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.

scholarly journals Genomic Selection for Any Dairy Breeding Program via Optimized Investment in Phenotyping and Genotyping

2021 ◽  
Vol 12 ◽  
Author(s):  
Jana Obšteter ◽  
Janez Jenko ◽  
Gregor Gorjanc
Keyword(s):  
Genomic Selection ◽  
Genetic Gain ◽  
Breeding Program ◽  
Progeny Testing ◽  
Initial Training ◽  
Training Population ◽  
Breeding Programs ◽  
Use Of Resources ◽  
Dairy Cattle Breeding ◽  
Close Relatives

This paper evaluates the potential of maximizing genetic gain in dairy cattle breeding by optimizing investment into phenotyping and genotyping. Conventional breeding focuses on phenotyping selection candidates or their close relatives to maximize selection accuracy for breeders and quality assurance for producers. Genomic selection decoupled phenotyping and selection and through this increased genetic gain per year compared to the conventional selection. Although genomic selection is established in well-resourced breeding programs, small populations and developing countries still struggle with the implementation. The main issues include the lack of training animals and lack of financial resources. To address this, we simulated a case-study of a small dairy population with a number of scenarios with equal available resources yet varied use of resources for phenotyping and genotyping. The conventional progeny testing scenario collected 11 phenotypic records per lactation. In genomic selection scenarios, we reduced phenotyping to between 10 and 1 phenotypic records per lactation and invested the saved resources into genotyping. We tested these scenarios at different relative prices of phenotyping to genotyping and with or without an initial training population for genomic selection. Reallocating a part of phenotyping resources for repeated milk records to genotyping increased genetic gain compared to the conventional selection scenario regardless of the amount and relative cost of phenotyping, and the availability of an initial training population. Genetic gain increased by increasing genotyping, despite reduced phenotyping. High-genotyping scenarios even saved resources. Genomic selection scenarios expectedly increased accuracy for young non-phenotyped candidate males and females, but also proven females. This study shows that breeding programs should optimize investment into phenotyping and genotyping to maximize return on investment. Our results suggest that any dairy breeding program using conventional progeny testing with repeated milk records can implement genomic selection without increasing the level of investment.

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 Genomic selection for any dairy breeding program via optimized investment in phenotyping and genotyping

2020 ◽  
Author(s):  
Jana Obšteter ◽  
Janez Jenko ◽  
Gregor Gorjanc
Keyword(s):  
Genomic Selection ◽  
Genetic Gain ◽  
Breeding Program ◽  
Progeny Testing ◽  
Initial Training ◽  
Training Population ◽  
Breeding Programs ◽  
Female Candidates ◽  
Use Of Resources ◽  
Close Relatives

AbstractThis paper evaluates the potential of maximizing genetic gain in dairy cattle breeding by optimizing investment into phenotyping and genotyping. Conventional breeding focuses on phenotyping selection candidates or their close relatives to maximize selection accuracy for breeders and quality assurance for producers. Genomic selection decoupled phenotyping and selection and through this increased genetic gain per year compared to the conventional selection. Although genomic selection is established in well-resourced breeding programs, small populations and developing countries still struggle with the implementation. The main issues include the lack of training animals and lack of financial resources. To address this, we simulated a case-study of a small dairy population with a number of scenarios with equal resources yet varied use of resources for phenotyping and genotyping. The conventional progeny testing scenario had 11 phenotype records per lactation. In genomic scenarios, we reduced phenotyping to between 10 and 1 phenotype records per lactation and invested the saved resources into genotyping. We tested these scenarios at different relative prices of phenotyping to genotyping and with or without an initial training population for genomic selection. Reallocating a part of phenotyping resources for repeated milk records to genotyping increased genetic gain compared to the conventional scenario regardless of the amount and relative cost of phenotyping, and the availability of an initial training population. Genetic gain increased by increasing genotyping, despite reduced phenotyping. High-genotyping scenarios even saved resources. Genomic scenarios expectedly increased accuracy for young non-phenotyped male and female candidates, but also cows. This study shows that breeding programs should optimize investment into phenotyping and genotyping to maximise return on investment. Our results suggest that any dairy breeding program using conventional progeny testing with repeated milk records can implement genomic selection without increasing the level of investment.

scholarly journals Investigations on breeding plans for organic dairy cattle

2004 ◽  
Vol 47 (2) ◽  
pp. 129-139 ◽  
Author(s):  
B. Harder ◽  
W. Junge ◽  
J. Bennewitz ◽  
E. Kalm
Keyword(s):  
Natural Selection ◽  
Population Size ◽  
Artificial Insemination ◽  
Genetic Gain ◽  
Selection Response ◽  
Breeding Program ◽  
Production Traits ◽  
Cattle Breeding ◽  
Economic Weights ◽  
The Impact

Abstract. facilities in Germany, different alternative breeding plans for organic cattle breeding were developed using the computer program ZPLAN. First the impact of the population size on the parameters of success was analysed. A conventional cattle breeding program was compared with an organic breeding program. The results indicate that the selection response increased with increasing population size due to improved selection of bull sires. The EU Regulations on organic farming say that the proportion of artificial insemination has to be reduced as much as possible. According to this, the influence of different proportions of artificial insemination on the monetary genetic gain was investigated. The reduction of artificial insemination below 50% led to high losses in the discounted profit. The influence of higher economic weights for functional traits on the natural selection response was investigated. An increase of the economic weights by 50% led to tolerable decreases in the natural selection response of production traits with regard to a more ecological orientated breeding goal. The effect of the variation of the test capacity and the number of test bulls on the monetary genetic gain was analysed. The optimum for the monetary genetic gain was located at a test capacity of 50%, 30 test bulls and 99 daughters per test bull.

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 A multi-part strategy for introgression of exotic germplasm into elite plant breeding programs using genomic selection

2022 ◽  
Author(s):  
Irene S. Breider ◽  
R. Chris Gaynor ◽  
Gregor Gorjanc ◽  
Steve Thorn ◽  
Manish K. Pandey ◽  
...  
Keyword(s):  
Genetic Variation ◽  
Plant Breeding ◽  
Genomic Selection ◽  
Genetic Gain ◽  
Genetic Improvement ◽  
Breeding Strategy ◽  
Breeding Program ◽  
Breeding Programs ◽  
Polygenic Inheritance ◽  
Polygenic Traits

Abstract Some of the most economically important traits in plant breeding show highly polygenic inheritance. Genetic variation is a key determinant of the rates of genetic improvement in selective breeding programs. Rapid progress in genetic improvement comes at the cost of a rapid loss of genetic variation. Germplasm available through expired Plant Variety Protection (exPVP) lines is a potential resource of variation previously lost in elite breeding programs. Introgression for polygenic traits is challenging, as many genes have a small effect on the trait of interest. Here we propose a way to overcome these challenges with a multi-part pre-breeding program that has feedback pathways to optimise recurrent genomic selection. The multi-part breeding program consists of three components, namely a bridging component, population improvement, and product development. Parameters influencing the multi-part program were optimised with the use of a grid search. Haploblock effect and origin were investigated. Results showed that the introgression of exPVP germplasm using an optimised multi-part breeding strategy resulted in 1.53 times higher genetic gain compared to a two-part breeding program. Higher gain was achieved through reducing the performance gap between exPVP and elite germplasm and breaking down linkage drag. Both first and subsequent introgression events showed to be successful. In conclusion, the multi-part breeding strategy has a potential to improve long-term genetic gain for polygenic traits and therefore, potential to contribute to global food security.

scholarly journals Genetic Gain and Inbreeding from Genomic Selection in a Simulated Commercial Breeding Program for Perennial Ryegrass

2016 ◽  
Vol 9 (1) ◽  
Author(s):  
Zibei Lin ◽  
Noel O. I. Cogan ◽  
Luke W. Pembleton ◽  
German C. Spangenberg ◽  
John W. Forster ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Perennial Ryegrass ◽  
Genetic Gain ◽  
Breeding Program ◽  
Commercial Breeding

scholarly journals Modeling Illustrates That Genomic Selection Provides New Opportunities for Intercrop Breeding

2021 ◽  
Vol 12 ◽  
Author(s):  
Jon Bančič ◽  
Christian R. Werner ◽  
R. Chris Gaynor ◽  
Gregor Gorjanc ◽  
Damaris A. Odeny ◽  
...  
Keyword(s):  
Grain Yield ◽  
Genetic Correlation ◽  
Genomic Selection ◽  
Genetic Gain ◽  
Phenotypic Selection ◽  
Breeding Program ◽  
Weed Suppression ◽  
Selection Strategy ◽  
Agricultural Practice ◽  
Breeding Programs

Intercrop breeding programs using genomic selection can produce faster genetic gain than intercrop breeding programs using phenotypic selection. Intercropping is an agricultural practice in which two or more component crops are grown together. It can lead to enhanced soil structure and fertility, improved weed suppression, and better control of pests and diseases. Especially in subsistence agriculture, intercropping has great potential to optimize farming and increase profitability. However, breeding for intercrop varieties is complex as it requires simultaneous improvement of two or more component crops that combine well in the field. We hypothesize that genomic selection can significantly simplify and accelerate the process of breeding crops for intercropping. Therefore, we used stochastic simulation to compare four different intercrop breeding programs implementing genomic selection and an intercrop breeding program entirely based on phenotypic selection. We assumed three different levels of genetic correlation between monocrop grain yield and intercrop grain yield to investigate how the different breeding strategies are impacted by this factor. We found that all four simulated breeding programs using genomic selection produced significantly more intercrop genetic gain than the phenotypic selection program regardless of the genetic correlation with monocrop yield. We suggest a genomic selection strategy which combines monocrop and intercrop trait information to predict general intercropping ability to increase selection accuracy in the early stages of a breeding program and to minimize the generation interval.

scholarly journals Simulation studies to optimize genomic selection in honey bees

2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Richard Bernstein ◽  
Manuel Du ◽  
Andreas Hoppe ◽  
Kaspar Bienefeld
Keyword(s):  
Genomic Selection ◽  
Genetic Gain ◽  
Honey Bees ◽  
Reference Population ◽  
Breeding Program ◽  
Genomic Relationship Matrix ◽  
Relationship Matrix ◽  
Genomic Relationship ◽  
Genomic Breeding ◽  
Breeding Values

Abstract Background With the completion of a single nucleotide polymorphism (SNP) chip for honey bees, the technical basis of genomic selection is laid. However, for its application in practice, methods to estimate genomic breeding values need to be adapted to the specificities of the genetics and breeding infrastructure of this species. Drone-producing queens (DPQ) are used for mating control, and usually, they head non-phenotyped colonies that will be placed on mating stations. Breeding queens (BQ) head colonies that are intended to be phenotyped and used to produce new queens. Our aim was to evaluate different breeding program designs for the initiation of genomic selection in honey bees. Methods Stochastic simulations were conducted to evaluate the quality of the estimated breeding values. We developed a variation of the genomic relationship matrix to include genotypes of DPQ and tested different sizes of the reference population. The results were used to estimate genetic gain in the initial selection cycle of a genomic breeding program. This program was run over six years, and different numbers of genotyped queens per year were considered. Resources could be allocated to increase the reference population, or to perform genomic preselection of BQ and/or DPQ. Results Including the genotypes of 5000 phenotyped BQ increased the accuracy of predictions of breeding values by up to 173%, depending on the size of the reference population and the trait considered. To initiate a breeding program, genotyping a minimum number of 1000 queens per year is required. In this case, genetic gain was highest when genomic preselection of DPQ was coupled with the genotyping of 10–20% of the phenotyped BQ. For maximum genetic gain per used genotype, more than 2500 genotyped queens per year and preselection of all BQ and DPQ are required. Conclusions This study shows that the first priority in a breeding program is to genotype phenotyped BQ to obtain a sufficiently large reference population, which allows successful genomic preselection of queens. To maximize genetic gain, DPQ should be preselected, and their genotypes included in the genomic relationship matrix. We suggest, that the developed methods for genomic prediction are suitable for implementation in genomic honey bee breeding programs.

scholarly journals Genetic Progress in 53 Years of the Peach Breeding Program of Embrapa: Canning Genotypes

2021 ◽  
Author(s):  
Maicon Nardino ◽  
Elisia Rodrigues Corrêa ◽  
Maria do Carmo Bassols Raseira ◽  
Isadora Moreira da Luz Real ◽  
Willian Silva Barros ◽  
...  
Keyword(s):  
Genetic Gain ◽  
Agricultural Research ◽  
Meta Analysis ◽  
Fruit Weight ◽  
Breeding Program ◽  
Raw Material ◽  
Soluble Solids ◽  
Genetic Progress ◽  
Cycle Number ◽  
Peach Breeding

Abstract Peach is a traditional crop in the south of Rio Grande do Sul State, Brazil, where 30 to 53 million cans of peaches in syrup are produced annually. All the raw material produced in the region consists of fruits originating from the peach breeding program of the Brazilian Agricultural Research Corporation (Embrapa Temperate Agriculture), which started even before Embrapa at the Experimental Station of Pelotas, Ministry of Agriculture. The objective was to estimate the genetic progress in phenological traits and production of canning peach resulting from the peach breeding program of Embrapa Temperate Agriculture in 53 years. We divided the data records considered in the estimation of genetic progress into two periods, 1964-1984 and 1985-2017, totaling 53 years. The following traits: maturing period, cycle, number of fruits, fruit weight, yield, and soluble solids content were evaluated. We initially tabulated data and analyzed descriptive statistics. Subsequently, we conducted analysis of mixed models and obtained the estimates of genetic progress through meta-analysis. Genetic gain for earliness, shortening the cycle from flowering to maturation, and genetic gain for fruit yield were observed.

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