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Nature Research, Nature Communications, 1(13), 2022

DOI: 10.1038/s41467-022-32276-2

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The whole blood transcriptional regulation landscape in 465 COVID-19 infected samples from Japan COVID-19 Task Force

Journal article published in 2022 by Qingbo S. Wang, Ryuya Edahiro, Ho Namkoong, Takanori Hasegawa, Yuya Shirai, Kyuto Sonehara, Hiromu Tanaka, Ho Lee, Ryunosuke Saiki ORCID, Takayoshi Hyugaji, Eigo Shimizu, Kotoe Katayama ORCID, Masahiro Kanai ORCID, Tatsuhiko Naito ORCID, Noah Sasa and other authors.
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Data provided by SHERPA/RoMEO

Abstract

AbstractCoronavirus disease 2019 (COVID-19) is a recently-emerged infectious disease that has caused millions of deaths, where comprehensive understanding of disease mechanisms is still unestablished. In particular, studies of gene expression dynamics and regulation landscape in COVID-19 infected individuals are limited. Here, we report on a thorough analysis of whole blood RNA-seq data from 465 genotyped samples from the Japan COVID-19 Task Force, including 359 severe and 106 non-severe COVID-19 cases. We discover 1169 putative causal expression quantitative trait loci (eQTLs) including 34 possible colocalizations with biobank fine-mapping results of hematopoietic traits in a Japanese population, 1549 putative causal splice QTLs (sQTLs; e.g. two independent sQTLs at TOR1AIP1), as well as biologically interpretable trans-eQTL examples (e.g., REST and STING1), all fine-mapped at single variant resolution. We perform differential gene expression analysis to elucidate 198 genes with increased expression in severe COVID-19 cases and enriched for innate immune-related functions. Finally, we evaluate the limited but non-zero effect of COVID-19 phenotype on eQTL discovery, and highlight the presence of COVID-19 severity-interaction eQTLs (ieQTLs; e.g., CLEC4C and MYBL2). Our study provides a comprehensive catalog of whole blood regulatory variants in Japanese, as well as a reference for transcriptional landscapes in response to COVID-19 infection.