Tierärztliche Hochschule Hannover Application of horse ...

Tierärztliche Hochschule Hannover

Application of horse genomics to identify quantitative trait loci

(QTL) for chronic pastern dermatitis in German draft horses

INAUGURAL-DISSERTATION

zur Erlangung des Grades einer

Doktorin der Veterinärmedizin

- Doctor medicinae veterinariae -

(Dr. med. vet.)

Vorgelegt von

Evelyn Henrike Mittmann Wuppertal

Hannover 2009

Table of contents

Scientific supervisor: Univ.-Prof. Dr. Dr. Ottmar Distl

Institute for Animal Breeding and Genetics

University of Veterinary Medicine Hannover

Examiner: Univ.-Prof. Dr. Dr. Ottmar Distl

Co-examiner: Univ.-Prof. Dr. Marion Hewicker-Trautwein

Oral examination: 10. November 2009

Dedicated to my beloved mother and father

Table of contents

Parts of this work have been published in the following journals:

1. Animal Genetics

2. Journal of Heredity

Table of contents

Table of contents

1 Introduction ...........................................................................................................2

2 Characterisation of a minimal microsatellite set for whole genome scans informative in warmblood and coldblood horse breeds....................................6

2.1 Abstract ......................................................................................................6

2.2 Introduction.................................................................................................6

2.3 Materials and Methods ...............................................................................7 2.3.1 Development of the marker set and animals for genotyping.......................7 2.3.2 PCR and genotyping of markers.................................................................9

2.4 Results......................................................................................................10

2.5 Discussion ................................................................................................11

2.6 References ...............................................................................................13

3 Identification of 21 781 equine microsatellites on the horse genome assembly 2.0 ........................................................................................................86

3.1 Source/description ....................................................................................86

3.2 Identification of microsatellites and their assignment on the horse genome.....................................................................................................86

3.3 PCR-primers.............................................................................................86

3.4 Comments ................................................................................................87

3.5 Acknowledgements...................................................................................87

3.6 References ...............................................................................................87

3.7 Tables.......................................................................................................88

3.8 Figures 1-32 .............................................................................................88

4 Whole genome scan identifies quantitative trait loci for chronic pastern dermatitis in German draft horses...................................................................108

4.1 Summary ................................................................................................108

4.2 Introduction.............................................................................................108

4.3 Material and Methods .............................................................................110 4.3.1 Animals...................................................................................................110 4.3.2 Microsatellite markers.............................................................................112 4.3.3 Genotyping .............................................................................................113 4.3.4 Statistical analysis ..................................................................................114

4.4 Results and discussion ...........................................................................115

4.5 References .............................................................................................119

Table of contents

4.6 Pedigrees of Family 2 - 32......................................................................148

5 General discussion ...........................................................................................156

5.1 Minimal screening set for the horse (MSSH) ..........................................156

5.2 Identification of new microsatellites ........................................................157

5.3 Whole genome scan for chronic pastern dermatitis ................................159

6 Summary............................................................................................................166

7 Erweiterte Zusammenfassung .........................................................................170

8 Appendix............................................................................................................184

9 List of publications ...........................................................................................190

10 Acknowledgements...........................................................................................192

Abbreviations

List of abbreviations A adenine

ABGe Animal Breeding & Genetics equus caballus

Acc. No Accession number

APS ammonium persulphate

ATP2A2 ATPase, Ca++ transporting, cardiac muscle, slow twitch 2

BLAST basic local alignment search tool

BLAT BLAST-like alignment tool

bp base pairs

C cytosine

CB Coldblood Horses

CD109 activated T-cell marker CD109

chrUn chromosome unknown

cM centiMorgan

CPD chronic pastern dermatitis

CPL chronic progressive lymphedema

DMSO dimethyl sulfoxide

DNA deoxyribonucleic acid

dNTP deoxy nucleoside 5’triphosphates (N is A, C, G or T)

E6AP E6-associated protein

ECA Equus caballus autosome

EDTA ethylenediaminetetraacetic acid

EquCab2.0 Equus caballus assembly 2.0

F forward

FOXC2 forkhead box C2 (MFH-1, mesenchyme forkhead 1)

G guanine

HET heterozygosity

HW Hanoverian warmblood

HSA Homo sapiens autosome

IBD identical by descent

INRA institut national de la recherche agronomique

IRD infrared dye

Abbreviations

kb kilobase

LD linkage disequilibrium

LOD logarithm of the odds

Mb megabase

MERLIN multipoint engine for rapid likelihood influence

MgCl2 Magnesium Chloride

MHC major histocompatibility complex

min minutes

MNA mean allele number

mM millimolar

MTMR6 myotubularin related protein 6

MSSH minimal screening set for the horse

n number

NA number of alleles

NC North Carolina

NCBI National Center for Biotechnology Information

NE Nebraska

ng nanogram

NPL non-parametric linkage

P or p error probability

PL error probability of LOD score

PZ error probability of Zmean

PCR polymerase chain reaction

PIC polymorphism information content

POS position

QTL quantitative trait locus or quantitative trait loci

R reverse

RG Rhenish German draft horse(s)

RH radiation hybrid

SAS statistical analysis system

SCHL Schleswig draft horse(s)

sec seconds

Abbreviations

SG South German draft horse(s)

SNP single nucleotide polymorphism

ST Saxon-Thuringian draft horse(s)

T thymine

Taq Thermus aquaticus (bacterium)

Ta annealing temperature

TBE tris-borate-ethylenediamine tetraacetic acid

T cell lymphocytes with a special receptor (T for thymus)

TEMED N,N,N’,N’-tetrametylenediamine

TGF-β transformimg growth factor-β

U unit

UBE3A ubiquitin protein ligase E3A

UCSC University of California Santa Cruz

USA United States of America

μl microliter

WB Warmblood horse(s)

CHAPTER 1

Introduction

2 Introduction

1 Introduction

The chronic pastern dermatitis is a severe localized skin infliction of horses’ feet that

has been known for centuries and has been the bane of existence for draft horse

breeders ever since. The chronic stage of pastern dermatitis is the verrucous form

that has early on been considered to represent a distinct disease entity that is only

found in heavy draft horses. Still, in Germany the pastern dermatitis is called ‘Mauke’

and no distinction is made between the ‘scratches’ or ‘grease heel’ of warmblood

breeds and the chronic verrucous form in draft horses. The disease processes start

at an early age, progress throughout the life until the horse presents with a

combination of progressive, severe, firm limb swelling associated with skin folds,

nodules and verrucous skin lesions that are non-responsive to therapy.

Environmental factors such as exposure to moisture and poor housing conditions

may exacerbate clinical signs and contribute to manifestations early in life.

The aetiology of chronic pastern dermatitis is still unknown, and this lack of

understanding towards the underlying pathogenic processes has resulted in new

descriptive terms for this disease. In the late 1980’s a study classified the histo-

pathologic changes that occur in affected skin regions as ’Pastern Leukocytoclastic

Vasculitis’ and provided a new effort to differentiate the common ‘grease heal’ from

the chronic pastern dermatitis in draft horses. Recent publications from the USA have

defined the new term ‘chronic progressive lymphedema’ that is derived from a

disease known in humans. The new research is neither complete nor conclusive as

to the cause and effect of the chronic pastern dermatitis, but the idea of a

generalized systemic disease with a localized affection area is becoming increasingly

popular in the scientific approaches.

Our approach was focused on the genetic factors that play an important role in the

development of chronic pastern dermatitis with heritability estimates ranging from 20

to 90% in German draft horse breeds. Due to the rapid development in equine

molecular genetics that has evolved around the second assembled genome

sequence of the horse (EquCab2.0), the prospects of dissecting the genetic

components of multigenic traits such as chronic pastern dermatitis have risen

Introduction 3

immensely. The first important step on this way has been achieved by this thesis.

This study was performed with the objective to identify the genomic regions which

are harbouring gene loci responsible for chronic pastern dermatitis in German draft

horses. Linkage studies such as this whole genome scan are based on highly

polymorphic microsatellite markers that are evenly distributed over the whole

genome. The availability of the second horse genome assembly has been essential

for the selection of equidistantly placed markers. Moreover, we made use of

EquCab2.0 to identify more than 20 000 new microsatellites by taking a

bioinformatics approach. The resulting five-fold increase of mapped microsatellites

has provided a so far unknown marker density for the horse close enough for very

successful fine mapping of quantitative trait loci. The newly identified microsatellites

were one of the new resources that helped us to increase the significance of the

whole genome scan for chronic pastern dermatitis in German draft horses.

Overview of chapters The contents of the thesis are presented in single papers according to §4(4) of the

Rules of Graduation (Promotionsordnung) of the University of Veterinary Medicine

Hanover.

Chapter 2 presents a minimal screening set for the horse that was developed to

facilitate microsatellite-based genome scans.

Chapter 3 provides the up to date largest number of mapped microsatellite markers

in the horse which were newly identified.

Chapter 4 contains a whole genome scan that was performed to identify quantitative

trait loci for chronic pastern dermatitis in German draft horses.

Chapter 5 comprises a general discussion and conclusions referring to chapters 2-4.

Chapter 6 is a concise English summary of this thesis.

Chapter 7 is an expanded, detailed German summary which takes into consideration

the overall research context.

CHAPTER 2

Characterisation of a minimal microsatellite set for whole genome

scans informative in warmblood and coldblood horse breeds

Evelyn H. Mittmann, Virginie Lampe, Stefanie Mömke, Alexandra Zeitz and Ottmar

Distl

Institute for Animal Breeding and Genetics, University of Veterinary Medicine

Hannover, Foundation, Germany

Journal of Heredity

Epub DOI 10.1093/jhered/esp091

6 Minimal screening set fort he horse (MSSH)

2 Characterisation of a minimal microsatellite set for whole genome scans informative in warmblood and coldblood horse breeds

2.1 Abstract

The availability of a high-quality draft sequence of the horse makes known the

physical location of microsatellites. The aim of the present study was to establish a

highly polymorphic minimal screening set of microsatellite markers for horses

(MSSH) annotated on the horse genome assembly EquCab2.0. We have used the

previously reported linkage and radiation hybrid maps and have extended these

marker sets by filling in gaps as noted from annotation on the horse sequence. This

MSSH covers all autosomes and the X chromosome with 322 evenly spaced

microsatellites whose positions were determined on the horse genome assembly

(EquCab2.0). The average chromosomal distance among markers amounts to 7.44

Mb. The characteristics established for this microsatellite set were the number of

alleles, the observed heterozygosity (HET), and the polymorphism information

content (PIC) for Hanoverian warmblood (HW) and several German coldblood horse

breeds (CB). The average number of alleles was 7.3 and 8.0 in HW and CB,

respectively. HET was at 71% for HW and CB, PIC at 65% (HW) and 67% (CB). This

MSSH allows scanning of the whole horse genome at close to 7-10 Mb resolution.

Key words: genome, heterozygositiy, horse, microsatellite, PIC The equine genome is estimated to contain approximately 2.7 billion base pairs

distributed on 31 autosomes and the X chromosome. The identification of genetic loci

and markers as well as their spacing on the chromosomes is one of the necessary

preconditions for linkage studies including fine mapping of quantitative trait loci,

parentage identification or genetic diversity analyses. We used the latest horse

Minimal screening set for the horse (MSSH) 7

genome assembly (EquCab2.0) to develop this minimal screening set of

microsatellites for horses (MSSH).

The majority of microsatellites reported in the horse are dinucleotide repeats (CA).

The comprehensive linkage maps reported by Swinburne et al. (2006), Penedo et al.

(2005) and Tozaki et al. (2004, 2007) and the radiation hybrid (RH) maps of

Chowdhary et al. (2003) and Raudsepp et al. (2008) have provided diverse sets of

microsatellite markers.

The aim of the present study was to establish a highly polymorphic microsatellite

marker set which covers the whole equine genome with evenly spaced markers and

which is anchored on the horse genome assembly EquCab2.0. This marker set

should be highly informative and facilitate mapping studies in the horse. Therefore,

the number of alleles, heterozygosity (HET) and polymorphism information content

(PIC) were determined in a random sample of the worldwide distributed Hanoverian

warmblood horse and a panel of several German coldblood horse breeds.

2.2 Materials and Methods

2.2.1 Development of the marker set and animals for genotyping

The Horsemap database at the INRA Biotechnology Laboratories Home Page

(http://locus.jouy.inra.fr) contains information on 1538 microsatellites. At the NCBI

nucleotide database (http://www.ncbi.nlm.nih.gov/) 24 124 equine microsatellites are

available since July 2009 (queries on 8/19/2009). The MSSH was developed using

microsatellites from the HORSEMAP homepage, the linkage maps of Swinburne et

al. (2006), Penedo et al. (2005) and Tozaki et al. (2004, 2007) and the RH maps of

Chowdhary et al. (2003) and Raudsepp et al. (2008). For all of these markers their

physical location on the horse genome assembly EquCab2.0 was determined using

BLAT (BLAST-like alignment tool) and an e-value of 10-5 as threshold. The expected

size range of the markers was determined using the polymerase chain reaction

(PCR) in silico function and the genome version ‘September 2007’ of the UCSC


(University of California, Santa Cruz) Genome Browser (http://genome.ucsc.edu/cgi-

bin/hgPcr?command=start). The distances between the markers were calculated and

for all chromosomal regions being not covered by microsatellites within a distance of

10-20 Mb, we developed a total of 54 new markers to bridge these gaps in this

preliminary marker set.

Therefore, we generated permutation sequences with all variations of di-, tri- and

tetrarepeat motifs with a minimum length of 15 repeats and a maximum length of 30

repeats. For these sequences alignments were identified in the EquCab2.0 assembly

within 1-2 Mb of the targeted location. These newly developed markers are

distributed on 14 autosomes (ECA1, 2, 4, 5, 7, 10, 11, 15, 16, 18, 21, 22, 26 and 31).

Each of the chosen markers in the MSSH has been characterized using the number

of alleles, the observed HET and the PIC. HET shows the proportion of heterozygote

individuals. PIC is defined as the probability that the marker genotype of a given

offspring will, in the absence of crossing-over, allow to deduce which one of the two

marker alleles the offspring received from its parents.

The values for HET and PIC were used to test the information content of all markers

in this preliminary set. Markers which did not show a minimum value of four alleles

and a HET and PIC greater than 0.50 were removed and replaced by more

polymorphic markers as far as possible. Particularly in genomic regions with a low

marker density or with markers exhibiting low information content, new microsatellites

had to be developed. We chose several horse breeds for evaluation of the

information content of the marker set. The reason for testing the degree of

polymorphisms of the microsatellites in several breeds was that HET and PIC vary

among breeds and through genotyping of horses from several breeds, markers with a

breed specific abundance of polymorphisms should be excluded. We have chosen

the HW as a representative breed for warmblood horses. This breed may also exhibit

a similar degree of polymorphisms like Thoroughbreds or breeds with larger

proportions of Thoroughbred genes because the average proportion of Thoroughbred

genes amounts to 35% in HW. Further significant contributions to Hanoverians have

been made by Arabians, Anglo-Arabians, Trakehner and other German warmblood

strains (Hamann and Distl 2008). The HW is the largest warmblood breed worldwide

http://genome.ucsc.edu/cgi-bin/hgPcr?command=start

http://genome.ucsc.edu/cgi-bin/hgPcr?command=start


and has influenced many European warmblood breeds. The second group of horses

comprised several German coldblood horse breeds (CB), such as South German,

Rhenish German, Saxon-Thuringian and Schleswig coldblood. Each of these breeds

has been influenced and refined by a number of European coldblood lines. Rhenish

German and Saxon-Thuringian coldblood were strongly influenced by Belgian and

Ardenner coldblood horses (Aberle et al. 2004).

The markers were genotyped on an average number of 362 HW horses and 299

German coldblood horses.

2.2.2 PCR and genotyping of markers

Genomic DNA was isolated from ethylenediaminetetraacetic acid blood samples

using the QIAamp® 96 DNA Spin Blood Kit (Qiagen, Hilden, Germany). PCR was

performed on PTC 100™, PTC 200™ (MJ Research, Watertown, MA, USA) or

professional thermocyclers (Biometra, Göttingen, Germany). A PCR program with

varying annealing temperatures (Ta) and a general procedure has been used as

follows: after 4 minutes of initial denaturation at 94°C, 36 cycles of 30 seconds at

94°C, 60 sec at optimum annealing temperature, 30 sec at 72°C and the final cooling

to 4°C for 10 min were carried out. All PCR reactions were performed in 12 μl

reaction volumes using 10 ng DNA, 1.2 μl 10x incubation buffer containing 15 mM

MgCl2, 0.6 μl dimethyl sulfoxide, 0.2 μl each dNTP (100μM each) and 0.1 μl Taq

Polymerase (Qbiogene, Heidelberg, Germany). All forward primers were labelled

with the IRD700 or IRD800 at the 5’ end.

To increase efficiency, primer pairs were pooled into PCR multiplex groups of two to

nine markers. The remaining primer pairs were amplified separately. The multiplex

groups and the separately amplified PCR products were pooled according to their

size and labelling and diluted with formamide loading buffer in ratios from 1:10 to

1:30. For the analysis of the marker genotypes, the PCR products were size-

fractionated by gel electrophoresis on an automated sequencer (LI-COR 4200/S-2,

LI-COR 4300, Lincoln, NE, USA) using 4% and 6% polyacrylamide denaturing gels

(Rotiphorese Gel40, Carl Roth, Karlsruhe, Germany). Allele sizes were scored


against IRD700- and IRD800-labeled DNA ladders. SAS/Genetics, version 9.2 (SAS

Institute, Cary, NC, USA) was employed to calculate number of alleles, HET and PIC.

2.3 Results

The mean number of alleles of the whole equine marker set including 322

microsatellite markers was 7.3 in HW and 8.0 in CB (Table 1). Seven microsatellites

had < 4 alleles for the HW and for the CB there were six markers with < 4 alleles.

HET and PIC with values less than 0.50 were seen in 7 and 19 markers (HW) and in

4 and 11 markers (CB). However, these markers with a lower heterozygosity were

retained as no other suitable markers were found to cover these regions.

The marker with the lowest number of alleles was UMNe244 which is located in a

region on ECA14 where only very few polymorphic microsatellites were identified.

The marker ABGe001 had the highest number of alleles with 24 for HW and 32 for

CB.

The mean values for HET and PIC slightly differed among HW and CB but overall the

CB showed a higher degree of polymorphisms. The distribution of the HET and PIC

values for the MSSH is shown in Tables 2 and 3.

The characteristics of the MSSH such as average chromosomal marker coverage,

number of markers per chromosome, mean number of alleles per chromosome, HET

and PIC of the horses genotyped are given in Table 4. The mean spacing among the

markers of the MSSH according to the horse genome assembly EquCab2.0 was 7.44

Mb. There were two gaps of more than 20 Mb on ECA3 (21.8 Mb) and on the X

chromosome (21.67 Mb). All microsatellites of the MSSH including marker names

and references, primer sequences, chromosomal location of the markers on

EquCab2.0, accession numbers for markers, number of alleles, HET and PIC for HW

and CB are given in Supplementary Table 1. Multiplex groups for the MSSH are

provided in Supplementary Table 2. All the markers of the MSSH had amplicons in

the expected size ranges, strong and clear bands to distinguish the alleles, as low as

possible stutter bands and no multiple products.


Due to the different lengths of the chromosomes, the number of markers ranged from

three on ECA13 to 25 markers on ECA1. On ECA3, we found the markers with the

highest average number of alleles (8.6) in the HW. For the CB, ECA31 showed an

average number of alleles of 12.5. The highest marker HET was observed on ECA21

for the HW (HET: 0.80) and for the CB on ECA31 (HET: 0.81). The highest PIC

values for HW were on ECA26 and 31 (PIC: 0.72) and for CB on ECA30 and 31

(PIC: 0.77).

2.4 Discussion

The aim of this study was to establish a minimal screening set of microsatellite

markers for the horse (MSSH) that benefits scientists using a microsatellite-based

approach. Considering the 2 groups of horse breeds genotyped, the mean values for

number of alleles, observed HET and PIC of this marker set differed only slightly.

This may indicate that the markers chosen may also be similarly polymorphic in other

horse breeds. However, a number of microsatellites exhibited large differences in

their polymorphisms. Eleven markers had high information content in the HW or in

the CB but not vice versa. These markers were distributed on ten chromosomes

(ECA1, 2, 5, 14, 15, 17, 19, 22, 27 and the X chromosome). When comparing the positions of the microsatellites in the MSSH with the positions

of the markers in the linkage map of Swinburne et al. (2006), the order of the markers

on most chromosomes was consistent with the annotation on the horse genome

assembly EquCab2.0. Marker order had been switched between TKY601 and

COR020 on chromosome 10. A less subtle difference in marker position was evident

for COR062, which had been located on the proximal end of ECA19 on EquCab1.0 in

agreement with the linkage map of Swinburne et al. (2006). Using the BLAT results

from EquCab2.0, it turned out that COR062 has to be re-located on ECA5.

Swinburne et al. (2006) could neither make a distinction between the location of

COR068 and COR073 on ECA21, nor between TKY011 and LEX051 on ECA15. In

the present physical map, distances of 1.8 Mb and 3.0 Mb were found in between

these two microsatellites. On ECA25, the orientation of the markers in the linkage


maps of Swinburne et al. (2006) and Penedo et al. (2005) as well is completely

reversed in comparison to the present annotation on EquCab2.0.

The order of the markers in the linkage map of Penedo et al. (2005) on ECA1, 4, 6,

10, 16, 18, 20 and 26 reflects minor discrepancies with the ordering in the MSSH.

Some adjacent markers switched their positions, like LEX058 and COR046 on ECA1,

UCD465 and COR070 on ECA6, TKY867 and LEX017 on ECA10 as well as TKY101

and TKY016 on ECA18. On ECA4, 10 and 20, 1 or 2 markers changed their positions

in comparison with the MSSH and were annotated more distally or proximally on the

respective chromosome. Chromosome 26 is oriented in opposite directions in the

linkage maps of Penedo et al. (2005) and Swinburne et al. (2006). The comparative

RH map of Raudsepp et al. (2008) and the linkage map of Swinburne et al. (2006)

would support the marker order found in the horse genome assembly EquCab2.0.

Between the most recently published RH map of Raudsepp et al. (2008) and the

horse genome assembly EquCab2.0, there is a strong colinearity. Only a few minor

discrepancies were found in marker positions due to local rearrangements on

ECA17, 18 and 25. The marker UMNe448 changed its location from ECA1 in the RH

map to ECA2 on horse sequence EquCab2.0. The marker UCD464 was located on

the proximal end of ECA25 using EquCab2.0, whereas its position has been

reversed to the distal end of ECA25 in the RH map.

This MSSH presents a useful tool for linkage studies in many horse breeds including

warmblood and coldblood breeds, Thoroughbreds and breeds with a significant

proportion of Thoroughbred blood. In standardbreds, ponies and primitive horse

breeds this marker set was not yet tested for its information content. A similar marker

set comprising 507 markers at 5 Mb spacing was developed for the dog (Sargan et

al. 2007). However, this MSSH does not allow identification of disease-associated

haplotypes by linkage disequilibrium (LD) mapping. Due to the high costs for LD

mapping using beadchips based on single nucleotide polymorphisms (SNPs) like the

Illumina equine 50K beadchip, microsatellite-based linkage studies may often be an

alternative and can be employed as a first option for disease and trait mapping.

When the approach for disease and quantitative trait mapping is mainly based on

affected individuals or animals with extreme phenotypes, linkage studies can be


performed and in the case of the availability of a very informative marker set, these

studies will be very worthwhile and attractive.

In summary, we have developed a resource for conducting genome-wide scans at

about 7- to 10 Mb resolution in the horse. Previously published linkage and RH maps

and especially the horse genome assembly enabled us to develop this 322

microsatellite-based marker set for horses. The integration of this marker set with

EquCab2.0 should further serve fine mapping, identification of positional candidate

genes and follow-up studies using SNP panels or SNP chips.

2.5 References Aberle K, Wrede J, Distl O. 2004. Analysis of relationships between German heavy

horse breeds based on pedigree information. Berl Münch Tierärztl Wschr.

117:72–75.

Chowdhary BP, Raudsepp T, Kata SR, Goh G, Millon LV, Allan V, Piumi F, Guérin G,

Swinburne J, Binns M, Lear TL, Mickelson J, Murray J, Antczak DF, Womack JE,

Skow LC. 2003. The first-generation whole-genome radiation hybrid map in the

horse identifies conserved segments in human and mouse genomes. Genome

Res. 13:742–751.

Hamann H, Distl O. 2008. Genetic variability in Hanoverian warmblood horses using

pedigree analysis. J Anim Sci. 86:1503–1513.

Penedo MCT, Millon LV, Bernoco D, Bailey E, Binns M, Cholewinski G, Ellis N, Flynn

J, Gralak B, Guthrie A, Hasegaw T, Lindgren G, Lyons LA, Røed KH, Swinburne

JE, Tozaki T. 2005. International equine gene mapping workshop report: a

comprehensive linkage map constructed with data from new markers and by

merging four mapping resources. Cytogenet Genome Res. 111:5–15.

Raudsepp T, Gustafson-Seabury A, Durkin K, Wagner ML, Goh G, Seabury CM,

Brinkmeyer-Langford C, Lee EJ, Agarwala R, Stallknecht-Rice E, Schäffer AA,

Skow LC, Tozaki T, Yasue H, Penedo MC, Lyons LA, Khazanehdari KA, Binns

MM, MacLeod JN, Distl O, Guérin G, Leeb T, Mickelson JR, Chowdhary BP.

2008. A 4,103 marker integrated physical and comparative map of the horse

genome. Cytogenet Genome Res. 122:28–36.


Sargan DR, Aguirre-Hernandez J, Galibert F, Ostrander EA. 2007. An extended

microsatellite set for linkage mapping in the domestic dog. J Hered. 98:221–231.

Swinburne JE, Boursnell M, Hill G, Pettitt L, Allen T, Chowdhary B, Hasegawa T,

Kurosawa M, Leeb T, Mashima S, Mickelson JR, Raudsepp T, Tozaki T, Binns M.

2006. Single linkage group per chromosome genetic linkage map for the horse,

based on two three-generation, full-sibling, crossbred horse reference families.

Genomics. 87:1–29.

Tozaki T, Penedo MCT, Oliveira RP, Katz JP, Millon LV, Ward T, Pettigrew DC,

Brault LS, Tomita M, Kurosawa M, Hasegawa T, Hirota K. 2004. Isolation,

characterization and chromosome assignment of 341 newly isolated equine

microsatellite markers. Anim Genet. 35:462–504.

Tozaki T, Swinburne J, Hirota K, Hasegawa T, Ishida N, Tobe T. 2007. Improved

resolution of the comparative horse-human map: investigating markers with in

silico and linkage mapping approaches. Gene. 392:181–186.


Table 1. Distribution of the number of alleles for the MSSH

Number of markers Number of alleles

Hanoverian horses n

%

German coldblood horses n

%

< 4 6 1.9 6 2.34-6 119 38.3 83 31.17-9 138 44.4 126 47.2

10-12 39 12.5 30 11.213-15 5 1.6 13 4.9>15 4 1.3 9 3.4

Table 2. Distribution of the observed HET for the MSSH

Number of markers HET (%) Hanoverian horses

n

% German coldblood horses

n

% 30 – 40 2 0.6 0 040 – 50 5 1.6 4 1.550 – 60 37 11.9 32 12.060 – 70 98 31.5 83 31.170 – 80 117 37.6 98 36.780 – 90 46 14.8 48 18.0

90 – 100 6 1.9 2 0.7 Table 3. Distribution of the PIC for the MSSH

Number of markers PIC (%) Hanoverian horses

n

% German coldblood horses

n

% 20 – 30 1 0.3 0 030 – 40 2 0.6 2 0.740 – 50 16 5.1 9 3.450 – 60 67 21.5 52 19.560 – 70 111 35.7 99 37.170 – 80 100 32.2 80 30.080 – 90 14 4.5 25 9.4


Table 4. Average chromosomal coverage of the MSSH, number of markers per chromosome, average number of alleles (NA), observed heterozygosity (HET) and polymorphism information content (PIC) per chromosome (ECA)

NA HET PIC NA HET PIC ECA

Average distance

(Mb)

Number of

markers Hanoverian warmblood German coldblood

1 7.15 25 7.8 0.69 0.65 9.0 0.73 0.69 2 5.11 23 8.1 0.69 0.65 9.4 0.75 0.70 3 9.53 12 8.6 0.76 0.71 8.9 0.73 0.69 4 4.72 22 8.2 0.71 0.64 10.4 0.76 0.73 5 5.25 18 6.7 0.70 0.65 7.4 0.71 0.67 6 7.06 11 6.4 0.70 0.64 7.1 0.70 0.66 7 14.08 5 6.8 0.64 0.59 6.6 0.69 0.66 8 11.76 8 5.9 0.66 0.62 7.8 0.72 0.67 9 9.28 8 5.9 0.68 0.61 6.6 0.66 0.61

10 4.67 17 7.6 0.72 0.66 8.4 0.74 0.70 11 7.66 7 7.9 0.73 0.68 8.1 0.69 0.65 12 6.62 4 7.8 0.69 0.69 9.3 0.70 0.67 13 10.64 3 5.7 0.71 0.63 7.3 0.75 0.67 14 9.81 9 5.4 0.65 0.55 7.0 0.65 0.61 15 4.58 19 7.7 0.72 0.67 7.7 0.68 0.66 16 4.60 18 6.5 0.73 0.68 7.2 0.69 0.67 17 8.97 8 5.9 0.63 0.59 7.4 0.66 0.63 18 4.13 19 7.3 0.74 0.67 7.2 0.75 0.68 19 8.57 6 6.3 0.63 0.57 7.3 0.66 0.64 20 8.02 7 6.9 0.68 0.64 7.2 0.68 0.58 21 4.81 11 7.6 0.80 0.70 7.0 0.67 0.61 22 5.55 8 7.8 0.66 0.65 7.8 0.72 0.67 23 11.14 4 6.5 0.72 0.61 6.8 0.75 0.67 24 7.79 5 7.2 0.76 0.70 7.4 0.71 0.66 25 7.91 4 6.8 0.65 0.59 7.3 0.69 0.60 26 4.65 8 7.9 0.76 0.72 8.4 0.74 0.71 27 6.66 5 7.4 0.68 0.61 7.0 0.68 0.67 28 9.24 4 6.5 0.75 0.66 8.3 0.77 0.68 29 4.81 6 6.8 0.68 0.60 7.2 0.69 0.64 30 6.01 4 6.8 0.69 0.66 9.0 0.78 0.77 31 5.00 4 8.3 0.77 0.72 12.5 0.81 0.77 X 10.34 11 7.2 0.70 0.70 7.6 0.65 0.67

Supplementary Table 1. Microsatellite markers of the minimal screening set for horses, the chromosome (Chr), size range,

number of alleles (Al), observed heterozygosity (HET) and polymorphism information content (PIC) in Hanoverian warmblood and

German coldblood horses, primer sequences and position on the horse genome assembly EquCab2.0 in base pairs (bp).

Minim

al screening set for the horse (MS

SH

)__ _17

Chr Marker Reference Acc. No. Size Size range

Warmbloods Al HET PIC

Coldbloods Al HET PIC Primer sequence (5’–3’) Position

(bp) 1 ABGe105 AM946984 207 186-

232 12 0.65 0.73 GGGTCTTTTGACTGCCTGAG TTGGGAGACGAGAACAAAGG

2748651 -2748857

1 ABGe001 AM900755 287 237- 333 24 0.79 0.82 32 0.85 0.84 TGGTGACGTAAGGGTTCTGG

GAGGGGATATGTGGATGTGG 6935534

-6935819

1 ABGe003

AM900757 193 182- 200 6 0.62 0.63 7 0.66 0.66 TCCCAAAGGAGGAAATGTTG

TCCCAAAGGAGGAAATGTTG 11115190

-11115382

1 VIAS-H34 Ewen et al. 1994 L23549 - 137-

153 7 0.71 0.71 5 0.73 0.69 TGAGTGTTTGCGTGTGTGTG TCCCGTCTCCTCTCTTGTTC

13844424 -13844443

1 ASB41 Irwin et al. 1998 AF004771 155 147-

159 7 0.66 0.57 6 0.72 0.68 AAAGTTCACTTAGTCCTTGG CCACCTGTTTGCACTTGC

18265676 -18265770

1 LEX020 Coogle et al. 1996a AF075622 205 192-

213 9 0.68 0.63 9 0.81 0.75 GGAATAGGTGGGGGTCTGTT AGGGTACTAGCCAAGTGACTGC

20590361 -20590618

1 TKY597 Tozaki et al. 2004 AB103815 141 129-

145 7 0.73 0.69 7 0.85 0.77 AGTGCCAAGGAGGCTGTCT TCTTCTCCCCATGAGTCACC

31471407 -31471658

1 NV1002 Røed et al. 1998 AF056399 209 190-

210 8 0.82 0.76 12 0.82 0.80 CCAAAGCAGAACATGTGAAGTT TGGCATAGATGTTAGCTCAGTGA

42499593 -42499855

1 COR100 Tallmadge et al. 1999 AF154953 210 192-

222 8 0.68 0.77 9 0.83 0.76 CCCAGAGGTTTCAGAGGG ATTCTAGGGCATATTATGACAA

50781045 -50781359

1 ASB12 Breen et al. 1997 - 168-

182 6 0.64 0.59 5 0.68 0.63 TCAGCAATAGAAGCCAGCTCC TCCTATGGAGGTGACCTTCCC

68983167 -68983595


175 6 0.55 0.58 8 0.65 0.62 AGCAACAGAGTAATGCCAAG TAGGCGGGTTTTAAACATGG

76312670 -76312990

1 AHT40 Swinburne et al. 2000 AJ271525 214 199-

215 8 0.81 0.75 8 0.79 0.73 TCCAAGTTGCTGAATGGATC ACGGCCTGATTCTCTCTTTG

89894655 -89894868

1 COR046 Ruth et al. 1999 AF108363 253 249-

257 6 0.68 0.62 7 0.63 0.63 TGTTTGCAAAGATATTGGGG ACCTGGTCAGGCCTATTACC

97524758 -97525028

1 LEX058 Coogle & Bailey 1997 AF075665 - 222-

232 6 0.90 0.75 8 0.71 0.69 GCAATCCGCTAGATAGAGTG ACCTTTACTTTACGGGTCACA

102643932 -102644134

1 TKY2 Sakagami et al. 1995 - 105-

117 9 0.73 0.66 TTCCCTCCCATGGTTATTTTTC TCTCTACTTTCATATACATTTGG

111173194 -111173216

http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nucleotide&val=170285428

http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nuccore&val=158392453



http://www.ncbi.nlm.nih.gov/nuccore/2738484

http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nucleotide&val=AF075622

http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nucleotide&val=AB103815



http://www.ncbi.nlm.nih.gov/nuccore/28465971?ordinalpos=1&itool=EntrezSystem2.PEntrez.Sequence.Sequence_ResultsPanel.Sequence_RVDocSum




Supplementary Table 1. continued Chr Marker Reference Acc. No. Size

Size range Warmbloods

Al HET PIC Coldbloods

Al HET PIC Primer sequence (5’–3’) Position (bp)

1 HMS15 Guérin & Bertaud 1996 U35401 218 214-

234 9 0.62 0.65 8 0.57 0.63 ATATCTCTTGCTGTCCTACTTTCC AATGTGACACGTAAGATAGGCCTC

136853390 -136853785

1 UM026 George et al. 1998 AF195573 206 204-

216 3 0.44 0.42 8 0.71 0.74 CCCAAAATCAATTAGGTCTC ATCAGTTGCTCTCTACTTTTC

150234788 -150234999

1 1CA16 Chowdhary et al. 2003 AF043200 125 114-

124 4 0.60 0.54 TCACTGGGGGGTATATGCAT GATCCTACTCCACCTGAAGTGG

157183217 -157183628

1 HMS7 Guérin et al. 1994 X74636 177 170-

182 7 0.81 0.75 8 0.70 0.70 CAGGAAACTCATGTTGATACCATC TGTTGTTGAAACATACCTTGACTGT

162381788 -162381964


332 5 0.56 0.50 7 0.79 0.76 TGGAACACATTCCTCACCAG GTTCTCCTTCCACCCCAAAT

170123409 -170124134


194 5 0.56 0.47 6 0.60 0.52 CAAGGACAACTTCTCAGGAG AATAAGAAAGGAGCCAGTCG

177976527 -177976972

1 COR053 Ruth et al. 1999 AF108370 200 171-

195 6 0.64 0.54 7 0.80 0.71 AATTGACTGTGGAAGCCTTG GGCTGAGGAGTAAGCTGAAAG

182814726 -182815075

2 COR065 Tallmadge et al. 1999b AF142602 286 266-

290 7 0.55 0.56 13 0.83 0.75 CAAAAGCACACACAAAGTGC TCCGGAAAGTGCAAAGTTAG

1737439 -1737788

2 ABGe109 AM946988 216 204- 218 12 0.76 0.75 15 0.82 0.82 GGGTGGCTCCTTAGAGCTTC

CCCCTCCCTTGTTTATATGC 6212019

-6212434


131 11 0.66 0.62 13 0.84 0.85 TGCAGCAAGAAACCTAAACA CTTCAGTTGTAATCAGGCTC

9002979 -9003590

2 ABGe144 FM165574 134 10 0.78 0.75 CAAAAATGGCAAGATTTCATCC TGCCCACTGACAGATGAATG

12436648 -12436787

2 ABGe111 Lopes et al. 2009 AM946990 161 165-

161 7 0.75 0.63 CCATGTTCACGTCCATTTTG CTAGGGGGTTCAAGGCACTC

15658048 -15658394

2 UMNe323 Wagner et al. 2003 AY391338 - 166-

212 13 0.79 0.73 19 0.86 0.87 GATCCTGCAGGAAAGCATGT CCGCTCGGAATATTTCATTG

19178919 -19179464

2 COR090 Tallmadge et al. 1999a AF154943 94 91-

101 5 0.60 0.56 7 0.76 0.63 GGTTTGTCTCTTTGAGGTGTG TGCTCATATCTTCACCCTGC

22451992 -22452193

2 ABGe006 Lopes et al. 2009 AM900760 199 175-

209 9 0.73 0.78 CTGAAACCAGCCAGGAAAAG TCTCCTAGCCGGGAGAAAAC

26891960 -26892358

2 ASB17 Breen et al. 1997 X93531 105 89-

115 12 0.79 0.75 15 0.83 0.81 GAGGGCGGTACCTTTGTACC ACCAGTCAGGATCTCCACCG

30600776 -30601268

18 Minim


SH

)

http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nucleotide&val=U35401





http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nuccore&id=6174759















2 ABGe342 FM179616 232 220- 237 7 0.80 0.77 ACCCACTGTGAAACCCTTTG

TGTGGAGAATGGAGGAGACC 34428083

-34428314

2 ABGe344 Lopes et al.

2009 FM179618 172 164-

178 7 0.73 0.68 GCACAGGAAGACCACACAAG TGAGCAGGAGAGGACTAGGG

36892745 -36892916


214 7 0.79 0.72 9 0.70 0.72 GATCAGTACTTTGCAAATGGATAAC GTAACTCCAAGGCTACGTTC

38799183 -38799462

2 ABGe065 AM940026 248 232- 312 16 0.71 0.69 ATGCTCGTTTCACAAAGAGG

TCTTTTTGTGCAGGGTGTG 41116698

-41117145

2 UMNe448 Mickelson et al. 2004 AY731396 164 151-

169 6 0.72 0.67 5 0.78 0.71 CCATTCTGCCCTGATTGG TTCAAGACCCCTCAATCTGC

45740375 -45741120

2 ABGe347 Lopes et al. 2009 FM179621 269 257-

269 6 0.68 0.64 ACTACTAGGGTGCATTGTTTTTAGG GTGAACATGTTGCCCCTCTG

49299479 -49299747

2 ABGe349 Lopes et al. 2009 FM179623 257 247-

263 7 0.77 0.73 CCAGGAATATGAAACCACAGG GGTCCCTCACTCCTTCAATG

52420974 -52421211


99 6 0.69 0.59 3 0.65 0.54 TCTGCTAAGTTCAATGGGTC TTCTTACTTAACAACCATCG

55379847 -55380237

2 AHT12 Swinburne et al. 1997 - 102-

114 6 0.66 0.65 5 0.74 0.65 ACCCAAAGTCATGGGAATCA TTGTTGCCGACAACATGC

59080884 -59081386

2 HMS16 Godard et al. 1997 U89806 148 142-

158 7 0.41 0.46 7 0.64 0.67 AGTGTAATCAATGGATGAGTGGAC TGTTGTCGCAAATGGCAGGCATC

65145788 -65146309


164 5 0.64 0.56 GAAGCAGTGCCTCTTATGTG CAGAACAGTCAGGACTTGAC

70519723 -70520218

2 A - 14 Marti et al. 1998 Y10239 220 208-

236 10 0.72 0.66 9 0.80 0.66 CAGCTGGGTGACACAGAGAG GTCATCACTACTCCCTACAC

74473583 -74474001

2 TKY798 Tozaki et al. 2004 AB104016 - 237-

247 6 0.56 0.53 6 0.61 0.61 GAGCAGAAGGTACGAGAAGA AACTTAACCAGGCTGTTCTG

93955346 -93955727

2 VHL123 van

Haeringen et al.1998

Y08446 150 149- 161 4 0.52 0.43 6 0.60 0.53 CCTCCTTCACAGTGAAGTGC

GAGTATATAGCTCCAGACCTC 109818905

-109819452

3 AHT36 Swinburne et al. 2000b AJ271521 144 136-

148 8 0.82 0.74 8 0.77 0.73 TGCTGCTCCAGTGTCCT TAGATTTCACAGGCGGGTG

2947849 -2948297

3 COR028 AF101397 233 229- 243 8 0.73 0.71 7 0.70 0.62 TAAAGAGGAAGGCAATGGAC

ACCTTTTGTGCTAGGCACTG 11070089

-11070513

3 COR033

Murphie et al. 1999

AF101402 242 213- 245 11 0.67 0.66 11 0.77 0.76 CCTCCCCTACTTCCTCTCTG

CATTTTCTTTCCAGGTTCCC 13467219

-13467518

Minim


SH

) _19





http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nucleotide&val=AY731396









http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nucleotide&val=AJ271521







3 TKY937 Tozaki et al. 2004 AB104155 120-

150 8 0.79 0.74 8 0.67 0.64 TCCTGCGGAAATACATTAGG AGTTCAAAGTGGTCCCATAG

16759941 -16760177

3 UMNe158 Wagner et al. 2004a AY391305 148 125-

149 10 0.81 0.78 10 0.75 0.77 AATTGAGAGCCAAGATGACACC GGCACCATTTGAGGAAGATG

20876242 -20876491


296 17 0.89 0.86 25 0.83 0.8 TGAGCATCTTGAAGATGAGCA CAACAGTTGTTAGCTCAGGTGC

24229107 -24229579

3 UCD4371 Eggleston-Stott et al.

1997 U67408 178 163-

187 7 0.71 0.64 6 0.63 0.61 CTGTTCTGGGCAGGCTTCTCTA TTGCTGGCTTGGCTGGTC

31285390 -31285349

3 UMNe231 Mickelson et al. 2003 AF536304 234 230-

240 5 0.89 0.68 TAGTCGTTGTCCACCAGCTG GATGGAATGACACAGCACATG

44184855 -44185254

3 TKY353 Tozaki et al. 2001 AB044853 - 177-

191 6 0.58 0.55 6 0.67 0.66 TTCTTACTTAACAACCATCG TGTCACTGACAGATGAATGG

57506460 -57506681

3 ASB23 Irvin et al. 1998 X93537 149 147-

167 6 0.95 0.80 8 0.74 0.71 GAGGTTTGTAATTGGAATG GAGAAGTCATTTTTAACACCT

79278924 -79279280

3 LEX007 Coogle et al. 1996c AF075610 173 192-

200 7 0.69 0.67 6 0.65 0.61 GGTAGGGCTCTGGGATGA AACACTGGGGAAAAGTCAG

86981061 -86981460


163 7 0.73 0.71 7 0.74 0.73 AGTTCGGTAACTTGCCCATG GTTCATGGGCAGAATGGC

99036283 -99036709


190 13 0.86 0.77 13 0.81 0.77 ACACAAGTGACAGGAGCGTG TGGAAGCATGCAAGAGGTC

2915239 -2915540


208 15 0.77 0.74 23 0.91 0.84 TGGCAATCTGCAGGGAAC GATCTTGTGATTGTGTGTGTG

6340788 -6340853

4 ABGe067 AM940028 144 136- 144 5 0.76 0.76 CCACTGGGGTGTAAATCTGA

CTGCGTGGAAGCTGTTTTAT 8754662

-8754733

4 ABGe069

AM940030 198 158- 208 10 0.69 0.70 CATGGCAACGACAATACAAA

TGGATTTACAGTGCAAGCAG 11479500

-11479697


104 6 0.55 0.61 TTGTGCAGCTGTGGCTTAG ATAGGTGAGGGGCTGTGAG

15666943 -15667471

4 ABGe073 AM940034 271 255- 295 8 0.70 0.64 AGTCTTGCCCTTGCCTTTT

AGGCAGAGCAAAAGGATCA 18611029

-18611299

4 ASB3 Breen et al. 1997 X93517 203 196-

208 6 0.57 0.54 6 0.75 0.72 AATTCATCTCAGTGCTCTACCAGC TTCATTTTCTACATGCACTACAGC

23511931 -23512309

4 TKY337+ 238 222- 242 7 0.74 0.68 ACTCAAGAGGTCAATCAGAGG

CTCTTCCACTCTGCATTCTG 29877251

-29877531

20 Minim


SH

)





















4 COR057 Ruth et al. 1999 AF108374 233 235-

243 5 0.74 0.65 8 0.71 0.68 GGAGGAGAGGAAGAGAGTGG ATCCAGGGCTCTCCATAGTC

32740935 -32741325

4 ABGe082 AM940043 93 86- 98 5 0.62 0.58 TCAATGACAATCATCCTCCTG

CAGAGCAAGGGTGGAAATC 39761266

-39761358

4 LEX061 Breen et al. 1995 AF075661 149 142-

160 7 0.72 0.72 9 0.69 0.68 TCAGTGTTCCCATCTGTA TGAAATCACACCTTTACTTTA

42309507 -42309993

4 LEX050 Coogle & Bailey 1997 AF075652 116 112-

124 5 0.55 0.50 9 0.68 0.68 ATAGTCTGGGGTTAGGTAAGG TCTAGCCCAATGTAAATGC

49364086 -49364563

4 ABGe084 AM940045 204 180- 210 7 0.78 0.71 CCCACATAAAGAATGTGAAACA

CGCCTGAACATAGAATAACAAA 52070194

-52070403

4 ABGe086

AM940047 151 135- 149 7 0.77 0.72 ATCTCAACGTGGGATGTCTG

GCTTTGGAGTGCAAATTAGG 54684268

-54684322


208 9 0.80 0.77 CTGAGATTAACGGCCCAGTA TCAGTCATGTATTCCTGTGCAT

57309052 -57309158


298 10 0.76 0.70 7 0.80 0.74 CCTGCCATAAATTTGTTTCC TCCCTACCTCATCTCCACAC

59842920 -59843508

4 ABGe091 AM940052 140 130- 142 6 0.66 0.60 AAACAAAAGCTGCATGTTGA

CTGAATTGTATTGGGGGAGA 62371980

-62372019

4 ABGe059

AM919498 214 192- 212 8 0.77 0.71 8 0.72 0.70 AGTTGCCTCTGGTCTTGCAG

GCTGGCAGAATGTCTGTTTTC 63975130

-63975343


174 12 0.67 0.69 9 0.75 0.72 AGTGAGGTCTTCCTTGACTG TGTTAGATGGTGGTAAGTGC

69237880 -69238237


152 4 0.65 0.60 5 0.60 0.59 CGAGGTCTTGGAACCTATCC TTCACTTCAGACAACTCTATTGAAGA

77941489 -77941636


176 8 0.76 0.72 8 0.81 0.75 CTCAGACTAAGCGGTACTAG ATGGATACATTCTGGGGAAC

96435463 -96435817

4 SGCV23 Godard et al. 1997 U90601 196 198-

246 17 0.78 0.82 20 0.91 0.89 GGCTTAAGATATGGGTGAGTAAGG GCCCACCCTCTTACTTTTCTCAA

102735745 -102736063

5 TKY1175 Penedo et al. 2005 AB104393 203 193-

215 6 0.66 0.62 6 0.52 0.44 TTATCACCAGTTTCCAGAGC CTTATTCCACCCACTAATTCAC

10153063 -10152872


236 11 0.82 0.74 9 0.70 0.67 GTCATCCAGTGACGAACACA AGGAAGTGCGCAGTAGAGAA

14848168 -14848671


310 6 0.65 0.61 6 0.64 0.58 GGGAGGAAACCCAGTCAATT GGTCCCTCATCACTTCCACA

24996071 -24995543

Minim


SH

) _21





5 HMS63 154 154- 174 5 0.73 0.69 8 0.83 0.80 GGCACCTCCTAGAATTGTGC

AGTCTTCTAATCCTCTCCCTG 28648069

-28648222

5 UMNe567 Wagner et al. 2004 AY735272 226 222-

230 4 0.80 0.63 5 0.57 0.53 GGTGCAGCTGCTAGCTCAG AGACCCAGTCATTGGGAGG

45380665 -45381150

5 COR023 Murphie et al. 1999 AF101392 275 269-

279 5 0.31 0.39 5 0.72 0.71 CGTTTAGCACCTCTCATGAAC TCTTTGCAAAATAGGGCTTG

53414359 -53414708


1999 U67402 - 95-

113 5 0.64 0.58 6 0.81 0.74 CGCTTTCCTGCTGTCACC GAGGGACTGTGGGGGAGGT

56678370 -56678158

5 ABGe3282 Mittmann et al. 2009 FN406321 149 142-

170 8 0.71 0.74 11 0.66 0.76 TTTCTTTTTCCCACTTAAAGC TGGGACTTAGCAGTATGAAAC

63737939 -63737858

5 UMNe582 AY735282 173 165- 183 5 0.61 0.54 TCTTGACCTTCTTTACTTAGTACACA

CCTGGGCATAGACCTACACA 68632972

-68632859

5 UMNe534 Wagner et al.

2004 AY735262 250 236-

253 6 0.81 0.73 8 0.85 0.80 ATGTTGTTGCAAATGGTAGGG TCCATCAATCCCTCTTCTGG

73632601 -73632283

5 ASB10 Breen et al. 1997 X93524 148 142-

152 6 0.72 0.63 6 0.76 0.65 GTTGTCTAGGTGCAGAATCTGG GTTATGTCTCCCCTTTCTCTACC

78287521 -78286962

5 ABGe012 Lampe et al. 2009 AM905690 126 109-

127 8 0.72 0.69 TCGAGTGCAACAATGTGTAGG AGTCGAAGGCTTCCCACTAC

80978898 -80978962


148 7 0.62 0.58 7 0.67 0.60 GATCTTTAGAATCAGCTTGTTG CTCGCCACGTTAGTTGATG

83737970 -83737836

5 UMNe455 Mickelson et al. 2004 AY731398 126 124-

134 5 0.73 0.65 6 0.71 0.69 TGAGGTACTGTGCCTTGCTG CTGGGAAGACAGAGCCAGTC

86268203 -86268452


213 12 0.85 0.80 GAATTAGTTGTTTACTAGATTGGGAG TTGTTGCAAAAATTGATGAGTG

89313457 -89313492


108 8 0.85 0.77 GTGTCCATCAATGGATGAAG CTTAAGGCTAAATAATATCCC

92502135 -92501321


162 6 0.71 0.61 CGAGAGCTTCCACCTTCTTG AAAGATTGCACGGTTCTTGC

95403958 -95404040


200 7 0.68 0.60 13 0.75 0.78 ATCTAACCAGAGCGCAACGT CCCGACACAGAAGATGGG

98389072 -98389292


145 5 0.71 0.60 9 0.68 0.69 CTCTGTAACCCTTATATCCTTA TGTTGATTGCTCCTCCCCT

4340398 -4340475

6 NV822 Bjornstad et al. 2000 AJ245770 129 123-

137 6 0.55 0.55 8 0.67 0.62 TGTGGCAGCATCCCACAAAC CCTCCATTTTTGTCGGTTAGCG

15512464 -15512852

22 Minim


SH

)







http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nucleotide&val=X93524














6 LEX065 Coogle & Bailey 1999 149 144-

156 5 0.77 0.65 5 0.74 0.66 GAAGGCACAATTCAATCTACT GCCCAGTCCCATTCTAAC

20784689 -20784837

6 UM015 Meyer et al. 1997 AF195133 308 302-

312 7 0.69 0.68 6 0.78 0.69 AGTCTGGCTGAGGATACTG GGTGAGAAAGGAGATAAATG

34558248 -34558641

6 TKY3193 AB217136 123 122- 134 7 0.69 0.65 7 0.74 0.78 CTAAATCACTCATGGTGGTG

GATCAGATTCTCCTTGAAAGAA 43522552

-43522812

6 TKY1162

Tozaki et al. 2007

AB104380 218 218- 234 3 0.62 0.52 4 0.66 0.62 TTTAATTATCACTCCCTTGACA

TGACTCTGGTTTCTGACTTC 52818647

-52818877

6 UCD4651 Shiue et al. 1999 U67414 204 198-

202 3 0.51 0.46 AACCAGTCCCTACATAGAAC CTCACAACCAAGCATACA

61228971 -61228705


299 11 0.85 0.80 12 0.80 0.80 CATCTGTTCCGTGGCATTA TTCAGGTGTGGGTTTTGAATC

65850871 -65851209


228 8 0.76 0.75 GTGTGGGACAGGAAGTTTGG ATTCTTGGGTCCCCTCATCT

70589219 -70589501


173 7 0.75 0.71 9 0.62 0.62 CTGGACTAGAGTCAGATTGC AACAGGATTCCCCCAATGCC

73768376 -73768601


222 7 0.78 0.63 GATCGGTAAGTGTCGGGAC TAAAATGACTGGGTGGAGAC

79472759 -79472984


156 7 0.53 0.53 8 0.59 0.61 CTGGTTTACCTTCCCTACAG CCAATGGTTCCTCTGAGAAG

12115180 -12115324

7 LEX038 Coogle et al. 1997 AF075640 144 133-

145 4 0.60 0.53 3 0.65 0.57 CTGCATTCCCATCATCACAT TGCCTTGCCTCTTTCTGTTTA

28957132 -28957698

7 ABGe102 Giesecke et al. 2009 AM946385 221 204-

226 9 0.79 0.74 10 0.78 0.80 AGGAAGCACGATCTGGTCTG AAGGATGCCCTGAGGAAGTC

33817944 -33818164


218 8 0.66 0.56 6 0.74 0.67 TACCTCTGGTGGTGATGCTT CCCACACTTACTCCCATCAC

54216081 -54216476


161 6 0.63 0.58 6 0.69 0.66 CTGTGGCAGCTGTCATCTTGG CCCAATTCCAGCCCAGCTTGC

71099323 -71099848


244 5 0.70 0.55 5 0.68 0.64 GGGATTTCTGAGATGCTGAA ATGGCTGGCTAGAGTTTGTG

1441807 -1442164

8 UMNe070 Roberts et al. 2000 AF191703 150 150-

156 3 0.54 0.55 TGGGCATTATTTCACAGTATGC TACATTAGGCCTGGAATGGG

14397243 -14397719


1996 U25171 230 228-

234 4 0.75 0.63 5 0.60 0.56 GCCAAACGCTGGAGGGTT CCACATTCACACACATGCACAC

17803970 -17803995

Minim


SH

) _23






















247 6 0.65 0.67 11 0.82 0.78 GGATGAAACAGGGAAGGAAA CCAACGGATTCATGAAAGCTA

25943823 -25944415

8 COR012 AF083455 173 166- 176 6 0.65 0.65 6 0.66 0.61 TCTAGGAAAGACCCATCACG

AGTAAGTGGAGGCCAAGGAT 46413115

-46413484

8 COR003

Hopman et al. 1999

AF083446 193 188- 210 7 0.60 0.59 10 0.85 0.80 TAGGGAAACTCCTCAAAGCC

GAAACCAAAACCTTCATCCA 64251041

-64251240

8 COR056 Ruth et al. 1999 AF108373 205 186-

212 10 0.73 0.74 10 0.70 0.65 AGATTCCAGGCATTAGGACC TCAGGGACAATCTTCCTCAAG

84105121 -84105348

9 HTG4 Ellegren et al. 1992 AF169165 127 127-

137 6 0.62 0.51 7 0.68 0.64 CTATCTCAGTCTTCATTGCAGGAC CTCCCTCCCTCCCTCTGTTCTC

1497882 -1497980

9 HMS3 Guérin et al. 1994 FJ915131 163 149-

167 7 0.76 0.71 8 0.65 0.67 CCAACTCTTTGTCACATAACAAGA CCATCCTCACTTTTTCACTTTGTT

16895741 -16896180

9 HTG8 Marklund et al. 1994 AF169292 188 178-

194 6 0.76 0.68 9 0.74 0.66 CAGGCCGTAGATGACTACCAATGA TTTTCAGAGTTAATTGGTATCACA

30021181 -30021494


251 4 0.58 0.46 4 0.56 0.52 GCAACAGATGTTGGCTCAG GGAGATGTCCTTGACCACAG

38001843 -38002092


159 3 0.64 0.52 3 0.50 0.37 GGTTAAATTAATCCAAGGTATTTTATTC AGAGGAAGACTGGCACAGATG

46073501 -46073661


206 6 0.84 0.73 7 0.68 0.65 TGCCTTTTTCTCTCATCACC AGACTAGTCTGCAAGTTCAG

51846360 -51846621

9 ASB4 X93518 121 120- 144 10 0.57 0.57 8 0.81 0.76 TAAATTGTAAAAGCTGGAGCCG

GCAAATAGTAGTTAAGTCCTC 61727515

-61727589

9 ASB5

Breen et al. 1997

X93519 115 105- 117 5 0.67 0.63 7 0.67 0.57 TCGAGGAGCTCATGACCTGG

TTGTACAACTCTCCACCATAGC 71663785

-71664087

10 ABGe351 FM179625 154 144- 180 8 0.67 0.65 TTCCCAGGATTGGAGCTATG

GACCAAGGTGGGTGTGTAGG 162441

-162794


286 10 0.66 0.58 8 0.80 0.74 CGAGGGGGAATTTTGTTTGT ATAGAGCCATGCAGGGGAAA

7559330 -7559649

10 COR020 Hopman et al. 1999a AF083463 166 146-

162 8 0.77 0.78 9 0.75 0.69 TCTCTACCGCAAGTGAAACC CTGAATTGTAGGACATCCCG

9995119 -9995488

10 COR048 Ruth et al. 1999 AF108365 172 167-

187 6 0.62 0.62 7 0.76 0.74 GATTGGGATGCAAAGATGAG CAAGAGGATTGGGAACAAAGG

12137860 -12138104

10 NV182 Røed et al. 1997 AF011404 117 112-

168 10 0.88 0.68 20 0.86 0.85 GGAGGAGACAGTGGCCCCAGTC GCTGAGCTCTCCCATCCCATCG

15382038 -15382479

24

M

inimal screening set for the horse (M

SS

H)























166 6 0.77 0.71 6 0.78 0.70 ACTGGAGGGGTGAAACAGATTCAGA GGAAGGGAGGTCATCAGAA

19349920 -19350033

10 ABGe353 FM179627 132 119- 137 6 0.69 0.68 5 0.64 0.61 CCAAAGAGGGTGACAGAGAAAG

CATATTTTAAAACCTTACCTGCATAC 23647748

-23648079


1997 AF000011 205 186-

206 8 0.68 0.69 11 0.87 0.78 AGAGGAAGGCGACAGGTC CATCCGTCCATCCATCAG

26936149 -26936639

10 ABGe354 FM179628 221 204- 230 8 0.74 0.72 8 0.72 0.69 TCAGTACCAGCAAATGAGTGC

GCCAGGTGTGGTCTGTCTTC 30255055

-30255473

10 ABGe356

FM179630 227 209- 233 10 0.82 0.83 9 0.83 0.80 AAGTCCTGTGCACGTGTGTG

TGACTTGATGCCTTGCTCTG 38480735

-38480769


226 7 0.66 0.61 6 0.53 0.52 AGCTAATGTCAGTAGGTTGG TTCCAAGCATCTTAAGGAGG

41439510 -41439790

10 HMS2 Guérin et al. 1994a X74631 226 220-

240 8 0.74 0.67 9 0.77 0.77 ACGGTGGCAACTGCCAAGGAAG CTTGCAGTCGAATGTGTATTAAATG

52713346 -52713758

10 ASB9 Breen et al. 1997 X93523 - 88-

102 8 0.85 0.74 9 0.73 0.67 GTGCGCATGTATGTGCGTGCC ATTTCCACAAGGGACATGAGG

54960553 -54960854


378 5 0.61 0.49 4 0.55 0.52 AAAGCCGTAAGATTGGGACA TCCATTGTGAGGGTGTAACA

61886938 -61887610


221 6 0.63 0.57 ATCATTCCTGGGGCTAAAGG GATCAACCAGGGAGGAGGAG

65756913 -65757272


217 9 0.67 0.57 7 0.79 0.72 CCCAATGAAGTCCAAGATGG GAAATCTCTAGCAAGACCCAGG

76695568 -76695883

10 ABGe357 FM179631 240 216- 258 9 0.72 0.64 TGCACCAGCACTGGTAAAAG

TGTACCTTTGCATTCTTTGTGG 81401443

-81401882


168 7 0.79 0.74 8 0.54 0.55 AAATCCCGAGCTAAAATGTA TAGGAAGATAGGATCACAAGG

10464311 -10464468

11 ABGe099 Giesecke et al. 2009 AM946382 181 164-

194 11 0.88 0.80 7 0.67 0.65 TTCCTTCTGATTGCACCACTC ATTGTGGGTGACTCCCTCTG

15170532 -15170653

11 SGCV24 U90602 118 107- 127 10 0.77 0.74 11 0.81 0.76 CTACCATTGAAGAGGGGTGGC

GAAACGAGCAGGAAGTGAATCTCC 19537447

-19537960

11 SGCV13

Godard et al. 1997

U90592 175 163- 189 6 0.60 0.56 5 0.62 0.56 GGACTAAAGCCCAACCATCCAGC

CTCACCAGTAAGGGGTTATGGGGC 26147195

-26147780


234 5 0.68 0.65 6 0.62 0.57 TCAGGAGTTTGGATAGATTTTGC TGGAATAACTGAAATGTCCAACA

34679091 -34679345

Minim


SH

) _25






















11 TKY424 Eggleston-Stott et al.

1999 AB103642 149 138-

154 6 0.65 0.57 8 0.73 0.70 ATACAGGAGTGCGCTTTTCC AAACCATCCTCCACCTTTCC

41500229 -41500479

11 UCD4571 U67412 89 71- 95 10 0.72 0.70 12 0.82 0.78 GGGGCGTGAGCATAAAGG

CGCTGGATGAGTGAGGGA 51569553

-51569863

12 SGCV10 U90591 178 173- 181 6 0.53 0.59 9 0.74 0.76 CATCCATCCTTTCCAGCTCGATATTC

CAAGACCGTAACTCAGGAGCCC 9502701

-9503196

12 SGCV08

Godard et al. 1997

U90590 130 121- 139 9 0.80 0.75 11 0.66 0.61 GAGTTCATTCTTTTTCGTGGCTG

GGAAACACCCTAAGTGTCCCTTG 21196067

-21196536

12 COR058 Ruth et al. 1999 AF108375 213 206-

230 12 0.81 0.79 13 0.82 0.78 GGGAAGGACGATGAGTGAC CACCAGGCTAAGTAGCCAAAG

27946783 -27947012


1999 U67419 110 105-

109 4 0.62 0.61 4 0.59 0.54 GTGGGAGGCAGCAGGAAC CCCCAGACACCGTGTGAT

32574328 -32574627


279 7 0.68 0.66 9 0.87 0.81 AGCCACCAGTCTGTTCTCTG AATGTCCTTTGGTGGATGAAC

6098891 -6099011

13 VHL47 van

Haeringen et al. 1998

X86449 138 126- 142 5 0.72 0.62 7 0.76 0.68 GTTTGCTGTGGTTACCAGGCAGA

GCAAATTGAATATTTGAAGTTGAGC 16894519

-16895111

13 ASB1 Breen et al. 1997 X93515 159 153-

167 5 0.72 0.60 6 0.63 0.53 AGCAGAAACCCACTCAAGCC GCATAATACCCTCAAGGTC

31743327 -31743536


103 7 0.68 0.62 6 0.61 0.68 ATACTGGCTTTACGTCACAG ATCACCACCAGAGTTAATGG

7169420 -7169458


244 5 0.51 0.44 6 0.63 0.56 CATTAAGCAACAAAAAGCATC GGAAAAGCATGACAAGACACT

16145063 -16144566

14 UM010 Meyer et al. 1997 AF195129 106 106-

120 7 0.78 0.73 9 0.80 0.75 TACAGCCATTGGAAATCTAC CACCATTACATTTTCCCAG

25466136 -25466345

14 VHL209 van


Y08451 83 83- 97 4 0.32 0.27 7 0.77 0.73 TCTTACATCCTTCCATTACAACTA

TGATACATATGTACGTGAAAGGAT 32966905

-32967040


142 8 0.92 0.81 9 0.66 0.59 TAACTAAGGGGAACAGATGG CAACTAAGGCTTATGCATGC

45639386 -45639665


214 6 0.72 0.58 10 0.78 0.78 GTTCGTCTGTTTCTAGCCTC TATCTCCACATGGTACTCTC

66594714 -66595164


170 2 0.53 0.37 3 0.51 0.36 TCCAAGGGTTTGTCCAAAAG TCTCTTGGTTGAAAATGGGG

72428874 -72429219

26

M


SS

H)













http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nucleotide&val=Y08451









268 5 0.77 0.52 6 0.45 0.42 CCTCTTGGGACAGAGGACAG TCTCTCAGGAGCCTGTGTTG

81175527 -81176020


173 5 0.59 0.61 ATCAAAGGTTCATCAGTTGGTG TTCTTTCACTCAGCGTGGTG

91640789 -91640980


149 7 0.74 0.71 6 0.61 0.58 ACCGCATTCTCTTTCAGGTG CTGGGTAACTGACTGGAAAAGG

7054694 -7055129


184 7 0.66 0.53 5 0.66 0.62 ACAGTGCTGGATGAGGAAGA CTGGTTCTTCTTGGGGACAC

11364482 -11364854


154 6 0.61 0.50 CAAGAACTGGCATCAGAATTTC TCTTGGGTCTCACTCACTCTCC

17647551 -17647773

15 B-8 Y10240 87 79- 101 10 0.66 0.67 8 0.70 0.65 TCCTCAGTCCTTTCTCATGC

AGCTGAAGGCAATCTGTACC 21788079

-21788009

15 TKY11 Hirota et al. 2001 AB048322 126-

136 6 0.70 0.66 3 0.59 0.53 ATGAGAGAGGTCACCAAAAT CCCTGCCAACAAAAACCTTG

32217580 -32217775

15 LEX051 AF075653 144 130- 146 6 0.61 0.58 7 0.69 0.69 CCTACGTGTCTCTTTCTCTTT

GTAACGCAATAATACAGCACT 35204838

-35204311

15 LEX046

Coogle et al. 1997

AF075648 127 115- 129 8 0.78 0.74 9 0.82 0.85 ATAAGCCAATCCACTTTTCC

ATTACCACCCCATTTCCTT 39365783

-39366146

15 TKY571 AB103789 124 114- 128 6 0.66 0.59 ACAGCACAGCAGCAAACAAA

CAGTGGGCCCAGGTGTATAG 41526499

-41526748

15 TKY1033 Penedo et al.

2005 AB104251 158 142-

162 7 0.79 0.69 9 0.60 0.58 AGACATGGATTTAGGGAGTG GCAGAGCCATGCTAAAACTG

43584317 -43583550

15 ASB15 X93529 139 121- 145 11 0.71 0.71 8 0.77 0.76 GTCCCAAAGGGACTCAGGAAGG

TGGATGCCAGTGCATAGACAG 50503906

-50503625

15 ASB2 X93516 181 234- 252 9 0.74 0.74 8 0.72 0.64 CCTTCCGTAGTTTAAGCTTCTG

CACAACTGAGTTCTCTGATAGG 54612560

-54612934

15 ASB19

Breen et al. 1997

X93533 176 162- 190 9 0.66 0.55 12 0.88 0.83 GAGTTGGAGCTCAAGTCTGTC

GTTTAGCAACTACAGCGTAGG 58313184

-58312495

15 ABGe 16406

Mittmann et al. 2009 FN411866 144 123

-146 8 0.77 0.75 10 0.80 0.82 ATGTTGTGCAAATGGGATGA TGCCCATTGATTGATGATTG

65460494 -65460353

15 ABGe145 FM165575 246 216- 250 8 0.75 0.77 7 0.43 0.73 TCAGGATTTAGGGCAAAAGG

CGTGACAATGAGTTCATCAAAG 74517317

-74517562

15 ABGe147

FM165577 197 181- 205 9 0.68 0.76 TGGAATATCCCAGTCAAAATG

CACTCCCTGAACCACAGGAG 77558749

-77558951

Minim


SH

) _27






http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nucleotide&val=Y10240
















15 HMB2 Binns et al. 1995 Y07730 106 95-

109 9 0.79 0.67 GTGCCACCACCTCTGTGATT TGGAGAAGGATCTTGGGCTC

81103814 -81104383

15 ABGe114 AM946993 141 124- 144 6 0.73 0.68 7 0.58 0.51 AACAGTTGTGGGGAGAGTGG

CCTCCTCCTAGCCTGTTTCC 84207465

-84207520


208 9 0.83 0.76 10 0.68 0.65 GCCCTAGTTAGCAACCAACA AAGATTGATTCCTCAGCACG

86778708 -86779060

15 15CA001 Pettigrew et al. 2005 AY878926 124 124-

134 4 0.61 0.58 CCCTATCTTCATGGGGTTTC AAGTGCATTTCCACTTCCTG

88658791 -88658755

16 ABGe094 AM942735 104 94- 108 7 0.85 0.77 10 0.78 0.81 AACTGCTGGCTGGATCTCTG

AAGACTGCCCCATTCAATACTC 2652145

-2652186

16 ABGe096

Lampe et al. 2009

AM942737 204 200- 214 5 0.56 0.56 GAGGAGGATTTTGGCCTACC

ACCACCCAAACCTCTCCAC 5016629

-5016779

16 HTG3 Ellegren et al. 2002 AF169164 116 114-

124 5 0.80 0.72 7 0.59 0.55 TAACCTGGGTGCAAAGCCACCCAT TCAGGGCCAATCTTCCTCAC

8149681 -8149612


244 5 0.81 0.69 7 0.67 0.62 GGGTTTGCTTGTGAACTTCTG GTGAAGCCCTGACTTTGAGC

17595723 -17595966

16 COR011 Hopman et al. 1999 AF083454 278 267-

277 4 0.58 0.56 5 0.57 0.56 CCTTCCGGTCTTTATTCACA GGTGGCTGGAGACACAATAG

23122963 -23122642

16 ABGe037 Lampe et al. 2009 AM919476 107 94

110 7 0.62 0.59 CTCTTACCATGCCAATCCAAG TCTAATTTGAGTTTTACCAGGTTCC

25309422 -25309528

16 HMS20 Lindgren et al. 1998 U35402 123 122-

136 5 0.64 0.58 6 0.69 0.70 TGGGAGAGGTACCTGAAATGTAC GTTGCTATAAAAAATTGTCTCCCTAC

27965789 -27966107


138 7 0.71 0.68 5 0.76 0.71 TTCATGGCCTTCAAAACTCC CCAGCTGGGGATACTTACCA

30274166 -30274658


195 7 0.69 0.65 TCCTAGGGAATTCACAGTTG TAACAGAACTACAAGGCCC

33483877 -33483472

16 ABGe054 AM919493 230 216- 228 4 0.75 0.61 5 0.61 0.59 TGGGGACCCAGGACTATCTC

TGTTTGGTGACCCTCCCTAC 38428866

-38429016

16 ABGe055 AM919494 116 96- 118 7 0.73 0.71 CCAGGATTGGCTTTTATTTTATTC

AAAGTTGTCAGGAGGTTGTTCAC 40685625

-40685679

16 ABGe058

Lampe et al. 2009

AM919497 193 186- 206 10 0.80 0.79 9 0.73 0.69 CCACACAGTATTCCCCCAAG

GGAGAGAGGGTTCAGTGCAG 43403311

-43403416


254 6 0.77 0.69 6 0.59 0.56 TTGGTTTAGGTTTTTAATTACTCTG GGTGTTGAAACAACTGGCTG

53171693 -53173389

28

M


SS

H)






















16 UMNe562 AY464528 142 129- 145 6 0.71 0.67 TGCTGTGACTATGCTGTGTCC

ATCAGCTGGTCAATGATGAGG 64762055

-64762532


1997 U67421 183 175-

197 7 0.72 0.69 8 0.70 0.68 ATCACTCTCTTGTTGAGATAAC GGGATTTCCTTCTTTCTC

68069022 -68069455


227 8 0.85 0.80 10 0.79 0.81 GACCTACAGGCCACTCATCAA GGCAGTTTCCTCCATCCTTA

70125332 -70125175

16 I-18 Marti et al. 1998 Y10244 101 97-

111 7 0.68 0.62 10 0.75 0.77 CAACAAAGATGTTGCAAGGG TGTGCCTCTTGTCTCTTAGG

74985289 -74985157


308 10 0.85 0.79 GGTCAAGCTTTTGGTTTTTCC CCTAAGGAAGAGCTGTTCTTGC

81441998 -81441686


192 7 0.76 0.74 7 0.67 0.66 TTTCCTCATTGCTTCCTGAG CCCAAGGTCTGTCTTGCTCTC

4767501 -4767676


182 9 0.70 0.68 13 0.74 0.74 GTGTTGGATGAAGCGAATGA GACTTGCCTGGCTTTGAGTC

6608591 -6608903


136 10 0.68 0.66 12 0.80 0.81 TTTTGAGGGGTGTGTTACAGC TTACCAGAGTTCTTACCTGGGG

23835784 -23836434


1996 U35423 128 128-

134 4 0.57 0.53 4 0.68 0.59 GCATTTGCTCACTGGCTAC ACTCCTCCACTCCCACCTA

28632533 -28632660


255 4 0.48 0.43 7 0.66 0.61 GCCCTCTTAGAGCATTTTCC CAGAGATGGCTGGAGTAAGG

41428027 -41428309


173 4 0.75 0.66 5 0.60 0.56 TTCACCTATGAGTTTGAGGTA CGTCATAATGCAGACTCTTTG

57221612 -57221813

17 HMS25 Godard et al. 1997 U89811 124 124-

128 3 0.48 0.42 5 0.52 0.48 CAAACATAAAATATGCATGTCCATT CTTTTGGATATGTAAGGCTTGAGG

61873305 -61873729


152 6 0.64 0.62 CAGTTCCATCCATCAGTGAC ATTCCCAAAGGGCCTTTTTC

75624074 -75624334

18 TKY19 Kakoi et al. 1999 AB048330 151 144-

160 8 0.78 0.73 8 0.85 0.79 CTTCTGCTGATTCCTGAATG GGATCTCCTTAAATGGAACA

539058 -539575


1997 U67401 - 111-

119 5 0.66 0.63 4 0.67 0.59 CTTTGGGCCTTTCCTCCAT CGAGCCTGGGAGTGATAC

4234196 -4234386

18 ABGe151 Lampe et al. 2009 FM177589 194 194-

212 8 0.75 0.69 CTCACTCTGGGCCCACTATC CGGAGTGAGAAGACAGTCCAG

10775307 -10775348

Minim


SH

) _29





















164- 182 7 0.74 0.63 7 18 LEX054 Coogle &

Bailey 1997 AF075656 0.72 0.60 TGCATGAGCCAATTCCTTAT TGGACAGATGACAGCAGTTC

16952929 -16953146 177


150 9 0.82 0.80 CCCTAGGTCCCCCACTTTAG CCATCCCTTCAGGAATACCAC

21926471 -21926521

18 HMS46 Godard et al. 1997 126 122-

134 7 0.71 0.65 U89814 4 0.65 0.49 GTCTCAGCCAAAAGGTATTCAAGC TGGCACCAATATAGGTCACCTGG

26460548 -26460958

18 ABGe153 Lampe et al. 2009 FM177591 240 220- TTGGACACAAAAAGGTAGGC 9 0.77 0.77 252 TTCCTTAGTTGGATATAGACACACAC

31834459 -31834495

18 COR096 AF154949 316 307- 319 10 0.65 Tallmadge et

al. 1999a 0.61 8 0.85 0.76 CCCCTCTTTTGCTTGAGAAT GCGTGTATGTGAGGATTGAAG

37306749 -37307216


136 8 0.63 0.60 9 0.87 0.81 GGTCAGAAGACAGTCAAGAGTCC CCTCTCAGGCCTCTTACCAC

40625246 -40625294


138 4 0.64 0.54 GCAGCTTCCCTCTGTCCAC TGACCTACGGCTTTGGTTTT

43786902 -43786955


168 7 0.75 0.70 TTAGTCATTGTCTCAAGACCTAAACAG ATTGTTAATCTTGGGCTAAGGATG

48409467 -48409507


137 8 0.77 0.72 10 0.85 0.80 CCTTCCTTCTCCTAACTCAGTCC TGGAAACCAGGAATAGGTGTG

51610274 -51610647


137 7 0.70 0.62 9 0.86 0.83 TGCAAACACTTGTGAACTGC AACCTAGTGTAATTGCTACC

54222239 -54222484


161 4 0.72 0.57 7 0.65 0.61 GCTATCCCTCCTGAGTCTTA AGGTAATTTGAAATAAAATACAC

57922256 -57922364

18 TKY101 Mashima et al. 1999 - 197-

217 8 0.83 0.73 9 0.72 0.69 TCTGAAATACCGTGTGCCT TTCTGCCTCCCTCCAACTTT

63528459 -63528478

18 TKY16 Hirota et al. 1997 AB048327 123 112-

128 5 0.72 0.66 8 0.68 0.66 GGTTATGGTTTGGTATCTGTC AAAACAATGGCTTCCTGGTCA

66838828 -66839217


184 10 0.88 0.77 GAGGGAGTCATTCCTGTACCC CCTCAGCCATGAATCTACCAG

70994530 -70994564


1999 U67404 80 76-

86 7 0.65 0.53 4 0.61 0.54 ACCCCCGCCCCAGCAC TGCCCCGTCATTCTGC

75253243 -75253043


230 8 0.83 0.74 7 0.78 0.66 TCGGCTCTTTTCTTCTATTTGC TCGGGCTCTGAATGAGAAAC

82252750 -82252784


240 5 0.62 0.58 6 0.70 0.72 CACCTCCATCACATTGGTCA GGCTGGAGTCAGCTGACATT

85306 -85632

30

M


SS

H)

















161 8 0.59 0.55 8 0.64 0.62 ATCAGCCCAGCCTCTTCA AACAACCGGCNAAATAGTGC

17854307 -17854786

19 LEX073 Bailey et al. 1999 AF213359 226 234-

272 7 0.75 0.74 11 0.72 0.75 CCAGCCATCCACTGGTAGAG GGGAAAAGGGGAACCTTCTA

24403431 -24403924

19 HMS8 Guérin et al. 1994 X74637 210 207-

215 5 0.66 0.58 6 0.62 0.57 GGTGAGGAATTATCTCTTTGAAGG GCAGGTAGGATTGGATAGGTACAT

40875827 -40876270

19 NV112 Røed et al. 1997 AF011403 121 120-

130 5 0.60 0.54 5 0.54 0.51 GGCCCCACCCACTAAATATCACTG CGGGGTCTTGGAAATTTATGAAGG

44118143 -44118426


163 8 0.53 0.45 8 0.74 0.7 TGAAAATACACCCAGCTACGC GGGAGATATTTCTTGGCTTGC

53488575 -53488712

20 AHT18 Guérin et al. 1999 167 168-

178 6 0.52 0.46 6 0.61 0.53 TTTTCCAGTGACTCTGAGTGTG GTTGTGGGAAAACTAGTCTGGC

10046144 -10046269

20 LEX064 Coogle & Bailey 1999 203 195-

215 7 0.74 0.72 6 0.73 0.59 ACCCTTTCCGCAGACAA CACATCAGAGCCCATCTTCTC

15298099 -15298299

20 UM011 Meyer et al. 1997 AF195130 167 160-

180 14 0.79 0.77 12 0.74 0.65 TGAAAGTAGAAAGGGATGTGG TCTCAGAGCAGAAGTCCCTG

33509960 -33510218


141 7 0.69 0.75 8 0.77 0.67 CACCTGCCTACAGTCCAAGC TTTGTGCTTAATGCCTTTGTG

44743346 -44743987


157 4 0.65 0.61 CATTTCAAGGGCTACTTTGACC CATACGTTTTGCACCCTCTC

49182522 -49182649

20 COR050 Ruth et al. 1999 AF108367 292 287-

297 5 0.66 0.52 5 0.54 0.44 TCTGTTGCCTTTATCCACAA ATGAAAACCCTGGGAATAGC

56106195 -56106525

20 HMS42 Godard et al. 1998 113 111-

135 5 0.68 0.62 6 0.67 0.61 TAGATTTCTTAAGTGCCAATAGTGG GAACTGCTATAGATATACCTAACTC

63743901 -63744013

21 SGCV16 Godard et al. 1997 U90594 - 146-

188 7 0.77 0.67 7 0.71 0.62 AATTCTCAAATGGTTCAGTGA CTCCCTCCCTTCCTTCTA

1927421 -1928006

21 ABGe261 Lampe et al. 2009 FM179765 348 7 0.89 0.63 TTGGCAAAATGTTGGATAAATG

GAATACAGGGGCTTTTTCTGC 4942554

-4942717


184 9 0.81 0.78 TGGAACTGTGATGATGTTGC TCTTTCTTCCCTTCCGAGAG

6997368 -6997806


178 7 0.96 0.63 CCTCCAGGCAGATGATGAAC TGAAGCAAGAGCCTCAAAGAG

11064632 -11064722


149 5 0.62 0.56 CCAAAATAATCAACCAGTTTAAAAG TGTTTGTTTATGCGATATCAGTG

15115561 -15115591

M


SS

H) _31




















113 10 0.84 0.81 7 0.65 0.52 CAATTCCCGCCCCACCCCCGGCA TTTTTATTCTGATCTGTCACATTT

17139032 -17139129

21 COR073 AF142610 226 180- 198 7 0.81 0.79 7 0.62 0.57 GCCAAGACATGGAAACAATC

GTTCTCAAGGTGCATCCCTA 20250383

-20250638

21 COR068

Tallmadge et al. 1999

AF142605 153 146- 156 6 0.82 0.70 6 0.73 0.65 AACCAATTGTGAGATTTTTGCT

GGCTAGTCCTGGATCATGTG 22008159

-22008507


115 5 0.43 0.41 AGGCAACATGAGAAGGCACA ATAGCACCTGTTCCCTGGAG

26399000 -26399213


191 11 0.79 0.76 11 0.80 0.77 CTCTCACTTCCAAGACACTC ATCAAACGTACAGGAAGAGC

44535031 -44535538


299 7 0.73 0.66 6 0.78 0.72 CAGTGTGGGTGGGCTTTATC ACCACTAGGGTGTGCATGTG

53467456 -53467789


149 9 0.63 0.65 7 0.88 0.80 CCAGTCTAAGTTTGTTGGCTAGAA CAAAGGTGAGTGATGGATGGAAGC

14279178 -14279307


263 5 0.55 0.56 4 0.53 0.53 AAGACGTGATGGGAAATCAA AGAAAGTTTTCAAATGTGCCA

22898530 -22899131

22 HTG21 Lindgren et al. 1999 - 124-

134 11 0.59 0.58 16 0.79 0.78 ATTACTTCCTCCAGGTATCTCAG AGGCAGGGCTGGGAGACGT

26806740 -26807240


181 6 0.76 0.68 7 0.62 0.53 AGGCAGCTTGACTACCCTGA AAAGTCTCCCCTGCGTGTT

31131498 -31131809


232 7 0.59 0.71 CTGCCCGGAAGTTGTAAGAC GCTCCCAGATATGGCTCTCC

34181813 -34182110

22 ABGe121 AM947000 238 226- 261 12 0.81 0.79 AGGAGCTGGAACTGACACAG

GCTTCTCAGGGCAGTATTCC 38154548

-38154785

22 ABGe122

AM947001 215 202- 228 6 0.76 0.75 GATTGCAGTCCTTTTGAAAGTAAG

CATCCCAAAGAGCAAGTGTG 41798945

-41798971


147 6 0.54 0.49 5 0.78 0.73 GCCCCCACCTGCTCCACC GGGGCAAAGTGGAAATCC

47778235 -47778257

23 UM019 Meyer et al. 1997 AF195136 160 154-

168 8 0.64 0.57 8 0.83 0.70 TACTGCCAGCACTTGTACC TCTCTCAGTTTCTCTCTCTGTC

3457790 -3458208

23 ASB39 Irvin et al. 1998 AF004769 168 156-

172 6 0.63 0.54 7 0.71 0.65 ACAGCTGCCTGGATATGTGG GCAGAGAGAAATAGAGATGC

20914650 -20914726


250 6 0.86 0.65 7 0.69 0.65 CGGGGTGTGCATCTCTTAGG TGGCGAATGCTGAATCTGG

29650202 -29650694

32

M


SS

H)





















23 LEX053 AF075655 133 123- 133 6 0.74 0.69 5 0.77 0.67 TTATTCCTGCTTCGTANATGA

ACACACTTGGGTTCAAATC 37435964

-37436489


175 6 0.80 0.70 8 0.74 0.71 AGTTGTGGCTTGCTTTCTAC TTGCACTTGAGCACTTAGTC

9791581 -9792025

24 AHT4 Binns et al. 1995 151 148-

164 9 0.77 0.67 6 0.67 0.59 AACCGCCTGAGCAAGGAAGT CCCAGAGAGTTTACCCT

23415644 -23415826

24 EA2C4 Gralak et al. 1994 Z29341 - 142-

166 5 0.63 0.66 7 0.78 0.66 ATGTATCTTCGAGGGATGAT GGCAGTTAATGGTGAGTAAG

25248572 -25248752

24 LEX032 AF075634 208 205- 217 9 0.81 0.73 8 0.59 0.64 CAAAAGTGATTGCCTTCGAT

TTGGAAGCTGGGTGATTG 36117547

-36117934

24 COR024

Murphie et al. 1999

AF101393 178 172- 178 7 0.76 0.74 8 0.77 0.69 ACAGAGCTGACTGCCTATGG

TCCTCTTCTCAGGGAGACCT 41000128

-41000446


1999 U67413 98 90-

102 4 0.59 0.47 7 0.66 0.53 ATGCTCTGAGAATAAGTCTGG AAAAGGCGAGAATGGAAT

1979447 -1979903


275 7 0.54 0.51 7 0.66 0.58 AGTCTGGCAATATTGAGGATGT AGCAGCTACCCTTTGAATACTG

15686913 -15687380


1997 272 244-

270 9 0.77 0.68 6 0.68 0.57 ACCTCGTCTGGCTGTTGTAAG ACTTGCTGTGCGACTCTG

19001568 -19001909

25 NV432 Røed et al. 1998 AF056396 157 135-

158 7 0.70 0.69 9 0.76 0.73 TGACACAAGATAAAAGCCCCAGG GATTGGGAAAAGAGCACAGCC

31067763 -31068076

26 ABGe126 AM947005 256 251- 271 7 0.79 0.73 13 0.85 0.76 AACCCTGAAATAACCAAAGTGC

CGCTTTGAAAGAGCTTTTACTCC 3050929

-3051113


158 5 0.65 0.61 6 0.74 0.75 TTCCAGTGGTTAGGATGTAG TTGAGCATAGTGATAGCATATG

6024034 -6024408

26 ABGe124 AM947003 228 206- 248 11 0.73 0.71 11 0.65 0.67 TAACACAAAGCCCCCAGTTG

GCCAAACCACACATGAGAGC 13163108

-13162965


153 9 0.80 0.73 8 0.78 0.81 GTGCTGGAGTGAGCTGACC ATCCAAATCGGAGACCATATG

18511390 -18511644

26 A-17 Marti et al. 1998 X94446 94 95-

111 9 0.80 0.79 6 0.72 0.67 GTGGAGAGATAAAAGAAGATCC GGCCACAAGGAATGAACACAC

21072789 -21072843

26 UM005 Meyer et al. 1997 AF195127 209 210-

224 7 0.79 0.71 7 0.53 0.51 CCCTACCTGAAATGAGAATTG GGCAAAAGATCAGGCCAT

26044385 -26044705

26 NV702 Bjornstad et al. 2000 AJ245765 198 189-

203 7 0.73 0.67 7 0.87 0.77 GCTGGTCAAGTCACACTGTG AACCTCACCCCAAGTTGTAT

30252524 -30253087

Minim


SH

) _33





















183 8 0.82 0.80 9 0.76 0.76 TGCACACCCATTCTAGCTCA GTGGCTCACTCCTCGCTTAC

38283750 -38284242


214 5 0.68 0.63 6 0.79 0.71 CAATTGCCATTTGTTCCAGTG GCTTAAGAAACACCAGGCAG

1374633 -1374857


1996 U35423 238 226-

240 9 0.78 0.71 10 0.76 0.76 AGCGGAAGTGCTGCGAAAG CCAGCATCTCTGGGCAGG

14048704 -14048941


107 8 0.54 0.47 8 0.72 0.74 GATGCCTCGAACTAGCTTG GATCTTCCATGTTTTTGTTTGG

20774870 -20775400

27 LEX005 Coogle et al. 1996c AF075609 244 243-

265 8 0.57 0.52 6 0.59 0.51 AAGGCAATGCTTATCAAATGC TTACCCGCAGTGACTTCTATT

26149342 -26149832


253 7 0.83 0.74 7 0.63 0.62 GAAGGCCTGAAGCATTTACA CGTAATGTTGACCAAACTTCA

35275619 -35275945

28 TKY333 Tozaki et al. 2001 AB044834 113 8 0.75 0.78 9 0.82 0.76 CCTTCACTAGCCTTCAAATG

TTGTGTTTAGACAGTGCTGC 2475434

-2475865

28 HTG30 Lindgren et al. 2000

226 224- 242 4 0.67 0.53 TCAAGGCAAATCTTTCCCAG

GTAAAATAACAAGTTGTTCCAG 10876313

-10876497


125 7 0.95 0.73 8 0.72 0.58 CCTGGGTGTCGTGTGTTTTA TTCCTCTCTCCTGCCTCATC

29125140 -29125543


1997 U67406 242 233-

245 7 0.65 0.59 8 0.77 0.71 AGCTGCCTCGTTAATTCA CTCATGTCCGCTTGTCTC

43085531 -43085844

29 LEX018 Coogle et al. 1996a AF075620 - 228-

246 8 0.65 0.61 6 0.72 0.62 TTTCATCACTTTCTGCTTCC TTCTCTTCCTTTGCTCATCCT

71054 -71649


228 7 0.85 0.77 7 0.70 0.68 GCTTTTGTTTCTCAATCCTAGC TGAAGTCAAATCCCTGCTTC

4277101 -4277427

29 TKY715 AB103933 - 214- 238 7 0.64 0.61 CAGTTTCACAGGAGAGAGAGTCC

CTGGAGTCCCACCTCCAAC 12285763

-12286003

29 TKY628 Tozaki et al.

2004 AB103846 228 240-

250 5 0.46 0.45 7 0.74 0.68 TGACACACAGGACCATCTCG AAGTGCACTGAGACCCCATT

18046426 -18046731


243 8 0.64 0.56 9 0.76 0.73 CAGCTCTGCAATTTCTCCTC AATGACCAAGGCATTGAAAG

22227248 -22227664

29 ASB43 Irvin et al. 1998 AF004773 91 85-

99 6 0.79 0.62 7 0.58 0.52 TCACTTAGTAGGGGCATGC GTGTTTGTCCTTGACTCTCC

30338025 -30338070

30 LEX025 Coogle et al. 1996b AF075627 158 141-

157 7 0.73 0.65 8 0.74 0.76 CAATCGTGGCCCGGTAAC TTCACTCCAATCCTCAGTCA

2041690 -2042135

34

M


SS

H)





















30 ABGe 11561

Mittmann et al. 2009 FN418917 - 144-

158 6 0.62 0.67 ATATGTCATATTTGAACAAGTCG GCACTGAAATCGAACATCTAA

7292962 -7292983

30 VHL20 van


X75970 87 88- 106 9 0.80 0.75 11 0.77 0.76 CAAGTCCTCTTACTTGAAGACTAG

AACTCAGGGAGAATCTTCCTCAG 18793793

-18793938

30 LEX075 Bailey et al. 2000 AF213361 153 148-

160 5 0.61 0.55 8 0.84 0.80 TGAAAAGTTGCAGTTTGAGA CAACCTCTTGCTACCAGAATA

26871756 -26872074


165 9 0.74 0.70 14 0.81 0.77 CTGAGGGCGTAAGTCGAGTC GTTAATAGGAGCGGTTGTTTGG

602105 -602350


262 7 0.79 0.69 14 0.88 0.85 CGAAGCTTCCACTCTTTTCC CCGAATTATCCCTGCCCTAA

11153663 -11154116

31 ABGe241 Giesecke et al. 2009 FM179521 243 214-

250 10 0.80 0.77 15 0.81 0.80 AAAACCAGTCATGCGGAATC TGAGCTTGTTCCTGCTAGGG

16164482 -16164935

31 AHT34 AJ271519 138 121- 141 7 0.76 0.72 7 0.73 0.68 CTCAGGGCGAATGTTCCTC

CCCCACCATGAGTCAAAAAC 21679437

-21679605

X UCD4281 Del-Valle et al. 1996 U67407 139 125-

137 4 0.64 0.58 5 0.68 0.68 CTTTTCCCCGAACCTCCTAC TTGGATGCTCCGAGAAGAGT

5103087 -5103517

X UMNe202 Mickelson et al. 2003 AF536285 187 4 0.57 0.60 4 0.63 0.56 ATGATTCCAAATGAGGCCTG

AGCAATCCTTGCAGGCAG 10034139

-10034166

X UCD5021 Eggleston-Scott et al.

1997 U67420 157 143-

165 7 0.75 0.79 10 0.82 0.77 CCTTGGGCTTTAGCAACT CCATTGGAAACTGAGAGG

18552172 -18552442

X LEX027 Coogle et al. 1996b AF075629 192 187-

199 7 0.72 0.67 7 0.65 0.71 ACCACTGGGAAACTGTGTAA GCCCAGAATCCGAACC

23668468 -23668785

X LEX010 Coogle et al. 1996a AF075613 200 198-

206 9 0.81 0.79 8 0.66 0.7 TGGGCTAAAATTTAATTTGGG ACCAAAACATATGCAAATTAA

36479399 -36479893

X AHT28 Swinburne et al. 2000b AJ271513 193 178-

222 13 0.79 0.82 16 0.75 0.83 CCTGGCTTATAGATGGCTGC ATTTGGAGATGGGGGTCTTT

53996907 -53997260

X UMNe060 Roberts et al. 2000 AF191694 146-

154 4 0.67 0.68 4 0.49 0.47 TGTGGCAGGAAAAACACATG CCATAATCCATGAGCCTATTCC

60776667 -60776844

X LEX013 AF075615 123 122- 128 5 0.55 0.61 5 0.65 0.59 TGCTAGAGGAAGGGATAAAGG

CTCTGCTCTTCCATTTCTTGC 82443939

-82444439

X LEX024 AF075626 134 132- 150 10 0.85 0.82 8 0.69 0.67 GGGGGTAGAGGGAAAAAGAG

TTGTTGGCAGATCCCAGG 85572552

-85572859

X LEX022

Coogle et al. 1996a

AF075624 107 101- 113 6 0.61 0.62 7 0.57 0.61 AACATATCCATCGCCTCACA

TGCAAATTCACTGAGAGTGG 104717060

-104717366

Minim


SH

) _35


















36

M


SS

H)





X LEX003 Coogle et al. 1996c AF075607 158 143-

164 10 0.71 0.73 10 0.59 0.78 ACATCTAACCAGTGCTGAGACT GAAGGAAAAAAAGGAGGAAGAC

110523778 -110524338

1 UCD = UCDEQ or UCD-E-CA 2 NV = NVHEQ + TKY337 another Forward Primer was used



References Bailey E, Skow L, Bernoco D, DelValle A, Scavone MD, Bowling AT, Murray JD,

2000. Equine dinucleotide repeat loci LEX071 through LEX078. Anim Genet.

31:286-287.

Bjørnstad G, Midthjell L, Røed KH, 2000. Characterization of ten equine dinucleotide

microsatellite loci: NVHEQ21. NVHEQ54. NVHEQ67. NVHEQ70. NVHEQ75.

NVHEQ77. NVHEQ79. NVHEQ81. NVHEQ82 and NVHEQ83. Anim Genet.

31:78-79.

Breen M, Lindgren G, Binns MM, Norman J, Irvin Z, Bell K, Sandberg K, Ellegren H,

1997. Genetical and physical assignments of equine microsatellites-first

integration of anchored markers in horse genome mapping. Mamm Genome.

8:267-273.



Skow LC, 2003. The first-generation whole-genome radiation hybrid map in the


Res 13:742-751.

Coogle L, Bailey E, Reid R, Russ M, 1996a. Equine dinucleotide repeat

polymorphisms at loci LEX002. -003. -004. -005. -007. -008. -009. -010. -011. -

013 and -014. Anim Genet. 27:126-127.

Coogle L, Reid R, Bailey E, 1996b. Equine dinucleotide repeat loci LEX015-LEX024.

Anim Genet. 27:217-218.

Coogle L, Reid R, Bailey E, 1996c.Equine dinucleotide repeat loci from LEX025 to

LEX033. Anim Genet. 27:289-290.

Coogle L, Reid R, Bailey E, 1997. Equine dinucleotide repeat loci LEX034-LEX048.

Anim Genet. 28:309.

Coogle L, Bailey E, 1997. Equine dinucleotide repeat loci LEX049-LEX063. Anim

Genet. 28:378.

Coogle L, Bailey E, 1999. Equine dinucleotide repeat loci LEX064 through LEX070.

Anim Genet. 30:71-72.

38_______________________________Minimal screening set for the horse (MSSH)

Eggleston-Stott ML, DelValle A, Bowling AT, Bautista M, Zahorchak R, Malyj W,

1996. Four equine dinucleotide repeats at microsatellite loci UCDEQ5.

UCDEQ14. UCDEQ46 and UCDEQ62. Anim Genet. 27:129.

Eggleston-Stott ML, DelValle A, Bautista M, Dileanis S, Wictum E, Bowling AT, 1997.

Nine equine dinucleotide repeats at microsatellite loci UCDEQ136. UCDEQ405.

UCDEQ412. UCDEQ425. UCDEQ437. UCDEQ467. UCDEQ487. UCDEQ502

and UCDEQ505. Anim Genet. 28:370-371.

Eggleston-Stott ML, DelValle A, Bautista M, Dileanis S, Wictum E, 1999. Twelve

equine dinucleotide repeats at microsatellite loci UCDEQ304. UCDEQ380.

UCDEQ387. UCDEQ411. UCDEQ439. UCDEQ440. UCDEQ455. UCDEQ457.

UCDEQ464. UCDEQ465. UCDEQ482 and UCDEQ497. Anim Genet. 30:69-70.

Ellegren H, Johansson M, Sandberg K, Andersson L, 1992. Cloning of highly

polymorphic microsatellites in the horse. Anim Genet. 23:133-142.

Godard S, Vaiman D, Oustry A, Nocart M, Bertaud M, Guzylack S, Mériaux JC,

Cribiu EP, Guérin G, 1997. Characterization. genetic and physical mapping

analysis of 36 horse plasmid and cosmid-derived microsatellites. Mamm Genome.

8:745-750.

Gralak B, Coppieters W, Van de Weghe A, 1994. Two new equine dinucleotide

repeat microsatellites at the EA2C4 and EB2E8 loci. Anim Genet. 25:285.

Guérin G, Bertaud M, Amigues Y, 1994. Characterization of seven new horse

microsatellites: HMS1. HMS2. HMS3. HMS5. HMS6. HMS7 and HMS8. Anim

Genet. 25:62.

Guérin G, Bertaud M, 1996. Characterization of two polymorphic horse

microsatellites: HMS15 and HMS20. Anim Genet. 27:123.

Hirota K, Tozaki T, Mashima S, Miura N, 2001. Cytogenetic assignment and genetic

characterization of the horse microsatellites. TKY4-18. TKY20. TKY22-24.

TKY30-41 derived from a cosmid library. Anim Genet. 32:160-162.

Hopman TJ, Han EB, Story MR, Schug MD, Aquadro CF, Bowling AT, Murray JD,

Caetano AR, Antczak DF, 1999. Equine dinucleotide repeat loci COR001-

COR020. Anim Genet. 30:225-226.

Irvin Z, Giffard J, Brandon R, Breen M, Bell K, 1998. Equine dinucleotide repeat

polymorphisms at loci ASB 21. 23. 25 and 37-43. Anim Genet. 29:67.


Lampe V, Dierks C, Distl O, 2009. Refinement of a quantitative trait locus on equine

chromosome 5 responsible for fetlock osteochondrosis in Hanoverian warmblood

horses. Anim Genet. 40:553-555.

Lampe V, 2009. Fine mapping of quantative trait loci (QTL) for osteochondrosis in

Hanoverian warmblood horses. Hanover: University of Veterinary Medicine

Hanover. 210p. http://elib.tiho-hannover.de/dissertations/lampev_ss09.pdf

Lindgren G, Persson H, Ellegren H, 1999. Five equine dinucleotide microsatellite loci

HTG17. HTG20. HTG21. HTG28 and HTG31. Anim Genet. 30:70-71.

Lindgren G, 2000. Ten equine dinucleotide microsatellite repeats HTG18-19. HTG22-

24. HTG26-27. HGT29-30 and HTG32. Anim Genet. 31:145-146.

Lopes MS, Diesterbeck U, da Câmara Machado A, Distl O. 2009. Fine mapping a

quantative trait locus on horse chromosome 2 associated with radiological signs

of navicular disease in Hanoverian warmblood horses. Anim Genet. Epub DOI

10.1111/j.1365-2052.2009.01923

Marklund S, Ellegren H, Eriksson S, Sandberg K, Andersson L, 1994. Parentage

testing and linkage analysis in the horse using a set of highly polymorphic

microsatellites. Anim Genet. 25:19-23.

Marti E, Breen M, Fischer P, Swinburne J, Binns MM, 1998. Six new cosmid derived

and physically mapped equine dinucleotide repeat microsatellites. Anim Genet.

29:236-238.

Mashima S, Tozaki T, Swinburne J, Kakoi H, Binns M, Miura N, 2001.

Characterization of equine microsatellite loci. TKY102-TKY112. Anim Genet.

32:117-119.

Meyer AH, Valberg SJ, Hillers KR, Schweitzer JK, Mickelson JR, 1997. Sixteen new

polymorphic equine microsatellites. Anim Genet. 28:69-70.

Mickelson JR, Wu JT, Morrison LY, Swinburne JE, Binns MM, Reed KM, Alexander

LJ, 2003. Eighty-three previously unreported equine microsatellite loci. Anim

Genet. 34:71-74.

Milenkovic D, Mariat D, Swinburne J, Chadi-Taourit S, Binns M, Guérin G, 2005.

Characterization and localization of 17 microsatellites derived from BACs in the

horse. Anim Genet. 36:164-166.

http://elib.tiho-hannover.de/dissertations/lampev_ss09.pdf


Murphie AM, Hopman TJ, Schug MD, Aquadro CF, Bowling AT, Murray JD, Caetano

AR, Antczak DF, 1999. Equine dinucleotide repeat loci COR021-COR040. Anim

Genet. 30:235-237.



JE, Tozaki T, 2005. International equine gene mapping workshop report: a


merging four mapping resources. Cytogenet Genome Res. 111:5-15.

Røed KH, Midthjell L, Bjørnstad G, Olsaker I, 1997. Equine dinucleotide repeat

microsatellites at the NVHEQ5. NVHEQ7. NVHEQ11. NVHEQ18 and NVHEQ24

loci. Anim Genet. 28:381-382.

Røed KH, Midthjell L, Bjørnstad G, 1998. Eight new equine dinucleotide repeat

microsatellites at the NVHEQ26. NVHEQ29. NVHEQ31. NVHEQ40. NVHEQ43.

NVHEQ90. NVHEQ98 and NVHEQ100 loci. Anim Genet. 29:470.

Ruth LS, Hopman TJ, Schug MD, Antczak DF, 1999. Equine dinucleotide repeat loci

COR041-COR060. Anim Genet. 30:320-321.

Sargan DR, Aguirre-Hernandez J, Galibert F, Ostrander EA, 2007. An extended

microsatellite set for linkage mapping in the domestic dog. J Hered. 98:221-31.

Swinburne JE, Marti E, Breen M, Binns MM, 1997. Characterization of twelve new

horse microsatellite loci: AHT12-AHT23. Anim Genet. 28:453.

Swinburne J, Gerstenberg C, Breen M, Aldridge V, Lockhart L, Marti E, Antczak D,

Eggleston-Stott M, Bailey E, Mickelson J, Røed K, Lindgren G, von Haeringen W,

Guérin G, Bjarnason J, Allen T, Binns M, 2000a. First comprehensive low-density

horse linkage map based on two 3-generation. full-sibling. cross-bred horse

reference families. Genomics. 66:123-34.

Swinburne JE, Lockhart L, Aldridge V, Marti E, Breen M, Binns MM, 2000b.

Characterisation of 25 new physically mapped horse microsatellite loci: AHT24++-

48. Anim Genet. 31:237-238.

Swinburne JE, Turner A, Alexander LJ, Mickleson JR, Binns MM, 2003.

Characterization and linkage map assignments for 61 new horse microsatellite

loci (AHT49-109). Anim Genet. 34:65-68.



Kurosawa M, Leeb T, Mashima S, Mickelson JR, Raudsepp T, Tozaki T, Binns M,

2005. Single linkage group per chromosome genetic linkage map for the horse.

based on two three-generation. full-sibling. crossbred horse reference families.

Genomics 87:1-29.

Tallmadge RL, Hopman TJ, Schug MD, Aquadro CF, Bowling AT, Murray JD,

Caetano AR, Antczak DF, 1999a. Equine dinucleotide repeat loci COR061-

COR080. Anim Genet. 30:462-463.

Tallmadge RL, Evans KG, Hopman TJ, Schug MD, Aquadro CF, Bowling AT, Murray

JD, Caetano AR, Antczak DF, 1999b. Equine dinucleotide repeat loci COR081-

COR100. Anim Genet. 30:470-471.

Tozaki T, Kakoi H, Mashima S, Hirota K, Hasegawa T, Ishida N, Miura N, Tomita M,

2000. The isolation and characterization of 34 equine microsatellite loci. TKY290-

TKY323. Anim Genet. 30:234-236.

Tozaki T, Mashima S, Hirota K, Miura N, Choi-Miura NH, Tomita M,

2001.Characterization of equine microsatellites and microsatellite-linked repetitive

elements (eMLREs) by efficient cloning and genotyping methods. DNA Res. 28;

8:33-45.

Tozaki T, Penedo MCT, Oliveira RP, Katz JP, Millon LV, Ward T, Pettigrew DC,

Brault LS, Tomita M, Kurosawa M, Hasegawa T, Hirota K, 2004. Isolation.

characterization and chromosome assignment of 341 newly isolated equine

microsatellite markers. Anim Genet. 35:462-504.

Tozaki T, Swinburne J, Hirota K, Hasegawa T, Ishida N, Tobe T, 2007. Improved

resolution of the comparative horse-human map: investigating markers with in

silico and linkage mapping approaches. Gene 392:181-186.

van Haeringen H, Bowling AT, Stott ML, Lenstra JA, Zwaagstra KA, 1994. A highly

polymorphic horse microsatellite locus: VHL20. Anim Genet. 25:207.

van Haeringen WA, van de Goor LH, van der Hout N, Lenstra JA, 1998.

Characterization of 24 equine microsatellite loci. Anim Genet. 29:153-156.

Wagner ML, Goh G, Wu JT, Morrison LY, Alexander LJ, Raudsepp T, Skow LC,

Chowdhary BP, Mickelson JR, 2004. Sixty-seven new equine microsatellite loci

assigned to the equine radiation hybrid map. Anim Genet. 35:484-486.


Supplementary Table 2. Multiplex groups (n = 54) with the optimum annealing

temperatures and the expected size ranges for markers of the minimal screening set

for horses (MSSH).

Multiplex Annealing temperature

Marker Size range IRD Primer μl/plate

1 50 EA2C4 138 - 166 8 5,25 LEX061 142 - 160 7 5,25 LEX064 195 - 217 8 5,25

2 52 ABGe082 86 - 98 7 9,45 TKY1162 122 - 134 8 5,88 TKY462 155 - 165 7 10,50 ABGe141 181 - 213 8 5,25 UMNe202 186 - 192 7 6,30 LEX010 198 - 218 8 9,45

3 55 UCD304 95 - 113 7 4,20 UMNe158 125 - 149 8 5,25 TKY597 158 - 170 7 6,30 TKY350 165 - 195 8 5,25 LEX020 192 - 211 7 6,30 LEX058 222 - 232 8 9,45 COR046 247 - 259 7 5,25 UM015 302 - 312 7 4,20 LEX009 366 - 378 7 5,25

4 55 HTG10 93 - 113 8 10,50 TKY344 90 - 108 7 10,50 TKY310 130 - 142 8 6,30 UCD502 143 - 165 7 7,35 TKY296 169 - 191 8 5,25 ABGe084 182 - 210 7 9,45 ABGe065 232 - 312 7 12,60 LEX073 232 - 270 8 5,25

5 55 TKY315 79 - 107 7 4,20 ASB4 120 - 144 7 8,40 LEX038 131 - 145 8 4,83 TKY924 164 - 174 7 6,30


Supplementary Table2. continued Multiplex Annealing

temperature Marker Size range IRD Primer

μl/plate 5 continued 55 TKY101 197 - 217 7 4,20

UCD465 200 - 206 8 7,35 6 55 HTG3 114 - 124 8 4,83 ABGe3459 128 - 149 7 4,20 UMNe448 151 - 169 8 5,25 TKY353 177 - 191 7 6,30 AHT92 243 - 296 7 5,25 7 55 UCD464 90 - 102 7 5,25 HMS42 111 - 135 8 7,35 ASB23 147 - 167 7 10,50 TKY281 174 - 192 8 5,25 LEX007 192 - 200 7 5,25 8 55 LEX050 108 - 126 8 5,25 HTG4 127 - 139 7 5,25 UMNe323 166 - 212 8 7,35 TKY435 184 - 214 7 5,88 LEX063 222 - 250 7 5,25 9 55 TKY384 103 - 135 7 4,41 SGCV16 140 - 188 8 5,25 UMNe116 154 - 168 7 7,35 UCD425 233 - 245 7 5,25

10 55 UM010 106 - 120 7 5,25 VHL47 126 - 142 8 7,35 ABGe3951 134 - 156 7 4,83 TKY363 171 - 183 8 5,25 UCD505 183 - 195 7 10,50 LEX056 211 - 227 7 6,30 UCD5 224 - 242 8 7,35 LEX005 243 - 265 7 6,30

11 55 TKY333 91 - 111 8 5,25 TKY19 144 - 160 8 3,15 COR075 145 - 161 7 4,20

HTG8 178 - 190 8 7,35




μl/plate 11 continued 55 UM005 209 - 224 7 8,40

HTG30 224 - 242 8 7,35 LEX032 249 - 267 7 5,25

12 55 TKY11 126 - 136 7 6,30 UM019 134 - 180 8 9,45 LEX054 164 - 182 7 6,30

13 55 HTG14 129 - 149 8 3,15 LEX036 141 - 161 7 3,15 LEX027 187 - 199 7 7,35

14 55 LEX022 101 - 113 7 6,30 LEX053 123 - 133 8 6,30 LEX025 141 - 157 7 4,83 ABGe069 158 - 208 8 9,45

15 55 LEX024 132 - 152 7 8,40 LEX003 143 - 163 8 5,25

16 58 TKY942 90 - 104 7 5,25 TKY507 114 - 120 8 6,30 15CA001 124 - 130 7 5,25 ABGe086 135 - 149 8 5,25 UMNe103 145 - 159 7 5,25 TKY899 161 - 179 8 6,30 ABGe354 204 - 230 7 5,25 ABGe054 216 - 228 8 4,20

17 58 COR090 91 - 101 8 10,50 ABGe055 96 - 118 7 5,25 HMS20 122 - 136 8 16,80 AHT97 149 - 163 7 5,25 ASB19 155 -190 8 6,30 TKY867 202 - 226 7 5,25 TKY524 232 - 250 8 5,25

18 58 UCD387 76 - 86 7 6,30 I-18 97 - 111 8 16,80

ASB10 142 - 152 7 6,30




μl/plate 18 continued 58 VHL123 145 - 151 8 5,25

COR053 171 - 199 8 3,15 ABGe006 176 - 210 7 5,25 LEX043 224 - 244 7 6,30 UMNe534 236 - 253 8 9,45

19 58 VIAS-H34 102 - 116 7 6,30 1CA16 114 - 124 8 5,25 ABGe067 136 - 144 7 5,25 UMNe070 150 - 156 8 5,25 ABGe157 166 - 184 7 6,30 TKY1451 190 - 208 8 6,30 ABGe151 194 - 212 7 3,15 ABGe033 236 - 244 8 8,40

20 58 TKY1053 88 - 94 8 4,83 TKY911 132 - 148 8 4,83 AHT107 128 - 200 7 4,83 UMNe582 165 - 183 8 5,25 HMS8 207 - 215 8 8,40 ABGe357 216 - 258 7 8,40 COR097 236 - 242 8 9,45

21 58 UCD440 105 - 115 7 10,50 UMNe222 135 - 151 8 5,25 ABGe3282 142 - 170 7 6,30 COR056 186 - 212 8 5,25 COR100 192 - 218 7 6,30 COR027 229 - 243 8 4,20

22 58 A-17 95 - 111 8 6,30 1CA16 114 - 124 7 5,25 AHT38 130 - 140 8 4,20 ABGe156 146 - 168 7 5,25 NVHEQ70 189 - 205 7 5,25 COR062 206 - 236 8 5,25

COR057 235 - 243 7 5,25




μl/plate 22 continued 58 COR089 276 - 298 8 7,35

23 58 LEX046 115 - 127 8 5,25 LEX051 130 - 146 7 5,25 AHT018 168 - 178 7 5,25 AHT058 164 - 196 8 4,83 UCD493 198 - 215 7 6,30 TKY628 228 - 238 8 6,30

24 58 UCD428 125 - 137 7 7,35 UMNe060 142 - 152 8 7,35 COR048 167 - 187 7 16,80 COR058 206 - 230 7 4,20 ABGe241 211 - 250 8 7,98 UMNe566 246 - 254 7 5,88

25 58 ABGe353 119 - 137 8 6,30 LEX013 122 - 131 7 7,35 ABGe351 144 - 180 7 5,25 ABGe354 204 - 230 7 5,25 TKY3193 218 - 234 8 8,40 COR022 256 - 264 8 8,40

26 58 VHL209 83 - 97 8 8,40 TKY741 119 - 137 8 6,30 UMNe198 136 - 154 7 4,83 UM011 160 - 180 8 10,50 TKY2489 166 - 184 7 5,88 TKY496 199 - 221 8 6,30 ABGe159 214 - 230 7 5,25 COR098 237 - 251 8 5,25 COR050 287 - 297 7 4,20

27 58 UCD136 111 - 119 7 6,30 AHT55 147 - 163 8 5,25 ASB1 153 - 167 7 4,20 COR073 180 - 198 7 4,20

SGCV23 198 - 246 8 3,15




μl/plate 27 continued 58 COR033 213 - 245 7 5,25

COR065 272 - 290 7 10,50 28 58 VHL20 83 - 97 7 5,25

ASB43 85 - 99 8 7,35 HTG21 120 - 138 7 5,25 COR007 153 - 173 8 4,20 TKY582 169 - 181 7 12,60 TKY805 190 - 206 8 5,25 COR024 204 - 219 7 5,25 COR069 259 - 283 8 6,30 COR023 269 - 279 7 5,25

29 58 TKY352 77 - 95 7 5,25 TKY1053 88 - 94 8 4,83 TKY937 120 - 150 7 6,30 TKY1033 142 - 162 8 5,25 ABGe058 186 - 204 7 5,25 ASB2 181 8 4,20 COR032 249 - 255 7 6,30

30 58 SGCV24 107 - 127 7 9,45 SGCV8 121 - 129 8 9,45 HMS3 149 - 167 8 7,98 COR012 166 - 176 7 5,25 COR095 198 - 218 7 7,35 COR003 192 - 206 8 7,35 COR018 253 - 275 7 3,15 TKY623 257 - 275 8 5,25 ABGe261 348 7 5,25

31 58 ABGe155 112 - 136 7 4,62 TKY934 142 - 158 8 6,30 TKY806 159 - 181 7 5,25 ABGe147 181 - 205 8 10,50 ABGe145 216 - 250 7 9,45

ABGe153 220 - 252 8 8,40




μl/plate 32 58 ASB15 121 - 145 7 6,30 ABGe152 132 - 150 8 5,25 ABGe096 200 - 214 7 6,30 COR031 202 - 214 8 5,25 COR017 235 - 253 7 4,20

33 58 HMS46 122 - 137 7 5,25 SGCV19 139 - 145 8 3,15 COR082 198 - 220 8 5,25 HMS8 207 - 215 7 10,50

34 58 AHT33 135 - 173 8 9,45 35 60 UCD497 105 - 109 7 5,25 TKY2 106 - 120 8 7,35 ASB41 133 - 159 7 7,35 UMNe244 168 - 170 8 6,30 HMS15 214 - 234 8 7,35 HMS2 218 - 236 7 6,30 TKY491 256 - 277 7 6,30

36 60 B-8 79 - 101 7 5,25 TKY425 91 - 107 8 6,30 UMNe222 135 - 151 7 6,30 ABGe11561 144 - 158 8 4,83 UMNe239 165 - 173 7 6,30 AHT86 195 - 216 7 4,20 TKY784 200 - 214 8 5,25 LEX023 223 - 247 7 6,30

37 60 AHT12 102 - 114 8 6,30 ABGe144 115 - 141 7 7,35 TKY661 140 - 152 8 7,35 NVHEQ100 184 - 210 7 5,88 AHT40 199 - 215 8 5,25 ABGe126 251 - 271 7 6,30

38 60 ABGe16406 122 - 146 8 12,60 UMNe455 124 - 134 7 4,83




μl/plate 38 continued 60 AHT43 156 - 190 7 5,25

ASB12 168 - 182 8 8,40 TKY412 214 - 228 7 6,30 ABGe342 220 - 237 8 8,40 TKY601 256 - 286 7 6,30

39 60 AHT84 94 - 208 7 6,30 ABGe012 109 - 127 8 5,25 ABGe111 145 - 161 8 5,25 ABGe109 204 - 218 8 8,40 ABGe001 237 - 333 7 5,25

40 60 UCD457 73 - 94 7 7,35 ABGe037 94 - 110 8 5,25 TKY792 139 - 151 8 6,30 AHT36 134 - 146 7 5,25 SGCV10 173 - 181 7 7,35 ASB3 196 - 208 8 4,20 UMNe567 222 - 228 8 5,88 COR028 229 - 243 7 6,30

41 60 UCD14 128 - 134 8 6,30 UMNe562 129 - 145 7 5,25 COR020 150 - 164 8 5,25 UCD437 165 - 187 7 5,25 COR072 174 - 192 8 7,35 TKY755 200 - 246 7 5,88 A-14 208 - 236 8 13,65

42 60 UMNe176 103 - 136 7 6,30 NVHEQ11 120 - 130 8 6,30 ABGe11561 144 - 158 8 4,20 TKY358 154 - 164 7 5,25 TKY1175 176 - 204 8 8,40 UM026 204 - 216 7 6,30 TKY710 228 - 238 8 8,40

AHT94 232 - 240 7 4,20




μl/plate 42 continued 60 TKY466 311 - 323 7 5,25

43 60 ABGe094 94 - 108 7 5,25 HMS25 124 - 128 7 9,45 TKY661 140 - 152 8 8,40 TKY354 164 - 174 7 5,25 ABGe003 182 - 200 7 7,98 NVHEQ100 190 - 210 8 6,30 UCD46 228 - 234 7 7,35 ABGe073 255 - 295 7 13,65 AHT68 292 - 310 8 5,25

44 60 ASB17 89 - 115 8 8,40 TKY16 112 - 128 7 5,25 HMS16 142 - 154 7 4,20 TKY424 158 - 174 8 6,30 ABGe059 192 - 212 7 5,25 UMNe231 230 - 240 8 4,20 ABGe349 247 - 263 7 5,25

45 60 TKY671 99 - 115 7 3,15 NVHEQ82 123 - 137 8 5,25 TKY337_2 140 - 174 7 5,25 ABGe344 164 - 178 8 5,25 TKY952 208 - 222 7 6,30 TKY2802 198 - 232 8 4,20 UCD405 252 - 270 7 4,20

46 60 LEX065 144 - 156 8 3,15 ABGe029 148 - 162 7 3,15 ABGe122 202 - 228 8 8,40 ABGe121 226 - 261 7 4,20

47 60 TKY571 114 - 128 8 3,15 ABGe114 124 - 144 7 4,83 ASB39 156 - 172 8 9,45 SGCV13 162 - 189 7 3,15

TKY715 214 - 238 7 6,30




μl/plate 47 continued 60 LEX018 228 - 246 8 4,20

48 60 TKY322 115 - 137 8 6,30 UMNe151 143 - 157 7 3,15 AHT28 178 - 222 7 5,25

49 60 COR068 146 - 156 7 4,20 AHT4 148 - 164 8 6,30

50 60 AHT34 121 - 148 7 6,30 NVHEQ43 130 - 158 8 11,55

51 62 HMB2 95 - 111 8 6,30 NVHEQ18 112 - 184 7 4,83 HMS63 145 - 174 8 8,40 ABGe105 186 - 232 7 12,60 ABGe102 204 - 226 8 5,25 ABGe347 257 - 269 8 5,25

52 62 ASB5 105 - 117 8 5,25 SGCV28 149 - 165 7 3,15 TKY284 157 - 173 8 4,20 UCD412 184 - 206 7 6,30 ABGe356 209 - 233 8 9,45 COR011 267 - 277 7 7,35 COR070 273 - 299 8 7,35

53 62 ASB9 84 - 102 7 5,25 ABGe091 130 - 142 7 10,50 SGCV30 156 - 166 8 10,50 ABGe099 173 - 193 7 6,30 ABGe124 206 - 250 8 9,45 AHT60 282 - 308 8 4,20

54 62 ABGe167 135 - 143 7 10,50 *Please note that the primers of the MSSH were selected from a large number of markers and therefore the multiplex PCRs have been optimized according to annealing temperature, primer concentration and size range. PCR products were size-fractionated on LI-COR 4200 or LI-COR 4300. IRD: 7 or 8 means that forward primers were labelled with IRD700 or IRD800.

52

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84_________________________________________________________________

CHAPTER 3

Identification of 21 781 equine microsatellites on the horse genome assembly 2.0

E. H. Mittmann, J. Wrede, J. Pook and O. Distl


Hannover, Foundation, Germany

Animal Genetics

Epub DOI 10.1111/j.1365-2052.2009.01979.x

86________________ Identification of 21 781 equine microsatellites on EquCab2.0

3 Identification of 21 781 equine microsatellites on the horse genome assembly 2.0

3.1 Source/description We identified a total of 21,781 equine microsatellites on the second horse genome

assembly EquCab2.0 (Table S1) which can be accessed at http://www.tiho-

hannover.de/einricht/zucht/index.htm. These microsatellites cover the horse genome

with a mean distance of 112 kb and a median distance of 75 kb (Table2). Most of the

repeat motifs (72 %) were dinucleotide repeats of AC, GT and AT with frequencies of

26.4%, 26.0% and 19.4%. Trinucleotide repeats represented 3.8%, tetranucleotide

repeats 17.6% and pentanucleotide repeats 1.6% of all simple sequence repeats

(SSR). We identified adjacent SINE and LINE elements and the corresponding gene

ID for 5549 (25.5%) intragenic microsatellites. Primer pairs were designed for 12 246

microsatellites after masking repetitive elements.

3.2 Identification of microsatellites and their assignment on the horse genome

Permutation sequences including all repeat motifs of two to five bases with at least 15 and up to 30 repeats were generated for the search of EquCab2.0. We then employed a stand-alone BLAST with the Blastn mode (ftp://ftp.ncbi.nih.gov/blast/executable/) with maximum e-values of e-6 to e-10 for alignment of the permutated SSRs with di-, tri-, tetra-, or pentanucleotide sequences. The maximum number of repeat motifs was set at 30 for two to four bases and at 15 for five bases. 3.3 PCR-primers

Primer pairs were designed using PRIMER3 (version 0.4.0, http://frodo.wi.mit.edu/cgi-bin/primer3/primer3_www.cgi) after masking repetitive elements using the REPEATMASKER (version3.1.5, available at http://repeatmasker.org). Using the N-masked sequences, we could design primer pairs for 12 114 microsatellites (55.8%). For an additional 1810 microsatellites, primer pairs (8.3%) were designed using the small-masked sequences.

http://www.tiho-hannover.de/einricht/zucht/index.htm


ftp://ftp.ncbi.nih.gov/blast/executable/

http://frodo.wi.mit.edu/cgi-bin/primer3/primer3_www.cgi

http://repeatmasker.org/

Identification of 21 781 equine microsatellites on EquCab2.0 87

3.4 Comments Most of the previously published microsatellites (n=4553) could be confirmed through

a BLAT search (http://genome.ucsc.edu/cgi-bin/hgBlat) on EquCab2.0 and only 97

microsatellites were assigned to ChrUn. The number of microsatellites matching with

our set was 2240. The reason why we did not retrieve all of these 4553

microsatellites was due to compound repeat motifs with a length below our threshold

of 15 repeats. Repeat motifs with less than 15 repeats were omitted as a result of

their expected lower content of polymorphisms according to the previous studies by

Karlskov-Mortensen et al.1.2 The low number of repeat motifs with more than two

base pairs is in agreement with reports for porcine microsatellites.1.2 This new

resource of microsatellites may be useful in linkage studies, for QTL and candidate

gene approaches using gene-associated markers and refinement of QTL.

3.5 Acknowledgements The authors thank the German Research Community, Bonn (DI 333/12-2) for the

financial support.

3.6 References Karlskov-Mortensen P., Hu Z. L., Gorodkin J., Reecy J. M. & Fredholm M. (2007)

Identification of 10882 porcine microsatellite sequences and virtual mapping of

4528 of these sequences. Animal Genetics 38, 401-5.

Karlskov-Mortensen P., Hu Z.L., Reecy J.M. & Fredholm M. (2008) A data resource

of 838 porcine microsatellite sequences with repeat motifs of three to six bases.

Animal Genetics 39, 85-6.

http://genome.ucsc.edu/cgi-bin/hgBlat


3.7 Table S1

The Table S1 is available at http://www.tiho-hannover.de/einricht/zucht/mol_gen/ms_horse/horse_ms.htm as a compressed exel file which is updated regularly. Due to the size of this table we refrain from printing out the data. The following data can be found in Table S1:

approximately 800 base pairs of the sequence surrounding the microsatellite the chromosome (ECA) the marker name (Marker) the Accession number (Acc.No.) with the link to the NCBI Nucleotide database the repeat motif the marker length in bp for uniform and for compound repeat motifs the BLAST position of the microsatellite on EquCab2.0 in bp the annealing temperature (Ta ) the GC content (GC%) the sequence of the forward and reverse primer the PCR product size, the position of the microsatellite in the PCR product sequence in bp the PCR product sequence the information if SINE or LINE elements occur in the sequence the Ensembl gene ID and/or the gene name if the marker is located within

and if available - the number of alleles (Alleles) the observed heterozygosity (HET) the polymorphism information content (PIC) the number of horses genotyped (n) the horse breed

3.8 Supplementary Figures 1-32

These figures show the distribution of the newly identified microsatellites across each equine chromosome per megabase and depending on the size of the chromosome per 5 Mb or per 10 Mb. The position of the centromere is indicated to explain the decrease of microsatellites around this position.




Table S2 Number of microsatellites per horse chromosome and the mean, median and 5 - 95% confidence interval (CI) of their average distance

Horse chromosome Chromosome size (Mb) Number of

microsatellitesAverage distance (kb)

Mean Median CI 5-95%

1 186 1579 118 80 6 - 356 2 121 1001 121 80 5 - 355 3 119 1036 115 75 4 - 357 4 109 948 114 76 4 -354 5 100 863 115 83 4 - 341 6 85 729 116 74 4 - 363 7 99 912 108 70 5 - 339 8 94 775 121 78 4 - 388 9 84 749 111 81 6 - 321

10 84 738 114 78 3 - 348 11 61 510 120 86 6 - 373 12 33 323 102 64 2 - 345 13 43 383 111 76 5 - 323 14 94 798 118 80 6 - 363 15 92 793 115 77 4 - 357 16 87 732 119 76 5 - 383 17 81 695 116 79 5 - 367 18 83 748 110 79 5 - 317 19 60 544 110 75 4 - 352 20 64 579 111 74 5 - 346 21 58 527 109 79 6 - 306 22 50 426 117 84 3 - 344 23 56 491 113 75 4 - 340 24 47 405 115 71 6 - 332 25 40 358 110 74 4 - 366 26 42 407 103 65 5 - 329 27 40 607 108 70 3 - 321 28 46 400 115 74 4 - 361 29 34 289 116 73 5 - 401 30 30 275 109 77 6 - 318 31 25 228 109 75 5 - 345 X 124 1407 88 54 3 - 284

chrUn 108 526 - - - Genome 2501 21,781 112 75 4 - 345


ECA1

0

4

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0 20 40 60 80 100 120 140 160 180

Mb

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Centromere

ECA2

0

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120


Centromere


ECA3

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Centromere

ECA4

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ECA5

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ECA6

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ECA7

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ECA8

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ECA9

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ECA10

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ECA11

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ECA12

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ECA13

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ECA14

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ECA15

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ECA16

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ECA17

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ECA18

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ECA19

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ECA20

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ECA21

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ECA22

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ECA23

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ECA24

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ECA25

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ECA26

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ECA27

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0 5 10 15 20 25 30 35 40 45 50 55 60 65Mb

Num

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ECA28

0

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ECA29

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ECA30

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ECA31

0

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Chromosome X

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Centromere

106________________

CHAPTER 4

Whole genome scan identifies quantitative trait loci for chronic pastern dermatitis in German draft horses

Evelyn Henrike Mittmann, Stefanie Mömke and Ottmar Distl


Hannover, Foundation

Bünteweg 17p, 30559 Hannover, Germany

Submitted for publication

108 Genome scan for chronic pastern dermatitis

4 Whole genome scan identifies quantitative trait loci for chronic pastern dermatitis in German draft horses

4.1 Summary

Chronic pastern dermatitis (CPD), also known as chronic progressive lymphedema

(CPL), is a skin disease that affects draft horses. This disease causes painful lower

leg swelling, nodule formation and skin ulceration interfering with movement. The aim

of this whole genome scan was to identify quantitative trait loci (QTL) for CPD in

German draft horses. We recorded clinical data in 917 German draft horses for CPD

and collected blood samples from these horses. Out of these 917 horses, 31 paternal

half-sib families with 378 horses from the breeds Rhenish German, Schleswig,

Saxon-Thuringian and South German were chosen for genotyping. Each half-sib

family was constituted by only one draft horse breed. Genotyping was done for 318

polymorphic microsatellites evenly distributed on all equine autosomes and the X

chromosome with a mean distance of 7.5 Mb. An across-breed multipoint linkage

analysis revealed chromosome-wide significant QTL on horse chromosomes (ECA)

1, 9, 16 and 17. Analyses by breed confirmed the QTL on ECA1 in South German

and the QTL on ECA 9, 16 and 17 in Saxon-Thuringian draft horses. For the Rhenish

German and Schleswig draft horses, further QTL on ECA4 and 10 and for the South

German draft horses an additional QTL on ECA7 were found. This is the first whole

genome scan for CPD in draft horses and it is an important step towards the

identification of candidate genes.

Key Words: chronic pastern dermatitis, chronic progressive lymphedema, draft

horses, genome scan, quantitative trait loci

4.2 Introduction

Chronic pastern dermatitis (CPD), also known as chronic progressive lymphedema

(CPL), is a common inflammatory skin disease in draft horses, particularly in heavy

and strongly feathered breeds. CPD has been described in many heavy draft horse

Genome scan for chronic pastern dermatitis 109

breeds across Europe for centuries. This condition is characterized by progressive

painful swelling, hyperkeratosis, nodule formation and fibrosis of the distal limbs (De

Cock et al. 2003). First signs of CPD can be observed at an early age. Since there is

no successful treatment, the disease progresses throughout the horse’s life with

severe symptoms in later life. CPD often leads to a heavy disfigurement and a

pronounced discomfort that interferes with movement. Severely affected horses must

be euthanized (De Cock et al. 2003, 2006, Wallraf et al. 2004a).

CPD starts as a diffuse inflammatory dermatitis with the earliest lesions found at the

pasterns that develop from scaling into hyperkeratotic and hyperplastic plaque-like

lesions (crusts) (Geburek et al. 2005). These early signs of CPD may often go

unnoticed because of the long fetlock hairs. The papular lesions can rapidly coalesce

and produce large areas of malodorous ulceration and suppuration. Affected horses

are susceptible to secondary infections by mites and bacteria. As a result, the lower

leg enlargement becomes permanent and shows the distinctive nodules and

verrucous skin lesions that can extend and encircle the entire leg.

Histopathological examination of skin biopsies from 37 CPD-affected draft horses

from several breeds showed epidermal hyperplasia and hyperkeratosis as the

predominant pathological characteristics in all horses (Geburek et al. 2005).

Depending on the stage of CPD, an orthokeratotic and parakeratotic hyperkeratosis

and acanthosis of the epithelium could be found. All horses examined showed

perivascular dermatitis dominated by T lymphocytes with an associated increase of

MHC class II-positive dendritic-like cells. Changes in the immuno-histo-chemical

labelling for cytokeratines indicated abnormal differentiation of keratinocytes. Similar

changes in the expression of cytokeratins have been seen in human skin diseases

like psoriasis (Geburek et al. 2005).

The aetiology of CPD is unknown but genetic factors play a significant role in this

disease (Wallraf et al. 2004b, Schäper 1950). The heritability for chronic pastern

dermatitis across the different German draft horse breeds was h2=0.21. Heritability

estimates within breeds were at h2=0.98 in the Rhenish German, h2=0.82 in the

Schleswig, h2=0.20 in the Saxon-Thuringian and h2=0.14 in the South German draft

horses (Wallraf et al. 2004b). Environmental factors such as exposure to moisture

and poor housing conditions may exacerbate clinical signs and contribute to

manifestations early in life.


The objective of the study was to identify QTL using a whole genome scan for CPD

in German draft horse breeds. We chose four German draft horse breeds for the

present study since clinical and histopathological changes associated with CPD are

identical in German draft horse breeds. Furthermore, these German draft horse

breeds exhibited high prevalences of CPD and were to some degree related with

each other through a few common ancestors in their breed history. The Rhenish

German and Saxon-Thuringian draft horses were founded on the basis of Belgian

stallions and mares at the end of the 19th century (Aberle et al. 2004a). As all

German draft horse breeds underwent severe bottlenecks in the 1960’s (Aberle et al.

2004b), QTL may be different among breeds due to genetic drift and thus, linkage

analysis using several breeds should detect more QTL than an analysis within a

single draft horse breed.

4.3 Material and Methods

4.3.1 Animals

Clinical data on CPD and data on management, housing and feeding conditions were

collected from a random sample of 917 German draft horses from breeders

distributed all over Germany. This survey was performed in 2001-2002 (Wallraf et al.

2004). For each horse, data including the date of birth, breed, housing system,

feeding regime, working capacity, development and progression of the skin lesions

for each limb was collected. The clinical examination of the horses included a general

examination and a specific examination of the four limbs with emphasis on clinical

signs such as erythema, exudation, crust formation, scaling, hyperkeratotic and

hyperplastic plaque-like lesions, malodorous skin surface, nodular masses,

verrucous masses with rugged surfaces as well as other skin alterations like scars

and tumors. The clinical status and appearance of the skin lesions were documented

using schematic drawings and evaluated using a scoring system that ranged from 1-

5 depending on the size of the affected area. A superficial skin scraping was taken

from CPD-affected regions, pruritic regions or if neither one present from the pastern

region and examined for chorioptes equi mite infestation. Out of these horses, 32

horses were included in a histopathological examination (Geburek et al. 2005). The


alterations of the skin characteristic for CPD were identical for all different draft horse

breeds examined.

We selected 378 German draft horses from the breeds Rhenish German, Schleswig,

Saxon-Thuringian and South German for the present study. These horses could be

grouped into 31 paternal half-sib families that segregated for CPD. Additional 302

horses with unknown phenotypes were included in the data set for linkage analyses

in order to build up pedigrees for CPD-affected horses.

The Rhenish German was represented by 2 families and 11 affected horses, the

Schleswig by 5 families and 56 affected horses, the Saxon-Thuringian by 9 families

and 87 affected horses and the South German by 15 families and 146 affected

horses. The number of family members genotyped ranged from 5 to 24 horses. The

pedigrees are shown in Supplementary Figure 1. As the genetic background of the

four selected draft horse breeds is diverse and upgrading with other European draft

horse breeds had not been uncommon in the breed history, we calculated the genetic

contributions from the founder breeds for the horses genotyped in this study. The

calculation of the proportion of ancestral genes for the draft horses was based on all

available pedigree information going back to the late 19th century. The Rhenish

German draft horses had 34% of ancestral genes from Belgian draft horses and the

Saxon-Thuringian draft horses had 81% of ancestral genes from Rhenish German

draft horses. The sample of Schleswig draft horses had a proportion of 6% ancestral

genes from South German draft horses and 28% founder genes from Jutland draft

horses. In the South German draft horses the proportion of ancestral genes from the

Noric draft horse was 48%.

Female and male horses were unequally distributed in the genotyped sample

because preferably older horses were sampled and in this age group, the male

horses are underrepresented in these draft horse populations.

As we used a design for affected pedigree members, preferably CPD-affected horses

were genotyped. The prevalence of CPD was 70.6% in South German draft horses.

Prevalence in the other horse breeds was at 82.3% (Saxon-Thuringian), 84.1%

(Schleswig) and 90.9% (Rhenish German). The draft horses included in this study

were born in the years from 1982-2000. The mean age of unaffected horses was 4

years (Rhenish German), 4.7 years (Schleswig), 7 years (Saxon-Thuringian) and 8.8

years (South German). The mean age of CPD-affected horses was 7 years (Rhenish


German), 8.7 years (South German), 9.3 years (Schleswig) and 9.8 years (Saxon-

Thuringian) (Supplementary Table 1).

4.3.2 Microsatellite markers

For the whole genome scan, we used 318 microsatellite markers that were chosen

from the equine linkage maps (Penedo et al. 2005, Swinburne et al. 2006), the INRA

HORSEMAP database (http://locus.jouy.inra.fr), the equine RH map (Chowdhary et

al. 2003, Raudsepp et al. 2008) and from a panel of newly identified microsatellites

on the horse genome assembly EquCab2.0 (Mittmann et al. 2009). The markers

were chosen according to their position on the horse genome assembly EquCab2.0

in order to provide an even and equidistant coverage. Then their number of alleles,

polymorphism information content (PIC) and heterozygosity (HET) was evaluated to

achieve a highly informative and equidistant marker set. HET shows the proportion of

heterozygote individuals. PIC is defined as the probability that the marker genotype

of a given offspring will, in the absence of crossing-over, allow to deduce which one

of the two marker alleles the offspring received from its parents. In order to achieve a

high information content for linkage analysis over the whole equine genome, markers

with more than four alleles, HET and PIC larger than 50 % were preferentially

employed. Markers that did not fulfil these requirements were discarded from the

genome scan as long as alternate more polymorphic microsatellites could be

identified.

This whole genome scan was performed as a two step analysis. In the first step, 178

polymorphic microsatellite markers that were evenly distributed over the 31 equine

autosomes and the X-chromosome were genotyped using 8 paternal half-sib families

consisting of 68 South German draft horses with 61 CPD-affected horses. The

average distance among these markers was 12.6 Mb, the mean number of alleles

was 6.8, the average HET was 66.5% and the average PIC 60.1%. The number of

horses was consecutively increased and additional samples from further German

draft horse breeds were then genotyped using the 178 microsatellites. In the second

step, the marker density was increased for horse chromosomes (ECA) with putative

QTL regions. We chose 20 additional markers on ECA1, 6 additional markers on

ECA10 and 13 additional markers on ECA26. After the release of the second horse

genome assembly in September 2007 all positions of the markers were verified on

Equcab2.0 using a stand-alone BLAST with the Blastn mode


(ftp://ftp.ncbi.nih.gov/blast/executable). All gaps larger than 20 Mb that had emerged

in the first marker set were filled with informative markers. Thus, the marker set was

supplemented by 101 microsatellites distributed on all chromosomes with the

exception of ECA12, 13, 20, 23 and 30. The average distance between the 318

microsatellites on EquCab2.0 was 7.5 ± 8.2 Mb, with 4.76 Mb on ECA1, 3.06 Mb on

ECA10, 2.62 Mb on ECA26 and 7.9 Mb on all other chromosomes (Supplementary

Table 2). The complete marker set used for this genome scan had an average

number of 7.1 alleles, the mean value for HET was 65.7% and for PIC 63.8%,

respectively (Supplementary Table 2).There were 4 markers with an allele number of

2 (1.3%), and 26 markers with an allele number of 3 (8.2%). The number of

microsatellites with HET and PIC < 0.40 was 24 (7.5%) and 30 (9.4%). These less

polymorphic microsatellites amount to less than 10% of the marker set.

4.3.3 Genotyping

Genomic DNA was isolated from EDTA blood samples using the QIAamp® 96 DNA

Spin Blood Kit (Qiagen, Hilden, Germany). PCR was performed on PTC 100™, PTC

200™ (MJ Research, Watertown, MA, USA) or professional thermocyclers (Biometra,

Göttingen, Germany). A PCR program with varying annealing temperatures (Ta)

(Supplementary Table 1) and a general procedure has been used as follows: after 4

minutes of initial denaturation at 94°C, 36 cycles of 30 seconds at 94°C, 60 sec at

optimum annealing temperature, 30 sec at 72°C and the final cooling to 4°C for 10

min were carried out. All PCR reactions were performed in 12 μl reaction volumes

using 10 ng DNA, 1.2 μl 10x incubation buffer containing 15 mM MgCl2, 0.6 μl

DMSO, 0.2 μl each dNTP (100μM each) and 0.1 μl Taq Polymerase (Qbiogene,

Heidelberg, Germany). All forward primers were labelled with IRD700 or IRD800 at

the 5’ end.

To increase efficiency, primer pairs were pooled into PCR multiplex groups of two to

nine markers. The remaining primer pairs were amplified separately. The multiplex

groups and the separately amplified PCR products were pooled according to their

size and labelling and diluted with formamide loading buffer in ratios from 1:3 to 1:20.

For the analysis of the marker genotypes, the PCR products were size-fractionated

by gel electrophoresis on automated sequencers LI-COR 4200/S-2, LI-COR 4300

(LI-COR, Lincoln, NE, USA) using 4% and 6% polyacrylamide denaturing gels

ftp://ftp.ncbi.nih.gov/blast/executable


(Rotiphorese Gel40, Carl Roth, Karlsruhe, Germany). Allele sizes were scored

against IRD700- and IRD800-labeled DNA ladders.

4.3.4 Statistical analysis

Mendelian inheritance correctness of marker transmission in the pedigrees

genotyped was confirmed using Pedstats (Wigginton and Abecasis 2005).

SAS/Genetics, version 9.2 (SAS Institute, Cary, NC, USA) was employed to calculate

number of alleles, HET and PIC. Multipoint non-parametric linkage (NPL) analyses

for the whole genome scan were performed using MERLIN software (multipoint

engine for rapid likelihood inference, version 1.1.2) (Abecasis et al. 2002). Analysis

was performed using a binary affection status for CPD (affected versus unaffected

horses). Linkage between CPD-affection and microsatellites was estimated by the

proportion of alleles identical by descent (IBD) for affected animals (Kong and Cox

1997; Kruglyak et al. 1996; Whittemore and Halpern 1994). Non-parametric linkage

analyses do not require assumptions on the mode of inheritance and the genetic

parameters of the specified model and so this approach should be useful for traits

like CPD. The Whittemore and Halpern NPL all and pairs statistics, the Zmeans and

linear model LOD (logarithm of odds) scores according to Kong and Cox (1997) were

employed for the multipoint chromosome-wide search for alleles shared among

affected family members. We employed multipoint analyses in order to use marker

information from the whole chromosome through linked informative markers and to

increase power of linkage analysis. The maximum (minimum) achievable Zmean and

linear LOD score across breeds were 160.38 (-2.97) and 33.82 (-0.02) indicating

enough power to detect genome-wide significant linkage.

In the linkage analyses within single breeds, the maximum (minimum) achievable

Zmean and linear LOD score were lowest for the Rhenish German with 4.42 (-0.90)

and 1.49 (-0.14), 29.22 (-1.43) and 5.18 (-0.04) for the Schleswig, 32.67 (-2.14) and

8.49 (-0.10) for the Saxon-Thuringian and highest for the South German draft horses

with 186.77 (-1.45) and 20.88 (-0.01). In order to detect family specific QTL regions,

families with Zmeans > 1 from all breeds were analysed together. The maximum

(minimum) achievable Zmean within single breed linkage analyses ranged from

190.80 (-1.58) to 27.06 (-1.69), and the linear LOD score ranged from 17.95 (-0.01)

to 6.35 (-0.06) showing enough power to detect genome-wide significant linkage.


In the case of no linkage, Zmean approaches the minimum achievable value due to

an equal distribution of alleles among affected relatives. When linkage is present

under the alternative hypothesis, the proportion of alleles IBD significantly deviates

from the expected IBD proportions of the null hypothesis. We determined empirical

chromosome-wide significance levels using simulated marker genotypes under the

null hypothesis. The empirical distribution for the 5% error probability was obtained

after 1000 replicates.

A chromosome-wide co-segregation of a marker allele with the phenotypic

expression of CPD was assumed for p-values < 0.05. Genome-wide probabilities

were obtained by applying a Bonferroni correction for the chromosome-wide

significant p-values with Pgenome-wide = 1 – (1 – Pchromosome-wide)1/r, where r = length of

the respective horse chromosome in Mb divided by the total equine genome length

(2680 Mb). A genome-wide co-segregation was assumed when p-values after the

Bonferroni correction were p < 0.05.

4.4 Results and discussion

The across-breed linkage analysis including the 378 German draft horses could

identify chromosome-wide significant QTL on ECA1, 9, 16 and 17. Their locations

were at 102.6-117.8 Mb on ECA1, at 46.1-71.7 Mb on ECA9, at 2.7-38.4 Mb on

ECA16 and at 0.0-23.8 Mb on ECA17 (Table 1).

Analyses within single breeds confirmed all these QTL. The QTL on ECA1 was

mainly caused by the South German (Table 2 and Figure 1). The QTL on ECA9, 16

and 17 had chromosome-wide significant LOD scores for the Saxon-Thuringian

(Table 3). Additional chromosome-wide significant QTL could be identified for single

breeds on ECA7, 10 and 11. The QTL on ECA7 at 29.0-33.8 Mb is due to the marker

ABGe101 that was significant in the South German (Table 2). For the Schleswig

chromosome-wide significant QTL could be seen on ECA10 at 19.8-38.5 Mb

including 7 markers and on ECA11 at 41.5-61.3 Mb owing to chromosome-wide

significant linkage of the marker UCD425 (Table 4).

The linkage analysis for the South German including 197 horses showed a genome-

wide significant Zmean on ECA1 for the markers 1CA43 and TKY002. When the 10

South German families with the highest per family Zmean were selected, the region

at 18.3-50.8 Mb was genome-wide significant and the region at 76.3-136.9 Mb was


chromosome-wide significant (Supplementary Table 3). A further reduction of the

genome-wide error probability could be achieved by analyzing all families with a

Zmean > 1 from all draft horse breeds (Supplementary Table 4 and Figure 1).

In the single breed analysis of the Rhenish German, markers on ECA10 reached

chromosome-wide significant LOD scores (p < 0.04-0.05). The analysis for the

Schleswig alone also revealed chromosome-wide significant linkage on ECA10

(Table 4). Combining all Rhenish German (n=12) and all Schleswig (n=64) families

resulted in two chromosome-wide QTL that were at 15.4-52.7 Mb on ECA10 (Table

5, Figure 2) and at 97.6-102.7 Mb on ECA4. Genome-wide significance was reached

by 3 markers on ECA10 when additional families from the other breeds increased the

number of affected animals (n=190) for the linkage analysis (Supplementary Table 5,

Figure 2).

The QTL on ECA16 including 5 markers between 2.7-38.4 Mb (Supplementary Table

6 and Figure 3) reached the genome-wide significance level (Pgenome-wide < 0.003-

0.021) for ABGe033 and COR011 when families (n=130 horses) were analyzed with

Zmeans > 1. On ECA17, genome-wide significant linkage could be seen for the LOD

score (Pgenome-wide < 0.023-0.026) for the markers COR072 and COR007 on the

proximal end of ECA17 at 4.77 and 6.61 Mb for all draft horse breeds (Figure 4).

It is very likely that a number of loci influence the development of CPD and the

influence of these loci may be different on CPD expression among the German draft

horse breeds analyzed here. The across-breed analysis was justified by the same

disease entity, particularly as there are no differences in the type and severity of

signs of CPD, or the age of onset among the analysed horse breeds. Furthermore,

there are genealogical relationships among these four German draft horse breeds

(Aberle et al. 2004a, 2004b) and in addition, to Belgian draft horses. Belgian draft

horses are believed to have significantly contributed to the high prevalence of CPD in

German draft horses in the first half of the last century (Schäper 1950).

The strong influence of the South German draft horses which is seen for the QTL on

ECA1 may be caused by the significantly higher number of horses from this breed.

Using families with Zmeans > 1 from the different breeds increased the likelihood to

detect chromosome-wide significant QTL. This was evident for the QTL on ECA10

that was chromosome-wide significant in the Schleswig and the Rhenish German and

reached the genome-wide significant level when families highly informative for

linkage were selected across all breeds. These results indicate that QTL for CPD


may not be detected in within-breed analyses due to low numbers of families with co-

segregating markers, whereas the across-breed analysis may reveal such QTL. In

addition, drift variance might be large in German draft horse breeds due to their

breed history and this fact may contribute to QTL segregating only in a number of

families.

There have been two previous studies attempting to identify genes responsible for

CPD through candidate gene approaches based on similar diseases in humans. In

the first study ATP2A2 on ECA8 at Mb 21.5 was analysed as a candidate gene for

CPD in German draft horses (Mömke and Distl 2007). Mutations in this gene were

previously shown to be responsible for the Darier-White disease in humans. The

acral-hemorrhagic type of this disease has a similarity with clinical signs of CPD.

Neither this previous study nor the present analysis could find significant test

statistics in that chromosome region. Therefore, it is unlikely that the ATP2A2 gene is

involved in the pathogenesis of CPD.

The second study focused on the FOXC2 candidate gene that is located on ECA3 at

33.42 Mb (Young et al. 2007). Mutations in this gene are responsible for

lymphedema-distichiasis in humans, but the association analysis of the SNPs

identified in 6 horses did not yield significant results for CPL. In agreement with this

previous study we could not find significantly linked markers on ECA3.

The four genomic regions showing genome-wide significant QTL in this study should

help to choose appropriate positional candidate genes for association studies. We

searched potential candidate genes within these QTL regions, even if these genomic

regions extend over more than 5-10 Mb. The positions of potential positional

candidate genes within the CPD-QTL were verified through BLAST analyses for

EquCab2.0.

If we assume an overregulated inflammatory autoimmune response to an initial

vector as a possible cause for CPD, the ubiquitin protein ligase E3A, CD109

molecule and the myotubularin related protein 6 may be considered as positional

candidate genes for the QTL regions on ECA1, 10 and 17 (Table 6).

A recently published study demonstrated that the annexin A1 degradation is

mediated by UBE3A in immune-modulation (Shimoji et al. 2009). UBE3A is located

on ECA1 at 110.75-110.79 Mb. An overexpression of ubiquitin protein ligase E3A

caused enhanced proteasomal degradation of annexin A1 in vivo which would result

in a highly active neutrophil extravasation and an overshooting inflammatory


response in damaged or infected tissues. Annexin A1 is anti-inflammatory and has

also been implicated in pro-apoptotic mechanisms in the cell. Mutations that reduced

the ubiquitin ligase E3A activity are associated with the Angelman syndrome, a

severe neurological disorder in humans.

The candidate gene activated T-cell marker CD109 located on ECA10 at 28.58-28.72

Mb plays a role in immune regulation as well. CD109 binds and down regulates the

transformimg growth factor-β (TGF-β) in human keratinocytes (Finnson et al. 2006).

TGF-β plays a critical role in skin development, homeostasis and wound healing.

Perturbations in the action of TGF-β have been implicated in a variety of skin

disorders, including impaired wound healing, hypertrophic scarring, psoriasis and

cancer. Results indicated that CD109 also negatively regulates the anti-proliferative

effect of TGF-β on human keratinocytes. At least five amino acid substitutions have

been identified in the CD109 coding DNA-sequence, whether they alter the function

of CD109 has not yet been determined. Because of the proliferation that is seen in

the formation of nodules and verrucous masses in CPD of draft horses, CD109 may

be a suitable candidate gene.

Myotubularin related protein 6 (MTMR6) encodes a phosphatidylinositol-3

phosphatase. This gene is located within the QTL on ECA17 at 5.09-5.12 Mb and

plays a critical role in setting a minimum threshold for a stimulus to activate T-cells

(Srivastava et al. 2006). A point mutation in the phosphatase domain of MTMR6

resulted in a significant increase of the proliferation of reactivated human CD4 T

cells. MTMR6 could be involved in the perivascular dermatitis which was dominated

by T lymphocytes (Geburek et al. 2005) and found in all CPD-affected draft horses.

Further studies will be necessary to evaluate the effects of theses candidate genes in

the pathogenesis of CPD and to detect causative mutations in these genes.

The speed of whole genome mapping has been significantly increased by tools like

the equine 50K Illumina Beadchip. However, the often notable reduction of allele

numbers of microsatellites in draft horse breeds in comparison to warmblood horse

breeds suggests an even lower degree of polymorphic single nucleotide

polymorphisms in draft horses. Testing the equine 50K Illumina Beadchip for minor

allele frequencies, HET and PIC in draft horse breeds should be done in the future

and then the suitability of this tool for genome-wide association studies can be

evaluated for these horse breeds.


This study is the very first genome-wide analysis for CPD. We could identify genome-

wide significant QTL on four chromosomes in German draft horse breeds.

4.5 References

Abecasis GR, Cherny SS, Cookson WO, Cardon LR (2002) Merlin rapid analysis of

dense genetic maps using sparse gene flow trees. Nat Genet 30:97-101

Aberle K, Wrede J, Distl O (2004a) Analysis of relationships between German heavy

horse breeds based on pedigree information. Berl Münch Tierärztl Wochenschr

117:72–75

Aberle K, Hamann H, Drögemüller C, O Distl (2004b) Genetic diversity in German

draught horse breeds compared to a group of primitive, riding and wild horses by

means of microsatellite DNA markers. Anim Genet 35:270-277



Skow LC (2003) The first-generation whole-genome radiation hybrid map in the


Res 13:742–751

De Cock HE, Affolter VK, Wisner ER, Ferraro GL, MacLachlan NJ (2003) Progressive

swelling, hyperkeratosis, and fibrosis of distal limbs in Clydesdales, Shires, and

Belgian draft horses suggestive of primary lymphedema. Lymphat Res Biol

1:191-199

De Cock HE, Affolter VK, Farver TB, Van Brantegem L, Scheuch B, Ferraro GL

(2006) Mearurement of skin desmosine as an indicator of altered cutaneous

elastin in draft horses with chronic progressive lymphedema. Lymphat Res Biol

4:67-72

Finnson KW, Tam BY, Liu K, Marcoux A, Lepage P, Roy S, Bizet AA, Philip A (2006)

Identification of CD109 as part of the TGF-beta receptor system in human

keratinocytes. Faseb J 20: -1527

Geburek F, Ohnesorge B, Deegen E, Doeleke R, Hewicker-Trautwein M (2005)

Alterations of epidermal proliferation and cytokeratin expression in skin biopsies

from heavy draught horses with chronic pastern dermatitis. Vet Dermatol 16:373-

84


Kong A, Cox NJ (1997) Allele-sharing models: LOD scores and accurate linkage

tests. Am J Hum Genet 61, 1179-1188

Kruglyak L, Daly MJ, Reeve-Daly MP, Lander ES (1996) Parametric and

nonparametric linkage analysis: a unified multipoint approach. Am J Hum Genet

58:1347-1363

Mittmann EH, Wrede J, Pook J, Distl O (2009) Identification of 21781 equine

microsatellites on the horse genome assembly 2.0. Anim Genet 10.1111/j. 1365-

2052.2009.01979.x

Mömke S, Distl O (2007a) Molecular characterization of the equine ATP2A2 gene.

Cytogenet Genome Res 116:256-262

Mömke S, Distl O (2007b) Molecular analysis of the ATP2A2 gene as candidate for

chronic pastern dermatitis in German draft horses. J Hered 98:267-271



JE, Tozaki T (2005) International equine gene mapping workshop report: a


merging four mapping resources. Cytogenet Genome Res 111:5–15

Raudsepp T, Gustafson-Seabury A, Durkin K, Wagner ML, Goh G, Seabury CM,

Brinkmeyer-Langford C, Lee EJ, Agarwala R, Stallknecht-Rice E, Schäffer AA,

Skow LC, Tozaki T, Yasue H, Penedo MC, Lyons LA, Khazanehdari KA, Binns

MM, MacLeod JN, Distl O, Guérin G, Leeb T, Mickelson JR, Chowdhary BP (2008)

A 4,103 marker integrated physical and comparative map of the horse genome.

Cytogenet Genome Res 122:28–36

Schäper W (1950) Die Verbesserung der Konstitution unserer Haustiere. Z

Tierzüchtg Züchtgsbiol 58:1-57

Shimoji T, Murakami K, Sugiyama Y, Matsuda M, Inubushi S, Nasu J, Shirakura M,

Suzuki T, Wakita T, Kishino T, Hotta H, Miyamura T, Shoji I (2009) Identification

of annexin A1 as a novel substrate for E6AP-mediated ubiquitylation. J Cell

Biochem 106:1123-1135

Srivastava S, Ko K, Choudhury P, Li Z, Johnson AK, Nadkarni V, Unutmaz D,

Coetzee WA, Skolnik EY (2006) Phosphatidylinositol-3 phosphatase

myotubularin-related protein 6 negatively regulates CD4 T cells. Mol Cell Biol

26:5595-5602



Kurosawa M, Leeb T, Mashima S, Mickelson JR, Raudsepp T, Tozaki T, Binns M

(2006) Single linkage group per chromosome genetic linkage map for the horse,


Genomics 87:1–29

Wallraf A, Hamann H, Deegen E, Ohnesorge B, Distl O (2004a) Analysis of the

prevalence of pastern dermatitis in German draft horse breeds. Berl Münch

Tierärztl Wochenschr 117:148-152

Wallraf A, Hamann H, Ohnesorge B, Deegen E, O Distl (2004b)

Populationsgenetische Untersuchung zum Auftreten von Mauke beim

Süddeutschen Kaltblut. Züchtungskunde 76:246-261

Whittemore AS, Halpern J (1994) A class of tests for linkage using affected pedigree

members. Biometrics 50:118-127

Wigginton JE, Abecasis GR (2005) PEDSTATS: descriptive statistics, graphics, and

quality assessment for gene mapping data. Bioinformatics 21:3445-3447

Young AE, Bower LP, Affolter VK, De Cock HE, Ferraro GL, Bannasch DL (2007)

Evaluation of FOXC2 as a candidate gene for chronic progressive lymphedema in

draft horses. Vet J 174:397-399


Table 1 Multipoint chromosome-wide significant Zmeans and LOD scores, their

chromosome-wide error probabilities (Pz, PL) and marker positions in Mb using a

whole genome scan for chronic pastern dermatitis in all 378 German draft horses

ECA Position (Mb)

Marker Zmean PZ LOD score PL

1 102.64 LEX058 1.38 0.08 0.44 0.08

1 110.28 1CA43 4.59 <0.000001*** 0.79 0.03

1 111.17 TKY002 4.94 <0.000001*** 0.83 0.02

1 117.76 1CA25 0.96 0.2 0.16 0.2

9 46.07 UMNe103 1.36 0.09 0.66 0.04

9 51.85 TKY805 1.63 0.05 0.87 0.02

9 71.66 ASB5 0.75 0.2 0.46 0.07

16 2.65 ABGe094 1.50 0.07 0.83 0.03

16 8.15 HTG3 1.70 0.04 1.24 0.008

16 17.60 ABGe033 2.23 0.013 1.59 0.003

16 23.12 COR011 1.98 0.02 1.48 0.005

16 38.43 ABGe054 0.02 0.5 0.00 0.5

17 4.77 COR072 1.82 0.03 2.16 0.0008*

17 6.61 COR007 1.89 0.03 2.20 0.0007*

17 23.84 UMNe176 1.49 0.07 0.85 0.02

*** Pgenome-wide < 0.001 * Pgenome-wide < 0.05




whole genome scan for chronic pastern dermatitis in South German draft horses

ECA Position (Mb)

Marker Zmean PZ LOD score PL

1 20.59 LEX020 1.34 0.09 0.50 0.061 31.47 TKY597 2.61 0.005 1.35 0.0061 42.50 NVHEQ100 1.66 0.05 1.00 0.021 42.91 1CA12 1.65 0.05 1.12 0.0111 50.78 COR100 1.03 0.2 0.60 0.051 76.31 TKY899 1.07 0.14 0.29 0.131 89.89 AHT40 2.01 0.02 0.71 0.041 97.52 COR046 2.61 0.004 0.89 0.021 102.58 1CA20 1.89 0.03 0.65 0.041 102.64 LEX058 1.88 0.03 0.64 0.041 110.28 1CA43 5.46 <0.000001*** 1.02 0.021 111.17 TKY002 5.76 <0.000001*** 1.06 0.0141 117.76 1CA25 1.70 0.04 0.39 0.091 120.49 UMNe043 1.73 0.04 0.37 0.101 124.27 HTG12 2.37 0.009 0.79 0.031 127.64 AHT058 2.23 0.013 0.65 0.041 129.61 UM004 2.09 0.02 0.55 0.067 28.96 LEX015 0.84 0.2 0.29 0.127 32.37 ABGe101 1.95 0.03 1.00 0.027 33.82 ABGe102 1.47 0.07 0.79 0.03

*** Pgenome-wide < 0.001




whole genome scan for chronic pastern dermatitis in Saxon-Thuringian draft horses

ECA Position

(Mb)

Marker Zmean PZ LOD score

PL

9 1.5 HMS3 1.15 0.13 0.67 0.04 9 16.9 HTG08 1.24 0.11 0.58 0.05 16 17.60 ABGe033 1.43 0.08 0.98 0.02 16 23.12 COR011 0.95 0.2 0.63 0.04 17 4.77 COR072 0.92 0.2 0.81 0.03 17 6.61 COR007 0.93 0.2 0.82 0.03



whole genome scan for chronic pastern dermatitis in Schleswig draft horses

ECA Position (Mb)


PL

4 97.58 AHT61 0.81 0.2 0.13 0.2 4 102.74 SGCV23 1.88 0.03 0.53 0.0610 19.80 COR015 1.42 0.08 0.55 0.0510 23.65 ABGe353 2.22 0.013 0.88 0.0210 26.94 UCD412 2.43 0.008 0.94 0.0210 27.36 LEX062 2.43 0.007 0.94 0.0210 28.67 LEX017 2.31 0.010 0.90 0.0210 30.25 ABGe354 2.16 0.02 0.86 0.0210 38.48 ABGe356 1.51 0.06 0.57 0.05




whole genome scan for chronic pastern dermatitis in Rhenish German and Schleswig

draft horses

ECA Position (Mb)


PL

4 97.58 AHT61 1.29 0.10 0.36 0.104 102.74 SGCV23 2.22 0.013 0.92 0.0210 15.38 NVHEQ18 -0.48 0.7 -0.02 0.6 10 19.80 COR015 1.87 0.03 1.01 0.0210 20.39 LEX008 1.99 0.02 1.08 0.01310 23.65 ABGe353 2.62 0.004 1.38 0.00610 26.94 UCD412 2.79 0.003 1.45 0.00510 27.36 LEX062 2.80 0.003 1.45 0.00510 28.67 LEX017 2.70 0.004 1.42 0.00510 30.25 ABGe354 2.56 0.005 1.39 0.00610 38.48 ABGe356 1.88 0.03 1.03 0.01510 41.44 TKY867 1.65 0.05 0.80 0.0310 52.71 HMS2 1.45 0.07 0.46 0.07


Table 6 Locations of candidate genes for chronic pastern dermatitis within equine

QTL on horse chromosomes (ECA) 1, 10 and 17 using the Equus caballus genome

assembly (EquCab2.0) and their cytogenetic and physical location on the human

genome (HSA, Homo sapiens genome view, Build 37.1)

Gene Location on human genome

Start of the sequence in bp on the respective HSA

ECA Start and end of the sequence in bp on the respective ECA

ubiquitin protein ligase E3A (UBE3A)

15q11q13 25,582,396 1 110,754,535 to 110,7935,772

activated T-cell marker CD109 (CD109)

6q13 74,405,514 10 28,582,767 to 28,722,876

myotubularin related protein 6 (MTMR6)

13q12 25,820,339 17 5,086,361 to 5,123,060


ECA1

-1

0

1

2

3

4

5

6

7

8

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180

Chromosome size in Mb

Zmea

n

South German subsampleSouth GermanGerman draft horses

TKY0021CA43

Fig. 1 Multipoint chromosome-wide Zmeans on horse chromosome (ECA) 1 harbouring a quantitative trait locus for chronic pastern dermatitis in German draft horses

ECA10

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80Chromosome size in Mb

Zmea

n

Rhenish German and SchleswigSchleswigsubsample German draft horses

LEX017

Fig. 2 Multipoint chromosome-wide Zmeans on horse chromosome (ECA) 10 harbouring a quantitative trait locus for chronic pastern dermatitis in Schleswig, Rhenish German and Schleswig and a subsample of all German draft horse breeds.


ECA16

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85Chromosome size in Mb

Zmea

n

German draft horses

subsample German draft horsesABGe033

Fig. 3 Multipoint chromosome-wide Zmeans on horse chromosome (ECA) 16 harbouring a quantitative trait locus for chronic pastern dermatitis in German draft horses ECA17

0.0

0.5

1.0

1.5

2.0

2.5

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80Chromosome size in Mb

Zmea

n an

d LO

D s

core

German draft horses - ZmeanGerman draft horses - LOD score

COR007

Fig. 4 Multipoint chromosome-wide Zmeans and LOD scores on horse chromosome (ECA) 17 harbouring a quantitative trait locus for chronic pastern dermatitis in German draft horses


Supplementary Table 1 Distribution of the genotyped German draft horses by breed and family, the number of affected and unaffected horses and their mean age (years) at examination per family

Affected horses Unaffected horsesFamily Genotyped

horses Horse breed Number Mean

age Number Mean age

2 4 Saxon-Thuringian 3 11.8 1 9.0 3 12 Saxon-Thuringian 8 11.3 4 10.3 4 9 Saxon-Thuringian 8 7.2 1 5.0 5 8 Saxon-Thuringian 7 11.1 1 8.0 6 13 Saxon-Thuringian 10 9.3 3 6.7 7 17 Saxon-Thuringian 14 9.8 3 4.3 8 15 Saxon-Thuringian 11 11.5 4 10.3 9 7 Saxon-Thuringian 8 9.5 - -

10 20 Saxon-Thuringian 18 7.1 2 2.5

11 7 Rhenish German 6 6.3 1 4.0 12 5 Rhenish German 5 7.8 - -

13 21 Schleswig 18 9.5 3 6.7 14 11 Schleswig 11 10.5 - - 15 11 Schleswig 11 12.5 - - 16 10 Schleswig 8 6.8 2 3.5 17 11 Schleswig 9 7.4 2 4.0

18 17 South German 17 9.9 - - 19 12 South German 6 7.1 6 6.8 20 12 South German 11 8.8 1 12.0 21 8 South German 7 6.1 1 7.0 22 10 South German 8 8.8 2 9.5 23 21 South German 15 8.3 7 6.7 24 14 South German 11 8.2 2 8.0 25 8 South German 6 9.5 2 10.5 26 16 South German 9 11.1 7 12.3 27 10 South German 10 9.5 2 7.5 28 10 South German 9 8.0 2 7.0 29 23 South German 13 9.8 9 7.8 30 10 South German 10 8.4 - - 31 10 South German 9 9.8 2 11.0 32 16 South German 7 7.5 9 7.8

No. of families and horses per breed 9 105 Saxon-Thuringian 87 9.7 18 7.0 2 12 Rhenish German 11 7.0 1 4.0 5 64 Schleswig 56 9.3 8 4.7

15 197 South German 146 8.7 51 8.8 Total 378 All breeds 308 70


Supplementary Table 2 Microsatellite marker statistics per horse chromosome with the chromosome length, number of markers, mean allele number, HET% and PIC%

ECA Size ECA (Mb) mean distance (Mb) Number of markers Alleles HET % PIC %

1 186 4.76 37 8 66.1 63.2

2 121 5.25 22 8 58.9 58.0

3 119 8.53 13 8 66.7 65.5

4 109 5.17 19 8 68.8 69.6

5 100 5.86 15 7 67.6 65.3

6 85 7.70 10 7 68.8 67.9

7 99 10.95 8 6 66.1 65.3

8 94 15.68 5 7 74.0 73.6

9 84 10.44 7 6 65.5 60.8

10 84 3.06 24 7 61.6 59.5

11 61 6.81 8 8 63.5 63.9

12 33 6.62 4 9 68.1 67.8

13 43 10.64 3 6 73.6 69.9

14 94 7.83 11 6 60.0 57.1

15 92 6.54 13 7 63.2 63.0

16 87 7.28 11 7 66.0 64.4

17 81 10.09 7 7 65.9 64.9

18 83 6.88 11 6 66.7 60.1

19 60 8.57 6 6 69.4 66.0

20 64 8.02 7 7 67.6 61.4

21 58 5.46 9 6 61.8 56.0

22 50 6.24 7 6 61.6 59.6

23 56 7.96 6 7 69.1 63.8

24 47 6.68 6 7 64.0 60.8

25 40 9.88 3 7 71.1 65.3

26 42 2.62 14 8 67.7 68.7

27 40 6.66 5 7 69.0 67.0

28 46 9.24 4 7 72.8 69.3

29 34 5.61 5 7 60.0 58.7

30 30 6.20 3 7 73.5 76.3

31 25 5.00 4 12 80.3 80.3

X 124 12.41 9 6 58.7 62.5

2501 7.52 318 7.1 66.8 64.9


Supplementary Table 3 Multipoint chromosome-wide significant Zmeans and LOD

scores, their chromosome-wide error probabilities (Pz, PL) and marker positions in Mb

for 10 families of 160 South German draft horses

ECA Position (Mb)


PL

1 18.25 ASB41 1.30 0.10 0.44 0.08 1 20.59 LEX020 1.82 0.03 0.82 0.03 1 31.47 TKY597 3.26 0.0006** 1.88 0.002*1 42.50 NVHEQ100 1.95 0.03 1.24 0.0081 42.91 1CA12 1.99 0.02 1.45 0.0051 50.78 COR100 0.90 0.2 0.56 0.05 1 76.31 TKY899 1.10 0.14 0.23 0.2 1 89.89 AHT40 2.23 0.013 0.71 0.04 1 97.52 COR046 3.17 0.0008** 0.89 0.02 1 102.58 1CA20 2.43 0.008 0.65 0.04 1 102.64 LEX058 2.42 0.008 0.64 0.04 1 110.28 1CA43 6.73 <0.000001*** 1.02 0.02 1 111.17 TKY002 7.05 <0.000001*** 1.06 0.0141 117.76 1CA25 2.44 0.007 0.73 0.03 1 118.80 TKY106 2.13 0.02 0.46 0.07 1 119.39 UCDEQ493 2.35 0.009 0.62 0.05 1 120.49 UM043 2.51 0.006 0.74 0.03 1 124.27 HTG12 3.28 0.0005** 1.54 0.0041 127.64 AHT058 3.16 0.0008** 1.34 0.0071 129.61 UM004 2.98 0.0014* 1.06 0.0141 130.13 UCDEQ440 2.89 0.002* 1.02 0.0151 136.85 HMS015 1.57 0.06 0.70 0.04 7 28.96 LEX038 0.93 0.2 0.34 0.10 7 32.37 ABGe101 2.15 0.02 1.01 0.02 7 33.82 ABGe102 1.50 0.07 0.75 0.03 10 0.41 AHT49 1.58 0.06 0.58 0.05 10 2.71 HMS23 1.70 0.04 0.59 0.05 10 7.60 TKY601 0.13 0.4 0.55 0.06 20 10.51 HTG5 1.10 0.13 0.61 0.05 20 15.30 LEX064 1.63 0.05 0.90 0.02 20 33.50 UM011 1.71 0.04 0.88 0.02 20 44.74 TKY507 1.92 0.03 1.02 0.02



scores on ECA1, their chromosome-wide error probabilities (Pz, PL) and marker

positions in Mb for all families with a Zmean > 1 (Saxon-Thuringian families 3, 6, 7;

Rhenish German family 12; Schleswig families 15, 16; South German families 18, 19,

21 23, 26, 27, 29, 30, 32) including 217 horses from all breeds

ECA Position (Mb)


PL

1 13.84 VIAS-H34 1 15.77 TKY1016 1.66 0.05 1.10 0.0121 18.27 ASB041 1.73 0.04 0.93 0.02 1 20.59 LEX020 2.21 0.014 1.44 0.0051 31.47 TKY597 3.08 0.0011** 1.69 0.003*1 42.50 NVHEQ100 2.39 0.009 1.55 0.0041 42.91 1CA12 2.31 0.010 1.56 0.0041 50.78 COR100 1.12 0.13 0.58 0.05 1 76.31 TKY899 1.28 0.10 0.47 0.07 1 89.89 AHT40 2.32 0.010 1.04 0.0141 97.52 COR046 3.09 0.0010** 1.37 0.0061 102.58 1CA20 2.67 0.004 1.59 0.003*1 102.64 LEX058 2.66 0.004 1.58 0.003*1 110.28 1CA43 6.64 <0.000001*** 1.71 0.003*1 111.17 TKY002 7.03 <0.000001*** 1.77 0.002*1 117.76 1CA25 2.26 0.012 0.87 0.02 1 118.80 TKY106 1.80 0.04 0.43 0.08 1 119.39 UCDEQ493 2.02 0.02 0.60 0.05 1 120.49 UM043 2.21 0.014 0.80 0.03 1 124.27 HTG12 2.92 0.002* 1.59 0.003*1 127.64 AHT058 2.77 0.003* 1.38 0.0061 129.61 UM004 2.58 0.005 1.15 0.0111 130.13 UCDEQ440 2.50 0.006 1.11 0.0121 136.85 HMS15 1.75 0.04 1.33 0.0071 150.23 UM026 1.26 0.10 0.82 0.03

***Pgenome-wide < 0.001 **Pgenome-wide < 0.01 *Pgenome-wide < 0.05




positions in Mb for all families with a Zmean > 1 (Saxon-Thuringian families 4, 7;

Rhenish German families 11, 12; Schleswig families 14, 17; South German families

18-20, 23, 24, 26, 28, 29) including 190 horses of all breeds

ECA Position (Mb)


PL

10 19.80 COR015 2.14 0.02 1.73 0.00210 20.39 LEX008 2.21 0.013 1.79 0.00210 23.65 ABGe353 2.80 0.003 1.96 0.0013*10 26.94 UCD412 3.30 0.0005* 2.41 0.0004*

10 27.36 LEX062 3.31 0.0005* 2.42 0.0004*

10 28.67 LEX017 3.54 0.0002** 2.78 0.0002**10 30.25 ABGe354 3.04 0.0012 2.35 0.0005*

10 30.74 ABGe355 2.93 0.002 2.29 0.0006*10 36.38 NVHEQ7 2.12 0.02 1.89 0.00210 38.48 ABGe356 1.94 0.03 1.75 0.00210 41.44 TKY867 1.46 0.07 1.17 0.01010 52.71 HMS2 1.87 0.03 1.42 0.00510 61.89 LEX009 1.76 0.04 1.30 0.00710 76.69 AHT086 1.48 0.07 0.78 0.03

**Pgenome-wide < 0.01 *Pgenome-wide < 0.05 Supplementary Table 6 Multipoint chromosome-wide significant Zmeans and LOD


positions in Mb for all families with a Zmean > 1 including 130 draft horses from all

breeds

ECA Position Marker Zmean PZ LOD PL

16 2.65 ABGe094 2.47 0.007 1.36 0.006 16 8.15 HTG3 2.76 0.003 1.82 0.002 16 17.60 ABGe033 3.69 0.00011** 2.45 0.0004*16 23.12 COR011 3.38 0.0004* 2.20 0.0007*16 38.43 ABGe054 0.21 0.4 0.06 0.3

**Pgenome-wide < 0.01 *Pgenome-wide < 0.05


SupplementaryTable 7 Characteristics of the microsatellites used in this study

Chr Marker Accession number

Primer sequence forward/reverse (5’-3’)

size range

allele(n)

HET (%)

PIC (%) Mb

1 HLM005 U36497 GCTGAAATCCTGTGGGTCTCCA CCTGGCTCCCTTGGTGGTCTGA

120 - 132 3 42.3 39.4 1.63

1 COR054 AF108371 CAAGCAAAACAAGAAATCCC CTTTGTACACGTTGCAGTGG

229 - 243 5 69.9 70.6 3.67

1 ABGe001 AM900755 TGGTGACGTAAGGGTTCTGG GAGGGGATATGTGGATGTGG

237 - 333 34 85.2 87.8 6.94

1 ABGe003 AM900757 TCCCAAAGGAGGAAATGTTG CTGGCCAAAGATACCCTCAG

182 - 200 7 67.0 69.6 11.12

1 VIAS-H34 L23549 TGAGTGTTTGCGTGTGTGTG TCCCGTCTCCTCTCTTGTTC

137 - 153 5 70.0 69.4 13.84

1 TKY1016 AB104234 GAGCTATGCCCTGGGAAAAC ATCAGGTACGATGGGACACG

143 - 155 7 58.8 64.6 15.77

1 ASB41 AF004771 AAAGTTCACTTAGTCCTTGG CCACCTGTTTGCACTTGC

147 - 159 9 73.1 68.9 18.27

1 LEX020 AF075622 GGAATAGGTGGGGGTCTGTT AGGGTACTAGCCAAGTGACTGC

192 - 213 9 88.0 80.4 20.59

1 TKY597 AB103815 AGTGCCAAGGAGGCTGTCT TCTTCTCCCCATGAGTCACC

129 - 145 7 84.8 78.8 31.47

1 NV100 AF056399 CCAAAGCAGAACATGTGAAGTT TGGCATAGATGTTAGCTCAGTGA

190 - 210 12 77.9 81.3 42.50

1 1CA12 AF043199 GGGAGTGGTGATTACTTCTTGC TAGCCGTGAGAAGGTGTGTG

101 - 109 4 71.8 62.1 42.91

1 COR100 AF154953 CCCAGAGGTTTCAGAGGG ATTCTAGGGCATATTATGACAA

192 - 222 9 77.3 75.9 50.78

1 COR079 AF142616 TGCTGCCAGATCTTCTGAAT TGGAGAGCGTGAAATTAACC

204 - 212 3 49.6 43.6 51.20

1 ASB12 X93526 TCAGCAATAGAAGCCAGCTCC TCCTATGGAGGTGACCTTCCC

168 - 182 5 64.0 61.6 68.98

1 TKY899 AB104117 AGCAACAGAGTAATGCCAAG TAGGCGGGTTTTAAACATGG

157 - 175 8 66.6 66.1 76.31

1 AHT40 AJ271525 TCCAAGTTGCTGAATGGATC ACGGCCTGATTCTCTCTTTG

199 - 215 8 71.8 69.9 89.89

1 COR046 AF108363 TGTTTGCAAAGATATTGGGG ACCTGGTCAGGCCTATTACC

249 - 257 5 59.9 52.9 97.52

1 1CA20 AF043202 CTAAGCAGGTTCCCTATCATGG TCCACTACACAGGAAAACGAA

112 - 122 3 14.1 12.5 102.58

1 LEX058 AF075665 GCAATCCGCTAGATAGAGTG ACCTTTACTTTACGGGTCACA

222 - 232 8 72.3 71.8 102.64

1 1CA43 AF043215 ATGGCATGATTTGCTTCTCC TGGAAACAACCTAAATGTCCA

121 - 125 3 49.3 36.3 110.28

1 TKY2 TTCCCTCCCATGGTTATTTTTC TCTCTACTTTCATATACATTTGG

105 - 117 9 69.5 64.8 111.17


Supplementary Table 7 continued


Primer sequence forward/reverse ( 5’-3’)

size range

allele (n)

HET (%)

PIC (%) Mb

1 1CA25 AF043205 TCCAATTTTCCCCAATGGTA CTGCATTTTGACAATGGTGG

194 - 206 5 52.5 51.3 117.76

1 TKY106 AB053345 CTAATCTTCCTCAGCACACACA GGTTGCCATGTATCTTTAGTCTCC

128 - 140 6 60.1 58.9 118.80

1 UCD493 U67418 ATTGGATATTTAACACCAAATGC CCCAGCTCAGTGACTCCATT

200 - 244 18 70.3 75.3 119.39

1 UM043 AF195586 CCTCAATCTTTTCTTCTCC TCAAGAGAGACGCTACAC

147 - 153 3 52.2 45.5 120.49

1 HTG12 AF169296 CACTAGAGTCAGGGGGGGTGGGCT TTGGAGTACTCTTTCTCCCTTCCC

111 - 119 6 53.9 49.8 124.27

1 AHT58 AJ507675 CAGTGATGAGCCGCAAATAG TCTACCTATAATCCGCCTCCC

163 - 193 12 80.2 78.9 127.64

1 UM004 AF195126 AGGTCAGGTTCACTTTTTC AGGTCACTGTGCCTAGTTG

109 - 122 8 77.4 69.7 129.61

1 UCD440 U67410 TGTTCGGACAGTGTGGAT GCAGGGTATGTGTGTGCT

105 - 111 5 69.9 56.9 130.13

1 HMS15 U35401 ATATCTCTTGCTGTCCTACTTTCC AATGTGACACGTAAGATAGGCCTC

214 - 234 136.85

1 UM026 AF195573 CCCAAAATCAATTAGGTCTC ATCAGTTGCTCTCTACTTTTC

204 - 216 7 70.2 73.8 150.23

1 TKY295 AB034604 GTCAAGTGTCAGTAGCTCTG GTTTCTAGGCTGATGCTGTA

145 - 169 9 52.8 45.0 153.15

1 1CA16 AF043200 TCACTGGGGGGTATATGCAT GATCCTACTCCACCTGAAGTGG

114 - 124 4 61.3 58.0 157.18

1 COR006 AF083449 GTTCTGCACATCCTGCTCTT AGTGCCCTGAAACTGTATGG

195 - 201 11 79.4 80.3 161.49

1 HMS7 X74636 CAGGAAACTCATGTTGATACCATC TGTTGTTGAAACATACCTTGACTGT

170 - 182 8 66.9 66.8 162.38

1 TKY466 AB103684 TGGAACACATTCCTCACCAG GTTCTCCTTCCACCCCAAAT

322 - 332 6 77.1 75.9 170.12

1 TKY281 AB033932 CAAGGACAACTTCTCAGGAG AATAAGAAAGGAGCCAGTCG

178 - 194 6 59.8 53.3 177.98

1 COR053 AF108370 AATTGACTGTGGAAGCCTTG GGCTGAGGAGTAAGCTGAAAG

171 - 195 6 78.6 71.7 182.81

2 COR065 AF142602 CAAAAGCACACACAAAGTGC TCCGGAAAGTGCAAAGTTAG

266 - 290 12 76.2 74.5 1.74

2 ASB18 X93532 TGCAGACAAAGCTGGACACTC CTGCTGAGAAAGCTTCTGC

186 - 214 13 81.5 80.2 5.26

2 ABGe109 AM946988 GGGTGGCTCCTTAGAGCTTC CCCCTCCCTTGTTTATATGC

204 - 218 15 79.2 84.2 6.21

2 TKY384 AB048290 TGCAGCAAGAAACCTAAACA CTTCAGTTGTAATCAGGCTC

103 - 131 13 81.9 87.2 9.00

2 HTG19 GTATGTGCTGTACCTTCTGC ATGAGAAAGACGATAGATGATAT

143 - 153 3 24.7 23.2 9.50

2 TKY615 AB103833 CCAGACCCACCCAAAAGATA GGGCAAAGTGGTCTGAGAAG

243 - 255 9 50.4 49.0 14.07





size range

allele (n)

HET (%)

PIC (%) Mb

2 UMNe323 AY391338 GATCCTGCAGGAAAGCATGT CCGCTCGGAATATTTCATTG

166 - 212 21 84.6 88.4 19.18

2 COR090 AF154943 GGTTTGTCTCTTTGAGGTGTG TGCTCATATCTTCACCCTGC

91 - 101 4 73.5 61.6 22.45

2 ASB17 X93531 GAGGGCGGTACCTTTGTACC ACCAGTCAGGATCTCCACCG

89 - 115 15 81.5 82.4 30.60

2 HMS51 AY775553 GAAAAGAAAGTGTCAGACTGCC GCTCCATGTTTCCTGACAGG

164 - 178 3 34.1 30.5 32.98

2 TKY474 AB103692 TGTCCTACTTCCCAGCTACG CCTGCCTTTGCAGTTCAGTG

135 - 159 6 39.6 42.7 38.50

2 TKY784 AB104002 GATCAGTACTTTGCAAATGGATAAC GTAACTCCAAGGCTACGTTC

200 - 214 9 71.1 75.4 38.80

2 UMNe448 AY731396 CCATTCTGCCCTGATTGG TTCAAGACCCCTCAATCTGC

151 - 169 6 77.4 71.8 45.74

2 TKY352 AB044852 TCTGCTAAGTTCAATGGGTC TTCTTACTTAACAACCATCG

77 - 99 3 63.1 53.7 55.38

2 TKY605 AB103823 AGTGCCATCTTGAATTGCTG CGATGAAATTGAGGATTTATGGA

157 - 165 2 58.8 37.2 60.64

2 A-14 Y10239 CAGCTGGGTGACACAGAGAG GTCATCACTACTCCCTACAC

208 - 236 7 81.5 67.5 74.47

2 ASB13 X93527 CTCTGAAAGAGCAGGATTGG GTCTTCTAAGTGGTAAGAGCC

122 - 132 5 36.1 54.8 75.64

2 100G3_T7 AJ542943 GGGTGAACAGTAGGGGAAAC CTGTTGTAGAGAGGGGGCTC

185 - 197 4 27.4 31.9 86.46

2 UMNe076 AF191706 CCCTCAGGTTGAGGACTCAG AGGTGACAACCTGGATTTGC

100 - 104 3 22.9 40.2 87.06

2 TKY798 AB104016 GAGCAGAAGGTACGAGAAGA AACTTAACCAGGCTGTTCTG

237 - 247 6 60.5 63.6 93.96

2 VHL123 Y08446 CCTCCTTCACAGTGAAGTGC GAGTATATAGCTCCAGACCTC

149 - 161 6 60.2 53.8 109.82

2 COR026 AF101395 GGCGTCCAACGTAAAGTAGA CCTCTTCGGAAACTCTGACA

228 - 230 2 29.9 22.2 117.55

3 AHT36 AJ271521 TGCTGCTCCAGTGTCCT TAGATTTCACAGGCGGGTG

136 - 148 8 72.8 72.4 2.95

3 COR033 AF101402 CCTCCCCTACTTCCTCTCTG CATTTTCTTTCCAGGTTCCC

213 - 245 11 84.9 83.1 13.47

3 TKY937 AB104155 TCCTGCGGAAATACATTAGG AGTTCAAAGTGGTCCCATAG

120 - 150 8 66.3 66.6 16.76

3 UMNe158 AY391305 AATTGAGAGCCAAGATGACACC GGCACCATTTGAGGAAGATG

125 - 149 11 72.1 80.2 20.88

3 AHT22 AAGCACAATGTGGGGGTTAG TCCACGTTCACACATACCTCA

189 - 201 5 66.7 63.4 21.13

3 AHT92 AJ507709 TGAGCATCTTGAAGATGAGCA CAACAGTTGTTAGCTCAGGTGC

254 - 296 25 83.9 82.9 24.23

3 LEX057 AF075659 TGGTCCCCTAATCAAATCAGA ACGGCATCCCACATAAAATAG

157 - 167 5 31.0 32.1 36.31





size range

allele (n)

HET (%)

PIC (%) Mb

3 UMNe231 AF536304 TAGTCGTTGTCCACCAGCTG GATGGAATGACACAGCACATG

230 - 240 6 68.4 65.0 44.18

3 TKY353 AB044853 TTCTTACTTAACAACCATCG TGTCACTGACAGATGAATGG

177 - 191 5 78.9 66.0 57.51

3 ASB23 X93537 GAGGTTTGTAATTGGAATG GAGAAGTCATTTTTAACACCT

147 - 167 7 74.8 70.9 79.28

3 HTG2 AF169163 GATTGGCAACAGATGTTAACTCGG CCCCATGAGAACTAACAATGTTAG

99 - 103 3 30.6 26.7 82.59

3 LEX007 AF075610 GGTAGGGCTCTGGGATGA AACACTGGGGAAAAGTCAG

192 - 200 6 63.7 69.1 86.98

3 AHT97 AJ507714 AGTTCGGTAACTTGCCCATG GTTCATGGGCAGAATGGC

149 - 163 8 73.0 73.5 99.04

4 AHT43 AJ271528 ACACAAGTGACAGGAGCGTG TGGAAGCATGCAAGAGGTC

156 - 190 2.92

4 AHT84 AJ507701 TGGCAATCTGCAGGGAAC GATCTTGTGATTGTGTGTGTG

94 - 208 24 89.3 90.2 6.34

4 HMS6 X74635 GAAGCTGCCAGTATTCAACCATTG CTCCATCTTGTGAAGTGTAACTCA

157 - 167 6 67.5 66.0 7.23

4 UMNe224 AF536301 ATGCTTAGCAAGGCCGTG TCCAAAGATAGCGGCAGTG

149 - 152 4 40.2 36.8 13.35

4 UMNe404 AY735245 TTGGAACTTTTAGCAAAGAACC GATCCATTCCCACATATGGC

162 - 174 6 53.3 62.9 16.51

4 UMNe063 AF191696 GGATTTTCTTCTTTTGAATGGC TTTACAATAGCCAAGATGCGG

128 - 144 6 48.6 56.0 19.57

4 ASB3 X93517 AATTCATCTCAGTGCTCTACCAGC TTCATTTTCTACATGCACTACAGC

196 - 208 5 73.7 73.7 23.51

4 COR057 AF108374 GGAGGAGAGGAAGAGAGTGG ATCCAGGGCTCTCCATAGTC

235 - 243 8 70.9 68.9 32.74

4 LEX061 AF075661 TCAGTGTTCCCATCTGTA TGAAATCACACCTTTACTTTA

142 - 160 7 69.5 80.5 42.31

4 LEX050 AF075652 ATAGTCTGGGGTTAGGTAAGG TCTAGCCCAATGTAAATGC

112 - 124 8 62.3 62.9 49.36

4 LEX033 AF075635 TTTAATCAAAGGATTCAGTTG TTTCTCTTCAGGTGTCCTC

178 - 204 7 80.0 75.3 59.50

4 ASB22 X93536 GAGGAATGTGAAATACAGGAGG TTTGTGGTCTTCCGTGCACC

149 - 167 10 66.9 81.9 59.51

4 ABGe059 AM919498 AGTTGCCTCTGGTCTTGCAG GCTGGCAGAATGTCTGTTTTC

192 - 212 8 69.1 71.9 63.98

4 HTG7 AF169291 CCTGAAGCAGAACATCCCTCCTTG ATAAAGTGTCTGGGCAGAGCTGCT

118 - 128 4 77.9 65.6 64.17

4 TKY354 AB044854 AGTGAGGTCTTCCTTGACTG TGTTAGATGGTGGTAAGTGC

140 - 174 10 75.2 74.4 69.24

4 HTG9 AF169293 TGTGGGAAGAGTGTCAATAGCTGT AGGCATCTGGTTTGCTGCAATTTC

118 - 138 8 67.7 70.4 71.14

4 TKY661 AB103879 CGAGGTCTTGGAACCTATCC TTCACTTCAGACAACTCTATTGAAGA

140 - 152 5 57.8 57.4 77.94





size range

allele (n)

HET (%)

PIC (%) Mb

4 TKY363 AB044863 CTCAGACTAAGCGGTACTAG ATGGATACATTCTGGGGAAC

164 - 176 8 79.0 74.0 96.44

4 AHT61 AJ507678 TCTGCATCCTCATGTTCAATG TTGACTTATTTCACTCAGCCCA

222 - 232 8 61.5 61.5 97.24

4 SGCV23 U90601 GGCTTAAGATATGGGTGAGTAAGG GCCCACCCTCTTACTTTTCTCAA

198 - 246 19 96.7 91.4 102.74

5 TKY1175 AB104393 TTATCACCAGTTTCCAGAGC CTTATTCCACCCACTAATTCAC

193 - 215 7 45.5 38.9 10.15

5 AHT68 AJ507685 GGGAGGAAACCCAGTCAATT GGTCCCTCATCACTTCCACA

298 - 310 6 67.9 60.4 25.00

5 HMS63 GGCACCTCCTAGAATTGTGC AGTCTTCTAATCCTCTCCCTG

154 - 174 8 83.2 79.8 28.65

5 HMS52 TCTTCTCAGGATTTGGGAGGT CCCTTCTGAACGGCTTATGA

217 - 223 5 74.4 69.1 28.65

5 TKY521 AB103739 GGTGCAGCTGCTAGCTCAG AGACCCAGTCATTGGGAGG

222 - 228 4 48.6 41.0 37.00

5 UMNe567 AY735272 CGTTTAGCACCTCTCATGAAC TCTTTGCAAAATAGGGCTTG

269 - 279 5 60.7 55.9 45.38

5 COR023 AF101392 TTTCTTTTTCCCACTTAAAGC TGGGACTTAGCAGTATGAAAC

142 - 170 5 70.5 73.4 53.41

5 ABGe3282 FN406321 ATGTTGTTGCAAATGGTAGGG TCCATCAATCCCTCTTCTGG

236 - 253 9 64.0 77.9 63.74

5 UMNe534 AY735262 GCGGAGGTAAGAAGTGGTAG GGCCTAAGATGAGGGTGAA

243 - 253 9 83.7 82.0 73.63

5 LEX034 AF075636 GTTGTCTAGGTGCAGAATCTGG GTTATGTCTCCCCTTTCTCTACC

142 - 152 6 67.1 56.8 76.17

5 ASB10 X93524 GATCTTTAGAATCAGCTTGTTG CTCGCCACGTTAGTTGATG

132 - 148 6 72.8 67.4 78.29

5 TKY911 AB104129 TGAGGTACTGTGCCTTGCTG CTGGGAAGACAGAGCCAGTC

124 - 134 7 65.2 69.6 83.74

5 UMNe455 AY731398 CCTTACTCACTGGGGAATAAA AGACTGAACACCTAACTATGA

390 - 400 6 69.0 68.2 86.27

5 LEX014 AF075616 ATCTAACCAGAGCGCAACGT CCCGACACAGAAGATGGG

152 - 200 6 64.9 57.9 88.96

5 ABGe024 AM9056702 CCACTACCCCTTAGGAGAACAC TGCCCCCAAATGTGAAAG

216 - 240 6 67.9 68.1 90.23

5 AHT107 AJ507724 CTCTGTAACCCTTATATCCTTA TGTTGATTGCTCCTCCCCT

135 - 145 14 75.9 79.1 98.39

6 ABGe3459 FN406422 TGTGGCAGCATCCCACAAAC CCTCCATTTTTGTCGGTTAGCG

123 - 137 9 71.0 77.2 4.34

6 NVHEQ82 AJ245770 AGTCTGGCTGAGGATACTG GGTGAGAAAGGAGATAAATG

302 - 312 8 68.9 65.7 15.51

6 UM015 AF195133 CTAAATCACTCATGGTGGTG GATCAGATTCTCCTTGAAAGAA

218 - 234 6 81.0 71.6 34.56

6 TKY3193 AB217136 TATGGCGATTTCTGGTCTGTGTC GATGACAACACTGGGAAGAAAGAG

125 - 131 7 75.2 77.8 43.52





size range

allele (n)

HET (%)

PIC (%) Mb

6 TKY111 AB053346 TTTAATTATCACTCCCTTGACA TGACTCTGGTTTCTGACTTC

122 - 134 4 50.0 45.7 45.05

6 TKY1162 AB104380 AACCAGTCCCTACATAGAAC CTCACAACCAAGCATACA

198 - 202 4 64.0 63.4 52.82

6 UCD465 U67414 CATCTGTTCCGTGGCATTA TTCAGGTGTGGGTTTTGAATC

273 - 299 3 54.4 49.5 61.23

6 COR070 AF142607 TTCAGCAGGGTCTCATGCCAC TTCGGCTCTGGTTCAAGAGG

271 - 359 10 78.9 80.9 65.85

6 TKY28 AB048335 CTGGACTAGAGTCAGATTGC AACAGGATTCCCCCAATGCC

157 - 173 10 78.5 79.8 66.84

6 TKY284 AB033935 CTGGTTTACCTTCCCTACAG CCAATGGTTCCTCTGAGAAG

144 - 156 9 65.6 67.5 73.77

7 ABGe3051 FN407104 CTGCATTCCCATCATCACAT TGCCTTGCCTCTTTCTGTTTA

133 - 145 8 61.4 71.9 12.12

7 LEX038 AF075640 GCATTCCCATCATCACAT CCTGCCTTGCCTCTTTCT

132 - 144 3 69.4 57.4 28.96

7 LEX015 AF075617 TACCTCTGGTGGTGATGCTT CCCACACTTACTCCCATCAC

200 - 218 3 58.5 54.7 28.96

7 ABGe101 AM946384 GAAAAATGCTGAATGCTTTGTG TTGATTTGCCTGTGATTCTCTC

94 - 108 7 66.0 66.6 32.37

7 ABGe102 AM946385 AGGAAGCACGATCTGGTCTG AAGGATGCCCTGAGGAAGTC

204 - 226 10 77.0 82.2 33.82

7 COR095 AF154948 CTGTGGCAGCTGTCATCTTGG CCCAATTCCAGCCCAGCTTGC

149 - 161 6 70.7 67.5 54.22

7 SGCV28 U90604 CATTTCTCTGGTGTATCTCCCA GGAATAGTCATAGTCCACGACC

131 - 145 6 70.3 66.2 71.10

7 ABGe4342 FN407827 GGGATTTCTGAGATGCTGAA ATGGCTGGCTAGAGTTTGTG

236 - 244 6 55.4 55.7 85.69

8 COR097 AF154950 GGATGAAACAGGGAAGGAAA CCAACGGATTCATGAAAGCTA

221 - 247 5 65.0 67.9 1.44

8 LEX023 AF075625 TCTAGGAAAGACCCATCACG AGTAAGTGGAGGCCAAGGAT

166 - 176 9 81.3 80.6 25.94

8 COR012 AF083455 TAGGGAAACTCCTCAAAGCC GAAACCAAAACCTTCATCCA

188 - 210 6 60.1 65.8 46.41

8 COR003 AF083446 AGATTCCAGGCATTAGGACC TCAGGGACAATCTTCCTCAAG

186 - 212 9 83.5 83.3 64.25

8 COR056 AF108373 CTATCTCAGTCTTCATTGCAGGAC CTCCCTCCCTCCCTCTGTTCTC

127 - 137 7 80.3 70.5 84.11

9 HTG4 AF169165 CCAACTCTTTGTCACATAACAAGA CCATCCTCACTTTTTCACTTTGTT

149 - 167 7 77.8 68.2 1.50

9 HMS3 X74632 CAGGCCGTAGATGACTACCAATGA TTTTCAGAGTTAATTGGTATCACA

178 - 194 7 65.4 70.2 16.90

9 HTG8 AF169292 GCAACAGATGTTGGCTCAG GGAGATGTCCTTGACCACAG

237 - 251 10 77.2 71.2 30.02

9 COR098 AF154951 GGTTAAATTAATCCAAGGTATTTTATT AGAGGAAGACTGGCACAGATG

145 - 159 4 53.9 55.2 38.00





size range

allele (n)

HET (%)

PIC (%) Mb

9 UMNe103 AF536248 TGCCTTTTTCTCTCATCACC AGACTAGTCTGCAAGTTCAG

190-206 3 48.0 37.5 46.07

9 TKY805 AB104023 TCGAGGAGCTCATGACCTGG TTGTACAACTCTCCACCATAGC

105 - 117 7 68.1 65.4 51.85

9 ASB5 X93519 TAATTGCCAAATACCACACAAA TGTCTTGTTCCTCAGGCTCT

297 - 313 5 68.0 57.6 71.66

10 AHT49 AJ507666 GATCCAATATTGTAAACCCCGCC CCTTCATAACCCTTATTGCAGCC

82 - 94 25.9 20.8 0.41

10 HMS23 U89810 CGAGGGGGAATTTTGTTTGT ATAGAGCCATGCAGGGGAAA

256 - 286 3 55.2 51.7 2.71

10 TKY601 AB103819 TCTCTACCGCAAGTGAAACC CTGAATTGTAGGACATCCCG

146 - 162 8 79.7 77.3 7.56

10 COR020 AF083463 CACAGCCCTGACCACTGA CCAAAACAGCCCTGGACT

114 - 126 8 75.0 71.1 10.00

10 UCD482 U67416 GATTGGGATGCAAAGATGAG CAAGAGGATTGGGAACAAAGG

167 - 187 4 56.4 54.5 10.74

10 COR048 AF108365 GGGTCCTGAGCAGGTCTTTT GAAGTAATGGCGGTCATGCT

137 - 159 7 66.7 72.0 12.14

10 AHT15 GGCACAGATGTTAGCTCAGC ATGGAACCAGCCTGGATTGC

188 - 204 7 30.1 28.6 12.76

10 ASB6 X93520 CAACCCCAATACCACCTCTGCC CCGGGAGTGCAGTGGGAGAC

157 - 166 9 66.3 60.3 14.46

10 HMS56 AY768956 GGAGGAGACAGTGGCCCCAGTC GCTGAGCTCTCCCATCCCATCG

112 - 168 4 28.2 27.7 14.61

10 NVHEQ18 AF011404 GGTGTGGAAACATTCCGTAT ACTGCATGTGTGGGAGAGAT

217 - 231 20 87.0 85.7 15.38

10 COR015 AF083458 AAACTGTCACAACGGTTAGGAC CGAAAAAGCCACTTGAGGTC

403 - 421 9 73.1 77.1 19.80

10 LEX008 AF075611 CCAAAGAGGGTGACAGAGAAAG CATATTTTAAAACCTTACCTGCATATC

119 - 137 6 77.9 68.8 20.39

10 ABGe353 FM179627 AGAGGAAGGCGACAGGTC CATCCGTCCATCCATCAG

186 - 206 5 64.2 60.3 23.65

10 UCD412 AF000011 GCTCTCAGTAACCTCGATGTT ATTAAGGAGAAGGTGGAAAAGAC

207 - 211 11 80.8 77.0 26.94

10 LEX062 AF075662 CCTGCCCAAGAAGAACTCAGA AGCAGTGTATTTTTGAAACAT

135 - 156 3 58.8 52.1 27.36

10 LEX017 AF075619 TCAGTACCAGCAAATGAGTGC GCCAGGTGTGGTCTGTCTTC

204 -230 6 59.4 51.7 28.67

10 ABGe354 FM179628 TCAGCGACAATCTTCCTCATACA ATTCACACCTCCCCCAGAAAT

217 - 223 7 72.9 70.1 30.26

10 ABGe355 FM179629 ATGATGGAACCTTGGCTGTC CCTCTCCCAAAGCAACTCTG

183 - 199 9 75.0 79.6 30.74

10 NVHEQ7 AF011402 AGCTAATGTCAGTAGGTTGG TTCCAAGCATCTTAAGGAGG

202 - 226 2 4.7 4.5 36.38

10 ABGe356 FM179630 AAGTCCTGTGCACGTGTGTG TGACTTGATGCCTTGCTCTG

209 - 233 9 80.8 79.1 38.48





size range

allele (n)

HET (%)

PIC (%) Mb

10 TKY867 AB104085 ACGGTGGCAACTGCCAAGGAAG CTTGCAGTCGAATGTGTATTAAATG

220 - 240 6 58.4 58.4 41.44

10 HMS2 X74631 AAAGCCGTAAGATTGGGACA TCCATTGTGAGGGTGTAACA

366 - 378 8 70.7 76.5 52.71

10 LEX009 AF075612 CCCAATGAAGTCCAAGATGG GAAATCTCTAGCAAGACCCAGG

185 - 217 4 53.0 49.1 61.89

10 AHT86 AJ507703 AAATCCCGAGCTAAAATGTA TAGGAAGATAGGATCACAAGG

154 - 168 7 78.1 73.6 76.70

11 UMNe116 AY735236 CTACCATTGAAGAGGGGTGGC GAAACGAGCAGGAAGTGAATCTCC

107 - 127 6 46.1 52.9 10.46

11 ABGe099 AM946382 TTCCTTCTGATTGCACCACTC ATTGTGGGTGACTCCCTCTG

164 - 194 7 66.1 65.4 15.17

11 ABGe100 AM946383 AACATCTCTCCCCAGTCCATC AAGGGGTCCTTGAAGTCCTG

217 - 247 5 54.4 49.8 16.23

11 SGCV24 U90602 GGACTAAAGCCCAACCATCCAGC CTCACCAGTAAGGGGTTATGGGGC

163 - 189 11 73.3 76.9 19.54

11 SGCV13 U90592 TCAGGAGTTTGGATAGATTTTGC TGGAATAACTGAAATGTCCAACA

228 - 234 5 61.3 60.5 26.15

11 TKY710 AB103928 ATACAGGAGTGCGCTTTTCC AAACCATCCTCCACCTTTCC

138 - 154 7 59.7 58.2 34.68

11 TKY424 AB103642 GGGGCGTGAGCATAAAGG CGCTGGATGAGTGAGGGA

71 - 95 8 75.4 71.6 41.50

11 UCD457 U67412 CATCCATCCTTTCCAGCTCGATATTC CAAGACCGTAACTCAGGAGCCC

173 - 181 14 71.8 75.6 51.57

12 SGCV10 U90591 GAGTTCATTCTTTTTCGTGGCTG GGAAACACCCTAAGTGTCCCTTG

121 - 139 9 77.8 78.7 9.50

12 SGCV8 U90590 GGGAAGGACGATGAGTGAC CACCAGGCTAAGTAGCCAAAG

206 - 230 10 77.1 77.2 21.20

12 COR058 AF108375 GTGGGAGGCAGCAGGAAC CCCCAGACACCGTGTGAT

105 - 109 13 82.5 79.3 27.95

12 UCD497 U67419 AGCCACCAGTCTGTTCTCTG AATGTCCTTTGGTGGATGAAC

265 - 279 3 35.0 35.9 32.57

13 COR069 AF142606 GTTTGCTGTGGTTACCAGGCAGA GCAAATTGAATATTTGAAGTTGAGAC

126 - 142 9 83.9 83.3 6.10

13 VHL047 X86449 AGCAGAAACCCACTCAAGCC GCATAATACCCTCAAGGTC

153 - 167 4 73.4 69.1 16.89

13 ASB1 X93515 ACTCATTCATTCACAAATCCCC AGAAAATTCCCTCCTGTCCC

282 - 284 5 63.4 57.4 31.74

14 AHT29 AJ271514 ATACTGGCTTTACGTCACAG ATCACCACCAGAGTTAATGG

87 - 103 6 51.9 54.5 5.41

14 TKY1053 AB104271 CATTAAGCAACAAAAAGCATC GGAAAAGCATGACAAGACACT

224 - 244 6 58.7 65.9 7.17

14 LEX043 AF075645 CTGAAACCTCATTTTATACAG TACTAGAACACAGAAAGCCTA

158 - 162 7 69.1 58.3 16.15

14 HTG18 TACAGCCATTGGAAATCTAC CACCATTACATTTTCCCAG

106 - 120 3 33.3 34.0 22.30





size range

allele (n)

HET (%)

PIC (%) Mb

14 UM010 AF195129 TCTTACATCCTTCCATTACAACTA TGATACATATGTACGTGAAAGGAT

83 - 97 8 78.3 75.8 25.47

14 VHL209 Y08451 TAACTAAGGGGAACAGATGG CAACTAAGGCTTATGCATGC

130 - 142 6 79.1 72.6 32.97

14 TKY310 AB034619 GTTCGTCTGTTTCTAGCCTC TATCTCCACATGGTACTCTC

184 - 214 9 61.9 57.0 45.64

14 TKY435 AB103653 TCCAAGGGTTTGTCCAAAAG TCTCTTGGTTGAAAATGGGG

168-170 10 76.7 76.6 66.59

14 UMNe244 AF536314 CCTCTTGGGACAGAGGACAG TCTCTCAGGAGCCTGTGTTG

256–268 3 49.0 35.0 72.43

14 TKY491 AB103709 AATGTGCGCATTTAACCACTGTG CAAGCCATGCTGTGGAAACG

160 - 164 7 46.2 43.6 81.18

14 LEX078 AF213364 ACCGCATTCTCTTTCAGGTG CTGGGTAACTGACTGGAAAAGG

135 - 149 5 55.5 54.3 87.48

15 UMNe222 AF536300 ACAGTGCTGGATGAGGAAGA CTGGTTCTTCTTGGGGACAC

166 - 184 6 60.5 58.7 7.05

15 TKY2489 AB216432 CAAGAACTGGCATCAGAATTTC TCTTGGGTCTCACTCACTCTCC

136 - 154 5 62.9 63.1 11.36

15 UMNe198 AF536283 TCCTCAGTCCTTTCTCATGC AGCTGAAGGCAATCTGTACC

79 - 101 5 59.9 49.5 17.65

15 B-8 Y10240 CCTACGTGTCTCTTTCTCTTT GTAACGCAATAATACAGCACT

130 - 146 9 64.4 64.1 21.79

15 LEX051 AF075653 AGACATGGATTTAGGGAGTG GCAGAGCCATGCTAAAACTG

142 - 162 6 64.6 62.6 35.20

15 TKY1033 AB104251 CCTTCCGTAGTTTAAGCTTCTG CACAACTGAGTTCTCTGATAGG

234 - 252 7 62.0 57.5 43.58

15 ASB2 X93516 GAGTTGGAGCTCAAGTCTGTC GTTTAGCAACTACAGCGTAGG

162 - 190 8 75.7 74.4 54.61

15 ASB19 X93533 ATGTTGTGCAAATGGGATGA TGCCCATTGATTGATGATTG

123 - 146 11 87.8 82.1 58.31

15 ABGe16406 FN411866 CCTGCTTGGAGGCTGTGATAAGAT GTTCACTGAATGTCAAATTCTGCT

82 - 104 10 79.5 84.2 65.46

15 HTG6 AF169167 TCAGGATTTAGGGCAAAAGG CGTGACAATGAGTTCATCAAAG

216 - 250 7 30.9 30.4 73.96

15 ABGe145 FM165575 GCCCTAGTTAGCAACCAACA AAGATTGATTCCTCAGCACG

192 - 208 7 41.0 72.8 74.52

15 HMB2 Y07730 GTGCCACCACCTCTGTGATT TGGAGAAGGATCTTGGGCTC

95 - 109 6 56.4 50.3 81.10

15 COR075 AF083457 AACTGCTGGCTGGATCTCTG AAGACTGCCCCATTCAATACTC

94 - 108 10 76.4 69.8 86.78

16 ABGe094 AM942735 TAACCTGGGTGCAAAGCCACCCAT TCAGGGCCAATCTTCCTCAC

114 - 124 9 84.7 82.3 2.65

16 HTG3 AF169164 GGGTTTGCTTGTGAACTTCTG GTGAAGCCCTGACTTTGAGC

236 - 244 6 55.4 62.7 8.15

16 ABGe033 AM919472 CCTTCCGGTCTTTATTCACA GGTGGCTGGAGACACAATAG

267 - 277 7 67.4 59.0 17.60





size range

allele (n)

HET (%)

PIC (%) Mb

16 COR011 AF083454 TGGGGACCCAGGACTATCTC TGTTTGGTGACCCTCCCTAC

216 - 228 5 55.0 55.0 23.12

16 ABGe054 AM919493 CCACACAGTATTCCCCCAAG GGAGAGAGGGTTCAGTGCAG

186 - 206 5 64.3 60.1 38.43

16 ABGe058 AM919497 TTGGTTTAGGTTTTTAATTACTCTG GGTGTTGAAACAACTGGCTG

246 - 254 9 72.5 69.6 43.40

16 UMNe566 AY735271 TGCTGTGACTATGCTGTGTCC ATCAGCTGGTCAATGATGAGG

129 - 145 6 58.5 58.0 53.17

16 UMNe562 AY464528 GACCTACAGGCCACTCATCAA GGCAGTTTCCTCCATCCTTA

211 - 227 6 69.2 68.9 64.76

16 LEX056 AF075658 CAACAAAGATGTTGCAAGGG TGTGCCTCTTGTCTCTTAGG

97 - 111 9 78.8 82.0 70.13

16 I-18 Y10244 TAGCTGTCTGCAAAGGCTCA CCAGTGTTCCACATGCCTC

108 - 118 7 75.2 75.5 74.99

16 AHT091 AJ507708 TTTCCTCATTGCTTCCTGAG CCCAAGGTCTGTCTTGCTCTC

174 - 192 5 45.0 35.8 84.05

17 COR072 AF142609 GTGTTGGATGAAGCGAATGA GACTTGCCTGGCTTTGAGTC

154 - 182 7 65.0 69.6 4.77

17 COR007 AF083450 TTTTGAGGGGTGTGTTACAGC TTACCAGAGTTCTTACCTGGGG

103 - 136 8 74.3 72.4 6.61

17 UMNe176 AF536275 GCATTTGCTCACTGGCTAC ACTCCTCCACTCCCACCTA

128 - 134 14 77.7 80.8 23.84

17 UCD014 U35423 GCCCTCTTAGAGCATTTTCC CAGAGATGGCTGGAGTAAGG

249 - 255 4 60.3 59.8 28.63

17 COR032 AF101401 TTCACCTATGAGTTTGAGGTA CGTCATAATGCAGACTCTTTG

163 - 173 4 59.9 52.9 41.43

17 TKY924 AB104142 CAGTTCCATCCATCAGTGAC ATTCCCAAAGGGCCTTTTTC

136 - 152 5 60.5 56.4 57.22

17 TKY792 AB104010 CTTTGGGCCTTTCCTCCAT CGAGCCTGGGAGTGATAC

111 - 119 6 63.6 62.7 75.62

18 UCD136 U67401 TGCATGAGCCAATTCCTTAT TGGACAGATGACAGCAGTTC

164 - 182 4 62.5 58.1 4.23

18 LEX054 AF075656 GTCTCAGCCAAAAGGTATTCAAGC TGGCACCAATATAGGTCACCTGG

122 - 134 7 68.2 57.2 16.95

18 HMS46 U89814 CCCCTCTTTTGCTTGAGAAT GCGTGTATGTGAGGATTGAAG

307 - 319 4 58.5 45.7 26.46

18 COR096 AF154949 AATCAACTAATATTAGGCCTCCT GAATACAGTTCTAGGGGCGT

156 - 160 9 82.7 80.1 37.31

18 HTG28 GGTCAGAAGACAGTCAAGAGTCC CCTCTCAGGCCTCTTACCAC

112 - 136 3 34.2 42.7 38.64

18 ABGe155 FM177593 GCTATCCCTCCTGAGTCTTA AGGTAATTTGAAATAAAATACAC

155 - 161 9 89.6 80.8 40.63

18 TKY462 AB103680 TCTGAAATACCGTGTGCCT TTCTGCCTCCCTCCAACTTT

197 - 217 4 61.3 56.7 57.92

18 TKY101 CAACTGTATGTTGACAGCACA CGGCCATATTAGGTTTATCTG

120 - 125 8 72.8 62.6 63.53





size range

allele (n)

HET (%)

PIC (%) Mb

18 TKY017 AB048328 ACCCCCGCCCCAGCAC TGCCCCGTCATTCTGC

76 - 86 3 61.1 57.0 66.81

18 UCD387 U67404 CACCTCCATCACATTGGTCA GGCTGGAGTCAGCTGACATT

232 - 240 4 63.6 54.6 75.25

18 ABGe159 FM177597 TCGGCTCTTTTCTTCTATTTGC TCGGGCTCTGAATGAGAAAC

214 - 230 7 79.5 65.9 82.25

19 AHT094 AJ507711 ATCAGCCCAGCCTCTTCA AACAACCGGCNAAATAGTGC

141 - 161 5 69.2 74.0 0.09

19 LEX036 AF075638 CCAGCCATCCACTGGTAGAG GGGAAAAGGGGAACCTTCTA

234 - 272 6 75.6 65.3 17.85

19 LEX073 AF213359 GGTGAGGAATTATCTCTTTGAAGG GCAGGTAGGATTGGATAGGTACAT

207 - 215 10 75.6 72.9 24.40

19 HMS8 X74637 GGCCCCACCCACTAAATATCACTG CGGGGTCTTGGAAATTTATGAAGG

120 - 130 5 65.2 61.5 40.88

19 NVHEQ11 AF011403 TGAAAATACACCCAGCTACGC GGGAGATATTTCTTGGCTTGC

147 - 163 4 63.9 55.6 44.12

19 AHT55 AJ507672 TTTTCCAGTGACTCTGAGTGTG GTTGTGGGAAAACTAGTCTGGC

168 - 178 7 67.0 66.5 53.49

20 AHT18 TGCTAAGCCTCAGCACATACA TGGAAATAAGGTTAGCAGGGATGC

79 - 91 6 58.7 53.7 10.05

20 HTG5 AF169166 ACCCTTTCCGCAGACAA CACATCAGAGCCCATCTTCTC

195 - 215 8 87.3 77.3 10.51

20 LEX064 TGAAAGTAGAAAGGGATGTGG TCTCAGAGCAGAAGTCCCTG

160 - 180 6 71.8 55.4 15.30

20 UM011 AF195130 CACCTGCCTACAGTCCAAGC TTTGTGCTTAATGCCTTTGTG

129 - 141 12 75.7 70.2 33.51

20 TKY507 AB103725 TCTGTTGCCTTTATCCACAA ATGAAAACCCTGGGAATAGC

287 - 297 8 76.2 69.5 44.74

20 COR050 AF108367 TAGATTTCTTAAGTGCCAATAGTGG GAACTGCTATAGATATACCTAACTC

111 - 135 5 46.8 38.5 56.11

20 HMS42 CTTCTCTGGACAAAGGGGTG CATGAATTTGCCAGTTTGATG

122 - 124 6 56.7 65.5 63.74

21 SGCV16 U90594 AATTCTCAAATGGTTCAGTGA CTCCCTCCCTTCCTTCTA

146 - 188 5 73.3 62.3 1.93

21 ABGe161 FM177705 TGCTTGCTGGAATTCAGTTTC GCTGATCACAGAACCCTACCC

106 - 134 10 64.7 59.6 3.3

21 UMNe229 AF536303 CAATTCCCGCCCCACCCCCGGCA TTTTTATTCTGATCTGTCACATTT

93 - 113 3 18.1 16.4 11.55

21 HTG10 AF169294 AACCAATTGTGAGATTTTTGCT GGCTAGTCCTGGATCATGTG

146 - 156 7 71.1 60.3 17.14

21 COR068 AF142605 CCCATTAAGAACTTTTCATCCTG GGCAAGCCCCACAAAATTAT

252 - 258 6 76.1 69.8 22.01

21 LEX031 AF075633 CTCTCACTTCCAAGACACTC ATCAAACGTACAGGAAGAGC

169 - 191 4 35.7 29.2 36.13

21 TKY296 AB034605 GGATTCCTCAACCTCCTAAA AGGGATAAGTGACCACCAC

191 - 199 11 78.1 77.5 44.54





size range

allele (n)

HET (%)

PIC (%) Mb

21 LEX037 AF075639 CCAGTCTAAGTTTGTTGGCTAGAA CAAAGGTGAGTGATGGATGGAAGC

129 - 149 5 63.0 56.2 47.82

21 TKY623 AB103841 CAGTGTGGGTGGGCTTTATC ACCACTAGGGTGTGCATGTG

281 - 299 6 76.4 73.0 53.47

22 HTG14 AF169298 AAGACGTGATGGGAAATCAA AGAAAGTTTTCAAATGTGCCA

255 - 263 7 92.8 81.1 14.28

22 COR022 AF101391 ATTACTTCCTCCAGGTATCTCAG AGGCAGGGCTGGGAGACGT

124 - 134 4 45.1 49.7 22.90

22 HTG21 AGGCAGCTTGACTACCCTGA AAAGTCTCCCCTGCGTGTT

169 - 181 8 77.5 79.0 26.81

22 TKY582 AB103800 CAGCTCAGTAGATGATTGTCCA GCAAAGACAAGGAGGTTAAGTT

172 - 202 8 60.4 53.9 31.13

22 COR016 AF083459 CCTGCTGAGGACCTTGGAAGCT ATGTATTTTCAAGTCTAATATCTGCC

188 - 206 2 36.0 31.0 31.73

22 HMS47 U89815 GCCCCCACCTGCTCCACC GGGGCAAAGTGGAAATCC

139 - 147 5 46.7 46.3 39.95

22 SGCV19 U90597 TAGTGACGCCTACGGATTTC CCCAAGAGGGCTTAGAAAGAG

230 - 254 5 72.8 76.2 47.78

23 COR055 AF108372 ACAGCTGCCTGGATATGTGG GCAGAGAGAAATAGAGATGC

156 - 172 8 82.3 76.1 3.25

23 AHT39 AJ271524 CCATTTTCTCATCACAAGCG TGAATCCCTGCTGCTTCC

77 - 95 9 62.7 61.0 4.79

23 ASB39 AF004769 CGGGGTGTGCATCTCTTAGG TGGCGAATGCTGAATCTGG

222 - 250 5 75.6 69.1 20.91

23 LEX063 AF075663 TTATTCCTGCTTCGTANATGA ACACACTTGGGTTCAAATC

123 - 133 7 68.9 63.6 29.65

23 LEX053 AF075655 CGACGCCTCCTCCTAAAC CAGCTGTGTGCCTTTGATTAT

207 - 213 5 74.1 68.2 37.44

23 SGCV4 U90587 AGTTGTGGCTTGCTTTCTAC TTGCACTTGAGCACTTAGTC

143 - 175 7 51.2 44.5 51.90

24 TKY524 AB103742 ACATACAAACCTGCTCAACAT CCTACACATCGCTCATCAA

212 - 222 8 69.4 70.9 9.79

24 LEX042 AF075644 ATGTATCTTCGAGGGATGAT GGCAGTTAATGGTGAGTAAG

142 - 166 7 41.4 38.3 18.56

24 EA2C4 Z29341 CAAAAGTGATTGCCTTCGAT TTGGAAGCTGGGTGATTG

205 - 217 7 76.8 65.1 25.25

24 LEX032 AF075634 CGTAGTAGGGTTTTGGGTCC TTGCGTTTCAATTTTTAATGAC

249 - 267 6 61.6 63.3 36.12

24 COR024 AF101393 ACAGAGCTGACTGCCTATGG TCCTCTTCTCAGGGAGACCT

172 - 178 8 68.9 67.1 41.00

24 COR025 AF101394 ATGCTCTGAGAATAAGTCTGG AAAAGGCGAGAATGGAAT

90 - 102 4 65.7 59.8 44.43

25 UCD464 U67413 AGTCTGGCAATATTGAGGATGT AGCAGCTACCCTTTGAATACTG

253 - 275 7 67.1 55.6 1.98

25 COR018 AF083461 TGACACAAGATAAAAGCCCCAGG GATTGGGAAAAGAGCACAGCC

135 - 158 6 64.4 63.4 15.69





size range

allele (n)

HET (%)

PIC (%) Mb

25 NVHEQ43 AF056396 AACCCTGAAATAACCAAAGTGC CGCTTTGAAAGAGCTTTTACTCC

251 - 271 8 81.8 76.9 31.07

26 ABGe126 AM947005 TTCCAGTGGTTAGGATGTAG TTGAGCATAGTGATAGCATATG

142 - 158 7 72.4 74.7 3.05

26 ABGe10547 FN417334 CGGTCTTGCTTGGGTTAGTC TGTGATTGGTTTTGACACAGG

153 - 167 6 68.6 69.6 4.82

26 TKY934 AB104152 ACTTATTCCAGGAGCGATGC TTCTATGCATCTTGGTCTTGC

246 - 266 6 75.4 74.4 6.02

26 ABGe125 AM947004 ACTTTGCACCTGTGCAAAAAG CTGATTCTTGGCATCTGGAAA

82 - 100 9 82.5 79.3 8.72

26 TKY766 AB103984 TAACACAAAGCCCCCAGTTG GCCAAACCACACATGAGAGC

206 - 248 8 70.7 67.8 11.34

26 ABGe124 AM947003 GAATCCCATCTTTCCTTTCAG ACGTGGAGAATTATCCTGCG

122 - 126 12 70.2 74.0 13.16

26 UMNe066 AF191699 GTGCTGGAGTGAGCTGACC ATCCAAATCGGAGACCATATG

135 - 153 3 38.3 32.6 14.18

26 UMNe153 AF536265 CTTGGGCTACAACAGGGAATA CTGCTATTTCAAACACTTGGA

182 - 208 9 75.2 82.2 18.51

26 COR071 AF142608 GGTTTCTTGTTCATGGAATTATCC TTTGTCAAACTTTGCTTCATCTTC

174 - 210 9 71.8 71.7 19.05

26 ABGe123 AM947002 GTGGAGAGATAAAAGAAGATCC GGCCACAAGGAATGAACACAC

95 - 111 10 78.0 78.3 20.17

26 A-17 X94446 CCCTACCTGAAATGAGAATTG GGCAAAAGATCAGGCCAT

210 - 224 6 65.5 73.3 21.07

26 UM005 AF195127 GGGGTTGTGGAGAATGTCTG CCACAGGTTTTCATGGGTCT

183 - 197 7 55.2 52.0 26.04

26 NVHEQ70 AJ245765 TGCACACCCATTCTAGCTCA GTGGCTCACTCCTCGCTTAC

119 - 183 7 80.4 76.3 30.25

26 TKY523 AB103741 CAATTGCCATTTGTTCCAGTG GCTTAAGAAACACCAGGCAG

190 - 214 9 72.7 75.6 38.28

27 COR031 AF101400 AGCGGAAGTGCTGCGAAAG CCAGCATCTCTGGGCAGG

226 - 240 6 75.3 71.0 1.37

27 UCD005 U35423 GATGCCTCGAACTAGCTTG GATCTTCCATGTTTTTGTTTGG

79 - 107 10 77.5 79.0 14.05

27 TKY315 AB034624 AAGGCAATGCTTATCAAATGC TTACCCGCAGTGACTTCTATT

243 - 265 7 69.7 76.4 20.77

27 LEX005 AF075609 GAAGGCCTGAAGCATTTACA CGTAATGTTGACCAAACTTCA

235 - 253 5 56.0 45.6 26.15

27 COR017 AF083460 CCTTCACTAGCCTTCAAATG TTGTGTTTAGACAGTGCTGC

137 - 157 6 66.7 63.0 35.28

28 TKY333 AB044834 GGAGGGACGATAGAGAGTAAG GCAGAGATAACGGACATGG

149 - 155 9 82.2 78.1 2.48

28 UM003 AF195125 CCTGGGTGTCGTGTGTTTTA TTCCTCTCTCCTGCCTCATC

109 - 125 4 66.1 62.5 10.56

28 TKY425 AB103643 AGCTGCCTCGTTAATTCA CTCATGTCCGCTTGTCTC

233 - 245 7 65.7 64.5 29.13





size range

allele (n)

HET (%)

PIC (%) Mb

28 UCD425 U67406 CTTGTTGTCACAAGAGGCAG ACCTAAATGTCCTTTGGTGG

120 - 130 6 77.1 72.2 43.09

29 TKY900 AB104118 CAAGGACAACTTCTCAGGAG AATAAGAAAGGAGCCAGTCG

194 - 208 3 34.2 36.0 8.24

29 TKY332 AB044833 TGACACACAGGACCATCTCG AAGTGCACTGAGACCCCATT

240 - 250 8 45.6 48.2 12.31

29 TKY628 AB103846 CAGCTCTGCAATTTCTCCTC AATGACCAAGGCATTGAAAG

229 - 243 6 73.1 70.5 18.05

29 COR027 AF101396 TCACTTAGTAGGGGCATGC GTGTTTGTCCTTGACTCTCC

85 - 99 9 80.8 80.1 22.23

29 ASB43 AF004773 CAATCGTGGCCCGGTAAC TTCACTCCAATCCTCAGTCA

141 - 157 8 66.4 58.6 30.34

30 LEX025 AF075627 CAAGTCCTCTTACTTGAAGACTAG AACTCAGGGAGAATCTTCCTCAG

88 - 106 6 65.8 68.9 2.04

30 VHL020 X75970 TGAAAAGTTGCAGTTTGAGA CAACCTCTTGCTACCAGAATA

148 - 160 9 77.6 81.0 18.79

30 LEX075 AF213361 CTGAGGGCGTAAGTCGAGTC GTTAATAGGAGCGGTTGTTTGG

145 - 165 7 77.1 79.0 26.87

31 AHT33 AJ271518 CGAAGCTTCCACTCTTTTCC CCGAATTATCCCTGCCCTAA

226 - 262 14 81.0 82.6 0.60

31 TKY755 AB103973 AAAACCAGTCATGCGGAATC TGAGCTTGTTCCTGCTAGGG

214 - 250 13 86.5 87.1 11.15

31 ABGe241 FM179521 CTCAGGGCGAATGTTCCTC CCCCACCATGAGTCAAAAAC

121 - 141 14 80.6 82.9 16.16

31 AHT34 AJ271519 CTTTTCCCCGAACCTCCTAC TTGGATGCTCCGAGAAGAGT

125 - 137 7 73.2 68.6 21.68

X UCD428 U67407 ATGATTCCAAATGAGGCCTG AGCAATCCTTGCAGGCAG

125 - 137 5 56.9 68.6 5.10

X UMNe202 AF536285 CCTTGGGCTTTAGCAACT CCATTGGAAACTGAGAGG

143 - 165 4 53.3 57.6 10.03

X UCD502 U67420 TGGGCTAAAATTTAATTTGGG ACCAAAACATATGCAAATTAA

198 - 206 12 70.2 81.2 18.55

X LEX010 AF075613 TGTGGCAGGAAAAACACATG CCATAATCCATGAGCCTATTCC

146 - 154 8 65.8 69.5 36.48

X UMNe060 AF191694 AAGCAGGGATAGTAAAGGAC TCTTCCTACCCTTTTTTGGA

146 - 154 5 40.7 47.3 60.78

X TKY039 AB048345 TGCTAGAGGAAGGGATAAAGG CTCTGCTCTTCCATTTCTTGC

122 - 128 3 25.5 27.5 73.27

X LEX013 AF075615 GCTGAAATCCTGTGGGTCTCCA CCTGGCTCCCTTGGTGGTCTGA

120 - 132 5 54.3 56.6 82.44

X LEX024 AF075626 GGGGGTAGAGGGAAAAAGAG TTGTTGGCAGATCCCAGG

132 - 150 8 89.7 80.6 85.57

X LEX022 AF075624 AACATATCCATCGCCTCACA TGCAAATTCACTGAGAGTGG

101 - 113 7 71.6 74.0 104.72


4.6 Pedigrees of Family 2 - 32 .6 Pedigrees of Family 2 - 32

Family 2 - Saxon-Thuringian Family 3 - Saxon-Thuringian Family 2 - Saxon-Thuringian Family 3 - Saxon-Thuringian

? ? ? ? ? ? ?

? ? ?

?

Family 4 - Saxon-Thuringian Family 5 - Saxon-Thuringian Family 4 - Saxon-Thuringian Family 5 - Saxon-Thuringian

? ?

? ?

? ?

Family 6 - Saxon-Thuringian Family 6 - Saxon-Thuringian

? ? ? ? ? ? ?

? ? ? ? ?

Family 7 - Saxon-Thuringian Family 7 - Saxon-Thuringian

? ? ? ?

?

? ?

?


Family 8 - Saxon-Thuringian

? ? ? ? ? ? ?

? ? ?

? ?Family 9 - Saxon-Thuringian Family 10 - Saxon-Thuringian

? ? ? ? ? ?

? ? ? ?

?

Family 11 - Rhenish German ? ? ? ? ?

? ? ? ?Family 12 - Rhenish German


Family 13 - Schleswig

?

? ? ? ? ?

? ?

?

?

? ? ? ?Family 14 - Schleswig

?

? ?

Family 15 - Schleswig Family 16 - Schleswig ?

? ? ? ? ? ? ? ?

?


Family 17 - Schleswig ? ?

? ? ? ? ? ?

Family 18 - South German

? ? ? ? ? ? ? ? ? ? ? ?

? ?

? ? ? ?


? ? ? ? ? ? ? ? ?


? ? ? ? ? ? ? ? ?

? ? ? ? ? ?

?


Family 21- South German

? ? ? ? ? ? ? ?


?

? ? ? ? ? ? ? ? ? ?


? ?

? ? ? ? ? ? ? ? ? ? ?

? ? ? ? ?


? ? ? ? ? ? ? ? ? ? ? ? ? ?


Family 25 - South German ? ? ? ? ? ? ? ?

? ? ? ? ?


? ? ? ? ? ? ? ? ? ? ? ? ? ?

? ? ?

? ?


? ? ? ? ?


? ? ? ? ? ?


Family 29 - South German ? ?

? ? ? ? ? ? ? ? ?

?

? ? ? ? ? ? ?


? ? ? ? ? ? ? ? ? Family 31 and 32 - South German

? ? ? ? ? ? ? ? ?

? ? ? ? ? ? ? ? ? ? ? ? ?

Legend Stallion or Gelding not affected Mare not affected Mare affected with CPD on the right hind leg Horses of unknown phenotype Mare affected with CPD on all four legs, not genotyped

? ?

CHAPTER 5

General discussion

156 General discussion

5 General discussion Many of the hereditary diseases that have been described in the horse are of a

quantitative genetic nature. The objective of this thesis was to identify quantitative

trait loci for chronic pastern dermatitis (CPD) in German draft horses and thus

localize genomic regions harbouring gene loci that play a role in the relatively

unresolved pathogenesis of the disease.

5.1 Minimal screening set for the horse (MSSH)

When the approach for disease and quantitative trait mapping is mainly based on

affected individuals or animals with extreme phenotypes, linkage studies can be

performed using microsatellites. ‘Simple-sequence repeats’ (SSRs), ‘short-tandem

repeats’ (STRs) or simply ‘microsatellites’ have become one of the most popular

classes of genetic markers owing to their high reproducibility, multi-allelic nature,

codominant mode of inheritance, abundance and wide genome coverage. High

mutability at microsatellite loci plays an important role in genome evolution by

creating genetic variation within a gene pool. The number of alleles at individual

microsatellite loci is defined by a variation in the number of repeat units.

In order to conduct a whole genome scan with microsatellites, these markers have to

be evenly distributed across the whole genome and in particular highly polymorphic

to reveal significant loci in a linkage analysis. So, the first step towards the whole

genome scan for chronic pastern dermatitis was to establish a highly polymorphic,

equidistant marker set that is anchored on the second horse genome assembly

(EquCab2.0) with the characteristics such as the number of alleles, the observed

heterozygosity (HET), and the polymorphism information content (PIC). The minimal

screening set for the horse (MSSH) was developed by using markers from previously

published linkage maps (Penedo et al. 2005, Swinburne et al. 2006) and RH maps

(Chowdhary et al.2003). For all of these markers the physical location on the horse

genome assembly EquCab2.0 was determined by using BLAST. The distances

between markers were calculated and for all chromosomal positions showing

coverage above 20 Mb we developed new markers to bridge gaps (Mittmann et al.

2009). The values for HET and PIC were used to test the information content of all

the markers in the preliminary set. Markers which did not show a minimum value of

General discussion 157

four alleles and a HET and PIC greater than 50% were removed and replaced by

more polymorphic markers as far as possible. The markers were genotyped on an

average number of 362 Hanoverian warmblood horses and 299 German draft

horses. The reason for testing the degree of polymorphisms of the microsatellites in

several breeds was that HET and PIC vary among breeds and through genotyping

horses from several breeds, markers with a breed specific abundance of

polymorphisms could be excluded. The large number of horses that were used to

calculate the HET and PIC within one breed provides an exceptionally assured

prediction towards the utility of a specific microsatellite. Because HET and PIC have

often been calculated based on a low number of horses from different breeds, the

values tended to be artificially elevated. This resulted in low allele numbers when the

microsatellites were genotyped using only one horse breed. This marker set was

specifically designed to be highly informative in any horse breed.

The marker set covers all autosomes and the X chromosome with 322 evenly spaced

microsatellite markers. The average chromosomal distance among markers

amounted to 7.44 Mb. The average number of alleles was 7.3 and 8.0 in

Hanoverians and German draft horses, respectively. HET was at 71% for

Hanoverians and German draft horses, PIC at 65% and 67%. This minimal

microsatellite set allows scanning of the whole horse genome at close to 7-10 Mb

resolution.

For a whole genome scan using pure bred draft horse breeds like the one we

conducted for chronic pastern dermatitis, the highly polymorphic marker set is

particularly necessary as there has often been a notable reduction of allele numbers

in known microsatellite markers for the draft horses. This is easily explained by the

relatively small population size in some of the draft horse breeds and the

consecutively high degree of inbreeding (Aberle et al. 2004).

5.2 Identification of new microsatellites

In the course of developing the minimal equine marker set, the search for highly

polymorphic markers in a specific genome region did not always yield satisfying

results. With the first and then the second horse genome sequence (EquCab2.0) that

was made available by the Broad Institute, new possibilities arose for the localization


of microsatellites using a bioinformatics approach. The conventional method of

generating microsatellites from genomic libraries is costly and takes up a lot of time.

Since it cannot be restricted to a single region, microsatellite density varied greatly

across the equine genome.

Before we started our search for new markers, the number of equine microsatellites

accessible in the Horsemap database amounted to 1803, the NCBI nucleotide

database provided information on 4582 microsatellites, and the latest equine linkage

maps spanned 2772 cM and 3740 cM including 734 microsatellites at 3.7 cM

intervals (Swinburne et al. 2006) and 776 microsatellites at 6.3 cM intervals (Penedo

et al. 2005).

With this study we achieved the largest release of horse microsatellites to date with

19,541 newly submitted markers. The resulting five-fold increase of mapped

microsatellites has provided an as yet unknown marker density for the horse close

enough for very successful fine mapping of quantitative trait loci.

The search for new microsatellites was performed with permutation sequences

including all repeat motifs of two to five bases with at least 15 up to 30 repeats.

Repeat motifs with less than 15 repeats were omitted as a result of their expected

lower content of polymorphisms. These microsatellites cover the horse genome with

an average distance of 112 kb and a median distance of 75 kb. Most of the repeat

motifs (72 %) were dinucleotide repeats of AC, GT and AT with frequencies of

26.4%, 26.0% and 19.4%. Trinucleotide repeats represented 3.8%, tetranucleotide

repeats 17.6% and pentanucleotide repeats 1.6% of all simple sequence repeats

(SSR).We identified adjacent SINE and LINE elements as well as 5549 (25.5%)

intragenic microsatellites. Primer pairs were designed for 12,246 microsatellites after

masking repetitive elements.

Information on microsatellite distribution is a prerequisite for an in-depth

understanding of processes determining the formation of microsatellite regions. More

importantly, the high density of mapped markers now enables a fine mapping of QTL

regions with less than 1 Mb marker distance, which will support genetic research for

a lot of multigenic traits in horses such as chronic pastern dermatitis.


5.3 Whole genome scan for chronic pastern dermatitis

The aetiology of chronic pastern dermatitis (CPD) in draft horses is still relatively

unknown. Our approach was focused on the hereditary predisposition for CPD that

has been estimated to vary between 20% up to 90% in the different German draft

horse breeds (Wallraf et al. 2004). CPD has been described in draft horses across

Europe for centuries and by choosing more than one draft horse breed we tried to

encompass the wide distribution and high prevalences. The prevalences of CPD

differ between the horses of the four draft horse breeds included in the genome scan.

In the South German draft horses the prevalence of CPD was 70.6%. Prevalences in

the other horse breeds were higher, ranging from 82.3% (Saxon-Thuringian), 84.1%

(Schleswig) to 90.9% (Rhenish German).

A whole genome scan with 318 microsatellite markers was performed by using 378

German draft horses which could be grouped into 31 paternal half-sib families that

segregated for CPD. The Rhenish German draft horses were represented by 2

families and 11 affected horses, the Schleswig by 5 families and 56 affected horses,

the Saxon-Thuringian by 9 families and 87 affected horses and the South German by

15 families and 146 affected horses. The maximum achievable Zmean of the

different draft horse breeds were lowest for Rhenish German 4.42 and highest for

South German 186.77 indicating the high power of the data set used for linkage

analyses. The across-breed linkage analysis including the 378 German draft horses

could identify chromosome-wide significant QTL on ECA1, 9, 16 and 17. Their

locations were at 102.6-117.8 Mb on ECA1, at 46.1-71.7 Mb on ECA9, at 2.7-38.4

Mb on ECA16 and at 0.0-23.8 Mb on ECA17. Analyses within single breeds

confirmed all the QTL, on ECA1 the South German draft horses showed genome-

wide significant linkage, the QTL on ECA9, 16 and 17 had chromosome-wide

significant LOD scores for the Saxon-Thuringian draft horses. Additional

chromosome-wide significant QTL could be identified for single breeds on ECA7, 10

and 11. The QTL on ECA7 at 29.0-33.8 Mb is chromosome-wide significant for South

German draft horses, for the Schleswig draft horses QTL could be seen on ECA10 at

19.8-38.5 Mb and on ECA11 at 41.5-61.3 Mb. Genome-wide significance was

reached for the QTL on ECA1, 10 and 16 when families with Zmean > 1 were

selected across the different breeds. The increase in significance implies that a QTL

which might be predominant in one breed due to the family material could be an all-

German draft horse QTL for CPD.


There have been two previous candidate gene approaches, for ATP2A2 on ECA8

(Mömke and Distl 2007) and for FOXC2 on ECA3 (Young et al. 2007) that were

initiated because of similarities of the associated diseases in man but could not

identify significant association or linkage with CPD. None of the genotyped

microsatellites in the respective regions showed any linkage with CPD. Therefore it is

unlikely that the genes are involved in the pathogenesis of CPD. Interestingly

enough, the FOXC2 candidate gene approach has resulted in the new term ‘chronic

progressive lymphedema’ for CPD in draft horses and has thus created a name

distinction between the pastern dermatitis in warmbloods that often is easily treated

and the chronic pastern dermatitis that inflicts draft horses. The term ‘chronic

progressive lymphedema’ has not been used for this thesis since the associated

edema is usually a secondary change so that the term ‘chronic pastern dermatitis‘ or

‘verrucous pastern dermatitis’ is a more accurate description.

Potential positional candidate genes may be chosen by means of comparative

human-equine maps where the QTL positions can then be compared with the

conserved regions between horses and humans. The four genomic regions with the

genome-wide significant QTL that were identified should provide a closer margin for

the next positional candidate gene approach. Since some of these regions are more

than 10 Mb wide, a fine mapping with additional microsatellites could help to further

narrow the focus for the search of positional candidate genes. To conduct a search

for suitable candidate genes, it is essential to understand the pathogenesis of the

specific disease. As so little information about the aetiology of chronic pastern

dermatitis has been ascertained by more than one author, it is a bold approach to try

and find potential candidate genes responsible for CPD. If we assume an

overregulated inflammatory autoimmune response to an initial vector as the cause for

CPD, the ubiquitin protein ligase E3A, CD109 molecule and the myotubularin related

protein 6 may all be considered candidate genes because they are located in the

genome-wide significant QTL regions on ECA1, 10 and 17. A recently published

study on the interaction of E6AP with Annexin A1 (Shimoji et al. 2009) could

demonstrate that degradation of Annexin A1 is mediated by E6AP (UBE3A or E6AP,

110.75-110.79 Mb). The degradation of Annexin A1 would result in a highly active

neutrophil extravasation and an overshooting inflammatory response in damaged or

infected tissues like the progressing skin inflammation seen in CPD. The candidate

gene on ECA10 (CD109, 28.58-28.72 Mb), activated T-cell marker CD109 binds and


downregulates the transformimg growth factor-β (TGF-β) in human keratinocytes

(Finnson et al. 2006). TGF-β plays a critical role in skin development, homeostasis

and wound healing. Myotubularin related protein 6 (MTMR6, 5.09-5.12 Mb on

ECA17) is located in the QTL region on ECA17 and plays a critical role in setting a

minimum threshold for a stimulus to activate T-cells (Srivastava et al. 2006). The

histo-pathological study (Geburek et al. 2005) showed a perivascular dermatitis

which was dominated by T lymphocytes was found in all animals affected with CPD,

so MTMR6 could be functionally involved.

Although the speed of whole genome mapping has been significantly increased by

tools like the equine 50K Illumina Beadchip, the often notable reduction of allele

numbers of known microsatellites in the purebred draft horse breeds in comparison to

warmblood horse breeds suggests an even lower degree of polymorphic single

nucleotide polymorphisms (SNPs). The first tests with the equine 50K Illumina

Beadchip for genome-wide association in draft horse breeds have indicated such a

low number of polymorph SNPs (McCue et al. 2009), that it might be an alternative to

make use of the latest significant increase in mapped microsatellite markers. These

have been made available recently (Mittmann et al. 2009), and will be of great value

for future fine mapping of the QTL regions.

This thesis presents the very first genome wide analysis of chronic pastern dermatitis

in four German draft horse breeds. We were able to identify across-breed genome-

wide significant QTL on four chromosomes that could well be the focus of the next

candidate gene approach.

References Aberle K, Wrede J, Distl O (2004) Analysis of relationships between German heavy

horse breeds based on pedigree information. Berl Münch Tierärztl Wochenschr

117, 72–75



Skow LC, 2003. The first-generation whole-genome radiation hybrid map in the


Res 13:742-751

Geburek F, Ohnesorge B, Deegen E, Doeleke R, Hewicker-Trautwein M (2005)

Alterations of epidermal proliferation and cytokeratin expression in skin biopsies


from heavy draught horses with chronic pastern dermatitis. Vet Dermatol 16, 373-

84

Finnson KW, Tam BY, Liu K, Marcoux A, Lepage P, Roy S, Bizet AA, Philip A (2006)

Identification of CD109 as part of the TGF-beta receptor system in human

keratinocytes. Faseb J 20, 1525-1527

McCue M, Mickelson J, Bannasch D, Penedo C, Bailey E, Binns M, Distl O, Guerin

G, Hasegawa T, Hill E, Leeb T, Lindgren G, Rǿed K, Swinburne J, Tozaki T,

Vaudin V, Wade C (2009) The horse gentrain project: initial evaluation of the

EquineSNP50 BeadChips 8th Dorothy Russell Havemeyer Foundation

International Equine Genome Mapping Workshop

Mömke S, Distl O (2007) Molecular analysis of the ATP2A2 gene as candidate for

chronic pastern dermatitis in German draft horses. J Hered 98, 267-271



JE, Tozaki T (2005) International equine gene mapping workshop report: a


merging four mapping resources. Cytogenet Genome Res 111, 5–15

Shimoji T, Murakami K, Sugiyama Y, Matsuda M, Inubushi S, Nasu J, Shirakura M,

Suzuki T, Wakita T, Kishino T, Hotta H, Miyamura T, Shoji I (2009) Identification

of annexin A1 as a novel substrate for E6AP-mediated ubiquitylation. J Cell

Biochem 106, 1123-1135

Srivastava S, Ko K, Choudhury P, Li Z, Johnson AK, Nadkarni V, Unutmaz D,

Coetzee WA, Skolnik EY (2006) Phosphatidylinositol-3 phosphatase

myotubularin-related protein 6 negatively regulates CD4 T cells. Mol Cell Biol 26,

5595-5602


Kurosawa M, Leeb T, Mashima S, Mickelson JR, Raudsepp T, Tozaki T, Binns M

(2006) Single linkage group per chromosome genetic linkage map for the horse,


Genomics 87, 1–29

Wallraf A, Hamann H, Deegen E, Ohnesorge B, Distl O (2004) Analysis of the

prevalence of pastern dermatitis in German Coldblood horse breeds. Berl Münch

Tierärztl Wochenschr, 117, 148-152


Young AE, Bower LP, Affolter VK, De Cock HE, Ferraro GL, Bannasch DL (2007)

Evaluation of FOXC2 as a candidate gene for chronic progressive lymphedema in

draft horses. Vet J 174, 397-399

CHAPTER 6

Summary

166 Summary

6 Summary Evelyn Henrike Mittmann (2009)

Application of horse genomics to identify quantitative trait loci (QTL) for chronic pastern dermatitis in German draft horses The availability of a high-quality draft sequence of the horse makes known the

physical location of microsatellites. The objective of the first part of this thesis was to

establish a highly polymorphic minimal screening set of microsatellite markers for

horses (MSSH) annotated on the horse genome assembly EquCab2.0. We have

used the previously reported linkage and RH maps and have extended these marker

sets by filling in gaps as noted from annotation on the horse sequence. This MSSH

covers all autosomes and the X chromosome with 322 evenly spaced microsatellites

whose positions were determined on the horse genome assembly (EquCab2.0). The

average chromosomal distance among markers amounts to 7.44 Mb. The

characteristics established for this microsatellite set were the number of alleles, the

observed heterozygosity (HET), and the polymorphism information content (PIC) for

Hanoverian warmblood (HW) and several German coldblood horse breeds (CB). The

average number of alleles was 7.3 and 8.0 in HW and CB, respectively. HET was at

71% for HW and CB, PIC at 65% (HW) and 67% (CB). This MSSH allows scanning

of the whole horse genome at close to 7-10 Mb resolution. Furthermore, we

identified a total of 21,781 equine microsatellites on the second horse genome

assembly EquCab2.0 which can be accessed at http://www.tiho-

hannover.de/einricht/zucht/mol_gen/ms_horse/horse_ms.htm. These microsatellites

cover the horse genome with a mean distance of 112 kb and a median distance of 75

kb. Most of the repeat motifs (72 %) were dinucleotide repeats of AC, GT and AT with

frequencies of 26.4%, 26.0% and 19.4%. Trinucleotide repeats represented 3.8%,

tetranucleotide repeats 17.6% and pentanucleotide repeats 1.6% of all simple

sequence repeats (SSR). We identified adjacent SINE and LINE elements and the

corresponding gene ID for 5549 (25.5%) intragenic microsatellites. Primer pairs were

designed for 12,246 microsatellites after masking repetitive elements.

http://www.tiho-hannover.de/einricht/zucht/mol_gen/ms_horse/horse_ms.htm


Summary 167

The effort of developing a suitable markerset was made because the main objective

of this thesis was to perform a whole genome scan for chronic pastern dermatitis in

draft horses. Chronic pastern dermatitis (CPD), also known as chronic progressive

lymphedema (CPL), is a skin disease that affects draft horses. This disease causes

painful lower leg swelling, nodule formation and skin ulceration interfering with

movement. The aim of this whole genome scan was to identify quantitative trait loci

(QTL) for CPD in German draft horses. We recorded clinical data in 917 German

draft horses for CPD and collected blood samples from these horses. Out of these

917 horses, 31 paternal half-sib families with 378 horses from the breeds Rhenish

German, Schleswig, Saxon-Thuringian and South German were chosen for

genotyping. Genotyping was done for 318 polymorphic microsatellites evenly

distributed on all equine autosomes and the X chromosome with a mean distance of

7.44 Mb. An across-breed multipoint linkage analysis revealed chromosome-wide

significant QTL on horse chromosomes (ECA) 1, 9, 16 and 17. Analyses by breed

confirmed the QTL on ECA1 in South German and the QTL on ECA 9, 16 and 17 in

Saxon-Thuringian draft horses. For the Rhenish German and Schleswig draft horses,

further QTL on ECA4 and 10 and for the South German draft horses an additional

QTL on ECA7 were found. This is the first whole genome scan for CPD in draft

horses and it is an important step towards the identification of candidate genes.

CHAPTER 7

Zusammenfassung

170 Erweiterte Zusammenfassung

7 Erweiterte Zusammenfassung Anwendung molekulargenetischer Methoden zur Identifizierung von quantitativen Merkmalsgenorten (QTL) für chronische Mauke bei Deutschen Kaltblütern Evelyn Henrike Mittmann (2009)

Einleitung Mauke ist eine entzündliche, im Fesselbereich beginnende und in chronischen Fällen

bis zu den Karpal- und Tarsalgelenken ziehende Hauterkrankung. Schon im 17.

Jahrhundert wurde Mauke als eine sich kontinuierlich verschlimmernde, chronische

Erkrankung bei Kaltblütern beschrieben. Trotz zahlreicher Therapieansätze und

neuer topisch wie systemisch wirkender Pharmaka wurde seit dieser Zeit für die

Behandlung der Mauke kein wirksames Medikament gefunden. Die palliative

Behandlung ist mühsam und langwierig und nur selten kann eine dauerhafte

Linderung der Symptomatik erzielt werden

Betroffene Kaltblüter erkranken meist relativ früh im Alter von 2-4 Jahren und zeigen

einen progressiven Krankheitsverlauf. Die zu Beginn noch kleinen Hautläsionen

werden oft durch den bei vielen Kaltblutpferden stark ausgeprägten Kötenbehang

verdeckt. Im Frühstadium der Erkrankung sind nur die obersten Hautschichten in der

Fesselbeuge betroffen. Die Haut ist gerötet und schmerzhaft (Dermatitis

erythematosa). Die sich nach einigen Tagen bildenden kleinen Bläschen platzen und

das entstandene gelbliche Exsudat verklebt die Haare (Dermatitis madidans). Dieses

nässende Ekzem entsteht durch den Verlust der oberen Epidermisschichten. Die mit

teils eitrigem Sekret verklebten Haare und eine starke Schuppenbildung der

betroffenen Haut verschlimmern die Symptomatik (Dermatitis squamosa). Es

entstehen Krusten, die je nach Entzündungsgrad und Ausbreitung zur Lahmheit der

Pferde führen können (Dermatitis crustosa).

Im Anschluss kommt es oft zu einer überschießenden Verhornung der Haut

(Dermatitis hyperkeratotika-hyperplastica). Dieses Stadium stellt zusammen mit der

Dermatitis crustosa die über alle Altersklassen hinweg häufigste Maukeform dar.

Nach einiger Zeit kann es zur Bildung von Knoten in den betroffenen Hautbereichen

kommen (Dermatitis tuberosa). Alle diese charakteristischen, aufeinander folgenden

Erweiterte Zusammenfassung 171

Stadien der Entzündung werden unter dem Begriff der ekzematösen Mauke

zusammengefasst (Dermatitis eccematosa). Wegen der vorgeschädigten Haut kann

es im Verlauf einer Maukeerkrankungen zu verschiedensten Sekundärinfektionen

durch Bakterien, Pilze und Chorioptes Milben kommen, die aber nicht ursächlich an

der Entstehung der Erkrankung beteiligt sein müssen.

Die chronische Form der Mauke wird gemeinhin als Warzenmauke (Dermatitis

verrucosa) bezeichnet und kann sich sowohl aus der akuten, ekzematösen Mauke

als auch eigenständig entwickeln. Die klinische Symptomatik der Warzenmauke stellt

sich mit schmierigem, eitrigem und übelriechendem Exsudat dar, welches durch die

ständige Reizung der Haut zu geschwürartigen Substanzverlusten der

Hautoberfläche und zur Bildung von multiplen warzenartigen Granulomen führt.

Zusätzlich ist diese Form der Mauke durch schwielenförmige, umschriebene

Hautveränderungen, diffuse Sklerose der Haut und Unterhaut sowie borstenartiges

Aufrichten der Haare gekennzeichnet. In den Fällen, bei denen sich die Mauke bis

über die Karpal- oder Tarsalgelenke ausgebreitet hat, kommt es durch die

Beinmotorik zu Querrissen der Haut (Rhagaden), die mit starker Schuppen- und

Borkenbildung einher gehen können (Raspe). Im Verlauf einer Maukeerkrankung

kann es zu hochgradig gestörtem Allgemeinbefinden mit starker Lahmheit und

Behinderung der Bewegungsfreiheit kommen, so dass die Pferde letztendlich

euthanasiert werden müssen.

Die Ätiologie der Mauke konnte bis heute nicht abschließend geklärt werden,

allerdings können differentialdiagnostisch einige Hauterkrankungen von der

‚idiopathischen Mauke’ unterschieden werden. Eine Infektion mit Chorioptes equi

Milben kann zu Mauke-ähnlichen Symptomen führen. Allerdings wird Mauke nicht

ursächlich durch Chorioptes Milben ausgelöst, obgleich diese eine bereits

vorhandene Maukeerkrankung erheblich verschlimmern können. Außerdem sollten

Autoimmunerkrankungen wie Pemphigus foliaceus und durch Photosensibilität

hervorgerufene Hautveränderungen im Fesselbereich von Mauke eindeutig

differenziert werden. Durch die ungeklärte Pathogenese von Mauke haben sich im

Laufe der Zeit unterschiedliche, deskriptive Begriffe ergeben. So ist die Definition

‚Leukozytoclastische Vaskulitis der Fessel’ einer der Versuche, die histo-

pathologisch feststellbaren Veränderungen der Mauke zu beschreiben, jedoch nicht

die Bezeichnung für eine eigenständige Erkrankung. In der letzten Zeit hat sich in der

amerikanischen Literatur der Begriff des chronischen, progressiven Lymphödems bei


Kaltblutpferden durchgesetzt. Diese Beschreibung ist nach Meinung von

europäischen Dermatologen und Pathologen nicht korrekt, da das mit Mauke

einhergehende Lymphödem eher sekundärer Genese ist. Trotz allem hat diese neue

Definition dazu beigetragen, dass die chronische Mauke beim Kaltblut als eigene

Erkrankung wahrgenommen wird.

Es gibt eine starke genetische Prädisposition für das Auftreten von Mauke bei

Kaltblutpferden, dennoch spielen Umweltfaktoren bei der klinischen Ausprägung eine

wichtige Rolle. So können das Stallklima einschließlich der Sauberkeit der Einstreu,

matschige, feuchte Paddocks (vor allem im Winter) als auch die Art der Fütterung zu

einer erheblichen Verschlimmerung von Krankheitssymptomen beitragen.

Da die Mauke beim Kaltblut weder durch gutes Management noch durch

medikamentöse Therapie geheilt werden kann, ist das Ziel dieser Doktorarbeit zur

Aufklärung der genetischen Prädisposition von Kaltblutpferden zugrundeliegenden

Faktoren beizutragen. Der Vererbungsmodus von Mauke ist noch nicht bekannt, es

ist aber davon auszugehen, dass für die Entstehung von Mauke mehrere Gene

verantwortlich sind. Da die Mauke oft erst nach den ersten Zuchteinsätzen auftritt

und damit weiterhin großflächig weitervererbt wird, besteht auf lange Sicht die

Hoffnung, dass ein Gentest entwickelt werden kann. Damit könnten betroffene Tiere

bereits vor dem Auftreten von ersten Maukesymptomen von der Zucht

ausgeschlossen werden.

Die Suche nach signifikant mit Mauke gekoppelten Genombereichen, in denen die

für die Entwicklung von Mauke verantwortlichen Gene lokalisiert sind, wurde mit Hilfe

molekulargenetischen Untersuchungsmethoden durchgeführt. Für Kopplungsstudien

werden genetische Marker (Mikrosatelliten) untersucht, die gleichmäßig übers

gesamte Genom verteilt sind, daher auch der Begriff ‚Genomscan’. Die entsprechend

mit der Erkrankung gekoppelten Bereiche werden als ‚Merkmalsgenorte’, im

Englischen auch ‚quantitative trait loci’ (QTL), bezeichnet. QTL definieren eine

Region, die wahrscheinlich ein für die Entstehung der untersuchten Krankheit

relevantes Gen aufweisen. Die Voraussetzungen, um eine derartige Studie

durchführen zu können sind ein gutes, informatives Markerset und eine große Anzahl

miteinander verwandter, betroffener Tiere. Im Verlauf dieser Doktorarbeit wurde

daher mit neuer Methodik ein hoch polymorphes Mikrosatelliten Markerset für


Genomscans beim Pferd entwickelt, was die guten, genomweit signifikanten

Ergebnisse der nachfolgenden Kopplungsanalyse erst ermöglicht hat.

Entwicklung eines für Warmblut- und Kaltblutpferde informativen minimalen Mikrosatelliten Markersets zur Durchführung von Genomscans Einführung Aussagekräftige Kopplungsstudien können mit Merkmalen wie Mauke durchgeführt

werden, die auf extremen Phänotypen oder auf vielen betroffenen Individuen

beruhen, wenn entsprechend polymorphe Mikrosatelliten Marker zur Verfügung

stehen. Mikrosatelliten sind einfache, relativ kurze repetitive Sequenzmuster, die

durch ihre hohe Mutationsrate eine Vielzahl von verschiedenen Allelen aufweisen.

Die Anzahl von Allelen an einem spezifischen Microsatellitenlocus wird durch die

Variation an unterschiedlich vielen repetitiven Sequenzmustern definiert. Mit ihrer

einfachen Reproduzierbarkeit, ihrer ko-dominanten Vererbung, ihrer großen Anzahl

und der gleichmäßigen Verteilung über das gesamte Genom sind sie die

wahrscheinlich am meisten genutzten genetischen Marker.

Um einen Genomscan mit Mikrosatelliten durchzuführen, müssen sie gleichmäßig

über das gesamte Genom verteilt sein und außerdem einen hohen Polymorphiegrad

aufweisen, damit in einer Kopplungsstudie signifikante Regionen entdeckt werden

können.

Material und Methoden

Der erste Schritt zur erfolgreichen Durchführung einer Kopplungsstudie für Mauke

beim Deutschen Kaltblut war daher die Entwicklung eines entsprechend

polymorphen Markersets, welches anhand der zweiten Pferdegenomsequenz

(EquCab2.0) angeordnet wurde. Diese Anordnung läuft über einen Sequenzabgleich,

die Anwendung heißt BLAST (basic local alignment search tool) und ist auf der NCBI

Datenbank unter http://www.ncbi.nlm.nih.gov/ zu finden. Die Abstände zwischen den

Mikrosatelliten wurden berechnet, und alle chromosomalen Distanzen größer als 20

Megabasen (Mb) mit neu entwickelten Markern aufgefüllt (Methode im nächsten

Abschnitt erklärt). Marker für das minimale Mikrosatelliten Markerset wurden unter

anderem aus den schon veröffentlichen Kopplungs- und RH-Karten ausgewählt. Die

Mikrosatelliten wurden mit Hilfe von der im Labor ermittelten Anzahl Allele, der

http://www.ncbi.nlm.nih.gov/


Heterozygotie (HET) und dem Polymorphie Informationsgehalt (PIC) bewertet und

dann entsprechend selektiert. Mikrosatelliten, deren Anzahl von Allelen kleiner als 4,

und deren HET und PIC Werte unter 50% lagen, wurden ausgetauscht solang

bessere Marker zur Verfügung standen. Die Mikrosatelliten des Markersets wurden

an durchschnittlich 362 Hannoveranern und 299 Deutschen Kaltblütern genotypisiert.

Um die nur in einer Pferderasse hoch polymorphen Mikrosatelliten eliminieren zu

können, wurde der Polymorphiegrad der Marker an zwei unterschiedlichen

Pferderassen getestet. Durch die große Anzahl an Pferden, an denen die Marker

innerhalb der beiden Pferderassen getestet wurden, sind die HET und PIC Werte

sehr zuverlässig und so sind die Marker auch innerhalb einer Pferderasse informativ.

Ergebnisse Die 322 Mikrosatelliten des Markersets sind gleichmäßig auf allen Pferde Autosomen

und dem X-Chromosom verteilt. Der durchschnittliche chromosomale Abstand

zwischen Markern betrug 7,44 Mb, die durchschnittliche Anzahl der Allele bei den

Hannoveranern war 7,3 und bei den Deutschen Kaltblütern 8,0. Die Heterozygotie

war in beiden Pferderassen gleich hoch mit HET 71%, PIC lag bei den

Hannoveranern bei 65% und bei den Kaltblütern bei 67%. Mit diesem minimalen

Markerset können Genomscans in unterschiedlichsten Pferderassen mit einer

genomweiten Abdeckung von 7-10 Mb durchgeführt werden.

Diskussion Dieses neu entwickelte Mikrosatelliten Markerset für das Pferd ist das erste Set, das

an zwei unterschiedlichen Pferderassen, beziehungsweise Pferdetypen getestet

wurde. Die Selektion auf hoch polymorphe Mikrosatelliten ist der andere, große

Vorteil dieses Markersets, wodurch es sich für alle Pferderassen mit Warmblut-,

Vollblut- oder Kaltblutanteil sehr informativ erweisen wird.

Die zuletzt veröffentlichten Kopplungskarten für das Pferd umfassten 2772

centiMorgan (cM) und 3740 cM mit jeweils 743 und 776 Mikrosatelliten. Beide Karten

beinhalten deutlich mehr Mikrosatelliten als unser Markerset, aber da keine HET, PIC

und Allele für die Mikrosatelliten angegeben werden ist in Frage zu stellen, ob all die

zusätzlichen Marker auch einen höheren Informationsgehalt in einer Kopplungsstudie

zeigen würden. Außerdem sind die Positionen der Mikrosatelliten in dem neu

entwickelten Markerset anhand der physikalischen Karte des Pferdes (EquCab2.0)


von uns verifiziert worden, und daher viel genauer, als das mit Kopplungskarten und

der alten Einheit centiMorgan möglich ist.

Für einen Genomscan, in dem das Probenmaterial reinrassig gezüchtete

Kaltblutpferderassen enthält, ist dieses hoch polymorphe Markerset besonders

wichtig. Es wurde während des Selektionsprozesses der Mikrosatelliten für dieses

Markerset oft eine deutlich geringere Allelanzahl, kombiniert mit geringeren HET und

PIC Werten innerhalb einzelner Kaltblutrassen beobachtet. Dieser Effekt beruht auf

der relativ kleinen Anzahl an zugelassenen Zuchtpferden und dem dadurch

entsprechend hohen Inzuchtgrad in bestimmten Kaltblutpferderassen.

Identifikation von 21.781 neuen Pferde Mikrosatelliten auf der 2. Pferdegenomsequenz

Einleitung Im Zuge der Entwicklung des minimalen Mikrosatelliten Markersets war die Suche

nach polymorphen Mikrosatelliten in einer bestimmten Genomregion nicht immer von

Erfolg gekrönt. Mit der Veröffentlichung der ersten und im folgenden Jahr der zweiten

Pferdegenomsequenz (EquCab2.0) durch das Broad Institute ergaben sich neue

Möglichkeiten zur Identifikation von bis dahin unbekannten Mikrosatelliten mit Hilfe

der Bioinformatik.

Da die konventionelle Methode, Mikrosatelliten im Labor zu lokalisieren sehr

zeitaufwändig und kostspielig ist und nicht auf eine spezifische Region beschränkt

werden kann, gab es große Schwankungen in der chromosomalen Abdeckung mit

Markern. Die Anzahl der in der HORSEMAP Datenbank enthaltenen Mikrosatelliten

belief sich auf 1803, die NCBI Datenbank enthielt Informationen zu 4582

Mikrosatelliten, und die zuletzt veröffentlichten Kopplungskarten für das Pferd

umfassten 2772 centiMorgan (cM) und 3740 cM mit jeweils 743 und 776

Mikrosatelliten.

Methoden Die Suche nach neuen Mikrosatelliten wurde mit permutierten repetitiven

Sequenzmustern aller möglichen zweier bis fünfer Basenkombinationen mit

mindestens 15 und maximal 30 Wiederholungen durchgeführt. Wir haben die BLAST


Funktion (basic local alignment search tool) mit BLASTn Modus benutzt, welcher

unter (ftp://ftp.ncbi.nih.gov/blast/executable) zu finden ist. Die Selektion von Markern

mit Sequenzmustern von mehr als 15 Wiederholungen ist von großer Bedeutung für

den voraussichtlichen Polymorphiegrad, der bei kürzeren Sequenzen deutlich

erniedrigt ist.

Ergebnisse Unsere Suche hat mit 19.541 neuen Mikrosatelliten zu einem 400%igen Anstieg von

auf der 2. Pferdegenomsequenz angeordneten Markern geführt. Außerdem

entsprachen 2240 von den 4582 auf NCBI Homepage veröffentlichten Mikrosatelliten

Positionen von unseren Treffern, von denen allerdings 97 keiner chromosomalen

Position auf EquCab2.0 zugeordnet werden konnten. Die Übereinstimmung der

Treffer war deshalb bei etwa 50%, weil zusammengesetzte Sequenzmotive unter

einer Länge von 15 Wiederholungen nicht vom Suchlogarithmus entdeckt werden

sollten aufgrund der zu erwartenden geringeren Polymorphie. Es wurden 462

zusammengesetzte Mikrosatelliten neu identifiziert, deren Einzellängen jeweils

länger als 30 bp waren, und damit höchstwahrscheinlich einen sehr hohen

Polymorphiegrad aufweisen.

Die neu identifizierten Mikrosatelliten sind im durchschnittlichen Abstand von 112

Kilobasen (kb) über das gesamte Pferdegenom verteilt. Die meisten der repetitiven

Sequenzmuster mit 72% waren Muster mit zwei Basen, Dinukleotide; AC (26,4%),

GT (26,0%) und AT (19,4%). Trinukleotide waren zu 3,8%, Tetranukleotide zu

17,6%, und Pentanukleotide zu 1,6% aller identifizierten Mikrosatelliten enthalten.

Um den einzelnen Mikrosatelliten herum wurden ca. 800 bp der Sequenz

ausgeschnitten, und dieses Stück wurde zusätzlich noch auf eventuell vorhandene

SINE- oder LINE - Elemente untersucht. Der Positionsabgleich dieser Sequenz mit

bekannten Genen ergab, dass 25,5% der neuen Marker in einem intragenischen

Bereich lagen. Nach der Maskierung repetitiver Sequenzbereiche mit Hilfe des

REPEATMASKER konnten für insgesamt 12.246 Mikrosatelliten Primer mit

PRIMER3 designed werden. Es wurde die Gesamtdatei mit allen Ergebnissen auf


zugänglich gemacht. Die Gesamtdatei kann in einer komprimierten Exeldatei

heruntergeladen werden, zusätzlich kann nach einzelnen Markernamen, oder nach

Markern in einer bestimmten Region gesucht werden.

ftp://ftp.ncbi.nih.gov/blast/executable



In der Tabelle enthalten sind die folgenden Daten; die 800bp Sequenz einschließlich

des Mikrosatelliten, das Chromosom, der Markername, die „Accession number“ mit

dem entsprechenden Link zur „NCBI Nukleotide Datenbank“, das Sequenzmotiv und

die Markerlänge in Basenpaaren für einfache und zusammengesetzte

Mikrosatelliten, die BLAST Position des Mikrosatelliten auf EquCab2.0, die

„Annealing“ Temperatur (Ta) und der GC Gehalt des entsprechenden Vorwärts und

Rückwärts Primers, die PCR Produktgröße in Basenpaaren mit der entsprechenden

Sequenz, die Position des Mikrosatelliten innerhalb dieser Sequenz in Basenpaaren,

die Information ob sich SINE oder LINE Elemente in der 800bp Sequenz befinden,

die ENSEMBL Gen ID mit dem Link zur ENSEMBL Datenbank und/oder der

Genname, und falls bekannt auch die Anzahl Allele, HET, PIC, Anzahl der Pferde an

denen diese Werte erhoben wurden, und die Pferderasse.

Diskussion Die Gesamtdatei kann in einer komprimierten Exeldatei heruntergeladen werden,

zusätzlich kann nach einzelnen Markernamen, oder nach Markern in einer

bestimmten Region gesucht werden. Dies ist die erste Datenbank für Mikrosatelliten,

die die physikalische Position eines Markers direkt (in Mb) angibt. Das ist sehr

hilfreich für diejenigen, die nach neuen Markern in einem speziellen Bereich suchen.

Das Wissen über Verteilungsmuster von Mikrosatelliten im Genom ist eine der

Voraussetzungen, um Vorgänge wie die Bildung von Mikrosatellitenregionen besser

verstehen zu können. Für die nähere Charakterisierung von quantitativen

Merkmalsgenorten aber ist noch viel wichtiger, dass die durch diese Veröffentlichung

stark erhöhte Dichte von Mikrosatelliten auf dem Pferdegenom eine Feinkartierung

mit Markerabständen von unter einer Megabase möglich macht.

Genomscan zur Identifizierung von quantitativen Merkmalsgenorten (QTL) für chronische Mauke bei Deutschen Kaltblütern Einleitung Im Fokus dieser Doktorarbeit stand die genetische Prädisposition für Mauke, die für

die unterschiedlichen Deutschen Kaltblutpferderassen auf 20% bis zu 90% geschätzt

wurde.


Durch die vorangehende Doktorarbeit ist diese molekulargenetische Studie

ermöglicht worden, denn aussagekräftige Kopplungsstudien sollten auf möglichst

vielen betroffenen Individuen und präzisen Untersuchungsergebnissen beruhen.

Grundlage für den molekulargenetischen Ansatz zur Aufklärung der chronischen

Mauke sind klinische Daten und Informationen wie zum Beispiel zu den

Stallbedingungen, der Verwendung der Pferde und der Fütterung, die im Rahmen

der vorangegangenen Doktorarbeit in den Jahren 2001-2002 von Züchtern in ganz

Deutschland bei 917 Deutschen Kaltblütern erhoben wurden. Für jedes Pferd wurden

außerdem das Geburtsdatum, die Pferderasse und der beobachtete

Krankheitsverlauf der Mauke erfragt. Die Untersuchung beinhaltete eine klinische

Allgemeinuntersuchung und eine spezielle klinische Untersuchung der Haut von allen

vier Beinen. Die Ausbreitung der Hautläsionen wurde mit Hilfe von schematischen

Zeichnungen dokumentiert und anhand einer Bewertungsscala von 1 bis 5 klinisch

bewertet.

Material und Methoden Pedigreematerial

Aus den Blutproben von 917 Kaltblütern wurden 378 Pferde aus den Zuchtverbänden

des Rheinisch Deutschen Kaltbluts, des Schleswiger Kaltbluts, des Sächsisch-

Thüringischen Kaltbluts und des Süddeutschen Kaltbluts ausgewählt. Diese

Kaltblüter wurden in 31 Halbgeschwister Familien zusammengefasst, welche für das

Merkmal ‚Mauke’ segregierten. Die Anzahl von genotypisierten Kaltblütern pro

Familie variierte zwischen 5 und 24. Die Rheinisch Deutschen wurden repräsentiert

durch 2 Familien mit 11 betroffenen Pferden, die Schleswiger durch 5 Familien mit 56

betroffenen Pferden, die Sächsisch-Thüringischen durch 9 Familien mit 87

betroffenen Pferden, und die Süddeutschen durch 15 Familien mit 146 betroffenen

Pferden.

Die Maukeprävalenz im Familienmaterial des Genomscans war bei den einzelnen

Kaltblutrassen unterschiedlich, wobei die Süddeutschen mit 70,6%, die Sächsisch-

Thüringischen mit 82,3%, die Schleswiger mit 84,1% und die Rheinisch-Deutschen

sogar mit 90,9% von Mauke betroffen waren.

Die ausgewählten Kaltblüter wurden in den Jahren 1982-2000 geboren, das

Durchschnittsalter der klinisch gesunden Pferde lag zwischen 4 Jahren (Rheinisch


Deutsche), 4,7 Jahren (Schleswiger), 7 Jahren (Sächsisch-Thüringische) und 8,8

Jahren (Süddeutsche). Die erkrankten Pferde waren durchschnittlich älter mit 7

Jahren (Rheinisch Deutsche), 8,7 Jahren (Süddeutsche), 9,3 Jahren (Schleswiger)

und 9,8 Jahren (Sächsisch-Thüringische).

Markerset

Für den Genomscan wurden insgesamt 318 Mikrosatelliten typisiert. Dies erfolgte als

Zweistufenanalyse, wobei zuerst 178 gleichmäßig über alle Autosomen und das X-

Chromosom verteilte Mikrosatelliten an 8 Familien mit 68 Süddeutschen Kaltblütern

genotypisiert wurden. Die Zahl der Kaltblutpferde wurde anschließend erhöht und die

zusätzlichen Proben von weiteren Deutschen Kaltblutrassen sind auch an den 178

Mikrosatelliten genotypisiert worden. Im zweiten Schritt wurde die Markerdichte auf

den Chromosomen (ECA) mit mutmaßlichen Merkmalsgenorten (QTL) erhöht. Der

durchschnittliche Abstand zwischen den 318 Markern auf der Pferdegenomsequenz

war 7,5 ± 8,2 Mb groß, die durchschnittliche Anzahl Allele betrug 7,1, die Mittelwerte

von HET lagen bei 65,7% und von PIC bei 63,8%.

Genotypisierung der Mikrosatelliten

Von den ausgewählten Kaltblütern wurde DNA aus EDTA-Blutproben unter

Verwendung des QIAamp® 96 DNA Blood Kit (QIAGEN, Hilden, Deutschland)

isoliert. Die verwendeten Mikrosatelliten wurden unter identischen PCR Bedingungen

(Denaturierung bei 94°C für 4 min, 36 Zyklen mit jeweils 30 sec bei 94°C, 60 sec Ta,

30 sec bei 72°C und 10 min bei 4°C), die sich lediglich in den verschiedenen

Annealing Temperaturen (Ta ) unterschieden, in Anwesenheit von Taq Polymerase

und spezifischer, Fluoreszenzfarbstoff markierter Primer (IRD700 und IRD800)

amplifiziert. Zur Steigerung der Effizienz konnten Multiplex-PCR Ansätze von bis zu 8

Primer-Paaren erfolgreich durchgeführt werden. Die PCR-Produkte wurden

anschließend mit Formamid-Ladepuffer in variierendem Verhältnis von 1:3 bis 1:20

verdünnt. Die einzelnen Allele wurden mit Hilfe von 4 und 6%-igen

Polyacrylamidgelen durch Gelelektrophorese auf den automatischen

Sequenziergeräten LI-COR 4200/S2 und LICOR 4300 (LI-COR, Lincoln, USA)

aufgetrennt. Die Allelgrößen wurden in Basenpaaren ausgewertet. Anhand von

zusätzlich aufgetragenen, fluoreszierenden (IRD700/800) Längenstandards und


einem schon typisierten Referenztier (RT-1) konnten auf Ausdrucken der Genotyp

der Marker makroskopisch bestimmt werden.

Statistik

Es wurde eine nicht-parametrische Kopplungsanalyse unter der Verwendung der

Software MERLIN auf der Grundlage von abstammungsidentischen Markerallelen

(IBD) zwischen den verwandten Tieren durchgeführt. Dabei wurden die Markerallele

auf Kosegregation mit der phänotypischen Ausprägung von Mauke getestet.

Anschließend wurde die einer Normalverteilung folgende Teststatistik für den Anteil

an IBD-Markerallelen (Zmean) und ein daraus abgeleiteter LOD Score berechnet. Als

signifikant für die Kosegregation eines Markerallels mit dem Phänotyp der Mauke

gelten Irrtumswahrscheinlichkeiten (p) von 0,05 oder kleiner. Es wurden

verschiedene Kopplungsanalysen durchgeführt, für alle Deutschen Kaltblutpferde

gemeinsam, innerhalb jeder der vier Rassen und für Familienkombinationen über alle

Rassen hinweg, deren Zmean > 1 war.

Ergebnisse Durch den Genomscan an 378 Deutschen Kaltblütern ergab sich eine signifikante

Kopplung für das Auftreten der chronischen Mauke zu insgesamt 4

Chromosomregionen auf ECA1, 9, 16 und 17. Die Position der QTL liegt zwischen

102,6–117,8 Mb auf Chromosom 1, zwischen 46,1-71,7 Mb auf Chromosom 9,

zwischen 2,7-38,4 Mb auf Chromosom 16 und zwischen 0-23,8 auf Chromosom 17.

Die Kopplungsanalysen innerhalb der einzelnen Rassen konnten alle

Merkmalsgenorte (QTL) bestätigen, der QTL auf Chromosom 1 war besonders für

das Süddeutsche Kaltblut signifikant, und die QTL auf Chromosom 9, 16 und 17

zeigten signifikante LOD Scores beim Sächsisch-Thüringischen Kaltblut. Zusätzlich

konnten noch chromosomweit signifikante QTL für die Süddeutschen Kaltblüter auf

Chromosom 7 zwischen 29-33,8 Mb, und für die Schleswiger auf Chromosom 10

zwischen 19,8-38,5 Mb und auf Chromosom 11 zwischen 41,5-61,3 nachgewiesen

werden. Genomweite Signifikanz der Kopplung wurde für die QTL auf Chromosomen

1, 10 und 16 durch die Einbeziehung aller Familien mit Zmeans > 1 erreicht.


Diskussion Diese Kopplungsstudie ist die erste genomweite Untersuchung an von chronischer

Mauke betroffenen Kaltblütern. Da die Pferde aller untersuchten Kaltblutrassen die

gleiche Krankheitssymptomatik einschließlich der Schwere der Erkrankung

aufweisen und dem gleichen Altersprofil entsprechen, wurde der Ansatz einer

Kopplungsanalyse mit mehreren Pferderassen gut begründet. Außerdem gibt es

genetische Verwandschaftsverhältnisse zwischen den untersuchten Kaltblutrassen

und auch zum Belgischen Kaltblut. Durch das Einkreuzen von Belgischem Kaltblut in

einige Deutsche Kaltblutrassen zu Anfang des 20. Jahrhunderts wurde eine

signifikante Steigerung von an Mauke erkrankten Pferden bedingt. Den Belgiern wird

ein hoher Anteil an der genetischen Prädisposition für Mauke nachgesagt.

Im Rahmen unserer Studie wurden vier genomweit signifikante Chromosomregionen

auf ECA1, 10, 16 und 17 identifiziert. Es ist sehr wahrscheinlich, dass die

Entwicklung von Mauke von mehreren Genen beeinflusst wird. Diese QTL

ermöglichen eine Suche nach neuen positionellen Kandidatengenen.

Die zwei vorangegangenen Kandidatengenstudien für Mauke haben das auf

Chromosom 8 liegende Gen ATP2A2 und das auf Chromosom 3 liegende Gen

FOXC2 auf Grund von ähnlichen, beim Menschen nachgewiesenen Erkrankungen

untersucht. In beiden Studien konnte keine Kopplung bzw Assoziation von mit Mauke

gekoppelten Markern gefunden werden, und auch im Genomscan wurden keine

signifikant gekoppelten Mikrosatelliten in den entsprechenden Chromosombereichen

gefunden.

Um eine Suche nach Kandidatengenen erfolgreich durchführen zu können sollte die

Pathogenese der entsprechenden Krankheit berücksichtigt werden. Da die Ätiologie

der Mauke bisher nicht abschließend geklärt werden konnte, haben wir uns für die

Suche von Kandidatengenen auf die Ergebnisse der histopathologischen Studien

bezogen. Diese beschreiben eine epidermale Hyperplasie und Hyperkeratose in

allen betroffenen Kaltblütern, kombiniert mit einer von T-Lymphozyten dominierten

perivaskulären Dermatitis. Außerdem konnte eine abnormale Differenzierung von

Keratinozyten nachgewiesen werden.

Es konnten für die Entstehung der Mauke potentiell wichtige Kandidatengene auf den

Chromosomen 1, 10 und 17 gefunden werden, welche in den genomweit

signifikanten QTL angeordnet sind. Im Einzelnen sind dies die ubiquitin protein ligase

E3A UBE3A auf ECA1 (110,7 Mb), der aktivierte T Zell Marker CD109 auf ECA10


(28,6-28,7 Mb) und das Myotubularin verwandte Protein 6 MTMR6 auf Chromosom

17, welche alle eine immunmodulatorische Wirkung besitzen.

In dieser ersten genomweiten Studie zur chronischen Mauke bei Deutschen

Kaltblutpferden konnten vier genomweit signifikante QTL auf vier Chromosomen

entdeckt werden.

CHAPTER 8

Appendix

184 Appendix

8 Appendix Laboratory paraphernalia Equipment Thermocycler

PTC-100™ Programmable Thermal Controller (MJ Research, Watertown, USA)

PTC-100™ Peltier Thermal Cycler (MJ Research, Watertown, USA)

PTC-200™ Peltier Thermal Cycler (MJ Research, Watertown, USA)

Biometra TProfessional Thermocycler (Biometra, Göttingen, Germany)

Automated sequencers

LI-COR Gene Read IR 4200 DNA Analyzer (LI-COR, Inc., Lincoln, NE, USA)

LI-COR Gene Read IR 4300 DNA Analyzer (LI-COR, Inc., Lincoln, NE, USA)

Centrifuges

Sigma centrifuge 4-15 (Sigma Laborzentrifugen GmbH, Osterode)

Desk-centrifuge 5415D (Eppendorf, Hamburg)

Biofuge stratos (Heraeus, Osterode)

Megafuge 1. OR (Heraeus, Osterode)

Speed Vac® Plus (Savant Instruments, Farmingdale, NY, USA)

Pipettes

Multipette® plus (Eppendorf AG, Hamburg, Germany)

Pipetus®-akku (Hirschmann® Laborgeräte GmbH & Co.KG, Eberstadt, Germany)

Pipetman® (P2, P10, P20, P100, P200, P1000) (Gilson Medical Electronics S.A.,

Villiers-le-bel, France)

Pipettor, Multi 12 Channel (0.1 – 10 µl) (Micronic® systems, Lelystad, The

Netherlands)

12 Channel Manual Pipettor (0.5 – 10 µL) (Matrix Technologies Corporation, Cheshire

UK)

12 Channel Manual Pipettor (25 – 200 µL) (Matrix Technologies Corporation,

Cheshire UK)

8-Channel, gel loading syringe (Hamilton Bonaduz AG, Bonaduz, Switzerland)

Appendix 185

Others

Milli-Q® biocel water purification system (Millipore GmbH, Eschborn, Germany)

Kits Isolation of DNA

QIAamp 96 DNA Blood Kit (QIAGEN, Hilden, Germany)

Size standards IRDye™ 700 or 800 (LI-COR, Inc., Lincoln, NE, USA)

Primers Primers were produced by MWG-Biotech AG, Ebersberg, Germany

Reagents and buffers APS solution (10 %)

1 g APS

10 ml H2O

dNTP solution

100 µl dATP [100 mM]

100 µl dCTP [100 mM]

100 µl dGTP [100 mM]

100 µl dTTP [100 mM]

1600 µl H2O

The concentration of each dNTP in the ready-to-use solution is 5 mM

Gel solution (6%)

12.75 ml Urea/TBE solution (6%)

2.25 ml Rotiphorese® Gel 40 (38% acrylamide and 2% bisacrylamide)

95 µl APS solution (10%)

9.5 µl TEMED

186 Appendix

TBE-buffer (1x)

100 ml TBE-buffer (10x)

900 ml H2O

Urea/TBE solution (6 %)

425 g urea [60.06 M]

250 ml H2O

100 ml TBE-buffer (10x)

Solubilise in a water bath at 65°C

H2O ad 850 ml

Chemicals Ammonium persulfate (APS) ≥ 98 % (Sigma-Aldrich Chemie GmbH, Taufkirchen)

Boric acid ≥ 99.8 %, p.a. (Carl Roth GmbH & Co, Karlsruhe)

dATP, dCTP, dGTP, dTTP > 98% (Carl Roth GmbH & Co. KG, Karlsruhe)

DMSO ≥ 99.5 %, p.a. (Carl Roth GmbH & Co, Karlsruhe)

dNTP-Mix (Qbiogene GmbH, Heidelberg)

Enhancer solution P 5x (peqlab Biotechnologie GmbH, Erlangen))

Formamide ≥ 99.5 %, p.a. (Carl Roth GmbH & Co, Karlsruhe)

Natriumdihydrogenphosphat (Biochrom AG, Berlin)

Paraffin (Merck KgaA, Darmstadt)

Rotiphorese®Gel40 (Carl Roth GmbH & Co, Karlsruhe)

TEMED 99 %, p.a. (Carl Roth GmbH & Co, Karlsruhe)

Tris PUFFERAN® ≥ 99.9 %, p.a. (Carl Roth GmbH & Co, Karlsruhe)

Urea ≥ 99.5 %, p.a. (Carl Roth GmbH & Co, Karlsruhe)

Water was taken from the water purification system Milli-Q®

Enzymes PCR

Taq-DNA-Polymerase 5 U/µl (Qbiogene GmbH, Heidelberg)

Incubation Mix (10x) T.Pol with MgCl2 [1.5mM] (Qbiogene, Heidelberg)

The polymerase was always used in the presence of Incubation Mix T. Pol 10x buffer

Appendix 187

Consumables PCR-Plate PP, nature, 96x0.2ml, skirted, RNase-, DNA- und pyrogenfree (nerbe plus)

Combitips® plus (Eppendorf AG, Hamburg, Germany)

Pipette tips 0.1 – 10 µl (K138.1), 0.1 – 10 µl (A407.1), 5 – 200 µl (7058.1) (Carl Roth

GmbH & Co, Karlsruhe, Germany)

Pipette tips 0.1 – 10 µl (7600) (Matrix Technologies Corporation, Lowell, USA)

Reaction tubes 1.5 ml and 2.0 ml (nerbe plus GmbH, Winsen/Luhe, Germany)

Reaction tubes 10 and 50 ml (Falcon) (Renner, Darmstadt, Germany)

Software BLASTN, trace archive http://www.ncbi.nlm.nih.gov

BLAT Search Genome http://genome.ucsc.edu/cgi-bin/hgBlat

ENSEMBL Genome browser http://www.ensembl.org/index.html

HORSEMAPdatabase http://dga.jouy.inra.fr/cgibin/lgbc/loci_

micro.operl?BASE=horse

MERLIN software

package version 1.0.1 http://www.sph.umich.edu/csg/abecasis/Merlin

Order of primers MWG Biotech-AG, Ebersberg

(https://ecom. mwgdna.com/register/index.tcl)

PED5.0 Dr. H. Plendl et al. (2005) Institute for Human

Genetics, Kiel/

Primer design (Primer3) http://frodo.wi.mit.edu/cgi-bin/primer3/primer3_

www.cgi

Repeat Masker http://www.repeatmasker.genome. washington.edu/

SUN Ultra Enterprise 450 Sun microsystems GmbH, Kirchheim-Heimstetten

SUN FIRE V490 Sun microsystems GmbH, Kirchheim-Heimstetten

CHAPTER 9

List of publications

190 List of publications

9 List of publications Journal articles Evelyn H. Mittmann, Virginie Lampe, Stefanie Mömke, Alexandra Zeitz and Ottmar

Distl (2009). Characterisation of a minimal microsatellite set for whole genome

scans informative in warmblood and coldblood horse breeds. Journal of

Heredity, DOI 10.1093/jhered/esp091

Mittmann E H, Wrede J, Pook J, Distl O (2009). Identification of 21 781 equine

microsatellites on the horse genome assembly 2.0. Animal Genetics, DOI

10.1111/j.1365-2052.2009.01979.x

Oral presentation E. H. Mittmann, S. Mömke, O. Distl. Identifizierung von mit Mauke gekoppelten QTL

bei Deutschen Kaltblütern anhand eines Genomscans. Vortragstagung der

Deutschen Gesellschaft für Züchtungskunde e.V. und der Gesellschaft für

Tierzuchtwissenschaft, 17. und 18. September 2008, Bonn

CHAPTER 10

Acknowledgements

192 Acknowledgements

10 Acknowledgements

First of all I wish to thank Prof. Dr. Dr. habil. Ottmar Distl, the supervisor of my

doctoral thesis, for offering me the opportunity to work on an interesting dissertation.

His academic guidance, constructive criticism and support in the course of this work

were invaluable.

I want to express my heartfelt thanks to Jörn Wrede for his help with the statistical

analyses; but even more so for the never ending patience and perseverance with the

new microsatellites. Without him this would not have been possible.

I wish to thank Dr. Virginie Lampe for her support, particularly in the laboratory and

the first steps of microsatellite analysis. Her soothing presence and readiness to help

pulled me through the tough times.

I am very grateful to Heike Klippert-Hasberg and Stefan Neander for teaching me the

laboratory techniques and for their help during the work in the laboratory.

I want to thank C. Mrusek and D. Böhm for their support concerning administrative

questions.

I wish to thank R. I. Schwan for the graphical assistance. My special thanks go to all colleagues and friends of the Institute for Animal Breeding

and Genetics of the University of Veterinary Medicine Hannover for their support,

humour and the friendly atmosphere. You all made me feel at home at work.

Very special thanks go to my mother. Thank you for your tireless encouragement and

support in every way throughout all the years. I couldn’t have done it without you.

My special gratitude for your understanding, undemanding love and support in my life

goes to Jupp, Maren, Johannes and Lisa Knepper.

Acknowledgements

193

I want to thank all of my family for their support and help during the tough times. My

father will always remain my exemplar in doing what is right and staying true to

myself.

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