Combining multi-OMICs information to identify key-regulator genes for pleiotropic effect on fertility and production traits in beef cattle
Fonseca, Pablo Augusto de Souza (Universidade Federal de Minas Gerais. Departamento de Biologia Geral)
Id-Lahoucine, Samir (University of Guelph. Department of Animal Biosciences)
Reverter, Antonio (CSIRO Agriculture and Food. Queensland Bioscience Precinct)
Medrano, Juan F. (University of California-Davis. Department of Animal Science)
Fortes, Marina S. (The University of Queensland. School of Chemistry and Molecular Biosciences)
Casellas Vidal, Joaquim (Universitat Autònoma de Barcelona. Departament de Ciència Animal i dels Aliments)
Miglior, Filippo (Canadian Dairy Network)
Brito, Luiz (University of Guelph. Department of Animal Biosciences)
Carvalho, Maria Raquel S. (Universidade Federal de Minas Gerais. Departamento de Biologia Geral)
Schenkel, Flavio S (University of Guelph. Department of Animal Biosciences)
Nguyen, Loan T. (The University of Queensland. School of Chemistry and Molecular Biosciences)
Porto-Neto, Laercio R. (CSIRO Agriculture and Food. Queensland Bioscience Precinct)
Thomas, Milton G. (Colorado State University. Department of Animal Science)
Cánovas, Angela (University of Guelph. Department of Animal Biosciences)

Data:	2018
Resum:	The identification of biological processes related to the regulation of complex traits is a difficult task. Commonly, complex traits are regulated through a multitude of genes contributing each to a small part of the total genetic variance. Additionally, some loci can simultaneously regulate several complex traits, a phenomenon defined as pleiotropy. The lack of understanding on the biological processes responsible for the regulation of these traits results in the decrease of selection efficiency and the selection of undesirable hitchhiking effects. The identification of pleiotropic key-regulator genes can assist in developing important tools for investigating biological processes underlying complex traits. A multi-breed and multi-OMICs approach was applied to study the pleiotropic effects of key-regulator genes using three independent beef cattle populations evaluated for fertility traits. A pleiotropic map for 32 traits related to growth, feed efficiency, carcass and meat quality, and reproduction was used to identify genes shared among the different populations and breeds in pleiotropic regions. Furthermore, data-mining analyses were performed using the Cattle QTL database (CattleQTLdb) to identify the QTL category annotated in the regions around the genes shared among breeds. This approach allowed the identification of a main gene network (composed of 38 genes) shared among breeds. This gene network was significantly associated with thyroid activity, among other biological processes, and displayed a high regulatory potential. In addition, it was possible to identify genes with pleiotropic effects related to crucial biological processes that regulate economically relevant traits associated with fertility, production and health, such as MYC, PPARG, GSK3B, TG and IYD genes. These genes will be further investigated to better understand the biological processes involved in the expression of complex traits and assist in the identification of functional variants associated with undesirable phenotypes, such as decreased fertility, poor feed efficiency and negative energetic balance.
Nota:	Altres ajuts: Fundação de Amparo à Pesquisa do Estado de Minas Gerais APQ APQ-01377-17. Conselho Nacional de Desenvolvimento Científico e Tecnológico CNPq 312068/2015-8
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Genetic networks ; Cattle ; Gene regulation ; Puberty ; Energy metabolism ; Beef ; Genetics ; Genomics
Publicat a:	PloS one, Vol. 13 (october 2018) , ISSN 1932-6203

DOI: 10.1371/journal.pone.0205295
PMID: 30335783

22 p, 2.8 MB

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