How health-relevant outcomes are influenced by genetics.

We will use the HRS data to pursue two complementary strategies. One is the discovery of particular genetic polymorphisms associated with social-science outcomes. Because the effect of an individual genetic polymorphism on the outcome is likely to be very small, the HRS sample is too small, taken alone, to be used to discover new associations. Hence, we will pursue this strategy with HRS data in conjunction with other datasets that we have organized in the Social Science Genetic Association Consortium (SSGAC; www.thessgac.org).
Our second strategy focuses on exploiting the uniquely rich social-science data in the HRS. We will conduct analyses that will shed light on the genetic architecture of a range of social-science outcomes. We will apply statistical methods that use the information contained in the dense SNP data taken as a whole and are thus well-powered in a sample size such as that of the HRS.
Our specific aims are:
1. To incorporate data from the HRS into ongoing meta-GWAS efforts from the SSGAC for a range of social-science outcomes, such as educational attainment, and personality.
2. To continue to include HRS in the future releases of the Polygenic Index (PGI) Repository. PGIs (aka polygenic scores) are summaries of a person's genetic predisposition to a particular trait. HRS was included in the first release of the Repository, for which we created PGIs for 47 phenotypes in 11 datasets, which were returned to the datasets to be shared with users according to the datasets’ own data sharing procedures. We will regularly update the existing PGIs and add new phenotypes as larger GWAS and better methodologies become available. Details on the Repository can be found in Becker et al. (2021, Resource profile and user guide of the Polygenic Index Repository. Nat. Hum. Behav.).
3. To use the HRS genotype data to conduct polygenic prediction analyses for a range of social-science traits. Besides the direct interest in assessing the degree of predictive power in PGIs, we will examine how these PGIs interact with environmental factors to influence life outcomes.
4. To estimate heritability and genetic correlations for social science traits in an older population.

We will use HRS data to explore the genetic architecture of social-science outcomes. To do so, we will either use HRS data together with other datasets to identify specific genetic variants associated with these outcomes, or analyze the aggregate effect of all genetic variants in HRS alone using heritability analyses and polygenic indexes (PGIs). PGIs are summaries of a person's known genetic predisposition to a particular trait. We will use PGIs to examine the pathways underlying the relationship between genetic variants and outcomes of interest, including analyses of how genes and environment interact. We will also include HRS in future releases of the PGI Repository, an initiative that makes PGIs for a wide range of traits available in a number of datasets that may be useful to social scientists (https://www.thessgac.org/pgi-repository ). HRS was included in the first release of the Repository, and we wish to continue to update the HRS PGIs and add PGIs for new phenotypes as more data or better methodologies become available.