An Evaluation of Bias in Estimated Breeding Values for Weaning Weight in August Beef Cattle Field Records. II. Estimates of Bias Due to Genetic Trend

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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Estimated breeding values and genetic trend for milk yield in Nili Ravi buffaloes

Italian Journal of Animal Science ◽

10.4081/ijas.2007.s2.393 ◽

2007 ◽

Vol 6 (sup2) ◽

pp. 393-396

Author(s):

M. Ahmad

Keyword(s):

Milk Yield ◽

Genetic Trend ◽

Breeding Values ◽

Estimated Breeding Values

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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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AlphaPart—R implementation of the method for partitioning genetic trends

Genetics Selection Evolution ◽

10.1186/s12711-021-00600-x ◽

2021 ◽

Vol 53 (1) ◽

Author(s):

Jana Obšteter ◽

Justin Holl ◽

John M. Hickey ◽

Gregor Gorjanc

Keyword(s):

Genetic Gain ◽

R Package ◽

Breeding Programme ◽

Genetic Trend ◽

Breeding Values ◽

Poultry Breeding ◽

Breeding Programmes ◽

Estimated Breeding Values ◽

The Relationship ◽

Genetic Trends

Abstract Background In this paper, we present the AlphaPart R package, an open-source implementation of a method for partitioning breeding values and genetic trends to identify the contribution of selection pathways to genetic gain. Breeding programmes improve populations for a set of traits, which can be measured with a genetic trend calculated from estimated breeding values averaged by year of birth. While sources of the overall genetic gain are generally known, their realised contributions are hard to quantify in complex breeding programmes. The aim of this paper is to present the AlphaPart R package and demonstrate it with a simulated stylized multi-tier breeding programme mimicking a pig or poultry breeding programme. Results The package includes the main partitioning function AlphaPart, that partitions the breeding values and genetic trends by pre-defined selection paths, and a set of functions for handling data and results. The package is freely available from the CRAN repository at http://CRAN.R-project.org/package=AlphaPart. We demonstrate the use of the package by partitioning the nucleus and multiplier genetic gain of the stylized breeding programme by tier-gender paths. For traits measured and selected in the multiplier, the multiplier selection generated additional genetic gain. By using AlphaPart, we show that the additional genetic gain depends on accuracy and intensity of selection in the multiplier and the extent of gene flow from the nucleus. We have proven that AlphaPart is a valuable tool for understanding the sources of genetic gain in the nucleus and especially the multiplier, and the relationship between the sources and parameters that affect them. Conclusions AlphaPart implements the method for partitioning breeding values and genetic trends and provides a useful tool for quantifying the sources of genetic gain in breeding programmes. The use of AlphaPart will help breeders to improve genetic gain through a better understanding of the key selection points that are driving gains in each trait.

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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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AlphaPart - R implementation of the method for partitioning genetic trends

10.1101/2020.04.24.059071 ◽

2020 ◽

Author(s):

Jana Obšteter ◽

Justin Holl ◽

John M. Hickey ◽

Gregor Gorjanc

Keyword(s):

Gene Flow ◽

Open Source ◽

Open Source Software ◽

Genetic Gain ◽

R Package ◽

Genetic Trend ◽

Breeding Values ◽

Breeding Programmes ◽

Estimated Breeding Values ◽

Genetic Trends

AbstractBackgroundIn this paper we present the AlphaPart R package, an open-source software that implements a method for partitioning breeding values and genetic trends to identify sources of genetic gain. Breeding programmes improve populations for a set of traits, which can be measured with a genetic trend calculated from averaged year of birth estimated breeding values of selection candidates. While sources of the overall genetic gain are generally known, their realised contributions are hard to quantify in complex breeding programmes. The aim of this paper is to present the AlphaPart R package and demonstrate it with a simulated pig breeding example.ResultsThe package includes the main partitioning function AlphaPart, that partitions the breeding values and genetic trends by analyst defined paths, and a set of functions for handling data and results. The package is freely available from CRAN repository at http://CRAN.R-project.org/package=AlphaPart. We demonstrate the use of the package by examining the genetic gain in a pig breeding example, in which the multiplier achieved higher breeding values than the nucleus for traits measured and selected in the multiplier. The partitioning analysis revealed that these higher values depended on the accuracy and intensity of selection in the multiplier and the extent of gene flow from the nucleus. For traits measured only in the nucleus, the multiplier achieved comparable or smaller genetic gain than the nucleus depending on the amount of gene flow.ConclusionsAlphaPart implements a method for partitioning breeding values and genetic trends and provides a useful tool for quantifying the sources of genetic gain in breeding programmes. The use of AlphaPart will help breeders to better understand or improve their breeding programmes.

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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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