scholarly journals Upgrading breeding value estimation in beef cattle

2018 ◽  
pp. 451-458
Author(s):  
Ferenc Szabó ◽  
Márton Szűcs ◽  
Károly Tempfli ◽  
Berry Donagh

This paper gives a summary of the possibility for applying genomic information for breeding value estimation in beef cattle breeding. This process is called genomic prediction and is now widely used in dairy cattle globally as well as in some beef and sheep populations. The advantage of genomic prediction is a more accurate estimate of the genetic merit of an individual at a young age thereby facilitating greater annual genetic gain, predominantly through shorter generation intervals. Genomic predictions are more advantageous for sex-linked (e.g., milk yield), low heritability (e.g., fertility) and difficult-to-measure (e.g., feed intake) traits. The larger the reference population, on average, the more accurate the genomic predictions; additionally, the closer genetically the reference population is to the candidate population, the greater the accuracy of genomic predictions. Research is continuing on strategies to generate accurate genomic predictions using a reference population consisting of multiple breeds (and crossbred). Retrospective analysis of real-life data where genomic predictions have been operation for several years clearly shows a benefit of this technology.

2016 ◽  
Vol 52 ◽  
pp. 82-94 ◽  
Author(s):  
A. Ye. Pochukalin

One of the ways of increasing level of animal economically useful traits is selection work with farm families. In pedigree cattle breeding of Ukraine families are a statistical component of breed genealogy. Among the main scientific works on working with families, it should be noted minimum number of female ancestors, proposed by D. T. Vinnichuk, to determine the breeding value, different categories, classification and techniques for evaluating related groups of females. The aim of our research was to analyse importance of farm families for genealogical structure of the breed. The research was on basis of data of primary breeding records at the herd of Volyn Beef cattle of “Zorya” breeding farm, Kovel district, Volyn region. Akula 102, Galka 37 and Galka 1537 families belonging to Krasavchyk 3004 bloodline, Smorodyna 613, Korona 2382 and Visla 1016 families – Tsebryk 3888 bloodline, Kalyna 212, Verba 1536 and Garna 536 families – Yamb 3066 bloodlines, Kazka 433, Galka 421 and Bystra 1124 families – Buinyi 3042 bloodline, Rozetka 1313, Arfa 599 and Bulana 943 families – Sonnyi-Kaktus 3307-9828 bloodline, and Palma 275, Desna 870 and Veselka 444 families – Mudryi 9100 bloodline were characterized. Belonging to a bloodline was determined by the father's side of female ancestors. Structural units of families: branches, branching with identifying the best individuals on breeding traits were submitted to identify the best combinations and successful use of closely related breeding. Comparing assessment of related groups of females on the main breeding traits belonging to Krasavchyk 3004 bloodline, it was noted that the cows of Akula 102 family predominated in live weight at 5 years’ age, milk ability and economic use duration, whereas the cows of Galka 1537 family – on traits of reproductive ability. Smorodyna 613 family of Tsebryk 3888 bloodline had high duration of economic use and cows’ live weight at 5 years’ age compared with Korana 2382 and Visla 1016 families with equal values of the exterior traits (height measures) and coefficient of reproductive ability. The families of Mudryi 9100 bloodline in terms of reproduction (calving interval, coefficient of reproductive ability) had the highest figures of cows’ milk ability and live weight. The cows of Bulana 943 family had a considerable predominance over representatives of Rozetka 1313 and Arfa 599 families of Sonnyi-Kaktus 3307-9828 bloodline by main economically useful traits. High indices of reproductive ability were noted in these families. Heifers of the families of Buinyi 3042 bloodline had high live weight at 18 months’ age at average values of milk ability and cows’ live weight at 5 years’ age. More equal figures of growth rate, exterior and economic use duration were observed in the cows of Kalyna 212, Verba 1536 and Garna 536 families of Yamb 3066 bloodline. Breeding by families in beef cattle breeding is an important element of selection, because it allows to evaluate not only related group of female ancestor, but also to analyse a successful combination with lines and purposeful use of closely related breeding by the best representatives of a breed.


2014 ◽  
Vol 54 (5) ◽  
pp. 544 ◽  
Author(s):  
N. Moghaddar ◽  
A. A. Swan ◽  
J. H. J. van der Werf

The objective of this study was to predict the accuracy of genomic prediction for 26 traits, including weight, muscle, fat, and wool quantity and quality traits, in Australian sheep based on a large, multi-breed reference population. The reference population consisted of two research flocks, with the main breeds being Merino, Border Leicester (BL), Poll Dorset (PD), and White Suffolk (WS). The genomic estimated breeding value (GEBV) was based on GBLUP (genomic best linear unbiased prediction), applying a genomic relationship matrix calculated from the 50K Ovine SNP chip marker genotypes. The accuracy of GEBV was evaluated as the Pearson correlation coefficient between GEBV and accurate estimated breeding value based on progeny records in a set of genotyped industry animals. The accuracies of weight traits were relatively low to moderate in PD and WS breeds (0.11–0.27) and moderate to relatively high in BL and Merino (0.25–0.63). The accuracy of muscle and fat traits was moderate to relatively high across all breeds (between 0.21 and 0.55). The accuracy of GEBV of yearling and adult wool traits in Merino was, on average, high (0.33–0.75). The results showed the accuracy of genomic prediction depends on trait heritability and the effective size of the reference population, whereas the observed GEBV accuracies were more related to the breed proportions in the multi-breed reference population. No extra gain in within-breed GEBV accuracy was observed based on across breed information. More investigations are required to determine the precise effect of across-breed information on within-breed genomic prediction.


2012 ◽  
Vol 52 (3) ◽  
pp. 100 ◽  
Author(s):  
D. J. Johnston ◽  
B. Tier ◽  
H.-U. Graser

Opportunities exist in beef cattle breeding to significantly increase the rates of genetic gain by increasing the accuracy of selection at earlier ages. Currently, selection of young beef bulls incorporates several economically important traits but estimated breeding values for these traits have a large range in accuracies. While there is potential to increase accuracy through increased levels of performance recording, several traits cannot be recorded on the young bull. Increasing the accuracy of these traits is where genomic selection can offer substantial improvements in current rates of genetic gain for beef. The immediate challenge for beef is to increase the genetic variation explained by the genomic predictions for those traits of high economic value that have low accuracies at the time of selection. Currently, the accuracies of genomic predictions are low in beef, compared with those in dairy cattle. This is likely to be due to the relatively low number of animals with genotypes and phenotypes that have been used in developing genomic prediction equations. Improving the accuracy of genomic predictions will require the collection of genotypes and phenotypes on many more animals, with even greater numbers needed for lowly heritable traits, such as female reproduction and other fitness traits. Further challenges exist in beef to have genomic predictions for the large number of important breeds and also for multi-breed populations. Results suggest that single-nucleotide polymorphism (SNP) chips that are denser than 50 000 SNPs in the current use will be required to achieve this goal. For genomic selection to contribute to genetic progress, the information needs to be correctly combined with traditional pedigree and performance data. Several methods have emerged for combining the two sources of data into current genetic evaluation systems; however, challenges exist for the beef industry to implement these effectively. Changes will also be needed to the structure of the breeding sector to allow optimal use of genomic information for the benefit of the industry. Genomic information will need to be cost effective and a major driver of this will be increasing the accuracy of the predictions, which requires the collection of much more phenotypic data than are currently available.


2010 ◽  
Vol 39 (suppl spe) ◽  
pp. 247-255 ◽  
Author(s):  
Stephen Miller

Genomics will improve the efficiency of beef cattle genetic improvement programs through the incorporation of genomic predictions into traditional genetic evaluations. The global dairy cattle breeding industry has been changed considerably in the last year through the implementation of genomic selection. Now proven to work in dairy cattle breeding, the challenge remains for the beef industry to successfully implement this technology. The primary challenge in beef cattle is the required resource population that relates genomic profile to phenotypic performance, which is quite large and its establishment will require collaboration or a significant investment by any one enterprise. Another challenge in beef cattle is the requirement for genomic predictions to function across breeds, which will require denser marker panels. Opportunities to increase genetic progress include increased accuracy of selection, reduced generation interval, increased selection intensity and better utilization of limited recording capacity, such as individual feed intake, along with opportunities to genetically change novel traits. Implementation of a low density panel at the commercial level will allow informative decisions based on genetic potential at all levels of the production chain. This reduced panel will include predictive SNP based on fine QTL mapping efforts, combined with additional SNP to enable imputation of genotypes from a high density SNP panel, when combined with high density genotypes of key ancestors, such as sires. With electronic recording in cattle, a single genotyping event on each animal would provide information throughout the beef production chain, which will create the incentive for genetic change. Genomics will create new opportunities for reproductive technologies such as embryo transfer as elite females will be identified with increased accuracy. Potential changes to the structure of the breeding industry are discussed including changes to recording strategies and the development of novel beef products.


BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Masayuki Takeda ◽  
Keiichi Inoue ◽  
Hidemi Oyama ◽  
Katsuo Uchiyama ◽  
Kanako Yoshinari ◽  
...  

Abstract Background Size of reference population is a crucial factor affecting the accuracy of prediction of the genomic estimated breeding value (GEBV). There are few studies in beef cattle that have compared accuracies achieved using real data to that achieved with simulated data and deterministic predictions. Thus, extent to which traits of interest affect accuracy of genomic prediction in Japanese Black cattle remains obscure. This study aimed to explore the size of reference population for expected accuracy of genomic prediction for simulated and carcass traits in Japanese Black cattle using a large amount of samples. Results A simulation analysis showed that heritability and size of reference population substantially impacted the accuracy of GEBV, whereas the number of quantitative trait loci did not. The estimated numbers of independent chromosome segments (Me) and the related weighting factor (w) derived from simulation results and a maximum likelihood (ML) approach were 1900–3900 and 1, respectively. The expected accuracy for trait with heritability of 0.1–0.5 fitted well with empirical values when the reference population comprised > 5000 animals. The heritability for carcass traits was estimated to be 0.29–0.41 and the accuracy of GEBVs was relatively consistent with simulation results. When the reference population comprised 7000–11,000 animals, the accuracy of GEBV for carcass traits can range 0.73–0.79, which is comparable to estimated breeding value obtained in the progeny test. Conclusion Our simulation analysis demonstrated that the expected accuracy of GEBV for a polygenic trait with low-to-moderate heritability could be practical in Japanese Black cattle population. For carcass traits, a total of 7000–11,000 animals can be a sufficient size of reference population for genomic prediction.


2020 ◽  
Vol 33 (12) ◽  
pp. 1912-1921
Author(s):  
Chiemela Peter Nwogwugwu ◽  
Yeongkuk Kim ◽  
Hyunji Choi ◽  
Jun Heon Lee ◽  
Seung-Hwan Lee

Objective: This study assessed genomic prediction accuracies based on different selection methods, evaluation procedures, training population (TP) sizes, heritability (h<sup>2</sup>) levels, marker densities and pedigree error (PE) rates in a simulated Korean beef cattle population.Methods: A simulation was performed using two different selection methods, phenotypic and estimated breeding value (EBV), with an h<sup>2</sup> of 0.1, 0.3, or 0.5 and marker densities of 10, 50, or 777K. A total of 275 males and 2,475 females were randomly selected from the last generation to simulate ten recent generations. The simulation of the PE dataset was modified using only the EBV method of selection with a marker density of 50K and a heritability of 0.3. The proportions of errors substituted were 10%, 20%, 30%, and 40%, respectively. Genetic evaluations were performed using genomic best linear unbiased prediction (GBLUP) and single-step GBLUP (ssGBLUP) with different weighted values. The accuracies of the predictions were determined.Results: Compared with phenotypic selection, the results revealed that the prediction accuracies obtained using GBLUP and ssGBLUP increased across heritability levels and TP sizes during EBV selection. However, an increase in the marker density did not yield higher accuracy in either method except when the h<sup>2</sup> was 0.3 under the EBV selection method. Based on EBV selection with a heritability of 0.1 and a marker density of 10K, GBLUP and ssGBLUP_0.95 prediction accuracy was higher than that obtained by phenotypic selection. The prediction accuracies from ssGBLUP_0.95 outperformed those from the GBLUP method across all scenarios. When errors were introduced into the pedigree dataset, the prediction accuracies were only minimally influenced across all scenarios.Conclusion: Our study suggests that the use of ssGBLUP_0.95, EBV selection, and low marker density could help improve genetic gains in beef cattle.


2014 ◽  
Vol 25 (4) ◽  
pp. 233-238 ◽  
Author(s):  
Martin Peper ◽  
Simone N. Loeffler

Current ambulatory technologies are highly relevant for neuropsychological assessment and treatment as they provide a gateway to real life data. Ambulatory assessment of cognitive complaints, skills and emotional states in natural contexts provides information that has a greater ecological validity than traditional assessment approaches. This issue presents an overview of current technological and methodological innovations, opportunities, problems and limitations of these methods designed for the context-sensitive measurement of cognitive, emotional and behavioral function. The usefulness of selected ambulatory approaches is demonstrated and their relevance for an ecologically valid neuropsychology is highlighted.


Sign in / Sign up

Export Citation Format

Share Document