bioRxivGeneticsPreprint
Multivariate genomic analysis of 1.5 million people identifies genes related to addiction, antisocial behavior, and health
Richard Karlsson LinnériD, Travis T. MallardiD, Peter B. BarriD, Sandra Sanchez‐RoigeiD, James W. MadoleiD, Morgan N. DriveriD, Holly E. PooreiD, Andrew David GrotzingeriD, Jorim J. TielbeekiD, Emma C. JohnsoniD, Mengzhen LiuiD, Hang ZhouiD, Rachel L. KemberiD, Joëlle A. PasmaniD, Karin J. H. VerweijiD, Dajiang J. LiuiD, Scott I. VriezeiD, COGA Collaborators, Henry R. KranzleriD, Joel E. GelernteriD, Kathleen Mullan HarrisiD, Elliot M. Tucker–DrobiD, Irwin D. WaldmaniD, Abraham A. PalmeriD, K. Paige HardeniD, , Danielle M. DickiD
Abstract
Behaviors and disorders related to self-regulation, such as substance use, antisocial conduct, and ADHD, are collectively referred to as externalizing and have a shared genetic liability. We applied a multivariate approach that leverages genetic correlations among externalizing traits for genome-wide association analyses. By pooling data from ~1.5 million people, our approach is statistically more powerful than single-trait analyses and identifies more than 500 genetic loci. The identified loci were enriched for genes expressed in the brain and related to nervous system development. A polygenic score constructed from our results captures variation in a broad range of behavioral and medical outcomes that were not part of our genome-wide analyses, including traits that until now lacked well-performing polygenic scores, such as opioid use disorder, suicide, HIV infections, criminal convictions, and unemployment. Our findings are consistent with the idea that persistent difficulties in self-regulation can be conceptualized as a neurodevelopmental condition.
- organism: human
- assay: GWAS summary statistics
- research focus: GWAS / association mapping
- approach: statistical model
- Citations
- 25
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