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357 results on '"Jamrozik, J."'

9. Two-step and random regression analyses of weight gain of station-tested beef bulls

Author

Schenkel, F.S., Miller, S.P., Jamrozik, J., and Wilton, J.W.

Subjects
Weight gain -- Research, Cattle -- Research, Zoology and wildlife conservation
Abstract

Our objectives were to compare a two-step model and a joint procedure via random regression model for evaluating weight gain of beef bulls, weighed every 28 d on 140-d test, and to estimate genetic, environmental, and phenotypic parameters. Two-step analysis consisted of fitting fixed linear regressions to weights of each bull to determine weight gain on test. In the second step, gain on test was analyzed by a mixed model that included fixed effects of breed, test group, and starting age and random effects of weaning herd-year group and animal (additive genetic). The random regression model included the same effects as the two-step mixed-model analysis with an additional random animal permanent environment effect. Fourth-order Legendre polynomials of days on test were fitted for all fixed and random effects in the random regression model, except for breed. Breed effects and residual variances varied for each measurement period. Variance components and EBV for gain were obtained from the covariance function and estimates of random regression coefficients for weight, respectively. Random regression heritability estimates for gain on test increased over time, being maximum at end of test (0.38) and equal to two-step estimate. Permanent environment variance ratio estimates also increased over time and were greater than heritability estimates. Estimate of weaning herd-year variance ratio was approximately constant over time, being equal to 0.07 at end of test and similar to two-step estimate. Genetic correlations between gain through different periods on test given by random regression model were high (from 0.81, between 28 and 140-d gain on test, to 0.99, between 112 and 140-d gain on test). Genetic correlations between gain on discrete 28-d intervals were moderate to high (e.g., 0.49 and 0.99 between the last 28 d on test and the first and fourth 28 d, respectively). Rank correlations between EBV for 140-d gain by the two procedures were 0.98, 0.84, and 0.73 for all bulls and the 5% and 1% of bulls with highest random regression EBV, respectively. Results indicated that the two procedures rank top bulls quite differently for 140-d gain on test. Random regression model accounted for changes over time of genetic and environmental effects on the test weight gain curve of the bulls. Use of 112-d instead of a 140-d test provided similar ranking of bulls on the basis of EBV for gain on test. Key Words: Cattle, Random Regressions, Test Station, Weight Gain

Published
2002

21. Genetic analyses of mid-infrared predicted milk fat globule size and milk lactoferrin

Author

Nayeri, S., Schenkel, Flavio S., Martin, Pauline, Fleming, Alison S., JAMROZIK, J., Brito, L.F., Malchiodi, F., Baes, C.F., Miglior, F., Génétique Animale et Biologie Intégrative (GABI), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, and Canadian Dairy Network

Subjects
[SDV.OT]Life Sciences [q-bio]/Other [q-bio.OT], genetic, somatic cell score, heritability, milk lactoferrin
Abstract

International audience; Genetic parameters for mid-infrared (MIR) predicted milk fat globule (MFG) size and lactoferrin were estimated for first parity Canadian Holsteins. A total of 109,029 records from 22,432 cows (105,737 pedigree animals) for lactoferrin and 109,212 records from 22,424 cows (105,070 pedigree animals) for MFG size were available. The linear animal model included fixed effects of herd-test day and days in milk, fixed regressions for age-season of calving effect, random regressions for herd-year of calving, permanent environment and additive genetic effects. Regression curves (on DIM) were modelled using orthogonal Legendre polynomials of order 4 for fixed and order 5 for random effects. Co-variance components were estimated by Bayesian methods via Gibbs sampling using custom-written software and 270,000 samples after a burn-in period of 30,000 samples. Multi-trait analyses using the same model with the MIR-predicted milk components along with test-day milk, fat and protein yields, fat and protein percentage, and somatic cell score were used to estimate genetic correlations among the traits. Average daily heritability estimates over the entire lactation were 0.34 for lactoferrin and 0.50 for MFG size. Milk lactoferrin content was found to have a moderate lactation average genetic correlation with protein % (0.41) and a positive genetic correlation with fat % (0.21). Weak genetic correlations were estimated between lactoferrin and fat and protein yields, and somatic cell score at 0.07, 0.08, and 0.07, respectively. Milk fat globule size had average genetic correlations of -0.23, 0.36, and 0.54 with milk yield, fat yield, and fat %, respectively. Posterior standard deviations for all estimates were low (below 0.001). The moderate genetic correlation with fat percentage suggested that MIR prediction for MFG size may not be perfectly related to fat percentage and thus the prediction equations are not simply predicting fat percentage.

Published
2018

33. Enhancing the data pipeline for novel traits in the genomic era: From farms to DHI to evaluation centres

Author

Miglior, Filippo, Finocchiaro, R., Malchiodi, F., Fleming, A., Brito, L., Baes, C.F., JAMROZIK, J., Martin, Pauline, CHESNAIS, J., Schenkel, Flavio S., ProdInra, Migration, Canadian Dairy Network, University of Guelph, Associazione Nazionale Allevatori Frisona Italiana, Partenaires INRAE, Semex Alliance, and International Committee for Animal Recording.

Subjects
[SDV] Life Sciences [q-bio], [SDV]Life Sciences [q-bio], ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2016

38. Genetic correlation patterns between somatic cell score and protein yield in the Italian Holstein-Friesian Population

Author

Samore, A., Groen, A.F., Boettcher, P., Jamrozik, J., Canavesi, F., Bagnato, A., Samore, A., Groen, A.F., Boettcher, P., Jamrozik, J., Canavesi, F., and Bagnato, A.

Abstract

Genetic parameters for somatic cell score (SCS) in the Italian Holstein-Friesian population were estimated addressing the pattern of genetic correlation with protein yield in different parities (first, second, and third) and on different days in milk within each parity. Three approaches for parameter estimation were applied using random samples of herds from the national database of the Italian Holstein Association. Genetic correlations for lactation measures (305-d protein yield and lactation SCS) were positive in the first parity (0.31) and close to zero in the second (0.01) and third (0.09) parities. These results indicated that larger values of SCS were genetically associated with increased production. The second and third sets of estimates were based on random regression test-day models, modeling the shape of lactation curve with the Wilmink function and fourth-order Legendre polynomials, respectively. Genetic correlations from both random regression models showed a specific pattern associated with days in milk within and across parities. Estimates varied from positive to negative in the first and second parity, and from null to negative in the third parity. Patterns were similar for both random regression models. The average overall correlation between SCS and protein yield was zero or slightly positive in the first lactation and ranged from zero to negative in later lactations. Correlation estimates differed by parity and stage of lactation. They also demonstrated the dubiousness of applying a single genetic correlation measure between SCS and protein in setting selection strategies. Differences in magnitude and the sign of genetic correlations between SCS and yields across and within parities should be accounted for in selection schemes.

Published
2008

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