Cho, Seung BinAliev, FazilClark, Shaunna L.Adkins, Amy E.Edenberg, Howard J.Bucholz, Kathleen K.Porjesz, Bernice2024-09-292024-09-2920170001-82441573-3297https://doi.org/10.1007/s10519-017-9844-4https://hdl.handle.net/20.500.14619/3926Twin studies indicate that latent genetic factors overlap across comorbid psychiatric disorders. In this study, we used a novel approach to elucidate shared genetic factors across psychiatric outcomes by clustering single nucleotide polymorphisms based on their genome-wide association patterns. We applied latent profile analysis (LPA) to p-values resulting from genome-wide association studies across three phenotypes: symptom counts of alcohol dependence (AD), antisocial personality disorder (ASP), and major depression (MD), using the European-American case-control genome-wide association study subsample of the collaborative study on the genetics of alcoholism (N = 1399). In the 3-class model, classes were characterized by overall low associations (85.6% of SNPs), relatively stronger association only with MD (6.8%), and stronger associations with AD and ASP but not with MD (7.6%), respectively. These results parallel the genetic factor structure identified in twin studies. The findings suggest that applying LPA to association results across multiple disorders may be a promising approach to identify the specific genetic etiologies underlying shared genetic variance.eninfo:eu-repo/semantics/openAccessComorbidityPsychiatric disorderGenetic etiologyLatent profile analysisGWASArticle10.1007/s10519-017-9844-42-s2.0-85016221913415428343281Q140547WOS:000403569500004Q2