Behrouz Mohammad Nazari; Ardeshir Nejati Javaremi; Mohammad Moradi Shahre Babak; Rostam AbdolahiArpanahi
Volume 22, Issue 4 , December 2020, , Pages 515-527
Abstract
In order to evaluate the effect of genotype by environment interaction on production traits of Holstein cattle of Iran, first lactation test day records of 344170, 135000 and 156840 of milk, fat and protein yield on 34417, 13500 and 15684 cows and SNP markers of 1935 genotyped bulls were used. The ...
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In order to evaluate the effect of genotype by environment interaction on production traits of Holstein cattle of Iran, first lactation test day records of 344170, 135000 and 156840 of milk, fat and protein yield on 34417, 13500 and 15684 cows and SNP markers of 1935 genotyped bulls were used. The production data were retrieved from the Animal Breeding Center and Productions Improvement of Iran’s database which were collected from 2013 to 2018. To consider the interaction of genotype and environment, mean of temperature-humidity index (THI) in three days before each test day records as continuous environmental effect were retrieved from the 35 closest meteorological stations in the vicinity of 139 Holstein herds from 13 provinces. Variance and covariance components were estimated through a single-trait random regression model with orthogonal Legendre polynomials of second order for days in milk and THI using AIREMLF90 software. The results showed that changes in THI across lactation led tofluctuations in additive genetic variance over time. The change in heritability of milk production traits over lactation followed the same trend as additive genetic variance. The results from cross-validation analysis showed that including genomic information into the predictive model, increased prediction accuracy and including THI information increased unbiasedness. Due to the changes in milk production of daughters of bulls across days and THI , genotype by environment interaction should be considered when selecting bulls under different conditions.
Behzad Rajabi Marand; Hossein Moradi Shahrbabak; Mostafa Sadeghi; Rostam AbdolahiArpanahi
Volume 21, Issue 4 , January 2020, , Pages 419-430
Abstract
The aim of current study was to evaluate the accuracy of genomic breeding values (GEBV) for two important economical traits of milk yield and somatic cell score using SNP markers and LD-based haplotype blocks (haploblocks) by two statistical methods of GBULP and Bayes B. The data set consisted ...
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The aim of current study was to evaluate the accuracy of genomic breeding values (GEBV) for two important economical traits of milk yield and somatic cell score using SNP markers and LD-based haplotype blocks (haploblocks) by two statistical methods of GBULP and Bayes B. The data set consisted of 1654 bulls genotyped with different marker densities. When SNPs were used, the accuracy of breeding values obtained by Bayes B was better than GBLUP. In other words, for milk yield and somatic cell score traits, the prediction accuracy of GBLUP was 0.54 and 0.44 and by Bayes B was 0.58 and 0.44,respectively. For milk yield, the prediction accuracy of using haploblocks in both statistical methods was higher than the prediction accuracy using SNPs, while for the somatic cell score, this increase was more pronounced when GBLUP was used. However, when Bayes B was used this superiority was only obtained when the r2 statistic used to build the haploblocks was higher than 0.2. The results showed that the optimum level of r2 for building haploblocks depends on the trait type and its heritability. As a result, using r2 statistic more than 0.2 for building haploblocks can increase the accuracy of breeding valuesfoe both traits compared to SNP markers.
ali ashrafian; nasser emam jomeh kashan; Rostam AbdolahiArpanahi; Mohammad Bagher Sayyadnejad
Volume 20, Issue 3 , November 2018, , Pages 401-409
Abstract
In order to determine the optimum number of test-day records for the progeny test program of Holstein bulls, 732,140 milk yield test-day were used. These milk yield test-days, which were related to 73,214 first parity dairy cows belonging to 62 herds, had been collected by the Animal Breeding Center ...
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In order to determine the optimum number of test-day records for the progeny test program of Holstein bulls, 732,140 milk yield test-day were used. These milk yield test-days, which were related to 73,214 first parity dairy cows belonging to 62 herds, had been collected by the Animal Breeding Center of Iran from 1992 to 2016. The correlation of predicted breeding value (EBV) of bulls from ten test-day of their daughters compared with EBV predicted from different number of recorded test-days. The Correlation of predicted EBV from ten test-days with EBV from even, odd, (second, fifth, seventh), (second, fifth, tenth) and (second, sixth) test-day records were estimated to be 0.99, 0.98, 0.98, 0.97 and 0.94 respectively. The results showed that to reduce cost of recording, number of records and generation interval in EBV prediction of bulls with random regression model it is possible to use only second, fifth and seventh test-days instead of ten test-days.
Rostam AbdolahiArpanahi
Volume 19, Issue 2 , August 2017, , Pages 255-264
Abstract
In order to estimate the genetic parameters for days from calving to first service (DFS) in Iranian Holstein cattle by using repeatability, multiple-trait (MT) and random regression (RR) models, 159,482 records of parities 1 to 6 collected during 1981 to 2013 and distributed over 33 large Holstein herds ...
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In order to estimate the genetic parameters for days from calving to first service (DFS) in Iranian Holstein cattle by using repeatability, multiple-trait (MT) and random regression (RR) models, 159,482 records of parities 1 to 6 collected during 1981 to 2013 and distributed over 33 large Holstein herds were used. Bayesian information criterion of MT model was lower than other models and Bayesian factor of MT model was significant compared to other models (P< 0.05). Estimates of heritability of DFS with RR model decreased from parity 1 (0.09±0.01) to parity 6 (0.03±0.01). Estimated heritability by MT model decreased from parity 1 (0.08) to parity 5 (0.04) and increased in parity 6 (0.10). The obtained heritability using repeatability model was 0.055±0.01. Genetic correlations between DFS in different parities were reduced continuously with increasing distance between parities in RR and MT models. Overall, the result of this study indicate that multiple trait model performs better than other models in estimation of genetic parameter for DFS.
farahnaz jamshidizad
Volume 19, Issue 2 , August 2017, , Pages 311-320
Abstract
In this study production, reproduction, management and economic parameters obtained from 7 flocks with 600 head of native sheep were used during annual cycle of production in village systems. The economic values of traits were estimated using maximizing profit, revenue per cost and minimizing cost method. ...
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In this study production, reproduction, management and economic parameters obtained from 7 flocks with 600 head of native sheep were used during annual cycle of production in village systems. The economic values of traits were estimated using maximizing profit, revenue per cost and minimizing cost method. The results showed that the average profit per sheep per year, revenue per cost ratio and cost per revenue ratio for this system were 3,211,391Rials, 1.40 and 0.713, respectively. Feeding and husbandry costs represented about 64% and 20% of total cost, respectively. In maximizing profit method, average economic values (relative) of traits were 7,550.139 (1.102) Rials for survival rate, 7,630.49 (1.1143) Rials for conception rate, 7,172.632 (1.0475) Rials for lambing frequency, 6,520.575 (0.95228) Rials for lambing rate, 7,969 (0.9164) Rials for lamb survival rate to weaning, 8,150.719 (1.1903) Rials for kid survival rate to yearling, 6,847.3565 (1.00) Rials for lamb live weight at sale, -2610 (-1.93) Rials for body weight of sheep. In revenue per cost method the average economic values of aforementioned traits in all systems were 0.677, 0,683, 0.646, 0.592, 0.711, 0.726, 0.566, -0.293 respectively. The sensitivity of economic values to changes in prices of input parameters was low and to changes of prices in output parameters was high. Since the input parameters used in this study were collected from a wide range of management and climates conditions, estimated economic values could be used for designing the appropriate selection index for Kordi sheep.
Rostam AbdolahiArpanahi
Volume 19, Issue 1 , May 2017, , Pages 1-12
Abstract
The objective of this study was to compare three parametric (GBLUP, BayesB and RKHS) and two resampling (Bagging GBLUP and Random Forest) statistical methods in genomic prediction of traits with different genetic architecture. A genome consisting of three chromosomes, 1 Morgan each, was simulated on ...
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The objective of this study was to compare three parametric (GBLUP, BayesB and RKHS) and two resampling (Bagging GBLUP and Random Forest) statistical methods in genomic prediction of traits with different genetic architecture. A genome consisting of three chromosomes, 1 Morgan each, was simulated on which 5000 SNPs and 50, 100 and 200 QTLs were distributed. The substitutions effects of QTLs were modeled with normal, gamma and uniform distributions with a level of heritability equal to 0.30. The predictive performance of statistical models was evaluated using the correlation between predicted and true breeding values as well as the regression of predicted values on true breeding values. In the target population, Random Forest resulted in overestimation of estimated regression coefficients while GBLUP, BayesB and RKHS led to an underestimation of regression coefficients of true breeding values on predicted breeding values. In exception of Bagging GBLUP, the performance of all statistical methods was the same in three gene effect distributions. However, the performance of GBLUP and BayesB was better than other statistical methods. A reason for this superiority could be the additive architecture of simulated traits. In conclusion, GBLUP and BayesB were superior over resampling methods in genomic predictions.