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Deep neural networks (DNNs) capture complex relationships among variables, however, because they require copious samples, their potential has yet to be fully tapped for understanding relationships between gene expression and human phenotypes. Here we introduce an analysis framework, namely MD-AD (Multi-task Deep learning for Alzheimer’s Disease neuropathology), which leverages an unexpected synergy between DNNs and multi-cohort settings. In these settings, true joint analysis can be stymied using conventional statistical methods, which require “harmonized” phenotypes and tend to capture cohort-level variations, obscuring subtler true disease signals. Instead, MD-AD incorporates related phenotypes sparsely measured across cohorts, and learns interactions between genes and phenotypes not discovered using linear models, identifying subtler signals than cohort-level variations which can be uniquely recapitulated in animal models and across tissues. We show that MD-AD exploits sex-specific relationships between microglial immune response and neuropathology, providing a nuanced context for the association between inflammatory genes and Alzheimer’s Disease.

Genome‐wide association studies (GWAS) of alcohol dependence (AD) have reliably identified variation within alcohol metabolizing genes (eg,ADH1B) but have inconsistently located other signals, which may be partially attributable to symptom heterogeneity underlying the disorder. We conducted GWAS of DSM‐IV AD (primary analysis), DSM‐IV AD criterion count (secondary analysis), and individual dependence criteria (tertiary analysis) among 7418 (1121 families) European American (EA) individuals from the Collaborative Study on the Genetics of Alcoholism (COGA). Trans‐ancestral meta‐analyses combined these results with data from 3175 (585 families) African‐American (AA) individuals from COGA. In the EA GWAS, three loci were genome‐wide significant: rs1229984 inADH1Bfor AD criterion count (P= 4.16E−11) andDesire to cut drinking(P= 1.21E−11); rs188227250 (chromosome 8,Drinking more than intended,P= 6.72E−09); rs1912461 (chromosome 15,Time spent drinking,P= 1.77E−08). In the trans‐ancestral meta‐analysis, rs1229984 was associated with multiple phenotypes and two additional loci were genome‐wide significant: rs61826952 (chromosome 1, DSM‐IV AD,P= 8.42E−11); rs7597960 (chromosome 2,Time spent drinking,P= 1.22E−08). Associations with rs1229984 and rs18822750 were replicated in independent datasets. Polygenic risk scores derived from the EA GWAS of AD predicted AD in two EA datasets (P < .01; 0.61%‐1.82% of variance). Identified novel variants (ie, rs1912461, rs61826952) were associated with differential central evoked theta power (loss − gain;P= .0037) and reward‐related ventral striatum reactivity (P= .008), respectively. This study suggests that studying individual criteria may unveil new insights into the genetic etiology of AD liability.