Addiction BiologyGenetic & Genomic MedicinePeer-reviewed journal article
Genetic liability for substance use associated with medical comorbidities in electronic health records of African- and European-ancestry individuals
Emily E. HartwelliD, Alison K. MerikangasiD, Shefali S. VermaiD, Marylyn DeRiggi RitchieiD, Henry R. KranzleriD, Rachel L. KemberiD
Abstract
Polygenic risk scores (PRS) represent an individuals summed genetic risk for a trait and can serve as biomarkers for disease. Less is known about the utility of PRS as a means to quantify genetic risk for substance use disorders (SUDs) than for many other traits. Nonetheless, the growth of large, electronic health record-based biobanks makes it possible to evaluate the association of SUD PRS with other traits. We calculated PRS for smoking initiation, alcohol use disorder (AUD), and opioid use disorder (OUD) using summary statistics from the Million Veteran Program sample. We then tested the association of each PRS with its primary phenotype in the Penn Medicine BioBank (PMBB) using all available genotyped participants of African or European ancestry (AFR and EUR, respectively) (N=18,612). Finally, we conducted phenome-wide association analyses (PheWAS) separately by ancestry and sex to test for associations across disease categories. Tobacco use disorder was the most common SUD in the PMBB, followed by AUD and OUD, consistent with the population prevalence of these disorders. All PRS were associated with their primary phenotype in both ancestry groups. PheWAS results yielded cross-trait associations across multiple domains, including psychiatric disorders and medical conditions. SUD PRS were associated with their primary phenotypes, however they are not yet predictive enough to be useful diagnostically. The cross-trait associations of the SUD PRS are indicative of a broader genetic liability. Future work should extend findings to additional population groups and for other substances of abuse.
- organism: human
- assay: electronic health records
- research focus: GWAS / association mapping
- approach: statistical model
- Citations
- 19
- FWCI
- 1.69
- Field percentile
- top 16%
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