Genetic diversity fuels gene discovery for tobacco and alcohol use

Abstract

Tobacco and alcohol use are heritable behaviours associated with 15% and 5.3% of worldwide deaths, respectively, due largely to broad increased risk for disease and injury1–4. These substances are used across the globe, yet genome-wide association studies have focused largely on individuals of European ancestries5. Here we leveraged global genetic diversity across 3.4 million individuals from four major clines of global ancestry (approximately 21% non-European) to power the discovery and fine-mapping of genomic loci associated with tobacco and alcohol use, to inform function of these loci via ancestry-aware transcriptome-wide association studies, and to evaluate the genetic architecture and predictive power of polygenic risk within and across populations. We found that increases in sample size and genetic diversity improved locus identification and fine-mapping resolution, and that a large majority of the 3,823 associated variants (from 2,143 loci) showed consistent effect sizes across ancestry dimensions. However, polygenic risk scores developed in one ancestry performed poorly in others, highlighting the continued need to increase sample sizes of diverse ancestries to realize any potential benefit of polygenic prediction.

Multi-ancestry study of the genetics of problematic alcohol use in >1 million individuals

“…To compare within- and cross-ancestry fine-mapping, we performed fine-mapping for the above 92 regions using the same SNP sets and EUR-only LD information (Figure 2b & 2c). The median number of SNPs in the credible sets is 13, with 7 credible sets containing a single variant and 26 containing ≤5 variants, indicating that cross-ancestry fine-mapping improved causal variant identification, consistent with other studies reporting improved fine-mapping by including other ancestries 11 .…”

“…Maximum habitual alcohol intake 9 ( r g =0.67, SE=0.15, p =8.13×10 −6 ) showed the highest correlation with AUD, followed by opioid use disorder 41 ( r g =0.62, SE=0.10, p =6.70×10 −10 ), and smoking trajectory 42 ( r g =0.57, SE=0.08, p =3.64×10 −4 ). Major depressive disorder 43 and smoking initiation 11 showed nominally significant ( p <0.05) positive correlation with AUD and type 2 diabetes 44 showed a nominally significant negative correlation.…”

“…We confirmed significant positive genetic correlations ( r g ) in EUR between PAU and substance use and psychiatric traits 6 (Supplementary Table 15). AD 5 showed the highest correlation with PAU ( r g =0.85, SE=0.07, p =4.49×10 −34 ), followed by maximum habitual alcohol intake 9 ( r g =0.79, SE=0.03, p =1.24×10 −191 ), opioid use disorder 41 ( r g =0.78, SE=0.04, p =1.20×10 −111 ), drinks per week 11 ( r g =0.76, SE=0.02, p <1×10 −200 ), smoking trajectory 42 ( r g =0.63, SE=0.02, p =2.47×10 −176 ), and cannabis use disorder 14 ( r g =0.61, SE=0.04, p =4.85×10 −63 ). We next tested r g between AUD and 13 published traits with large GWAS in AFR (Figure 3, Supplementary Table 16).…”

“…Alcohol use disorder (AUD) is a chronic relapsing disease associated with a host of adverse medical, psychiatric, and social consequences 1 . Given observed heritability (h 2 ~50% 2 ), there has been substantial progress made in genome-wide association studies (GWAS) of AUD and problematic drinking [3][4][5][6][7][8][9] , and also for measures of alcohol consumption 10,11 . A prior GWAS of problematic alcohol use (PAU, a phenotype based on a meta-analysis of highly genetically correlated (genetic correlations >0.7) AUD 7 , alcohol dependence [AD] 5 , and AUDIT-P [Alcohol Use Disorders Identification Test-Problem score, a measure of problematic drinking] 4,12 , N=435,563) identified 29 independent risk variants, predominantly in European (EUR) ancestry subjects 6 .…”

Trans-ancestry epigenome-wide association meta-analysis of DNA methylation with lifetime cannabis use

“…The large sample set included in this study provides a good representation for a wide range of populations across countries, sexes, ancestry groups, and age ranges, empowering more generalizable findings in identifying a common and robust DNAm signature for lifetime cannabis use. A recent genetic study 59 has shown that increased sample sizes of diverse ancestries improved detection power and generated more generalizable polygenic risk scores. However, one limitation of this approach is that associations with ancestry effects may have been attenuated when all data were combined, as heterogeneity across different cohorts would have reduced power to detect such specific associations in underrepresented populations.…”