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Systematic Exploration in Tissue-Pathway Associations of Complex Traits Using Comprehensive eQTLs Catalog
The collection of expression quantitative trait loci (eQTLs) is an important resource to study complex traits through understanding where and how transcriptional regulations are controlled by genetic variations in the non-coding regions of the genome. Previous studies have focused on associating eQT...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8595594/ https://www.ncbi.nlm.nih.gov/pubmed/34805976 http://dx.doi.org/10.3389/fdata.2021.719737 |
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author | Wang, Boqi Yang, James Qiu, Steven Bai, Yongsheng Qin, Zhaohui S. |
author_facet | Wang, Boqi Yang, James Qiu, Steven Bai, Yongsheng Qin, Zhaohui S. |
author_sort | Wang, Boqi |
collection | PubMed |
description | The collection of expression quantitative trait loci (eQTLs) is an important resource to study complex traits through understanding where and how transcriptional regulations are controlled by genetic variations in the non-coding regions of the genome. Previous studies have focused on associating eQTLs with traits to identify the roles of trait-related eQTLs and their corresponding target genes involved in trait determination. Since most genes function as a part of pathways in a systematic manner, it is crucial to explore the pathways’ involvements in complex traits to test potentially novel hypotheses and to reveal underlying mechanisms of disease pathogenesis. In this study, we expanded and applied loci2path software to perform large-scale eQTLs enrichment [i.e., eQTLs’ target genes (eGenes) enrichment] analysis at pathway level to identify the tissue-specific enriched pathways within trait-related genomic intervals. By utilizing 13,791,909 eQTLs cataloged in the Genotype-Tissue Expression (GTEx) V8 data for 49 tissue types, 2,893 pathway sets reported from MSigDB, and query regions derived from the Phenotype-Genotype Integrator (PheGenI) catalog, we identified intriguing biological pathways that are likely to be involved in ten traits [Alzheimer’s disease (AD), body mass index, Parkinson’s disease (PD), schizophrenia, amyotrophic lateral sclerosis, non-small cell lung cancer (NSCLC), stroke, blood pressure, autism spectrum disorder, and myocardial infarction]. Furthermore, we extracted the most significant pathways for AD, such as BioCarta D4-GDI pathway and WikiPathways sulfation biotransformation reaction and viral acute myocarditis pathways, to study specific genes within pathways. Our data presented new hypotheses in AD pathogenesis supported by previous studies, like the increased level of caspase-3 in the amygdala that cleaves GDP dissociation inhibitor and binds to beta-amyloid, leading to increased apoptosis and neuronal loss. Our findings also revealed potential pathogenesis mechanisms for PD, schizophrenia, NSCLC, blood pressure, autism spectrum disorder, and myocardial infarction, which were consistent with past studies. Our results indicated that loci2path′s eQTLs enrichment test was valuable in unveiling novel biological mechanisms of complex traits. The discovered mechanisms of disease pathogenesis and traits require further in-depth analysis and experimental validation. |
format | Online Article Text |
id | pubmed-8595594 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-85955942021-11-18 Systematic Exploration in Tissue-Pathway Associations of Complex Traits Using Comprehensive eQTLs Catalog Wang, Boqi Yang, James Qiu, Steven Bai, Yongsheng Qin, Zhaohui S. Front Big Data Big Data The collection of expression quantitative trait loci (eQTLs) is an important resource to study complex traits through understanding where and how transcriptional regulations are controlled by genetic variations in the non-coding regions of the genome. Previous studies have focused on associating eQTLs with traits to identify the roles of trait-related eQTLs and their corresponding target genes involved in trait determination. Since most genes function as a part of pathways in a systematic manner, it is crucial to explore the pathways’ involvements in complex traits to test potentially novel hypotheses and to reveal underlying mechanisms of disease pathogenesis. In this study, we expanded and applied loci2path software to perform large-scale eQTLs enrichment [i.e., eQTLs’ target genes (eGenes) enrichment] analysis at pathway level to identify the tissue-specific enriched pathways within trait-related genomic intervals. By utilizing 13,791,909 eQTLs cataloged in the Genotype-Tissue Expression (GTEx) V8 data for 49 tissue types, 2,893 pathway sets reported from MSigDB, and query regions derived from the Phenotype-Genotype Integrator (PheGenI) catalog, we identified intriguing biological pathways that are likely to be involved in ten traits [Alzheimer’s disease (AD), body mass index, Parkinson’s disease (PD), schizophrenia, amyotrophic lateral sclerosis, non-small cell lung cancer (NSCLC), stroke, blood pressure, autism spectrum disorder, and myocardial infarction]. Furthermore, we extracted the most significant pathways for AD, such as BioCarta D4-GDI pathway and WikiPathways sulfation biotransformation reaction and viral acute myocarditis pathways, to study specific genes within pathways. Our data presented new hypotheses in AD pathogenesis supported by previous studies, like the increased level of caspase-3 in the amygdala that cleaves GDP dissociation inhibitor and binds to beta-amyloid, leading to increased apoptosis and neuronal loss. Our findings also revealed potential pathogenesis mechanisms for PD, schizophrenia, NSCLC, blood pressure, autism spectrum disorder, and myocardial infarction, which were consistent with past studies. Our results indicated that loci2path′s eQTLs enrichment test was valuable in unveiling novel biological mechanisms of complex traits. The discovered mechanisms of disease pathogenesis and traits require further in-depth analysis and experimental validation. Frontiers Media S.A. 2021-11-03 /pmc/articles/PMC8595594/ /pubmed/34805976 http://dx.doi.org/10.3389/fdata.2021.719737 Text en Copyright © 2021 Wang, Yang, Qiu, Bai and Qin. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Big Data Wang, Boqi Yang, James Qiu, Steven Bai, Yongsheng Qin, Zhaohui S. Systematic Exploration in Tissue-Pathway Associations of Complex Traits Using Comprehensive eQTLs Catalog |
title | Systematic Exploration in Tissue-Pathway Associations of Complex Traits Using Comprehensive eQTLs Catalog |
title_full | Systematic Exploration in Tissue-Pathway Associations of Complex Traits Using Comprehensive eQTLs Catalog |
title_fullStr | Systematic Exploration in Tissue-Pathway Associations of Complex Traits Using Comprehensive eQTLs Catalog |
title_full_unstemmed | Systematic Exploration in Tissue-Pathway Associations of Complex Traits Using Comprehensive eQTLs Catalog |
title_short | Systematic Exploration in Tissue-Pathway Associations of Complex Traits Using Comprehensive eQTLs Catalog |
title_sort | systematic exploration in tissue-pathway associations of complex traits using comprehensive eqtls catalog |
topic | Big Data |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8595594/ https://www.ncbi.nlm.nih.gov/pubmed/34805976 http://dx.doi.org/10.3389/fdata.2021.719737 |
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