Mortality selection in a genetic sample and implications for association studies

Abstract

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.

Keywords: Mortality; genetic epidemiology; genotype.

Figure 1

Description of two-step selection process into sample of genotyped respondents (inset). Main figure shows Kaplan-Meier survival curves for those who died before 2006 (type 1), those who survived through 2006 but were not genotyped (type 2) and those who were genotyped (type 3).

Figure 2

Kaplan-Meier survival curves for genotyped and non-genotyped HRS respondents, split by race and sex.

Figure 3

Phenotypic means [with LOcally WEighted Scatter-plot Smoother (LOESS) fits] as a function of respondent birth year in genotyped and non-genotyped sample.

Figure 4

Mean polygenic score for non-Hispanic Whites split by sex as a function of birth year.

Figure 5

Birth-cohort variation in effect sizes for polygenic scores in models with enhanced mortality selection, naïve models and models correcting for mortality selection using inverse probability weighting. Effect sizes are estimated from Equation 4 in the Supplementary data (available at IJE online)/ Analyses were conducted for non-Hispanic White HRS respondents born 1919–55. Slope plots show changes in polygenic score effects for fitted values one SD above PGS mean minus fitted value one SD below PGS mean (y-axis) across birth cohorts (x-axis, showing year of birth). Barplots show estimated interaction coefficients for all scenarios. Data for women are plotted on the left side of the figure. Data for men are plotted on the right side of the figure.