BACKGROUND: Genome-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.
METHODS: We 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.
RESULTS: 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 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.
CONCLUSIONS: 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.
Bibliographical noteThis work was supported by the Wellcome Trust through a Strategic Award Reference No. 104036/Z/14/Z, the Dr. Mortimer and Theresa Sackler Foundation (T-KC and AMM), the Medical Research Council (MRC) to the Human Genetics Unit (PN and CSH), and the Biotechnology and Biological Sciences Research Council Grant No. BB/J004235/1 (PN and CSH). The Chief Scientist Office of the Scottish Government and the Scottish Funding Council provided core support for Generation Scotland (GS). GS: The Scottish Family Health Study (SFHS) was funded by a grant from the Scottish Government Health Department, Chief Scientist Office, No. CZD/16/6. This work was also supported by National Institutes of Health Grant No. UO1MH105630.
We thank the families who took part in GS:SFHS, the general practitioners, and Scottish School of Primary Care for their help in recruiting them, and the whole GS team, which includes academic researchers, clinic staff, laboratory technicians, clerical workers, information technology staff, statisticians, and research managers. YZ thanks Mr. Ian White for the suggestion for analysis of polygenic score.
AMF-P, LSH, BHS, LJH, SP, CH, and NRW report no biomedical financial interests or potential conflicts of interest. YZ received support from China Scholarship Council. PN and CSH received support from the MRC. T-KC and AMM received financial support for this work from the Dr. Mortimer and Theresa Sackler Foundation. PAT, IJD, DJP, and AMM are members of The University of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology, part of the cross council Lifelong Health and Wellbeing Initiative (MR/K026992/1). DJM is an NRS (National Health Service Research Scotland) Fellow, funded by the Chief Scientist Office. AMM previously received grant support from Pfizer, Lilly, and Janssen; those studies are not connected to the present investigation.
- Pathway analysis
- Polygenic risk score
- Regional heritability
- Journal Article