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

项目摘要/摘要：我们的医疗保健系统的大部分负担来自复杂的人类 疾病，其发病和结果受多种基因组变异的影响（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。这些研究一般 确定的变异赋予相对较小的疾病风险增量。我们提出了另一种解释 对于GWA在人类研究中未实现的承诺：而不是缺乏正确的数据来研究 与医学相关的特征，人类基因组学缺乏准确的理论模型， 描述了我们物种的种群历史以及自然选择的伴随效应。 在寻找疾病相关变异时忽略人群历史， 基因型和疾病状态之间的相关性，以及疾病相关变异的识别 在一个群体中，这些基因不会在多个祖先基因组背景中复制。许多共同 不同种族和/或性别的疾病发病率不同，但相关研究没有提供框架， 确定这些疾病的危险等位基因。 该应用程序的目的是将人类人口的共同人口统计历史， 将变量显性模型应用于全基因组关联研究。核心假设是人类 人口统计学历史推动了不同种族和性别的常见疾病发病率。的目标 建议是：1）开发计算方法，以确定具有差异的疾病的风险等位基因， 跨种族的发病率; 2）开发群体遗传方法来推断突变的适应性效应 与不同性别之间发生的疾病有关; 3）应用新开发的方法， dbGAP关联性--来自发病率存在种族和性别差异的疾病的研究数据。的方法 所开发的基因组分析技术将适用于疾病关联的全基因组、全基因组和外显子组研究。

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.