Most transcriptome-wide association studies (TWASs) so far focus on European ancestry and lack diversity. 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. 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. By exploiting shared phenotypic effects between ancestries and accommodating potential effect heterogeneities, TESLA improves power over other TWAS methods. When applied to tobacco use phenotypes, TESLA identified 273 new genes, up to 55% more compared with alternative TWAS methods. These hits and subsequent fine mapping using TESLA point to target genes with biological relevance. 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.

到目前为止，大多数转录组范围的关联研究 （TWAS） 都集中在欧洲血统上，缺乏多样性。为了克服这一限制，我们汇总了来自不同祖先的全基因组关联研究 （GWAS） 汇总统计、全基因组序列和表达数量性状位点 （eQTL） 数据。我们开发了一种新方法，TESLA （使用关联统计的最佳线性组合的多祖先综合研究），将 eQTL 数据集与多祖先 GWAS 整合。通过利用祖先之间共享的表型效应并适应潜在的效应异质性，TESLA 提高了优于其他 TWAS 方法的功效。当应用于烟草使用表型时，TESLA 鉴定了 273 个新基因，与其他 TWAS 方法相比，多出 55%。这些命中和随后使用 TESLA 的精细定位指向具有生物学相关性的靶基因。计算机模拟药物再利用分析突出了几种已知有效的药物，包括右美沙芬和加兰他敏，以及可能重新用于治疗尼古丁成瘾的新药，例如肌肉松弛剂。