Quantitative Genetic Analysis of Longitudinal Data Using Robust Bayesian Methods

生物体中的许多特征具有复杂的遗传基础，因此必须使用统计方法来研究它们在自然选择或人工选择下的行为。通常，观察是纵向的;例如，对一棵树进行重复测量，动物是否在多次发作时感染寄生虫，繁殖史中的窝仔数。本计画将利用贝氏推论与“强健”分布，发展复杂纵向性状之数量遗传学研究技术。贝叶斯分析使用概率来描述关于未知数的不确定性，例如，一个未来的观察或模型的数学形式。“稳健”分布旨在防止在统计模型中做出不正确的假设。重点关注三种类型的纵向数据：1）连续（小鼠运动，绵羊产奶量），2）多次测量中每次测量的离散（存在牛疾病），3）计数（猪的窝仔数）。计算机模拟将在几种遗传学情景下检验稳健模型的性能，结果将扩展和改进用于复杂性状遗传分析的统计方法。这些方法将在从产生纵向信息的实验或调查中获得的小样本中得出精确的推断。

Many characteristics in organisms have a complex genetic basis, so statistical methods must be used to study their behavior under natural or artificial selection. Often, observations are longitudinal; for example, repeated measurements taken on a tree, whether or not an animal is infested with parasites at a number of episodes, litter sizes during reproductive history. This project will develop techniques for studying quantitative genetics of complex longitudinal characters using Bayesian inference and "robust" distributions. Bayesian analysis uses probability to describe uncertainty about unknowns, e.g., a future observation or the mathematical form of a model. "Robust" distributions aim to protect from making incorrect assumptions in statistical models. Focus will be on three types of longitudinal data: 1) continuous (exercise in mice, milk yield in sheep), 2) discrete (presence of a cattle disease) at each of a number of measurements, and 3) counts (litter size of pigs). Computer simulations will examine the performance of the robust models under several genetics scenarios.Results will extend and improve the battery of statistical methods employed for genetic analysis of complex traits. The methods will draw exact inferences in small samples obtained from experiments or surveys generating longitudinal information.