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Perspectives from Human Studies and Low Density Chip Jeffrey R. O’Connell University of Maryland School of Medicine October 28, 2008.

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Presentation on theme: "Perspectives from Human Studies and Low Density Chip Jeffrey R. O’Connell University of Maryland School of Medicine October 28, 2008."— Presentation transcript:

1 Perspectives from Human Studies and Low Density Chip Jeffrey R. O’Connell University of Maryland School of Medicine October 28, 2008

2 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

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

4 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 kb regions still prohibitive –Characterizing biologic mechanism through functional studies

5 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

6 Ingredients for Success Data 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

7 Human Height Polygenic Trait with h 2 = 0.8

8 Study Design

9 Results from Two Loci

10 Results Ten newly identified and two previously reported loci were strongly associated with variation in height –P values from 4x10E-7 to 8xE –Together 12 loci account for < 2% of the population variation in height Individuals with 16 height-increasing alleles differ in height by< 3.5 cm. Sample sizes > 100,00O have identified over 60 height alleles

11 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

12 Animal Model Quantitative Trait Association Y i =  +   j c ij + kg i + a i + e i, –Y i is the phenotype of the i th individual –c ij are covariates,  j is the covariate effect –g i is the genotype, k is the genotype effect –a i the additive polygenic effect –e i is the residual error

13 DGAT

14

15 Chr 29 LD Plot 1000 OLD Animals Chr 29 LD Plot 1000 YNG Animals

16 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

17 Animal Model and Genetic Prediction Y predictee = + 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

18 Net Merit Predicted vs. Observed PTA Genomic Matrix R 2 = 0.32

19 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

20 Imputing Low Density ? High Low 100 missing markers

21 Imputing Low Density High Low

22 Imputing Low Density High Low

23 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

24 Acknowledgements University of Maryland Brackie Mitchell Toni Pollin Alan Shuldiner USDA AIPL / BFGL Paul VanRaden Tad Sonstegard Curt Van Tassell George Wiggans Funding NIH U01 HL NRI


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