Design Considerations in Large-Scale Genetic Association Studies

Michael Boehnke,Andrew Skol, Laura Scott, Cristen Willer,

Gonçalo Abecasis, Anne Jackson, and the FUSION Study Investigators

Department of Biostatistics Center for Statistical Genetics

University of Michigan

Outline

• Assess the utility of HapMap samples for tagSNP selection in a study of type 2 diabetes in Finnish subjects

• Discuss the impact of several design factors on cost and efficiency of genome-wide association (GWA) studies

FUSION Study: Finland-United States Investigation of NIDDM Genetics

National Public Health Institute, HelsinkiUSC Keck School of Medicine, Los AngelesNational Human Genome Research Institute, BethesdaUniversity of Michigan School of Public Health, Ann ArborUniversity of North Carolina School of Medicine, Chapel Hill

Chromosome 14 SNP Selection

• Used early HapMap (May 2004) to select tagSNPs in 18 Mb linkage interval on chr 14

• MAF > .05, Illumina design score > .40

• Unselected SNPs had r2 > .8 with 1 tagSNP

• Added annotation-based SNPs

• Double tagged large bins, filled large gaps

Chromosome 14 SNP Selection

HapMap SNPs in region (MAF > .05) 2276

HapMap tagSNPs (r2 > .8) 1132

Annotation-based SNPs 28

Double-tag SNPs (large bins) 11

Gap-filling SNPs 211

Total SNPs attempted 1382

Total SNPs genotyped 1230

Total SNPs polymorphic and in HWE 1192

Utility of HapMap for tagSNP Selection

for Finnish Subjects

• Question: How comparable were allele, haplotype frequency and r2 in HapMap, Finnish data?

• Compared HapMap data and 1448 Finnish samples from FUSION and Finrisk 2002 studies

• Poster 1621, Willer et al., Friday 1:30 3:30 pm

Allele Frequencies: FUSION vs. HapMap

CEU YRI

CHB JPT

Allele Frequencies: FUSION vs. CEU

7.5% SNP frequencies differ at p < .01 r = .98

LD r2 : FUSION vs. CEU

r = .91

Haplotype Frequencies: FUSION vs. CEU

r = .97

Summary: Chromosome 14 SNP Selection• CEU excellent basis for tagSNP selection in Finns

• Strong correlation between allele frequencies, haplotype frequencies, LD in two samples

• Excess of significant allele and haplotype frequency differences (7% at .01 level), but mostly small

• Nearly all common haplotypes (frequency > .05) in one sample present in both samples– 579/583 from CEU in FUSION – 557/563 from FUSION in CEU

Design of Genome-wide Association Studies

• GWA provides unprecedented opportunity to identify genetic variants predisposing to disease

• Enabled by HapMap, genotyping costs

• Since we may type 100s-1000s of samples on 100Ks of SNPs, efficient study design critical

• Examine two-stage designs for large-scale genetic studies (see Satagopan, Elston, Thomas)

1,2,

3,…

……

……

……

……

,N

1,2,3,……………………………,MSNPs

Sam

ples

One-Stage DesignOne-Stage Design

Stage 1

Sta

ge 2

samples

markers

Two-Stage DesignTwo-Stage Design

1,2,3,……………………………,MSNPs

Sam

ples

1,2,

3,…

……

……

……

……

,N

One- and Two-Stage GWA Designs

SNPs

Sam

ples

Replication-based analysisSNPs

Sam

ples

Stage 1

Stag

e 2

One-Stage DesignOne-Stage Design

Joint analysisSNPs

Sam

ples

Stage 1

Stag

e 2

Two-Stage DesignTwo-Stage Design

Joint Analysis is More Powerful than Replication-Based Analysis Skol et al., Friday 8:45, 180, Hall 3

300,000 markers genotyped on 1000 cases, 1000 controlsMultiplicative model, prevalence 10%, GRR = 1.4

One-stage power

Factors that Influence Cost and Efficiency of GWAs

• Fraction samples typed in Stage 1 (samples)

• Fraction SNPs typed in Stage 2 (markers)

• Stage 2 to Stage 1 per genotype cost ratio (R)

For a two-stage GWA study, what is the optimal fraction of samples genotyped in Stage 1 (samples) ?

Stage 2 per genotype costR =

Stage 1 per genotype cost

Case 1: R = 1

Case 2: R = 1, 2, 5, 10

Cost as a Function of Samples Typed in Stage 1 Per Genotype Cost Ratio R=1

Fraction of Markers Followed-up Varies to Ensure Constant Power

For a two-stage GWA study, what is the optimal fraction of samples genotyped in Stage 1 (samples) ?

Stage 2 per genotype costR =

Stage 1 per genotype cost

Case 1: R = 1

Case 2: R = 1, 2, 5, 10

Cost as a Function of Samples Typed in Stage 1 Per Genotype Cost Ratio R = 1, 2, 5, 10

Fraction of Markers Followed-up Varies to Ensure Constant Power

R=10

R=1

R=5

R=2

Summary: Two-Stage GWA Designs

• Two-stage GWA designs efficient, cost-effective; joint analysis more powerful than replication

• For equal Stage 1, 2 per genotype costs (R=1), 250K SNPs, genomewide significance =.05, genotype 20-30% of samples in Stage 1

• For R>1, less stringent significance, fewer SNPs, genotype 30-40% SNPs in Stage 1

Acknowledgements

• Chromosome 14: Cristen Willer, Anne Jackson; FUSION, CIDR, and HapMap investigators

• Two-stage designs: Andrew Skol, Laura Scott, Gonçalo Abecasis

• Thanks!

Excluded slides follow

0

1

2

3

0 40 80 120Position (cM)

ML

S

FUSION 1: 495 ASP families

FUSION 2: 242 ASP families

FUSION 1+2

FUSION Chromosome 14 T2D Linkage

Power of One- and Two-Stage Designs

How does a change in significance level change the optimal proportion

of samples in Stage 1 (samples)?

Case 1: =.05/250,000 genomewide significance

Case 2: =10/250,000 less stringent significance

Case 2’: =.05/1,250 candidate gene significance

Impact of Significance Level on Optimal Proportion of Samples in Stage 1