Abstract
BackgroundGenome-wide association studies (GWASs) 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 that contribute to disease risk. MethodsWe integrated pathway analysis and multilevel regional heritability analyses in a pipeline designed to identify MDD-associated pathways. The pipeline was applied to two independent GWAS samples [Generation Scotland: The Scottish Family Health Study (GS:SFHS, N = 6455) and Psychiatric Genomics Consortium (PGC:MDD) (N = 18,759)]. A polygenic risk score (PRS) composed of single nucleotide polymorphisms from the pathway most consistently associated with MDD was created, and its accuracy to predict MDD, using area under the curve, logistic regression, and linear mixed model analyses, was tested. ResultsIn 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 subregion levels. The NETRIN1 signaling pathway showed the most consistent association with MDD across the two samples. PRSs from this pathway showed competitive predictive accuracy compared with the whole-genome PRSs when using area under the curve statistics, logistic regression, and linear mixed model. ConclusionsThese 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.
Highlights
Genome-wide association studies (GWASs) of major depressive disorder (MDD) have identified few significant associations
Among the four pathways that were significant in pathway analysis, two yielded significant p values based on the likelihood ratio test (LRT) for pathway-level regional heritability in MDD, with the highest regional heritability estimated in the NETRIN1 signaling pathway [pathway-level regional heritability attributable to the pathway SNPs (h2RÞ 5 0.014, SE 5 0.009, plrt_FDR 5 .019] (Table 2)
Disease-associated pathways have been assumed to have the following features: 1) the genetic variants in them are shown to be associated with disease in association tests, 2) the genetic variants in them explain a significant proportion of phenotypic variance, and 3) the genetic proxies such as polygenic risk score (PRS) derived from them have valuable predictive power for the disease
Summary
Genome-wide association studies (GWASs) of major depressive disorder (MDD) have identified few significant associations. METHODS: We integrated pathway analysis and multilevel regional heritability analyses in a pipeline designed to identify MDD-associated pathways. A polygenic risk score (PRS) composed of single nucleotide polymorphisms from the pathway most consistently associated with MDD was created, and its accuracy to predict MDD, using area under the curve, logistic regression, and linear mixed model analyses, was tested. RESULTS: In GS:SFHS, four pathways were significantly associated with MDD, and two of these explained a significant amount of pathway-level regional heritability. 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 subregion levels. The NETRIN1 signaling pathway showed the most consistent association with MDD across the two samples. The NETRIN1 signaling pathway is identified as a candidate pathway for MDD and should be explored in further large population studies
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