# Presentation: 898920

## 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 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

## Human HeightPolygenic Trait with h2 = 0.8

- 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

## Slide 14

## 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 Matrix

- R2 = 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**
**2** **1**

**1** **1**
**? ?**
**2** **2**

**1 ****2**
**1 ****2**
**1 ****2**

- High

- High

- Low

- 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

- Low

## Imputing Low Density

**1** **2**
**1** **2**
**2** **1**

**1** **1**
**1**** ****2**
**2** **2**

**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 Mitchell
- Toni Pollin
- Alan Shuldiner

**USDA AIPL / BFGL**
- Paul VanRaden
- Tad Sonstegard
- Curt Van Tassell
- George Wiggans
- Funding
- NIH U01 HL084756
- NRI  2007-32205-17883