Abstract:

In human genetics, many quantitative traits, such as blood pressure, are thought to be influenced by particular genes, but are also affected by environmental factors, making the associated genes difficult to identify and locate from genetic data alone.  For this reason, it is difficult to detect and localize single nucleotide polymorphisms (SNPs) associated with quantitative traits in genome-wide association study (GWAS) data using classical statistics. I will present a coalescent approach to search for SNPs associated with quantitative traits in GWAS data by taking into account the evolutionary history among SNPs, and evaluate its performance using simulation data.  Results of applying the statistical methodology developed to a real-data set to search for SNPs associated with high-density lipoprotein cholesterol in mice will also be presented.  By combining methods from stochastic processes and phylogenetics, this work provides an innovative avenue for the development of new statistical methodology in statistical genetics.