SUMMIT: An integrative approach for better transcriptomic data imputation improves causal gene identification.
Nat Commun
; 13(1): 6336, 2022 Oct 25.
Article
in English
| MEDLINE | ID: covidwho-2087204
ABSTRACT
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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Transcriptome
/
COVID-19
Type of study:
Etiology study
/
Prognostic study
Limits:
Humans
Language:
English
Journal:
Nat Commun
Journal subject:
Biology
/
Science
Year:
2022
Document Type:
Article
Affiliation country:
S41467-022-34016-y
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