Bayesian Methods for Mapping Complex Epistatic Genes

描述(申请人提供)：上位性，基因之间的相互作用，被推测在大多数常见人类疾病的遗传控制中普遍存在，例如肥胖症、高血压和癌症。动物模型已被证明是了解人类常见疾病的遗传结构和病因的有效方法。在近亲交配的动物群体中控制基因和环境的能力极大地简化了复杂交互作用的分析。对数量性状基因座(QTL)间互作效应的统计建模必须适应大量潜在的遗传效应，即使假设只有适度数量的QTL。这一基本的统计学挑战是确定关于QTL的数量、它们的基因组位置和它们的遗传效应的遗传模型的主要障碍。 这项拟议的研究将开发统计方法和计算机软件，利用贝叶斯框架和马尔可夫链蒙特卡罗(MCMC)算法识别具有复杂相互作用模式的多个基因。这里提出的方法将主要用于从两个自交系(例如，F2、回交、重组自交系、高级异交系)或多个自交系(例如，四向杂交、八向杂交)衍生的任意交配设计。该建议的具体目标是：(1)建立新的贝叶斯模型选择和搜索策略，用于识别整个基因组中的上位性QTL，并联合推断来自两个自交系的任意交配设计中QTL的数量、它们的基因组位置及其主效应和上位性效应；(2)发展贝叶斯方法和MCMC算法，用于定位复杂顺序性状(例如，疾病易感性和严重性)的上位性QTL，并联合分析多变量连续和有序性状；(3)发展贝叶斯方法和MCMC算法，用于定位来自多个自交系的任意交配设计的上位性QTL，(4)评估通过广泛的模拟研究开发的所有程序的特性；(5)将开发的方法应用于多个真实数据集，并将所提出的方法与现有的一些方法进行比较；(6)发布高质量的、用户友好的软件来实现所提出的方法。 所提出的方法有望帮助发现更多的QTL，提高估计其基因组位置及其遗传效应的准确性，最终增强我们理解人类疾病的能力。

DESCRIPTION (provided by applicant): Epistasis, the interaction among genes, is speculated to be ubiquitous in the genetic control of most common human diseases, e.g., obesity, hypertension, and cancer. The animal models have proved to be a powerful approach to understanding genetic architectures and etiologies of common human diseases. The ability to control both genotype and environment in inbred populations of animals greatly simplifies analysis of complex interactions. The statistical modeling of interaction effects among quantitative trait loci (QTL) must accommodate a very large number of potential genetic effects, even when one assumes only a moderate number of QTL. This fundamental statistical challenge presents a major barrier to determining genetic model with respect to the number of QTL, their genomic positions and their genetic effects. The proposed research will develop statistical methodologies and computer software for identifying multiple genes with complex interaction patterns using the Bayesian framework and Markov chain Monte Carlo (MCMC) algorithms. The methods proposed herein will be developed primarily for arbitrary mating designs derived from two inbred lines (e.g., F2, backcrosses, recombinant inbred lines, advanced intercross lines) or multiple inbred lines (e.g., four-way crosses, eight-way crosses). The specific objectives of this proposal are to: (1) establish novel Bayesian model choice and search strategies for identifying epistatic QTL across the entire genome and jointly inferring the number of QTL, their genomic positions and their main and epistatic effects in arbitrary mating designs derived from two inbred lines, (2) develop Bayesian methods and MCMC algorithms for mapping epistatic QTL for complex ordinal traits (e.g., disease susceptibility and severity), and jointly analyzing multivariate continuous and ordinal traits, (3) develop Bayesian methods and MCMC algorithms for mapping epistatic QTL in arbitrary mating designs derived from multiple inbred lines, (4) evaluate the properties of all procedures developed by extensive simulation studies, (5) apply the methods developed to multiple real data sets and compare the proposed methods with some existing methods, and (6) release high quality, user-friendly software to implement the proposed methods. The proposed methods are expected to aid the discovery of a greater number of QTL, improve the accuracy of estimating their genomic positions and their genetic effects, and finally enhance our ability to understand human diseases.