Genome-Wide Analyses of Health and Well-Being Phenotypes

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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.