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Related Experiment Videos

Mortality selection in a genetic sample and implications for association studies.

Benjamin W Domingue1, Daniel W Belsky2, Amal Harrati3

  • 1Graduate School of Education, Stanford University, 520 Galvez Mall, Stanford, CA 94305, USA.

International Journal of Epidemiology
|April 13, 2017
PubMed
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Mortality selection can bias genetic association estimates, particularly gene-by-cohort interactions. Adjusting for this bias using probability weights in genetic epidemiology studies is crucial for accurate results.

Area of Science:

  • Genetic epidemiology
  • Population genetics
  • Biostatistics

Background:

  • Mortality selection, where deceased individuals are unobserved, is a concern in health and social sciences.
  • Its impact on genetic epidemiology has been understudied.
  • This study investigates mortality selection bias in genetic association estimates using US Health and Retirement Study (HRS) data.

Purpose of the Study:

  • To test the hypothesis that mortality selection biases genetic association estimates.
  • To quantify mortality selection effects within the HRS genetic database.
  • To assess the impact of correcting for mortality selection on genetic association analyses.

Main Methods:

  • Compared HRS genetic database participants with non-participants to identify mortality selection.
Keywords:
Mortalitygenetic epidemiologygenotype

Related Experiment Videos

  • Modeled mortality selection based on demographic, health, and social factors to derive probability weights.
  • Analyzed polygenic score associations before and after applying inverse-probability weighting.
  • Examined simple and time-varying (gene-by-cohort interaction) genetic associations.

    • Main Results:

    • Confirmed mortality selection into the HRS genetic database based on observable characteristics.
    • Inverse-probability weighting did not alter simple genetic association estimates.
    • Correction for mortality selection significantly altered gene-by-cohort interaction estimates, often in the opposite direction of observed selection.

    Conclusions:

    • Mortality selection poses a potential bias for gene-by-cohort interaction estimates in genetic association studies.
    • Inverse-probability weighting methods can adjust for mortality selection based on observable factors.
    • The magnitude of mortality selection bias varies depending on the specific genetic trait analyzed.