A hierarchical Bayesian approach to multi-trait clinical quantitative trait locus modeling

Abstract

Recent advances in high-throughput genotyping and transcript profiling technologies have enabled the inexpensive production of genome-wide dense marker maps in tandem with huge amounts of expression profiles. These large-scale data encompass valuable information about the genetic architecture of important phenotypic traits and the subtle molecular networks and pathways involved. Integrated models combining molecular markers and gene transcript levels are increasingly advocated as an effective approach to dissecting the genetic architecture of complex phenotypic traits. The simultaneous utilization of marker and gene expression data to explain variation in clinical quantitative traits is termed clinical quantitative trait locus (cQTL) mapping. Clinical quantitative trait locus analysis poses challenges that are both conceptual and a computational. Nonetheless, the hierarchical Bayesian (HB) modeling approach, in combination with modern computational tools such as Markov chain Monte Carlo (MCMC) simulation techniques, provides much versatility for combining information from various data sources and accommodating uncertainty at different levels, which makes it invaluable for cQTL analysis. Sillanpää and Noykova developed a HB model for single-trait cQTL analysis in inbred line cross-data using molecular markers, gene expressions, and marker–gene expression pairs. However, phenotypic traits generally relate to one another through environmental correlations and pleiotropy. A multi-trait approach can provide enhanced power to dissect the genetic architecture of complex clinical traits. Here we extend the HB cQTL model for inbred line crosses proposed by Sillanpää and Noykova to a multi-trait setting. We conduct an extensive simulation study to evaluate the performance of our HB multi-trait cQTL model, using the single trait model as benchmark for comparison. The model fitting to the data is carried out by MCMC simulation through the Bayesian freeware OpenBUGS. Our result