Identifying Genome-scale Interaction Effects in Human Traits and Diseases

识别人类特征和疾病的基因组规模相互作用效应

• 批准号: 9146373
• 负责人: Hugues Aschard
• 金额: $ 20.19万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-18 至 2018-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9146373
• 关键词: Accounting; Address; Affect; African; African American; Age; Animal Model; Architecture; Asthma; Biological Models; Biological Process; Breeding; Consumption; Data; Data Set; Diploidy; Disease; Environmental Exposure; Environmental Risk Factor; Epidemiologist; Epidemiology; General Population; Genes; Genetic; Genetic Risk; Goals; Health; Human; Individual; Life; Literature; Lung; Malignant neoplasm of prostate; Measures; Methods; Modeling; Non-Insulin-Dependent Diabetes Mellitus; Nurses' Health Study; Outcome; Pattern; Phenotype; Play; Population; Research Proposals; Risk; Risk Factors; Role; Sample Size; Single Nucleotide Polymorphism; Smoking; Testing; Time; Validation; Work; cancer type; case control; cohort; gene environment interaction; genetic risk factor; genetic variant; genome wide association study; genome-wide; high risk; human data; human disease; improved; innovation; interest; malignant breast neoplasm; non-genetic; pulmonary function; risk variant; screening; sugar; sweetened beverage; trait

 DESCRIPTION (provided by applicant): Thousands of studies searching for gene-gene and gene-environment interaction effects on human traits and diseases have been conducted over the past 10 years. Almost all of them considered pairwise interaction only, i.e. testing for interaction effects between a single genetic variant and a single interacting factor (i.e. another genetic variant or an environmental exposure). Despite this huge effort, no pairwise interaction has been clearly established and replicated in independent data. In this proposal, we hypothesize that interaction effects take place at a higher structural level, involving scaling variables that affect the association between a phenotype and multiple genetic variants at the same time, and resulting in very low marginal pairwise interaction effects. We aim at developing and applying innovative strategies that to detect such association patterns. We especially target risk modifiers that can alter the global effect of genetic backgrounds, and vice versa genetic background that can alter the marginal association of identified genetic and non-genetic risk factors. We devised two complementary approaches to identify such effects. We will first develop and evaluate strategies for the identification of factors modifying the effect of genetic risk scores (GRS) built from SNPs found associated to human traits in genome-wide association scan (GWAS). These approaches will be used to test for interaction effects between the GRS of several human traits in the NHS and EGEA studies. Second, we will explore the possibility of interaction effects between established genetic and non-genetic risk factors and genetic background as measured by global and local ancestry in African-American admixed population using data from the COPDGene study. This analysis will focus on pulmonary phenotypes, which are known to be associated with the proportion of African ancestry. The proposed approach differs profoundly from existing ones and represents a pioneering work. Its validation has the potential to change the perspective on how genetic epidemiologists define strategies for the identification of interaction effects in human traits.

 描述（由申请人提供）：在过去的 10 年里，已经进行了数千项研究，寻找基因-基因和基因-环境相互作用对人类特征和疾病的影响，几乎所有这些研究都只考虑成对相互作用，即测试之间的相互作用。尽管付出了巨大的努力，但在独立数据中尚未明确建立和复制单个遗传变异和单个相互作用因素（即另一个遗传变异或环境暴露）。我们的目标是开发和应用创新策略来检测这种关联模式，同时影响表型和多个遗传变异之间的关联。可以改变遗传背景的整体影响的风险调节因素，反之亦然，可以改变已识别的遗传和非遗传风险因素的边际关联的遗传背景我们设计了两种互补的方法来识别此类影响。用于识别影响效果的因素根据全基因组关联扫描 (GWAS) 中发现的与人类特征相关的 SNP 构建遗传风险评分 (GRS)，这些方法将用于测试 NHS 和 EGEA 研究中多个人类特征的 GRS 之间的相互作用。我们将利用 COPDGene 研究的数据，探讨已确定的遗传和非遗传风险因素与遗传背景之间相互作用的可能性，该遗传背景是通过非裔美国人混合人群的全球和当地血统测量的。已知是所提出的方法与现有方法有很大不同，它的验证有可能改变遗传流行病学家如何定义识别人类特征相互作用效应的策略的观点。

项目成果

lme4qtl: linear mixed models with flexible covariance structure for genetic studies of related individuals.

• DOI: 10.1186/s12859-018-2057-x
• 发表时间: 2018-02-27
• 期刊: BMC bioinformatics
• 影响因子: 3
• 作者: Ziyatdinov A;Vázquez-Santiago M;Brunel H;Martinez-Perez A;Aschard H;Soria JM
• 通讯作者: Soria JM