RESUMO
Ticks cause substantial production losses for beef and dairy cattle. Cattle resistance to ticks is one of the most important factors affecting tick control, but largely neglected due to the challenge of phenotyping. In this study, we evaluate the pooling of tick resistance phenotyped reference populations from multi-country beef cattle breeds to assess the possibility of improving host resistance through multi-trait genomic selection. Data consisted of tick counts or scores assessing the number of female ticks at least 4.5 mm length and derived from seven populations, with breed, country, number of records and genotyped/phenotyped animals being respectively: Angus (AN), Brazil, 2,263, 921/1,156, Hereford (HH), Brazil, 6,615, 1,910/2,802, Brangus (BN), Brazil, 2,441, 851/851, Braford (BO), Brazil, 9,523, 3,062/4,095, Tropical Composite (TC), Australia, 229, 229/229, Brahman (BR), Australia, 675, 675/675, and Nguni (NG), South Africa, 490, 490/490. All populations were genotyped using medium density Illumina SNP BeadChips and imputed to a common high-density panel of 332,468 markers. The mean linkage disequilibrium (LD) between adjacent SNPs varied from 0.24 to 0.37 across populations and so was sufficient to allow genomic breeding values (GEBV) prediction. Correlations of LD phase between breeds were higher between composites and their founder breeds (0.81 to 0.95) and lower between NG and the other breeds (0.27 and 0.35). There was wide range of estimated heritability (0.05 and 0.42) and genetic correlation (-0.01 and 0.87) for tick resistance across the studied populations, with the largest genetic correlation observed between BN and BO. Predictive ability was improved under the old-young validation for three of the seven populations using a multi-trait approach compared to a single trait within-population prediction, while whole and partial data GEBV correlations increased in all cases, with relative improvements ranging from 3% for BO to 64% for TC. Moreover, the multi-trait analysis was useful to correct typical over-dispersion of the GEBV. Results from this study indicate that a joint genomic evaluation of AN, HH, BN, BO and BR can be readily implemented to improve tick resistance of these populations using selection on GEBV. For NG and TC additional phenotyping will be required to obtain accurate GEBV.
Assuntos
Cruzamento , Bovinos/genética , Resistência à Doença/genética , Genoma , Genômica/métodos , Infestações por Carrapato/veterinária , Carrapatos/fisiologia , Animais , Brasil , Bovinos/fisiologia , Feminino , Genótipo , Desequilíbrio de Ligação , Masculino , Fenótipo , Polimorfismo de Nucleotídeo Único , Característica Quantitativa Herdável , África do Sul , Infestações por Carrapato/genéticaRESUMO
The aim of this study was to identify genomic regions underlying milk production traits in the Valle del Belice dairy sheep using regional heritability mapping (RHM). Repeated measurements for milk yield (MY), fat percentage and yield (F% and FY) and protein percentage and yield (P% and PY), collected over a period of 6 years (2006-2012) on 481 Valle del Belice ewes, were used for the analysis. Animals were genotyped with the Illumina 50k SNP chip. Variance components, heritabilities and repeatabilities within and across lactations were estimated, fitting parity, litter size, season of lambing and fortnights in milk, as fixed; and additive genetic, permanent environment within and across lactations, flock by test-day interaction and residual as random effects. For the RHM analysis, the model included the same fixed and random effects as before, plus an additional regional genomic additive effect (specific for the region being tested) as random. While the whole genomic additive effect was estimated using the genomic relationship matrix (GRM) constructed from all SNPs, the regional genomic additive effect was estimated from a GRM matrix constructed from the SNPs within each region. Heritability estimates ranged between 0.06 and 0.15, with repeatabilities being between 0.14 and 0.24 across lactations and between 0.23 and 0.39 within lactation for all milk production traits. A substantial effect of flock-test-day on milk production traits was also estimated. Significant genomic regions at either genome-wide (p < .05) or suggestive (i.e., one false positive per genome scan) level were identified on chromosome (OAR) 2, 3 and 20 for F% and on OAR3 for P%, with the regions on OAR3 in common between the two traits. Our results confirmed the role of LALBA and AQP genes, on OAR3, as candidate genes for milk production traits in sheep.
Assuntos
Lactação , Leite , Carneiro Doméstico/genética , Animais , Feminino , Genômica , Lactação/genética , Fenótipo , Gravidez , Ovinos/genéticaRESUMO
Using receiver-operating characteristic (ROC) curve methodology this study was designed to assess the diagnostic effectiveness of somatic cell count (SCC) and the California mastitis test (CMT) in Valle del Belice sheep, and to propose and evaluate threshold values for those tests that would optimally discriminate between healthy and infected udders. Milk samples (n=1357) were collected from 684 sheep in four flocks. The prevalence of infection, as determined by positive bacterial culture was 0.36, 87.7% of which were minor and 12.3% major pathogens. Of the culture negative samples, 83.7% had an SCC<500,000/mL and 97.4% had <1,000,000cells/mL. When the associations between SC score (SCS) and whole sample status (culture negative vs. infected), minor pathogen status (culture negative vs. infected with minor pathogens), major pathogen status (culture negative vs. infected with major pathogens), and CMT results were evaluated, the estimated area under the ROC curve was greater for glands infected with major compared to minor pathogens (0.88 vs. 0.73), whereas the area under the curve considering all pathogens was similar to the one for minor pathogens (0.75). The estimated optimal thresholds were 3.00 (CMT), 2.81 (SCS for the whole sample), 2.81 (SCS for minor pathogens), and 3.33 (SCS for major pathogens). These correctly classified, respectively, 69.0%, 73.5%, 72.6% and 91.0% of infected udders in the samples. The CMT appeared only to discriminate udders infected with major pathogens. In this population, SCS appeared to be the best indirect test of the bacteriological status of the udder.
Assuntos
Leite/citologia , Doenças dos Ovinos/diagnóstico , Animais , Indústria de Laticínios , Feminino , OvinosRESUMO
The aim of this work was to study ß-defensin 1 (SBD1) and ß-defensin 2 (SBD2) genes in Valle del Belice dairy sheep in order to identify polymorphisms that can be utilized as markers of the analyzed genes, and search for the functional effects and roles of the identified polymorphisms (variation of the amino acid sequence of the protein and stability of mRNA molecule). The study was conducted on 300 randomly selected animals belonging to four flocks. A total of seven SNPs were identified, two in SBD1 and five in SBD2. The two SNPs identified in SBD2 coding region, at position 1659 and position 1667, were non-synonymous, leading to amino acid changes in the protein product. Nevertheless, the functional effects predicted by the two SNPs demonstrated that amino acid substitutions may not have effect on ß-defensin 2 protein function. Moreover, we demonstrated that SBD2 mutant sequence shows changes in mRNA secondary structure. These results suggest that identified SNPs could play a role in the modulation of the immune response.
Assuntos
Indústria de Laticínios , Polimorfismo de Nucleotídeo Único/genética , Carneiro Doméstico/genética , beta-Defensinas/genética , Animais , Sequência de Bases , Biologia Computacional , Frequência do Gene/genética , Genótipo , Dados de Sequência Molecular , Conformação de Ácido Nucleico , RNA Mensageiro/química , RNA Mensageiro/genéticaRESUMO
BACKGROUND: Somatic cell score (SCS) has been promoted as a selection criterion to improve mastitis resistance. However, SCS from healthy and infected animals may be considered as separate traits. Moreover, imperfect sensitivity and specificity could influence animals' classification and impact on estimated variance components. This study was aimed at: (1) estimating the heritability of bacteria negative SCS, bacteria positive SCS, and infection status, (2) estimating phenotypic and genetic correlations between bacteria negative and bacteria positive SCS, and the genetic correlation between bacteria negative SCS and infection status, and (3) evaluating the impact of imperfect diagnosis of infection on variance component estimates. METHODS: Data on SCS and udder infection status for 1,120 ewes were collected from four Valle del Belice flocks. The pedigree file included 1,603 animals. The SCS dataset was split according to whether animals were infected or not at the time of sampling. A repeatability test-day animal model was used to estimate genetic parameters for SCS traits and the heritability of infection status. The genetic correlation between bacteria negative SCS and infection status was estimated using an MCMC threshold model, implemented by Gibbs Sampling. RESULTS: The heritability was 0.10 for bacteria negative SCS, 0.03 for bacteria positive SCS, and 0.09 for infection status, on the liability scale. The genetic correlation between bacteria negative and bacteria positive SCS was 0.62, suggesting that they may be genetically different traits. The genetic correlation between bacteria negative SCS and infection status was 0.51. We demonstrate that imperfect diagnosis of infection leads to underestimation of differences between bacteria negative and bacteria positive SCS, and we derive formulae to predict impacts on estimated genetic parameters. CONCLUSIONS: The results suggest that bacteria negative and bacteria positive SCS are genetically different traits. A positive genetic correlation between bacteria negative SCS and liability to infection was found, suggesting that the approach of selecting animals for decreased SCS should help to reduce mastitis prevalence. However, the results show that imperfect diagnosis of infection has an impact on estimated genetic parameters, which may reduce the efficiency of selection strategies aiming at distinguishing between bacteria negative and bacteria positive SCS.