Statistical Modeling of Multiparental and Genetic Reference Populations

项目摘要/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起着至关重要的作用。 相关特征。传统上，这样的杂交往往是小规模的，检测基因的能力有限 影响，定位因果变异的能力有限，复制的选择有限。然而，在过去的十年里， 更大规模跨学科研究的出现，更便宜的基因分型和人类平行进步 遗传学，刺激了更复杂和更强大的实验设计的发展。最重要的是 其中包括两个现代遗传设计概念：多亲种群(MPP)，其中每个 受试者是从一组遗传多样性的小的、特征良好的近交系中进化而来的，目标是 有效地探索广泛的遗传图景；以及遗传参考群体(GRP)，受试者 来自一大组遗传多样性的近交系，目的是研究种群，以及 因此，研究本身可以无限复制。他们的组合，多亲遗传参考群体(MP-GRP)，代表了复杂性状遗传学的最新水平，并已被实施 在许多模式生物中，包括植物、苍蝇和啮齿动物。 拟议的研究方案侧重于开发统计和计算工具，以 推进使用MPS、GRPS和MP-GRPS的研究的设计和分析。它以寻址为中心 三个相互关联的问题。 1)如何在遗传变异的种群中利用生物复制？考虑的方向包括：检测遗传诱导的表型离群值的更稳定的方法；使用遗传诱导的 异方差以提高统计能力和寻找控制变异的基因；以及通过使用因果推断的原理更严格和更广泛地表征逐个处理的基因效应。 2)如何在MP-GRP及其衍生交叉的复杂设计空间中导航？考虑的指示 包括：将决策理论应用于试点数据的贝叶斯分析；纳入方差异质性 以控制可能的重复性。 3)如何对MPP和MP-GRP进行数量性状座位(QTL)分析？考虑的方向包括：使基于单倍型的关联对单倍型状态的不确定性更加稳健；将单倍型- 基于变量的作图；QTL复杂性的自适应建模；等位基因序列的机器学习； 基于下降排列的家族式误码率控制。 在这些方面的进展不仅将填补在使用MPP、GRPS和MP-GRPS的研究方面的重大空白，而且 还将提供工具和见解，使这些设计能够以新的、更强大的方式使用。

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 decade, however, the emergence of larger-scale interdisciplinary research, cheaper genotyping 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 on the development of statistical and computational tools to advance the design and analysis of studies using MPPs, GRPs and MP-GRPs. It centers around addressing three interconnected questions. 1) How to take advantage of biological replicates in a genetically varying population? Directions considered include: more stable methods to detect genetically-induced phenotypic outliers; use of genetically-induced heteroskedasticity to improve statistical power and find variance-controlling genes; and more rigorous and expansive characterization of gene-by-treatment effects by using principles from causal inference. 2) How to navigate the complex design space of MP-GRPs and their derived crosses? Directions considered include: use of decision theory applied to Bayesian analysis of pilot data; incorporation of variance heterogeneity to control likely reproducibility. 3) How to approach quantitative trait locus (QTL) analysis in MPPs and MP-GRPs? Directions considered include: making haplotype-based association more robust to uncertainty in haplotype state; combining haplotype- based with variant-based mapping; adaptive modeling of QTL complexity; machine learning of the allelic series; familywise error rate control through descent-based permutation. Progress on these fronts will not only fill significant gaps in studies using MPPs, GRPs and MP-GRPs, but will also provide tools and insights that will allow these designs to be used in new and more powerful ways.