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Dissecting Cis Regulation of Gene Expression in Human Metabolic Tissues

Complex diseases such as obesity and type II diabetes can result from a failure in multiple organ systems including the central nervous system and tissues involved in partitioning and disposal of nutrients. Studying the genetics of gene expression in tissues that are involved in the development of t...

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Autores principales: Dobrin, Radu, Greenawalt, Danielle M., Hu, Guanghui, Kemp, Daniel M., Kaplan, Lee M., Schadt, Eric E., Emilsson, Valur
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3166146/
https://www.ncbi.nlm.nih.gov/pubmed/21912597
http://dx.doi.org/10.1371/journal.pone.0023480
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author Dobrin, Radu
Greenawalt, Danielle M.
Hu, Guanghui
Kemp, Daniel M.
Kaplan, Lee M.
Schadt, Eric E.
Emilsson, Valur
author_facet Dobrin, Radu
Greenawalt, Danielle M.
Hu, Guanghui
Kemp, Daniel M.
Kaplan, Lee M.
Schadt, Eric E.
Emilsson, Valur
author_sort Dobrin, Radu
collection PubMed
description Complex diseases such as obesity and type II diabetes can result from a failure in multiple organ systems including the central nervous system and tissues involved in partitioning and disposal of nutrients. Studying the genetics of gene expression in tissues that are involved in the development of these diseases can provide insights into how these tissues interact within the context of disease. Expression quantitative trait locus (eQTL) studies identify mRNA expression changes linked to proximal genetic signals (cis eQTLs) that have been shown to affect disease. Given the high impact of recent eQTL studies, it is important to understand what role sample size and environment plays in identification of cis eQTLs. Here we show in a genotyped obese human population that the number of cis eQTLs obey precise scaling laws as a function of sample size in three profiled tissues, i.e. omental adipose, subcutaneous adipose and liver. Also, we show that genes (or transcripts) with cis eQTL associations detected in a small population are detected at approximately 90% rate in the largest population available for our study, indicating that genes with strong cis acting regulatory elements can be identified with relatively high confidence in smaller populations. However, by increasing the sample size we allow for better detection of weaker and more distantly located cis-regulatory elements. Yet, we determined that the number of tissue specific cis eQTLs saturates in a modestly sized cohort while the number of cis eQTLs common to all tissues fails to reach a maximum value. Understanding the power laws that govern the number and specificity of eQTLs detected in different tissues, will allow a better utilization of genetics of gene expression to inform the molecular mechanism underlying complex disease traits.
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spelling pubmed-31661462011-09-12 Dissecting Cis Regulation of Gene Expression in Human Metabolic Tissues Dobrin, Radu Greenawalt, Danielle M. Hu, Guanghui Kemp, Daniel M. Kaplan, Lee M. Schadt, Eric E. Emilsson, Valur PLoS One Research Article Complex diseases such as obesity and type II diabetes can result from a failure in multiple organ systems including the central nervous system and tissues involved in partitioning and disposal of nutrients. Studying the genetics of gene expression in tissues that are involved in the development of these diseases can provide insights into how these tissues interact within the context of disease. Expression quantitative trait locus (eQTL) studies identify mRNA expression changes linked to proximal genetic signals (cis eQTLs) that have been shown to affect disease. Given the high impact of recent eQTL studies, it is important to understand what role sample size and environment plays in identification of cis eQTLs. Here we show in a genotyped obese human population that the number of cis eQTLs obey precise scaling laws as a function of sample size in three profiled tissues, i.e. omental adipose, subcutaneous adipose and liver. Also, we show that genes (or transcripts) with cis eQTL associations detected in a small population are detected at approximately 90% rate in the largest population available for our study, indicating that genes with strong cis acting regulatory elements can be identified with relatively high confidence in smaller populations. However, by increasing the sample size we allow for better detection of weaker and more distantly located cis-regulatory elements. Yet, we determined that the number of tissue specific cis eQTLs saturates in a modestly sized cohort while the number of cis eQTLs common to all tissues fails to reach a maximum value. Understanding the power laws that govern the number and specificity of eQTLs detected in different tissues, will allow a better utilization of genetics of gene expression to inform the molecular mechanism underlying complex disease traits. Public Library of Science 2011-08-31 /pmc/articles/PMC3166146/ /pubmed/21912597 http://dx.doi.org/10.1371/journal.pone.0023480 Text en Dobrin et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Dobrin, Radu
Greenawalt, Danielle M.
Hu, Guanghui
Kemp, Daniel M.
Kaplan, Lee M.
Schadt, Eric E.
Emilsson, Valur
Dissecting Cis Regulation of Gene Expression in Human Metabolic Tissues
title Dissecting Cis Regulation of Gene Expression in Human Metabolic Tissues
title_full Dissecting Cis Regulation of Gene Expression in Human Metabolic Tissues
title_fullStr Dissecting Cis Regulation of Gene Expression in Human Metabolic Tissues
title_full_unstemmed Dissecting Cis Regulation of Gene Expression in Human Metabolic Tissues
title_short Dissecting Cis Regulation of Gene Expression in Human Metabolic Tissues
title_sort dissecting cis regulation of gene expression in human metabolic tissues
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3166146/
https://www.ncbi.nlm.nih.gov/pubmed/21912597
http://dx.doi.org/10.1371/journal.pone.0023480
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