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genome-wide association studies of Major Depressive Disorder (MDD) have identified few significant associations. Testing the aggregation of genetic variants in particular biological pathways may be more powerful. Regional heritability analysis can be used to detect genomic regions contributing to disease risk.
we integrated pathway analysis and multi-level regional heritability analyses in a pipeline designed to identify MDD-associated pathways. The pipeline was applied to two independent GWAS studies (GS:SFHS, N=6,455) and PGC:MDD (N=18,755). A polygenic risk score (PRS) composed of SNPs from the pathway most consistently associated with MDD was created and its accuracy to predict MDD using AUC, logistic regression and linear mixed model (LMM) analysis was tested.
In GS:SFHS, four pathways were significantly associated with MDD, and two of these explained a significant amount of pathway-level regional heritability. In PGC:MDD, one pathway was significantly associated with MDD. Pathway-level regional heritability was significant in this pathway in one subset of PGC:MDD. For both samples the regional heritabilities were further localized to the gene and sub-region levels. The NETRIN1 signaling pathway showed the most consistent association with MDD across the two samples. PRSs from this pathway showed competitive predictive accuracy when compared with the whole-genome PRSs when using AUC statistics, logistic regression and LMM.
these post-GWAS analyses highlight the value of combining multiple methods on multiple GWAS data for the identification of risk pathways for MDD. The NETRIN1 signaling pathway is identified as a candidate pathway for MDD, and should be explored in further large population studies.
1/09/13 → 31/08/19