Abstract

Background: Proper adjustment for population substructure is essential in epigenome-wide association studies (EWAS), particularly in cohorts with diverse ancestries. EPISTRUCTURE offers a genotype-free approach to ancestry inference, originally developed using a European reference population from the Cooperative Health Research in the Region of Augsburg (KORA) study. However, its effectiveness in genetically diverse, multi-ancestry cohorts remains insufficiently evaluated. Methods: For EWAS using cord-blood samples from the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study, we systematically assessed the ancestry adjustment performance of EPISTRUCTURE principal components (PCs) derived from the widely used KORA-based reference set versus new reference sets generated from genotyping data of the multi-ancestry HAPO cohort. HAPO-based reference sets were defined by varying SNP–CpG $R^2$ thresholds (e.g., RS30: $R^2 > 0.3$) to identify ancestry-informative CpGs. We applied these reference sets for population substructure adjustment in EWAS of three newborn adiposity traits: birthweight, cord C-peptide, and sum of skinfolds, to evaluate their impact on association detection and biological interpretation. Results: Compared to the KORA reference, the HAPO RS30 reference consistently produced lower genomic inflation and identified more biologically relevant associations for birthweight and cord blood C-peptide in EWAS of HAPO cord blood samples (n = 3,116). Pathway enrichment analyses revealed strong immune and metabolic signals, including pathways uniquely captured by EWAS when using the HAPO-derived reference for ancestry adjustment. Trait enrichment using the EWAS Catalog further confirmed associations with fetal growth, maternal metabolic traits, and glucose regulation. Conclusion: Our findings demonstrate that reference sets derived from multi-ancestry cohorts like HAPO better capture underlying population substructure and improve ancestry adjustment in diverse EWAS settings.