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Effectively identifying regulatory hotspots while capturing expression heterogeneity in gene expression studies

Expression quantitative trait loci (eQTL) mapping is a tool that can systematically identify genetic variation affecting gene expression. eQTL mapping studies have shown that certain genomic locations, referred to as regulatory hotspots, may affect the expression levels of many genes. Recently, stud...

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Detalles Bibliográficos
Autores principales: J Joo, Jong Wha, Sul, Jae Hoon, Han, Buhm, Ye, Chun, Eskin, Eleazar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4053820/
https://www.ncbi.nlm.nih.gov/pubmed/24708878
http://dx.doi.org/10.1186/gb-2014-15-4-r61
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author J Joo, Jong Wha
Sul, Jae Hoon
Han, Buhm
Ye, Chun
Eskin, Eleazar
author_facet J Joo, Jong Wha
Sul, Jae Hoon
Han, Buhm
Ye, Chun
Eskin, Eleazar
author_sort J Joo, Jong Wha
collection PubMed
description Expression quantitative trait loci (eQTL) mapping is a tool that can systematically identify genetic variation affecting gene expression. eQTL mapping studies have shown that certain genomic locations, referred to as regulatory hotspots, may affect the expression levels of many genes. Recently, studies have shown that various confounding factors may induce spurious regulatory hotspots. Here, we introduce a novel statistical method that effectively eliminates spurious hotspots while retaining genuine hotspots. Applied to simulated and real datasets, we validate that our method achieves greater sensitivity while retaining low false discovery rates compared to previous methods.
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spelling pubmed-40538202014-06-12 Effectively identifying regulatory hotspots while capturing expression heterogeneity in gene expression studies J Joo, Jong Wha Sul, Jae Hoon Han, Buhm Ye, Chun Eskin, Eleazar Genome Biol Method Expression quantitative trait loci (eQTL) mapping is a tool that can systematically identify genetic variation affecting gene expression. eQTL mapping studies have shown that certain genomic locations, referred to as regulatory hotspots, may affect the expression levels of many genes. Recently, studies have shown that various confounding factors may induce spurious regulatory hotspots. Here, we introduce a novel statistical method that effectively eliminates spurious hotspots while retaining genuine hotspots. Applied to simulated and real datasets, we validate that our method achieves greater sensitivity while retaining low false discovery rates compared to previous methods. BioMed Central 2014 2014-04-07 /pmc/articles/PMC4053820/ /pubmed/24708878 http://dx.doi.org/10.1186/gb-2014-15-4-r61 Text en Copyright © 2014 Joo et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Method
J Joo, Jong Wha
Sul, Jae Hoon
Han, Buhm
Ye, Chun
Eskin, Eleazar
Effectively identifying regulatory hotspots while capturing expression heterogeneity in gene expression studies
title Effectively identifying regulatory hotspots while capturing expression heterogeneity in gene expression studies
title_full Effectively identifying regulatory hotspots while capturing expression heterogeneity in gene expression studies
title_fullStr Effectively identifying regulatory hotspots while capturing expression heterogeneity in gene expression studies
title_full_unstemmed Effectively identifying regulatory hotspots while capturing expression heterogeneity in gene expression studies
title_short Effectively identifying regulatory hotspots while capturing expression heterogeneity in gene expression studies
title_sort effectively identifying regulatory hotspots while capturing expression heterogeneity in gene expression studies
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4053820/
https://www.ncbi.nlm.nih.gov/pubmed/24708878
http://dx.doi.org/10.1186/gb-2014-15-4-r61
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