In addition, because most causal variants have been observed to be consistent across ancestries 11, 12, 13, MATCH-TWAS can suffer from substantial power loss by using only the GWAS data from the matched ancestry, due simply to smaller sample size. MATCH-TWAS may be difficult or even impossible to implement in practice because eQTL data may not be broadly available for disease-relevant tissues in non-European ancestries. An alternative strategy is to use ancestry-matched eQTL data from disease-relevant tissues and perform TWAS separately for each ancestry (which we call MATCH-TWAS). The results may also be difficult to interpret because causal variants underlying GWAS hits or eQTLs may differ between ancestries. Direct integration of eQTLs with GWAS data from nonmatched ancestries (for example, integrating European-derived eQTLs with non-European GWASs) was shown to have suboptimal power 10. TWAS in its original form requires GWAS and eQTL data to be from matched ancestries. Various TWAS methods have been widely applied to different complex traits to understand the functional consequences of regulatory variations 7, 8, 9. TWAS approaches (for example, FUSION 3, TIGAR 4, PrediXcan 5 and UTMOST 6) use eQTLs to predict gene expression levels in silico, which the method then uses to identify genes associated with the phenotype of interest. In the present study, we combined GWAS datasets totaling 1.3 million individuals: 1.2 million from the GWAS and Sequencing Consortium of Alcohol and Nicotine use (GSCAN) and 150,000 diverse ancestries from the Trans-Omics Precision Medicine (TOPMed) 2 to further empower gene discovery and elucidate the genetic architecture of smoking behavior.ĭissecting the mechanisms of GWAS hits for tobacco use is crucial to understand the etiology of nicotine addiction and related disease outcomes. On top of this, the genetic architecture of tobacco use outside of European populations remains understudied. Although some of these associations point to genes and pathways of known biological importance, including the nicotinic receptor and dopaminergic signaling pathway genes 1, the underlying mechanisms for most of the identified loci are unknown. The availability of large datasets has enabled a breakthrough in the genetics of smoking addiction, with >400 loci discovered to date 1. In silico drug-repurposing analyses highlight several drugs with known efficacy, including dextromethorphan and galantamine, and new drugs such as muscle relaxants that may be repurposed for treating nicotine addiction.Ĭigarette smoking is a major heritable risk factor for human diseases. These hits and subsequent fine mapping using TESLA point to target genes with biological relevance. When applied to tobacco use phenotypes, TESLA identified 273 new genes, up to 55% more compared with alternative TWAS methods. By exploiting shared phenotypic effects between ancestries and accommodating potential effect heterogeneities, TESLA improves power over other TWAS methods. We developed a new approach, TESLA (multi-ancestry integrative study using an optimal linear combination of association statistics), to integrate an eQTL dataset with a multi-ancestry GWAS. To overcome this limitation, we aggregated genome-wide association study (GWAS) summary statistics, whole-genome sequences and expression quantitative trait locus (eQTL) data from diverse ancestries. Most transcriptome-wide association studies (TWASs) so far focus on European ancestry and lack diversity. Nature Genetics volume 55, pages 291–300 ( 2023) Cite this article Multi-ancestry transcriptome-wide association analyses yield insights into tobacco use biology and drug repurposing
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