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Copy number variations in Friesian horses and genetic risk factors for insect bite hypersensitivity

Overview of attention for article published in BMC Genomic Data, July 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (76th percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

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6 X users
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1 Wikipedia page

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21 Dimensions

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36 Mendeley
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Title
Copy number variations in Friesian horses and genetic risk factors for insect bite hypersensitivity
Published in
BMC Genomic Data, July 2018
DOI 10.1186/s12863-018-0657-0
Pubmed ID
Authors

Anouk Schurink, Vinicius H. da Silva, Brandon D. Velie, Bert W. Dibbits, Richard P. M. A. Crooijmans, Liesbeth Franҫois, Steven Janssens, Anneleen Stinckens, Sarah Blott, Nadine Buys, Gabriella Lindgren, Bart J. Ducro

Abstract

Many common and relevant diseases affecting equine welfare have yet to be tested regarding structural variants such as copy number variations (CNVs). CNVs make up a substantial proportion of total genetic variability in populations of many species, resulting in more sequence differences between individuals than SNPs. Associations between CNVs and disease phenotypes have been established in several species, but equine CNV studies have been limited. Aim of this study was to identify CNVs and to perform a genome-wide association (GWA) study in Friesian horses to identify genomic loci associated with insect bite hypersensitivity (IBH), a common seasonal allergic dermatitis observed in many horse breeds worldwide. Genotypes were obtained using the Axiom® Equine Genotyping Array containing 670,796 SNPs. After quality control of genotypes, 15,041 CNVs and 5350 CNV regions (CNVRs) were identified in 222 Friesian horses. Coverage of the total genome by CNVRs was 11.2% with 49.2% of CNVRs containing genes. 58.0% of CNVRs were novel (i.e. so far only identified in Friesian horses). A SNP- and CNV-based GWA analysis was performed, where about half of the horses were affected by IBH. The SNP-based analysis showed a highly significant association between the MHC region on ECA20 and IBH in Friesian horses. Associations between the MHC region on ECA20 and IBH were also detected based on the CNV-based analysis. However, CNVs associated with IBH in Friesian horses were not often in close proximity to SNPs identified to be associated with IBH. CNVs were identified in a large sample of the Friesian horse population, thereby contributing to our knowledge on CNVs in horses and facilitating our understanding of the equine genome and its phenotypic expression. A clear association was identified between the MHC region on ECA20 and IBH in Friesian horses based on both SNP- and CNV-based GWA studies. These results imply that MHC contributes to IBH sensitivity in Friesian horses. Although subsequent analyses are needed for verification, nucleotide differences, as well as more complex structural variations like CNVs, seem to contribute to IBH sensitivity. IBH should be considered as a common disease with a complex genomic architecture.

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X Demographics

The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 36 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 6 17%
Other 4 11%
Student > Ph. D. Student 4 11%
Student > Postgraduate 2 6%
Student > Doctoral Student 1 3%
Other 4 11%
Unknown 15 42%
Readers by discipline Count As %
Agricultural and Biological Sciences 10 28%
Veterinary Science and Veterinary Medicine 6 17%
Unspecified 1 3%
Biochemistry, Genetics and Molecular Biology 1 3%
Pharmacology, Toxicology and Pharmaceutical Science 1 3%
Other 2 6%
Unknown 15 42%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 16 April 2023.
All research outputs
#4,590,757
of 25,385,509 outputs
Outputs from BMC Genomic Data
#146
of 1,204 outputs
Outputs of similar age
#81,025
of 340,947 outputs
Outputs of similar age from BMC Genomic Data
#2
of 22 outputs
Altmetric has tracked 25,385,509 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,204 research outputs from this source. They receive a mean Attention Score of 4.3. This one has done well, scoring higher than 87% 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 340,947 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 76% of its contemporaries.
We're also able to compare this research output to 22 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.