Generalized and multiple-trait extensions to Quantitative-Trait Locus mapping

Abstract

QTL (quantitative-trait locus) analysis aims to locate and estimate the effects of genes that are responsible for quantitative traits, by means of statistical methods that evaluate the association of genetic variation with trait (phenotypic) variation. Quantitative traits are typically controlled by multiple genes with varying degrees of influence on the phenotype. I describe a new QTL analysis method based on shrinkage and a unifying framework based on the generalized linear model for non-normal data. I develop their extensions to multiple-trait QTL analysis. Expression QTL, or eQTL, analysis is QTL analysis applied to gene expression data to reveal the eQTLs controlling transcript-abundance variation, with the goal of elucidating gene regulatory networks. For exploiting eQTL data, I develop a novel extension of the graphical Gaussian model that produces an undirected graph of a gene regulatory network. To reduce the dimensionality, the extension constructs networks one cluster at a time. However, because Fuzzy-K, the clustering method of choice, relies on subjective visual cutoffs for cluster membership, I develop a bootstrap method to overcome this disadvantage. Finally, I describe QGene, an extensible QTL- and eQTL-analysis software platform written in Java and used for implementation of all analyses.