Version 1
: Received: 5 January 2024 / Approved: 7 January 2024 / Online: 8 January 2024 (09:56:39 CET)
Version 2
: Received: 8 April 2024 / Approved: 8 April 2024 / Online: 8 April 2024 (15:09:22 CEST)
Version 3
: Received: 24 September 2024 / Approved: 25 September 2024 / Online: 25 September 2024 (12:40:43 CEST)
How to cite:
Nikpay, M. Trans-eQTLs Can Be Used to Identify Tissue-Specific Gene Regulatory Networks. Preprints2024, 2024010546. https://doi.org/10.20944/preprints202401.0546.v2
Nikpay, M. Trans-eQTLs Can Be Used to Identify Tissue-Specific Gene Regulatory Networks. Preprints 2024, 2024010546. https://doi.org/10.20944/preprints202401.0546.v2
Nikpay, M. Trans-eQTLs Can Be Used to Identify Tissue-Specific Gene Regulatory Networks. Preprints2024, 2024010546. https://doi.org/10.20944/preprints202401.0546.v2
APA Style
Nikpay, M. (2024). Trans-eQTLs Can Be Used to Identify Tissue-Specific Gene Regulatory Networks. Preprints. https://doi.org/10.20944/preprints202401.0546.v2
Chicago/Turabian Style
Nikpay, M. 2024 "Trans-eQTLs Can Be Used to Identify Tissue-Specific Gene Regulatory Networks" Preprints. https://doi.org/10.20944/preprints202401.0546.v2
Abstract
Previous high throughput screening studies indicated trans-eQTLs tend to be tissue specific. In this study, I probed if this feature can be used to identify tissue-specific gene regulatory networks. eQTL data (P<5e-8) for 16,259 genes were identified and their summary association statistics were obtained from the eQTLGene database. Next, eQTLs that display both cis and trans regulatory effects were selected and the association between their corresponding genes were examined using Mendelian randomization. A total of 169 genes that exerted trans-regulatory impacts on 692 genes were identified. 90% of the genes (N=749) aggregated into a gene regulatory network significantly enriched in hemo-immune processes. The robustness of finding was confirmed through simulation. The identified network displayed the scale-free topology. This provided the reason to examine the association of the network’s hub genes with the phenome. The outcome of analyses revealed GSDMB and ORMDL3 impact several disorders of immune origin and ALDH2 overexpression contributes to obesity. This study reports trans-eQTLs can be used to identify tissue-specific gene regulatory networks and describes a workflow to achieve this purpose. The network identified in this study showed scale-free topology indicating the hub genes of a GRN could be targeted to prevent disease outcomes.
Biology and Life Sciences, Biochemistry and Molecular Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
