Abstract

BackgroundBovine 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.ResultsWe 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).ConclusionsData obtained represent a step forward to understand the biology of BLV–bovine interaction, and provide genetic information potentially applicable to selective breeding programs.

Highlights

  • Bovine leukemia virus (BLV) infection is omnipresent in dairy herds causing direct economic losses due to trade restrictions and lymphosarcoma-related deaths

  • International trade restrictions of livestock products from affected herds [9] impact on the regional economy, but the total economic loss is significantly higher if we consider milk production dropping and faster culling of asymptomatic BLV carriers compared to BLV free herds [10, 11]

  • Genome-wide distributed Single Nucleotide Polymorphisms (SNPs) were tested for association with both proviral load (PVL) and white blood cells (WBC) traits using logistic regressions assuming an additive model that incorporated as covariates 8 Principal Components (PCs) and other confounding factors Age (A); Herd (H); Lactation number (L); Percentage of Holstein (PH) and Bull (B)

Read more

Summary

Introduction

Bovine leukemia virus (BLV) infection is omnipresent in dairy herds causing direct economic losses due to trade restrictions and lymphosarcoma-related deaths. International trade restrictions of livestock products from affected herds [9] impact on the regional economy, but the total economic loss is significantly higher if we consider milk production dropping and faster culling of asymptomatic BLV carriers compared to BLV free herds [10, 11]. These conditions are emphasised in lymphocytotics animals and in those with high anti-BLV humoral response [12,13,14,15,16]. Genetic variation for resistance/susceptibility to diseases in cattle is usually polygenic, i.e. susceptibility to paratuberculosis [35, 36], mastitis [37, 38] bovine respiratory disease complex (BRDC) [39] and recently observed in a genome-wide mapping for genetics determinants of BLV incidence [40, 41]

Objectives
Methods
Results
Discussion
Conclusion
Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.