Multi‐Omics Insights Into The Heterogeneity Of Depression In Alzheimer’s Disease

Abstract

AbstractBackgroundDepression is a common, though heterogenous, comorbidity in late‐onset Alzheimer’s Disease (LOAD) patients. Moreover, individuals with depression are at greater risk to develop LOAD. In prior work, we performed pleiotropy analysis and found shared genetic etiology between LOAD and Major Depressive Disorder (MDD), suggesting inter‐relationship between LOAD and depression symptoms. However, the underpinning genetic, epigenetic, and transcriptomic etiologies predisposing the comorbidity of depression in LOAD, are largely unknown.MethodThis work applied various bioinformatics methods including analyses of polygenic risk scores (PRS), differential gene expression and DNA‐methylation. The discovery analyses were performed using ROSMAP datasets including, genotypes, transcriptomic, DNA‐methylomic and depression diagnosis. When available the NACC dataset was used for validation. In addition, MDD‐GWAS summary statistics from the Psychiatric Genomics Consortium were used to create the PRS.ResultWe constructed PRS based on MDD‐GWAS data and assessed its performance in predicting depression onset in LOAD patients. The PRS showed marginal results in standalone models for predicting depression onset in both ROSMAP (AUC = 0.540) and NACC (AUC = 0.527). Full models, with baseline age, sex, education, and APOEε4 allele count, showed improved prediction of depression onset (ROSMAP AUC: 0.606, NACC AUC: 0.581). In time‐to‐event analysis, standalone PRS models showed significant effects in ROSMAP (P = 0.0051), but not in NACC cohort. Full models showed significant performance in predicting depression in LOAD for both datasets (P<0.001). Next, we investigated differential gene expression in LOAD patients with and without comorbid depression and didn’t reveal significant association between differences in gene expression and the risk of depression in LOAD. Upon sex‐stratification, we identified 25 differential expressed genes (DEGs) in males, of which CHI3L2 showed the strongest upregulation, and only 3 DEGs in females. Finally, we tested differences in DNA‐methylation and found significant hypomethylation with only one CpG probe.ConclusionOur study is the first multi‐omics genome‐wide exploration of the genomic landscapes underlying the heterogeneity of depression in LOAD. Our developed PRS accurately predicted LOAD patients with depressive symptoms. Furthermore, we discovered sex‐dependent differences in gene expression associated with the risk of depression symptoms in LOAD. Collectively, our results have clinical implications for the diagnosis and treatment of depression in LOAD patients.