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Genome-wide CNV analysis reveals variants associated with growth traits in Bos indicus

Overview of attention for article published in BMC Genomics, June 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (85th percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

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1 tweeter


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Genome-wide CNV analysis reveals variants associated with growth traits in Bos indicus
Published in
BMC Genomics, June 2016
DOI 10.1186/s12864-016-2461-4
Pubmed ID

Yang Zhou, Yuri T. Utsunomiya, Lingyang Xu, El Hamidi abdel Hay, Derek M. Bickhart, Pamela Almeida Alexandre, Benjamin D. Rosen, Steven G. Schroeder, Roberto Carvalheiro, Haroldo Henrique de Rezende Neves, Tad S. Sonstegard, Curtis P. Van Tassell, José Bento Sterman Ferraz, Heidge Fukumasu, Jose Fernando Garcia, George E. Liu


Apart from single nucleotide polymorphism (SNP), copy number variation (CNV) is another important type of genetic variation, which may affect growth traits and play key roles for the production of beef cattle. To date, no genome-wide association study (GWAS) for CNV and body traits in beef cattle has been reported, so the present study aimed to investigate this type of association in one of the most important cattle subspecies: Bos indicus (Nellore breed). We have used intensity data from over 700,000 SNP probes across the bovine genome to detect common CNVs in a sample of 2230 Nellore cattle, and performed GWAS between the detected CNVs and nine growth traits. After filtering for frequency and length, a total of 231 CNVs ranging from 894 bp to 4,855,088 bp were kept and tested as predictors for each growth trait using linear regression analysis with principal components correction. There were 49 significant associations identified among 17 CNVs and seven body traits after false discovery rate correction (P < 0.05). Among the 17 CNVs, three were significant or marginally significant for all the traits. We have compared the locations of associated CNVs with quantitative trait locus and the RefGene database, and found two sets of 9 CNVs overlapping with either known QTLs or genes, respectively. The gene overlapping with CNV100, KCNJ12, is a functional candidate for muscle development and plays critical roles in muscling traits. This study presents the first CNV-based GWAS of growth traits using high density SNP microarray data in cattle. We detected 17 CNVs significantly associated with seven growth traits and one of them (CNV100) may be involved in growth traits through KCNJ12.

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Mendeley readers

The data shown below were compiled from readership statistics for 76 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 76 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 20 26%
Student > Master 14 18%
Researcher 12 16%
Other 6 8%
Student > Bachelor 4 5%
Other 8 11%
Unknown 12 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 38 50%
Biochemistry, Genetics and Molecular Biology 10 13%
Medicine and Dentistry 4 5%
Veterinary Science and Veterinary Medicine 2 3%
Physics and Astronomy 1 1%
Other 2 3%
Unknown 19 25%

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 07 May 2018.
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Outputs of similar age from BMC Genomics
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Altmetric has tracked 12,902,491 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,573 research outputs from this source. They receive a mean Attention Score of 4.3. This one has done particularly well, scoring higher than 90% of its peers.
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 264,322 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 85% of its contemporaries.
We're also able to compare this research output to 15 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.