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A resource for integrated genomic analysis of the human liver
In this study, we generated whole-transcriptome RNA-Seq from n = 192 genotyped liver samples and used these data with existing data from the GTEx Project (RNA-Seq) and previous liver eQTL (microarray) studies to create an enhanced transcriptomic sequence resource in the human liver. Analyses of geno...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9452507/ https://www.ncbi.nlm.nih.gov/pubmed/36071064 http://dx.doi.org/10.1038/s41598-022-18506-z |
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author | Zhou, Yi-Hui Gallins, Paul J. Etheridge, Amy S. Jima, Dereje Scholl, Elizabeth Wright, Fred A. Innocenti, Federico |
author_facet | Zhou, Yi-Hui Gallins, Paul J. Etheridge, Amy S. Jima, Dereje Scholl, Elizabeth Wright, Fred A. Innocenti, Federico |
author_sort | Zhou, Yi-Hui |
collection | PubMed |
description | In this study, we generated whole-transcriptome RNA-Seq from n = 192 genotyped liver samples and used these data with existing data from the GTEx Project (RNA-Seq) and previous liver eQTL (microarray) studies to create an enhanced transcriptomic sequence resource in the human liver. Analyses of genotype-expression associations show pronounced enrichment of associations with genes of drug response. The associations are primarily consistent across the two RNA-Seq datasets, with some modest variation, indicating the importance of obtaining multiple datasets to produce a robust resource. We further used an empirical Bayesian model to compare eQTL patterns in liver and an additional 20 GTEx tissues, finding that MHC genes, and especially class II genes, are enriched for liver-specific eQTL patterns. To illustrate the utility of the resource to augment GWAS analysis with small sample sizes, we developed a novel meta-analysis technique to combine several liver eQTL data sources. We also illustrate its application using a transcriptome-enhanced re-analysis of a study of neutropenia in pancreatic cancer patients. The associations of genotype with liver expression, including splice variation and its genetic associations, are made available in a searchable genome browser. |
format | Online Article Text |
id | pubmed-9452507 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-94525072022-09-09 A resource for integrated genomic analysis of the human liver Zhou, Yi-Hui Gallins, Paul J. Etheridge, Amy S. Jima, Dereje Scholl, Elizabeth Wright, Fred A. Innocenti, Federico Sci Rep Article In this study, we generated whole-transcriptome RNA-Seq from n = 192 genotyped liver samples and used these data with existing data from the GTEx Project (RNA-Seq) and previous liver eQTL (microarray) studies to create an enhanced transcriptomic sequence resource in the human liver. Analyses of genotype-expression associations show pronounced enrichment of associations with genes of drug response. The associations are primarily consistent across the two RNA-Seq datasets, with some modest variation, indicating the importance of obtaining multiple datasets to produce a robust resource. We further used an empirical Bayesian model to compare eQTL patterns in liver and an additional 20 GTEx tissues, finding that MHC genes, and especially class II genes, are enriched for liver-specific eQTL patterns. To illustrate the utility of the resource to augment GWAS analysis with small sample sizes, we developed a novel meta-analysis technique to combine several liver eQTL data sources. We also illustrate its application using a transcriptome-enhanced re-analysis of a study of neutropenia in pancreatic cancer patients. The associations of genotype with liver expression, including splice variation and its genetic associations, are made available in a searchable genome browser. Nature Publishing Group UK 2022-09-07 /pmc/articles/PMC9452507/ /pubmed/36071064 http://dx.doi.org/10.1038/s41598-022-18506-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Zhou, Yi-Hui Gallins, Paul J. Etheridge, Amy S. Jima, Dereje Scholl, Elizabeth Wright, Fred A. Innocenti, Federico A resource for integrated genomic analysis of the human liver |
title | A resource for integrated genomic analysis of the human liver |
title_full | A resource for integrated genomic analysis of the human liver |
title_fullStr | A resource for integrated genomic analysis of the human liver |
title_full_unstemmed | A resource for integrated genomic analysis of the human liver |
title_short | A resource for integrated genomic analysis of the human liver |
title_sort | resource for integrated genomic analysis of the human liver |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9452507/ https://www.ncbi.nlm.nih.gov/pubmed/36071064 http://dx.doi.org/10.1038/s41598-022-18506-z |
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