Multi-ancestry meta-analysis of tobacco use disorder prioritizes novel candidate risk genes and reveals associations with numerous health outcomes

Abstract

Tobacco use disorder (TUD) is the most prevalent substance use disorder in the world. Genetic factors influence smoking behaviors, and although strides have been made using genome-wide association studies (GWAS) to identify risk variants, the majority of variants identified have been for nicotine consumption, rather than TUD. We leveraged five biobanks to perform a multi-ancestral meta-analysis of TUD (derived via electronic health records, EHR) in 898,680 individuals (739,895 European, 114,420 African American, 44,365 Latin American). We identified 88 independent risk loci; integration with functional genomic tools uncovered 461 potential risk genes, primarily expressed in the brain. TUD was genetically correlated with smoking and psychiatric traits from traditionally ascertained cohorts, externalizing behaviors in children, and hundreds of medical outcomes, including HIV infection, heart disease, and pain. This work furthers our biological understanding of TUD and establishes EHR as a source of phenotypic information for studying the genetics of TUD.

Figure 1.. Overview of the cohorts and analysis pipeline (a) and genetic correlations among the sites (b).

(a) We conducted independent GWAS of TUD cases and controls in individuals of European (EUR) ancestry across four PsycheMERGE sites (BioVU, MGBB, PMBB, and MVP) and performed a GWAS meta-analysis (“TUD-EUR”); these summary results were used for all secondary analyses. For African American (AA), we conducted GWAS meta-analysis of TUD cases and controls from the PMBB and MVP cohorts (“TUD-AA”). For Latin American (LA), we conducted GWAS of TUD cases and controls from the MVP cohort. Next, we performed a multi-ancestral GWAS meta-analysis (“TUD-multi”), which combined the results from all seven cohorts. We also obtained summary statistics from UKBB, which used a less stringent case definition in individuals of EUR ancestry and performed a GWAS meta-analysis within EUR individuals (“TUD-EUR+UKBB”) and across ancestries (“TUD-multi+UKBB”). Supplementary Table 2 summarizes the datasets used for the analyses. We subjected the TUD-EUR summary statistics to several secondary analyses to characterize the genetic architecture of TUD. (b) LDSC genetic correlations (r_g) for TUD between EUR sites were positive and high, ranging from 0.51 to unity, with most confidence intervals overlapping (Supplementary Figure 1). LDSC genetic correlation for TUD between the two AA samples was strongly positive (r_g=0.93) but not significant (p=0.45). LDSC SNP-heritability estimates (h²_SNP 5–15%) are shown in the diagonal. UKBB=UK Biobank, BioVU=Vanderbilt University Medical Center’s biobank, MGBB=Mass General Brigham Biobank, PMBB=Penn Medicine Biobank, MVP=Million Veteran Program.

Figure 2.. Manhattan and porcupine plots for the TUD-multi meta-analysis and ancestry-specific GWAS.

(a) TUD-multi identified 88 independent risk loci, all of which were recently identified by the GSCAN study. (b) Porcupine plot of ancestry-specific meta-analyses identified 63 loci in the European cohort (EUR, in red), and 2 loci in the African-ancestry cohort (AA, in blue). No significant associations were detected in the Latin American-ancestry (LA) cohort. We used a sign test to examine the 74 EUR lead SNPs in the AA and HA cohorts, of which 57 and 53, respectively, were directly analyzed or had proxy SNPs in these populations (Supplementary Table 10). Most SNPs had the same direction of effect in both populations (AA = 45 out of 57, HA = 41 out of 53; sign test AA p = 1.31E-05, LA p = 8.17E-05; Supplementary Figure 5). Only 25 SNPs (AAs = 12, HAs = 13) were nominally associated across populations (p < 0.05), none of which survived multiple testing correction.

Figure 3.. Integration with functional genomic data implicated 461 unique TUD candidate risk genes.

(a) Of 461 associated genes, 56 converged with at least 3 methods, and were dispersed throughout the chromosomes. (b) LDSC (SNP-based) and MAGMA tissue-specific gene expression of TUD risk genes reveals substantial brain enrichment (Supplementary Tables 25–26). (c) The genetic findings across multiple levels of analysis (LDSC, MAGMA, MultiXcan, BrainXcan) implicated brain regions exhibiting anatomical differences in cases. (d) Cell type-specific expression of TUD risk genes. Results from MAGMA property analyses and gene expression using human single-cell RNA-sequencing datasets (Supplementary Table 28 for full list). After multiple testing correction for all datasets, only genes expressed in GABAergic neurons were associated with TUD (Supplementary Table 28).

Figure 4.. Sankey Diagram showing drug repurposing results from S-PrediXcan brain tissues.

20 medications/perturbagens grouped by ATC category membership from the Library of Integrated Network-Based Cellular Signatures (LINCS) database. ATC categories connected to perturbagen edges represent corresponding ATC category membership. Perturbagens connected to gene target edges are associated with the reversal of the TUD transcriptomic profile from S-PrediXcan brain tissue results. Only medications that targeted at least one mapped/independent gene from our GWAS are plotted.

Figure 5.. FDR-significant genetic correlations between TUD-EUR and 113 complex traits, including smoking and related phenotypes (b).

(a) TUD consists of multiple components, progressing from experimental use to regular use, compulsive use, cessation, and relapse. Therefore, high genetic correlations (r_g) are to be expected between the age of smoking initiation (AgeSmkInit), smoking initiation (SmkInit), cigarettes per day (CPD), smoking cessation (SmkCess), nicotine dependence measured using the Fagerström Test for Nicotine Dependence (FTND), and tobacco use disorder (see Supplementary Table 31 for full results). (b) Genetic correlations with an extended list of traits from publicly available GWAS. Traits with positive r_g values are plotted above the line; traits with negative r_g values below the line. All r_gs are significant using a 5% FDR correction for multiple testing. (c-e) Systematic comparison of significant genetic correlation estimates between TUD and SmkInit (c), CPD (d) and FTND (e) reveal overlapping (black dots) and trait-specific (blue and yellow dots) relations between TUD and these other smoking phenotypes. r_g estimates were generally higher for TUD than CPD - even with a smaller sample size (TUD, N=495,005; CPD, N=784,353) - and FTND. On the contrary, r_g’s were generally smaller for TUD than SmkInit, possibly because of the larger sample for SmkInit (N=3,383,199) than TUD. Overall, these results indicate that these smoking behaviors, including SmkInit, CPD, FTND, and TUD, represent both unique and interrelated polygenic influences, which are complementary to those associated with other complex behaviors and disorders at the genetic level.

Figure 6.. TUD PGS PheWAS in the (a) Mayo Clinic, (b) Yale-Penn, and (c) ABCD European cohorts.

Only selected Bonferroni-significant traits are shown. In (a) and (b), association of TUD PGS (in black) is conditioned on PGS for FTND, CPD, and SmkInit (in green). The exact values for each association and extended lists of traits can be found in Supplementary Tables 34, 36 and 38.