|Title||Mortality selection in a genetic sample and implications for association studies.|
|Publication Type||Journal Article|
|Year of Publication||2017|
|Authors||Domingue, BW, Belsky, DW, Harrati, A, Dalton C. Conley, Weir, DR, Boardman, JD|
|Journal||Int J Epidemiol|
|Date Published||2017 08 01|
|Keywords||Age Distribution, Aged, Aged, 80 and over, Bias, Female, Genotype, Humans, Kaplan-Meier Estimate, Male, Middle Aged, Models, Theoretical, Molecular Epidemiology, Mortality, Sex Distribution, United States|
Background: 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).
Methods: 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).
Results: 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.
Conclusions: 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.
|User Guide Notes|
|Alternate Journal||Int J Epidemiol|
|PubMed Central ID||PMC5837559|
|Grant List||RC4 AG039029 / AG / NIA NIH HHS / United States |
DP5 OD009162 / OD / NIH HHS / United States
R01 AG026291 / AG / NIA NIH HHS / United States
U01 AG009740 / AG / NIA NIH HHS / United States
R21 HD078031 / HD / NICHD NIH HHS / United States
P2C HD047879 / HD / NICHD NIH HHS / United States
P30 AG028716 / AG / NIA NIH HHS / United States
RC2 AG036495 / AG / NIA NIH HHS / United States
P2C HD066613 / HD / NICHD NIH HHS / United States
P30 AG034424 / AG / NIA NIH HHS / United States
R01 AG032282 / AG / NIA NIH HHS / United States