Cho, Seung BinAliev, FazilClark, Shaunna L.Adkins, Amy E.Edenberg, Howard J.Bucholz, Kathleen K.Porjesz, BerniceDick, Danielle M.2017-12-072017-12-072017-07Cho, S. B., Aliev, F., Clark, S. L., Adkins, A. E., Edenberg, H. J., Bucholz, K. K., ... & Dick, D. M. (2017). Using Patterns of Genetic Association to Elucidate Shared Genetic Etiologies Across Psychiatric Disorders. Behavior Genetics, 47(4), 405-415. https://doi.org/10.1007/s10519-017-9844-4https://hdl.handle.net/1805/14739Twin 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.enPublisher Policycomorbiditypsychiatric disordergenetic etiologyArticle