Association mapping:

finding genetic variants for common

traits & diseases

Manuel Ferreira

Queensland Institute of Medical Research

Brisbane

Genetic Epidemiology

1WEHI Postgraduate seminar, 31 May 2010

2

Predict disease risk / drug response Personalized Medicine

Lancet 2010; 375: 1525–35

Understand disease aetiology

Why?

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Rare, monogenic traits

Ng et al. Nature Genetics 2010; 42: 30-35.

G E

O

DISEASERISK

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Common, complex traits

Phenotypic modelling

Linkage analysis

Association analysis

GENETICS OF COMMON DISEASES

1990

2000

2005

2008

2009

2010

2015

Recent advances assays/analysis genetic

variationHapMap, 1000 Genomes

High-throughput genotyping & sequencing

Analytic Methods

Genome-wide association, imputation, stratification, CNVs, risk prediction

genes env

other

DISEASERISK

genes

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HapMap project

“The HapMap was designed to determine the frequencies and patterns of association among roughly 3 million common Single Nucleotide Polymorphisms (SNPs) in four populations, for use in

genetic association studies.” [4]

1. GOALS

[1] The International HapMap Consortium. Nature 2003; 426: 789. [2] International HapMap Consortium. Nature 2005; 437: 1299.[3] International HapMap Consortium. Nature 2007; 449: 851.[4] Manolio et al. J Clin Invest 2008; 118: 1590.

Individuals

SNPs

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HapMap project

2. STRATEGY

30 trios Yoruba in Ibadan, Nigeria (YRI)30 trios European descent in Utah (CEU)45 unrelated Han Chinese from Beijing (CHB)45 unrelated Japanese from Tokyo (JPT)

Genome-wide SNP discovery1,7 million dbSNP 9,2 million

2002 200514,7 million (6,5 million validated)

2009

Genotyping

Phase 1: MAF>0.05, validated, non-synonymous SNPs prioritised (1,27 million total)

Phases 2 and 3 expanded SNP (4 million) and population (11) coverage

http://www.hapmap.org/

SNP selection

7 genotyping platforms used/developed by 12 centres

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HapMap project

3. OUTCOMES

“Systematic” catalogue of common human variation

Linkage disequilibrium (LD) or correlation between SNPs(tagging, fine-mapping, imputation)

Designing and refining high-throughput genotyping platforms

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Population genetics (selection, sub-structure, recombination & mutation)

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Gene A

Haplotypes

HapMap SNPs

D’ and r2

Correlation (LD) between SNPs

Haploview, TaggerSNP tags

Genetic CoverageProportion of known SNPs taggedHaploview

Fine-mappingInteresting SNPs to follow-upCross-study comparisons

eg. SNP 1 ‘tags’ 4/10 variants

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1000 Genomes project

GOAL

http://www.1000genomes.org/

“The 1000 Genomes Project aims to achieve a nearly complete catalog of common human genetic variants (defined as frequency 1% or higher) by generating high-quality sequence

data for >85% of the genome for three sets of 400-500 individuals (...)”

2,500 samples at 4x by 2011

High-throughput genotyping & sequencing

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Whole-genome genotyping (from $300 USD/sample)

Whole-genome sequencing (from $10,000 USD/sample)

Illumina:

HiSeq 200030x coverage

100 bp read length

Complete Genomics

40x coverage35 bp read length

Affymetrix:

6.0 chip>900,000 SNPs

CNV probes82% coverage CEU HapMap

Accuracy 99.90%

Illumina:

Human1M BeadChip>1 million SNPs

CNV probes95% coverage CEU HapMap

Accuracy 99.94%

Recent advances assays/analysis genetic

variationHapMap, 1000 Genomes

High-throughput genotyping & sequencing

Analytic Methods

Genome-wide Association, stratification, imputation, CNV, risk prediction

Examples: recent GWAS.

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Analytic methods

1. GENOME-WIDE ASSOCIATION

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Indi

vidu

als

SNPs

cases

controls

cases controls

No association

Association

Analytic methods

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Association testsStudy designs

Unrelated individuals

Families

Software

Between individual effects

Between + Within family effects

Many (eg. PLINK)

Merlin, etc

Unrelated individuals

Families

More power / $ spent, easier to collect, analyse

Assess inheritance (CNVs), robust population stratification

Pros

Analytic methods

2. POPULATION STRATIFICATION

Ind1 Ind2 % shared

A1 A2 100

A1 A3 50

A1 A4 25

A1 A5 10

A1 A6 8

A1 B1 5

Genetic matching

AB

BA

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A

B

B

A

cases controls

Analytic methods

3. IMPUTATION OF UNMEASURED GENOTYPES

Reference panel (eg. HapMap)

Genotyped Dataset

Individuals

SNPs MAF N SNPs Imputation Info score

Proportion of SNPs

Average Imputation

Rate

Average Concordance

0.01-0.05 27,078 Not imputed 0.000 - -

0-0.5 0.325 0.841 0.966

0.5-0.8 0.149 0.917 0.979

≥0.8 0.526 0.992 0.992

0.05-0.15 71,984 Not imputed 0.002 - -

0-0.5 0.164 0.525 0.934

0.5-0.8 0.175 0.750 0.961

≥0.8 0.659 0.967 0.989

0.15-0.25 65,918 Not imputed 0.004 - -

0-0.5 0.082 0.248 0.874

0.5-0.8 0.164 0.554 0.939

≥0.8 0.750 0.939 0.986

0.25-0.50 146,253 Not imputed 0.004 - -

0-0.5 0.053 0.094 0.777

0.5-0.8 0.145 0.389 0.907

≥0.8 0.798 0.917 0.981

MACH, IMPUTE, BEAGLE17

Shaun Purcell, Doug Ruderfer (PLINK)

Genotyped + Imputed Dataset

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Affy

Illumina

Perlegen

HapMap

Combine data from studies genotyped using different platforms

Example 1: Bipolar Disorder GWAS

WTCCC STEP-UCL ED-DUB-STEP2 Overall

Sample Size

N (% males) 4,764 (45) 3,467 (47) 2,365 (40) 10,596 (44)

Cases (% males) 1,829 (38) 1,460 (43) 1,098 (44) 4,387 (41)

Controls (% males) 2,935 (49) 2,007 (50) 1,267 (36) 6,209 (47)

Genotype missing rate 0.0027 0.0057 0.0031 0.0038

MAF GRR Power (α = 5 × 10-8)

0.05 1.40 0.05 0.02 <0.01 0.61

0.20 1.20 0.03 <0.01 <0.01 0.48

0.40 1.15 0.02 <0.01 <0.01 0.31

Ferreira et al (2008) Nature Genetics 40: 105619

325,690 SNPs

>1,7 million SNPs

ANK3: Ankyrin G

Cases: 7.0% Controls: 5.3%Odds ratio = 1.45

Not related to sex, psychosis or age-of-onset

Smith et al (2009) Mol Psychiatry 14: 755-63.

Scott et al (2009) Proc Natl Acad Sci USA 106: 7501-6.

[Lee et al (2010) Mol Psychiatry Apr 13 – Han Chinese population]

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Replicated recently

Example 2: analysis of lymphocyte subsets

Ferreira et al. (2010) Am J Hum Genet 86: 88-92 21

2,538 individuals | CD4+ T cell levels, CD8+ T cell levels, CD4:CD8 ratio

MHC class I• rs2524054, C• Increased CD8+ T levels• Improved host control of HIV (OR=0.32, P=10-9)

MHC class II• rs9270986, A• Increased CD4+ T levels• Protective effect for type-1 diabetes (OR = 0.04, P=10-125)• Protective effect Rheum. Arthritis (OR=0.60, P=10-15)

Structural Variants

Genomic alterations involving segment of DNA >1kb

Quantitative

(Copy Number Variants)

Positional (Translocations)

Orientational (Inversions)

Deletions

Duplications

Insertions

Analytic methods

4. Structural Variants

Detection of CNVs

Non-polymorphic probesMcCarroll et al 2008 Nat Genet 40: 1166

Detection of CNVs

Use polymorphic probes from genotyping arrays to Identify and genotype new, potentially rarer CNVs

Example: rs1006737 A/G ... AGCCCGAAATGTTTTCAGA...

... AGCCCGAAGTGTTTTCAGA...

probe 1

probe 2AAAGGG

Intensity of probe 2

Inte

nsity

of

prob

e 1

Detection of CNVs

1 A/G 1 1 2

2 A/- 1 0 1

3 AA/- 2 0 2

4 -/G 0 1 1

5 -/- 0 0 0

6 AAA/G 3 1 4

...CG ATG...

ATG......CGATG......CG

ATG......CG

ATG......CGATG......CG

ATG......CGATG......CG

ATG......CGATG......CG

ATG......CGATG......CG

A/G

A

AG

A

AA

G

A A AG

ATG......CG

Mat/PatIndGenotype Copy number for:

A G TotalPattern

Detection of CNVs ...CG ATG...

A/G

A

Normalized intensity of allele A

Nor

mal

ized

inte

nsity

of

alle

le G

Polymorphic probe in CNV region

A/A

A/G

G/G

Individuals with

deletion(s)

Individuals with

duplication(s)ie. total CN > 2

ie. total CN < 2

Detection of CNVs

Combine information across probes to identify new CNVs

For example... Cases Controls

100kb deletion chr. 2 10/5,000 1/5,000

Korn et al 2008 Nat Genet 40: 1253

BirdseyeAffy 5.0, 6.0

Wang et al 2007 Genome Res 17: 1665

PennCNVAffymetrix and Illumina

Example 3: Autism whole-genome CNV

analysisSample 16p11 Cases Controls P

Discovery Del (600kb) 5/1,441 3/4,2341.1 x 10-4

[Affy 500K] Dup 7/1,441 2/4,234

Replication 1 (CHB) Del 5/512 0/4340.007

[array-CGH] Dup 4/512 0/434

Replication 2 (deCODE) Del 3/299 2/18,8344.2 x 10-4

[Illumina] Dup 0/299 5/18,834

Deletion frequency Iceland

Autism 1%Psychiatric disorder 0.1%General population 0.01%

Weiss et al. N Engl J Med 2008; 358: 667

COPPERBirdseye

CNAT

del dup

inherited 2 6de novo 10 1unknown 1 4

Example 4: SCZ whole-genome CNV

analysis

Shaun Purcell

CasesCases

ControlsControlsChromosome Chromosome →→

Genome-wide burden

Specific loci

3,391 patients with SCZ, 3,181 controlsFilter for <1% MAF, >100kb

6,753 CNVs

Cases have greater rate of CNVs than controls1.15-fold increase

P = 3×10-5

Cases have greater rate of CNVs than controls1.15-fold increase

P = 3×10-5

Rate of genic CNVs in cases versus controls1.18-fold increase

P = 5×10-6

Rate of genic CNVs in cases versus controls1.18-fold increase

P = 5×10-6

Rate of non-genic CNVs in cases versus controls1.09-fold increase

P = 0.16

Rate of non-genic CNVs in cases versus controls1.09-fold increase

P = 0.16

Results invariant to obvious statistical controlsArray type, genotyping plate, sample collection site, mean probe intensity

Results invariant to obvious statistical controlsArray type, genotyping plate, sample collection site, mean probe intensity

Genome-wide burden of rare CNVs in SCZ

Shaun Purcell

Similar successes for Similar successes for other common diseasesother common diseases

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Jan 2006 to

Jan 2008

before Jan 2006

Crohn’s Disease (31 loci, ~10% variance)

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20

30

0

5

http://www.genome.gov/gwastudies

Altshuler, Daly & Lander. Science 2008; 322: 881Manolio, Brooks & Collins. J Clin Invest 2008 118: 1590

N c

onfir

med

loci

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Summary

Tremendous recent technological advances

Large-scale genetic association studies feasible

>150 disease loci unequivocally identified since 2006

Provide a solid base to build our knowledge about disease mechanisms

Hundreds of loci yet to be identified for most diseases

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