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SUMMIT: An integrative approach for better transcriptomic data imputation improves causal gene identification

Author

Listed:
  • Zichen Zhang

    (Florida State University)

  • Ye Eun Bae

    (Florida State University)

  • Jonathan R. Bradley

    (Florida State University)

  • Lang Wu

    (Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa)

  • Chong Wu

    (The University of Texas MD Anderson Cancer Center)

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.

Suggested Citation

  • Zichen Zhang & Ye Eun Bae & Jonathan R. Bradley & Lang Wu & Chong Wu, 2022. "SUMMIT: An integrative approach for better transcriptomic data imputation improves causal gene identification," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-34016-y
    DOI: 10.1038/s41467-022-34016-y
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    1. Lida Wang & Chachrit Khunsriraksakul & Havell Markus & Dieyi Chen & Fan Zhang & Fang Chen & Xiaowei Zhan & Laura Carrel & Dajiang. J. Liu & Bibo Jiang, 2024. "Integrating single cell expression quantitative trait loci summary statistics to understand complex trait risk genes," Nature Communications, Nature, vol. 15(1), pages 1-13, December.

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