Efficient Statistical Methods for Association Studies with Dense Genotypes and Family History of Disease

密集基因型与疾病家族史关联研究的有效统计方法

• 批准号: 9191432
• 负责人: Annie J Lee
• 金额: $ 4.03万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2019-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9191432
• 关键词: Accounting; Address; African American; Age; Aging; Algorithms; Alzheimer' s Disease; Alzheimer' s disease risk; Biological Markers; Blood specimen; Caucasians; Cessation of life; Collection; Complication; Computer software; Data; Data Set; Decision Making; Dementia; Disease; Elderly; Environmental Risk Factor; Epidemiologic Studies; Equation; Ethnic group; Event; Family; Family Sizes; Family Study; Family health status; Family history of; Family member; Genetic; Genetic Counseling; Genetic Markers; Genetic Risk; Genetic screening method; Genetic study; Genome; Genotype; Goals; Height; Hispanics; Individual; Interview; Lead; Life Style; Linear Regressions; Logistic Regressions; Longitudinal Studies; Maps; Methods; Modeling; Multivariate Analysis; Onset of illness; Patient Self-Report; Patients; Pattern; Persons; Phenotype; Population; Principal Component Analysis; Probability; Process; Recording of previous events; Recruitment Activity; Research; Risk; Sample Size; Sampling; Severities; Statistical Methods; Structure; Techniques; Testing; Time; Washington; base; case control; computerized tools; cost; design; disorder risk; family genetics; family structure; genetic association; genetic pedigree; genetic variant; genome wide association study; genome-wide; hazard; improved; meetings; multilevel analysis; novel; population based; proband; prognostic; risk variant; tool; trait; wasting

PROJECT SUMMARY / ABSTRACT In many genetic studies, case-control samples (probands) are recruited and phenotypes in their relatives are collected through a family health history interview on the probands. In these designs with combined genome-wide association study (GWAS) data in probands and family history in relatives (GWAS+FH), family member’s dense genotypes are often not collected due to the high cost of in-person collection of blood sample or death of a relative. Discarding relatives’ phenotypes lead to waste of much useful information because by examining patterns of the phenotypes among relatives with combination of genetic factors, environmental conditions, and lifestyle choices, a GWAS+FH leads to improved power of identifying an individual at risk of disease than using probands data alone. Multilevel models are powerful tools to test for association between genetic markers and correlated phenotypes because of their ability to account for varying degrees of relatedness among individuals. Improved power is expected from increased sample size by including relatives, higher chance to detect genuine genetic associations, and better type I error control compared to probands only analyses. However, analysis is highly challenging due to missing genotypes in relatives and correlation among family members’ phenotypes. The use of mixed effects multilevel model tools is rare in genetic association studies until recently, mainly due to the bottleneck of sub-optimal computational tools that do not meet requirements to handle large-scale GWAS and large sample size. This proposal addresses these challenges by providing fast and comprehensive statistical tools to increase our ability to map genetic variants in the combined data of proband GWAS and family history in relatives. Through multilevel mixed effects models, we will achieve improved power of association testing while controlling for correlation and confounding by: (1) use dense genotypes in probands to estimate between-family genetic similarities and expected values of missing relative genotypes; and (2) combine with within-family relatedness represented by polygenic effects. We will apply our methods to analyze Washington Heights-Inwood Columbia Aging Project, which offers golden opportunities to discover genetic variants associated with the risk of Alzheimer's disease in multiple ethnicity groups (Caucasian, African American and Hispanics). The novel statistical methods will ultimately allow personalized risk estimation of disease to each individual’s unique biomarkers, and aid in important decision making including genetic testing and genetic counseling.

项目总结/摘要 在许多遗传学研究中，招募病例对照样本（先证者），并在其表型中进行分析。 通过对先证者的家族健康史访谈收集亲属。在这些 在先证者和家系中结合全基因组关联研究（GWAS）数据的设计 亲属病史（GWAS+FH），家庭成员的密集基因型往往不收集， 亲自采集血样或亲属死亡的高昂费用。丢弃 亲属的表型导致浪费了许多有用的信息，因为通过检查模式， 表型的亲属与遗传因素，环境条件， 和生活方式的选择，GWAS+FH导致识别个体风险的能力提高， 而不是仅仅使用先证者的数据。多级模型是测试的强大工具 遗传标记和相关表型之间的关联，因为它们能够 解释了个体之间不同程度的相关性。预计电力将有所改善， 通过包括亲属来增加样本量，更有机会检测到真正的遗传 关联，以及与仅先证者分析相比更好的I型错误控制。然而，在这方面， 分析是非常具有挑战性的，因为亲属中缺失的基因型和 家族成员的表型。使用混合效应多水平模型工具是罕见的遗传 关联研究直到最近，主要是由于次优计算的瓶颈 不符合处理大规模GWAS和大样本量要求的工具。这 建议通过提供快速和全面的统计工具来应对这些挑战， 提高我们在先证者GWAS和家族的组合数据中绘制遗传变异的能力 亲戚的历史通过多层次混合效应模型，我们将实现改进的功效。 关联测试，同时控制相关性和混杂：（1）使用密集 先证者的基因型，以估计家庭间的遗传相似性和预期值 缺失相关基因型;和（2）联合收割机与家族内相关性， 多基因效应我们将应用我们的方法来分析华盛顿高地-因伍德哥伦比亚 老龄化项目，它提供了黄金机会，发现与遗传变异相关的 阿尔茨海默病的风险在多个种族群体（高加索人，非洲裔美国人和西班牙裔）。 新的统计方法最终将允许对每个人进行个性化的疾病风险评估。 个人独特的生物标志物，并有助于重要的决策，包括基因检测和 遗传咨询