Trans-ancestry Genome-Wide Analyses in UK Biobank Yield Novel Risk Loci for Major Depression

Most genome-wide association studies (GWASs) of depression focus on broad, heterogeneous outcomes, limiting the discovery of genomic risk loci specific to major depressive disorder (MDD). Previous UK Biobank (UKB) studies had limited ability to pinpoint MDD-associated loci due to a smaller sample with strictly defined MDD outcomes and further exclusion of many participants based on ancestry or relatedness, significantly underutilizing this resources potential for elucidating the genetic architecture of MDD. Here, we present novel genomic insights into MDD by fully utilizing existing UKB data through (1) a trans-ancestry GWAS pipeline using two complementary approaches controlling for population structure and relatedness and (2) an increased sample with MDD symptom-level data across two mental health assessments. We identified strict MDD outcomes among 211,535 participants, representing a 38% increase in eligible participants from prior studies with only one assessment. Ancestrally inclusive analyses yielded 61 genomic risk loci across depression phenotypes, compared to 47 in the analyses restricted to participants genetically similar to European ancestry. Fourteen of these loci, including five novel, were associated with strict MDD phenotypes, whereas only one locus has been previously reported in UKB. MDD-associated genomic loci and predicted gene expression levels showed little overlap with broad depression, indicating higher specificity. Notably, polygenic scores based on these results were significantly associated with depression diagnoses across ancestry groups in the All of Us Research Program, highlighting the shared genetic architecture across populations. While the trans-ancestry analyses, which included non-European participants, increased the number of associated loci, the discovery of non-European ancestry-specific loci was limited, underscoring the need for larger, globally representative studies of MDD. Importantly, beyond these results, our GWAS pipeline will facilitate inclusive analyses of other traits and disorders, helping improve statistical power, representation, and generalizability in genomic studies.