Genetics of Complex Traits, College of Medicine and Health, University of Exeter, Exeter, UK.
MRC Integrative Epidemiology Unit at the University of Bristol, University of Bristol, Bristol, UK.
Nat Commun. 2021 Feb 9;12(1):886. doi: 10.1038/s41467-021-21073-y.
Large studies such as UK Biobank are increasingly used for GWAS and Mendelian randomization (MR) studies. However, selection into and dropout from studies may bias genetic and phenotypic associations. We examine genetic factors affecting participation in four optional components in up to 451,306 UK Biobank participants. We used GWAS to identify genetic variants associated with participation, MR to estimate effects of phenotypes on participation, and genetic correlations to compare participation bias across different studies. 32 variants were associated with participation in one of the optional components (P < 6 × 10), including loci with links to intelligence and Alzheimer's disease. Genetic correlations demonstrated that participation bias was common across studies. MR showed that longer educational duration, older menarche and taller stature increased participation, whilst higher levels of adiposity, dyslipidaemia, neuroticism, Alzheimer's and schizophrenia reduced participation. Our effect estimates can be used for sensitivity analysis to account for selective participation biases in genetic or non-genetic analyses.
大型研究，如英国生物库，越来越多地被用于全基因组关联分析（GWAS）和孟德尔随机化（MR）研究。然而，研究中的选择和淘汰可能会导致遗传和表型相关性产生偏差。我们研究了影响多达 451306 名英国生物库参与者参加四个可选组成部分的遗传因素。我们使用全基因组关联分析（GWAS）来确定与参与相关的遗传变异，使用孟德尔随机化（MR）来估计表型对参与的影响，并使用遗传相关性来比较不同研究中的参与偏差。有 32 个变异与一个可选组件的参与相关（P<6×10），其中包括与智力和阿尔茨海默病有关的基因座。遗传相关性表明，参与偏差在研究中普遍存在。MR 表明，教育持续时间更长、初潮年龄更大和身高更高会增加参与度，而肥胖程度、血脂异常、神经质、阿尔茨海默病和精神分裂症水平较高则会降低参与度。我们的效应估计值可用于敏感性分析，以在遗传或非遗传分析中考虑选择性参与偏差。
