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Genomic structure and marker-derived gene networks for growth and meat quality traits of Brazilian Nelore beef cattle

Overview of attention for article published in BMC Genomics, March 2016
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Title
Genomic structure and marker-derived gene networks for growth and meat quality traits of Brazilian Nelore beef cattle
Published in
BMC Genomics, March 2016
DOI 10.1186/s12864-016-2535-3
Pubmed ID
Authors

Maurício A. Mudadu, Laercio R. Porto-Neto, Fabiana B. Mokry, Polyana C. Tizioto, Priscila S. N. Oliveira, Rymer R. Tullio, Renata T. Nassu, Simone C. M. Niciura, Patrícia Tholon, Maurício M. Alencar, Roberto H. Higa, Antônio N. Rosa, Gélson L. D. Feijó, André L. J. Ferraz, Luiz O. C. Silva, Sérgio R. Medeiros, Dante P. Lanna, Michele L. Nascimento, Amália S. Chaves, Andrea R. D. L. Souza, Irineu U. Packer, Roberto A. A. Torres, Fabiane Siqueira, Gerson B. Mourão, Luiz L. Coutinho, Antonio Reverter, Luciana C. A. Regitano

Abstract

Nelore is the major beef cattle breed in Brazil with more than 130 million heads. Genome-wide association studies (GWAS) are often used to associate markers and genomic regions to growth and meat quality traits that can be used to assist selection programs. An alternative methodology to traditional GWAS that involves the construction of gene network interactions, derived from results of several GWAS is the AWM (Association Weight Matrices)/PCIT (Partial Correlation and Information Theory). With the aim of evaluating the genetic architecture of Brazilian Nelore cattle, we used high-density SNP genotyping data (~770,000 SNP) from 780 Nelore animals comprising 34 half-sibling families derived from highly disseminated and unrelated sires from across Brazil. The AWM/PCIT methodology was employed to evaluate the genes that participate in a series of eight phenotypes related to growth and meat quality obtained from this Nelore sample. Our results indicate a lack of structuring between the individuals studied since principal component analyses were not able to differentiate families by its sires or by its ancestral lineages. The application of the AWM/PCIT methodology revealed a trio of transcription factors (comprising VDR, LHX9 and ZEB1) which in combination connected 66 genes through 359 edges and whose biological functions were inspected, some revealing to participate in biological growth processes in literature searches. The diversity of the Nelore sample studied is not high enough to differentiate among families neither by sires nor by using the available ancestral lineage information. The gene networks constructed from the AWM/PCIT methodology were a useful alternative in characterizing genes and gene networks that were allegedly influential in growth and meat quality traits in Nelore cattle.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 94 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 94 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 19 20%
Researcher 13 14%
Student > Bachelor 8 9%
Student > Doctoral Student 7 7%
Student > Ph. D. Student 7 7%
Other 19 20%
Unknown 21 22%
Readers by discipline Count As %
Agricultural and Biological Sciences 40 43%
Biochemistry, Genetics and Molecular Biology 9 10%
Veterinary Science and Veterinary Medicine 9 10%
Medicine and Dentistry 2 2%
Psychology 2 2%
Other 4 4%
Unknown 28 30%
Attention Score in Context

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 15 July 2016.
All research outputs
#15,364,458
of 22,856,968 outputs
Outputs from BMC Genomics
#6,694
of 10,661 outputs
Outputs of similar age
#178,713
of 299,392 outputs
Outputs of similar age from BMC Genomics
#152
of 214 outputs
Altmetric has tracked 22,856,968 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,661 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 29th percentile – i.e., 29% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 299,392 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 214 others from the same source and published within six weeks on either side of this one. This one is in the 19th percentile – i.e., 19% of its contemporaries scored the same or lower than it.