Integrative multi-omics framework for causal gene discovery in Long COVID
Sindy Pinero, Xiaomei Li, Lin Liu, Jiuyong Li, Sang Hong Lee, Marnie Winter, Thin Nguyen, Junpeng Zhang, Thuc Duy Le
Abstract
Long COVID, or Post-Acute Sequelae of SARS-CoV-2 infection (PASC), affects an estimated 10–20% of COVID-19 patients and presents persistent multisystemic symptoms. Although demographic and clinical factors, such as age, sex, and comorbidities, contribute to risk, the genetic mechanisms underlying this risk remain poorly defined. To address this gap, we developed a multi-omics framework that integrates Transcriptome-Wide Mendelian Randomization (TWMR), Control Theory (CT), Expression Quantitative Trait Loci (eQTL), Genome-Wide Association Studies (GWAS), RNA sequencing (RNA-seq), and Protein-Protein Interaction (PPI) network to identify putative causal genes and network drivers in Long COVID.
Introduction
Long COVID, also known as Post-Acute Sequelae of COVID-19 (PASC), is a complex condition characterized by the persistence or onset of symptoms after SARS-CoV-2 infection. Long COVID is defined differently by various organizations. For example, the World Health Organization (WHO) and the Centers for Disease Control and Prevention (CDC) describe it as symptoms that persist three months after infection and last at least two months [1,2].
Materials and method
Overview of the causal gene discovery framework
The causal gene discovery framework integrates various data sources, including eQTL, GWAS, RNA-seq, and PPI networks, to identify genes with putative causal roles in Long COVID (Fig 1). It begins by processing multi-omics input data (Fig 1A) and then applies an integrative scoring method (Fig 1B) that combines TWMR with CT-based network analysis.
Results
Putative causal genes of Long COVID
By varying the α values in our model, we identified a comprehensive set of putative causal genes for Long COVID, each with distinct roles. Fig 2 shows the sets of these causal genes that correspond to specific values of α.
Discussion
Long COVID, or PASC, is a multisystemic disorder whose respiratory, neurological, cardiovascular, and gastrointestinal manifestations can persist for months after the acute phase [1,2,4–6]. Despite its growing clinical impact, decisive genetic drivers remain elusive. We address this gap with a multi-omics framework that combines TWMR with CT concepts to prioritize genes that show evidence of expression-mediated effects on disease risk and occupy critical positions within the network for controllability.
Acknowledgments
The authors thank our colleagues and collaborators for their insightful feedback during the study design and synthesis phases. The authors also acknowledge institutional support from the University of South Australia.
Citation: Pinero S, Li X, Liu L, Li J, Lee SH, Winter M, et al. (2025) Integrative multi-omics framework for causal gene discovery in Long COVID. PLoS Comput Biol 21(12): e1013725. https://doi.org/10.1371/journal.pcbi.1013725
Editor: Boyang Ji, BioInnovation Institute, DENMARK
Received: May 8, 2025; Accepted: November 9, 2025; Published: December 1, 2025
Copyright: © 2025 Pinero et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the manuscript and its Supporting Information files. All processed data matrices, gene lists, statistical results, and analysis code required to replicate our findings are available on our GitHub repository (https://github.com/SindyPin/Causal-Multiomics-Method) and as interactive results hosted on https://sindypin.shinyapps.io/github/.
Funding: This work was partly supported by the Australian Research Council Discovery Project under Grant DP230101122 (to LL, JL, and TDL) and the University Presidents Scholarship (UPS) stipend (to SP). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors declare that they have no competing interests.
