@article {10.1093/geronb/gbz156, title = {Mortality and Obesity among US Older Adults: The Role of Polygenic Risk}, journal = {The Journals of Gerontology, Series B}, volume = {76}, year = {2021}, pages = {343-347}, abstract = {To examine the relationship between obesity and mortality as a function of polygenic risk for obesity among older US adults.Using data from the 1994-2014 Health and Retirement Study in conjunction with genome-wide data, we evaluated the risk of mortality as a function of obesity classification, an individual{\textquoteright}s polygenic risk score (PGS) for obesity, and their interaction, stratified by sex. We conducted our analyses using cox proportional hazard models.Among those with an average PGS for obesity (8,143 [68.8 \%]), obese I (HR = 0.7}, keywords = {Mortality, Obesity, polygenic risk, Polygenic risk score}, issn = {1079-5014}, doi = {10.1093/geronb/gbz156}, author = {Vinneau, Justin M and Huibregtse, Brooke M and Laidley, Thomas M and Joshua A Goode and Jason D Boardman} } @article {9049, title = {Mortality selection in a genetic sample and implications for association studies.}, journal = {International Journal of Epidemiolpgy}, volume = {46}, year = {2017}, month = {08/2017}, 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 = {Genetics, GWAS, Mortality}, issn = {1464-3685}, doi = {10.1093/ije/dyx041}, author = {Benjamin W Domingue and Daniel W. Belsky and Harrati, Amal and Dalton C Conley and David R Weir and Jason D Boardman} }