Design and Analysis of Human Gene Mapping Studies

人类基因组计划和后续项目，如1000个基因组，GTEx，ENCODE和TOPMed 提供强大的资源，以确定影响人类健康和疾病的基因， 疾病相关的数量性状（QTs）。沿着这些资源而来的是越来越有效的工具， 基因分型、测序和注释基因组，并支持跨这些数据的计算。这些资源 在我们继续探索人类疾病和与疾病相关的QT的遗传基础时， 在这个提议中，我们描述了人类基因定位中出现的统计和计算问题， 特别关注序列分析、基因型估算和质量控制。我们描述统计 解决这些问题的方法以及便于使用的软件工具和网络服务。我们将测试 通过计算机模拟和分析来自复杂特征的数据， 我们参与的遗传学研究。具体而言，我们将： (1)开发工具来检测和估计与遗传血统无关的DNA样本污染; (2)在以下情况下，开发基于序列或插补基因型的Hardy-Weinberg平衡检验： 种群结构和对样本污染的鲁棒性; (3)在存在以下情况下，能够从DNA序列数据中进行更准确的变体过滤和基因型调用： 种群结构和/或样本污染; (4)开发检测RNA和表观基因组序列数据中样品污染的方法; (5)扩展密歇根插补服务器（MIS），以增加基于序列的关联研究的功效， 支持使用来自现有序列数据资源的外部控制，增强现有估算 参照研究者测序样本的样本组，并检查污染情况;以及 (6)记录、分发和支持有效的软件工具来支持这些方法。 在单独的资助下，我们将应用由此产生的方法来帮助理解2型的遗传基础。 糖尿病和相关QT间期，精神分裂症和双相情感障碍。 这些目标的成功将能够更快地识别易患人类疾病的变异 并解释了疾病相关QT的变异性，并有可能导致对基础QT的新见解。 生物学和疾病病因学，确定新的治疗方法，提高治疗的靶向，帮助疾病 分类，并支持更准确的疾病风险预测。统计和 计算方法的发展，以及这些方法在许多研究中的影响，使我们的 建议的研究具有很高的成本效益。

The Human Genome Project and follow-on projects such as 1000 Genomes, GTEx, ENCODE, and TOPMed provide powerful resources to identify genes that influence human health and disease and variability in disease-related quantitative traits (QTs). Along with these resources have come increasingly efficient tools to genotype, sequence, and annotate the genome, and to support computation across these data. These resources and tools will be critical as we continue to explore the genetic basis of human disease and disease-related QTs. In this proposal, we describe statistical and computational problems that arise in human gene mapping, with a particular focus on sequence analysis, genotype imputation, and quality control. We describe statistical methods to address these problems and software tools and web services to facilitate their use. We will test resulting methods, tools, and web services via computer simulation and analysis of data from complex trait genetics studies in which we are involved. Specifically, we will: (1) develop tools to detect and estimate DNA sample contamination that are agnostic to genetic ancestry; (2) develop a test for Hardy-Weinberg equilibrium of sequence-based or imputed genotypes in the presence of population structure and robust to sample contamination; (3) enable more accurate variant filtering and genotype calling from DNA sequence data in the presence of population structure and/or sample contamination; (4) develop methods to detect sample contamination in RNA- and epigenomic sequence data; (5) extend the Michigan Imputation Server (MIS) to increase power of a sequence-based association studies by supporting use of external controls from existing sequence data resources, augmenting an existing imputation reference panel with the investigator's sequenced samples, and checking for contamination; and (6) document, distribute, and support efficient software tools to support these methods. Under separate funding, we will apply the resulting methods to help understand the genetic basis of type 2 diabetes and related QTs, and of schizophrenia and bipolar disorder. Success in these aims will enable more rapid identification of variants that predispose to human disease and account for variability in disease-related QTs, and has the potential to lead to new insights into basic biology and disease etiology, identify novel therapies, improve targeting of therapies, assist in disease classification, and support more accurate disease risk prediction. The modest cost of statistical and computational methods development, and the impact of these methods across many studies, makes our proposed research highly cost effective.