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.

The social and genetic inheritance of educational attainment: Genes, parental education, and educational expansion

“…Third, HRS retroactively obtained the parental education, thus increasing the chances of respondents forgetting or subjectively modifying their parents' educational attainment, potentially resulting in recall bias. Another age-related issue is the mortality or morbidity selection bias of the HRS sample (Domingue et al 2017). Individuals who have a better polygenic score for education might live longer.…”

Detecting the Effects of Early-Life Exposures: Why Fecundity Matters

“…Our argument builds on a long history of research on mortality selection. Scarring-selection models are standard tools in population science (Vaupel and Yashin 1985;Bozzoli et al 2009;Wrigley-Field 2014;Domingue et al 2017). Here, we argue that these models are also applicable for the period before birth (Bruckner and Catalano 2018;Liew et al 2015).…”

Genetic Nature or Genetic Nurture? Quantifying Bias in Analyses Using Polygenic Scores

“…It is not clear that the methodology used to identify the underlying genetics of proximal, biological phenotypes can be used without side effect to interrogate the genetics of complex, socially contextualized phenotypes. Especially in the case of traits like depression and educational attainment, it is critical that GWAS results be interpreted cautiously (Martschenko, Trejo, and Domingue 2019). While PGSs have been shown to predict complex phenotypes, the relationship between an individual's PGS captures a broad range of information and associations with downstream outcomes and therefore cannot be readily interpreted as the causal effect of genes.…”

“…In the short term, PGSs may be used as control variables in studies of environmental effects (Rietveld et al 2013), used in gene-environment interaction studies to probe whether genetic effects are environmentally contingent (Trejo et al 2018;Barcellos, Carvalho, and Turley 2018), and used to better understand how genetic factors influence developmental processes (Belsky et al 2016;Belsky et al 2013). In the long run, PGSs might be used to identify those who would benefit most from early medical or educational interventions (say, for a developmental disorder like dyslexia (Martschenko, Trejo, and Domingue 2019)).…”