Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
Oncode Institute, Utrecht, The Netherlands.
Genome Biol. 2024 Jan 22;25(1):29. doi: 10.1186/s13059-023-03151-0.
Expression quantitative trait loci (eQTL) offer insights into the regulatory mechanisms of trait-associated variants, but their effects often rely on contexts that are unknown or unmeasured. We introduce PICALO, a method for hidden variable inference of eQTL contexts. PICALO identifies and disentangles technical from biological context in heterogeneous blood and brain bulk eQTL datasets. These contexts are biologically informative and reproducible, outperforming cell counts or expression-based principal components. Furthermore, we show that RNA quality and cell type proportions interact with thousands of eQTLs. Knowledge of hidden eQTL contexts may aid in the inference of functional mechanisms underlying disease variants.
表达数量性状基因座（eQTL）为研究与性状相关变异的调控机制提供了线索，但它们的作用往往依赖于未知或未测量的背景。我们引入了 PICALO，这是一种用于推断 eQTL 背景隐藏变量的方法。PICALO 可以识别和区分异质血液和大脑整体 eQTL 数据集中的技术背景和生物学背景。这些背景具有生物学意义且可重复，优于细胞计数或基于表达的主成分分析。此外，我们还表明 RNA 质量和细胞类型比例与数千个 eQTL 相互作用。对隐藏 eQTL 背景的了解可能有助于推断疾病变异背后的功能机制。
