Genes, Alzheimer’s Disease and Related Dementias (ADRD) and Economic Outcomes

Recent advances in behavioral genetics have led to the discovery of specific genetic variants that predict complex health outcomes, such as Alzheimer’s disease (AD) (Lambert et al, 2013; Kunkle et al, 2019). We aim to study associations between these variants, summarized as a linear index known as a polygenic score, and economic outcomes. Loosely, a higher polygenic score for AD (henceforth: the AD score) means an individual possesses more of the genetic variants associated with AD. We will also study one particular marker – APOE ε4 – separately given its substantial established links to AD. To examine relationships between genetic measures and individual outcomes, we will use data from the Health and Retirement Study (HRS), which follows a nationally representative sample of U.S. adults above age 50. The data include detailed health and economic variables for older Americans. Moreover, a large portion of the sample has been genotyped, which means the AD score and other genetic measures can be computed for thousands of individuals and paired with detailed lifecycle health and economic information. The overarching goal of the proposed research is to understand how a genetic predisposition for AD affects lifecycle behavior and outcomes; through which particular channels this occurs; and, ultimately, what policies or measures could mitigate the potential detrimental effects of a genetic predisposition for AD. Our study has three specific aims.
• We will estimate descriptive relationships between our genetic measures for AD, the development of Alzheimer’s disease and related dementias (ADRD), and economic behavior and outcomes, including employment, labor income, retirement age and wealth accumulation.
• We will explore mechanisms that explain any empirical associations between genetic variants related to AD and economic outcomes. For example, we find a negative relationship between genetic variants for AD and wealth at retirement. Additional analyses can help determine whether this relationship is due to medical expenditures, poor financial and investment decisions, or other factors.
• We will provide initial evidence on whether policies or institutional features, such as defined benefit pension (DB) schemes or default long-term care insurance (LTCI) provision, could potentially counteract negative consequences of genetic variants related to AD.