一种协方差增强的多组织联合eQTL定位方法及其在全转录组关联研究中的应用
A covariance-enhanced approach to multi-tissue joint eQTL mapping with application to transcriptome-wide association studies
摘要：
基于遗传预测基因表达的全转录组关联研究有可能识别与各种复杂性状相关的新区域。已经表明，整合对应于多种组织类型的表达数量性状基因座（eQTL）可以提高涉及复杂病因学的关联研究的能力。在这篇文章中，我们提出了一个新的多变量响应线性回归模型和方法，同时预测基因在多个组织中的表达。与现有的多组织联合eQTL定位方法不同，我们的方法结合了组织-组织表达相关性，这使我们能够更有效地处理缺失的表达测量，并使用eQTL基因型的加权总和更准确地预测基因表达。我们通过模拟研究表明，我们的方法在许多情况下比现有的方法表现更好。我们使用我们的方法来估计GTEx收集的29个组织的eQTL权重，并表明我们的方法与竞争对手相比显着提高了表达预测的准确性。使用我们的eQTL权重，我们进行了一个基于多组织的S-MultiXcan全转录组关联研究，并表明我们的方法在新区域和整体上比现有方法有更多的发现。估计的eQTL权重可在github.com/ajmolstad/MTeQTLResults在线下载。
英文摘要：
Transcriptome-wide association studies based on genetically predicted gene expression have the potential to identify novel regions associated with various complex traits. It has been shown that incorporating expression quantitative trait loci (eQTLs) corresponding to multiple tissue types can improve power for association studies involving complex etiology. In this article, we propose a new multivariate response linear regression model and method for predicting gene expression in multiple tissues simultaneously. Unlike existing methods for multi-tissue joint eQTL mapping, our approach incorporates tissue-tissue expression correlation, which allows us to more efficiently handle missing expression measurements and more accurately predict gene expression using a weighted summation of eQTL genotypes. We show through simulation studies that our approach performs better than the existing methods in many scenarios. We use our method to estimate eQTL weights for 29 tissues collected by GTEx, and show that our approach significantly improves expression prediction accuracy compared to competitors. Using our eQTL weights, we perform a multi-tissue-based S-MultiXcan transcriptome-wide association study and show that our method leads to more discoveries in novel regions and more discoveries overall than the existing methods. Estimated eQTL weights are available for download online at github.com/ajmolstad/MTeQTLResults.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
