title: Mortality selection in a genetic sample and implications for association studies.
abstract: 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.
authors.affiliation: Graduate School of Education, Stanford University, 520 Galvez Mall, Stanford, CA 94305, USA.
authors.forename: Benjamin W
authors.initials: BW
authors.lastname: Domingue
authors.affiliation: Department of Medicine, Duke University School of Medicine; Duke University Population Research Institute, Duke University, 2020 W. Main St., Durham NC, 27705.
authors.forename: Daniel W
authors.initials: DW
authors.lastname: Belsky
authors.affiliation: School of Medicine, Stanford University, 1070 Arastradero Rd Palo Alto, CA 94304.
authors.forename: Amal
authors.initials: A
authors.lastname: Harrati
authors.affiliation: Office of Population Research, Department of Sociology, Princeton University, 153 Wallace Hall Princeton, NJ 08544.
authors.forename: Dalton
authors.initials: D
authors.lastname: Conley
authors.affiliation: Population Studies Center, Survey Research Center, University of Michigan, 426 Thompson St, Ann Arbor, MI 48104.
authors.forename: David R
authors.initials: DR
authors.lastname: Weir
authors.affiliation: Institute of Behavioral Science, Department of Sociology, University of Colorado Boulder, 483 UCB Boulder, CO 80309-0483.
authors.forename: Jason D
authors.initials: JD
authors.lastname: Boardman
mesh_terms: D017677:Age Distribution; D000368:Aged; D000369:Aged, 80 and over; D015982:Bias; D005260:Female; D005838:Genotype; D006801:Humans; D053208:Kaplan-Meier Estimate; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D017720:Molecular Epidemiology / Q000379:methods*; D009026:Mortality; D017678:Sex Distribution; D014481:United States
keywords: Mortality; genetic epidemiology; genotype