Usage of crossbred cattle for the progeny testing of Japanese black cattle (Wagyu)

1997 ◽  
Vol 1997 ◽  
pp. 146-146
Author(s):  
Y. Nagamine ◽  
K. Nirasawa ◽  
H. Takahashi

The Japanese black cattle (Wagyu) is well known for their high marbling and price meat. Usually purebred calves from Japanese black cattle are used for the station or field progeny testing. If genetic variance of particular traits of the dam breed is much smaller than that of the sire breed, breeding values of sires can be estimated accurately from their crossbred progeny because genetic difference among dams is relatively small and can be ignored. The objective of this study was to investigate the possibility of using crossbred for progeny testing of Japanese black sires. Since marbling scores or carcass prices of Holstein cattle are low and do not vary widely, crossbred between Japanese black and Holstein was chosen in this study.

1995 ◽  
Vol 60 (3) ◽  
pp. 379-387 ◽  
Author(s):  
Z. W. Luo ◽  
J. A. Woolliams ◽  
R. Thompson

AbstractA nucleus dairy population using multiple ovulation and embryo transfer (MOET) was stochastically modelled with overlapping generations. The aim was to investigate the feasibility of controlling inbreeding in MOET breeding schemes using more realistic parameters for embryo recovery and best linear unbiased prediction (BLUP) for genetic evaluation. Four different cases (involving the culling of donors, more donors and the use of organized progeny testing of nucleus bulls) were studied in combination with nested and factorial designs. Further studies involved modifications of the selection index, including subtracting parental breeding values, inflating the genetic variance in the BLUP evaluation and penalizing inbred animals; these options were examined both with and without organized progeny testing. The effects of applying these schemes on both genetic response and rate of inbreeding were investigated. The results stressed the importance of incorporating progeny testing into MOET schemes for value of reducing inbreeding whilst maintaining genetic progress. There was no significant difference between nested and factorial designs. In the absence of progeny testing the inflation of genetic variance was more effective than subtracting parental breeding values at controlling inbreeding; however incorporating progeny testing made the latter strategy more potent and the superiority of inflating the genetic variance was in this case much smaller and non-significant.


1956 ◽  
Vol 47 (4) ◽  
pp. 367-375 ◽  
Author(s):  
I. L. Mason ◽  
Alan Robertson

1. An analysis has been made of milk records from 13,000 cows bred by artificial insemination in Denmark.2. The herds were divided into three equal groups on the basis of their average production. The variance of yield within herds increased as the average yield increased, but the coefficient of variation declined slightly. The genetic variance was more than five times as large in the high-yielding herds than in the low, and correspondingly the heritabilities in low, medium and high herds were 0·05, 0·15 and 0·22 respectively. These were estimated from the variation observed between progeny groups of the same 152 bulls at each production level.3. No evidence was obtained of any sire-herd interaction for yield, either within or between management levels. The true ranking of bulls for breeding value was apparently the same at all levels.4. The heritability of fat content in the three groups was 0·27, 0·47 and 0·49 respectively, and no evidence of sire-herd interaction was found.5. The contemporary comparison method of assessing A.I. bulls for yield was found to have the accuracy expected in theory.6. These results are discussed in relation to those of other workers with which there are some discrepancies. On our results, a policy of choosing bulls on the basis of their daughters' performance in high-yielding herds should be the most satisfactory way of progeny-testing bulls used in artificial insemination.


Genetics ◽  
1994 ◽  
Vol 138 (3) ◽  
pp. 913-941 ◽  
Author(s):  
M Turelli ◽  
N H Barton

Abstract We develop a general population genetic framework for analyzing selection on many loci, and apply it to strong truncation and disruptive selection on an additive polygenic trait. We first present statistical methods for analyzing the infinitesimal model, in which offspring breeding values are normally distributed around the mean of the parents, with fixed variance. These show that the usual assumption of a Gaussian distribution of breeding values in the population gives remarkably accurate predictions for the mean and the variance, even when disruptive selection generates substantial deviations from normality. We then set out a general genetic analysis of selection and recombination. The population is represented by multilocus cumulants describing the distribution of haploid genotypes, and selection is described by the relation between mean fitness and these cumulants. We provide exact recursions in terms of generating functions for the effects of selection on non-central moments. The effects of recombination are simply calculated as a weighted sum over all the permutations produced by meiosis. Finally, the new cumulants that describe the next generation are computed from the non-central moments. Although this scheme is applied here in detail only to selection on an additive trait, it is quite general. For arbitrary epistasis and linkage, we describe a consistent infinitesimal limit in which the short-term selection response is dominated by infinitesimal allele frequency changes and linkage disequilibria. Numerical multilocus results show that the standard Gaussian approximation gives accurate predictions for the dynamics of the mean and genetic variance in this limit. Even with intense truncation selection, linkage disequilibria of order three and higher never cause much deviation from normality. Thus, the empirical deviations frequently found between predicted and observed responses to artificial selection are not caused by linkage-disequilibrium-induced departures from normality. Disruptive selection can generate substantial four-way disequilibria, and hence kurtosis; but even then, the Gaussian assumption predicts the variance accurately. In contrast to the apparent simplicity of the infinitesimal limit, data suggest that changes in genetic variance after 10 or more generations of selection are likely to be dominated by allele frequency dynamics that depend on genetic details.


2008 ◽  
Vol 52 (No. 4) ◽  
pp. 83-87 ◽  
Author(s):  
A. Matějíček ◽  
J. Matějíčková ◽  
E. Němcová ◽  
O.M. Jandurová ◽  
M. Štípková ◽  
...  

The objective of this study was to estimate the joint effects of <i>CSN3</i> and <i>LGB</i> genotypes on breeding values of milk production parameters. <i>CSN3</i> (kappa-casein) and <i>LGB</i> (beta-lactoglobulin) genotypes of 120 Czech Fleckvieh sires were detected using the PCR-RFLP method. Breeding values of sires were obtained from the Official Database of Progeny Testing. Ten genotype combinations were detected. Genotypes <i>ABAB</i> (25.0%), <i>ABAA</i> (13.3%) and <i>ABBB</i> (13.3%) were the most frequent. Significant effects of genotype combinations on breeding values for fat and protein content were found. The highest breeding values for milk (+621 kg) and protein (+15.8 kg) yields were associated with genotype combination <i>ABAA</i>, while the highest breeding values for content parameters (+0.15% for protein content and +0.55% for fat content) were associated with genotype combination <i>BBAB</i>.


Animals ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 2048
Author(s):  
Liyuan Liu ◽  
Jinghang Zhou ◽  
Chunpeng James Chen ◽  
Juan Zhang ◽  
Wan Wen ◽  
...  

High-yield and high-quality of milk are the primary goals of dairy production. Understanding the genetic architecture underlying these milk-related traits is beneficial so that genetic variants can be targeted toward the genetic improvement. In this study, we measured five milk production and quality traits in Holstein cattle population from China. These traits included milk yield, fat, and protein. We used the estimated breeding values as dependent variables to conduct the genome-wide association studies (GWAS). Breeding values were estimated through pedigree relationships by using a linear mixed model. Genotyping was carried out on the individuals with phenotypes by using the Illumina BovineSNP150 BeadChip. The association analyses were conducted by using the fixed and random model Circulating Probability Unification (FarmCPU) method. A total of ten single-nucleotide polymorphisms (SNPs) were detected above the genome-wide significant threshold (p < 4.0 × 10−7), including six located in previously reported quantitative traits locus (QTL) regions. We found eight candidate genes within distances of 120 kb upstream or downstream to the associated SNPs. The study not only identified the effect of DGAT1 gene on milk fat and protein, but also discovered novel genetic loci and candidate genes related to milk traits. These novel genetic loci would be an important basis for molecular breeding in dairy cattle.


1952 ◽  
Vol 1953 (1) ◽  
pp. 79-105 ◽  
Author(s):  
Ivar Johansson ◽  
Alan Robertson

The progress in animal improvement depends on the accuracy with which the breeding value of each individual in the breed, or herd, is estimated, and how the animals chosen on the basis of our estimates are combined in matings. We are concerned here only with the first part of the problem, i.e. the estimation of breeding values. The estimates are made in regard to certain characters, or traits, which are of particular interest from an economic point of view, and they may be based on the phenotypic merit of the individual, or on the merits of its ancestors or collateral relatives, or on the merits of its progeny, in regard to the character in question. Often a combination of two, or three, or all four methods may be used. Their relative importance depends on the heritability of the character, as will be discussed later.


1987 ◽  
Vol 44 (1) ◽  
pp. 29-38 ◽  
Author(s):  
M. E. Goddard

ABSTRACTIn the breeding of dairy cattle the selection of bulls to breed young bulls for progeny testing is a crucial process. This paper compares several policies for making this selection based on the criteria-selection response, inbreeding depression, loss of genetic variance and variability of response. A number called the ‘effective number of new bulls to breed bulls selected each year’ (NBBe) is defined which is closely related to the last three of these criteria. Past studies of the design of dairy cattle breeding programmes have assumed that selection is within a group of bulls progeny tested in the same year (policy I). However, modern sire evaluation methods allow comparison of sires tested in different years. To evaluate the effect of selecting bulls to breed bulls from all available bulls (policy II) a computer simulation program was used. Policy II results in an increase in the response to selection but a substantial decrease in NBBe. When compared at the same NBBe, policy II results in a smaller selection response than policy I. A policy which allows the best bulls to be used for more than 1 year but which limits the maximum number of years for which they can be used, results in the best compromise. If bulls are to be used for several years there is little advantage to be gained from making more matings within each year to more high-rated bulls or to older, more reliably evaluated bulls.


2010 ◽  
Vol 42 (1) ◽  
Author(s):  
David Habier ◽  
Jens Tetens ◽  
Franz-Reinhold Seefried ◽  
Peter Lichtner ◽  
Georg Thaller

1999 ◽  
Vol 29 (6) ◽  
pp. 724-736 ◽  
Author(s):  
P X Lu ◽  
D A Huber ◽  
T L White

Potential biases associated with incomplete linear models in the estimation of heritability and the prediction of breeding values have been investigated. Results indicate that estimates of additive genetic variance and heritability as well as predicted parental breeding values from incomplete models will inevitably be biased as long as the true variance components of ignored effects are not zero. While models ignoring the interaction effect of males and females (SCA) × environment (E) interaction downwardly biased the estimates of additive genetic variance and heritability, models ignoring SCA and (or) the additive genetic effect (GCA) × E interaction yielded upward biases. The magnitudes of biases are functions of population genetic architecture, mating design, and field experimental design and can be precisely assessed with formulae derived for balanced data. Numerical simulations using unbalanced data of different mating and field experimental designs suggest that the formulae from balanced data can be used to approximate the minimum biases associated with unbalanced data. Because of the magnitudes of biases for some typical forest genetic scenarios, it is suggested that models ignoring SCA and (or) GCA × E should be avoided when the numbers of test sites and crosses per parent are small. However, incomplete model ignoring SCA × E interaction may be used to reduce computational demand with only negligible consequences.


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