Refining bias correction in genome-wide association analyses of case-control studies

Abstract

Genome-wide association studies are vulnerable to confounding factors. This study provides evidence-based guidance for minimizing bias associated with genetic relatedness, SNP-specific non-additive allelic interactions, predisposed genotypes among controls, and multi-allelic polymorphism in case-control studies. The analyses demonstrated that genetic similarity within case or control groups introduces experimental bias, whereas genetic relatedness across case-control samples reduces this bias. These findings establish a general framework that can filter genetically related sub-communities or paired samples, whilst preserving maximal statistical power with minimal false-positive rates. Moreover, the skewed odds ratios resulting from predisposed genotypes among controls underscored the importance of age-related filtering to minimize this confounding effect. To ensure accurate genetic estimates, such as polygenic risk scores, the identification of SNP-specific allelic interaction models was also emphasized in case-control studies, contingent on normalization for within-population differences in genotype frequencies. Here, we introduce the Allelic-effect aware Case-Control GWAS (AlleliC-GWAS) tool for identifying SNP-specific allelic effects on binary traits. Finally, we recommend a strategy to accurately capture genetic effects at multi-allelic genomic positions.