Transcriptome-wide association studies (TWASs) are a powerful approach to identify genes whose expression is associated with complex disease risk. However, non-causal genes can exhibit association signals due to confounding by linkage disequilibrium (LD) patterns and eQTL pleiotropy at genomic risk regions, which necessitates fine-mapping of TWAS signals. Here, we present MA-FOCUS, a multi-ancestry framework for the improved identification of genes underlying traits of interest. We demonstrate that by leveraging differences in ancestry-specific patterns of LD and eQTL signals, MA-FOCUS consistently outperforms single-ancestry fine-mapping approaches with equivalent total sample sizes across multiple metrics. We perform TWASs for 15 blood traits using genome-wide summary statistics (average n_EA = 511 k, n_AA = 13 k) and lymphoblastoid cell line eQTL data from cohorts of primarily European and African continental ancestries. We recapitulate evidence demonstrating shared genetic architectures for eQTL and blood traits between the two ancestry groups and observe that gene-level effects correlate 20% more strongly across ancestries than SNP-level effects. Lastly, we perform fine-mapping using MA-FOCUS and find evidence that genes at TWAS risk regions are more likely to be shared across ancestries than they are to be ancestry specific. Using multiple lines of evidence to validate our findings, we find that gene sets produced by MA-FOCUS are more enriched in hematopoietic categories than alternative approaches (p = 2.36 × 10⁻¹⁵). Our work demonstrates that including and appropriately accounting for genetic diversity can drive more profound insights into the genetic architecture of complex traits.

全转录组关联研究 (TWAS) 是一种强大的方法，可用于识别其表达与复杂疾病风险相关的基因。然而，由于基因组风险区域的连锁不平衡 (LD) 模式和 eQTL 多效性的混杂，非因果基因可能会表现出关联信号，这需要对 TWAS 信号进行精细定位。在这里，我们介绍了 MA-FOCUS，这是一个多祖先框架，用于改进感兴趣特征的基因识别。我们证明，通过利用 LD 和 eQTL 信号的祖先特定模式的差异，MA-FOCUS 在跨多个指标的总样本量相等的情况下，始终优于单一祖先精细定位方法。我们使用全基因组汇总统计（平均 n _EA  = 511 k，n_AA  = 13 k) 和淋巴母细胞系 eQTL 数据来自主要是欧洲和非洲大陆血统的队列。我们概括了证明两个祖先群体之间 eQTL 和血液性状的共享遗传结构的证据，并观察到基因水平效应在祖先之间的相关性比 SNP 水平效应强 20%。最后，我们使用 MA-FOCUS 进行精细定位，并发现证据表明 TWAS 风险区域的基因更有可能在祖先之间共享，而不是特定于祖先。使用多行证据来验证我们的发现，我们发现 MA-FOCUS 产生的基因组比其他方法更丰富造血类别 (p = 2.36 × 10 ^-15). 我们的工作表明，包括并适当考虑遗传多样性可以推动对复杂性状遗传结构的更深刻洞察。