AI Insight
Researchers developed traceCB, a statistical framework that improves the identification of cell-type-specific genetic variants affecting gene expression in underrepresented populations by combining single-cell and bulk tissue data across diverse ancestries. The method increases statistical power by borrowing information from well-studied European cohorts while accounting for ancestry-specific genetic differences, effectively increasing sample sizes up to 2.9-fold and identifying 40% more regulatory genes in East Asian and African populations. TraceCB demonstrated over 90% replication rate in independent datasets and improved identification of genetic variants associated with blood and immune-related diseases.
Why it matters
This tool addresses a critical gap in genomic research by enabling more effective study of disease-causing genetic variants in non-European populations, which have historically been underrepresented in genetic studies. By revealing cell-type-specific disease mechanisms across diverse ancestries, traceCB could help reduce health disparities and improve precision medicine approaches for underserved populations.
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⚠️ Preprint – Noch nicht peer-reviewed
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Mapping cell-type-specific expression quantitative trait loci (ct-eQTLs) is essential for interpreting disease-associated variants, yet studies in underrepresented populations are hindered by limited statistical power. Here, we present traceCB, a statistical framework that enhances ct-eQTL mapping in target ancestries by integrating summary statistics from single-cell and bulk-tissue eQTL studies across diverse populations. By explicitly modeling trans-ancestry genetic architecture and accounting for cellular heterogeneity in bulk tissues, traceCB optimizes information borrowing from well-powered European cohorts while robustly controlling for type I error. Simulation studies demonstrate that traceCB achieves superior statistical power compared to original ct-eQTL, particularly when leveraging tissue-level data. In an application to immune cells in East Asian and African cohorts, traceCB increased the effective sample size by up to 2.9-fold and identified approximately 40% more eGenes than single-ancestry analyses, with a replication rate exceeding 90% in independent datasets. Furthermore, traceCB improved the colocalization of regulatory variants with GWAS signals for blood and immune-related traits, revealing cell-type-specific mechanisms underlying complex diseases. These findings establish traceCB as a powerful and scalable tool for leveraging global genomic resources to improve regulatory variant discovery at the cellular level across diverse populations.
Source: traceCB: Trans-ancestry cell-type-specific eQTLs mapping by integrating scRNA-seq and bulk data