SUMMIT: An integrative approach for better transcriptomic data imputation improves causal gene identification.
SUMMIT: An integrative approach for better transcriptomic data imputation improves causal gene identification.
复制标题
DOI：
10.1038/s41467-022-34016-y
复制
发表时间：
2022-10-25
影响因子：
16.6
通讯作者：
中科院分区：
文献类型：
--
作者：
具有中度至低度表达遗传力的基因可以解释大部分复杂性状病因，但在传统的全转录组关联研究（TWAS）中无法充分捕获此类基因，部分原因是用于开发表达遗传预测模型以捕获基因表达的中度至低度遗传调控成分的可用参考数据集相对较小。在这里，我们介绍了一种方法，即摘要级统一建模集成转录组方法（SUMMIT），通过使用大型表达数量性状位点（eQTL）摘要级数据集来提高表达预测模型的准确性和 TWAS 的功能。我们将 SUMMIT 应用于 eQTLGen 联盟提供的 eQTL 摘要级数据。通过模拟研究和对24个复杂性状的全基因组关联研究汇总统计分析，我们表明SUMMIT提高了血液中表达预测的准确性，成功地建立了低表达遗传力基因的表达预测模型，并取得了比几种基准方法更高的统计功效。最后，我们与 SUMMIT 进行了一项有关 COVID-19 严重程度的案例研究，并确定了 11 个可能与 COVID-19 严重程度相关的致病基因。传统的 TWAS 无法充分捕获具有中低表达遗传力的基因。本研究引入了一种新方法，即摘要级统一转录组建模方法（SUMMIT），通过使用 eQTL 摘要级数据来改进 TWAS 的表达预测。
Genes with moderate to low expression heritability may explain a large proportion of complex trait etiology, but such genes cannot be sufficiently captured in conventional transcriptome-wide association studies (TWASs), partly due to the relatively small available reference datasets for developing expression genetic prediction models to capture the moderate to low genetically regulated components of gene expression. Here, we introduce a method, the Summary-level Unified Method for Modeling Integrated Transcriptome (SUMMIT), to improve the expression prediction model accuracy and the power of TWAS by using a large expression quantitative trait loci (eQTL) summary-level dataset. We apply SUMMIT to the eQTL summary-level data provided by the eQTLGen consortium. Through simulation studies and analyses of genome-wide association study summary statistics for 24 complex traits, we show that SUMMIT improves the accuracy of expression prediction in blood, successfully builds expression prediction models for genes with low expression heritability, and achieves higher statistical power than several benchmark methods. Finally, we conduct a case study of COVID-19 severity with SUMMIT and identify 11 likely causal genes associated with COVID-19 severity. Genes with moderate-low expression heritability cannot be sufficiently captured with conventional TWAS. This study introduces a new method, Summary-level Unified Method for Modeling Integrated Transcriptome (SUMMIT), to improve the expression prediction of TWAS by using eQTL summary-level data.
登录
查看更多内容
遗传对人体组织基因表达的影响。
影响因子：
64.8
作者：
GTEx Consortium;Laboratory, Data Analysis &Coordinating Center (LDACC)—Analysis Working Group;Statistical Methods groups—Analysis Working Group;Enhancing GTEx (eGTEx) groups;NIH Common Fund;NIH/NCI;NIH/NHGRI;NIH/NIMH;NIH/NIDA;Biospecimen Collection Source Site—NDRI;Biospecimen Collection Source Site—RPCI;Biospecimen Core Resource—VARI;Brain Bank Repository—University of Miami Brain Endowment Bank;Leidos Biomedical—Project Management;ELSI Study;Genome Browser Data Integration &Visualization—EBI;Genome Browser Data Integration &Visualization—UCSC Genomics Institute, University of California Santa Cruz;Lead analysts:;Laboratory, Data Analysis &Coordinating Center (LDACC):;NIH program management:;Biospecimen collection:;Pathology:;eQTL manuscript working group:;Battle A;Brown CD;Engelhardt BE;Montgomery SB
通讯作者：
Montgomery SB
影响因子：
30.8
作者：
Finucane HK;Bulik-Sullivan B;Gusev A;Trynka G;Reshef Y;Loh PR;Anttila V;Xu H;Zang C;Farh K;Ripke S;Day FR;ReproGen Consortium;Schizophrenia Working Group of the Psychiatric Genomics Consortium;RACI Consortium;Purcell S;Stahl E;Lindstrom S;Perry JR;Okada Y;Raychaudhuri S;Daly MJ;Patterson N;Neale BM;Price AL
通讯作者：
Price AL
影响因子：
64.5
作者：
Boyle EA;Li YI;Pritchard JK
通讯作者：
Pritchard JK
影响因子：
64.8
作者：
COVID-19 Host Genetics Initiative
通讯作者：
COVID-19 Host Genetics Initiative
影响因子：
30.8
作者：
Hu, Yiming;Li, Mo;Yu, Lei
通讯作者：
Yu, Lei