Genetics Workshop

Association Mapping with LD-Based Imputation and Haplotypes

Paul Scheet
Dept. of Biostatistics, University of Michigan

Current high-throughput technologies have enabled large-scale surveys of population genetic data, such as those for genome-wide association (GWA) studies of complex traits. These data demand computationally tractable models for inference. In this talk I present a statistical model for patterns of linkage disequilibrium (the correlation of alleles at nearby loci; LD) among tightly-linked SNPs. I demonstrate how this model may be used to improve association mapping techniques by imputing genotypes from a dense reference panel of individuals and by directly modeling haplotype variation to detect associations with rare SNPs. I also present a new LD-based quality control tool for genotype data, which can detect, and in some cases correct, genotyping errors.

Michigan State University | Department of Statistics and Probability | Statistical Genetics Lab