A statistical framework for cross-tissue transcriptome-wide association analysis
A statistical framework for cross-tissue transcriptome-wide association analysis
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DOI：
10.1038/s41588-019-0345-7
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发表时间：
2019-03-01
期刊：
影响因子：
30.8
通讯作者：
Yu, Lei
中科院分区：
文献类型：
--
作者：
Hu, Yiming;Li, Mo;Yu, Lei
全转录组关联分析是研究复杂性状遗传结构的有力方法。该方法的一个关键组成部分是建立一个模型，通过在给定组织中使用具有匹配基因型和基因表达数据的样本，从基因型中推算基因表达水平。然而，对于任何单一组织，在有限的样本量下开发稳健和准确的植入模型是具有挑战性的。在这里，我们首先引入了一种多任务学习方法来联合计算44个人体组织的基因表达。与单组织方法相比，我们的方法平均提高了39%的植入准确性，平均多生成120%的有效基因植入模型。我们描述了一个基于汇总统计的测试框架，将多个单组织关联结合成一个强大的度量来量化整体基因-性状关联。我们将我们的方法称为most（分子特征统一测试）应用于多个全基因组关联结果，并证明了其优于单组织策略的优势。
Transcriptome-wide association analysis is a powerful approach to studying the genetic architecture of complex traits. A key component of this approach is to build a model to impute gene expression levels from genotypes by using samples with matched genotypes and gene expression data in a given tissue. However, it is challenging to develop robust and accurate imputation models with a limited sample size for any single tissue. Here, we first introduce a multi-task learning method to jointly impute gene expression in 44 human tissues. Compared with single-tissue methods, our approach achieved an average of 39% improvement in imputation accuracy and generated effective imputation models for an average of 120% more genes. We describe a summary-statistic-based testing framework that combines multiple single-tissue associations into a powerful metric to quantify the overall gene-trait association. We applied our method, called UTMOST (unified test for molecular signatures), to multiple genome-wide-association results and demonstrate its advantages over single-tissue strategies.