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Identification of putative regulatory regions and transcription factors associated with intramuscular fat content traits

Overview of attention for article published in BMC Genomics, June 2018
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Title
Identification of putative regulatory regions and transcription factors associated with intramuscular fat content traits
Published in
BMC Genomics, June 2018
DOI 10.1186/s12864-018-4871-y
Pubmed ID
Authors

Aline S. M. Cesar, Luciana C. A. Regitano, James M. Reecy, Mirele D. Poleti, Priscila S. N. Oliveira, Gabriella B. de Oliveira, Gabriel C. M. Moreira, Maurício A. Mudadu, Polyana C. Tizioto, James E. Koltes, Elyn Fritz-Waters, Luke Kramer, Dorian Garrick, Hamid Beiki, Ludwig Geistlinger, Gerson B. Mourão, Adhemar Zerlotini, Luiz L. Coutinho

Abstract

Integration of high throughput DNA genotyping and RNA-sequencing data allows for the identification of genomic regions that control gene expression, known as expression quantitative trait loci (eQTL), on a whole genome scale. Intramuscular fat (IMF) content and carcass composition play important roles in metabolic and physiological processes in mammals because they influence insulin sensitivity and consequently prevalence of metabolic diseases such as obesity and type 2 diabetes. However, limited information is available on the genetic variants and mechanisms associated with IMF deposition in mammals. Thus, our hypothesis was that eQTL analyses could identify putative regulatory regions and transcription factors (TFs) associated with intramuscular fat (IMF) content traits. We performed an integrative eQTL study in skeletal muscle to identify putative regulatory regions and factors associated with intramuscular fat content traits. Data obtained from skeletal muscle samples of 192 animals was used for association analysis between 461,466 SNPs and the transcription level of 11,808 genes. This yielded 1268 cis- and 10,334 trans-eQTLs, among which we identified nine hotspot regions that each affected the expression of > 119 genes. These putative regulatory regions overlapped with previously identified QTLs for IMF content. Three of the hotspots respectively harbored the transcription factors USF1, EGR4 and RUNX1T1, which are known to play important roles in lipid metabolism. From co-expression network analysis, we further identified modules significantly correlated with IMF content and associated with relevant processes such as fatty acid metabolism, carbohydrate metabolism and lipid metabolism. This study provides novel insights into the link between genotype and IMF content as evident from the expression level. It thereby identifies genomic regions of particular importance and associated regulatory factors. These new findings provide new knowledge about the biological processes associated with genetic variants and mechanisms associated with IMF deposition in mammals.

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Mendeley readers

The data shown below were compiled from readership statistics for 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 58 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 10 17%
Student > Bachelor 8 14%
Researcher 8 14%
Student > Ph. D. Student 7 12%
Student > Doctoral Student 4 7%
Other 9 16%
Unknown 12 21%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 16 28%
Agricultural and Biological Sciences 15 26%
Veterinary Science and Veterinary Medicine 2 3%
Nursing and Health Professions 2 3%
Pharmacology, Toxicology and Pharmaceutical Science 1 2%
Other 5 9%
Unknown 17 29%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 28 June 2018.
All research outputs
#10,485,813
of 13,153,703 outputs
Outputs from BMC Genomics
#5,972
of 7,744 outputs
Outputs of similar age
#201,754
of 268,813 outputs
Outputs of similar age from BMC Genomics
#7
of 7 outputs
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