↓ Skip to main content

Genome-wide scan for commons SNPs affecting bovine leukemia virus infection level in dairy cattle

Overview of attention for article published in BMC Genomics, February 2018
Altmetric Badge

About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (51st percentile)
  • Above-average Attention Score compared to outputs of the same age and source (54th percentile)

Mentioned by

twitter
4 X users

Citations

dimensions_citation
19 Dimensions

Readers on

mendeley
68 Mendeley
Title
Genome-wide scan for commons SNPs affecting bovine leukemia virus infection level in dairy cattle
Published in
BMC Genomics, February 2018
DOI 10.1186/s12864-018-4523-2
Pubmed ID
Authors

Hugo A. Carignano, Dana L. Roldan, María J. Beribe, María A. Raschia, Ariel Amadio, Juan P. Nani, Gerónimo Gutierrez, Irene Alvarez, Karina Trono, Mario A. Poli, Marcos M. Miretti

Abstract

Bovine leukemia virus (BLV) infection is omnipresent in dairy herds causing direct economic losses due to trade restrictions and lymphosarcoma-related deaths. Milk production drops and increase in the culling rate are also relevant and usually neglected. The BLV provirus persists throughout a lifetime and an inter-individual variation is observed in the level of infection (LI) in vivo. High LI is strongly correlated with disease progression and BLV transmission among herd mates. In a context of high prevalence, classical control strategies are economically prohibitive. Alternatively, host genomics studies aiming to dissect loci associated with LI are potentially useful tools for genetic selection programs tending to abrogate the viral spreading. The LI was measured through the proviral load (PVL) set-point and white blood cells (WBC) counts. The goals of this work were to gain insight into the contribution of SNPs (bovine 50KSNP panel) on LI variability and to identify genomics regions underlying this trait. We quantified anti-p24 response and total leukocytes count in peripheral blood from 1800 cows and used these to select 800 individuals with extreme phenotypes in WBCs and PVL. Two case-control genomic association studies using linear mixed models (LMMs) considering population stratification were performed. The proportion of the variance captured by all QC-passed SNPs represented 0.63 (SE ± 0.14) of the phenotypic variance for PVL and 0.56 (SE ± 0.15) for WBCs. Overall, significant associations (Bonferroni's corrected -log10p > 5.94) were shared for both phenotypes by 24 SNPs within the Bovine MHC. Founder haplotypes were used to measure the linkage disequilibrium (LD) extent (r2 = 0.22 ± 0.27 at inter-SNP distance of 25-50 kb). The SNPs and LD blocks indicated genes potentially associated with LI in infected cows: i.e. relevant immune response related genes (DQA1, DRB3, BOLA-A, LTA, LTB, TNF, IER3, GRP111, CRISP1), several genes involved in cell cytoskeletal reorganization (CD2AP, PKHD1, FLOT1, TUBB5) and modelling of the extracellular matrix (TRAM2, TNXB). Host transcription factors (TFs) were also highlighted (TFAP2D; ABT1, GCM1, PRRC2A). Data obtained represent a step forward to understand the biology of BLV-bovine interaction, and provide genetic information potentially applicable to selective breeding programs.

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 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 68 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 68 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 16%
Researcher 11 16%
Student > Master 10 15%
Student > Bachelor 7 10%
Other 6 9%
Other 8 12%
Unknown 15 22%
Readers by discipline Count As %
Agricultural and Biological Sciences 22 32%
Veterinary Science and Veterinary Medicine 9 13%
Biochemistry, Genetics and Molecular Biology 8 12%
Medicine and Dentistry 4 6%
Immunology and Microbiology 3 4%
Other 5 7%
Unknown 17 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 14 August 2018.
All research outputs
#13,065,845
of 23,023,224 outputs
Outputs from BMC Genomics
#4,596
of 10,699 outputs
Outputs of similar age
#210,659
of 446,078 outputs
Outputs of similar age from BMC Genomics
#87
of 202 outputs
Altmetric has tracked 23,023,224 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,699 research outputs from this source. They receive a mean Attention Score of 4.7. This one has gotten more attention than average, scoring higher than 55% 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 446,078 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.
We're also able to compare this research output to 202 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 54% of its contemporaries.