Using ancestral information to detect and localize quantitative trait loci in genome-wide association studies

In mammalian genetics, many quantitative traits, such as blood pressure, are thought to be influenced by specific genes, but are also affected by environmental factors, making the associated genes difficult to identify and locate from genetic data alone. In particular, the application of classical statistical methods to single nucleotide polymorphism (SNP) data collected in genome-wide association studies has been especially challenging. We propose a coalescent approach to search for SNPs associated with quantitative traits in genome-wide association study (GWAS) data by taking into account the evolutionary history among SNPs.

We evaluate the performance of the new method using simulated data, and find that it performs at least as well as existing methods with an increase in performance in the case of population structure. Application of the methodology to a real data set consisting of high-density lipoprotein cholesterol measurements in mice shows the method performs well for empirical data, as well.

By combining methods from stochastic processes and phylogenetics, this work provides an innovative avenue for the development of new statistical methodology in the analysis of GWAS data.