Search

Strictly defined major depressive disorder (MDD) phenotypes in the UK Biobank (UKB) can yield more specific genome-wide association study (GWAS) results compared to broad depression. However, as MDD is deeply phenotyped in only a third of the UKB participants, prior GWASs in further subsets of European ancestry (EUR) or "White British" unrelated individuals have been underpowered for variant-level discovery. Here, we apply a pan-UKB, cross-ancestry GWAS pipeline that enhances power and provides deeper insights into the genetic architecture of MDD. We assigned the UKB samples (application ID 30782) to reference ancestry groups using the POP-MaD pipeline (Population grouping by Mahalanobis Distance), identifying ∼458k EUR and 28k non-EUR individuals (across five non-EUR groups). We derived an expanded set of strictly defined MDD cases and controls by combining data from the 2016 and 2022 online mental health questionnaires. For comparison, we also obtained broad depression phenotypes based on prior studies. We then performed ancestry-stratified GWAS meta-analyses as well as joint trans-ancestry mixed-effects GWAS, while using REGENIE to include related samples in both approaches. Follow-up analyses involved conditional and joint association (COJO) analyses to identify independent genomic loci, fine-mapping and colocalization between different depression phenotypes, and gene-level post-GWAS analyses, including transcriptome-wide association studies (TWAS) and summary statistics-based Mendelian Randomization (SMR) using gene expression data from brain tissues. We identified over 62k cases and 118k controls of strictly defined MDD, with significant prevalence differences between ancestry groups. The EUR and cross-ancestry GWAS approaches revealed, in total, 61 independent genomic loci associated with various depression definitions. Notably, 14 of these loci were discovered in cross-ancestry analyses only, highlighting increased power. Sensitivity analyses and LD score regression in EUR showed no evidence of bias due to relatedness or more stringently defined ancestry outliers. Through additional trans-ancestry analyses with down sampled EUR, we also compared the impacts of increased sample size vs increased genetic diversity on genomic discovery for MDD. Analyses of strictly defined MDD phenotypes revealed 13 genomic risk loci, compared to only one locus previously reported in the unrelated, White-British subset. Of these, eleven loci were not significantly associated with broad depression despite the latter's much larger sample, suggesting higher specificity for MDD. Further, colocalization analyses indicated little overlap of causal SNPs underlying MDD and broad depression. Follow-up TWAS and SMR analyses revealed genes and brain tissues specific to MDD (e.g., LRRC37A4P in the hypothalamus, anterior cingulate cortex, and nucleus accumbens) vs those shared with broad depression (e.g., DFNA5 in the caudate nucleus), providing novel MDD-specific biological insights. Finally, for both MDD and broad depression, we compared the prediction accuracy of EUR and cross-ancestry polygenic scores in the All of Us Research Program. Inclusive, pan-UKB cross-ancestry analyses of depression maximize the genomic insights from extant data, augmenting the power for variant-level associations and post-GWAS analyses of deeply phenotyped MDD. These analyses help elucidate MDD-specific biological pathways and potential therapeutic targets. Nothing to disclose. [ABSTRACT FROM AUTHOR]

Epigenome-wide association studies (EWAS) aim to identify differentially methylated loci associated with complex traits and disorders. EWAS of cigarette smoking shows some of the most widespread DNA methylation (DNAm) associations in blood. However, traditional EWAS cannot differentiate between causation and confounding, leading to ambiguity in etiological interpretations. Here, we apply an integrated approach combining Mendelian Randomization and twin-based Direction-of-Causation analyses (MR-DoC) to examine causality underlying smoking-associated blood DNAm changes in the Netherlands Twin Register (N=2577). Evidence across models suggests that current smoking's causal effects on DNAm likely drive many of the previous EWAS findings, implicating functional pathways relevant to several adverse health outcomes of smoking, including hemopoiesis, cell- and neuro-development, and immune regulation. Additionally, we find evidence of potential reverse causal influences at some DNAm sites, with 17 of these sites enriched for gene regulatory functional elements in the brain. The top three sites with evidence of DNAm's effects on smoking annotate to genes involved in G protein-coupled receptor signaling ( GNG7 , RGS3 ) and innate immune response ( SLC15A4 ), elucidating potential biological risk factors for smoking. This study highlights the utility of integrating genotypic and DNAm measures in twin cohorts to clarify the causal relationships between health behaviors and blood DNAm., Competing Interests: Conflicts of Interest Nothing to declare.