The Health and Retirement Study (HRS) has the potential to become a critical resource in social-science genetics in general, and the behavioral genomics of aging in particular, due to its relatively large sample size, its rich longitudinal behavioral measures, and the availability of dense genomic data for approximately 13,000 older Americans, with data on 7,000 more on the way. The proposed research will use the HRS's rich phenotypic, genetic, and environmental data to pursue two complementary strategies. One is to incorporate HRS data into large consortium meta-analyses of behavioral phenotypes conducted under the auspices of the Social Science Genetic Association Consortium (SSGAC), which the applicants organize. The second strategy is to use the HRS data to test specific hypotheses arising from the consortium's findings and to shed light on the genetic architecture-i.e., the joint distribution of genetic effect sizes and allele frequencies-of the rich set of behavioral phenotypes measured in the HRS. Our general aim is to use the phenotypic, genetic, and environmental data from the HRS to significantly advance understanding of behavioral genomics in general, and of the economic behavior, health, and well- being of older Americans in particular. Among the goals of our proposal are: (a) discoveries of specific genetic polymorphisms that are associated with important behavioral outcomes, psychological characteristics, and economic preferences; (b) analysis of biological pathways that underlie these associations; (c) development of polygenic scores (indexes of many polymorphisms) that, when constructed with weights estimated in large samples, will have substantial predictive power for behavioral phenotypes; (d) identification of behavioral mechanisms (i.e., endophenotypes) that mediate associations with specific polymorphisms and polygenic scores; (e) analysis of the genetic architecture of a range of phenotypes measured in the HRS; and (f) examination of gene-environment interactions.

Public Health Relevance

The ever-increasing availability of genomic data has the potential to provide individuals with much more information about themselves that could be used to make better health and financial decisions. This project aims to use the Health and Retirement Study, along with other datasets, to shed light on the potential for genomic data to be informative about economic behaviors and outcomes.

Agency
National Institute of Health (NIH)
Institute
National Institute on Aging (NIA)
Type
Research Project (R01)
Project #
3R01AG042568-03S1
Application #
9525117
Study Section
Social Sciences and Population Studies A Study Section (SSPA)
Program Officer
King, Jonathan W
Project Start
2015-09-01
Project End
2018-05-31
Budget Start
2017-09-15
Budget End
2018-05-31
Support Year
3
Fiscal Year
2017
Total Cost
Indirect Cost
Name
University of Southern California
Department
Social Sciences
Type
Schools of Arts and Sciences
DUNS #
072933393
City
Los Angeles
State
CA
Country
United States
Zip Code
90033
Xue, Angli; Wu, Yang; Zhu, Zhihong et al. (2018) Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes. Nat Commun 9:2941
Barcellos, Silvia H; Carvalho, Leandro S; Turley, Patrick (2018) Education can reduce health differences related to genetic risk of obesity. Proc Natl Acad Sci U S A 115:E9765-E9772
Qi, Ting; Wu, Yang; Zeng, Jian et al. (2018) Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood. Nat Commun 9:2282
Turley, Patrick; Walters, Raymond K; Maghzian, Omeed et al. (2018) Multi-trait analysis of genome-wide association summary statistics using MTAG. Nat Genet 50:229-237
Karlsson Linnér, R; Marioni, R E; Rietveld, C A et al. (2017) An epigenome-wide association study meta-analysis of educational attainment. Mol Psychiatry 22:1680-1690
Yengo, Loic; Zhu, Zhihong; Wray, Naomi R et al. (2017) Detection and quantification of inbreeding depression for complex traits from SNP data. Proc Natl Acad Sci U S A 114:8602-8607
Barban, Nicola (see original citation for additional authors) (2016) Genome-wide analysis identifies 12 loci influencing human reproductive behavior. Nat Genet 48:1462-1472
Okbay, Aysu (see original citation for additional authors) (2016) Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533:539-42
Okbay, Aysu; Baselmans, Bart M L; De Neve, Jan-Emmanuel et al. (2016) Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses. Nat Genet 48:624-33
Bayarri, M J; Benjamin, Daniel J; Berger, James O et al. (2016) Rejection odds and rejection ratios: A proposal for statistical practice in testing hypotheses. J Math Psychol 72:90-103

Showing the most recent 10 out of 11 publications