ABSTRACT Depressive symptoms are present in up to 40% of individuals with Alzheimer’s disease (AD) and an ongoing debate regarding whether they represent a risk factor or a prodromal sign of AD. Chronic conditions such as depression impact the stress response and may accelerate biological aging further contributing to susceptibility to age-related conditions specifically cognitive decline. However, genetics of psychiatric traits and AD have been mostly studied separately. This proposal aims to identify coding and non-coding regulatory variants associated with shared genetic risk for depression and AD in multiple cohorts: UK biobank, Million Veteran Program, Alzheimer’s Disease Sequencing Project, National Health and Resilience in Veterans Study, and Yale-Penn study, and replicate findings in PsycheMERGE, cumulatively studying more than 1 million individuals. We will assess two major risk factors of AD - APOE-ε4 carrier status (Model-1) and parental history of AD (Model-2) with depression. Our previous findings from the genetically regulated expression study of depression in 1.2 million individuals using hippocampus-based expression quantitative trait loci (QTL) identified several genes which have roles in AD pathology (e.g. PARK2, NEGR1, HSPA1A, ITPR3, NLGN1, and DRD2). Therefore, we hypothesize that stratifying depression with AD phenotypes will uncover overlapping genetic contributions between depression and AD and elucidate the shared molecular mechanisms, and potential therapeutic targets. To test theses hypotheses, this proposal aims to develop a multi-modal framework to study neuropsychiatric comorbidities by investigating, Aim-1) whole exome profiles for coding regions associated with depression and genetic risk for AD (K99 phase), Aim-2) transcriptomic profiles to identify a shared molecular basis for depression and AD risk by integrating large-scale GWAS with brain tissue-based molecular QTL studies (R00 phase), and Aim-3) epigenome profiles to identify methylation sites associated with a combined polygenic score of depression and AD, and compare biological aging between depression and AD comorbidity, and either disorder alone (R00 phase). The accompanying training includes didactic courses in i) data analysis from multiple high throughput technologies, ii) developing biomarkers from large-scale datasets, iii) computational programming and iv) gerontological studies. The professional development training will include writing workshops, building mentoring portfolio, training opportunities to establish laboratory as an independent researcher. This training plan was developed under advisory team comprised of five members who are experts in AD, psychiatry, aging, large-scale genomics, and cohorts with electronic health records. Together they provide guidance on the proposed study and support a multidisciplinary neuropsychiatric research career for the candidate.