Academic Appointments


All Publications

  • To the freezers! Stored biospecimens from human randomized trials are an important new direction for studies of biological aging. The journals of gerontology. Series A, Biological sciences and medical sciences Belsky, D. W., Harrati, A. 2019; 74 (1): 89–90

    View details for PubMedID 30561553

  • Predictors and implications of accelerated cognitive aging BIODEMOGRAPHY AND SOCIAL BIOLOGY Levine, M. E., Harrati, A., Crimmins, E. M. 2018; 64 (2): 83–101
  • Mortality selection in a genetic sample and implications for association studies. International journal of epidemiology Domingue, B. W., Belsky, D. W., Harrati, A., Conley, D., Weir, D. R., Boardman, J. D. 2017


    Mortality selection occurs when a non-random subset of a population of interest has died before data collection and is unobserved in the data. Mortality selection is of general concern in the social and health sciences, but has received little attention in genetic epidemiology. We tested the hypothesis that mortality selection may bias genetic association estimates, using data from the US-based Health and Retirement Study (HRS).We tested mortality selection into the HRS genetic database by comparing HRS respondents who survive until genetic data collection in 2006 with those who do not. We next modelled mortality selection on demographic, health and social characteristics to calculate mortality selection probability weights. We analysed polygenic score associations with several traits before and after applying inverse-probability weighting to account for mortality selection. We tested simple associations and time-varying genetic associations (i.e. gene-by-cohort interactions).We observed mortality selection into the HRS genetic database on demographic, health and social characteristics. Correction for mortality selection using inverse probability weighting methods did not change simple association estimates. However, using these methods did change estimates of gene-by-cohort interaction effects. Correction for mortality selection changed gene-by-cohort interaction estimates in the opposite direction from increased mortality selection based on analysis of HRS respondents surviving through 2012.Mortality selection may bias estimates of gene-by-cohort interaction effects. Analyses of HRS data can adjust for mortality selection associated with observables by including probability weights. Mortality selection is a potential confounder of genetic association studies, but the magnitude of confounding varies by trait.

    View details for DOI 10.1093/ije/dyx041

    View details for PubMedID 28402496

  • Characterizing the Genetic Influences on Risk Aversion BIODEMOGRAPHY AND SOCIAL BIOLOGY Harrati, A. 2014; 60 (2): 185–98


    Risk aversion has long been cited as an important factor in retirement decisions, investment behavior, and health. Some of the heterogeneity in individual risk tolerance is well understood, reflecting age gradients, wealth gradients, and similar effects, but much remains unexplained. This study explores genetic contributions to heterogeneity in risk aversion among older Americans. Using over 2 million genetic markers per individual from the U.S. Health and Retirement Study, I report results from a genome-wide association study (GWAS) on risk preferences using a sample of 10,455 adults. None of the single-nucleotide polymorphisms (SNPs) are found to be statistically significant determinants of risk preferences at levels stricter than 5 × 10(-8). These results suggest that risk aversion is a complex trait that is highly polygenic. The analysis leads to upper bounds on the number of genetic effects that could exceed certain thresholds of significance and still remain undetected at the current sample size. The findings suggest that the known heritability in risk aversion is likely to be driven by large numbers of genetic variants, each with a small effect size.

    View details for DOI 10.1080/19485565.2014.951986

    View details for Web of Science ID 000343911700006

    View details for PubMedID 25343366