Lezioni di genetica medica - webalice.it · Testi consigliati • Neri-Genuardi Genetica umana e...

Lezioni di genetica medica

2017

Testi consigliati

• Neri-GenuardiGenetica umana e medicaEditore Elsevier Masson

• Moncharmont-LeonardiPatologia GeneraleEditore Idelson Gnocchi

• Strachan-ReadGenetica Molecolare UmanaEditore Zanichelli

• LewisGenetica UmanaEditore Piccin

• Sito web http://www.vincenzonigro.it (glossario)

Renato Dulbecco premio Nobel per la medicina 1975Catanzaro, 22 febbraio 1914 – La Jolla, 19 febbraio 2012

Science 7 marzo 1986: «leggere tutto il genoma umano»

• Costo 3 miliardi di dollari

• 13 anni (1988-2001)

Progetto genomaumano

1 245,203,898 218,712,898

2 243,315,028 237,043,673

3 199,411,731 193,607,218

4 191,610,523 186,580,523

5 180,967,295 177,524,972

6 170,740,541 166,880,540

7 158,431,299 154,546,299

8 145,908,738 141,694,337

9 134,505,819 115,187,714

10 135,480,874 130,710,865

11 134,978,784 130,709,420

12 133,464,434 129,328,332

13 114,151,656 95,511,656

14 105,311,216 87,191,216

15 100,114,055 81,117,055

16 89,995,999 79,890,791

17 81,691,216 77,480,855

18 77,753,510 74,534,531

19 63,790,860 55,780,860

20 63,644,868 59,424,990

21 46,976,537 33,924,742

22 49,476,972 34,352,051

X 152,634,166 147,686,664

Y 50,961,097 22,761,097

Lunghezza dei cromosomi totale eucromatina

UCSC Genome Browser

Screenshot from University of California at Santa Cruz http://genome.ucsc.edu

storia:Sanger sequencing

Frederick SangerNobel price 1958 and 1980born August 13 1918, died November 19 2013

I ddNTPs (ddATP, ddTTP, ddGTP, ddCTP) sono detti terminatori perché bloccano la polimerizzazione del DNA

“prima generazione”sequenziamento del DNA secondo il metodo Sanger

Il sequenziamento con tutte le versioni modificate del metodo Sanger ha dominato nella scienza e nell’industria per almeno 20 anni e ha consentito la lettura del genoma umano e la scoperta di oltre 2.500 malattie genetiche monogeniche

Il metodo Sanger rappresenta la tecnologia di “prima generazione”, mentre i nuovi metodi sono denominati “next-generation sequencing (NGS)”

Metodo “Sanger”

• prima si isolano segmenti di DNA chimicamente omogenei

• si replicano fino a nanogrammi di DNA

• poi partono le singole reazioni

Mutation detection by Sanger sequencinggene-by-gene, exon-by-exon, fragment-by-fragment

2a generazione“next-generation sequencing (NGS)”

• Il principale vantaggio è poter lavorare con milioni di molecole di DNA

senza doverle separare

• la possibilità tecnica di produrre un volume enorme di dati a costi

estremamente più bassi ed in tempi estremamente più rapidi

• Il potenziale dell’NGS è simile ai primi tempi della PCR con il limite

principale dovuto all’immaginazione

2a generazione“next-generation sequencing (NGS)”

• prima si frammenta il DNA casualmente

• si aggiungono adattatori comuni

• poi partono milioni di reazioni di sequenziamento individuali

Sistema Illumina

• ciascun frammento di DNA è immobilizzato su una superficie solida

• molecole di DNA spazialmente separate permettono di eseguire

simultaneamente milioni di reazioni di sequenziamento

• ciascuna reazione produce una fluorescenza puntiforme che è fotografata

• la scansione di queste immagini consente di leggere la sequenza per ogni

punto

la Solid-phase amplification può generare milioni di clusters di

molecole di DNA distinte per vetrino

flow cell

Bridge PCR

Genome Analyzer II

4.000.000.000 bp/7 giorni

= 42 ore per miliardo di basi

350$/miliardo di basi

2008

NovaSeq6000

6.000.000.000.000 bp/40 ore

= 40 secondi per miliardo di basi

7$/miliardo di basi

20172002

96-capillary 3730xl DNA Analyzer

2.100.000 bp/giorno

= 476 giorni per miliardo di basi

2.000.000$/miliardobasi

• Next generation sequencing = 25,594 papers

– Before 2010 : 221

• Exome = 8,341 papers

– Before 2010 : 8

http://www.ncbi.nlm.nih.gov/pubmedOctober 26, 2016

http://www.ncbi.nlm.nih.gov/pubmed

FASTQ

@SEQ_ID

GATTTGGGGTTCAAAGCAGTATCGATCAAATA

+

!''*((((***+))%%%++)(%%%%).1***1

ACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACCGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGAACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGACGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGATAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGACGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGACGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGACGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATTATAGCTCGCGACACACACAGATATATAGCGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGACGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTTATAGCTCGCGACACACACAGATATATAGCGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGACGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGATAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGACGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTT

Variazioni di sequenza in un segmento di DNA

Ogni cromosoma è differente da tutti gli altri

SNPssingle nucleotide polymorphisms

• Variazioni naturali che esistono tra le sequenze di qualsiasi cromosoma con un frequenza di almeno l’1%degli individui

• Consistono in sostituzioni di singoli nucleotidi, altre più rare consistono in delezioni o inserzioni di singoli nucleotidi

• Un SNP è identificato mediante sequenziamento del DNA di differenti cromosomi in individui diversi

• I due alleli possono essere identici (in omozigosi; T/T o C/C) o differenti (eterozigosi T/C o C/T) nel sitopolimorfico

penetrance

the penetrance of a disease-causing variant is

the proportion of individuals with the pathogenic gene variant who exhibit clinical symptoms

for example, if a gene variant transmitted as an autosomal dominant trait has 75% penetrance, then 75% of those with the variant will develop the disease, while 25% will not

expressivity

the expressivity of a disease-causing variant is

a variation in a phenotype among individuals carrying the same gene variant(s)

for example, in cystic fibrosis the range extends from early childhood death due to obstructive lung disease with bronchiectasis, to pancreatic insufficiency to recurrent sinusitis and bronchitis or male infertility in young adulthood

It arises when a person has a large amount of data at disposal, but only focuses on a small subset of that data

the Texas Sharpshooter Fallacy

Variant interpretation

• Population data EXac, EVS, 1,000 genomes• Internal database (local population, NIG?)• Segregation data• Functional studies and predictive tools• Impact of other variants on the same gene (Stop?)• Relationship with the observed phenotype

• very strong (PVS1)• strong (PS1–4)

• moderate (PM1–6)• supporting (PP1–5)

Pathogenic

likely pathogenic

uncertain significance

likely benign and benign

4,200,000/WGS 71,000/WES

sequence variants

interpretation

The genome of James Watson

7.4 x coverage

234 runs

24.5 billions bp

11 mutazioni causative di malattie genetiche

“Whole - Genome” Seq

Pedigree of the family and segregation of SH3TC2 mutations

Sequenze codificanti dei geni umani

Geni con mutazioni che causanomalattie umane

45M = ~ 21.000 geni

45 M >4.500 geni

Totale ~ 3.100 M

le mutazioni che causano le malattie monogeniche (mendeliane) sono concentrate nello 0,3% del genoma

1

5

50

500

Mendeliome: 5,000

whole exome: 20,000

whole genome

How much workload?How much money?

Il termine “NGS” indicatipologie di analisi molto differenti

Whole genomecoverage @40 x (~ 150 Gb of sequences/sample) = 1,000 Gbytes

Whole exomecoverage @60 x (~ 8 Gb/sample)= 60 Gbytes/sample

Solo 10-15 geni noticoverage @200 x (~ 0.1 Gb/sample)= 1 Gbytes/sample

http://www.illumina.com/Documents/systems/hiseq/datasheet_hiseq_systems.pdf

• whole genome (WGS)

• whole exome (WES)

• broad targeted sequencing

• focused targeted sequencing

• specific variants (deep sequencing) Whole genome

@30 x(~ 150 Gb)

Whole exome

@100 x(~ 10 Gb)

Targeted

@500 x(~ 0.1-1

Gb)

“Next Generation Sequencing”poor results?

30-40% 30-40% >40%

detection rate

NGS parents should be included to

• validate variants

• ruling out sample swaps

• discover de novo mutations and get phase

But run separately and examined together……..

The accuracy of NGS is improving

New kits improve coverage uniformity in Mendeliome, WES, WGS

New chemistry improve sequence signal

New instruments (NovaSeq) improve read length

More powerful bioinformatic softwares

Public databases are much larger

Progress of WES and WGS quality (from GWE Santen)

22/11/2017

39

2011 exome 30x

2011 genome 25X

2014 exome 160x

2014 exome 60x

2015 genome (X10)

Whole exome con CRE Agilent (sequenze almeno 20x) in 84 soggetti, di cui 1/3 fatti con doppio coverage (20Gb)

COL6A1 100 100 100 100 100 100 100 100 100

100 100 100 100 100 100 100 100 100

100 100 100 100 100 100 100 100 100

99.7 100 100 100 100 100 100 100 100

100 100 100 100 100 100 100 100 100

100 100 100 100 100 100 100 100 100

100 100 100 100 100 100 100 100 100

100 100 100 100 100 100 100 100 100

100 100 100 100 100 100 100 100 100

100 100 100

COL6A2 100 100 100 100 100 100 100 100 100

100 100 100 100 100 100 100 100 100

100 100 100 100 100 100 100 100 100

100 100 100 100 100 100 100 100 100

100 100 99.6 99.6 100 100 100 99.9 100

100 100 100 100 100 100 100 100 100

100 100 100 100 100 100 100 100 100

100 100 100 100 100 100 100 100 100

100 100 100 100 100 100 100 100 100

100 99.6 100

COL6A3 100 100 100 100 100 100 100 100 100

100 100 100 100 100 100 100 100 100

100 100 100 100 100 100 100 100 100

100 100 100 100 100 100 100 100 100

100 100 100 100 100 100 100 100 100

100 100 100 100 100 100 100 100 100

100 100 100 100 100 100 100 100 100

100 100 100 100 100 100 100 100 100

100 100 100 100 100 100 100 100 100

100 100 100

may be a new genetic disease? case submission

evaluation and deep phenotyping by the

three clinical partners

Experimental plan

Case 3

Case 1 Cas

e 2

selection and prioritization of 350

families

high-coverage whole exome sequencing and

validation

DNA samples from father-mother-

propositus

matching DNA variants with those of similar

phenotypesthroughout the world

return of results and counselling

The Starry Night, 1889

Vincent van Gogh

recognize the picture?

no idea

one crucial de novo mutation

absent absent

Come scoprire un’eventuale nuova malattia genetica da mutazioni denovo usando l’NGS?

l’idea di base

Data will be mainly shared through

PhenomeCentral (http://phenomecentral.org)

This has great potential for identifying

additional cases of phenotypically similar

patients required to validate the identified

putative disease causative variant(s)

>350 users worldwide, such as the

Canadian (Care for Rare), US

(Undiagnosed Diseases Network)

and European (Neuromics, AnDDI-

rare) rare disease sequencing

programs

Matching Sequence and Phenotypic Data

http://phenomecentral.org/

two mutations in the same gene (1/360,000)

1/300 1/300

heterozygous healthy carriers, rare patients

Come scoprire una nuova malattia genetica a trasmissione autosomica recessiva usando l’NGS?

Malattie da mutazioni in 2 alleli

patologie a trasmissione autosomica recessiva

in genere «loss-of-function»

le mutazioni nuove sono rare, di solito hanno una lunga storia (100-1000 generazioni) con un fondatore

hanno una «ethnical signature» con un pattern di distribuzione geografica riconoscibile

la consanguineità è pertanto il fattore principale di rischio

è più facile distinguere i polimorfismi, ma molti portatori

con la penetranza al 50% e la frequenza di 1/20.000, gli alleli causativi possono avere nella popolazione generale una frequenza fino a 0.02

accuracytechnique/variant class

Variant type Sanger Array CGH Targeted NGS WGS PCR-free

substitution +++ - +++ +++

indel +++ - + +

triplet + - - +

Exon del/dup - + - +

Large del/dup - +++ -/+ ++

Quali varianti del DNA possono essere perse dall’NGS?

Non identificate• quelle in regioni con

poca/assente copertura

• inserzioni/delezioni >10-20nt

• Espansioni di triplette

• copy number variations

• traslocazioni

• inversioni

Non riconosciute• Difetti di splicing elusivi

• Varianti in geni a funzione sconosciuta

• Varianti multiple di geni molto grandi

ACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACCGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGAACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGACGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGATAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGACGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGACGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGACGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATTATAGCTCGCGACACACACAGATATATAGCGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGACGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTTATAGCTCGCGACACACACAGATATATAGCGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGACGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGATAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGACGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGACGAGACGTAGGGCTCTCGATATAGCTCGCCTCGCGACACACACAGATATATAGCGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGACGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGA

ACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACCGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGAACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGACGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACTATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGACGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGATAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGACGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGACGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGACGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATTATAGCTCGCGACACACACAGATATATAGCGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGACGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTTATAGCTCGCGACACACACAGATATATAGCGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGACGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTGACCTGACACGTGCTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGACACACACAGATATATAGCGCTCCCTGAAACAGCTCCGACACAGCTCGCACACCGCTCGAGACCTTAGCTAGCTCCTCTCGAGACGTAGGGCTCTCGATATAGCTCGCGA

1

2

Copy Number VariationCNV

trovare un’informazione importante, senza sapere dove sia

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long range information, phasing, structural variant detectionand copy number determination

Lezioni di genetica medica - webalice.it · Testi consigliati • Neri-Genuardi Genetica umana e...

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Transcript of Lezioni di genetica medica - webalice.it · Testi consigliati • Neri-Genuardi Genetica umana e...