Genome-Wide Meta-Analyses Of Stratified Depression In Generation Scotland And UK Biobank
Lynsey S. Hall,
David M Howard,
Saskia P Hagenaars,
Ana Maria Fernandez-Pujals,
Eleanor M. Wigmore,
Thibaud S. Boutin,
Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium,
Pippa A. Thomson,
Chris S. Haley,
Posted 24 Apr 2017
bioRxiv DOI: 10.1101/130229 (published DOI: 10.1038/s41398-017-0034-1)
Posted 24 Apr 2017
Few replicable genetic associations for Major Depressive Disorder (MDD) have been identified. However recent studies of depression have identified common risk variants by using either a broader phenotype definition in very large samples, or by reducing the phenotypic and ancestral heterogeneity of MDD cases. Here, a range of genetic analyses were applied to data from two large British cohorts, Generation Scotland and UK Biobank, to ascertain whether it is more informative to maximize the sample size by using data from all available cases and controls, or to use a refined subset of the data - stratifying by MDD recurrence or sex. Meta-analysis of GWAS data in males from these two studies yielded one genome-wide significant locus on 3p22.3. Three associated genes within this region (CRTAP, GLB1, and TMPPE) were significantly associated in subsequent gene-based tests. Meta-analyzed MDD, recurrent MDD and female MDD were each genetically correlated with 6 of 200 health-correlated traits, namely neuroticism, depressive symptoms, subjective well-being, MDD, a cross-disorder phenotype and Bipolar Disorder. Meta-analyzed male MDD showed no statistically significant correlations with these traits after correction for multiple testing. Whilst stratified GWAS analysis revealed a genome-wide significant locus for male MDD, the lack of independent replication, the equivalent SNP-based heritability estimates and the consistent pattern of genetic correlation with other health-related traits suggests that phenotypic stratification in currently available sample sizes is currently weakly justified. Based upon existing studies and our findings, the strategy of maximizing sample sizes is likely to provide the greater gain.
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