scholarly journals Beef cattle breeding in Australia with genomics: opportunities and needs

2012 ◽  
Vol 52 (3) ◽  
pp. 100 ◽  
Author(s):  
D. J. Johnston ◽  
B. Tier ◽  
H.-U. Graser
Keyword(s):  
Beef Cattle ◽  
Genomic Selection ◽  
Genetic Gain ◽  
Information Needs ◽  
Genomic Information ◽  
Female Reproduction ◽  
Genetic Progress ◽  
Cattle Breeding ◽  
Phenotypic Data ◽  
Genomic Predictions

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.

scholarly journals Industrial perspective: capturing the benefits of genomics to Irish cattle breeding

2012 ◽  
Vol 52 (3) ◽  
pp. 172 ◽  
Author(s):  
B. W. Wickham ◽  
P. R. Amer ◽  
D. P. Berry ◽  
M. Burke ◽  
S. Coughlan ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Artificial Insemination ◽  
Genetic Gain ◽  
Selection Index ◽  
Breeding Program ◽  
Training Population ◽  
Genetic Progress ◽  
Cattle Breeding ◽  
Program Costs ◽  
The Cost

Genomics is a technology for increasing the accuracy with which the genetic merit of young potential breeding animals can be determined. It enables earlier selection decisions, thus reducing generation intervals and gives rise to more rapid annual rates of genetic gain. Recently, the cost of genomics has reduced to the point where it enables breeding-program costs to be reduced substantially. Ireland has been a rapid adopter of genomics technology in its dairy-cattle breeding program, with 40% of dairy-cow artificial inseminations in 2010 being from bulls evaluated using genomic information. This rapid adoption has been facilitated by a comprehensive database of phenotypes and genotypes, strong public funding support for applied genomics research, an international network of collaborators, a short path between research and implementation, an overall selection index which farmers use in making breeding decisions, and a motivated and informed breeding industry. The shorter generation interval possible with genomic selection strategies also allows exploitation of the already accelerating rate of genetic progress in Ireland, because elite young dairy bulls are considerably superior to the small numbers of bulls that entered progeny test 6 years ago. In addition, genomics is having a dramatic impact on the artificial-insemination industry by substantially reducing the cost of entry, the cost of operation, and shifting the focus of breeding from bulls to cows. We believe that the current industry structures must evolve substantially if Irish cattle farmers are to realise the full benefits of genomics and be protected from related risks. Our model for future dairy breeding envisages a small number of ‘next generation research herds’, 1000 ‘bull breeder herds’ and an artificial-insemination sector using 30 new genomically selected bulls per year to breed the bulk of replacements in commercial milk-producing herds. Accurate imputation from a low-density to a higher-density chip is a key element of our strategy to enable dairy farmers to afford access to genomics. This model is capable of delivering high rates of genetic gain, realising cost savings, and protecting against the risks of increased inbreeding and suboptimal breeding goals. Our strategy for exploiting genomic selection for beef breeding is currently focussed on genotyping, using a high-density chip, a training population of greater than 2000 progeny-tested bulls representing all the main beef breeds in Ireland. We recognise the need for a larger training population and are seeking collaboration with organisations in other countries and populations.

A maternal productive index for beef cows

1972 ◽  
Vol 12 (59) ◽  
pp. 573 ◽  
Author(s):  
RG Beilharz
Keyword(s):  
Beef Cattle ◽  
Reproductive Performance ◽  
Genetic Parameters ◽  
Selection Index ◽  
Beef Cows ◽  
Early Summer ◽  
Direct Measure ◽  
Important Goal ◽  
Genetic Progress ◽  
Cattle Breeding

To evaluate beef cows on their reproductive performance a maternal productive index (M.P.I.) was developed as an alternative to their evaluation in terms of simpler traits, or in terms of a conventional selection index based on simple traits. Data on M.P.I. were obtained from Hereford cows on three grazing treatments each containing three groups of cows differentiated by last breeding season (i.e. presence and age of calf at foot). The same cows were also scored for coat type on two occasions in late spring and early summer. The magnitude and change of coat score are explained by the hypothesis that nutritional stress delays the cycle of shedding of winter coat and its replacement by a sleek coat. Analysis of the correlations between coat score data and M.P.I. shows that low M.P.I. is also associated with a delay in change of coat type. This suggests that M.P.I. is an indication of adaptation of cows to their environment with poorly adapted animals suffering a greater stress. Because M.P.I. is a direct measure of a very important goal of beef cattle breeding it should be used widely in selection (or culling) of beef cows. Whether genetic progress will be faster than through the use of simpler traits, may be judged once genetic parameters have been estimated for M.P.I.

scholarly journals Opportunities and challenges from the use of genomic selection for beef cattle breeding in Latin America

2012 ◽  
Vol 2 (1) ◽  
pp. 23-29 ◽  
Author(s):  
Hugo H. Montaldo ◽  
Eduardo Casas ◽  
José Bento Sterman Ferraz ◽  
Vicente E. Vega-Murillo ◽  
Sergio Iván Román-Ponce
Keyword(s):  
Latin America ◽  
Beef Cattle ◽  
Genomic Selection ◽  
Cattle Breeding ◽  
Selection For

Genomic selection for female reproduction in Australian tropically adapted beef cattle

2014 ◽  
Vol 54 (1) ◽  
pp. 16 ◽  
Author(s):  
Y. D. Zhang ◽  
D. J. Johnston ◽  
S. Bolormaa ◽  
R. J. Hawken ◽  
B. Tier
Keyword(s):  
Genomic Selection ◽  
Cross Validation ◽  
Population Data ◽  
Northern Australia ◽  
Female Reproduction ◽  
Genetic Complexity ◽  
Reproduction Traits ◽  
Experimental Populations ◽  
Snp Panels ◽  
Genomic Predictions

The usefulness of genomic selection was assessed for female reproduction in tropically adapted breeds in northern Australia. Records from experimental populations of Brahman (996) and Tropical Composite (1097) cattle that had had six calving opportunities were used to derive genomic predictions for several measures of female fertility. These measures included age at first corpus luteum (AGECL), at first calving and subsequent postpartum anoestrous interval and measures of early and lifetime numbers of calves born or weaned. In a second population, data on pregnancy and following status (anoestrous or pregnancy) were collected from 27 commercial herds from northern Australia to validate genomic predictions. Cows were genotyped with a variety of single nucleotide polymorphism (SNP) panels and, where necessary, genotypes imputed to the highest density (729 068 SNPs). Genetic parameters of subsets of the complete data were estimated. These subsets were used to validate genomic predictions using genomic best linear unbiased prediction using both univariate cross-validation and bivariate analyses. Estimated heritability ranged from 0.56 for AGECL to 0.03 for lifetime average calving rate in the experimental cows, and from 0.09 to 0.25 for early life reproduction traits in the commercial cows. Accuracies of predictions were generally low, reflecting the limited number of data in the experimental populations. For AGECL and postpartum anoestrous interval, the highest accuracy was 0.35 for experimental Brahman cows using five-fold univariate cross-validation. Greater genetic complexity in the Tropical Composite cows resulted in the corresponding accuracy of 0.23 for AGECL. Similar level of accuracies (from univariate and bivariate analyses) were found for some of the early measures of female reproduction in commercial cows, indicating that there is potential for genomic selection but it is limited by the number of animals with phenotypes.

scholarly journals Genome-wide association study and low-cost genomic predictions for growth and fillet yield in Nile tilapia (Oreochromis niloticus)

2019 ◽  
Author(s):  
Grazyella M. Yoshida ◽  
Jean P. Lhorente ◽  
Katharina Correa ◽  
Jose Soto ◽  
Diego Salas ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Nile Tilapia ◽  
Cost Effective ◽  
Genotype Imputation ◽  
Genome Wide Association ◽  
Genomic Information ◽  
Breeding Programs ◽  
Genome Wide ◽  
Fillet Yield ◽  
Genomic Predictions

ABSTRACTFillet yield (FY) and harvest weight (HW) are economically important traits in Nile tilapia production. Genetic improvement of these traits, especially for FY, are lacking, due to the absence of efficient methods to measure the traits without sacrificing fish and the use of information from relatives to selection. However, genomic information could be used by genomic selection to improve traits that are difficult to measure directly in selection candidates, as in the case of FY. The objectives of this study were: (i) to perform genome-wide association studies (GWAS) to dissect the genetic architecture of FY and HW, (ii) to evaluate the accuracy of genotype imputation and (iii) to assess the accuracy of genomic selection using true and imputed low-density (LD) single nucleotide polymorphism (SNP) panels to determine a cost-effective strategy for practical implementation of genomic information in tilapia breeding programs. The data set consisted of 5,866 phenotyped animals and 1,238 genotyped animals (108 parents and 1,130 offspring) using a 50K SNP panel. The GWAS were performed using all genotyped and phenotyped animals. The genotyped imputation was performed from LD panels (LD0.5K, LD1K and LD3K) to high-density panel (HD), using information from parents and 20% of offspring in the reference set and the remaining 80% in the validation set. In addition, we tested the accuracy of genomic selection using true and imputed genotypes comparing the accuracy obtained from pedigree-based best linear unbiased prediction (PBLUP) and genomic predictions. The results from GWAS supports evidence of the polygenic nature of FY and HW. The accuracy of imputation ranged from 0.90 to 0.98 for LD0.5K and LD3K, respectively. The accuracy of genomic prediction outperformed the estimated breeding value from PBLUP. The use of imputation for genomic selection resulted in an increased relative accuracy independent of the trait and LD panel analyzed. The present results suggest that genotype imputation could be a cost-effective strategy for genomic selection in tilapia breeding programs.

scholarly journals Genetic improvement of beef cattle through opportunities in genomics

2010 ◽  
Vol 39 (suppl spe) ◽  
pp. 247-255 ◽  
Author(s):  
Stephen Miller
Keyword(s):  
Beef Cattle ◽  
Dairy Cattle ◽  
Genetic Improvement ◽  
Reproductive Technologies ◽  
High Density ◽  
Cattle Breeding ◽  
Production Chain ◽  
Dairy Cattle Breeding ◽  
Improvement Programs ◽  
Genomic Predictions

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.

scholarly journals Integration of genomic information into beef cattle and sheep genetic evaluations in Australia

2012 ◽  
Vol 52 (3) ◽  
pp. 126 ◽  
Author(s):  
Andrew A. Swan ◽  
David J. Johnston ◽  
Daniel J. Brown ◽  
Bruce Tier ◽  
Hans-U. Graser
Keyword(s):  
Beef Cattle ◽  
Covariance Matrices ◽  
Relationship Matrix ◽  
Genomic Information ◽  
Genetic Covariance ◽  
Breeding Values ◽  
Snp Panels ◽  
Genetic Evaluations ◽  
Genomic Predictions ◽  
Cattle And Sheep

Genomic information has the potential to change the way beef cattle and sheep are selected and to substantially increase genetic gains. Ideally, genomic data will be used in combination with pedigree and phenotypic data to increase the accuracy of estimated breeding values (EBVs) and selection indexes. The first example of this in Australia was the integration of four markers for tenderness into beef cattle breeding values. Subsequently, the availability of high-density single nucleotide polymorphism (SNP) panels has made selection using genomic information possible, while at the same time creating significant challenges for genetic evaluation with regard to both data management and statistical modelling. Reference populations have been established in both the beef cattle and sheep industries, in which an extensive range of phenotypes have been collected and animals genotyped mainly using 50K SNP panels. From this information, genomic predictions of breeding value have been developed, albeit with varying levels of accuracy. These predictions have been incorporated into routine genetic evaluations using three approaches and trial results are now available to breeders. In the first, genomic predictions have been included in genetic evaluation models as additional traits. The challenges with this method have been the construction of consistent genetic covariance matrices, and a significant increase in computing time. The second approach has been to use a selection index procedure to blend genomic predictions with existing EBVs. This method has been shown to produce very similar results, and has the advantage of being simple to implement and fast to operate, although consistent genetic covariance matrices are still required. Third, in sheep a single-step analysis combining a genomic relationship matrix with a standard pedigree-based relationship matrix has been used to estimate breeding values for carcass and eating-quality traits. It is likely that this procedure or one similar will be incorporated into routine evaluations in the near future. While significant progress has been made in implementing methods of integrating genomic information in both beef and sheep evaluations in Australia, the major challenges for the future will be to continue to collect the phenotypes needed to derive accurate genomic predictions, and in managing much larger volumes of genomic data as the number of animals genotyped and the density of markers increase.

Economic evaluation of beef cattle breeding schemes incorporating performance testing of young bulls for feed intake

2004 ◽  
Vol 44 (5) ◽  
pp. 393 ◽  
Author(s):  
J. A. Archer ◽  
S. A. Barwick ◽  
H.-U. Graser
Keyword(s):  
Beef Cattle ◽  
Feed Intake ◽  
Genetic Gain ◽  
Selection Criterion ◽  
Performance Testing ◽  
Cattle Breeding ◽  
Commercial Unit ◽  
Base Scenario ◽  
Breeding Schemes ◽  
The Impact

A model beef cattle breeding scheme consisting of a breeding unit and a commercial unit was used to evaluate the impact on genetic gain and profitability of incorporating feed intake measurements as an additional selection criterion in breeding programmes. Costs incurred by the breeding unit were compared with returns generated in the commercial unit, with bulls from the breeding unit being used as sires in the commercial unit. Two different market objectives were considered — a grass-fed product for the Australian domestic market, and a grain-fed product for the Japanese market. Breeding units utilising either artificial insemination or natural service were also considered. A base scenario was modelled incorporating a range of criteria available to Australian cattle breeders. A second scenario incorporated selection of sires for the breeding unit using a 2-stage selection process, with a proportion of bulls selected after weaning for measurement of (residual) feed intake. Measurement of feed intake of bulls improved accuracy of breeding unit sire selection by 14–50% over the equivalent base scenario, and genetic gain in the breeding objective was improved for all scenarios, with gains ranging from 8 to 38% over the base scenario. After accounting for the cost of measuring feed intake ($150–450), additional profit was generated from inclusion of feed intake measurement on a proportion of bulls for all breeding schemes considered. Profit was generally maximised where 10–20% of bulls were selected at weaning for measurement of intake, with improvement in profit ranging from 9 to 33% when optimal numbers of bulls were selected for intake measurement.

Utilization of the sex-determining region Y gene in beef cattle breeding schemes

1991 ◽  
Vol 53 (2) ◽  
pp. 157-164 ◽  
Author(s):  
S. C. Bishop ◽  
J. A. Woolliams
Keyword(s):  
Beef Cattle ◽  
Male Offspring ◽  
Biological Efficiency ◽  
Normal Male ◽  
Sry Gene ◽  
Breeding Scheme ◽  
Genetic Progress ◽  
Cattle Breeding ◽  
Breeding Schemes ◽  
Normal Males

AbstractIn mammals ‘maleness’, i.e. the presence of testes, is thought to be controlled by a single gene on the Y chromosome. Recently, a candidate gene termed the SRY (sex-determining region Y) gene has been located. If the SRY gene is the gene causing maleness then a transgenic male with the SRY gene on an autosome would produce a greater proportion of male offspring than a normal male. This would be advantageous in situations where male offspring are more valuable than females. Such transgenic males have a reduced probability of propagating their genotype and an effort has to be made to avoid their extinction. This is at the cost of genetic progress which must be made to enable the transgenics to remain competitive with normal males.In a simulated beef cattle breeding scheme if half of the annual matings were made to transgenics then after 15 years of selection the transgenic males fell the equivalent of 2·6 years of selection behind males in a traditional herd. If all matings were made to transgenics they fell over 9 years behind. Selection for lean food conversion ratio was considered as an example. After 15 years of selection the gain in biological efficiency from more male offspring outweighed the loss from reduced genetic progress only when more than 0·5 of the bulls used in the breeding scheme were normal males. In practice, the difficulty of maintaining a small population of transgenic males along with other costs not included in the calculations suggest that breeding schemes in beef cattle with an SRY transgene would not be practicable without further technology.

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