Statistical Modeling of Multiparental and Genetic Reference Populations

项目总结/摘要 模式生物中的遗传杂交在理解遗传因素如何影响医学方面发挥着重要作用 相关特征传统上，这种杂交往往是小规模的，在检测遗传多样性方面的能力有限。 影响，有限的能力，本地化的因果变异，有限的复制选项。然而，在过去的15年里， 更大规模的跨学科研究、更便宜的基因组技术和平行进展的出现 人类遗传学的发展促进了更复杂和强大的实验设计的发展。 最重要的是那些结合了两个现代遗传设计概念的基因：多亲群体（MPP）， 其中每个受试者都是遗传多样性近交系的小的、充分表征的集合的后代， 目标是有效地探索广泛的遗传景观;遗传参考群体（GRP）， 其中受试者来自一个大的和遗传多样性的近交系，目的是该研究 人口，从而研究本身，可以无限复制。他们的组合，多父母 遗传参考群体（MP-GRP），代表了复杂性状遗传学的最新发展， 在许多模式生物中实施，包括植物、啮齿动物和啮齿动物。 拟议的研究计划主要集中在统计和计算的发展 使用MPP、GRP和MP-GRP推进研究设计和分析的工具，其次是 这些工具如何更广泛地应用于包括人类在内的遗传多样性群体。五个主要 研究主题被认为： 1)如何最大限度地利用GRP研究治疗反应表型的遗传效应，例如 对药物治疗的不良反应、对病毒感染的发病机制或对饮食的代谢反应。 考虑的方向包括新的方法，a）在统计学上消除遗传对治疗反应的影响 从基线结果中，B）检测自然分布或非线性的遗传效应。 2)如何使用相关GRP的多项研究，以a）评估那些重复的重复性和再现性， 源，以及B）联合收割机分析以获得更大的功率。 3)如何适应和利用遗传和非遗传驱动的残余表型差异 QTL作图和基因组分析中的方差。 4)如何最大限度地利用基于单倍型的QTL定位，无论是在单倍型推断的情况下， 基于血统，并且其中它们通过经验聚类程序被指定为伪单倍型。 5)多组学因果关系的方法如何利用基于单倍型和伪单倍型的遗传锚。 这些方面的进展将增加遗传变异模型研究的力量、价值和范围 生物种群以及更广泛的遗传变异种群。

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.