Health Research Using Genomic...

Health Research Using Genomic Information

김호

서울대학교 보건대학원

CONTENTS

• Linkage Analysis

• Segregation Analysis

• SNP and haplotype analysis

• Association Studies

• Discussion

Putative gene(locus)

Gene ?Phenotype

Linkage analysisLinkage analysis(LD, (LD, sibpairsibpair et al)et al)

Association studyAssociation study

New GeneNew GeneDiscoveryDiscovery

SegregationSegregation

Biological Basis of Linkage

• If two loci are on different chromosome, they recombine with probability 0.5

• Similarly, if two loci are very far apart on the same chromosome,..

• But then the two loci are very close together, recombination tends towards zero.

· PARAMETRIC LINKAGE ANALYSISTo estimate the recombination fraction between markers and a hypothesized trait locus, where inheritance parameters of the trait locus (mode of inheritance, penetrance, phenocopy rate, allele frequencies etc) must be specified.

Ex. Lod score method

· LOD SCORE

The common logarithm of the likelihood ratio:

Z(θ) = log10 [L(θ ) / L(½)]

where θ is the recombination fraction between two loci

· Purpose Of The Lod Score Method

1. Estimation of the recombination fraction, θ

2. Hypothesis testing

H0: θ = ½ (absence of linkage)

H1: θ < ½ (linkage)

max 10( ) log [ ( ) / (1/ 2)]Z Z L L= =θ̂ θ̂

· Scale For Testing Linkage

Zmax ≥ 3 : Strong linkage

Zmax > 0 : Support linkage

Zmax < 0 : Against linkage

Zmax = 0 : No support

(not related to recombination in linkage or no linkage)

· Asymptotic Distribution

2 ln [L(θ ) / L(½)] = 4.6 × Zmax ~ χ21

under the null hypothesis of no linkage

P (Zmax ≥ 3) = P (χ21 ≥ 13.8) = 0.0002

α = 0.0001

Phase known pedigree

Figure 2 Phase known pedigree

• The maximum likelihood estimator of is 2/6=1/3

2 46 2 4

10 102 4

(1 )( ) log log 2 (1 )0.5 0.5

Z θ θθ θ θ−= = −⋅

(1/ 3) 0.1475Z =

θ

Phase-unknown pedigree

Figure 3 Phase-unknown pedigree

• The maximum likelihood estimator of is not so trivial

• The MLE is found to be 0.5 by numerical method

4 2 2 4

2 2 2 2

1 1( ) (1 ) (1 )2 21 = (1 ) [ (1 ) ]2

L θ θ θ θ θ

θ θ θ θ

= − + −

− + −

θ

Genotype Unknown-Phenotype known

Figure 4 Genotype Unknown-Phenotype known

( ; ) Pr( ) Pr( )

Pr( | , ; )

and we know thatPr( ) Pr( ) Pr( | )

ma pa

offs ma paoffspring

ma G

L data Ph Ph

Ph Ph Ph

Ph G Ph G

θ

θ

=

×

=

∏

∑

· NONPARAMETRIC LINKAGE ANALYSISInheritance parameters of the trait locus are not specified. Rather, one focuses on pairs (or multiples) of affected individuals and investigates marker allele sharing among these individuals, contrasting observed allele sharing with that expected when the marker has nothing to do with the trait.

Ex. IBD (identical by descent) test

AN EXAMPLE FAMILY WITH DISEASE LOCUS AT THE MARKER

3 4+ –

3 2+ –

3 3+ +

3 4+ –

2 3– +

2 4– –

• Only ‘+ +’ indicates as “affected”(‘+’ is recessive to ‘–’)

** Qualitative Trait

Sib-Pair Markers

sib1 sib23 | 3 3 | 33 | 3 3 | 43 | 3 2 | 33 | 3 2 | 43 | 4 3 | 43 | 4 2 | 33 | 4 2 | 42 | 3 2 | 32 | 3 2 | 42 | 4 2 | 4

Disease Status

d1 d2+ ++ -+ -+ -- -- -- -- -- -- -

# ofShared i.b.d.

2110201212

C

10.250.250.250.50.50.50.50.50.5

• Cj = (dj1 – µ) (dj2 – µ)

= α + β IBDj + εj

· Linkage And LD

- The two loci can be assumed to reside on different chromosomes.

The presence of LD does not necessarily imply linkage between the loci considered.

- Although LD originally referred to an association of alleles at different loci, it has become customary to take LD to mean association among alleles due to close linkage. “allelic association”

· Another Approach To LD Analysis(“Family-Based Study”)

1. Haplotype relative risk (HRR) method

: Falk and Rubinstein (1987)

2. Haplotype-based haplotype relative risk (HHRR) method: Terwilliger and Ott (1992)

3. Transmission/ disequilibrium test (TDT)

: Spielman et al. (1993)

4. Sib-Transmission/ disequilibrium test (S-TDT): Spielman and Ewens (1998)

· Transmission/ disequilibrium test

1 2 1 2

1 1 0(d)0 (c)Allele2 (A2)

2(b)0 (a)Allele 1 (A1)Transm

itted

Allele2 (A2)

Allele1 (A1)

Not transmitted

- Focus on heterozygous parents only, and allow the use of multiple affected siblings.- McNemar’s test (standard χ2 test) H0: b = cThe TDT statistic:

- Powerful only in the presence of LD. cb

cb+−=

221

)(χ

• Genomewide Linkage Analysis of Bipolar Disorder by Use of a High-Density Single-Nucleotide Polymorphism (SNP) Genotyping Assay: A Comparison with MicrosatelliteMarker Assays and Finding of Significant Linkage to Chromosome 6q22

• F. A. Middleton,1,2,3 M. T. Pato,2,3,4 K. L. Gentile,1,2 C. P. Morley,2 X. Zhao,1,2 A. F. Eisener,2 A. Brown,1,2 T. L. Petryshen,6 A. N. Kirby,5,6 H. Medeiros,2,4 C. Carvalho,2 A. Macedo,8 A. Dourado,8 I. Coelho,8 J. Valente,8 M. J. Soares,8 C. P. Ferreira,9 M. Lei,9 M. H. Azevedo,4 J. L. Kennedy,10 M. J. Daly,5 P. Sklar,6,7 and C. N. Pato2,3,4,9

• Am. J. Hum. Genet., 74:000, 2004

We performed a linkage analysis on 25 extended multiplex Portuguese families segregating for bipolar disorder, by use of a high-density single-nucleotide polymorphism (SNP) genotyping assay, the GeneChip Human Mapping 10K Array (HMA10K). Of these families, 12 were used for a direct comparison of the HMA10K with the traditional 10-cM microsatellite marker set and the more dense 4-cM marker set. This comparative analysis indicated the presence of significant linkage peaks in the SNP assay in chromosomal regions characterized by poor coverage and low information content on the microsatellite assays. The HMA10K provided consistently high information and enhanced coverage throughout these regions. Across the entire genome, the HMA10K had an average information content of 0.842 with 0.21-Mb intermarker spacing. In the 12-family set, the HMA10K-based analysis detected two chromosomal regions with genomewide significant linkage on chromosomes 6q22 and 11p11; both regions had failed to meet this strict threshold with the microsatelliteassays. The full 25-family collection further strengthened the findings on chromosome 6q22, achieving genomewide significance with a maximum nonparametric linkage (NPL) score of 4.20 and a maximum LOD score of 3.56 at position 125.8 Mb. In addition to this highly significant finding, several other regions of suggestive linkage have also been identified in the 25-family data set, including two regions on chromosome 2 (57 Mb, NPL = 2.98; 145 Mb, NPL = 3.09), as well as regions on chromosomes 4 (91 Mb, NPL = 2.97), 16 (20 Mb, NPL = 2.89), and 20 (60 Mb, NPL = 2.99).We conclude that at least some of the linkage peaks we have identified may have been largely undetected in previous whole-genome scans for bipolar disorder because of insufficient coverage or information content, particularly on chromosomes 6q22 and 11p11.

• Figure 2 Linkage signals obtained with 10-cM spaced and 4-cM spaced microsatellite assays, as well as the HMA10K SNP genotyping assay. These assays were performed on the same individuals from each of the same 12 families. Note the high correlation of the different assays in general, and that for both chromosomes 6 and 11, the SNP assay detected major linkage peaks at locations where the information content and coverage of the microsatellite panels were relatively low. Mb, megabaseposition; MSM, microsatellitemarkers.

• Figure 3 NPL analysis of 25 families with bipolar disorder from the Portuguese Island Collection. The number of each chromosome is shown at the top of each plot. The X-axis indicates the physical position (Mb) of the SNP marker. The Y-axis indicates the NPL Z score (black) or Kong and Cox LOD score (gray). For this scan, the empirical limit for genomewide significance was an NPL score of 3.85 and a LOD score of 3.15. Note that only the peak on chromosome 6 at 125.8 Mb was significant when both NPL Z and LOD thresholds were used.

Figure 4 Comparison of the 12-family (gray) and 25-family (black)genomewide linkage scans for selected

chromosomes showing suggestive or

significant linkage (see table 1). The X-axis indicates physical position (Mb). Notethat for both scans,

the signal on chromosome 6 at

position 125.8 Mb is the only genomic

region that achievesgenomewide

significance (of NPLscore and/or LOD

score).

· QUANTITATIVE TRAITA phenotype with a continuous (normal/ lognormal) distribution.

Ex. Height, blood pressure, head circumstance and the cholesterol level in the blood

· QUALITATIVE TRAITA phenotype with a discrete distribution. Ex. Signs and symptoms indicate whether a disease state is present or absent.

· HERITABILITY Of The Trait (H2)

The fraction of the variation caused by genetic variation.

H2 = Vg / Vp =Vg / (Vg + Ve ) (broad sense)

= Va / Vp (narrow sense)

· QUANTITATIVE TRAIT LOCI (QTL)

The location of a gene that affects a trait that is measured on a quantitative (linear) scale. The loci that are determinants of quantitative trait expression.

예제: Descriptive statistics

1.8 0 CHOL≥240(%)

10.07 8.78 BMI≥30(%)

21.2 14.8 HP(%)

38.7 9.4 ALCHOL(%)

18.7 14.3 SMOK(%)

69.9 90.5 MADE(%)

39.9 46.3 MALE(%)

8.0 8.2 EDU-YR(MEAN)

38.2 29.4 AGE (MEAN)

SelengeDornod D (지역) S (지역)

16.8 10.5 16.6 7.3 SKIN FOLD

163.4 165.4 159.7 154.2 CHOL

74.1 75.0 74.1 69.2 WC

76.1 82.0 73.2 67.4 DBP

116.0 127.0 114.0 107.6 SBP

24.3 22.3 24.1 21.0 BMI

54.9 58.1 54.7 48.0 WEIGHT

151.9 159.8 149.6 147.5 HEIGHT

Female Male Female Male

Selenge Dornod D S

**0.50WC ^

**

**

**

**

**

**

*

**

**

유의성

0.53DBP ^

0.35BMI ^

0.38SKIN_FOLD ^

0.43BMD_LF1 ^

0.50HDL-C

0.42HC

0.17HEIGHT

0.39WEIGHT

0.51SBP

유전율변수

** P-value <0.05 * P-value < 0.1^ 정규성, 왜도, 첨도를 위해 변환을 실행한 변수들HEIGHT의 경우는 첨도에 문제가 있어서 유전율이 낮게 나왔음. Covariate으로 age sex age^2 age*sex bmi 중에서 사용하였는데 변수마다그 covariate 들이 다르다

SOLARSOLAR로로 살펴본살펴본 일부일부 PHENOTYPEPHENOTYPE의의 유전율유전율

SNPs(pronounced snips)

SNPs as DNA Landmarks

• Help in DNA sequencing

• Help in the discovery of genes responsible for many major diseases:

– asthma, diabetes, heart disease, schizophrenia and cancer among others

From SNP to Haplotype

DNA Sequence

GATATTCGTACGGA-TGATGTTCGTACTGAATGATATTCGTACGGA-TGATATTCGTACGGAATGATGTTCGTACTGAATGATGTTCGTACTGAAT

SNP

SNP

1 2

3

4

5 6

AG- 2/6

GTA 3/6AGA 1/6

Haplotypes

PhenotypeBlack eyeBrown eyeBlack eyeBlue eyeBrown eyeBrown eye

In-silico Haplotyping: Approaches

1) Clark’s algorithm

2) E-M algorithm (expectation-maximization algorithm)

3) Bayesian algorithm

Clark’s Algorithm

1) Find Homozygotes or heterozygotes at one locus

SNP1 T T

SNP2 A A

SNP3 C C

T-A-C

T-A-C

SNP1 T T

SNP2 A A

SNP3 C G

T-A-C

T-A-G

Unambiguously defined

Clark’s Algorithm2) Try to solve ambiguous haplotype as a combination of solved ones

SNP1 A T

SNP2 A A

SNP3 C G

T-A-C : solved one

A-A-G

Continue until either all haplotypes have been solved or until no more haplotypes can be found in this way

……………………………

Clark’s Algorithmproblems

• No homozygotes or single SNP heterozygotes -> chain might never get started

•Many unsolved haplotypes left at the end

•Quite useful in practice !!

EM Algorithm• Use multinomial likelihood with HWE

Pr(AT//AA//CG)

=pr(AAC/TAG)+pr(AAG/TAC)

=pr(AAC)pr(TAG)+pr(AAG)pr(TAC)

Falling and Schork(2000) showed that EM is better than Clark’s algorithm

A Gibbs sampler, Stephens et al (2001)

• G=(G1, …, Gn) observed multilocus genotype freq

H=(H1, …, Hn) unknown haplotype pairs

F=(F1, …, FM) M unknown pop’n hap freq

1. Choose individual i from all ambiguous individuals

2. Sample Hi(t+1) from pr(Hi|G,H-i

(t))

3. Set Hj(t+1)=Hj

(t) for j=1,2,…,i-1,i+1,…n

Haplotype InferenceA: SNP data: 0 (MM), 1 (Mm), 2 (mm) for a single locus

B: Haplotype data: 0(M), 1 (m) for a single locus

• Traditional linkage studies > use recombination information only in pedigrees

• Association methods > use recombination information at the population level

• Association methods have greater power to detect small and moderate genetic effects than does linkage analysis (Risch and Merikangas 1996)

Strategy for Suggested AssoSt for complex disease

1. Small # of people (10-20) genotyped at a very dense SNP map, haps also determined

2. Hap block partitioning algorithm : hap block and tag SNPs

3. Large # of people genotyped at tag SNP marker loci

4. Association study analysis

Polymorphisms in the XRCC1gene and alcohol consumption are associated with colorectal

cancer risk • a case-control study of 209

colorectal cancer cases and 209 age- and sex-matched controls in the Korean population

• Allelic variants of the XRCC1 gene at codons 194, 280 and 399 were analyzed in lymphocyte DNA by PCR-RFLP

Table 1. Frequencies of single nucleotide polymorphisms and the odds ratios of colorectal cancer

0.0171.61 (1.09, 2.39)73 (34.9)97 (46.4)Arg/Gln or Gln/Gln

0.6911.21 (0.47, 3.16)9 (4.3)9 (4.3)Gln/Gln

0.0141.67 (1.11, 2.51)64 (30.6)88 (42.1)Arg/Gln

1136 (65.1)112 (53.6)Arg/Arg

XRCC1 Codon 399

0.1441.43 (0.88, 2.32)36 (17.2)48 (23.0)Arg/His or His/His

0.6130.54 (0.05, 5.98)2 (1.0)1 (0.5)His/His

0.1141.49 (0.91, 2.43)34 (16.2)47 (22.5)Arg/His

1173 (82.8)161 (77.0)Arg/Arg

XRCC1 Codon 280

0.2801.24 (0.84, 1.82)108 (51.7)119 (57.0)Arg/Trp or Trp/Trp

0.8091.08 (0.58, 2.00)26 (12.5)25 (12.0)Trp/Trp

0.2291.29 (0.85, 1.94)82 (39.2)94 (45.0)Arg/Trp

1101 (48.3)90 (43.0)Arg/Arg

XRCC1 Codon 194

P-valueOR (95% CI)*Controls (%)Patients (%)

Table 2. Estimated haplotype frequencies and odds ratios of colorectal cancer based on haplotypes

0.0021.78 (1.23, 2.59)82 (19.6)106 (25.4)194Arg-280Arg-399Gln

0.0151.81 (1.12, 2.94)38 (9.1)50 (12.0)194Arg-280His-399Arg

0.0231.47 (1.05, 2.05)134 (32.1)143 (34.2)194Trp-280Arg-399Arg

1164 (39.2)119 (28.4)194Arg-280Arg-399Arg

P-valueOR (95% CI)Controls (%)Patients (%)XRCC1*

* The frequencies of 194Trp-280His-399Arg, 194Trp-280Arg-399Gln, 194Arg-280His-399Gln, 194Trp-280His-399Gln were zero in both groups.

Table 3. Estimated genotype frequencies and the odds ratios of colorectal cancer aftercontrolling for alcohol intake, smoking, dietary habits and exercise

0.4861.28 (0.64, 2.54)9 (4.3)9 (4.3)194Arg-280Arg-399Gln /194Arg-280Arg-399Gln

0.0043.69 (1.53, 8.90)6 (2.9)17 (8.1)194Arg-280His-399Arg /194Arg-280Arg-399Gln

0.2801.79 (0.62, 5.14)2 (1.0)1 (0.5)194Arg-280His-399Arg /194Arg-280His-399Arg

0.0042.08 (1.27, 3.40)22 (10.5)42 (20.1)194Trp-280Arg-399Arg /194Arg-280Arg-399Gln

0.5641.54 (0.36, 6.60)12 (5.7)14 (6.7)194Trp-280Arg-399Arg /194Arg-280His-399Arg

0.6091.32 (0.46, 3.75)26 (12.4)25 (12.0)194Trp-280Arg-399Arg /194Trp-280Arg-399Arg

0.7701.07 (0.68, 1.69)36 (17.2)29 (13.9)194Arg-280Arg-399Arg /194Arg-280Arg-399Gln

0.5401.14 (0.76, 1.71)16 (7.7)17 (8.1)194Arg-280Arg-399Arg /194Arg-280His-399Arg

0.8260.90 (0.34, 2.35)48 (23.0)37 (17.7)194Arg-280Arg-399Arg /194Trp-280Arg-399Arg

132 (15.3)18 (8.6)194Arg-280Arg-399Arg /194Arg-280Arg-399Arg

P-value

OR (95% CI)Controls (%)Patients (%)

Table 4. Risk of colorectal cancer associated with alcohol intake after controlling for smoking, dietary habits and exercise, and the risk modification by genotype

0.3154.14 (0.26, 66.36)1 (16.7)7 (41.2)A bottle or more a week

5 (83.3)10 (58.8)Less than a bottle a week

0.0317.15 (1.20, 42.46)19 (86.4)3 (13.6)

27 (64.3)15 (35.7)

194Trp-280Arg-399Arg /194Arg-280Arg-399Gln Less than a bottle a weekA bottle or more a week

194Arg-280His-399Arg /194Arg-280Arg-399Gln



194Arg-280Arg-399Arg /194Arg-280Arg-399Arg



All subjects

P-valueOR (95% CI)Controls (%)Patients (%)Amount of alcohol intake

Haplotype 분석시의 유의점

• Haplotype estimation에서의 불확실성

• LD를 살펴봄

• Sub-cell의 freq 가 너무 적은 경우에는비모수적인 방법 등을 고려해야함

• Population mixture의 문제

무유

0.7134/0.1333=5.52상대위험도

28/(28+182)=0.133318228a

81/(81+29)=0.73642981A

위험도질병상태

유전정보

표1. 질병상태의 유전정보에 따른 위험도 (예제1)

결론 : 유전정보와 질병상태에는 연관이 있다.

표2. 혼란변수 유무에 따른 위험도 (예제1)

무유

1.00상대위험도

0.80028a

0.8002080A

위험도질병상태유전

정보무유

1.00상대위험도

0.10018020a

0.10091A


정보

A 인종 B 인종

결론 : 두 인종 모두에서 유전정보와 질병상태에는 연관이 없다.

요약하면

• 전체 집단에서는 질병과 유전정보에 연관 있다.

• A 인종에서는 질병과 유전정보에 연관 없다.

• A 인종에서는 질병과 유전정보에 연관 없다.

• ? ? ?

무유

1.0000상대위험

도

0.3636350200a

0.3636420240A

위험도질병상태

유전정보

표3. 질병상태의 유전정보에 따른 위험도 (예제2)

결론 : 질병상태와 유전정보에는 연관이 없다.

표4. 혼란변수 유무에 따른 위험도 (예제2)

무유

2.45상대위험도

0.3900305195a

0.95455105A


정보무유

2.45상대위험도

0.1000455a

0.2455415135A


정보

A 인종 B 인종

결론 : 두 인종 모두에서 유전정보와 질병상태에는 연관이 없다.

요약하면

• 전체 집단에서는 질병과 유전정보에 연관 없다.

(RR=1.00)

• A 인종에서는 질병과 유전정보에 연관 있다. (RR=2.45)

• B 인종에서는 질병과 유전정보에 연관 있다. (RR=2.45)

• ? ? ?

• 질병상태와 유전정보는 인종에 의해 혼란

(Confounding) 되고 있다

• 이러한 경우 올바른 자료의 분석을 위해서는인종은 질병상태와 유전정보와 함께 반드시 고려해야 한다. (성별을 혼란변수라고 부른다.)

• Population mixture 문제

Time line of developments in human statistical genetics

Theory Technology Study design

References• Clark (1990). Inference of haplotypes from PCR-amplified samples of diploid populations. Mol Bio Evol 7: 111-122

• Escoffier and Slatkin (1995). Maximum likelihood estimation of molecular haplotype frequencies in a diploid population. Mol Bio Evol 12: 921-927.

• Stephens, Smith, and Donnelly (2001) A new statistical method for haplotype reconstruction from population data. Am J Hum Genet 68, 978-989.

• Niu, Qin, Xu and Liu (2002) Bayesian haplotype inference for multiple linked single-nucleotide ploymorphisms. Am J Hum Genet 70;157-169

•Patil et al (2001) Science 294: 1719-1723

•Escoffier and Slatkin (1995). Maximum likelihood estimation of molecular haplotype frequencies in a diploid population. Mol Bio Evol 12: 921-927.

• Stephens, Smith, and Donnelly (2001) A new statistical method for haplotype reconstruction from population data. Am J Hum Genet 68, 978-989.

• Niu, Qin, Xu and Liu (2002) Bayesian haplotype inference for multiple linked single-nucleotide ploymorphisms. Am J Hum Genet 70;157-169

•Toivonen et al. (2000) Data Mining Applied to Linkage Disequilibrium Mapping. AM J Hum Genet 67: 133-145

•Petteri Sevon, Hannu T.T. Toivonen, Vesa Ollikainen. TreeDT: Gene Mapping by Tree Disequilibrium Test. The Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2001), pp. 365-370. San Francisco, California, August 2001.

• Wallenstein, Hodge, Weston (1998) Logistic regression model for analyzing extended haplotype data, Genet Epidemiol 15:173-181.

•Http://www.genome.helsinki.fi/eng/research/projects/DM/index.html

•ZHAOHUI S. QIN, TIANHUA NIU, JUN S. LIU (2002) Partition-Ligation–Expectation-Maximization Algorithm for Haplotype Inference with Single-Nucleotide Polymorphisms Am. J. Hum. Genet. 71:1242–1247, 2002

•Petteri Sevon, Vesa Ollikainen, Päivi Onkamo, Hannu Toivonen, Heikki Mannila, and Juha Kere.

•Johnson et al. (2001) Nat Genetics 29: 233-237

인간의 건강

유전적 요인환경적 요인

사회적 요인

Environmental Epidemiology

Genetic Epidemiology

Social Epidemiology

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상호

작용 상

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용

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