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Tal-Stein R.,Hebrew University of Jerusalem | Fontanesi L.,University of Bologna | Dolezal M.,University of Veterinary Medicine Vienna | Scotti E.,University of Bologna | And 6 more authors.
Journal of Dairy Science | Year: 2010

Mastitis is an important and common dairy cattle disease affecting milk yield, quality, and consumer safety as well as cheese yields and quality. Animal welfare and residues of the antibiotics used to treat mastitis cause public concern. Considerable genetic variation may allow selection for increased resistance to mastitis. Because of high genetic correlation to milk somatic cell score (SCS), SCS can serve as a surrogate trait for mastitis resistance. The present study intended to identify quantitative trait loci (QTL) affecting SCS in Israeli and Italian Holstein dairy cattle (IsH and ItH, respectively), using selective DNA pooling with single and multiple marker mapping. Milk samples of 4,788 daughters of 6 IsH and 7 ItH sires were used to construct sire-family high- and low-tail pools, which were genotyped at 123 (IsH) and 133 (ItH) microsatellite markers. Shadow correction was used to obtain pool allele frequency estimates. Frequency difference between the tails and empirical standard error of D, SE(D), were used to obtain P-values. All markers significant by single marker mapping were also significant by multiple marker mapping, but not vice versa. Combining both populations, 22 QTL on 21 chromosomes were identified; all corresponded to previous reports in the literature. Confidence intervals were set by chi-squared drop method. Heterozygosity of QTL was estimated at 44.2%. Allele substitution effects ranged from 1,782 to 4,930 cells/mL in estimated breeding value somatic cell count units. Most (80%) of the observed variation in estimated breeding value somatic cell score could be explained by the QTL identified under the stringent criteria. The results found here can be used as a basis for further genome-wide association studies for the same trait. © 2010 American Dairy Science Association. Source

Bernabucci U.,University of Tuscia | Biffani S.,Associazione Nazionale Allevatori Frisona Italiana ANAFI | Buggiotti L.,University of Tuscia | Vitali A.,University of Tuscia | And 2 more authors.
Journal of Dairy Science | Year: 2014

The data set for this study comprised 1,488,474 test-day records for milk, fat, and protein yields and fat and protein percentages from 191,012 first-, second-, and third-parity Holstein cows from 484 farms. Data were collected from 2001 through 2007 and merged with meteorological data from 35 weather stations. A linear model (M1) was used to estimate the effects of the temperature-humidity index (THI) on production traits. Least squares means from M1 were used to detect the THI thresholds for milk production in all parities by using a 2-phase linear regression procedure (M2). A multiple-trait repeatability test-model (M3) was used to estimate variance components for all traits and a dummy regression variable (t) was defined to estimate the production decline caused by heat stress. Additionally, the estimated variance components and M3 were used to estimate traditional and heat-tolerance breeding values (estimated breeding values, EBV) for milk yield and protein percentages at parity 1. An analysis of data (M2) indicated that the daily THI at which milk production started to decline for the 3 parities and traits ranged from 65 to 76. These THI values can be achieved with different temperature/humidity combinations with a range of temperatures from 21 to 36°C and relative humidity values from 5 to 95%. The highest negative effect of THI was observed 4 d before test day over the 3 parities for all traits. The negative effect of THI on production traits indicates that first-parity cows are less sensitive to heat stress than multiparous cows. Over the parities, the general additive genetic variance decreased for protein content and increased for milk yield and fat and protein yield. Additive genetic variance for heat tolerance showed an increase from the first to third parity for milk, protein, and fat yield, and for protein percentage. Genetic correlations between general and heat stress effects were all unfavorable (from -0.24 to -0.56). Three EBV per trait were calculated for each cow and bull (traditional EBV, traditional EBV estimated with the inclusion of THI covariate effect, and heat tolerance EBV) and the rankings of EBV for 283 bulls born after 1985 with at least 50 daughters were compared. When THI was included in the model, the ranking for 17 and 32 bulls changed for milk yield and protein percentage, respectively. The heat tolerance genetic component is not negligible, suggesting that heat tolerance selection should be included in the selection objectives. © 2014 American Dairy Science Association. Source

Pintus M.A.,University of Sassari | Nicolazzi E.L.,Catholic University of the Sacred Heart | Van Kaam J.B.C.H.M.,Associazione Nazionale Allevatori Frisona Italiana ANAFI | Biffani S.,Associazione Nazionale Allevatori Frisona Italiana ANAFI | And 4 more authors.
Journal of Animal Breeding and Genetics | Year: 2013

One of the main issues in genomic selection was the huge unbalance between number of markers and phenotypes available. In this work, principal component analysis is used to reduce the number of predictors for calculating direct genomic breeding values (DGV) for production and functional traits. 2093 Italian Holstein bulls were genotyped with the 54 K Illumina beadchip, and 39 555 SNP markers were retained after data editing. Principal Components (PC) were extracted from SNP matrix, and 15 207 PC explaining 99% of the original variance were retained and used as predictors. Bulls born before 2001 were included in the reference population, younger animals in the test population. A BLUP model was used to estimate the effect of principal component on deregressed proof (DRPF) for 35 traits and results were compared to those obtained by using SNP genotypes as predictors either with BLUP or with Bayes_A models. Correlations between DGV and DRPF did not substantially differ among the three methods except for milk fat content. The lowest prediction bias was obtained for the method based on the use of principal component. Regression coefficients of DRPF on DGV were lower than one for the approach based on the use of PC and higher than one for the other two methods. The use of PC as predictors resulted in a large reduction of number of predictors (approximately 38%) and of computational time that was approximately 2% of the time needed to estimate SNP effects with the other two methods. Accuracies of genomic predictions were in most of cases only slightly higher than those of the traditional pedigree index, probably due to the limited size of the considered population. © 2012 Blackwell Verlag GmbH. Source

Nicolazzi E.L.,Consorzio di Ricerca e Sperimentazione degli Allevatori CRSA | Biffani S.,Associazione Nazionale Allevatori Frisona Italiana ANAFI | Jansen G.,Associazione Nazionale Allevatori Frisona Italiana ANAFI
Journal of Dairy Science | Year: 2013

Routine genomic evaluations frequently include a preliminary imputation step, requiring high accuracy and reduced computing time. A new algorithm, PedImpute (http://dekoppel.eu/pedimpute/), was developed and compared with findhap (http://aipl.arsusda.gov/software/findhap/) and BEAGLE (http://faculty.washington.edu/browning/beagle/beagle.html), using 19,904 Holstein genotypes from a 4-country international collaboration (United States, Canada, UK, and Italy). Different scenarios were evaluated on a sample subset that included only single nucleotide polymorphism from the Bovine low-density (LD) Illumina BeadChip (Illumina Inc., San Diego, CA). Comparative criteria were computing time, percentage of missing alleles, percentage of wrongly imputed alleles, and the allelic squared correlation. Imputation accuracy on ungenotyped animals was also analyzed. The algorithm PedImpute was slightly more accurate and faster than findhap and BEAGLE when sire, dam, and maternal grandsire were genotyped at high density. On the other hand, BEAGLE performed better than both PedImpute and findhap for animals with at least one close relative not genotyped or genotyped at low density. However, computing time and resources using BEAGLE were incompatible with routine genomic evaluations in Italy. Error rate and allelic squared correlation attained by PedImpute ranged from 0.2 to 1.1% and from 96.6 to 99.3%, respectively. When complete genomic information on sire, dam, and maternal grandsire are available, as expected to be the case in the close future in (at least) dairy cattle, and considering accuracies obtained and computation time required, PedImpute represents a valuable choice in routine evaluations among the algorithms tested. © 2013 American Dairy Science Association. Source

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