Clinical, environmental, and genetic risk factors for substance use disorders: characterizing combined effects across multiple cohorts.

Substance use disorders (SUDs) incur serious social and personal costs. The risk for SUDs is complex, with risk factors ranging from social conditions to individual genetic variation. We examined whether models that include a clinical/environmental risk index (CERI) and polygenic scores (PGS) are able to identify individuals at increased risk of SUD in young adulthood across four longitudinal cohorts for a combined sample of N = 15,134. Our analyses included participants of European (NEUR = 12,659) and African (NAFR = 2475) ancestries. SUD outcomes included: (1) alcohol dependence, (2) nicotine dependence; (3) drug dependence, and (4) any substance dependence. In the models containing the PGS and CERI, the CERI was associated with all three outcomes (ORs = 01.37-1.67). PGS for problematic alcohol use, externalizing, and smoking quantity were associated with alcohol dependence, drug dependence, and nicotine dependence, respectively (OR = 1.11-1.33). PGS for problematic alcohol use and externalizing were also associated with any substance dependence (ORs = 1.09-1.18). The full model explained 6-13% of the variance in SUDs. Those in the top 10% of CERI and PGS had relative risk ratios of 3.86-8.04 for each SUD relative to the bottom 90%. Overall, the combined measures of clinical, environmental, and genetic risk demonstrated modest ability to distinguish between affected and unaffected individuals in young adulthood. PGS were significant but added little in addition to the clinical/environmental risk index. Results from our analysis demonstrate there is still considerable work to be done before tools such as these are ready for clinical applications.

Principal Component Analysis Reduces Collider Bias in Polygenic Score Effect Size Estimation.

In this study, we test principal component analysis (PCA) of measured confounders as a method to reduce collider bias in polygenic association models. We present results from simulations and application of the method in the Collaborative Study of the Genetics of Alcoholism (COGA) sample with a polygenic score for alcohol problems, DSM-5 alcohol use disorder as the target phenotype, and two collider variables: tobacco use and educational attainment. Simulation results suggest that assumptions regarding the correlation structure and availability of measured confounders are complementary, such that meeting one assumption relaxes the other. Application of the method in COGA shows that PC covariates reduce collider bias when tobacco use is used as the collider variable. Application of this method may improve PRS effect size estimation in some cases by reducing the effect of collider bias, making efficient use of data resources that are available in many studies.

Alcohol use disorder, psychiatric comorbidities, marriage and divorce in a high-risk sample.

OBJECTIVE: To examine associations between alcohol use disorder (AUD), its psychiatric comorbidities, and their interactions, with marital outcomes in a diverse high-risk, genetically informative sample. METHOD: Participants included European ancestry (EA; n = 4,045) and African ancestry (AA; n = 1,550) individuals from the multigenerational Collaborative Study on the Genetics of Alcoholism (COGA) sample (56% female, Mage ∼ 41 years). Outcomes were lifetime marriage and divorce. Predictors included lifetime AUD, an alcohol problems polygenic score (PRS), and AUD comorbidities, including conduct or antisocial personality disorder (ASP), cannabis dependence/abuse (CAN), frequent tobacco use (TOB), and major depressive disorder (MDD). Mixed effect Cox models and generalized linear mixed effects models were fit. RESULTS: Among EA participants, those with AUD and CAN were less likely to marry (hazard ratios [HRs] 0.70-0.83, ps < 0.01). Among AA participants, those with AUD and TOB were less likely to marry (HRs 0.66-0.82, ps < 0.05) and those with MDD were more likely to marry (HR = 1.34, ps < 0.01). Among EA participants, AUD, CAN, TOB, and MDD were associated with higher odds of divorce (odds ratios [ORs] 1.59-2.21, ps < 0.01). Among AA participants, no predictors were significantly associated with divorce. Significant random effects indicated genetic and environmental influences on marriage, but only environmental factors on divorce. CONCLUSIONS: In a high-risk sample, AUD was associated with reduced likelihood of marriage in EA and AA individuals and increased risk of divorce in EA individuals. These associations were largely independent of comorbidities. Genetic and environmental background factors contributed to marriage, while only environmental background factors contributed to divorce. (PsycInfo Database Record (c) 2022 APA, all rights reserved).