scholarly journals Efficient use of genomic information for sustainable genetic improvement in small cattle populations

2019 ◽  
Author(s):  
J. Obšteter ◽  
J. Jenko ◽  
J. M. Hickey ◽  
G. Gorjanc
Keyword(s):  
Genetic Variation ◽  
Genomic Selection ◽  
Conversion Efficiency ◽  
Genetic Gain ◽  
Progeny Testing ◽  
Effective Population ◽  
Generation Interval ◽  
Truncation Selection ◽  
Selection Accuracy ◽  
Sire Selection

ABSTRACTThis paper compares genetic gain, genetic variation, and the efficiency of converting variation into gain under different genomic selection scenarios with truncation or optimum contribution selection in a small dairy population by simulation. Breeding programs have to maximize genetic gain but also ensure sustainability by maintaining genetic variation. Numerous studies showed that genomic selection increases genetic gain. Although genomic selection is a well-established method, small populations still struggle with choosing the most sustainable strategy to adopt this type of selection. We developed a simulator of a dairy population and simulated a model after the Slovenian Brown Swiss population with ~10,500 cows. We compared different truncation selection scenarios by varying i) the method of sire selection and their use on cows or bull-dams, and ii) selection intensity and the number of years a sire is in use. Furthermore, we compared different optimum contribution selection scenarios with optimization of sire selection and their usage. We compared the scenarios in terms of genetic gain, selection accuracy, generation interval, genetic and genic variance, the rate of coancestry, effective population size, and the conversion efficiency. The results show that early use of genomically tested sires increased genetic gain compared to progeny testing as expected from changes in selection accuracy and generation interval. A faster turnover of sires from year to year and higher intensity increased the genetic gain even further but increased the loss of genetic variation per year. While maximizing intensity gave the lowest conversion efficiency, a faster turn-over of sires gave an intermediate conversion efficiency. The largest conversion efficiency was achieved with the simultaneous use of genomically and progeny tested sires that were used over several years. Compared to truncation selection optimizing sire selection and their usage increased the conversion efficiency by either achieving comparable genetic gain for a smaller loss of genetic variation or achieving higher genetic gain for a comparable loss of genetic variation. Our results will help breeding organizations to implement sustainable genomic selection.

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 Designing dairy cattle breeding schemes under genomic selection: a review of international research

2012 ◽  
Vol 52 (3) ◽  
pp. 107 ◽  
Author(s):  
J. E. Pryce ◽  
H. D. Daetwyler
Keyword(s):  
Dairy Cattle ◽  
Genomic Selection ◽  
Genetic Gain ◽  
Reproductive Technologies ◽  
Progeny Testing ◽  
Genomic Breeding ◽  
Breeding Values ◽  
Scheme Design ◽  
Dairy Cattle Breeding ◽  
Breeding Schemes

High rates of genetic gain can be achieved through (1) accurate predictions of breeding values (2) high intensities of selection and (3) shorter generation intervals. Reliabilities of ~60% are currently achievable using genomic selection in dairy cattle. This breakthrough means that selection of animals can happen at a very early age (i.e. as soon as a DNA sample is available) and has opened opportunities to radically redesign breeding schemes. Most research over the past decade has focussed on the feasibility of genomic selection, especially how to increase the accuracy of genomic breeding values. More recently, how to apply genomic technology to breeding schemes has generated a lot of interest. Some of this research remains the intellectual property of breeding companies, but there are examples in the public domain. Here we review published research into breeding scheme design using genomic selection and evaluate which designs appear to be promising (in terms of rates of genetic gain) and those that may have unfavourable side-effects (i.e. increasing the rate of inbreeding). The schemes range from fairly conservative designs where bulls are screened genomically to reduce numbers entering progeny testing, to schemes where very large numbers of bull calves are screened and used as sires as soon as they reach sexual maturity. More radical schemes that incorporate the use of reproductive technologies (in juveniles) and genomic selection in nucleus herds are also described. The models used are either deterministic and more recently tend to be stochastic, simulating populations of cattle. A key driver of the rate of genetic gain is the generation interval, which could range from being similar to that in conventional testing (~5 years), down to as little as 1.5 years. Generally, the rate of genetic gain is between 12% and 100% more than in conventional progeny testing, while the rate of inbreeding tends to be lower per generation than in progeny testing because Mendelian sampling terms can be estimated more accurately. However, short generation intervals can lead to higher rates of inbreeding per year in genomic breeding programs.

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 Mendelian sampling terms as a selective advantage in optimum breeding schemes with restrictions on the rate of inbreeding

2004 ◽  
Vol 83 (1) ◽  
pp. 55-64 ◽  
Author(s):  
S. AVENDAÑO ◽  
J. A. WOOLLIAMS ◽  
B. VILLANUEVA
Keyword(s):  
Genetic Variation ◽  
Genetic Gain ◽  
Selective Advantage ◽  
Breeding Values ◽  
Truncation Selection ◽  
Joint Management ◽  
Breeding Schemes ◽  
Estimated Breeding Values ◽  
Available Information ◽  
Mendelian Sampling

Quadratic indices are a general approach for the joint management of genetic gain and inbreeding in artificial selection programmes. They provide the optimal contributions that selection candidates should have to obtain the maximum gain when the rate of inbreeding is constrained to a predefined value. This study shows that, when using quadratic indices, the selective advantage is a function of the Mendelian sampling terms. That is, at all times, contributions of selected candidates are allocated according to the best available information about their Mendelian sampling terms (i.e. about their superiority over their parental average) and not on their breeding values. By contrast, under standard truncation selection, both estimated breeding values and Mendelian sampling terms play a major role in determining contributions. A measure of the effectiveness of using genetic variation to achieve genetic gain is presented and benchmark values of 0·92 for quadratic optimisation and 0·5 for truncation selection are found for a rate of inbreeding of 0·01 and a heritability of 0·25.

The potential for identifying heritable endocrine parameters associated with fertility in post-partum dairy cows

1999 ◽  
Vol 68 (2) ◽  
pp. 333-347 ◽  
Author(s):  
A. O. Darwash ◽  
G. E. Lamming ◽  
J. A. Woolliams
Keyword(s):  
Genetic Variation ◽  
Genetic Gain ◽  
Production Efficiency ◽  
Negative Impact ◽  
Post Partum ◽  
Genetic Correlations ◽  
Reproductive Efficiency ◽  
Steady Increase ◽  
High Fertility ◽  
Progeny Testing

AbstractFertility is an important component of herd production efficiency, as each additional oestrous cycle that does not result in a planned pregnancy adds to the cost of dairy farming. In addition to the negative impact on milk production, the high costs of veterinary intervention, re-insemination and herd replacement, subfertility can affect the rate of genetic gain in traits of economic merit. In contrast with the steady increase in average milk yield per cow during the last 30 years, there has been a decline in conception rate to artificial insemination in both the USA and in the UK.The genetic correlation between yield and fertility has been equivocal. Attempts to improve the reproductive efficiency in dairy cattle through breeding and selection have been frustrated to date by the lack of heritable reproductive parameters conducive to high fertility. However, the traditional fertility parameters of interval to first service, services per conception, days open and calving intervals are highly influenced by managerial decisions and have, as expected, heritabilities too low to permit a meaningful genetic gain through selection. An alternative approach is to use the growing body of evidence that the majority of endocrine factors affecting reproduction are a result of gene expression at the hypothalamic, pituitary, ovarian or uterine level. Mechanisms such as commencement of post-partum cyclicity, follicle wave patterns, manifestation of oestrus, luteal competence and level of embryo mortality are appropriate for study. Research is required to investigate the genetic component of the variation between animals in these parameters, their phenotypic and genetic correlations with fertility and their association with other production traits.In summary: (a) subfertility is a syndrome with multiple causes and only the symptom in common; (b) improvement in fertility will continue to be frustrated until recorded traits provide more accurate estimates of breeding values; (c) techniques are now available to estimate the genetic variation in physiological components conducive to improved reproductive efficiency; (d) once the heritable components of fertility are identified, these tools could be introduced into progeny testing and breeding nuclei, from which the genetic improvement can be widely disseminated. Selection for those components with sufficient genetic variation will result in the improvement of the integral endocrine and other physiological mechanisms favourably correlated with high fertility (e) these tools may also assist in detecting quantitative trait loci for faster genetic gain through markers-assisted selection.

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 Genomic selection for non-key traits in radiata pine when the documented pedigree is corrected using DNA marker information

BMC Genomics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Yongjun Li ◽  
Jaroslav Klápště ◽  
Emily Telfer ◽  
Phillip Wilcox ◽  
Natalie Graham ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Genetic Gain ◽  
Additive Genetic Variance ◽  
Predictive Ability ◽  
Radiata Pine ◽  
Exome Capture ◽  
Forward Selection ◽  
Generation Interval ◽  
Stem Straightness ◽  
Cluster Frequency

Abstract Background Non-key traits (NKTs) in radiata pine (Pinus radiata D. Don) refer to traits other than growth, wood density and stiffness, but still of interest to breeders. Branch-cluster frequency, stem straightness, external resin bleeding and internal checking are examples of such traits and are targeted for improvement in radiata pine research programmes. Genomic selection can be conducted before the performance of selection candidates is available so that generation intervals can be reduced. Radiata pine is a species with a long generation interval, which if reduced could significantly increase genetic gain per unit of time. The aim of this study was to evaluate the accuracy and predictive ability of genomic selection and its efficiency over traditional forward selection in radiata pine for the following NKTs: branch-cluster frequency, stem straightness, internal checking, and external resin bleeding. Results Nine hundred and eighty-eight individuals were genotyped using exome capture genotyping by sequencing (GBS) and 67,168 single nucleotide polymorphisms (SNPs) used to develop genomic estimated breeding values (GEBVs) with genomic best linear unbiased prediction (GBLUP). The documented pedigree was corrected using a subset of 704 SNPs. The percentage of trio parentage confirmed was about 49% and about 50% of parents were re-assigned. The accuracy of GEBVs was 0.55–0.75 when using the documented pedigree and 0.61–0.80 when using the SNP-corrected pedigree. A higher percentage of additive genetic variance was explained and a higher predictive ability was observed when using the SNP-corrected pedigree than using the documented pedigree. With the documented pedigree, genomic selection was similar to traditional forward selection when assuming a generation interval of 17 years, but worse than traditional forward selection when assuming a generation interval of 14 years. After the pedigree was corrected, genomic selection led to 37–115% and 13–77% additional genetic gain over traditional forward selection when generation intervals of 17 years and 14 years were assumed, respectively. Conclusion It was concluded that genomic selection with a pedigree corrected by SNP information was an efficient way of improving non-key traits in radiata pine breeding.

Genomic selection in crops, trees and forages: a review

2014 ◽  
Vol 65 (11) ◽  
pp. 1177 ◽  
Author(s):  
Z. Lin ◽  
B. J. Hayes ◽  
H. D. Daetwyler
Keyword(s):  
Family Structure ◽  
Genomic Selection ◽  
Genetic Gain ◽  
Population Level ◽  
Early Age ◽  
Marker Density ◽  
Breeding Strategies ◽  
Effective Population ◽  
Genomic Breeding ◽  
Factors Affecting

Genomic selection is now being used at an accelerating pace in many plant species. This review first discusses the factors affecting the accuracy of genomic selection, and then interprets results of existing plant genomic selection studies in light of these factors. Differences between genomic breeding strategies for self-pollinated and open-pollinated species, and between-population level v. within-family design, are highlighted. As expected, more training individuals, higher trait heritability and higher marker density generally lead to better accuracy of genomic breeding values in both self-pollinated and open-pollinated plants. Most published studies to date have artificially limited effective population size by using designs of bi-parental or within-family structure to increase accuracies. The capacity of genomic selection to reduce generation intervals by accurately evaluating traits at an early age makes it an effective tool to deliver more genetic gain from plant breeding in many cases.

scholarly journals Deep scoping: a breeding strategy to preserve, reintroduce and exploit genetic variation

2021 ◽  
Author(s):  
David Vanavermaete ◽  
Jan Fostier ◽  
Steven Maenhout ◽  
Bernard De Baets
Keyword(s):  
Genetic Variation ◽  
Genetic Gain ◽  
Breeding Population ◽  
Breeding Strategy ◽  
Premature Convergence ◽  
Financial Cost ◽  
Truncation Selection ◽  
Trait Locus ◽  
Genetic Value

Abstract Key message The deep scoping method incorporates the use of a gene bank together with different population layers to reintroduce genetic variation into the breeding population, thus maximizing the long-term genetic gain without reducing the short-term genetic gain or increasing the total financial cost. Abstract Genomic prediction is often combined with truncation selection to identify superior parental individuals that can pass on favorable quantitative trait locus (QTL) alleles to their offspring. However, truncation selection reduces genetic variation within the breeding population, causing a premature convergence to a sub-optimal genetic value. In order to also increase genetic gain in the long term, different methods have been proposed that better preserve genetic variation. However, when the genetic variation of the breeding population has already been reduced as a result of prior intensive selection, even those methods will not be able to avert such premature convergence. Pre-breeding provides a solution for this problem by reintroducing genetic variation into the breeding population. Unfortunately, as pre-breeding often relies on a separate breeding population to increase the genetic value of wild specimens before introducing them in the elite population, it comes with an increased financial cost. In this paper, on the basis of a simulation study, we propose a new method that reintroduces genetic variation in the breeding population on a continuous basis without the need for a separate pre-breeding program or a larger population size. This way, we are able to introduce favorable QTL alleles into an elite population and maximize the genetic gain in the short as well as in the long term without increasing the financial cost.

scholarly journals 30 Inbreeding in American Angus cattle before and after the implementation of genomic selection

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 14-14
Author(s):  
Emmanuel A Lozada-Soto ◽  
Francesco Tiezzi ◽  
Duc Lu ◽  
Stephen P Miller ◽  
John B Cole ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Effective Population ◽  
Generation Interval ◽  
Inbreeding Coefficients ◽  
Angus Cattle ◽  
Before And After ◽  
Population Sizes ◽  
The Mean ◽  
Time Periods ◽  
Genomic Inbreeding

Abstract The aim of this study was to characterize the American Angus cattle population in terms of changes to the inbreeding rate (ΔF) and effective population size (Ne) before and after the implementation of genomic selection (GS). Genomic information (89,206 SNPs) was obtained for 25,960 bulls and 134,962 cows born between the years 2000 and 2017. Bulls and cows were independently grouped into two groups based on year of birth, pre-GS (2000–2009), and post-GS (2010–2017). Genomic inbreeding (FGRM) was calculated assuming fixed allele frequencies (0.5). Inbreeding based on runs of homozygosity (FROH) was calculated using software SNP1101 (Sargolzaei, 2014). The yearly ΔF for each group was estimated by regressing the inbreeding coefficients on year of birth. The generation intervals (L) were calculated for each of the four pathways of selection at both time periods (pre-GS and post-GS), where the mean of the sires of sires and dams of sires pathways was taken to be the generation interval for the bulls and the mean of the sires of dams and dams of dams pathways was taken to be the generation interval for the cows. The L and ΔF of the three inbreeding coefficients were used to estimate the Ne. Estimates of ΔF and Ne for both sexes at the two time periods can be found in table 1. In both sexes, ΔFROH decreased and NeROH increased from pre-GS to post-GS. For bulls, ΔFGRM and NeGRM did not change, and for cows, ΔFGRM decreased and NeGRM increased from pre-GS to post-GS. These results suggest that the implementation of genomic selection in Angus cattle has not caused the increased inbreeding rates and reduced effective population sizes seen in other species, but instead has been beneficial for the preservation of genetic diversity.

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