Shared genetic etiology underlying Alzheimer's disease and major depressive disorder: Genetics/genetic factors of Alzheimer's disease. (7th December 2020) - Chiba‐Falek, Ornit; Lutz, Michael W; Sprague, Daniel; Barrera, Julio
Abstract: Background: Patients with late‐onset Alzheimer's disease (LOAD) frequently manifest comorbid neuropsychiatric symptoms with depression and anxiety being most frequent, and individuals with Major depressive disorder (MDD) have an increased prevalence of LOAD. This suggests shared etiologies and intersecting pathways between LOAD and MDD. Nonetheless, the genetic pleiotropic between LOAD and neuropsychiatric disorders, including MDD, has been understudied. Methods: We performed pleiotropy analyses by applying a robust new statistical strategy that integrates GWAS summary statistics results for two traits, LOAD and MDD. We used LOAD and MDD GWAS datasets from the International Genomics of Alzheimer's Project (IGAP) and the Psychiatric Genomics Consortium (PGC), respectively. We complement the study with functional genomic and bioinformatic analyses including, pathway, gene expression and eQTL analyses. Results: We found a moderate enrichment for SNPs associated with LOAD across increasingly stringent levels of significance with the MDD GWAS association (LOAD|MDD), of maximum 4 and 8‐folds including and excluding the APOE ‐region, respectively. Association analysis excluding the APOE ‐region identified numerous SNPs corresponding to 40 genes, 9 of which are known LOAD‐risk loci primarily on chromosome 11 regions that contain the SPI1 gene and MS4A genes‐cluster, and others were novel pleiotropic risk‐loci for LOAD conditional with MDD. The most significant associated‐SNPs on chromosome11 overlapped with eQTLs found in whole‐blood and monocytes, suggesting functional role in gene regulation. The reverse conditional association analysis (MDD|LOAD) showed a moderate level, ∼7‐fold, of polygenic overlap, however, no SNP showed significant association. Pathway analyses replicated previously reported LOAD biological pathways related to immune response and regulation of endocytosis. Conclusion: Here we provide insights into the overlapping genetic signatures underpinning the common phenotypic manifestations and inter‐relationship between LOAD and MDD. The outcomes of this study have several important implications to LOAD genetic architecture including, demonstration of genetic pleiotropy effects between LOAD and MDD, identification of new LOAD loci, validation of LOAD pathways. Furthermore, our data proposes genetic interpretation for the heterogeneity of depression in LOAD. These findings are impactful for the development of actionable targets for novel therapies to treat depression preceding dementia in an effort to delay or ultimately prevent the onset of LOAD.