Statistical Modeling of Complex Traits in Genetic Reference Super-Populations

Univ Of North Carolina Chapel Hill, Chapel Hill NC

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Abstract

DESCRIPTION (provided by applicant): Genetic crosses in model organisms play an essential role in understanding the heritable architecture of medically relevant phenotypes. Traditionally, such crosses have tended to be on a small scale with either limited power to detect genetic effects or limited resolution to localize causal variants. Recently, however, the emergence of larger-scale interdisciplinary research, cheaper genotyping and parallel advances in human genetics, have spurred the development of more sophisticated and powerful experimental designs. Genetic Resource Populations (GRPs) use economies of scale to provide cost-effective and replicable platforms for genetic studies. This project concerns the largest, most ambitious GRP in mouse genetics to date, the Collaborative Cross (CC), and a series of crosses and designs related to or derived from it: the Diversity Outbred (DO) cross, the CC Recombinant Inbred Cross (CC-RIX) and the diallel. Experiments on each separate cross provide distinct information about the heritable architecture of a target complex disease. In combination, this Genetic Reference Super-Population (GRSP) potentially provides an unparalleled basis for cross-study replication and integration in mouse genetics. This project aims to develop statistical methods that advance the current state of complex trait analysis of these populations separately, and, by exploiting the unique structure that connects them, proposes to develop a statistical framework that allows for their joint use. Aim 1 develops a Bayesian probabilistic framework for haplotype-based analysis of quantitative trait loci (QTL). Aim 1a develops a statistical software module for flexible haplotype-based analysis, which can be ex- tended by the researcher to model a rich variety of designs and disease types. Aim 1b will adapt machine learning techniques to provide posterior inference of the allelic series of a QTL. Aim 1c will incorporate Bayesian modeling of polygenic effects. Aim 2 and 3 concern joint analysis, building on the foundation set by Aim 1. Aim 2 develops methods to optimize experimental design of follow-up studies in one population given results from another. Aim 2a uses the diallel to inform design of CC/CC-RIX/DO experiments. Aim 2b uses partial data on CC/CC-RIX/DO to guide collection of additional data. Aim 3 explores models for jointly analyzing multiple populations in the GRSP, using complementary datasets to stabilize analysis at single QTL (Aim 3a) and across multiple QTL (Aim 3b). These aims address specific and persistent challenges in the cost-effective design and efficient analysis of multiparent genetic data, in particular the CC, DO, CC-RIX and diallel. The project will generate tools useful for a wide range of model organism crosses and can be applied to the genetic study of any complex disease.