Project 1: Incorporating Ethnic and Gender Disparities in Genomic Studies of Disease

项目摘要/摘要：我们医疗保健系统的大部分负担来自复杂的人类 其发病和转归受多种基因组变异影响的疾病(例如，心血管疾病， 癌症和糖尿病)。在过去的十年里，人类遗传学家进行了全基因组的关联 研究，扫描与复杂疾病表型相关的风险等位基因。这些研究大体上 确定了使疾病风险增加相对较小的变种。我们提出了另一种解释 GWA研究在人类身上未实现的希望：而不是缺乏正确的数据来进行研究 在与医学相关的特征方面，人类基因组学缺乏准确的理论模型 描述我们物种的种群水平历史和自然选择的伴随影响。 在寻找疾病相关变异时忽略种群历史会导致这两个错误 基因分型与疾病状态的相关性，以及疾病相关变异的识别 在一个种群中，不能在多个祖先基因组背景下繁殖。许多共同之处 疾病的发病率因种族和/或性别的不同而不同，但关联研究没有提供框架 确定此类疾病的风险等位基因。 这个应用程序的目标是结合人类人口的共享人口历史和 将变量显性模型引入全基因组关联研究。中心假设是人类 人口历史推动了不同种族和性别的常见疾病的发病率。该计划的目标是 建议是：1)开发计算方法，以识别存在差异的疾病的风险等位基因 跨种族发病率；2)发展群体遗传学方法来推断突变的适合性影响 与不同性别发生的疾病有关；以及3)将新开发的方法应用于 DBGaP协会-研究发病率存在种族和性别差异的疾病的数据。这些方法 开发的将适用于全基因组、全基因组和疾病关联的外显组研究。

Project Summary/Abstract: Most of the burden in our health care system comes from complex human diseases, whose onset and outcome are influenced by multiple genomic variants (e.g., cardiovascular disease, cancer, and diabetes). For the past decade, human geneticists have conducted genome-wide association studies, scanning for risk alleles associated with complex disease phenotypes. These studies have generally identified variants that confer relatively small increments in disease risk. We propose an alternative explanation for the unrealized promise of GWA studies in humans: rather than lacking the correct data with which to study medically-relevant traits, human genomics suffers from a lack of theoretical models that accurately characterize the population-level history of our species and the concomitant effects of natural selection. Overlooking population histories in the search for disease-associated variants leads to both spurious correlations between genotype and disease status, as well as the identification of disease-associated variants in one population that are not reproduced across multiple ancestral genomic backgrounds. Many common diseases vary in incidence across ethnicities and/or sexes, but association studies offer no framework to identify risk alleles for such diseases. The objective of this application is to incorporate the shared demographic history of human populations and models of variable dominance into genome-wide association studies. The central hypothesis is that human demographic history drives the incidence of common diseases across ethnicities and sexes. The aims of the proposal are to: 1) develop computational methods to identify risk alleles for diseases with disparities in incidence across ethnicities; 2) develop population-genetic methods to infer the fitness effects of mutations associated with diseases occurring differentially across sexes; and 3) apply newly developed methods to dbGAP association-study data from diseases with ethnic and sex-based disparities in incidence. The methods developed will be applicable to genome-wide, whole-genome and exome studies of disease association.