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

BACKGROUND: 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. RESULTS: 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). CONCLUSIONS: Data obtained represent a step forward to understand the biology of BLV-bovine interaction, and provide genetic information potentially applicable to selective breeding programs.

Fine-mapping quantitative trait loci with a medium density marker panel: efficiency of population structures and comparison of linkage disequilibrium linkage analysis models.

Recently, a Haley-Knott-type regression method using combined linkage disequilibrium and linkage analyses (LDLA) was proposed to map quantitative trait loci (QTLs). Chromosome of 5 and 25 cM with 0·25 and 0·05 cM, respectively, between markers were simulated. The differences between the LDLA approaches with regard to QTL position accuracy were very limited, with a significantly better mean square error (MSE) with the LDLA regression (LDLA_reg) in sparse map cases; the contrary was observed, but not significantly, in dense map situations. The computing time required for the LDLA variance components (LDLA_vc) model was much higher than the LDLA_reg model. The precision of QTL position estimation was compared for four numbers of half-sib families, four different family sizes and two experimental designs (half-sibs, and full- and half-sibs). Regarding the number of families, MSE values were lowest for 15 or 50 half-sib families, differences not being significant. We observed that the greater the number of progenies per sire, the more accurate the QTL position. However, for a fixed population size, reducing the number of families (e.g. using a small number of large full-sib families) could lead to less accuracy of estimated QTL position.