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Bjørg Heringstad - One of the best experts on this subject based on the ideXlab platform.
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information from later Lactations improves accuracy of genomic predictions of fertility related disorders in norwegian red
Journal of Dairy Science, 2015Co-Authors: K Haugaard, Morten Svendsen, Bjørg HeringstadAbstract:Our aim was to investigate whether including information from later Lactations improves accuracy of genomic breeding values for 4 fertility-related disorders: cystic ovaries, retained placenta, metritis, and silent heat. Data consisted of health records from 6,015,245 Lactations from 2,480,976 Norwegian Red cows, recorded from 1979 to 2012. These were daughters of 3,675 artificial insemination bulls. The mean frequency of these disorders for cows in Lactation 1 to 5 ranged from 0.6 to 2.4% for cystic ovaries, 1.0 to 1.5% for metritis, 1.9 to 4.1% for retained placenta, and 2.4 to 3.8% for silent heat. Genomic information was available for all sires, and the 312 youngest bulls were used for validation. After standard editing of a 25K/54K single nucleotide polymorphism data set that was imputed both ways, a total of 48,249 single nucleotide polymorphism loci were available for genomic predictions. Genomic breeding values were predicted using univariate genomic BLUP for the first Lactation only and for the first 5 Lactations and multivariate genomic BLUP with 5 Lactations for each disorder was also used for genomic predictions. Correlations between estimated breeding values for the 4 traits in 5 Lactations with predicted genomic breeding values were compared. Accuracy ranged from 0.47 and 0.51 for cystic ovaries, 0.50 to 0.74 for retained placenta, 0.21 to 0.47 for metritis, and 0.22 to 0.60 for silent heat. Including later Lactations in a multitrait genomic BLUP improved accuracy of genomic estimated breeding values for cystic ovaries, retained placenta, and silent heat, whereas for metritis no obvious advantage in accuracy was found.
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short communication genetic parameters for fertility related disorders in norwegian red
Journal of Dairy Science, 2015Co-Authors: K Haugaard, Bjørg HeringstadAbstract:Abstract Heritabilities and genetic correlations were estimated for the 4 most common fertility-related disorders in Norwegian Red: retained placenta, cystic ovaries, silent heat, and metritis. Data on 1,747,500 Lactations from 780,114 cows calving from January 2001 through December 2011 were analyzed using multivariate threshold sire models to estimate variance components for the 4 disorders in the first 5 Lactations. The traits were defined as binary within Lactation (0=unaffected, 1=affected), and each fertility-related disorder was analyzed separately with the 5 Lactations as correlated traits. The mean frequency of affected cows ranged from 0.5 to 1.7% for cystic ovaries, 0.7 to 1.1% for metritis, 1.3 to 3.4% for retained placenta, and 1.7 to 2.7% for silent heat. Posterior means (standard deviations) of heritability of liability ranged from 0.02 (0.01) to 0.12 (0.01), and were lowest for silent heat and highest for cystic ovaries. Genetic correlations across Lactation within disorder were positive and moderate to high, ranging from 0.79 to 0.95 for cystic ovaries, 0.40 to 0.75 for metritis, 0.53 to 0.94 for retained placenta, and 0.39 to 0.83 for silent heat.
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genetic parameters for fertility related disorders in norwegian red
Interbull Bulletin, 2013Co-Authors: K Haugaard, Bjørg HeringstadAbstract:Heritabilities and genetic correlations were estimated for the 4 most common fertility related disorders in Norwegian Red: retained placenta, cystic ovaries, silent heat and metritis. Each of the 4 disorders was analyzed separately with the first 5 Lactations as correlated traits using multivariate threshold sire models. Heritability estimates ranged from 0.03 to 0.14, and were lowest for metritis and highest for cystic ovaries. The genetic correlations between Lactations for cystic ovaries (0.74 – 0.95) was high and close to 1. So was also the correlations for retained placenta for Lactations 2 through 5 (0.86 – 0.94), while the correlation between first and later Lactations was lower (0.59 – 0.68). The genetic correlations between Lactations were moderate for metritis (0.40-0.77) and silent heat (0.37-0.79). The results suggested that cystic ovaries can be considered to be the same trait genetically across Lactations, while metritis and silent heat was different traits genetically across Lactations and retained placenta in first Lactation was genetically different from the subsequent Lactations.
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genetic analysis of clinical mastitis milk fever ketosis and retained placenta in three Lactations of norwegian red cows
Journal of Dairy Science, 2005Co-Authors: Bjørg Heringstad, Yumei Chang, Daniel Gianola, Gunnar KlemetsdalAbstract:Abstract The objectives were to infer heritability and genetic correlations between clinical mastitis (CM), milk fever (MF), ketosis (KET), and retained placenta (RP) within and between the first 3 Lactations and to estimate genetic change over time for these traits. Records of 372,227 daughters of 2411 Norwegian Red (NRF) sires were analyzed with a 12-variate (4 diseases×3 Lactations) threshold model. Within each Lactation, absence or presence of each of the 4 diseases was scored based on the cow's health recordings. Each disease was assumed to be a different trait in each of the 3 Lactations. The model for liability had trait-specific effects of year-season of calving and age of calving (first Lactation) or month-year of calving and calving interval (second and third Lactations), herd-5-yr, sire of the cow, and a residual. Posterior means of heritability of liability in first, second, and third Lactations were 0.08, 0.07, and 0.07, respectively, for CM; 0.09, 0.11, and 0.13 for MF; 0.14, 0.16, and 0.15 for KET, and 0.08 in all 3 Lactations for RP. Posterior means of genetic correlations between liability to CM, MF, KET, and RP, within disease between Lactations, ranged from 0.19 to 0.86, and were highest between KET in different Lactations. Correlations involving first Lactation MF were low and had higher standard deviations. Genetic correlations between diseases were low or moderate (from −0.10 to 0.40), within as well as between Lactations; the largest estimates were for MF and KET, and the lowest involved MF or KET and RP. Positive genetic correlations between diseases suggest that some general disease resistance factor with a genetic component exists. Trends of average sire posterior means by birth-year of daughters were used to assess genetic change, and the results indicated genetic improvement of resistance to CM and KET and no genetic change for MF and RP in the NRF population.
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multivariate threshold model analysis of clinical mastitis in multiparous norwegian dairy cattle
Journal of Dairy Science, 2004Co-Authors: Bjørg Heringstad, Y M Chang, Daniel Gianola, Gunnar KlemetsdalAbstract:Abstract A Bayesian multivariate threshold model was fitted to clinical mastitis (CM) records from 372,227 daughters of 2411 Norwegian Dairy Cattle (NRF) sires. All cases of veterinary-treated CM occurring from 30 d before first calving to culling or 300 d after third calving were included. Lactations were divided into 4 intervals: −30 to 0 d, 1 to 30 d, 31 to 120 d, and 121 to 300 d after calving. Within each interval, absence or presence of CM was scored as "0" or "1" based on the CM episodes. A 12-variate (3 Lactations×4 intervals) threshold model was used, assuming that CM was a different trait in each interval. Residuals were assumed correlated within Lactation but independent between Lactations. The model for liability to CM had interval-specific effects of month-year of calving, age at calving (first Lactation), or calving interval (second and third Lactations), herd-5-yr-period, sire of the cow, plus a residual. Posterior mean of heritability of liability to CM was 0.09 and 0.05 in the first and last intervals, respectively, and between 0.06 and 0.07 for other intervals. Posterior means of genetic correlations of liability to CM between intervals ranged from 0.24 (between intervals 1 and 12) to 0.73 (between intervals 1 and 2), suggesting interval-specific genetic control of resistance to mastitis. Residual correlations ranged from 0.08 to 0.17 for adjacent intervals, and between −0.01 and 0.03 for nonadjacent intervals. Trends of mean sire posterior means by birth year of daughters were used to assess genetic change. The 12 traits showed similar trends, with little or no genetic change from 1976 to 1986, and genetic improvement in resistance to mastitis thereafter. Annual genetic change was larger for intervals in first Lactation when compared with second or third Lactation. Within Lactation, genetic change was larger for intervals early in Lactation, and more so in the first Lactation. This reflects that selection against mastitis in NRF has emphasized mainly CM in early first Lactation, with favorable correlated selection responses in second and third Lactations suggested.
J Jamrozik - One of the best experts on this subject based on the ideXlab platform.
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single step genome wide association for longitudinal traits of canadian ayrshire holstein and jersey dairy cattle
Journal of Dairy Science, 2019Co-Authors: J Jamrozik, S Tsuruta, I Misztal, H R Oliveira, D A L Lourenco, Yutaka Masuda, Luiz F Brito, F F SilvaAbstract:ABSTRACT Estimating single nucleotide polymorphism (SNP) effects over time is essential to identify and validate candidate genes (or quantitative trait loci) associated with time-dependent variation of economically important traits and to better understand the underlying mechanisms of Lactation biology. Therefore, in this study, we aimed to estimate time-dependent effects of SNP and identifying candidate genes associated with milk (MY), fat (FY), and protein (PY) yields, and somatic cell score (SCS) in the first 3 Lactations of Canadian Ayrshire, Holstein, and Jersey breeds, as well as suggest their potential pattern of phenotypic effect over time. Random regression coefficients for the additive direct genetic effect were estimated for each animal using single-step genomic BLUP, based on 2 random regression models: one considering MY, FY, and PY in the first 3 Lactations and the other considering SCS in the first 3 Lactations. Thereafter, SNP solutions were obtained for random regression coefficients, which were used to estimate the SNP effects over time (from 5 to 305 d in Lactation). The top 1% of SNP that showed a high magnitude of SNP effect in at least 1 d in Lactation were selected as relevant SNP for further analyses of candidate genes, and clustered according to the trajectory of their SNP effects over time. The majority of SNP selected for MY, FY, and PY increased the magnitude of their effects over time, for all breeds. In contrast, for SCS, most selected SNP decreased the magnitude of their effects over time, especially for the Holstein and Jersey breeds. In general, we identified a different set of candidate genes for each breed, and similar genes were found across different Lactations for the same trait in the same breed. For some of the candidate genes, the suggested pattern of phenotypic effect changed among Lactations. Among the Lactations, candidate genes (and their suggested phenotypic effect over time) identified for the second and third Lactations were more similar to each other than for the first Lactation. Well-known candidate genes with major effects on milk production traits presented different suggested patterns of phenotypic effect across breeds, traits, and Lactations in which they were identified. The candidate genes identified in this study can be used as target genes in studies of gene expression.
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multiple trait estimates of genetic parameters for metabolic disease traits fertility disorders and their predictors in canadian holsteins
Journal of Dairy Science, 2016Co-Authors: J Jamrozik, A Koeck, G J Kistemaker, F MigliorAbstract:Producer-recorded health data for metabolic disease traits and fertility disorders on 35,575 Canadian Holstein cows were jointly analyzed with selected indicator traits. Metabolic diseases included clinical ketosis (KET) and displaced abomasum (DA); fertility disorders were metritis (MET) and retained placenta (RP); and disease indicators were fat-to-protein ratio, milk β-hydroxybutyrate, and body condition score (BCS) in the first Lactation. Traits in first and later (up to fifth) Lactations were treated as correlated in the multiple-trait (13 traits in total) animal linear model. Bayesian methods with Gibbs sampling were implemented for the analysis. Estimates of heritability for disease incidence were low, up to 0.06 for DA in first Lactation. Among disease traits, the environmental herd-year variance constituted 4% of the total variance for KET and less for other traits. First- and later-Lactation disease traits were genetically correlated (from 0.66 to 0.72) across all traits, indicating different genetic backgrounds for first and later Lactations. Genetic correlations between KET and DA were relatively strong and positive (up to 0.79) in both first- and later-Lactation cows. Genetic correlations between fertility disorders were slightly lower. Metritis was strongly genetically correlated with both metabolic disease traits in the first Lactation only. All other genetic correlations between metabolic and fertility diseases were statistically nonsignificant. First-Lactation KET and MET were strongly positively correlated with later-Lactation performance for these traits due to the environmental herd-year effect. Indicator traits were moderately genetically correlated (from 0.30 to 0.63 in absolute values) with both metabolic disease traits in the first Lactation. Smaller and mostly nonsignificant genetic correlations were among indicators and metabolic diseases in later Lactations. The only significant genetic correlations between indicators and fertility disorders were those between BCS and MET in both first and later Lactations. Results indicated a limited value of a joint genetic evaluation model for metabolic disease traits and fertility disorders in Canadian Holsteins.
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short communication estimates of genetic parameters of body condition score in the first 3 Lactations using a random regression animal model
Journal of Dairy Science, 2011Co-Authors: S Loker, J Jamrozik, F Miglior, L R Schaeffer, Catherine Bastin, A Sewalem, V R OsborneAbstract:The objective of this research was to estimate the genetic parameters of body condition score (BCS) in the first 3 Lactations in Canadian Holstein dairy cattle using a multiple-Lactation random regression animal model. Field staff from Valacta milk recording agency (Sainte-Anne-de-Bellevue, QC, Canada) collected BCS from Quebec herds several times throughout each Lactation. Approximately 32,000, 20,000, and 11,000 first-, second-, and third-parity BCS were analyzed, respectively, from a total of 75 herds. Body condition score was a moderately heritable trait over the Lactation for parity 1, 2, and 3, with average daily heritabilities of 0.22, 0.26, and 0.30, respectively. Daily heritability ranged between 0.14 and 0.26, 0.19 and 0.28, and 0.24 and 0.33 for parity 1, 2, and 3, respectively. Genetic variance of BCS increased with days in milk within Lactations. The low genetic variance in early Lactation suggests that the evolution of the ability to mobilize tissue reserves in early Lactation provided cattle with a major advantage, and is, therefore, somewhat conserved. The increasing genetic variance suggests that more genetic differences were related to how well cows recovered from the negative energy balance state. More specifically, increasing genetic variation as Lactation progressed could be a reflection of genetic differences in the ability of cows to efficiently control the rate of mobilization of tissue reserves, which would not be crucial in early Lactation. The shape of BCS curves was similar across parities. From first to third parity, differences included the progressively deeper nadir and faster rate of recovery of condition. Daily genetic correlations between parities were calculated from 5 to 305 DIM, and were summed and divided by 301 to obtain average daily genetic correlations. The average daily genetic correlations were 0.84 between parity 1 and 2, 0.83 between parity 1 and 3, and 0.86 between parity 2 and 3. Although not 1, these genetic correlations are still strong, so much of the variation observed in BCS was controlled by the same genes for each of the first 3 Lactations. If a genetic evaluation for BCS is developed, regular collection of first-Lactation BCS records should be sufficient for genetic evaluation.
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multiple trait random regression test day model for production traits
Interbull Bulletin, 1997Co-Authors: J Jamrozik, L R Schaeffer, Z Liu, G B JansenAbstract:Research on test day (TD) models began in Canada in 1991. At that time fixed regressions (within age and season of calving) were used to account for the shape of the Lactation curve of dairy cows, but the animal effect was used only to account for differences in height of these curves (Ptak and Schaeffer, 1993). Fixed regression TD model was applied to Canadian dairy goat data (Schaeffer and Sullivan, 1994) and to Canadian data on somatic cell score (SCS) (Reents et al., 1995a,b). Schaeffer and Dekkers (1994) suggested the possibility of using random regressions in a linear model (Henderson, 1982) for analysing TD data. Single trait random regression models were applied to first Lactation milk, fat and protein records, with different functions for fixed and random regressions (Jamrozik and Schaeffer, 1997; Jamrozik et al., 1997a,b). In the simulation study of Kistemaker (1997) random regression models were significantly better than an analysis of 305d yields in terms of correlation between estimated and true breeding values. A 2-3% increase in accuracy for bulls and 68% for cows was found for first Lactation milk yield. Changes occuring in Canadian milk recording system will require an application of multiple trait test day model in genetic evaluation for dairy production traits. Future milk recording programs would have TD records that have all yield values (milk, fat, protein, SCS) while other TD records might have only milk yield. Thus the multiple trait model for simultaneous analysis of all yields seems to be a logical choice. Also, preliminary work with milk yield in different Lactations showed that Lactation curves were different between first and second Lactation, and again between second and later Lactations. Thus, yields in different Lactations should also be considered as different traits. The objective of this research was to develop the multiple trait, random regression TD model for genetic evaluation of dairy bulls and cows for production traits. Both animal genetic and permanent environmental effects were modelled by random regressions in this model. Some preliminary results are presented.
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estimation of genetic parameters for test day records of somatic cell score
Journal of Dairy Science, 1995Co-Authors: R Reents, J Jamrozik, L R Schaeffer, Jack C M DekkersAbstract:Abstract The present study estimated variance components for test day records of somatic cell score and production traits. Data consisted of 235,100 test day observations recorded between 1986 and 1994 on Lactations 1 to 3 of 15,922 Holstein cows from 143 herds. Records were considered as repeated observations within a Lactation and as different traits across Lactations. The multiple-trait animal model for analysis included random animal additive genetic and permanent environment effects by Lactation. Fixed effects included herd test date and a set of four covariables for days in Lactation, estimated by parity, age, and season, which accounted for the shape of the Lactation curve. Gibbs sampling chains were carried out separately for somatic cell score and milk production and fat and protein yields. Heritabilities of somatic cell score for Lactations l to 3 were .09, .09, and .11, respectively. Genetic correlations between Lactations were high (.88, .79, and .95 between Lactations 1 and 2, 1 and 3, and 2 and 3, respectively). Correlations between permanent environment effects were smaller (.29, .19, and .46 between Lactations 1 and 2, 1 and 3, and 2 and 3, respectively). Heritabilities and correlations between permanent environment effects were higher for production traits than for somatic cell score. Genetic correlations between Lactations for production traits were similar to those for somatic cell score. Variances between Lactations differed significantly, indicating that observations from different Lactations should not be considered as repeated observations of the same trait.
H.d. Norman - One of the best experts on this subject based on the ideXlab platform.
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death losses for lactating cows in herds enrolled in dairy herd improvement test plans
Journal of Dairy Science, 2008Co-Authors: R H Miller, H.d. Norman, M T Kuhn, J.r. WrightAbstract:Factors that affect frequency of death of lactating cows were studied for cows with records that terminated from 1995 through 2005. Analyses included effects of herd, year, month, parity, and Lactation stage at Lactation termination as well as cow breed and milk yield. A national data set (15,025,035 Lactations in 45,032 herds) was analyzed with PROC GLM. Overall death frequency was 3.1% per Lactation (5.7% per cow). Death frequency increased by 1.6% from 1995 to 2005, with a sudden increase of 0.9% from 2003 to 2004, probably because of a USDA requirement in late 2003 for euthanizing downer cows. Death frequency was 16.5% greater for Lactations that terminated at or=251 d. Death frequency increased with parity (2% greater for eighth parity and later than for first parity) and with Lactation milk yield (0.4%/1,000 kg for Holsteins and Jerseys and 0.5%/1,000 kg for other breeds). Deaths were most frequent in July and least frequent in November. Within-herd breed differences (Holstein, Jersey, and other breeds) were small. The heritability of likelihood of death estimated from a sample of 79,162 Holstein cows was 1.3%. Death losses are increasing, perhaps partly because of increased milk yield and more intensive management regimens.
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dry period length to maximize production across adjacent Lactations and lifetime production
Journal of Dairy Science, 2006Co-Authors: M T Kuhn, J L Hutchison, H.d. NormanAbstract:The primary objectives of this research were to determine the dry period lengths that maximize production across adjacent Lactations and also dry period length that maximizes lifetime yield. Effect of days dry (DD) after Lactations 1 through 3 were determined separately for both adjacent Lactation sums and lifetime yield. Field data, collected through the Dairy Herd Improvement Association, on US Holstein cows first calving between January 1997 and January 2004 were utilized. Lifetime records were restricted to cows first calving no later than December 1999. Actual Lactation yields, in contrast to standardized records, were used to calculate Lactation sums and lifetime records. Herds were required to be on test for the entire period to avoid partial records. Another important edit was that actual calving dates had to agree with expected calving dates, based on reported days open, within 10 d. This edit ensured that the producer knew, at least at one point in time, when the cow was going to calve. Cow effects were corrected for in both the adjacent Lactation and lifetime analyses. The minimum DD to maximize production across adjacent Lactations depended on parity. For yield across first and second Lactations, there was little loss in production with a minimum of 40 to 45 DD. Longer dry periods (55 to 65 DD) were required after second and third Lactations however, presumably due to the lower persistency of second and later Lactation cows. Lifetime production was maximized by 40 to 50 DD after first Lactation and 30 to 40 DD after second and later Lactations. Fewer DD were required to maximize lifetime yield than adjacent Lactation yield because cows with fewer DD also had more lifetime days in milk. Although dry periods of 30 to 40 d can be used after second and later Lactations without cost in lifetime yield, their benefit to lifetime production is minimal. Dry periods shorter than 30 d or longer than 70 d are costly to lifetime yield and should be avoided. Dry periods longer than 80 d are even more costly than dry periods less than 30 d.
A E O Malauaduli - One of the best experts on this subject based on the ideXlab platform.
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comparative evaluation of a new Lactation curve model for pasture based holstein friesian dairy cows
Journal of Dairy Science, 2012Co-Authors: S A Adediran, D A Ratkowsky, D J Donaghy, A E O MalauaduliAbstract:Fourteen Lactation models were fitted to average and individual cow Lactation data from pasture-based dairy systems in the Australian states of Victoria and Tasmania. The models included a new “log-quadratic” model, and a major objective was to evaluate and compare the performance of this model with the other models. Nine empirical and 5 mechanistic models were first fitted to average test-day milk yield of Holstein-Friesian dairy cows using the nonlinear procedure in SAS. Two additional semiparametric models were fitted using a linear model in ASReml. To investigate the influence of days to first test-day and the number of test-days, 5 of the best-fitting models were then fitted to individual cow Lactation data. Model goodness of fit was evaluated using criteria such as the residual mean square, the distribution of residuals, the correlation between actual and predicted values, and the Wald-Wolfowitz runs test. Goodness of fit was similar in all but one of the models in terms of fitting average Lactation but they differed in their ability to predict individual Lactations. In particular, the widely used incomplete gamma model most displayed this failing. The new log-quadratic model was robust in fitting average and individual Lactations, and was less affected by sampled data and more parsimonious in having only 3 parameters, each of which lends itself to biological interpretation.
R W Blake - One of the best experts on this subject based on the ideXlab platform.
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an autoregressive repeatability animal model for test day records in multiple Lactations
Journal of Dairy Science, 2002Co-Authors: Julio Carvalheira, E J Pollak, R L Quaas, R W BlakeAbstract:Test-day (TD) models are becoming a standard for genetic evaluation of production traits in dairy cattle. Various approaches to model covariances between TD records include random regression, autoregressive repeatability, orthogonal polynomials, and models based on character processing. The applicability of these models is mainly associated with the number of parameters to estimate, incorporation of multiple Lactations, and the accuracy of correlations generated by the cow's repeated expression of milking performance (TD yields) within and across Lactations. We define and evaluate a multiple-Lactation, autoregressive-repeatability model that disentangles environmental effects due to cow within and between Lactations. Simulated records either included or ignored a long-term environmental effect between Lactations. Our autoregressive TD animal model correctly detected presence and the absence of this effect and accurately recovered the assumed variance components and correlations underlying the data (10 parameters for three Lactations). Estimates of variance components and autocorrelation coefficients were obtained using DFREML-simplex methodology. Given the value of this approach to reduce the size of residual variance components, autoregressive animal models are a preferable alternative to classical methods based on cumulative Lactation yield to improve milk production in dairy cattle.
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application of an autoregressive process to estimate genetic parameters and breeding values for daily milk yield in a tropical herd of lucerna cattle and in united states holstein herds
Journal of Dairy Science, 1998Co-Authors: Julio Carvalheira, R W Blake, E J Pollak, R L Quaas, C V DurancastroAbstract:The objectives of this study were to estimate from test day records the genetic and environmental (co)variance components, correlations, and breeding values to increase genetic gain in milk yield of Lucerna and US Holstein cattle. The effects of repeated observations (within cow) were explained by first-order autoregressive processes within and across Lactations using an animal model. Estimates of variance components and correlation coefficients between test days were obtained using derivative-free REML methodology. The autoregressive structure significantly reduced the model error component by disentangling the short-term environmental effects. The additional information and the more heterogeneous environmental variances between Lactations in the multiple-Lactation test day model than in the first Lactation model provided substantially larger estimates of additive genetic variance (0.62 kg2 for Lucerna; 14.73 kg2 for Holstein), heritability (0.13 for Lucerna; 0.42 for Holstein), and individual genetic merit. Rank correlations of breeding values from multiple Lactations and from first Lactations ranged from 0.18 to 0.37 for females and from 0.73 to 0.89 for males, respectively. Consequently, more selection errors and less genetic gain would be expected from selection decisions based on an analysis of first Lactation only, and greater accuracy would be achieved from multiple Lactations. Results indicated that substantial genetic gain was possible for milk yield in the Lucerna herd (34 kg/yr). Estimates of genetic variance for Holsteins were larger than previously reported, which portends more rapid genetic progress in US herds also; under our assumptions, increases would be from 173 to 197 kg/yr.
