Identification of Novel Loci and Cross-Disorder Pleiotropy Through Multi-Ancestry Genome-Wide Analysis of Alcohol Use Disorder in Over One Million Individuals

Abstract

Alcohol use disorder (AUD) is highly heritable and burdensome worldwide. Genome-wide association studies (GWASs) can provide new evidence regarding the aetiology of AUD. We report a multi-ancestry GWASs across diverse ancestries focusing on a narrow AUD phenotype, using novel statistical tools in a total sample of 1,041,450 individuals [102,079 cases; European, 75,583; African, 20,689 (mostly African-American); Hispanic American, 3,449; East Asian, 2,254; South Asian, 104; descent]. Cross-ancestry functional analyses were performed with European and African samples. Thirty-seven genome-wide significant loci were identified, of which seven were novel for AUD and six for other alcohol phenotypes. Loci were mapped to genes enriched for brain regions relevant for AUD (striatum, hypothalamus, and prefrontal cortex) and potential drug targets (GABAergic, dopaminergic and serotonergic neurons). African-specific analysis yielded a unique pattern of immune-related gene sets. Polygenic overlap and positive genetic correlations showed extensive shared genetic architecture between AUD and both mental and general medical phenotypes, suggesting they are not only complications of alcohol use but also share genetic liability with AUD. Leveraging a cross-ancestry approach allowed identification of novel genetic loci for AUD and underscores the value of multi-ancestry genetic studies. These findings advance our understanding of AUD risk and clinically-relevant comorbidities.

Figure 1

Independent cell types associated with the GWAS meta-analysis results in the African (AFR), European (EUR) and multi-ancestry (MA) samples. Results from FUMA step 3 analysis obtained with 217 Human cell types. woFetal, dataset considered without developing cells; GW, gestation week; PFC, prefrontal cortex; exCA1, hippocampal cornu ammonis excitatory neurons; The complete datasets description is available at https://fuma.ctglab.nl/tutorial#celltype.

Figure 2

Tissue-specific gene expression enrichment from the multi-ancestry (top panel). African (AFR, middle panel) and European (EUR, bottom panel) analyses. Significant enrichment is represented in pink. (p <0.05 after False discovery rate correction).

Figure 3. Ancestry Specific Genetic Architecture of AUD in the multi-ancestry analysis (top, red, AUD multi-ancestry) and for the European (top, blue, AUD EUR) and African (bottom, yellow, AUD AFR) samples.

−log10 (p-values) obtained by meta-analysis (METAL) are shown on the y-axis while the x-axis represents increasing chromosome numbers from 1 to 22 and positions in K-base pairs. Y-axis is truncated to −log10(P) = 32.

Figure 4

Polygenic overlap between AUD (blue) and clinically relevant phenotypes, after filtering based on estimation of MiXeR stability using the Akaike Informant Criterion. A) CUD, cannabis use disorder; OUD, opioid use disorder; DRINKS, drinks / week; SMOKE, age at smoking initiation (yellow); B) ADHD, attention deficit/hyperactivity disorder; BIP, bipolar disorder; DEP, major depression; SCZ, schizophrenia; NEUR, neuroticism (pink); C) SBP, systolic blood pressure (green).

Figure 5

Genetic correlation of AUD with mental traits and disorders (left) and with general medical conditions and risk factors (right), including neuropsychiatric diseases, for the AFR and the EUR samples, separately. The secondary traits are listed. *p <0.05, **p< 0.01, ***p <0.001 (Bonferroni-corrected). LVESF, left ventricular ejection systolic fraction; MRI, magnetic resonance imaging; HDL, hight density lipoproteins; APOE4, Apolipoprotein ε4 locus. AUD EUR, Alcohol Use disorder, European.