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Title: The association between lower educational attainment and depression owing to shared genetic effects? Results in ~25 000 subjects.
Authors: Peyrot WJ,  Lee SH,  Milaneschi Y,  Abdellaoui A,  Byrne EM,  Esko T,  de Geus EJ,  Hemani G,  Hottenga JJ,  Kloiber S,  Levinson DF,  Lucae S,  Major Depressive Disorder Working Group of the Psychiatric GWAS Consortium (Corporate Collaborator),  Martin NG,  Medland SE,  Metspalu A,  Milani L,  Noethen MM,  Potash JB,  Rietschel M,  Rietveld CA,  Ripke S,  Shi J,  Social Science Genetic Association Consortium (Corporate Collaborator),  Willemsen G,  Zhu Z,  Boomsma DI,  Wray NR,  Penninx BW,  Major Depressive Disorder Working Group of the Psychiatric GWAS Consortium Corporate Collaborator,  Social Science Genetic Association Consortium Corporate Collaborator
Journal: Mol Psychiatry
Date: 2015 Jun
Branches: BB
PubMed ID: 25917368
PMC ID: not available
Abstract: An association between lower educational attainment (EA) and an increased risk for depression has been confirmed in various western countries. This study examines whether pleiotropic genetic effects contribute to this association. Therefore, data were analyzed from a total of 9662 major depressive disorder (MDD) cases and 14 949 controls (with no lifetime MDD diagnosis) from the Psychiatric Genomics Consortium with additional Dutch and Estonian data. The association of EA and MDD was assessed with logistic regression in 15 138 individuals indicating a significantly negative association in our sample with an odds ratio for MDD 0.78 (0.75-0.82) per standard deviation increase in EA. With data of 884 105 autosomal common single-nucleotide polymorphisms (SNPs), three methods were applied to test for pleiotropy between MDD and EA: (i) genetic profile risk scores (GPRS) derived from training data for EA (independent meta-analysis on ~120 000 subjects) and MDD (using a 10-fold leave-one-out procedure in the current sample), (ii) bivariate genomic-relationship-matrix restricted maximum likelihood (GREML) and (iii) SNP effect concordance analysis (SECA). With these methods, we found (i) that the EA-GPRS did not predict MDD status, and MDD-GPRS did not predict EA, (ii) a weak negative genetic correlation with bivariate GREML analyses, but this correlation was not consistently significant, (iii) no evidence for concordance of MDD and EA SNP effects with SECA analysis. To conclude, our study confirms an association of lower EA and MDD risk, but this association was not because of measurable pleiotropic genetic effects, which suggests that environmental factors could be involved, for example, socioeconomic status.