Perspectives from Human Studies and Low

Density Chip

Jeffrey R. O’Connell

University of Maryland School of Medicine

October 28, 2008

What Can We Learn from Human Studies?

• 3 years of GWAS (genome-wide associations using) using high-density SNP panels has been successful in identifying alleles that contribute risk to disease such as diabetes, age-related macular degeneration, Crohns disease and cardiovascular events

• Genetic variation in CAPON associated with Type 2 diabetes, QT heart interval and schizophrenia

Allelic Architecture

McCarthy et al. Genome-wide association studies for complex traits: consensus, uncertainty and challenges, NatGenRev 2008

Lessons Learned

• Allelic architecture– Alleles found to date do not account for

majority of familial risk estimated from epidemiological studies

• Finding causal variants a challenge– Sequencing cost to identify all variation

in 50-100kb regions still prohibitive– Characterizing biologic mechanism

through functional studies

Ingredients for Success - Technology

• Human Genome Project– Genome sequence

• HapMap– Catalog of common variation and haplotype

structure in 4 target populations

• High density fixed-content chips– 1M chips Illumina and Affymetrix (combined

1.6M SNPs)– 50K targeted panels

• 1000 Genomes Project– Identify low frequency polymorphisms

Ingredients for SuccessData Sharing

• Increased (forced?) cooperation across groups– Essential for replication– Meta analyses to increase sample size power

• Public access to data– dbGAP (repository of GWA data)– Best minds have access to the data for

analysis and methods development• Reports of new findings on public data from

different methods

Human HeightPolygenic Trait with h2 = 0.8

Study Design

Results from Two Loci

Results

• Ten newly identified and two previously reported loci were strongly associated with variation in height – P values from 4x10E-7 to 8xE10-22. – Together 12 loci account for < 2% of the

population variation in height• Individuals with <= 8 height-increasing

alleles and >16 height-increasing alleles differ in height by< 3.5 cm.

• Sample sizes > 100,00O have identified over 60 height alleles

Lessons Learned• Sample sizes required to detect

common with low effect sizes are large• Replication is essential to confirm

findings– Initial results often not reproduced

• Meta analysis methods important to combine data across studies– SNP effects and ranking often change as

sample sizes increase

Animal Model Quantitative Trait

Association• Yi = + j cij + kgi + ai + ei,

– Yi is the phenotype of the ith individual

– cij are covariates, j is the covariate effect

– gi is the genotype, k is the genotype effect

– ai the additive polygenic effect

– ei is the residual error

DGAT

Chr 29 LD Plot 1000 OLD Animals

Chr 29 LD Plot 1000 YNG Animals

Low Density SNP Selection• Forward regression model building

– Add SNP to model– Compare to model without SNP

• If the model fit is better, keep the SNP

• Final set depends in order SNPs added to model

• Genomic matrix– Relationship between animals based on

genetic data rather than pedigree

Animal Model and Genetic Prediction

• Ypredictee = + WV-1(Y-X), – m is the contribution of SNP effects– V-1(Y-X) are the fitted residuals using predictor

set– W = Cov(Predictee,Predictor) is the covariance

matrix between predictee and predictor animals (A or G matrix)

• Predictive Ability– Predictor set: 3570 proven bulls from 2003– Predictee set: 1791 bulls from 2003 that have

proofs in 2008– Measure correlation of predicted with observed

Net Merit Predicted vs. Observed PTA

Genomic MatrixR2 = 0.32

Low Density to High Density

• Use high density of ancestors to infer genotypes of offspring– Inferred genotypes used in genomic

prediction for other phenotypes• 384 low density: 38,400 high density

– 100 SNPs between two high density– Low density SNP every 10 Mb– Crossovers every 100 Mb

Imputing Low Density

1 2

1 2

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1 1

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2 2

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100 missing markers

Imputing Low Density

1 2

1 2

2 1

1 1

1 2

2 2

1 2

1 2

1 2

High

High

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Imputing Low Density

1 2

1 2

2 1

1 1

1 2

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1 2

1 2

1 2

High

High

Low

Low Density to High Density

• Accuracy of low density to high density depends on number and proximity of high density genotyped relatives

• Current work will quantify the accuracy using the 15,000 Holstein samples with high density genotyping– Censor high density calls– Predict low density– Compare with observed data

Acknowledgements

University of Maryland

Brackie MitchellToni PollinAlan Shuldiner

USDA AIPL / BFGLPaul VanRadenTad SonstegardCurt Van TassellGeorge Wiggans

FundingNIH U01 HL084756NRI 2007-32205-17883