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Transcriptional regulatory mechanisms in adipose and muscle tissue associate with composite gluco-metabolic phenotypes
OBJECTIVE: Tissue-specific gene expression is associated with individual metabolic measures. However, these measures may not reflect the true but latent underlying biological phenotype. This study reports gene expression associations with multi-dimensional gluco-metabolic characterizations of obesit...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5821540/ https://www.ncbi.nlm.nih.gov/pubmed/29377571 http://dx.doi.org/10.1002/oby.22113 |
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author | Langefeld, Carl D. Comeau, Mary E. Sharma, Neeraj K. Bowden, Donald W. Freedman, Barry I. Das, Swapan K. |
author_facet | Langefeld, Carl D. Comeau, Mary E. Sharma, Neeraj K. Bowden, Donald W. Freedman, Barry I. Das, Swapan K. |
author_sort | Langefeld, Carl D. |
collection | PubMed |
description | OBJECTIVE: Tissue-specific gene expression is associated with individual metabolic measures. However, these measures may not reflect the true but latent underlying biological phenotype. This study reports gene expression associations with multi-dimensional gluco-metabolic characterizations of obesity, glucose homeostasis, and lipid traits. METHODS: Factor analysis was computed using orthogonal rotation to construct composite phenotypes (CPs) from 23 traits in 256 non-diabetic African Americans. Genome-wide transcript expression data from adipose and muscle were tested for association with CPs, and expression quantitative trait loci (eQTL) were identified by association between cis-SNPs and gene expression. RESULTS: The factor analysis identified six CPs. The CPs 1 through 6 individually explained 34%, 12%, 9%, 8%, 6% and 5% of the variation in 23 gluco-metabolic traits studied. There were 3994 and 929 CP–associated transcripts identified in adipose and muscle, respectively; CP2 had the largest number of associated transcripts. Pathway analysis identified multiple canonical pathways from the CP-associated transcripts. In adipose and muscle, significant cis-eQTL were identified for 558 and 164 CP-associated transcripts (q-value <0.01), respectively. CONCLUSIONS: Adipose and muscle transcripts comprehensively define pathways involved in regulating gluco-metabolic disorders. Cis-eQTLs for CP-associated eGenes may act as primary causal determinants of gluco-metabolic phenotypes by regulating transcription of key genes. |
format | Online Article Text |
id | pubmed-5821540 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
record_format | MEDLINE/PubMed |
spelling | pubmed-58215402018-07-29 Transcriptional regulatory mechanisms in adipose and muscle tissue associate with composite gluco-metabolic phenotypes Langefeld, Carl D. Comeau, Mary E. Sharma, Neeraj K. Bowden, Donald W. Freedman, Barry I. Das, Swapan K. Obesity (Silver Spring) Article OBJECTIVE: Tissue-specific gene expression is associated with individual metabolic measures. However, these measures may not reflect the true but latent underlying biological phenotype. This study reports gene expression associations with multi-dimensional gluco-metabolic characterizations of obesity, glucose homeostasis, and lipid traits. METHODS: Factor analysis was computed using orthogonal rotation to construct composite phenotypes (CPs) from 23 traits in 256 non-diabetic African Americans. Genome-wide transcript expression data from adipose and muscle were tested for association with CPs, and expression quantitative trait loci (eQTL) were identified by association between cis-SNPs and gene expression. RESULTS: The factor analysis identified six CPs. The CPs 1 through 6 individually explained 34%, 12%, 9%, 8%, 6% and 5% of the variation in 23 gluco-metabolic traits studied. There were 3994 and 929 CP–associated transcripts identified in adipose and muscle, respectively; CP2 had the largest number of associated transcripts. Pathway analysis identified multiple canonical pathways from the CP-associated transcripts. In adipose and muscle, significant cis-eQTL were identified for 558 and 164 CP-associated transcripts (q-value <0.01), respectively. CONCLUSIONS: Adipose and muscle transcripts comprehensively define pathways involved in regulating gluco-metabolic disorders. Cis-eQTLs for CP-associated eGenes may act as primary causal determinants of gluco-metabolic phenotypes by regulating transcription of key genes. 2018-01-29 2018-03 /pmc/articles/PMC5821540/ /pubmed/29377571 http://dx.doi.org/10.1002/oby.22113 Text en http://www.nature.com/authors/editorial_policies/license.html#terms Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:http://www.nature.com/authors/editorial_policies/license.html#terms |
spellingShingle | Article Langefeld, Carl D. Comeau, Mary E. Sharma, Neeraj K. Bowden, Donald W. Freedman, Barry I. Das, Swapan K. Transcriptional regulatory mechanisms in adipose and muscle tissue associate with composite gluco-metabolic phenotypes |
title | Transcriptional regulatory mechanisms in adipose and muscle tissue associate with composite gluco-metabolic phenotypes |
title_full | Transcriptional regulatory mechanisms in adipose and muscle tissue associate with composite gluco-metabolic phenotypes |
title_fullStr | Transcriptional regulatory mechanisms in adipose and muscle tissue associate with composite gluco-metabolic phenotypes |
title_full_unstemmed | Transcriptional regulatory mechanisms in adipose and muscle tissue associate with composite gluco-metabolic phenotypes |
title_short | Transcriptional regulatory mechanisms in adipose and muscle tissue associate with composite gluco-metabolic phenotypes |
title_sort | transcriptional regulatory mechanisms in adipose and muscle tissue associate with composite gluco-metabolic phenotypes |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5821540/ https://www.ncbi.nlm.nih.gov/pubmed/29377571 http://dx.doi.org/10.1002/oby.22113 |
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