Genetics of substance use disorders in the era of big data

Abstract

Substance use disorders (SUDs) are conditions in which the use of legal or illegal substances, such as nicotine, alcohol or opioids, results in clinical and functional impairment. SUDs and, more generally, substance use are genetically complex traits that are enormously costly on an individual and societal basis. The past few years have seen remarkable progress in our understanding of the genetics, and therefore the biology, of substance use and abuse. Various studies - including of well-defined phenotypes in deeply phenotyped samples, as well as broadly defined phenotypes in meta-analysis and biobank samples - have revealed multiple risk loci for these common traits. A key emerging insight from this work establishes a biological and genetic distinction between quantity and/or frequency measures of substance use (which may involve low levels of use without dependence), versus symptoms related to physical dependence.

Figure 1 |. From epidemiology and gene discovery to biology of substance use disorders.

Genome-wide association datasets can be used as the basis for multiple analytical approaches to disentangle the biology of the traits investigated (e.g., cellular processes and molecular functions) and to understand the mechanisms underlying the associations observed in epidemiological studies.

Figure 2 |. Genetic correlation among SUD traits and other phenotypes.

Genetic correlation of problematic alcohol use, cannabis use disorder, cocaine dependence, opioid use disorder, and nicotine dependence with psychiatric disorders, behavioural traits, and other complex phenotypes. The 95% confidence interval of the genetic correlation estimates were obtained from previous studies^{, , , ,} that applied the linkage disequilibrium score regression method. The traits included are those tested with respect to at least four out of the five addictions considered. There are some patterns of genetic correlation that are consistent across the five addictions presented. The major differences among them are related to the substance-specific genetic correlations, i.e. nicotine addiction vs. cigaretters per day, alcohol addiction vs. drinks per week, and cannabis addiction vs. cannabis use.

Figure 3 |. Twin-based versus SNP-based heritabilities of alcohol, cannabis, cocaine, opioid, and tobacco addictions.

Twin-based heritabilities (weighted means and ranges) were previously estimated in US surveys of addictive agents in adult twin pairs. SNP-based heritabilities were previously estimated by genome-wide association studies using linkage disequilibrium score regression^{, , , ,} . Bubble size represents the relative risk of addiction for each substance. Vertical bars represent 95% confidence intervals for the SNP-based estimate, and horizontal bars represent the twin-based range. Family-based and SNP-based heritabilities of complex traits previously calculated using UK Biobank data are also plotted. The differences between twin-based and SNP-based heritabilities (i.e., the ‘missing heritability’) for four of the addictions shown is in line with those observed among other human traits and diseases. The only exception appears to be cocaine addiction that presents a large 96% confidence interval due to the small sample size of the largest genome-wide association study available for this trait to date.