Simulation algorithms for genome-wide data and application to admixed data

人类遗传学的主要目标之一是确定影响复杂非孟德尔疾病易感性的遗传变异。一种常见的方法是关联作图，研究人员对许多标记进行基因分型，以找到与感兴趣的表型相关的标记。这些标记本身可能不影响疾病易感性，但可能与致病标记处于强连锁不平衡（LD）。计算机模拟是关联研究中规划和分析的一个重要工具。模拟有助于研究人员比较相互竞争的实验设计，并有助于解释所发现的任何关联。尽管如此重要，但目前缺乏经过验证的适用于目前正在产生的全基因组数据集的模拟方法。对于那些确实存在的方法，没有尝试测试所产生的数据是否准确反映了观察数据的属性，或者是否将其用于功效研究在功效的最终估计方面引入了偏倚。在这项提案中，我们专注于开发用于模拟全染色体遗传数据和分析全基因组关联研究数据的方法。我们将测试这些方法的准确性公开可用的数据，以及我们的合作者在南加州大学收集的基因型数据。我们将集中讨论如何分析混合人口的数据，如拉丁美洲人，其中人口分层使得大多数现有的分析方法不合适。这项工作也将帮助我们确定未来关联研究所需的标记密度和样本量。

DESCRIPTION (Proposal abstract): One of the main goals of human genetics is to identify the genetic variants that affect susceptibility to complex, non-Mendelian diseases. A common approach is association mapping, whereby researchers genotype many markers to find those correlated with the phenotype of interest. These markers may not affect disease susceptibility themselves, but are likely to be in strong linkage disequilibrium (LD) with causative markers. One essential tool in the planning and analysis of association studies is computer simulation. Simulations help researchers compare competing experimental designs, and aid in the interpretation of any associations that are found. Despite this importance, there is a lack of proven simulation methods that are appropriate for the genome-wide data sets now being produced. For those methods that do exist, no attempt has been made to test whether the data produced accurately reflects the properties of observed data, or whether their use for power studies introduces a bias in terms of the final estimates of power. In this proposal, we focus on developing methods for simulating whole chromosome genetic data and for analyzing whole-genome association study data. We will test the accuracy of these methods on publicly available data as well as on genotype data collected by our collaborators at the University of Southern California. We will concentrate on how to analyze data from admixed populations such as Latinos, where population stratification makes most existing analytical methods inappropriate. This work will also help us determine the marker density and sample size needed for future association studies.