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ABSTRACT: Background
People who opt to participate in scientific studies tend to be healthier, wealthier and more educated than the broader population. Although selection bias does not always pose a problem for analysing the relationships between exposures and diseases or other outcomes, it can lead to biased effect size estimates. Biased estimates may weaken the utility of genetic findings because the goal is often to make inferences in a new sample (such as in polygenic risk score analysis).Methods
We used data from UK Biobank, Generation Scotland and Partners Biobank and conducted phenotypic and genome-wide association analyses on two phenotypes that reflected mental health data availability: (i) whether participants were contactable by e-mail for follow-up; and (ii) whether participants responded to follow-up surveys of mental health.Results
In UK Biobank, we identified nine genetic loci associated (P <5 × 10-8) with e-mail contact and 25 loci associated with mental health survey completion. Both phenotypes were positively genetically correlated with higher educational attainment and better health and negatively genetically correlated with psychological distress and schizophrenia. One single nucleotide polymorphism association replicated along with the overall direction of effect of all association results.Conclusions
Re-contact availability and follow-up participation can act as further genetic filters for data on mental health phenotypes.
SUBMITTER: Adams MJ
PROVIDER: S-EPMC7266553 | biostudies-literature | 2020 Apr
REPOSITORIES: biostudies-literature
Adams Mark J MJ Hill W David WD Howard David M DM Dashti Hassan S HS Davis Katrina A S KAS Campbell Archie A Clarke Toni-Kim TK Deary Ian J IJ Hayward Caroline C Porteous David D Hotopf Matthew M McIntosh Andrew M AM
International journal of epidemiology 20200401 2
<h4>Background</h4>People who opt to participate in scientific studies tend to be healthier, wealthier and more educated than the broader population. Although selection bias does not always pose a problem for analysing the relationships between exposures and diseases or other outcomes, it can lead to biased effect size estimates. Biased estimates may weaken the utility of genetic findings because the goal is often to make inferences in a new sample (such as in polygenic risk score analysis).<h4> ...[more]