Cigarette smoking is a well-established major heritable risk factor for human diseases. And alcohol consumption has been increasingly associated with the development of chronic diseases and other serious problems, such as heart disease, stroke, and cancer. The availability of large datasets in recent years has enabled a breakthrough in the genetics of smoking and alcohol addiction, with more than 400 associated genomic loci discovered to date. These numbers keep growing thanks to rapidly increasing sample size and involvement of ethnically diverse populations. But it remains challenging to map non-coding variants to their target genes and translate their biological and clinical relevance.
To address this gap, we developed a novel trans-ethnic TWAS approach, TESLA (trans-ethnic transcriptome-wide association study approach using an optimal linear combination of association statistics), that has provably optimal power to integrate trans-ethnic GWAS with eQTL data from a possibly unmatched ancestry. TESLA uses a mixed effect meta-regression approach to model ancestry-specific effect across different studies in meta-analysis. We showed using simulation that TESLA substantially outperforms other strategies, including TWAS using fixed effect meta-analysis results and TWAS using only studies from matched ancestries.
Using TESLA, we aggregated trans-ethnic GWAS and whole-genome sequencing data from the GSCAN consortium (total N = 3.4 million) and GTEx v8 eQTL data in 49 tissues to further empower gene discovery for tobacco and alcohol use behaviors. We identified 2,652 genes; among them, 148 are novel genes that are outside 1 million basepair window of GWAS sentinel variants. These results provide many susceptibility genes for alcohol and smoking addiction than hitherto. Consistent with previous studies, our results also showed a general lack of tissue specificity across all phenotypes where the most significant genes in each tissue are often ranked the top across many tissues. Further, we performed a fine-mapping analysis in these risk TWAS loci to prioritize putative causal genes. The 90%-credible set of 778 loci were narrowed down to a single gene. These results could help us develop a deeper understanding and broad vision of the genetic architecture of smoking and alcohol use behaviors.