Despite the large toll of opioid use disorder (OUD), genome-wide association studies (GWAS) of OUD to date have yielded few susceptibility loci. We performed a large-scale GWAS of OUD in individuals of European (EUR) and African (AFR) ancestry, optimizing genetic informativeness by performing MTAG (Multi-trait analysis of GWAS) with genetically correlated substance use disorders (SUDs). Meta-analysis included seven cohorts: the Million Veteran Program, Psychiatric Genomics Consortium, iPSYCH, FinnGen, Partners Biobank, BioVU, and Yale-Penn 3, resulting in a total N = 639,063 (N_cases = 20,686;N_effective = 77,026) across ancestries. OUD cases were defined as having a lifetime OUD diagnosis, and controls as anyone not known to meet OUD criteria. We estimated SNP-heritability (h²_SNP) and genetic correlations (r_g). Based on genetic correlation, we performed MTAG on OUD, alcohol use disorder (AUD), and cannabis use disorder (CanUD). A leave-one-out polygenic risk score (PRS) analysis was performed to compare OUD and OUD-MTAG PRS as predictors of OUD case status in Yale-Penn 3. The EUR meta-analysis identified three genome-wide significant (GWS; p ≤ 5 × 10⁻⁸) lead SNPs—one at FURIN (rs11372849; p = 9.54 × 10⁻¹⁰) and two OPRM1 variants (rs1799971, p = 4.92 × 10⁻⁰⁹; rs79704991, p = 1.11 × 10⁻⁰⁸; r² = 0.02). Rs1799971 (p = 4.91 × 10⁻⁰⁸) and another OPRM1 variant (rs9478500; p = 1.95 × 10⁻⁰⁸; r² = 0.03) were identified in the cross-ancestry meta-analysis. Estimated h²_SNP was 12.75%, with strong r_g with CanUD (r_g = 0.82; p = 1.14 × 10⁻⁴⁷) and AUD (r_g = 0.77; p = 6.36 × 10⁻⁷⁸). The OUD-MTAG resulted in a GWAS N_equivalent = 128,748 and 18 independent GWS loci, some mapping to genes or gene regions that have previously been associated with psychiatric or addiction phenotypes. The OUD-MTAG PRS accounted for 3.81% of OUD variance (beta = 0.61;s.e. = 0.066; p = 2.00 × 10⁻¹⁶) compared to 2.41% (beta = 0.45; s.e. = 0.058; p = 2.90 × 10⁻¹³) explained by the OUD PRS. The current study identified OUD variant associations at OPRM1, single variant associations with FURIN, and 18 GWS associations in the OUD-MTAG. The genetic architecture of OUD is likely influenced by both OUD-specific loci and loci shared across SUDs.

Despite rapid progress in characterizing the role of host genetics in SARS-Cov-2 infection, there is limited understanding of genes and pathways that contribute to COVID-19. Here, we integrate a genome-wide association study of COVID-19 hospitalization (7,885 cases and 961,804 controls from COVID-19 Host Genetics Initiative) with mRNA expression, splicing, and protein levels (n = 18,502). We identify 27 genes related to inflammation and coagulation pathways whose genetically predicted expression was associated with COVID-19 hospitalization. We functionally characterize the 27 genes using phenome- and laboratory-wide association scans in Vanderbilt Biobank (n = 85,460) and identified coagulation-related clinical symptoms, immunologic, and blood-cell-related biomarkers. We replicate these findings across trans-ethnic studies and observed consistent effects in individuals of diverse ancestral backgrounds in Vanderbilt Biobank, pan-UK Biobank, and Biobank Japan. Our study highlights and reconfirms putative causal genes impacting COVID-19 severity and symptomology through the host inflammatory response.

Genome-wide association studies (GWAS) have been performed for many psychiatric disorders and revealed a complex polygenic architecture linking mental and physical health phenotypes. Psychiatric diagnoses are often heterogeneous, and several layers of trait heterogeneity may contribute to detection of genetic risks per disorder or across multiple disorders. In this review, we discuss these heterogeneities and their consequences on the discovery of risk loci using large-scale genetic data. We primarily highlight the ways in which sex and diagnostic complexity contribute to risk locus discovery in schizophrenia, bipolar disorder, attention deficit hyperactivity disorder, autism spectrum disorder, posttraumatic stress disorder, major depressive disorder, obsessive-compulsive disorder, Tourette’s syndrome and chronic tic disorder, anxiety disorders, suicidality, feeding and eating disorders, and substance use disorders. Genetic data also have facilitated discovery of clinically relevant subphenotypes also described here. Collectively, GWAS of psychiatric disorders revealed that the understanding of heterogeneity, polygenicity, and pleiotropy is critical to translate genetic findings into treatment strategies.