Statistical Modeling of Complex Traits in Genetic Reference Super-Populations

描述(由申请人提供)： 模式生物中的遗传杂交在理解与医学相关的表型的可遗传结构方面起着至关重要的作用。传统上，这种杂交往往是小规模的，检测遗传效应的能力有限，或者定位的分辨率有限。 因果变种。然而，最近出现了更大规模的跨学科研究，更便宜的基因分型和人类遗传学的平行进步，刺激了更复杂和更强大的实验设计的发展。遗传资源群体(GRP)利用规模经济为基因研究提供具有成本效益和可复制的平台。这个项目涉及迄今为止小鼠遗传学中最大、最雄心勃勃的GRP--合作杂交(CC)，以及与其相关或衍生的一系列杂交和设计：多样性远缘杂交(DO)杂交、CC重组近交系(CC-Rix)和双列杂交。在每个单独的杂交上的实验提供了关于目标复杂疾病的可遗传结构的不同信息。结合起来，这种遗传参考超群体(GRSP)潜在地为小鼠遗传学的交叉研究复制和整合提供了无与伦比的基础。该项目旨在开发统计方法，分别推进这些群体的复杂特征分析的现状，并通过利用将它们联系在一起的独特结构，建议开发一个允许它们共同使用的统计框架。目的1建立基于单倍型的数量性状基因座(QTL)的贝叶斯概率分析框架。目的1a开发了一个用于基于单倍型的灵活分析的统计软件模块，研究人员可以对其进行扩展，以模拟丰富的各种设计和疾病类型。目标1b将采用机器学习技术来提供对QTL等位基因序列的后验推断。Aim 1c将纳入多基因效应的贝叶斯建模。目标2和目标3涉及联合分析，建立在目标1确定的基础上。目标2开发方法来优化在一个人群中给出另一个人群的结果的随访研究的实验设计。Aim 2a使用双列杂交设计CC/CC-Rix/DO实验。AIM 2b使用CC/CC-Rix/DO上的部分数据来指导额外数据的收集。目标3探索了在GRSP中联合分析多个群体的模型，使用互补数据集在单个QTL(目标3a)和跨多个QTL(目标3b)进行稳定分析。这些目标旨在解决多亲本遗传数据，特别是CC、DO、CC-Rix和DILEL基因数据的成本效益设计和有效分析方面的具体和持久的挑战。该项目将产生对广泛的模式生物杂交有用的工具，并可应用于任何复杂疾病的遗传学研究。 公共卫生相关性： 这项拟议的研究将改进对人类疾病动物模型的遗传研究的分析和设计。由于该项目的重点是应用于实验小鼠种群的统计方法，因此该项目的科学成果预计将适用于专注于任何可以在小鼠身上研究的医学状况的基础研究。

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. PUBLIC HEALTH RELEVANCE: The proposed research will lead to improvements in the analysis and design of genetic studies on animal models of human disease. Because the project focuses on statistical methodology applied to experimental mouse populations, the scientific output of the project is expected to be applicable to basic research focusing on any medical condition that can be studied in the mouse.