An Evaluation of Bias in Estimated Breeding Values for Weaning Weight in Angus Beef Cattle Field Records. III. Estimates of Bias due to Nonrandom Mating

This study examined influences of sire (n = 9) estimated breeding values (EBVs), sire-group (Muscle, Growth, and Control), and nutrition (low and high quality and availability pasture) from birth to slaughter at ~8 months of age on indices of muscle cellularity and transcriptional and translational capacity in 56 castrate lambs. Effects of nutritional systems to 8 months of age were greater, overall, than those due to EBVs or sire-group. Amount of DNA increased with increasing EBV for post-weaning eye muscle depth (PEMD or Muscle EBV) in longissimus but not in semimembranosus and semitendinosus muscles, while Muscle EBV also had an inverse association with concentration of DNA. Protein to DNA and RNA to DNA were related positively to Muscle EBV, the associations being strongest for the semitendinosus muscle. Post-weaning weight (PWWT or Growth) EBV correlated positively with the RNA to DNA ratio and, among high but not low nutrition lambs, was inversely related to concentration of muscle DNA, whereas post-weaning fat depth (PFAT or Fat) EBV was correlated positively with RNA concentration. Overall, the magnitude of effects of sire-group was less than for sire EBVs, presumably due to differing selection pressures for muscling, fatness, and growth. High nutrition lambs had more protein to DNA than low nutrition lambs in the longissimus and semitendinosus muscles, but not in the semimembranosus muscle. In low compared with high nutrition lambs, concentration of DNA was greater in the longissimus and semitendinosus muscles. Total amount of DNA was reduced by more in low compared with high nutrition in the longissimus and semimembranosus than in the semitendinosus, and amount of protein was reduced by more in low compared with high nutrition in the longissimus than in the other two muscles. We conclude that genetic selection for eye muscle depth in sheep has differing effects on cellular characteristics of the longissimus, semimembranosus, and semitendinosus muscles, and has greater effects on muscle cellular characteristics than genetic selection for post-weaning weight or fat depth.

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Weaning results of beef Hungarian Fleckvieh calves 2. Genetic parameters, breeding values

Archives Animal Breeding ◽

10.5194/aab-53-26-2010 ◽

2010 ◽

Vol 53 (1) ◽

pp. 26-36 ◽

Cited By ~ 1

Author(s):

S. Bene ◽

I. Füller ◽

A. Fördős ◽

F. Szabó

Keyword(s):

Genetic Parameters ◽

Correlation Coefficients ◽

Breeding Value ◽

Phenotypic Variance ◽

Weaning Weight ◽

Breeding Values ◽

Estimated Breeding Values ◽

Genetic Value ◽

Maternal Heritability ◽

Daily Gain

Abstract. Weaning weight, preweaning daily gain and 205-day weight of Hungarian Fleckvieh calves (n=8 929, bulls =4 539, heifers =4 390) born from 232 sires between 1980 and 2003 were examined. Variance, covariance components and heritability values and correlation coefficients were estimated. The effect of the maternal permanent environment on genetic parameters and breeding values were examined. Two animal models were used for breeding value estimation. The direct heritability (hd2) of weaning weight, preweaning daily gain and 205-day weight was between 0.37 and 0.42. The maternal heritability (hm2) of these traits was 0.06 and 0.07. The direct-maternal correlations (rdm) were medium and negative −0.52 and −0.74. Contribution of the maternal heritability and maternal permanent environment to phenotype is smaller than that of direct heritabilities (hm2+c2< hd2). The ratio of the variance of maternal permanent environment in the phenotypic variance (c2) changed from 3 to 6 %. Estimated breeding values changed whether the permanent environmental effect of dam wasn’t taken into consideration but the rank of the animals was not modified. The genetic value for weaning results of Hungarian Fleckvieh population has increased since 1997.

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61 The impact of selective phenotyping and genotyping over generations in beef cattle

Journal of Animal Science ◽

10.1093/jas/skz122.068 ◽

2019 ◽

Vol 97 (Supplement_2) ◽

pp. 37-39

Author(s):

Andrea Plotzki Reis ◽

Rodrigo Fagundes da Costa ◽

Fabyano Fonseca e Silva ◽

Fernando Flores Cardoso ◽

Matthew L Spangler

Keyword(s):

Beef Cattle ◽

Prediction Accuracy ◽

Cost Benefit ◽

Single Step ◽

Breeding Values ◽

Additional Information ◽

Economic Framework ◽

Estimated Breeding Values ◽

The Cost ◽

The Impact

Abstract The aim of this study was to investigate selective phenotyping to maintain adequate prediction accuracy. A simulation was conducted, with 10 replicates, using QMSim to mimic the structure and size of a Braford population. A population with 50 generations, 500 animals per generation, was created with phenotyping and genotyping beginning in generation 11. The scenarios investigated were: 1) Randomly phenotype and genotype 10, 25, 50, 75, and 100% of individuals each generation and; 2) Randomly phenotype and genotype 10, 25, 50, 75, and 100% of individuals in every-other generation. Estimated breeding values (EBV) were obtained using single-step GBLUP and accuracy was determined as the correlation between true BV from simulation and those estimated from the blupf90 family of programs. For scenarios where phenotyping and genotyping occurred every generation, EBV accuracies in generation 11 and 50 ranged from 0.32 to 0.32, 0.42 to 0.43, 0.49 to 0.51, 0.53 to 0.56 and 0.57 to 0.59 when 10, 25, 50, 75, and 100% of animals were chosen, respectively. The highest accuracies were 0.40 and 0.50 in generation 38 for scenarios 10 and 25%; 0.56, 0.61 and 0.64 in generation 40 for scenarios 50, 75 and 100%, respectively. When animals were selected every-other generation, EBV accuracy in generation 11 and 50 ranged from 0.24 to 0.26, 0.36 to 0.36, 0.43 to 0.42, 0.48 to 0.44 and 0.53 to 0.48 for 10, 25, 50, 75 and 100% of selected animals, respectively. The highest accuracies were in generation 23 for scenario 10% (0.31), in generation 37 for scenarios 25 (0.43), 50 (0.50) and 75% (0.55) and in generation 39 for 100% (0.59). Although increasing the density of phenotyped and genotyped animals increased prediction accuracy, some gains were marginal. These differences in accuracy must be contemplated in an economic framework to determine the cost-benefit of additional information.

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Changes in genomic predictions when new information is added

Journal of Animal Science ◽

10.1093/jas/skab004 ◽

2021 ◽

Vol 99 (2) ◽

Author(s):

Jorge Hidalgo ◽

Daniela Lourenco ◽

Shogo Tsuruta ◽

Yutaka Masuda ◽

Stephen Miller ◽

...

Keyword(s):

Single Step ◽

Pedigree Information ◽

Weaning Weight ◽

Breeding Values ◽

Core Group ◽

Absolute Change ◽

Angus Cattle ◽

Average Maximum ◽

Estimated Breeding Values ◽

Genetic Evaluations

Abstract The stability of genomic evaluations depends on the amount of data and population parameters. When the dataset is large enough to estimate the value of nearly all independent chromosome segments (~10K in American Angus cattle), the accuracy and persistency of breeding values will be high. The objective of this study was to investigate changes in estimated breeding values (EBV) and genomic EBV (GEBV) across monthly evaluations for 1 yr in a large genotyped population of beef cattle. The American Angus data used included 8.2 million records for birth weight, 8.9 for weaning weight, and 4.4 for postweaning gain. A total of 10.1 million animals born until December 2017 had pedigree information, and 484,074 were genotyped. A truncated dataset included animals born until December 2016. To mimic a scenario with monthly evaluations, 2017 data were added 1 mo at a time to estimate EBV using best linear unbiased prediction (BLUP) and GEBV using single-step genomic BLUP with the algorithm for proven and young (APY) with core group fixed for 1 yr or updated monthly. Predictions from monthly evaluations in 2017 were contrasted with the predictions of the evaluation in December 2016 or the previous month for all genotyped animals born until December 2016 with or without their own phenotypes or progeny phenotypes. Changes in EBV and GEBV were similar across traits, and only results for weaning weight are presented. Correlations between evaluations from December 2016 and the 12 consecutive evaluations were ≥0.97 for EBV and ≥0.99 for GEBV. Average absolute changes for EBV were about two times smaller than for GEBV, except for animals with new progeny phenotypes (≤0.12 and ≤0.11 additive genetic SD [SDa] for EBV and GEBV). The maximum absolute changes for EBV (≤2.95 SDa) were greater than for GEBV (≤1.59 SDa). The average(maximum) absolute GEBV changes for young animals from December 2016 to January and December 2017 ranged from 0.05(0.25) to 0.10(0.53) SDa. Corresponding ranges for animals with new progeny phenotypes were from 0.05(0.88) to 0.11(1.59) SDa for GEBV changes. The average absolute change in EBV(GEBV) from December 2016 to December 2017 for sires with ≤50 progeny phenotypes was 0.26(0.14) and for sires with >50 progeny phenotypes was 0.25(0.16) SDa. Updating the core group in APY without adding data created an average absolute change of 0.07 SDa in GEBV. Genomic evaluations in large genotyped populations are as stable and persistent as the traditional genetic evaluations, with less extreme changes.

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A comprehensive comparison between single- and two-step GBLUP methods in a simulated beef cattle population

Canadian Journal of Animal Science ◽

10.1139/cjas-2017-0176 ◽

2018 ◽

Vol 98 (3) ◽

pp. 565-575 ◽

Cited By ~ 5

Author(s):

Mario L. Piccoli ◽

Luiz F. Brito ◽

José Braccini ◽

Fernanda V. Brito ◽

Fernando F. Cardoso ◽

...

Keyword(s):

Beef Cattle ◽

Production Systems ◽

Simulated Data ◽

Single Step ◽

Breeding Values ◽

True Breeding ◽

Blending Method ◽

Estimated Breeding Values ◽

Genetic Evaluations ◽

Genomic Predictions

The statistical methods used in the genetic evaluations are a key component of the process and can be best compared by using simulated data. The latter is especially true in grazing beef cattle production systems, where the number of proven bulls with highly reliable estimated breeding values is limited to allow for a trustworthy validation of genomic predictions. Therefore, we simulated data for 4980 beef cattle aiming to compare single-step genomic best linear unbiased prediction (ssGBLUP), which simultaneously incorporates pedigree, phenotypic, and genomic data into genomic evaluations, and two-step GBLUP (tsGBLUP) procedures and genomic estimated breeding values (GEBVs) blending methods. The greatest increases in GEBV accuracies compared with the parents’ average estimated breeding values (EBVPA) were 0.364 and 0.341 for ssGBLUP and tsGBLUP, respectively. Direct genomic value and GEBV accuracies when using ssGBLUP and tsGBLUP procedures were similar, except for the GEBV accuracies using Hayes’ blending method in tsGBLUP. There was no significant or slight bias in genomic predictions from ssGBLUP or tsGBLUP (using VanRaden’s blending method), indicating that these predictions are on the same scale compared with the true breeding values. Overall, genetic evaluations including genomic information resulted in gains in accuracy >100% compared with the EBVPA. In addition, there were no significant differences between the selected animals (10% males and 50% females) by using ssGBLUP or tsGBLUP.

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Accuracies of genomically estimated breeding values from pure-breed and across-breed predictions in Australian beef cattle

Genetics Selection Evolution ◽

10.1186/s12711-014-0061-9 ◽

2014 ◽

Vol 46 (1) ◽

Cited By ~ 6

Author(s):

Vinzent Boerner ◽

David J Johnston ◽

Bruce Tier

Keyword(s):

Beef Cattle ◽

Breeding Values ◽

Pure Breed ◽

Estimated Breeding Values

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Factors affecting birth weight and performance to weaning of Bunaji calves

The Journal of Agricultural Science ◽

10.1017/s0021859600083386 ◽

1988 ◽

Vol 111 (2) ◽

pp. 407-410 ◽

Cited By ~ 2

Author(s):

O. O. Oni ◽

V. Buvanendran ◽

N. I. Dim

Keyword(s):

Birth Weight ◽

Beef Cattle ◽

Beef Cows ◽

Genetic Differences ◽

Economic Returns ◽

Weaning Weight ◽

Breeding Values ◽

Factors Affecting ◽

Sources Of Variation ◽

And Performance

Birth weight and pre-weaning performance are recognized as important components in determing economic returns from beef cattle. The accuracy of ranking animals based on their breeding values, and hence the effectiveness of selection, will be increased when allowance is made for non-genetic sources of variation. Furthermore, the proper evaluation of identifiable sources of variation in calf weaning weight aids in more accurate appraisal of genetic differences in mothering ability among beef cows.

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Potential for selection to improve efficiency of feed use in beef cattle: a review

Australian Journal of Agricultural Research ◽

10.1071/a98075 ◽

1999 ◽

Vol 50 (2) ◽

pp. 147 ◽

Cited By ~ 200

Author(s):

J. A. Archer ◽

E. C. Richardson ◽

R. M. Herd ◽

P. F. Arthur

Keyword(s):

Genetic Variation ◽

Beef Cattle ◽

Feed Intake ◽

Production System ◽

Feed Efficiency ◽

Genetic Correlations ◽

Total Production ◽

Breeding Values ◽

Improve Efficiency ◽

Estimated Breeding Values

Evidence for genetic variation in feed efficiency of beef cattle is reviewed in this paper, and ways in which this variation might be used in selection programs to improve beef cattle in Australia are discussed. Efficiency of beef production systems is determined by feed and other inputs of all classes of animals in the production system as well as outputs in terms of slaughter progeny and cull cows. Different indices have been used to express aspects of efficiency on cattle over certain periods of the production cycle. Use of these indices is discussed, and then evidence for genetic variation in both growing animals and mature animals is reviewed. Genetic variation in feed efficiency exists in both growing and mature cattle, although information is lacking to determine whether variation in total production system efficiency exists. The physiological basis for observed variation in feed efficiency is discussed, with differences in requirements for maintenance, body composition, proportions of visceral organs, level of physical activity, and digestion efficiency identified as possible sources of variation. Selection to improve efficiency might be achieved by measuring feed intake of growing animals and utilising genetic correlations that are likely to exist between efficiency of growing animals and mature animals. Measurement of feed intake might occur in central test stations, or methods may be developed to measure feed intake on-farm. Ways of utilising information generated in genetic evaluations are discussed, and it is concluded that estimated breeding values for feed intake after a phenotypic adjustment for growth performance would be most practical, although not theoretically optimal. Such estimated breeding values would best be used in an economic selection index to account for genetic correlations with other traits, including feed intake of the breeding herd, and the economic value of feed in relation to other traits. Future research should be directed towards understanding the genetic relationships between feed intake and other traits in the breeding objective, and to find ways to reduce the cost of measurement of feed intake, including a search for genetic markers.

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