Genome-Wide Analyses of Health and Well-Being Phenotypes

项目总结/摘要 这项建议，“健康和幸福表型的全基因组分析”，是一个竞争性的更新， 目前已获资助，为期三年的R 01。当前的R 01专注于推进社会科学的研究 遗传协会联盟（SSGAC），一个跨学科的合作，进行大规模的遗传 行为表型研究，由三位申请人指导。R 01的所有目标都是 办妥了一批其结果被广泛用于健康和衰老相关的研究，包括社会科学遗传学， 医学遗传学和流行病学。 这一竞争性的延期提议继续SSGAC的工作。简而言之，我们建议： ·在更大的人群中进行健康和衰老相关行为表型的遗传关联研究 现在已经有了样品。我们将完成我们的饮食摄入量的研究， 当前R 01。此外，我们还将对其他表型进行大规模研究，包括物理学研究。 活动和自我报告的一般健康状况。这些项目中的每一个都将确定与基因突变相关的基因。 与表型，分析生物学途径，这些协会的基础，并构建多基因 评分（许多遗传变异的指数），可以对表型具有实质性的预测能力。 ·开发一种更强大的方法来联合分析多种表型，这将能够（a） 估计与一些表型集合相关而与其他表型集合无关的遗传变体的分数，以及（B） 识别可能与某些表型而不是其他表型相关的遗传变异。的结果 应用这种方法将有助于解开遗传变异对人类健康的影响的不同机制。 表型，并将使建设更多的预测多基因评分为每一个表型。 ·应用这种方法来阐明影响教育的共同和独特的遗传途径 老年痴呆症（Alzheimer's Disease）这项分析将：（a）确定许多新的遗传变异 与阿尔茨海默病相关;（B）产生更多的阿尔茨海默病的预测性多基因评分， 促进早期诊断和治疗;（c）阐明与潜在的 驱动阿尔茨海默病和教育程度之间遗传关系的机制; (d)使生物注释的遗传变异确定影响阿尔茨海默氏症的风险，但不是教育 因此，它可能通过更直接的生物途径对疾病风险起作用。

Project Summary/Abstract This proposal, “Genome-Wide Analyses of Health and Well-Being Phenotypes,” is a competing renewal for a currently funded, three-year R01. The current R01 is focused on advancing the research of the Social Science Genetic Association Consortium (SSGAC), an interdisciplinary collaboration for conducting large-scale genetic studies of behavioral phenotypes, which is directed by three of the applicants. All of the aims of the R01 were achieved. Its results are being widely used in health and aging-related research in social-science genetics, medical genetics, and epidemiology. This competing renewal proposes to continue the work of the SSGAC. In brief, we propose to: • Conduct genetic-association studies of health and aging-relevant behavioral phenotypes in much larger samples that have now become available. We will complete our study of dietary intake begun under the current R01. In addition, we will undertake large-scale studies of additional phenotypes, including physical activity and self-reported general health. Each of these projects will identify genetic variants associated with the phenotype, analyze biological pathways that underlie these associations, and construct polygenic scores (indexes of many genetic variants) that can have substantial predictive power for the phenotype. • Develop a more powerful method for joint analysis of multiple phenotypes, which will be able to (a) estimate the fraction of genetic variants associated with some set of phenotypes but not others, and (b) identify genetic variants likely to be associated with some set of phenotypes but not others. The results of applying the method will help disentangle different mechanisms by which the genetic variants matter for the phenotypes and will enable the construction of more predictive polygenic scores for each phenotype. • Apply this method to shed light on the shared and unique genetic pathways that influence educational attainment and (late-onset) Alzheimer's disease. This analysis will: (a) identify many new genetic variants associated with Alzheimer's disease; (b) generate more predictive polygenic scores for Alzheimer's disease, facilitating earlier diagnosis and treatment; (c) shed light on hypotheses related to the underlying mechanisms driving the genetic relationship between Alzheimer's disease and educational attainment; and (d) enable biological annotation of genetic variants identified to affect Alzheimer's risk but not educational attainment and which thus may operate through more direct biological pathways on disease risk.