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51.
Developments in quantitative genetics methodology as applied to national genetic improvement programs for swine
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Ignacy MISZTAL 《华南农业大学学报》2005,26(Z1)
For a long time,purebred pigs were evalua-ted in a nucleus for several growth,meat qualityand reproduction traits including growth,backfatand number of piglets alive[1].The evaluationwas using BLUP with all traits treated as linearand also assuming a normal distribution.Ani-mals down the pyramid were not evaluated;itwas assumed that most if not all of the gains ofselection at the nucleus level transferred to thecommercial level.The selection based on the e-valuations seemed to be successful … 相似文献
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Górski K Misztal T Dobek E Molik E Romanowicz K 《Reproduction in domestic animals》2012,47(5):746-751
Suckling by newborns induces a surge of lactogenic hormones, that is prolactin and growth hormone (GH), in mother's body, with endogenous opioid peptide (EOP) participating in generation of this surge. The aim of the current study was to investigate which types of opioid receptors are involved in generation of the GH surge in ewes during suckling. A series of intracerebroventricular infusions of opioid receptors antagonists: naloxone (for all types of receptors), naloxonazine (specific for μ receptor) and 5'-guanidinonaltrindole (GNTI--specific for κ receptor) and the vehicle (control) were performed in nursing sheep during the fifth week of lactation. All infusions were carried out in a serial manner: five 30-min infusions (60 μg/60 μl) from 10:00 to 15:00, at 30-min intervals. The period of the experiment consisted of the non-suckling (10:00-12:30) and suckling (12:30-15:00) periods. Simultaneously, blood samples were collected at 10-min intervals to determine plasma GH concentration by radioimmunoassay. Suckling evoked a rapid increase in GH concentration in control ewes. Naloxone and naloxonazine significantly decreased both the basal GH release in the non-suckling period and the suckling-induced GH surge. Specifically, the suppressive effect concerned either the duration or the amplitude of the GH surge. In contrast, GNTI did not significantly affect the GH release. In conclusion, the EOPs may affect the regulatory process of GH secretion in lactating sheep, especially through μ opioid receptor. 相似文献
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Genomic selection has been adopted nationally and internationally in different livestock and plant species. However, understanding whether genomic selection has been effective or not is an essential question for both industry and academia. Once genomic evaluation started being used, estimation of breeding values with pedigree best linear unbiased prediction (BLUP) became biased because this method does not consider selection using genomic information. Hence, the effective starting point of genomic selection can be detected in two possible ways including the divergence of genetic trends and Realized Mendelian sampling (RMS) trends obtained with BLUP and single-step genomic BLUP (ssGBLUP). This study aimed to find the start date of genomic selection for a set of economically important traits in three livestock species by comparing trends obtained using BLUP and ssGBLUP. Three datasets were used for this purpose: 1) a pig dataset with 117k genotypes and 1.3M animals in pedigree, 2) an Angus cattle dataset consisted of ~842k genotypes and 11.5M animals in pedigree, and 3) a purebred broiler chicken dataset included ~154k genotypes and 1.3M birds in pedigree were used. The genetic trends for pigs diverged for the genotyped animals born in 2014 for average daily gain (ADG) and backfat (BF). In beef cattle, the trends started diverging in 2009 for weaning weight (WW) and in 2016 for postweaning gain (PWG), with little divergence for birth weight (BTW). In broiler chickens, the genetic trends estimated by ssGBLUP and BLUP diverged at breeding cycle 6 for two out of the three production traits. The RMS trends for the genotyped pigs diverged for animals born in 2014, more for ADG than for BF. In beef cattle, the RMS trends started diverging in 2009 for WW and in 2016 for PWG, with a trivial trend for BTW. In broiler chickens, the RMS trends from ssGBLUP and BLUP diverged strongly for two production traits at breeding cycle 6, with a slight divergence for another trait. Divergence of the genetic trends from ssGBLUP and BLUP indicates the onset of the genomic selection. The presence of trends for RMS indicates selective genotyping, with or without the genomic selection. The onset of genomic selection and genotyping strategies agrees with industry practices across the three species. In summary, the effective start of genomic selection can be detected by the divergence between genetic and RMS trends from BLUP and ssGBLUP. 相似文献
54.
Utility of the preconditioned conjugate gradient algorithm with a diagonal preconditioner for solving mixed-model equations in animal breeding applications was evaluated with 16 test problems. The problems included single- and multiple-trait analyses, with data on beef, dairy, and swine ranging from small examples to national data sets. Multiple-trait models considered low and high genetic correlations. Convergence was based on relative differences between left- and right-hand sides. The ordering of equations was fixed effects followed by random effects, with no special ordering within random effects. The preconditioned conjugate gradient program implemented with double precision converged for all models. However, when implemented in single precision, the preconditioned conjugate gradient algorithm did not converge for seven large models. The preconditioned conjugate gradient and successive overrelaxation algorithms were subsequently compared for 13 of the test problems. The preconditioned conjugate gradient algorithm was easy to implement with the iteration on data for general models. However, successive overrelaxation requires specific programming for each set of models. On average, the preconditioned conjugate gradient algorithm converged in three times fewer rounds of iteration than successive overrelaxation. With straightforward implementations, programs using the preconditioned conjugate gradient algorithm may be two or more times faster than those using successive overrelaxation. However, programs using the preconditioned conjugate gradient algorithm would use more memory than would comparable implementations using successive overrelaxation. Extensive optimization of either algorithm can influence rankings. The preconditioned conjugate gradient implemented with iteration on data, a diagonal preconditioner, and in double precision may be the algorithm of choice for solving mixed-model equations when sufficient memory is available and ease of implementation is essential. 相似文献
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A. Legarra J.K. Bertrand T. Strabel R.L. Sapp J.P. Sánchez & I. Misztal 《Zeitschrift für Tierzüchtung und Züchtungsbiologie》2007,124(5):286-295
A multi-breed model was presented for the genetic evaluation of growth traits in beef cattle. In addition to the fixed effects, random direct and maternal genetic effects, and random maternal permanent environmental effects are considered; the model also fits direct and maternal heterosis and direct and maternal breed-of-founder (BOF) x generation group effects using a Bayesian approach that weights prior literature estimates relative to information supplied by the dataset to which the model will be applied. The multi-breed evaluation procedures also allow the inclusion of external evaluations for animals of other breeds. The multi-breed model was applied to a dataset provided by the American Gelbvieh Association. Different analyses were conducted by varying the weights given to the prior literature relative to the information provided by the dataset. Large differences were observed for the heterosis estimates, the BOF x generation group effect estimates, and the predicted breeding values across breeds due to the weights posed on prior literature estimates versus estimates derived directly from data. However, the rankings within breed were observed to be relatively robust to the different weights on prior information. 相似文献
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The objective of this study was to investigate the possibility of genotype x environment interactions for weaning weight (WWT) between different regions of the United States (US) and between Canada (CA), Uruguay (UY), and US for populations of Hereford cattle. Original data were composed of 487,661, 102,986, and 2,322,722 edited weaning weight records from CA, UY, and US, respectively. A total of 359 sires were identified as having progeny across all three countries; 240 of them had at least one progeny with a record in each environment. The data sets within each country were reduced by retaining records from herds with more than 500 WWT records, with an average contemporary group size of greater than nine animals, and that contained WWT records from progeny or maternal grand-progeny of the across-country sires. Data sets within each country were further reduced by randomly selecting among remaining herds. Four regions within US were defined: Upper Plains (UP), Cornbelt (CB), South (S), and Gulf Coast (GC). Similar sampling criteria and common international sires were used to form the within-US regional data sets. A pairwise analysis was done between countries and regions within US (UP-CB vs S-GC, UP vs CB, and S vs GC) for the estimation of (co)variance components and genetic correlation between environments. An accelerated EM-REML algorithm and a multiple-trait animal model that considered WWT as a different trait in each environment were used to estimate parameters in each pairwise analysis. Direct and maternal (in parentheses) estimated genetic correlations for CA vs UY, CA vs US, US vs UY, UP-CB vs S-GC, UP vs CB, and S vs GC were .88 (.84), .86 (.82), .90 (.85), .88 (.87), .88 (.84), and .87 (.85), respectively. The general absence of genotype x country interactions observed in this study, together with a prior study that showed the similarity of genetic and environmental parameters across the three countries, strongly indicates that a joint WWT genetic evaluation for Hereford cattle could be conducted using a model that treated the information from CA, UY, and US as a single population using single population-wide genetic parameters. 相似文献
59.
Calving records (n = 6,763) obtained from first, second, and third parities of 3,442 spring-calving, Uruguayan Aberdeen Angus cows were used to estimate heritabilities and genetic correlations for the linear trait calving day (CD) and the binary trait calving success (CS), using models that considered CD and CS at 3 calving opportunities as separate traits. Three approaches were defined to handle the CD observations on animals that failed to calve: 1) the cows were assigned a penalty value of 21 d beyond the last observed CD record within contemporary group (PEN); 2) the censored CD values were randomly obtained from a truncated normal distribution (CEN); and 3) the CD records were treated as missing, and the parameters were estimated in a joint threshold-linear analysis including CS traits (TLMISS). The models included the effects of contemporary group (herd x year of calving x mating management), age at calving (3 levels), physiological status at mating (nonlactating or lactating), animal additive genetic effects, and residual. Estimates of heritability for CD traits in the PEN and CEN data sets ranged from 0.20 to 0.31, with greater values in the first calving opportunity. Genetic correlations were positive and medium to high in magnitude, 0.57 to 0.59 in the PEN data set and 0.38 to 0.91 in the CEN data set. In the TLMISS data set, heritabilities ranged from 0.19 to 0.23 for CD and 0.37 to 0.42 for CS. Genetic correlations between CD traits varied between 0.82 and 0.88; between CS traits, genetic correlations varied between 0.56 and 0.80. Negative (genetically favorable), medium to high genetic correlations (-0.54 to -0.91) were estimated between CD and CS traits, suggesting that CD could be used as an indicator trait for CS. Data recording must improve in quality for practical applications in genetic evaluation for fertility traits. 相似文献
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The objectives of this study were to develop an efficient algorithm for calculating prediction error variances (PEVs) for genomic best linear unbiased prediction (GBLUP) models using the Algorithm for Proven and Young (APY), extend it to single-step GBLUP (ssGBLUP), and apply this algorithm for approximating the theoretical reliabilities for single- and multiple-trait models in ssGBLUP. The PEV with APY was calculated by block sparse inversion, efficiently exploiting the sparse structure of the inverse of the genomic relationship matrix with APY. Single-step GBLUP reliabilities were approximated by combining reliabilities with and without genomic information in terms of effective record contributions. Multi-trait reliabilities relied on single-trait results adjusted using the genetic and residual covariance matrices among traits. Tests involved two datasets provided by the American Angus Association. A small dataset (Data1) was used for comparing the approximated reliabilities with the reliabilities obtained by the inversion of the left-hand side of the mixed model equations. A large dataset (Data2) was used for evaluating the computational performance of the algorithm. Analyses with both datasets used single-trait and three-trait models. The number of animals in the pedigree ranged from 167,951 in Data1 to 10,213,401 in Data2, with 50,000 and 20,000 genotyped animals for single-trait and multiple-trait analysis, respectively, in Data1 and 335,325 in Data2. Correlations between estimated and exact reliabilities obtained by inversion ranged from 0.97 to 0.99, whereas the intercept and slope of the regression of the exact on the approximated reliabilities ranged from 0.00 to 0.04 and from 0.93 to 1.05, respectively. For the three-trait model with the largest dataset (Data2), the elapsed time for the reliability estimation was 11 min. The computational complexity of the proposed algorithm increased linearly with the number of genotyped animals and with the number of traits in the model. This algorithm can efficiently approximate the theoretical reliability of genomic estimated breeding values in ssGBLUP with APY for large numbers of genotyped animals at a low cost. 相似文献