Quantifying the extent to which index event biases influence large genetic association studies

Abstract

As genetic association studies increase in size to 100,000s of individuals, subtle biases may influence conclusions. One possible bias is \"index event bias\" (IEB), also called \"collider bias\", caused by the stratification by, or enrichment for, disease status when testing associations between gene variants and a disease-associated trait. We first provided a statistical framework for quantifying IEB then identified real examples of IEB in a range of study and analytical designs. We observed evidence of biased associations for some disease alleles and genetic risk scores, even in population-based studies. For example, a genetic risk score consisting of type 2 diabetes variants was associated with lower BMI in 113,203 type 2 diabetes controls from the population based UK Biobank study (-0.010 SDs BMI per allele, P=5E-4), entirely driven by IEB. Three of 11 individual type 2 diabetes risk alleles, and 10 of 25 hypertension alleles were associated with lower BMI at p<0.05 in UK Biobank when analyzing disease free individuals only, of which six hypertension alleles remained associated at p<0.05 after correction for IEB. Our analysis suggested that the associations between CCND2 and TCF7L2 diabetes risk alleles and BMI could (at least partially) be explained by IEB. Variants remaining associated after correction may be pleiotropic and include those in CYP17A1 (allele associated with hypertension risk and lower BMI). In conclusion, IEB may result in false positive or negative associations in very large studies stratified or strongly enriched for/against disease cases.