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

项目 1：将种族和性别差异纳入疾病基因组研究

• 批准号: 9433665
• 负责人: Sohini Ramachandran
• 金额: $ 27.06万
• 依托单位: BROWN UNIVERSITY
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/9433665
• 关键词: Acute Lymphocytic Leukemia; Africa; Autistic Disorder; Cardiovascular Diseases; Case-Control Studies; Centers of Research Excellence; Complex; Complex Genetic Trait; Computational Biology; Computer software; Computing Methodologies; DNA Sequence Alteration; Data; Diabetes Mellitus; Disease; Disease susceptibility; Ethnic Origin; Ethnic group; European; Female; Fruit; Gene Frequency; Genetic Diseases; Genetic Polymorphism; Genomics; Genotype; Goals; Healthcare Systems; Heart Diseases; Human; Human Genetics; Incidence; Individual; Joints; Link; Malignant Neoplasms; Malignant neoplasm of prostate; Masks; Medical; Methodology; Methods; Modeling; Modernization; Mutation; Natural Selections; Non-Insulin-Dependent Diabetes Mellitus; Outcome; Partner in relationship; Patients; Phenotype; Population; Population Genetics; Process; Publishing; Recording of previous events; Research; Role; Scanning; Schizophrenia; Sex Bias; Statistical Methods; Structure; Testing; Theoretical model; Treatment outcome; Variant; Work; base; database of Genotypes and Phenotypes; disease phenotype; disorder risk; ethnic disparity; exome; experience; fitness; gender disparity; genetic architecture; genetic variant; genome wide association study; genome-wide; human disease; human genomics; insight; male; novel; personalized medicine; pressure; public health relevance; risk variant; sex; trait; whole genome

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.

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 其发病和结果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）。在过去的十年中，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 确定了疾病风险增量相对较小的变异。我们提出另一种解释 对于人类 GWA 研究未实现的承诺：而不是缺乏用于研究的正确数据 医学相关的特征，人类基因组学缺乏准确的理论模型 描述我们物种的种群历史以及自然选择的随之而来的影响。 在寻找与疾病相关的变异时忽视人口历史会导致虚假的结果 基因型和疾病状态之间的相关性，以及疾病相关变异的鉴定 在一个没有跨多个祖先基因组背景复制的群体中。很多常见的 疾病的发病率因种族和/或性别而异，但关联研究没有提供框架 识别此类疾病的风险等位基因。 该应用程序的目的是整合人类共同的人口统计历史和 将变量优势模型纳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。该组织的目标 建议：1）开发计算方法来识别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度效应 与不同性别发生差异的疾病相关； 3）应用新开发的方法 dbGAP 关联研究来自具有种族和性别发病率差异的疾病的数据。方法 开发的将适用于疾病关联的全基因组、全基因组和外显子组研究。