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: 9320181
• 负责人: Annie J Lee
• 金额: $ 4.07万
• 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/9320181
• 关键词: 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; Equation; Ethnic group; Event; Family; Family Sizes; Family Study; Family health status; Family history of; Family member; First Degree Relative; Genetic; Genetic Counseling; Genetic Markers; Genetic Risk; Genetic screening method; Genetic study; Genome; Genotype; Goals; 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 epidemiology; genetic pedigree; genetic variant; genome wide association study; genome-wide; hazard; improved; multilevel analysis; novel; phenotypic data; 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.

项目摘要/摘要 在许多遗传学研究中，病例对照样本(先证者)被招募，他们的表型 亲属是通过对先证者的家庭健康史访谈来收集的。在这些 结合先证者和家系的全基因组关联研究(GWAS)数据的设计 亲属病史(GWAS+FH)，家庭成员的密集基因型常未及时收集 亲身采集血样或亲属死亡的高昂费用。丢弃 亲属的表型导致许多有用信息的浪费，因为通过检查模式 有遗传因素、环境条件、 和生活方式选择，GWAS+FH可以提高识别有高血压风险的个人的能力 这比单独使用先证者的数据更能反映疾病的发展趋势。多层模型是测试的强大工具 遗传标记与相关表型之间的关联，因为它们有能力 解释了个体之间不同程度的关联性。预计通过以下方式提高功率 通过包括亲属增加样本量，更高的机会检测到真正的基因 关联性，与仅分析先证者相比，I类错误控制更好。然而， 由于亲属中缺少基因类型，以及亲属之间的相关性，分析具有极大的挑战性 家庭成员的表型。混合效应多水平模型工具的使用在遗传学研究中很少见 联想研究直到最近，主要是由于次优计算的瓶颈 不符合要求的工具，不能处理大规模GWA和大样本量。这 Proposal通过提供快速而全面的统计工具来应对这些挑战 提高我们在先证者和家系综合数据中绘制遗传变异图谱的能力 亲人的历史。通过多层混合效果模型，我们将实现更强的 关联测试通过以下方式控制相关性和混杂：(1)使用密集 先证者的基因型别估计家系间的遗传相似性和期望值 缺少亲属基因类型；以及(2)与以下所代表的家庭内亲缘关系相结合 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，该项目提供了发现与基因变异相关的黄金机会 多种族群体(高加索人、非裔美国人和西班牙裔美国人)患阿尔茨海默病的风险。 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 个体独特的生物标志物，并帮助做出重要决策，包括基因检测和 遗传咨询。