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Made available in DSpace on 2022-04-29T08:37:08Z (GMT). No. of bitstreams: 0 Previous issue date: 2022-01-01 Context: Lactation persistency (LP) is an economically important characteristic to include in the selection objectives of the Gyr breed. Two persistency measures were tested to establish their contribution to the genetic evaluation of milk production in this genotype. The second measure of persistency studied would be the more appropriate measure to use in the genetic evaluation of lactation persistency. Aim: The aim of this work was to study LP in Gyr cows by using a random regression model (RRM) in two-Trait analysis. Methods: Test-day milk yields (TDMY) of the first two lactations of Gyr cows were analysed. RRM was performed by Bayesian inference using the GIBBS3F90 program. Fourth-order Legendre polynomials were used to describe the random additive genetic and permanent environmental effects of the animal. The fixed effects included in the model were contemporary group and, as covariates, age of cow at calving and the regression function according to the TDMY class of lactation as the average trajectories of lactation curve, distinguished by calving order. Two persistency measures (PSi, i.e. PS1 and PS2) were used in the analyses. Rank correlations were calculated between the predicted breeding values for PSi, 305-day lactation milk yield (MY305), and the regression coefficients a0 and a1, to determine the percentage of matching animals between rankings when different selection intensities are applied on the basis of the ranking for MY305. Key results: The heritability estimates for PS1, PS2 and for MY305 were 0.19, 0.12 and 0.41 respectively, in the first lactation, and 0.43, 0.27 and 0.38 in the second lactation. The genetic correlation between MY305 and PS1 was negative and of low magnitude. The rank correlation between breeding values for PSi, obtained for bulls with at least five daughters with production records, was higher than 0.80 in the two lactation periods. Conclusions: The results indicated that PS2 is the more suitable of the two persistency measures used in this work, for inclusion in genetic evaluations of lactation persistency in Brazilian Gyr cows. Implications: PS2 must be included as a selection criteria in a breeding program for the Gyr breed. Grupo de Investigación en Biodiversidad y Genética Molecular Facultad de Ciencias Agrarias Universidad Nacional de Colombia, Medellín Grupo de Melhoramento Animal de Mato Grosso Instituto de Ciências Agrárias e Tecnológicas Universidade Federal de Rondonópolis, Avenida dos Estudantes, N 5055, Rondonópolis, MT Centro de Pesquisas de Bovinos de Corte Instituto de Zootecnia, Rodovia Carlos Tonani Departamento de Zootecnia Faculdade de Ciências Agrárias e Veterinárias Universidade Estadual Paulista, Via de Acesso Prof. Paulo Donato Castellani, SP Departamento de Zootecnia Faculdade de Ciências Agrárias e Veterinárias Universidade Estadual Paulista, Via de Acesso Prof. Paulo Donato Castellani, SP

Remarkable increases in the production of dairy animals have negatively impacted their tolerance to heat stress (HS). The evaluation of the effect of HS on milk yield is based on the direct impact of HS on performance. However, in practical terms, HS also exerts its influence during gestation (indirect effect). The main purpose of this study was to identify and characterize the genotype by environment interaction (G × E) due to HS during the last 60 days of gestation (THI_g) and also the HS postpartum (THI_m) over first lactation milk production of Brazilian Holstein cattle. A total of 389 127 test day milk yield (TD) records from 1572 first lactation Holstein cows born in Brazil (daughters of 1248 dams and 70 sires) and the corresponding temperature–humidity index (THI) obtained between December 2007 and January 2013 were analyzed using different random regression models. Cows in the cold environment (THI_g = 64 to 73) during the last 60 days of gestation produced more milk than those cows in a hot environment (THI_g = 74 to 84), particularly during the first 150 days of lactation (DIM). The heritabilities (h2) of TD were similar throughout DIM for cows in THI_g hot (0.11 to 0.20) or (0.10 to 0.22), while the genetic correlations (rg) for TD between these two environments ranged from 0.11 to 0.52 along the first 250 DIM. The h2 estimates for TD across THI_m were similar for cows in THI_g hot (0.07 to 0.25) and THI_g cold (0.08 to 0.19). The rg estimates ranged from 0.17 to 0.42 along THI_m between TD of cows in cold and hot THI_g. The results were consistent in demonstrating the existence of an additional source of G × E for TD due to THI_g and THI_m. The present study is probably the first to provide evidence of this source of G × E; further research is needed because of its importance when the breeding objective is to select animals that are more tolerant to HS.

The objective of this study was to evaluate the 305-day cumulative milk yield (MY305) and the lactation curve trajectory of cows from different genetic groups originating from the crossing of Holstein and Dairy Gyr breeds, daughters of proven and unproven sires. Milk yield records of 1,032 animals with a genetic composition of ½, ¾ and ⅞ Holstein x Gyr were used. The analyses to determine the effects of the genetic groups on the shape of the lactation curve were carried out in two steps: 1) pre-adjusting test-day milk yields for fixed effects in a linear model, and 2) using the adjusted yields in a nonlinear model on days in milk to compare the lactation curves of animals from different genetic groups, born to proven and unproven sires. The effects included in the linear model for pre-adjustment of test-day yields were the contemporary group and age class of cow at calving. The data were analyzed by the least squares method. The same model was used for MY305, which included the effects of genetic group of the animal and sire category (proven or unproven). MY305 was influenced by all effects (P

The aim of this study was to estimate genetic parameters for month test-day milk yields of dairy Gyr cattle and to explore the additive genetic pattern of the milk production curve using cluster analysis in order to choose subgroups of animals that can result in genetic gain for milk production. The dataset of dairy Gyr cows was provided by the Brazilian Association of Zebu Breeders (ABCZ) and contained 123,035 first-lactation test-day records from 16,318 cows and calvings between 1983 and 2014. Random regression under an animal model was used to predict the breeding values of animals for monthly milk yields from 30 to 305 days, as the sum of breeding values of each day in milk, for coefficients of the lactation curve (intercept, linear, quadratic, and cubic), for peak yield, persistency of lactation, and total milk yield. Two cluster analyses were used to describe the additive genetic structure of the population: the hierarchical (Ward method) and non-hierarchical (k-means method) cluster analyses using the predicted breeding values of the above traits. Cluster analysis were used to figure out the patterns of the additive genetic lactation curves within the population that matched up with pre-defined breeding goals (improve the milk production) to suggest potential selection candidates. The estimates of heritability for test day milk yields ranged from 0.14 ± 0.02 to 0.20 ± 0.02. The intercept of the random regression had strong genetic correlation with peak yield and total milk yield while the linear random regression coefficient had strong genetic correlation with the persistency, as defined in this study, of the lactation curve. Cluster analysis resulted in four groups. The animals from Cluster 1 could be used as selection candidates because their additive genetic pattern presented EBVs above the mean population for all the traits. So, those animals could help to increase the milk production, and the persistency of lactation. Cluster analysis was effective to identify groups of animals suitable to attend the breeding goals, i.e. increase the persistency of lactation and the milk production along the lactation curve in dairy Gyr cattle.

In an attempt to determine the possible detrimental effects of continuous selection for milk yield on the genetic tolerance of Zebu cattle to heat stress, genetic parameters and trends of the response to heat stress for 86,950 test-day (TD) milk yield records from 14,670 first lactations of purebred dairy Gir cows were estimated. A random regression model with regression on days in milk (DIM) and temperature-humidity index (THI) values was applied to the data. The most detrimental effect of THI on milk yield was observed in the stage of lactation with higher milk production, DIM 61 to 120 (-0.099kg/d per THI). Although modest variations were observed for the THI scale, a reduction in additive genetic variance as well as in permanent environmental and residual variance was observed with increasing THI values. The heritability estimates showed a slight increase with increasing THI values for any DIM. The correlations between additive genetic effects across the THI scale showed that, for most of the THI values, genotype by environment interactions due to heat stress were less important for the ranking of bulls. However, for extreme THI values, this type of genotype by environment interaction may lead to an important error in selection. As a result of the selection for milk yield practiced in the dairy Gir population for 3 decades, the genetic trend of cumulative milk yield was significantly positive for production in both high (51.81kg/yr) and low THI values (78.48kg/yr). However, the difference between the breeding values of animals at high and low THI may be considered alarming (355kg in 2011). The genetic trends observed for the regression coefficients related to general production level (intercept of the reaction norm) and specific ability to respond to heat stress (slope of the reaction norm) indicate that the dairy Gir population is heading toward a higher production level at the expense of lower tolerance to heat stress. These trends reflect the genetic antagonism between production and tolerance to heat stress demonstrated by the negative genetic correlation between these components (-0.23). Monitoring trends of the genetic component of heat stress would be a reasonable measure to avoid deterioration in one of the main traits of Zebu cattle (i.e., high tolerance to heat stress). On the basis of current genetic trends, the need for future genetic evaluation of dairy Zebu animals for tolerance to heat stress cannot be ruled out.

The effect of selection for postweaning weight was evaluated within the growth curve parameters for both growth and carcass traits. Records of 2404 Nellore animals from three selection lines were analyzed: two selection lines for high postweaning weight, selection (NeS) and traditional (NeT); and a control line (NeC) in which animals were selected for postweaning weight close to the average. Body weight (BW), hip height (HH), rib eye area (REA), back fat thickness (BFT), and rump fat thickness (RFT) were measured and records collected from animals 8 to 20 (males) and 11 to 26 (females) months of age. The parameters A (asymptotic value) and k (growth rate) were estimated using the nonlinear model procedure of the Statistical Analysis System program, which included fixed effect of line (NeS, NeT, and NeC) in the model, with the objective to evaluate differences in the estimated parameters between lines. Selected animals (NeS and NeT) showed higher growth rates than control line animals (NeC) for all traits. Line effect on curves parameters was significant (P < 0.001) for BW, HH, and REA in males, and for BFT and RFT in females. Selection for postweaning weight was effective in altering growth curves, resulting in animals with higher growth potential.

Bioeconomic models were developed to calculate economic values (EV) for economically important traits in beef cattle, to evaluate the impact of these traits on production profitability, to assess possible market changes with a payment system and to develop economic selection indexes for Angus cattle for two production systems. Two beef cattle production systems were simulated as follows: a cow-calf cycle (CC) and a complete cycle (CoC). Following selection, positive changes in the EV were observed. In the CC, each 1.0% increment in weaning weight (WW), weaning rate (WR) and pregnancy rate (PR) resulted in increases in US$ 1.30, US$ 3.68 and US$ 3.55 per cow/year in profit, respectively. In the CoC, EV of US$ 1.01, US$ 1.79, US$ 1.19, US$ 1.34, US$ 6.84 and US$ 7.86 per cow/year were obtained for WW, year weight, yearling weight, final weight, WR and PR, respectively. The payment system for carcass quality showed that the scenario considering that 100% of the animals displayed uniform carcasses exhibited the highest EV and was considered optimal. Considering the sensitivity analysis, the price paid per animal was the factor that most affected the EV in both systems. The selection indexes obtained may be used in similar production systems, and the use of EV and selection indexes are important tools for any production system with positive change in profit after selection.

Given the importance of Guzera breeding programs for milk production in the tropics, the objective of this study was to compare alternative random regression models for estimation of genetic parameters and prediction of breeding values. Test-day milk yields records (TDR) were collected monthly, in a maximum of 10 measurements. The database included 20,524 records of first lactation from 2816 Guzera cows. TDR data were analyzed by random regression models (RRM) considering additive genetic, permanent environmental and residual effects as random and the effects of contemporary group (CG), calving age as a covariate (linear and quadratic effects) and mean lactation curve as fixed. The genetic additive and permanent environmental effects were modeled by RRM using Wilmink, Ali and Schaeffer and cubic B-spline functions as well as Legendre polynomials. Residual variances were considered as heterogeneous classes, grouped differently according to the model used. Multi-trait analysis using finite-dimensional models (FDM) for test-day milk records (TDR) and a single-trait model for 305-days milk yields (default) using the restricted maximum likelihood method were also carried out as further comparisons. Through the statistical criteria adopted, the best RRM was the one that used the cubic B-spline function with five random regression coefficients for the genetic additive and permanent environmental effects. However, the models using the Ali and Schaeffer function or Legendre polynomials with second and fifth order for, respectively, the additive genetic and permanent environmental effects can be adopted, as little variation was observed in the genetic parameter estimates compared to those estimated by models using the B-spline function. Therefore, due to the lower complexity in the (co)variance estimations, the model using Legendre polynomials represented the best option for the genetic evaluation of the Guzera lactation records. An increase of 3.6% in the accuracy of the estimated breeding values was verified when using RRM. The ranks of animals were very close whatever the RRM for the data set used to predict breeding values. Considering P305, results indicated only small to medium difference in the animals׳ ranking based on breeding values predicted by the conventional model or by RRM. Therefore, the sum of all the RRM-predicted breeding values along the lactation period (RRM305) can be used as a selection criterion for 305-day milk production.

The Gir cattle breed (Bos indicus) is an important genetic resource for milk production throughout the tropics. The small number of Gir animals introduced in Brazil, rapid dissemination of this breed recently, and the intensification of selection practices could contribute to increase of inbreeding level and reduce genetic diversity in this population. The population was analyzed in terms of pedigree completeness level, inbreeding coefficient, coancestry, generation interval, effective population size, effective number of founders and ancestors, among others. Despite the low mean inbreeding (around 2%), minor problems were identified in the population structure of the Brazilian Gir cattle, e.g., trend of narrower bottlenecks in the pedigree in recent years. The effective population sizes based on inbreeding (94) or coancestry (165.9) as well as the effective number of ancestors (76) and founders (143) were relativity high. The major subdivision of the Gir breed was observed between 1993 and 2002 (dairy and dual-purpose herds, wide use of within-herd matings). In this period the level of inbreeding remained at a higher level, there was a small increase in coancestry and the number of equivalent subpopulations was approximately 6. After 2002, there was genetic exchange between subpopulations, reduction in the average inbreeding, pronounced increase in the average coancestry, and the number of equivalent subpopulations was about 2. Furthermore, it was found that the mean generation interval of the population tended to increase in recent years (around 9 years). About 2.3% of genetic diversity has been lost since the first generation of founders. Based on the effective population size, number of equivalent subpopulations, inbreeding, coancestry, and loss of genetic diversity, the Gir population is still highly structured, but there is ample room for artificial selection. The results regarding the effective number of founders and ancestors in the present population demonstrate the existence of bottlenecks in the pedigree and indicate the need for population structure monitoring. Nevertheless, the Brazilian Gir breed can perfectly face a breeding program with high selection intensity.

Made available in DSpace on 2014-12-03T13:11:34Z (GMT). No. of bitstreams: 0 Previous issue date: 2014-01-01Bitstream added on 2014-12-03T13:22:36Z : No. of bitstreams: 1 WOS000336729500048.pdf: 554796 bytes, checksum: a8bd5adfb58fa1aa6bd3365fc27a58ed (MD5) The objective of this study was to estimate variance components and genetic parameters for accumulated 305-day milk yield (MY305) over multiple ages, from 24 to 120 months of age, applying random regression (RRM), repeatability (REP) and multi-trait (MT) models. A total of 4472 lactation records from 1882 buffaloes of the Murrah breed were utilized. The contemporary group (herd-year-calving season) and number of milkings (two levels) were considered as fixed effects in all models. For REP and RRM, additive genetic, permanent environmental and residual effects were included as random effects. MT considered the same random effects as did REP and RRM with the exception of permanent environmental effect. Residual variances were modeled by a step function with 1, 4, and 6 classes. The heritabilities estimated with RRM increased with age, ranging from 0.19 to 0.34, and were slightly higher than that obtained with the REP model. For the MT model, heritability estimates ranged from 0.20 (37 months of age) to 0.32 (94 months of age). The genetic correlation estimates for MY305 obtained by RRM (L23.res4) and MT models were very similar, and varied from 0.77 to 0.99 and from 0.77 to 0.99, respectively. The rank correlation between breeding values for MY305 at different ages predicted by REP, MT, and RRM were high. It seems that a linear and quadratic Legendre polynomial to model the additive genetic and animal permanent environmental effects, respectively, may be sufficient to explain more parsimoniously the changes in MY305 genetic variation with age. Univ Estadual Paulista, Dept Zootecnia, Jaboticabal, SP, Brazil Agencia Paulista Tecnol Agronegocios, Polo Reg Ctr Leste, Ribeirao Preto, SP, Brazil Univ Fed Vicosa, Inst Nacl Ciencia & Tecnol Ciencia Anim, Vicosa, MG, Brazil Univ Estadual Paulista, Dept Zootecnia, Jaboticabal, SP, Brazil

Submitted by Vitor Silverio Rodrigues (vitorsrodrigues@reitoria.unesp.br) on 2014-05-27T11:30:45Z No. of bitstreams: 0Bitstream added on 2014-05-27T14:43:34Z : No. of bitstreams: 1 2-s2.0-84884839371.pdf: 470689 bytes, checksum: e4a740515efdd38574083e62a8b41617 (MD5) Made available in DSpace on 2014-05-27T11:30:45Z (GMT). No. of bitstreams: 0 Previous issue date: 2013-09-27 We analyzed 46,161 monthly test-day records of milk production from 7453 first lactations of crossbred dairy Gyr (Bos indicus) x Holstein cows. The following seven models were compared: standard multivariate model (M10), three reduced rank models fitting the first 2, 3, or 4 genetic principal components, and three models considering a 2-, 3-, or 4-factor structure for the genetic covariance matrix. Full rank residual covariance matrices were considered for all models. The model fitting the first two principal components (PC2) was the best according to the model selection criteria. Similar phenotypic, genetic, and residual variances were obtained with models M10 and PC2. The heritability estimates ranged from 0.14 to 0.21 and from 0.13 to 0.21 for models M10 and PC2, respectively. The genetic correlations obtained with model PC2 were slightly higher than those estimated with model M10. PC2 markedly reduced the number of parameters estimated and the time spent to reach convergence. We concluded that two principal components are sufficient to model the structure of genetic covariances between test-day milk yields. © FUNPEC-RP. Universidade Estadual Paulista Júlio de Mesquita Filho, Jaboticabal, SP Agência Paulista de Tecnologia dos Agronegócios, Ribeirão Preto, SP Embrapa Gado de Leite, Juiz de Fora, MG Universidade Estadual Paulista Júlio de Mesquita Filho, Jaboticabal, SP

Tropical and sub-tropical climates are characterized by high temperature and humidity, during at least part of the year. Consequently, heat stress is common in Holstein cattle and productive and reproductive losses are frequent. Our objectives were as follows: (1) to quantify losses in production and quality of milk due to heat stress; (2) to estimate genetic correlations within and between milk yield (MY) and milk quality traits; and (3) to evaluate the trends of genetic components of tolerance to heat stress in multiple lactations of Brazilian Holstein cows. Thus, nine analyses using two-trait random regression animal models were carried out to estimate variance components and genetic parameters over temperature–humidity index (THI) values for MY and milk quality traits (three lactations: MY×fat percentage (F%), MY×protein percentage (P%) and MY×somatic cell score (SCS)) of Brazilian Holstein cattle. It was demonstrated that the effects of heat stress can be harmful for traits related to milk production and milk quality of Holstein cattle even though most herds were maintained in a modified environment, for example, with fans and sprinklers. For MY, the effect of heat stress was more detrimental in advanced lactations (−0.22 to −0.52 kg/day per increase of 1 THI unit). In general, the mean heritability estimates were higher for lower THI values and longer days in milk for all traits. In contrast, the heritability estimates for SCS increased with increasing THI values in the second and third lactation. For each trait studied, lower genetic correlations (different from unity) were observed between opposite extremes of THI (THI 47 v. THI 80) and in advanced lactations. The genetic correlations between MY and milk quality trait varied across the THI scale and lactations. The genotype×environment interaction due to heat stress was more important for MY and SCS, particularly in advanced lactations, and can affect the genetic relationship between MY and milk quality traits. Selection for higher MY, F% or P% may result in a poor response of the animals to heat stress, as a genetic antagonism was observed between the general production level and specific ability to respond to heat stress for these traits. Genetic trends confirm the adverse responses in the genetic components of heat stress over the years for milk production and quality. Consequently, the selection of Holstein cattle raised in modified environments in both tropical and sub-tropical regions should take into consideration the genetic variation in heat stress.

The objectives of the present study were: 1) to investigate variation in the genetic component of heat stress for nonreturn rate at 56 days after first artificial insemination (NR56); 2) to identify and characterize the genotype by environment interaction (G × E) due to heat stress for NR56 of Brazilian Holstein cattle. A linear random regression model (reaction norm model) was applied to 51,748 NR56 records of 28,595 heifers and multiparous cows. The decline in NR56 due to heat stress was more pronounced in milking cows compared to heifers. The age of females at first artificial insemination and temperature-humidity index (THI) exerted an important influence on the genetic parameters of NR56. Several evidence of G × E on NR56 were found as the high slope/intercept ratio and frequent intersection of reaction norms. Additionally, the genetic correlation between NR56 at opposite extremes of the THI scale reached estimates below zero, indicating that few of the same genes are responsible for NR56 under conditions of thermoneutrality and heat stress. The genetic evaluation and selection for NR56 in Holstein cattle reared under (sub)tropical conditions should therefore take into consideration the genetic variation on age at insemination and G × E due to heat stress.

The objective of this paper is to model variations in test-day milk yields of first lactations of Holstein cows by RR using B-spline functions and Bayesian inference in order to fit adequate and parsimonious models for the estimation of genetic parameters. They used 152,145 test day milk yield records from 7317 first lactations of Holstein cows. The model established in this study was additive, permanent environmental and residual random effects. In addition, contemporary group and linear and quadratic effects of the age of cow at calving were included as fixed effects. Authors modeled the average lactation curve of the population with a fourth-order orthogonal Legendre polynomial. They concluded that a cubic B-spline with seven random regression coefficients for both the additive genetic and permanent environment effects was to be the best according to residual mean square and residual variance estimates. Moreover they urged a lower order model (quadratic B-spline with seven random regression coefficients for both random effects) could be adopted because it yielded practically the same genetic parameter estimates with parsimony.

A total of 46,089 individual monthly test-day (TD) milk yields (10 test-days), from 7,331 complete first lactations of Holstein cattle were analyzed. A standard multivariate analysis (MV), reduced rank analyses fitting the first 2, 3, and 4 genetic principal components (PC2, PC3, PC4), and analyses that fitted a factor analytic structure considering 2, 3, and 4 factors (FAS2, FAS3, FAS4), were carried out. The models included the random animal genetic effect and fixed effects of the contemporary groups (herd-year-month of test-day), age of cow (linear and quadratic effects), and days in milk (linear effect). The residual covariance matrix was assumed to have full rank. Moreover, 2 random regression models were applied. Variance components were estimated by restricted maximum likelihood method. The heritability estimates ranged from 0.11 to 0.24. The genetic correlation estimates between TD obtained with the PC2 model were higher than those obtained with the MV model, especially on adjacent test-days at the end of lactation close to unity. The results indicate that for the data considered in this study, only 2 principal components are required to summarize the bulk of genetic variation among the 10 traits.

Made available in DSpace on 2013-09-27T14:53:35Z (GMT). No. of bitstreams: 1 WOS000300617600131.pdf: 615210 bytes, checksum: ad5b8ce3e478e0bf8e1e8d71046f3274 (MD5) Previous issue date: 2011-01-01 Made available in DSpace on 2013-09-30T18:03:38Z (GMT). No. of bitstreams: 1 WOS000300617600131.pdf: 615210 bytes, checksum: ad5b8ce3e478e0bf8e1e8d71046f3274 (MD5) Previous issue date: 2011-01-01 Submitted by Vitor Silverio Rodrigues (vitorsrodrigues@reitoria.unesp.br) on 2014-05-20T13:19:12Z No. of bitstreams: 1 WOS000300617600131.pdf: 615210 bytes, checksum: ad5b8ce3e478e0bf8e1e8d71046f3274 (MD5) Made available in DSpace on 2014-05-20T13:19:12Z (GMT). No. of bitstreams: 1 WOS000300617600131.pdf: 615210 bytes, checksum: ad5b8ce3e478e0bf8e1e8d71046f3274 (MD5) Previous issue date: 2011-01-01 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) We analyzed 152,145 test-day records from 7317 first lactations of Holstein cows recorded from 1995 to 2003. Our objective was to model variations in test-day milk yield during the first lactation of Holstein cows by random regression model (RRM), using various functions in order to obtain adequate and parsimonious models for the estimation of genetic parameters. Test-day milk yields were grouped into weekly classes of days in milk, ranging from 1 to 44 weeks. The contemporary groups were defined as herd-test-day. The analyses were performed using a single-trait RRM, including the direct additive, permanent environmental and residual random effects. In addition, contemporary group and linear and quadratic effects of the age of cow at calving were included as fixed effects. The mean trend of milk yield was modeled with a fourth-order orthogonal Legendre polynomial. The additive genetic and permanent environmental covariance functions were estimated by random regression on two parametric functions, Ali and Schaeffer and Wilmink, and on B-spline functions of days in milk. The covariance components and the genetic parameters were estimated by the restricted maximum likelihood method. Results from RRM parametric and B-spline functions were compared to RRM on Legendre polynomials and with a multi-trait analysis, using the same data set. Heritability estimates presented similar trends during mid-lactation (13 to 31 weeks) and between week 37 and the end of lactation, for all RRM. Heritabilities obtained by multi-trait analysis were of a lower magnitude than those estimated by RRM. The RRMs with a higher number of parameters were more useful to describe the genetic variation of test-day milk yield throughout the lactation. RRM using B-spline and Legendre polynomials as base functions appears to be the most adequate to describe the covariance structure of the data. Univ Estadual Paulista, Fac Ciencias Agr & Vet, Dept Zootecnia, Jaboticabal, SP, Brazil Agencia Paulista Tecnol Agronegocios, Ribeirao Preto, SP, Brazil Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA) Gado Corte, Campo Grande, MS, Brazil Univ São Paulo, Escola Super Agr Luiz de Queiroz, Dept Zootecnia, Piracicaba, SP, Brazil Univ Estadual Paulista, Fac Ciencias Agr & Vet, Dept Zootecnia, Jaboticabal, SP, Brazil

Made available in DSpace on 2013-09-27T14:53:36Z (GMT). No. of bitstreams: 1 WOS000295823500089.pdf: 996294 bytes, checksum: 37ca1e860210cf90c6f3215f2d46cb80 (MD5) Previous issue date: 2011-01-01 Made available in DSpace on 2013-09-30T18:03:36Z (GMT). No. of bitstreams: 1 WOS000295823500089.pdf: 996294 bytes, checksum: 37ca1e860210cf90c6f3215f2d46cb80 (MD5) Previous issue date: 2011-01-01 Submitted by Vitor Silverio Rodrigues (vitorsrodrigues@reitoria.unesp.br) on 2014-05-20T13:19:11Z No. of bitstreams: 1 WOS000295823500089.pdf: 996294 bytes, checksum: 37ca1e860210cf90c6f3215f2d46cb80 (MD5) Made available in DSpace on 2014-05-20T13:19:11Z (GMT). No. of bitstreams: 1 WOS000295823500089.pdf: 996294 bytes, checksum: 37ca1e860210cf90c6f3215f2d46cb80 (MD5) Previous issue date: 2011-01-01 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG) We investigated the effect of stage of pregnancy on estimates of breeding values for milk yield and milk persistency in Gyr and Holstein dairy cattle in Brazil. Test-day milk yield records were analyzed using random regression models with or without the effect of pregnancy. Models were compared using residual variances, heritabilities, rank correlations of estimated breeding values of bulls and cows, and number of nonpregnant cows in the top 200 for milk yield and milk persistency. The estimates of residual variance and heritabilities obtained with the models with or without the effect of pregnancy were similar for the two breeds. Inclusion of the effect of pregnancy in genetic evaluation models for these populations did not affect the ranking of cows and sires based on their predicted breeding values for 305-day cumulative milk yield. In contrast, when we examined persistency of milk yield, lack of adjustment for the effect of pregnancy overestimated breeding values of nonpregnant cows and cows with a long days open period and underestimated breeding values of cows with a short days open period. We recommend that models include the effect of days of pregnancy for estimation of adjustment factors for the effect of pregnancy in genetic evaluations of Dairy Gyr and Holstein cattle. Univ Estadual Paulista, Dept Zootecnia, Fac Ciencias Agr Vet, Jaboticabal, SP, Brazil Univ Fed Mato Grosso, Inst Ciencias Agr Tecnol, Rondonopolis, MT, Brazil Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA) Gado Leite, Juiz de Fora, MG, Brazil Agencia Paulista Tecnol Agronegocios, Secretaria Agr Abastecimento, Ribeirao Preto, SP, Brazil Univ Estadual Paulista, Dept Zootecnia, Fac Ciencias Agr Vet, Jaboticabal, SP, Brazil

Summary The objective of this work was to estimate covariance functions for additive genetic and permanent environmental effects and, subsequently, to obtain genetic parameters for buffalo’s test-day milk production using random regression models on Legendre polynomials (LPs). A total of 17 935 test-day milk yield (TDMY) from 1433 first lactations of Murrah buffaloes, calving from 1985 to 2005 and belonging to 12 herds located in Sao Paulo state, Brazil, were analysed. Contemporary groups (CGs) were defined by herd, year and month of milk test. Residual variances were modelled through variance functions, from second to fourth order and also by a step function with 1, 4, 6, 22 and 42 classes. The model of analyses included the fixed effect of CGs, number of milking, age of cow at calving as a covariable (linear and quadratic) and the mean trend of the population. As random effects were included the additive genetic and permanent environmental effects. The additive genetic and permanent environmental random effects were modelled by LP of days in milk from quadratic to seventh degree polynomial functions. The model with additive genetic and animal permanent environmental effects adjusted by quintic and sixth order LP, respectively, and residual variance modelled through a step function with six classes was the most adequate model to describe the covariance structure of the data. Heritability estimates decreased from 0.44 (first week) to 0.18 (fourth week). Unexpected negative genetic correlation estimates were obtained between TDMY records at first weeks with records from middle to the end of lactation, being the values varied from −0.07 (second with eighth week) to −0.34 (1st with 42nd week). TDMY heritability estimates were moderate in the course of the lactation, suggesting that this trait could be applied as selection criteria in milking buffaloes.

The objective of the present study was to estimate milk yield genetic parameters applying random regression models and parametric correlation functions combined with a variance function to model animal permanent environmental effects. A total of 152,145 test-day milk yields from 7,317 first lactations of Holstein cows belonging to herds located in the southeastern region of Brazil were analyzed. Test-day milk yields were divided into 44 weekly classes of days in milk. Contemporary groups were defined by herd-test-day comprising a total of 2,539 classes. The model included direct additive genetic, permanent environmental, and residual random effects. The following fixed effects were considered: contemporary group, age of cow at calving (linear and quadratic regressions), and the population average lactation curve modeled by fourth-order orthogonal Legendre polynomial. Additive genetic effects were modeled by random regression on orthogonal Legendre polynomials of days in milk, whereas permanent environmental effects were estimated using a stationary or nonstationary parametric correlation function combined with a variance function of different orders. The structure of residual variances was modeled using a step function containing 6 variance classes. The genetic parameter estimates obtained with the model using a stationary correlation function associated with a variance function to model permanent environmental effects were similar to those obtained with models employing orthogonal Legendre polynomials for the same effect. A model using a sixth-order polynomial for additive effects and a stationary parametric correlation function associated with a seventh-order variance function to model permanent environmental effects would be sufficient for data fitting.

The objective of the present study was to estimate genetic parameters for stayability at 60 months of age (STAY60) and its association with first lactation cumulative milk yield (P305), age at first calving (AFC), and first calving interval (FCI), in order to adopt these traits as selection criteria for longevity in Gir dairy cattle. Records for 2770 cows born between 1982 and 2008 from six herds in the Brazilian states of Minas Gerais, Sao Paulo, and Paraiba were analyzed. The (co)variance components were estimated by a Bayesian approach using bivariate animal models. The heritability estimates were 0.37 ± 0.09, 0.23 ± 0.04, 0.26 ± 0.06, and 0.07 ± 0.03 for STAY60, P305, AFC, and FCI, respectively. The genetic correlations of STAY60 with P305, AFC, and FCI were moderate to high, with values of 0.61 (0.17), -0.44 (0.23), and 0.88 (0.13), respectively. STAY60, P305, and AFC exhibited additive genetic variability, and these traits should be considered in selection indices. The indirect selection for longevity through the correlated responses of early-expression traits, such as milk production at first lactation, could be used to improve the ability of animals to remain in the herd.

Genetic parameters for the relation between the traits of milk yield (MY), age at first calving (AFC) and interval between first and second calving (IBFSC) were estimated in milk buffaloes of the Murrah breed. In the study, data of 1578 buffaloes at first lactation, with calvings from 1974 to 2006 were analyzed. The MTDFREML system was used in the analyses with models for the MY, IBFSC traits which included the fixed effects of herd-year-season of calving, linear and quadratic terms of calving age as covariate and the random animal effects and error. The model for AFC consisted of the herd-year-season fixed effects of calving and the random effects of animal and error. Heritability estimates MY, AFC and IBFSC traits were 0.20, 0.07 and 0.14, respectively. Genetic and phenotypic correlations between the traits were: MY and AFC = -0.12 and -0.15, MY and IBFSC = 0.07 and 0.30, AFC and IBFSC = 0.35 and 0.37, respectively. Genetic correlation between MY and AFC traits showed desirable negative association, suggesting that the daughters of the bulls with high breeding value for MY could be physiological maturity to a precocious age. Genetic correlation between MY and IBFSC showed that the selection of the animals that increased milk yield is also those that tend to intervals of bigger calving.

The objective of the present study was to estimate the genetic parameters for test-day milk yields (TDMY) in the first and second lactations using random regression models (RRM) in order to contribute to the application of these models in genetic evaluation of milk yield in Gyr cattle. A total of 53,328 TDMY records from 7118 lactations of 5853 Gyr cows were analyzed. The model included the direct additive, permanent environmental, and residual random effects. In addition, contemporary group and linear and quadratic effects of the age of cows at calving were included as fixed effects. A random regression model fitting fourth-order Legendre polynomials for additive genetic and permanent environmental effects, with five classes of residual variance, was applied. In the first lactation, the heritabilities increased from early lactation (0.26) until TDMY3 (0.38), followed by a decrease until the end of lactation. In the second lactation, the estimates increased from the first (0.29) to the fifth test day (0.36), with a slight decrease thereafter, and again increased on the last two test days (0.34 and 0.41). There were positive and high genetic correlations estimated between first-lactation TDMY and the remaining TDMY of the two lactations. The moderate heritability estimates, as well as the high genetic correlations between half the first-lactation TDMY and all TDMY of the two lactations, suggest that the selection based only on first lactation TDMY is the best selection strategy to increase milk production across first and second lactations of Gyr cows.

This study investigates the genetic association of the SNP present in the ACTA1 gene with performance traits, organs and carcass of broilers to help marker-assisted selection of a paternal broiler line (TT) from EMBRAPA Swine and Poultry, Brazil. Genetic and phenotypic data of 1,400 broilers for 68 traits related to body performance, organ weights, weight of carcass parts, and yields as a percentage of organs and carcass parts were used. The maximum likelihood method, considering 4 analytical models, was used to analyze the genetic association between the SNP and these important economic traits. The association analysis was performed using a mixed animal model including the random effect of the animal (polygenic), and the fixed effects of sex (2 levels), hatch (5 levels) and SNP (3 levels), besides the random error. The traits significantly associated (P

Made available in DSpace on 2015-03-18T15:56:26Z (GMT). No. of bitstreams: 0 Previous issue date: 2014-01-01Bitstream added on 2015-03-18T16:28:20Z : No. of bitstreams: 1 WOS000343049600119.pdf: 1345058 bytes, checksum: 8e3569aa60ce3095817dcce976f75367 (MD5) Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) We analyzed 75,555 test-day milk flow records from 2175 primiparous Holstein cows that calved between 1997 and 2005. Milk flow was obtained by dividing the mean milk yield (kg) of the 3 daily milking by the total milking time (min) and was expressed as kg/min. Milk flow was grouped into 43 weekly classes. The analyses were performed using a single-trait Random Regression Models that included direct additive genetic, permanent environmental, and residual random effects. In addition, the contemporary group and linear and quadratic effects of cow age at calving were included as fixed effects. Fourth-order orthogonal Legendre polynomial of days in milk was used to model the mean trend in milk flow. The additive genetic and permanent environmental covariance functions were estimated using random regression Legendre polynomials and B-spline functions of days in milk. The model using a third-order Legendre polynomial for additive genetic effects and a sixth-order polynomial for permanent environmental effects, which contained 7 residual classes, proved to be the most adequate to describe variations in milk flow, and was also the most parsimonious. The heritability in milk flow estimated by the most parsimonious model was of moderate to high magnitude. Univ Estadual Paulista, Fac Ciencias Agr & Vet, Dept Zootecnia, Jaboticabal, SP, Brazil Agencia Paulista Tecnol Agronegocios, Polo Reg Ctr Leste, Ribeirao Preto, SP, Brazil Univ Estadual Paulista, Fac Ciencias Agr & Vet, Dept Zootecnia, Jaboticabal, SP, Brazil

The objectives of the present study were to estimate genetic parameters of monthly test-day milk yield (TDMY) of the first lactation of Brazilian Holstein cows using random regression (RR), and to compare the genetic gains for milk production and persistency, derived from RR models, using eigenvector indices and selection indices that did not consider eigenvectors. The data set contained monthly TDMY of 3,543 first lactations of Brazilian Holstein cows calving between 1994 and 2011. The RR model included the fixed effect of the contemporary group (herd-month-year of test days), the covariate calving age (linear and quadratic effects), and a fourth-order regression on Legendre orthogonal polynomials of days in milk (DIM) to model the population-based mean curve. Additive genetic and nongenetic animal effects were fit as RR with 4 classes of residual variance random effect. Eigenvector indices based on the additive genetic RR covariance matrix were used to evaluate the genetic gains of milk yield and persistency compared with the traditional selection index (selection index based on breeding values of milk yield until 305 DIM). The heritability estimates for monthly TDMY ranged from 0.12 ± 0.04 to 0.31 ± 0.04. The estimates of additive genetic and nongenetic animal effects correlation were close to 1 at adjacent monthly TDMY, with a tendency to diminish as the time between DIM classes increased. The first eigenvector was related to the increase of the genetic response of the milk yield and the second eigenvector was related to the increase of the genetic gains of the persistency but it contributed to decrease the genetic gains for total milk yield. Therefore, using this eigenvector to improve persistency will not contribute to change the shape of genetic curve pattern. If the breeding goal is to improve milk production and persistency, complete sequential eigenvector indices (selection indices composite with all eigenvectors) could be used with higher economic values for persistency. However, if the breeding goal is to improve only milk yield, the traditional selection index is indicated.

Egg production curves describe the laying patterns of hen populations over time. The objectives of this study were to fit the weekly egg production rate of selected and nonselected lines of a White Leghorn hen population, using nonlinear and segmented polynomial models, and to study how the selection process changed the egg-laying patterns between these 2 lines. Weekly egg production rates over 54 wk of egg production (from 17 to 70 wk of age) were measured from 1,693 and 282 laying hens from one selected and one nonselected (control) genetic line, respectively. Six nonlinear and one segmented polynomial models were gathered from the literature to investigate whether they could be used to fit curves for the weekly egg production rate. The goodness of fit of the models was measured using Akaike's information criterion, mean square error, coefficient of determination, graphical analysis of the fitted curves, and the deviations of the fitted curves. The Logistic, Yang, Segmented Polynomial, and Grossman models presented the best goodness of fit. In this population, there were significant differences between the parameter estimates of the curves fitted for the selected and nonselected lines, thus indicating that the effect of selection changed the shape of the egg production curves. The selection for egg production was efficient in modifying the birds' egg production curve in this population, thus resulting in genetic gain from the 5th to the 54th week of egg laying and improved the peak egg production and the persistence of egg laying.

Made available in DSpace on 2013-09-27T14:53:22Z (GMT). No. of bitstreams: 1 WOS000308817800003.pdf: 864467 bytes, checksum: 77766f6d6020a86d941afbf11f9ecaf0 (MD5) Previous issue date: 2012-01-01 Made available in DSpace on 2013-09-30T17:51:52Z (GMT). No. of bitstreams: 1 WOS000308817800003.pdf: 864467 bytes, checksum: 77766f6d6020a86d941afbf11f9ecaf0 (MD5) Previous issue date: 2012-01-01 Submitted by Vitor Silverio Rodrigues (vitorsrodrigues@reitoria.unesp.br) on 2014-05-20T13:13:56Z No. of bitstreams: 1 WOS000308817800003.pdf: 864467 bytes, checksum: 77766f6d6020a86d941afbf11f9ecaf0 (MD5) Made available in DSpace on 2014-05-20T13:13:56Z (GMT). No. of bitstreams: 1 WOS000308817800003.pdf: 864467 bytes, checksum: 77766f6d6020a86d941afbf11f9ecaf0 (MD5) Previous issue date: 2012-01-01 Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) We estimated genetic parameters for egg production in different periods by means of random regression models, aiming at selection based on partial egg production from a generation of layers. The production was evaluated for each individual by recording the number of eggs produced from 20 to 70 weeks of age, with partial records taken every three weeks for a total of 17 periods. The covariance functions were estimated with a random regression model by the restricted maximum likelihood method. A model composed of third-order polynomials for the additive effect, ninth-order polynomials for the permanent environment, and a residual variance structure with five distinct classes, was found to be most suitable for adjusting the egg production data for laying hens. The heritability estimates varied from 0.04 to 0.14. The genetic correlations were all positive, varying from 0.10 to 0.99. Selection applied in partial egg production periods will result in greater genetic profit for the adjacent periods. However, as the distance in time between periods increases, selection becomes less efficient. Selection based on the second period (23 to 25 weeks of age), where greater heritability was estimated, would note benefit the final egg-laying cycle periods. Univ Estadual Paulista, Fac Ciencias Agr & Vet, Dept Ciencias Exatas, Jaboticabal, SP, Brazil Empresa Brasileira Pesquisa Agropecuria Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA) S, Concordia, SC, Brazil Agencia Paulista Tecnol Agronegocios, Polo Reg Ctr Leste, Ribeirao Preto, SP, Brazil Univ Estadual Paulista, Fac Ciencias Agr & Vet, Dept Ciencias Exatas, Jaboticabal, SP, Brazil

Studies investigating the use of random regression models for genetic evaluation of milk production in Zebu cattle are scarce. In this study, 59,744 test-day milk yield records from 7,810 first lactations of purebred dairy Gyr ( Bos indicus ) and crossbred (dairy Gyr × Holstein) cows were used to compare random regression models in which additive genetic and permanent environmental effects were modeled using orthogonal Legendre polynomials or linear spline functions. Residual variances were modeled considering 1, 5, or 10 classes of days in milk. Five classes fitted the changes in residual variances over the lactation adequately and were used for model comparison. The model that fitted linear spline functions with 6 knots provided the lowest sum of residual variances across lactation. On the other hand, according to the deviance information criterion (DIC) and Bayesian information criterion (BIC), a model using third-order and fourth-order Legendre polynomials for additive genetic and permanent environmental effects, respectively, provided the best fit. However, the high rank correlation (0.998) between this model and that applying third-order Legendre polynomials for additive genetic and permanent environmental effects, indicates that, in practice, the same bulls would be selected by both models. The last model, which is less parameterized, is a parsimonious option for fitting dairy Gyr breed test-day milk yield records.

The objective of this work was to estimate covariance functions for additive genetic and permanent environmental effects and, subsequently, to obtain genetic parameters for buffalo's test-day milk production using random regression models on Legendre polynomials (LPs). A total of 17 935 test-day milk yield (TDMY) from 1433 first lactations of Murrah buffaloes, calving from 1985 to 2005 and belonging to 12 herds located in São Paulo state, Brazil, were analysed. Contemporary groups (CGs) were defined by herd, year and month of milk test. Residual variances were modelled through variance functions, from second to fourth order and also by a step function with 1, 4, 6, 22 and 42 classes. The model of analyses included the fixed effect of CGs, number of milking, age of cow at calving as a covariable (linear and quadratic) and the mean trend of the population. As random effects were included the additive genetic and permanent environmental effects. The additive genetic and permanent environmental random effects were modelled by LP of days in milk from quadratic to seventh degree polynomial functions. The model with additive genetic and animal permanent environmental effects adjusted by quintic and sixth order LP, respectively, and residual variance modelled through a step function with six classes was the most adequate model to describe the covariance structure of the data. Heritability estimates decreased from 0.44 (first week) to 0.18 (fourth week). Unexpected negative genetic correlation estimates were obtained between TDMY records at first weeks with records from middle to the end of lactation, being the values varied from -0.07 (second with eighth week) to -0.34 (1st with 42nd week). TDMY heritability estimates were moderate in the course of the lactation, suggesting that this trait could be applied as selection criteria in milking buffaloes.