Genetic Effects on Transcriptome Profiles in Colon Epithelium Provide Functional Insights for Genetic Risk Loci.


Journal

Cellular and molecular gastroenterology and hepatology
ISSN: 2352-345X
Titre abrégé: Cell Mol Gastroenterol Hepatol
Pays: United States
ID NLM: 101648302

Informations de publication

Date de publication:
2021
Historique:
received: 02 07 2020
revised: 06 02 2021
accepted: 08 02 2021
pubmed: 19 2 2021
medline: 27 1 2022
entrez: 18 2 2021
Statut: ppublish

Résumé

The association of genetic variation with tissue-specific gene expression and alternative splicing guides functional characterization of complex trait-associated loci and may suggest novel genes implicated in disease. Here, our aims were as follows: (1) to generate reference profiles of colon mucosa gene expression and alternative splicing and compare them across colon subsites (ascending, transverse, and descending), (2) to identify expression and splicing quantitative trait loci (QTLs), (3) to find traits for which identified QTLs contribute to single-nucleotide polymorphism (SNP)-based heritability, (4) to propose candidate effector genes, and (5) to provide a web-based visualization resource. We collected colonic mucosal biopsy specimens from 485 healthy adults and performed bulk RNA sequencing. We performed genome-wide SNP genotyping from blood leukocytes. Statistical approaches and bioinformatics software were used for QTL identification and downstream analyses. We provided a complete quantification of gene expression and alternative splicing across colon subsites and described their differences. We identified thousands of expression and splicing QTLs and defined their enrichment at genome-wide regulatory regions. We found that part of the SNP-based heritability of diseases affecting colon tissue, such as colorectal cancer and inflammatory bowel disease, but also of diseases affecting other tissues, such as psychiatric conditions, can be explained by the identified QTLs. We provided candidate effector genes for multiple phenotypes. Finally, we provided the Colon Transcriptome Explorer web application. We provide a large characterization of gene expression and splicing across colon subsites. Our findings provide greater etiologic insight into complex traits and diseases influenced by transcriptomic changes in colon tissue.

Sections du résumé

BACKGROUND & AIMS
The association of genetic variation with tissue-specific gene expression and alternative splicing guides functional characterization of complex trait-associated loci and may suggest novel genes implicated in disease. Here, our aims were as follows: (1) to generate reference profiles of colon mucosa gene expression and alternative splicing and compare them across colon subsites (ascending, transverse, and descending), (2) to identify expression and splicing quantitative trait loci (QTLs), (3) to find traits for which identified QTLs contribute to single-nucleotide polymorphism (SNP)-based heritability, (4) to propose candidate effector genes, and (5) to provide a web-based visualization resource.
METHODS
We collected colonic mucosal biopsy specimens from 485 healthy adults and performed bulk RNA sequencing. We performed genome-wide SNP genotyping from blood leukocytes. Statistical approaches and bioinformatics software were used for QTL identification and downstream analyses.
RESULTS
We provided a complete quantification of gene expression and alternative splicing across colon subsites and described their differences. We identified thousands of expression and splicing QTLs and defined their enrichment at genome-wide regulatory regions. We found that part of the SNP-based heritability of diseases affecting colon tissue, such as colorectal cancer and inflammatory bowel disease, but also of diseases affecting other tissues, such as psychiatric conditions, can be explained by the identified QTLs. We provided candidate effector genes for multiple phenotypes. Finally, we provided the Colon Transcriptome Explorer web application.
CONCLUSIONS
We provide a large characterization of gene expression and splicing across colon subsites. Our findings provide greater etiologic insight into complex traits and diseases influenced by transcriptomic changes in colon tissue.

Identifiants

pubmed: 33601062
pii: S2352-345X(21)00036-9
doi: 10.1016/j.jcmgh.2021.02.003
pmc: PMC8102177
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

181-197

Commentaires et corrections

Type : ErratumIn

Informations de copyright

Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.

Auteurs

Virginia Díez-Obrero (V)

Oncology Data Analytics Program, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona, Spain; Colorectal Cancer Group, Molecular Mechanisms and Experimental Therapy in Oncology (ONCOBELL) Program, Bellvitge Biomedical Research Institute, Spain; Consortium for Biomedical Research in Epidemiology and Public Health, Madrid, Spain; Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain.

Christopher H Dampier (CH)

Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia; Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia; Department of Surgery, University of Virginia, Charlottesville, Virginia.

Ferran Moratalla-Navarro (F)

Oncology Data Analytics Program, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona, Spain; Consortium for Biomedical Research in Epidemiology and Public Health, Madrid, Spain; Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain.

Matthew Devall (M)

Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia; Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia.

Sarah J Plummer (SJ)

Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia; Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia.

Anna Díez-Villanueva (A)

Oncology Data Analytics Program, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona, Spain; Colorectal Cancer Group, Molecular Mechanisms and Experimental Therapy in Oncology (ONCOBELL) Program, Bellvitge Biomedical Research Institute, Spain; Consortium for Biomedical Research in Epidemiology and Public Health, Madrid, Spain.

Ulrike Peters (U)

Epidemiology Department, University of Washington, Seattle, Washington; Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington.

Stephanie Bien (S)

Epidemiology Department, University of Washington, Seattle, Washington; Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington.

Jeroen R Huyghe (JR)

Epidemiology Department, University of Washington, Seattle, Washington; Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington.

Anshul Kundaje (A)

Department of Genetics, Stanford University, Stanford, California.

Gemma Ibáñez-Sanz (G)

Oncology Data Analytics Program, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona, Spain; Colorectal Cancer Group, Molecular Mechanisms and Experimental Therapy in Oncology (ONCOBELL) Program, Bellvitge Biomedical Research Institute, Spain; Consortium for Biomedical Research in Epidemiology and Public Health, Madrid, Spain; Gastroenterology Department, Bellvitge University Hospital, L'Hospitalet de Llobregat, Barcelona, Spain.

Elisabeth Guinó (E)

Oncology Data Analytics Program, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona, Spain; Colorectal Cancer Group, Molecular Mechanisms and Experimental Therapy in Oncology (ONCOBELL) Program, Bellvitge Biomedical Research Institute, Spain; Consortium for Biomedical Research in Epidemiology and Public Health, Madrid, Spain.

Mireia Obón-Santacana (M)

Oncology Data Analytics Program, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona, Spain; Colorectal Cancer Group, Molecular Mechanisms and Experimental Therapy in Oncology (ONCOBELL) Program, Bellvitge Biomedical Research Institute, Spain; Consortium for Biomedical Research in Epidemiology and Public Health, Madrid, Spain.

Robert Carreras-Torres (R)

Oncology Data Analytics Program, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona, Spain; Colorectal Cancer Group, Molecular Mechanisms and Experimental Therapy in Oncology (ONCOBELL) Program, Bellvitge Biomedical Research Institute, Spain; Consortium for Biomedical Research in Epidemiology and Public Health, Madrid, Spain.

Graham Casey (G)

Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia; Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia. Electronic address: gc8r@virginia.edu.

Víctor Moreno (V)

Oncology Data Analytics Program, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona, Spain; Colorectal Cancer Group, Molecular Mechanisms and Experimental Therapy in Oncology (ONCOBELL) Program, Bellvitge Biomedical Research Institute, Spain; Consortium for Biomedical Research in Epidemiology and Public Health, Madrid, Spain; Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain. Electronic address: v.moreno@iconcologia.net.

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