SUMMIT-FA: a new resource for improved transcriptome imputation using functional annotations.
SUMMIT-FA: a new resource for improved transcriptome imputation using functional annotations.
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SUMMIT-FA：使用功能注释改进转录组插补的新资源。
DOI：
10.1093/hmg/ddad205
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发表时间：
2024
影响因子：
3.5
通讯作者：
Wu,Chong
中科院分区：
文献类型：
--
作者：
Melton,HunterJ;Zhang,Zichen;Wu,Chong
全转录组关联研究（TWAS）整合了基因表达预测模型和全基因组关联研究（GWAS）来识别基因-性状关联。 TWAS 的威力由 GWAS 的样本量和表达预测模型的准确性决定。在这里，我们提出了一种新方法，即使用功能注释对集成转录组进行建模的摘要级统一方法（SUMMIT-FA），该方法通过利用功能注释资源和大型表达数量性状位点（eQTL）摘要级数据集来提高基因表达预测的准确性。我们使用 SUMMIT-FA 以及来自 eQTLGen 联盟的综合功能数据库 MACIE 和 eQTL 摘要级数据构建全血基因表达预测模型。我们将这些模型应用于 24 个复杂性状的 GWAS，结果表明，与几种基准方法相比，SUMMIT-FA 识别出更多的基因-性状关联，并提高了识别“银标准”基因的预测能力。我们进一步进行了模拟研究来证明 SUMMIT-FA 的有效性。
Transcriptome-wide association studies (TWAS) integrate gene expression prediction models and genome-wide association studies (GWAS) to identify gene-trait associations. The power of TWAS is determined by the sample size of GWAS and the accuracy of the expression prediction model. Here, we present a new method, the Summary-level Unified Method for Modeling Integrated Transcriptome using Functional Annotations (SUMMIT-FA), which improves gene expression prediction accuracy by leveraging functional annotation resources and a large expression quantitative trait loci (eQTL) summary-level dataset. We build gene expression prediction models in whole blood using SUMMIT-FA with the comprehensive functional database MACIE and eQTL summary-level data from the eQTLGen consortium. We apply these models to GWAS for 24 complex traits and show that SUMMIT-FA identifies significantly more gene-trait associations and improves predictive power for identifying “silver standard” genes compared to several benchmark methods. We further conduct a simulation study to demonstrate the effectiveness of SUMMIT-FA.