Tree-Based Quantitative Trait Mapping in the Presence of External Covariates

• Activity Number: 71
• Type: Contributed
• Date/Time: Sunday, August 3, 2014 : 4:00 PM to 5:50 PM
• Sponsor: Biometrics Section
• Author(s): Katherine Thompson*+ and Laura Kubatko
• Companies: University of Kentucky and Ohio State University
• Keywords: phylogenetic analysis ; genome-wide association study (GWAS) data ; coalescent theory ; SNPs ; stochastic processes

Abstract

A central goal in the biological and biomedical sciences is to identify locations along the genome (single nucleotide polymorphisms, or SNPs) connected to variation in quantitative traits. Over the last decade, improvements in sequencing technologies coupled with the active development of association mapping methods have made it possible to link SNPs and quantitative traits. However, a major limitation of existing methods is that they are often unable to consider complex, but biologically-realistic, scenarios. Previous work showed that association mapping method performance can be improved by using the evolutionary history within each SNP to estimate the covariance structure among randomly-sampled individuals. The proposed method can be used to analyze a variety of data types, such as data including external covariates, while considering the evolutionary history among SNPs. Existing methods either do so at a computational cost, or fail to model these relationships, which may make it difficult to detect associated SNPs. By considering the broad-scale relationships among SNPs, the proposed approach is both computationally-feasible and informed by the evolutionary history among SNPs.