Multivariate genome-wide association meta-analysis of over 1 million subjects identifies loci underlying multiple substance use disorders

Abstract

Genetic liability to substance use disorders can be parsed into loci that confer general or substance-specific addiction risk. We report a multivariate genome-wide association meta-analysis that disaggregates general and substance-specific loci for published summary statistics of problematic alcohol use, problematic tobacco use, cannabis use disorder, and opioid use disorder in a sample of 1,025,550 individuals of European descent and 92,630 individuals of African descent. Nineteen independent SNPs were genome-wide significant (P < 5e-8) for the general addiction risk factor (addiction-rf), which showed high polygenicity. Across ancestries, PDE4B was significant (among other genes), suggesting dopamine regulation as a cross-substance vulnerability. An addiction-rf polygenic risk score was associated with substance use disorders, psychopathologies, somatic conditions, and environments associated with the onset of addictions. Substance-specific loci (9 for alcohol, 32 for tobacco, 5 for cannabis, 1 for opioids) included metabolic and receptor genes. These findings provide insight into genetic risk loci for substance use disorders that could be leveraged as treatment targets.

Figure 1.. Manhattan Plot of Addiction-rf Genome-Wide Significant Results.

The dotted line represents genome-wide significance at 5e-8. Each SNP peak is annotated with the closest mapped gene from FUMA (Table 1). We have not included all SNPs in the credible set in Table 1, but they are shown in Supplementary Table 4. Significance is set at genome-wide significance Bonferroni correction in a two-sided test (P < 5e-8).

Figure 2.. Manhattan Plot of TWAS Results for Addiction-rf.

A Transcriptome-Wide Association Study (TWAS) of the addiction-rf, plotted as a Manhattan plot. In Panel A, Analyses were conducted in S-MultiXcan with GTeX v8 data. In Panel B, the analysis was run using S-PredixCan with weights trained from PsychEncode. The y-axis is presented as −log10(p), the color of the data point represents the tissue in which correlation between gene expression and outcome was the highest. The dotted black line represents Bonferroni corrected TWAS significance of a two-sided test (Plot A has 9,944 genes, Bonferroni = 5×10^-6 and the line is at 5.3, Plot B has 13,850 genes, Bonferroni = 3.6×10^-6, line is at 5.4)

Figure 3.. PheWAS of Genetic Correlations using MASSIVE.

Genetic correlations between 1,547 traits and the addiction-rf, calculated in MASSIVE, mapped by their statistical significance (-log10(p) on the y-axis), and broad category. The top 20 correlations are annotated. The black dashed line represents Bonferroni significance for association of a two-sided test (p_bon= .05/1,574 = 3.232e-05).

Figure 4.. Polygenic Risk Score Prediction in Yale-Penn.

(A) Polygenic risk score (PRS) of the addiction-rf predicts lifetime alcohol (AUD), cannabis (CUD), opioid (OUD), tobacco (TD), and cocaine (CoUD) use disorders, and variables representing more than one lifetime substance use disorder diagnosis vs no SUDs diagnosis (Polysubstance Use Disorder, 2 level), more than one lifetime diagnosis vs. one lifetime diagnosis (polysubstance vs. unitary), as well as any substance use disorder diagnosis (Any Addiction) in an independent sample (Yale Penn 3; N=1,986 individuals of European genetic ancestry). (B) The addiction-rf PRS was associated with a comparable phenotypic substance use disorders (SUD) common factor in the Yale-Penn sample. Controlling for age, sex and 10 genetic principal components of ancestry, all path estimates are fully standardized. Estimates were significant at p < .001 of a two-sided test (LAVAAN does not report P-values lower than .001).