Using polygenic scores for identifying individuals at increased risk of substance use disorders in clinical and population samples

Abstract

Genome-wide, polygenic risk scores (PRS) have emerged as a useful way to characterize genetic liability. There is growing evidence that PRS may prove useful for early identification of those at increased risk for certain diseases. The current potential of PRS for alcohol use disorders (AUD) remains an open question. Using data from both a populationbased sample [the FinnTwin12 (FT12) study] and a high-risk sample [the Collaborative Study on the Genetics of Alcoholism (COGA)], we examined the association between PRSs derived from genome-wide association studies (GWASs) of (1) alcohol dependence/alcohol problems, (2) alcohol consumption, and (3) risky behaviors with AUD and other substance use disorder (SUD) criteria. These PRSs explain~2.5-3.5% of the variance in AUD (across FT12 and COGA) when all PRSs are included in the same model. Calculations of area under the curve (AUC) show PRS provide only a slight improvement over a model with age, sex, and ancestral principal components as covariates. While individuals in the top 20, 10, and 5% of the PRS distribution had greater odds of having an AUD compared to the lower end of the continuum in both COGA and FT12, the point estimates at each threshold were statistically indistinguishable. Those in the top 5% reported greater levels of licit (alcohol and nicotine) and illicit (cannabis and opioid) SUD criteria. PRSs are associated with risk for SUD in independent samples. However, usefulness for identifying those at increased risk in their current form is modest, at best. Improvement in predictive ability will likely be dependent on increasing the size of well-phenotyped discovery samples.

Genetics of substance use disorders: a review

“…PGS have shown promise for the stratification of individuals at risk by their polygenic ‘load’ for some health conditions; for example, one successful non-SUD application of PGS was reported for coronary disease, where individuals in the highest quintile of genetic risk had an approximately 90% increase in relative risk of experiencing an adverse coronary event compared to individuals in the lowest quintile of genetic risk (Khera et al, 2016 ). Current SUD PGS explain a relatively small proportion of variance (generally 1–5%) in SUD-related outcomes, especially relative to other known risk factors (SES, SUD family history, comorbid psychiatric disorders; Barr et al, 2020 ). This limits their current clinical utility.…”

Genetic feedback for psychiatric conditions: Where are we now and where are we going

“…Although GPS have been used in clinical settings to inform risk of diabetes (Grant et al, 2013) and guide treatment decisions for heart disease (Kullo et al, 2016), GPS are still in the very early stages of being used as a risk assessment tool for psychiatric conditions. Currently, GPS only capture a small amount of variance in most psychiatric phenotypes (Lewis & Vassos, 2017;Palk et al, 2019), limiting their utility in clinical settings (Barr et al, 2020). Further, the integration of genotypic information for psychiatric conditions into clinical care and decision-making will likely pose a number of additional complications, related to the complex etiological pathways through which risk for psychiatric and substance use disorders unfolds.…”

Alcohol and nicotine polygenic scores are associated with the development of alcohol and nicotine use problems from adolescence to young adulthood

“…Sample characteristics of the discovery samples (e.g. treatment‐seeking versus population‐based) and levels of substance use problems in the target sample [9,10] might also account for differences between the PAU and DPW PGSs. PGS may perform better in target samples with similar degrees of problematic substance use found in the PGS discovery sample.…”

“…PGS can be generated from a GWAS discovery sample by weighting genetic variants relative to the strength of their association with a given phenotype to calculate a measure of individual genetic risk in a target sample. For example, PGS calculated from GWAS-identified associations for alcohol use have demonstrated associations with alcohol-related outcomes in independent samples [8][9][10]. PGSs for alcohol and nicotine use have also been associated with use of other drugs (e.g.…”