Statistical Modeling of Multiparental and Genetic Reference Populations

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PROJECT SUMMARY / ABSTRACT Genetic crosses in model organisms play an essential role in understanding how heritable factors affect medically relevant traits. Such crosses have traditionally tended to be on a small scale with limited power to detect genetic effects, limited ability to localize causal variants, and limited options for replication. In the last 15 years, however, the emergence of larger-scale interdisciplinary research, cheaper genomic technologies, and parallel advances in human genetics has spurred the development of more sophisticated and powerful experimental designs. Foremost are those that incorporate two modern genetic design concepts: the multiparental population (MPP), whereby each subject is descended from a small, well-characterized set of genetically diverse inbred strains, with the goal of efficiently exploring a wide genetic landscape; and the genetic reference population (GRP), whereby subjects are drawn from a large and genetically diverse set of inbred strains, with the goal that the study population, and thereby the studies themselves, can be infinitely replicated. Their combination, the multiparental genetic reference population (MP-GRP), represents the state-of-the-art in complex trait genetics and has been implemented in a number of model organisms, including plants, flies, and rodents. The proposed program of research focuses primarily on the development of statistical and computational tools to advance the design and analysis of studies using MPPs, GRPs, and MP-GRPs, and secondarily on how those tools can be applied to genetically varying populations more broadly, including humans. Five main research themes are considered: 1) How to maximally exploit GRPs for studying genetic effects on treatment-response phenotypes, such as adverse reactions to drug therapy, pathogenesis in response to viral infection, or metabolic response to diet. Directions considered include new ways to a) statistically deconfound genetic effects on treatment response from those on baseline outcomes, b) detect genetic effects that are distributional or non-linear in nature. 2) How to use multiple studies on related GRPs to a) assess replicability and reproducibility of those re- sources, and b) combine analyses for greater power. 3) How to accommodate and exploit genetic- and non-genetically-driven differences in residual phenotypic variance in QTL mapping and genomic analyses. 4) How to maximally exploit QTL mapping based on haplotypes, both in cases where haplotypes are inferred based on descent and where they are assigned as pseudo-haplotypes through empirical clustering procedures. 5) How methods for multi-omic causal can exploit haplotype- and pseudo-haplotyped-based genetic anchors. Progress on these fronts will increase the power, value, and scope of studies on genetically varying model organism populations as well as on genetically varying populations more broadly.