Emphasis on resilience in dairy cattle breeding: Possibilities and consequences.

This study aimed to investigate dairy cattle breeding goals with more emphasis on resilience. We simulated the consequences of increasing weight on resilience indicators and an assumed true resilience trait (TR). Two environments with different breeding goals were simulated to represent the variability of production systems across Europe. Ten different scenarios were stochastically simulated in a so-called pseudogenomic simulation approach. We showed that many modern dairy cattle breeding goals most likely have negative genetic gain for TR and promising resilience indicators such as the log-transformed, daily deviation from the lactation curve (LnVAR). In addition, there were many ways of improving TR by increasing the breeding goal weight of different resilience indicators. The results showed that adding breeding goal weight to resilience indicators, such as body condition score and LnVAR, could reverse the negative trend observed for resilience indicators. Loss in the aggregate genotype calculated with only current breeding goal traits was 12 to 76%. This loss was mainly due to a reduction in genetic gain in milk production. We observed higher genetic gain in beef production, fertility, and udder health when breeding for more resilience, but from an economical point of view, this was not high enough to compensate for the reduction in genetic gain in milk production. The highest genetic gain in TR was obtained when adding the highest breeding goal weight to LnVAR or TR, both with 0.29 genetic standard deviation units. The indicators we used, body condition score and LnVAR, can be measured on a large scale today with relatively cheap methods, which is crucial if we want to improve these traits through breeding. Economic values for resilience have to be estimated to find the most optimal breeding goal for a more resilient dairy cow in the future.

Breeding schemes with optimum-contribution selection or truncation selection for beef cattle destined for use on dairy females.

We tested the hypothesis that the size of a beef cattle population destined for use on dairy females is smaller under optimum-contribution selection (OCS) than under truncation selection (TRS) at the same genetic gain (ΔG) and the same rate of inbreeding (ΔF). We used stochastic simulation to estimate true ΔG realized at a 0.005 ΔF in breeding schemes with OCS or TRS. The schemes for the beef cattle population also differed in the number of purebred offspring per dam and the total number of purebred offspring per generation. Dams of the next generation were exclusively selected among the one-year-old heifers. All dams were donors for embryo transfer and produced a maximum of 5 or 10 offspring. The total number of purebred offspring per generation was: 400, 800, 1,600 or 4,000 calves, and it was used as a measure of population size. Rate of inbreeding was predicted and controlled using pedigree relationships. Each OCS (TRS) scheme was simulated for 10 discrete generations and replicated 100 (200) times. The OCS scheme and the TRS scheme with a maximum of 10 offspring per dam required approximately 783 and 1,257 purebred offspring per generation to realize a true ΔG of €14 and a ΔF of 0.005 per generation. Schemes with a maximum of 5 offspring per dam required more purebred offspring per generation to realize a similar true ΔG and a similar ΔF. Our results show that OCS and multiple ovulation and embryo transfer act on selection intensity through different mechanisms to achieve fewer selection candidates and fewer selected sires and dams than under TRS at the same ΔG and a fixed ΔF. Therefore, we advocate the use of a breeding scheme with OCS and multiple ovulation and embryo transfer for beef cattle destined for use on dairy females because it is favorable both from an economic perspective and a carbon footprint perspective.

Competitive gene flow does not necessarily maximize the genetic gain of genomic breeding programs in the presence of genotype-by-environment interaction.

National and international across-population selection is often recommended and fairly common in the current breeding practice of dairy cattle, with the primary aims to increase genetic gain and genetic variability. The aim of this study was to test the hypothesis that the strategy of truncation selection of sires across populations [i.e., competitive gene flow strategy (CGF)] may not necessarily maximize genetic gain in the long term in the presence of genotype-by-environment interaction (G×E). Two alternative strategies used to be compared with CGF were forced gene flow (FGF) strategies, with 10 or 50% of domestic dams forced to be mated with foreign sires (FGF10%, FGF50%). Two equal-size populations (Ndams = 1,000) that were selected for the same breeding goal trait (h2 = 0.3) under G×E correlation (rg) of either 0.9 or 0.8 were simulated to test these 3 different strategies. Each population first experienced either 5 or 20 differentiation generations (Gd), then 15 migration generations. Discrete generations were simulated for simplicity. Each population performed a within-population conventional breeding program during differentiation generations and the 3 across-population sire selection strategies based on joint genomic prediction during migration generations. The 4 Gd_rg combinations defined 4 different levels of differentiation degree between the 2 populations at the start of migration. The true rate of inbreeding over the last 10 migration generations in each scenario was constrained at 0.01 to provide a fair basis for comparison of genetic gain across scenarios. Results showed that CGF maximized the genetic gain after 15 migration generations in 5_0.9 combination only, the case of the lowest differentiation degree, with a superiority of 0.4% (0.04 genetic SD units) over the suboptimal strategy. While in 5_0.8, 20_0.9, and 20_0.8 combinations, 2 FGF strategies had a superiority in genetic gain of 2.3 to 12.5% (0.21-1.07 genetic SD units) over CGF after 15 migration generations, especially FGF50%. The superiority of FGF strategies over CGF was that they alleviated inbreeding, introduced new genetic variance in the early migration period, and improved accuracy in the entire migration period. Therefore, we concluded that CGF does not necessarily maximize the genetic gain of across-population genomic breeding programs given moderate G×E. The across-population selection strategy remains to be optimized to maximize genetic gain.

Strategies for implementing genomic selection for feed efficiency in dairy cattle breeding schemes.

Alternative genomic selection and traditional BLUP breeding schemes were compared for the genetic improvement of feed efficiency in simulated Norwegian Red dairy cattle populations. The change in genetic gain over time and achievable selection accuracy were studied for milk yield and residual feed intake, as a measure of feed efficiency. When including feed efficiency in genomic BLUP schemes, it was possible to achieve high selection accuracies for genomic selection, and all genomic BLUP schemes gave better genetic gain for feed efficiency than BLUP using a pedigree relationship matrix. However, introducing a second trait in the breeding goal caused a reduction in the genetic gain for milk yield. When using contracted test herds with genotyped and feed efficiency recorded cows as a reference population, adding an additional 4,000 new heifers per year to the reference population gave accuracies that were comparable to a male reference population that used progeny testing with 250 daughters per sire. When the test herd consisted of 500 or 1,000 cows, lower genetic gain was found than using progeny test records to update the reference population. It was concluded that to improve difficult to record traits, the use of contracted test herds that had additional recording (e.g., measurements required to calculate feed efficiency) is a viable option, possibly through international collaborations.

Improving production efficiency in the presence of genotype by environment interactions in pig genomic selection breeding programmes.

We simulated a genomic selection pig breeding schemes containing nucleus and production herds to improve feed efficiency of production pigs that were cross-breed. Elite nucleus herds had access to high-quality feed, and production herds were fed low-quality feed. Feed efficiency in the nucleus herds had a heritability of 0.3 and 0.25 in the production herds. It was assumed the genetic relationships between feed efficiency in the nucleus and production were low (rg  = 0.2), medium (rg  = 0.5) and high (rg  = 0.8). In our alternative breeding schemes, different proportion of production animals were recorded for feed efficiency and genotyped with high-density panel of genetic markers. Genomic breeding value of the selection candidates for feed efficiency was estimated based on three different approaches. In one approach, genomic breeding value was estimated including nucleus animals in the reference population. In the second approach, the reference population was containing a mixture of nucleus and production animals. In the third approach, the reference population was only consisting of production herds. Using a mixture reference population, we generated 40-115% more genetic gain in the production environment as compared to only using nucleus reference population that were fed high-quality feed sources when the production animals were offspring of the nucleus animals. When the production animals were grand offspring of the nucleus animals, 43-104% more genetic gain was generated. Similarly, a higher genetic gain generated in the production environment when mixed reference population was used as compared to only using production animals. This was up to 19 and 14% when the production animals were offspring and grand offspring of nucleus animals, respectively. Therefore, in genomic selection pig breeding programmes, feed efficiency traits could be improved by properly designing the reference population.

Reproductive technologies combine well with genomic selection in dairy breeding programs.

The objective of the present study was to examine whether genomic selection of females interacts with the use of reproductive technologies (RT) to increase annual monetary genetic gain (AMGG). This was tested using a factorial design with 3 factors: genomic selection of females (0 or 2,000 genotyped heifers per year), RT (0 or 50 donors selected at 14 mo of age for producing 10 offspring), and 2 reliabilities of genomic prediction. In addition, different strategies for use of RT and how strategies interact with the reliability of genomic prediction were investigated using stochastic simulation by varying (1) number of donors (25, 50, 100, 200), (2) number of calves born per donor (10 or 20), (3) age of donor (2 or 14 mo), and (4) number of sires (25, 50, 100, 200). In total, 72 different breeding schemes were investigated. The profitability of the different breeding strategies was evaluated by deterministic simulation by varying the costs of a born calf with reproductive technologies at levels of €500, €1,000, and €1,500. The results confirm our hypothesis that combining genomic selection of females with use of RT increases AMGG more than in a reference scheme without genomic selection in females. When the reliability of genomic prediction is high, the effect on rate of inbreeding (ΔF) is small. The study also demonstrates favorable interaction effects between the components of the breeder's equation (selection intensity, selection accuracy, generation interval) for the bull dam donor path, leading to higher AMGG. Increasing the donor program and number of born calves to achieve higher AMGG is associated with the undesirable effect of increased ΔF. This can be alleviated, however, by increasing the numbers of sires without compromising AMGG remarkably. For the major part of the investigated donor schemes, the investment in RT is profitable in dairy cattle populations, even at high levels of costs for RT.

Genetic Parameter Estimates of Carcass Traits under National Scale Breeding Scheme for Beef Cattle.

Carcass and price traits of 72,969 Hanwoo cows, bulls and steers aged 16 to 80 months at slaughter collected from 2002 to 2013 at 75 beef packing plants in Korea were analyzed to determine heritability, correlation and breeding value using the Multi-Trait restricted maximum likelihood (REML) animal model procedure. The traits included carcass measurements, scores and grades at 24 h postmortem and bid prices at auction. Relatively high heritability was found for maturity (0.41±0.031), while moderate heritability estimates were obtained for backfat thickness (0.20±0.018), longissimus muscle (LM) area (0.23±0.020), carcass weight (0.28±0.019), yield index (0.20±0.018), yield grade (0.16±0.017), marbling (0.28±0.021), texture (0.14±0.016), quality grade (0.26±0.016) and price/kg (0.24±0.025). Relatively low heritability estimates were observed for meat color (0.06±0.013) and fat color (0.06±0.012). Heritability estimates for most traits were lower than those in the literature. Genetic correlations of carcass measurements with characteristic scores or quality grade of carcass ranged from -0.27 to +0.21. Genetic correlations of yield grade with backfat thickness, LM area and carcass weight were 0.91, -0.43, and -0.09, respectively. Genetic correlations of quality grade with scores of marbling, meat color, fat color and texture were -0.99, 0.48, 0.47, and 0.98, respectively. Genetic correlations of price/kg with LM area, carcass weight, marbling, meat color, texture and maturity were 0.57, 0.64, 0.76, -0.41, -0.79, and -0.42, respectively. Genetic correlations of carcass price with LM area, carcass weight, marbling and texture were 0.61, 0.57, 0.64, and -0.73, respectively, with standard errors ranging from ±0.047 to ±0.058. The mean carcass weight breeding values increased by more than 8 kg, whereas the mean marbling scores decreased by approximately 0.2 from 2000 through 2009. Overall, the results suggest that genetic improvement of productivity and carcass quality could be obtained under the national scale breeding scheme of Korea for Hanwoo and that continuous efforts to improve the breeding scheme should be made to increase genetic progress.

Genomic selection strategies in a small dairy cattle population evaluated for genetic gain and profit.

The objective of this study was to evaluate a genomic breeding scheme in a small dairy cattle population that was intermediate in terms of using both young bulls (YB) and progeny-tested bulls (PB). This scheme was compared with a conventional progeny testing program without use of genomic information and, as the extreme case, a juvenile scheme with genomic information, where all bulls were used before progeny information was available. The population structure, cost, and breeding plan parameters were chosen to reflect the Danish Jersey cattle population, being representative for a small dairy cattle population. The population consisted of 68,000 registered cows. Annually, 1,500 bull dams were screened to produce the 500 genotyped bull calves from which 60 YB were selected to be progeny tested. Two unfavorably correlated traits were included in the breeding goal, a production trait (h(2)=0.30) and a functional trait (h(2)=0.04). An increase in reliability of 5 percentage points for each trait was used in the default genomic scenario. A deterministic approach was used to model the different breeding programs, where the primary evaluation criterion was annual monetary genetic gain (AMGG). Discounted profit was used as an indicator of the economic outcome. We investigated the effect of varying the following parameters: (1) increase in reliability due to genomic information, (2) number of genotyped bull calves, (3) proportion of bull dam sires that are young bulls, and (4) proportion of cow sires that are young bulls. The genomic breeding scheme was both genetically and economically superior to the conventional breeding scheme, even in a small dairy cattle population where genomic information causes a relatively low increase in reliability of breeding values. Assuming low reliabilities of genomic predictions, the optimal breeding scheme according to AMGG was characterized by mixed use of YB and PB as bull sires. Exclusive use of YB for production cows increased AMGG up to 3 percentage points. The results from this study supported our hypothesis that strong interaction effects exist. The strongest interaction effects were obtained between increased reliabilities of genomic estimated breeding values and more intensive use of YB. The juvenile scheme was genetically inferior when the increase in reliability was low (5 percentage points), but became genetically superior at higher reliabilities of genomic estimated breeding values. The juvenile scheme was always superior according to discounted profit because of the shorter generation interval and minimizing costs for housing and feeding waiting bulls.

AI-assisted selection of mating pairs through simulation-based optimized progeny allocation strategies in plant breeding.

Emerging technologies such as genomic selection have been applied to modern plant and animal breeding to increase the speed and efficiency of variety release. However, breeding requires decisions regarding parent selection and mating pairs, which significantly impact the ultimate genetic gain of a breeding scheme. The selection of appropriate parents and mating pairs to increase genetic gain while maintaining genetic diversity is still an urgent need that breeders are facing. This study aimed to determine the best progeny allocation strategies by combining future-oriented simulations and numerical black-box optimization for an improved selection of parents and mating pairs. In this study, we focused on optimizing the allocation of progenies, and the breeding process was regarded as a black-box function whose input is a set of parameters related to the progeny allocation strategies and whose output is the ultimate genetic gain of breeding schemes. The allocation of progenies to each mating pair was parameterized according to a softmax function, whose input is a weighted sum of multiple features for the allocation, including expected genetic variance of progenies and selection criteria such as different types of breeding values, to balance genetic gains and genetic diversity optimally. The weighting parameters were then optimized by the black-box optimization algorithm called StoSOO via future-oriented breeding simulations. Simulation studies to evaluate the potential of our novel method revealed that the breeding strategy based on optimized weights attained almost 10% higher genetic gain than that with an equal allocation of progenies to all mating pairs within just four generations. Among the optimized strategies, those considering the expected genetic variance of progenies could maintain the genetic diversity throughout the breeding process, leading to a higher ultimate genetic gain than those without considering it. These results suggest that our novel method can significantly improve the speed and efficiency of variety development through optimized decisions regarding the selection of parents and mating pairs. In addition, by changing simulation settings, our future-oriented optimization framework for progeny allocation strategies can be easily implemented into general breeding schemes, contributing to accelerated plant and animal breeding with high efficiency.

Bayesian optimisation for breeding schemes.

Introduction: Advances in genotyping technologies have provided breeders with access to the genotypic values of several thousand genetic markers in their breeding materials. Combined with phenotypic data, this information facilitates genomic selection. Although genomic selection can benefit breeders, it does not guarantee efficient genetic improvement. Indeed, multiple components of breeding schemes may affect the efficiency of genetic improvement and controlling all components may not be possible. In this study, we propose a new application of Bayesian optimisation for optimizing breeding schemes under specific constraints using computer simulation. Methods: Breeding schemes are simulated according to nine different parameters. Five of those parameters are considered constraints, and 4 can be optimised. Two optimisation methods are used to optimise those parameters, Bayesian optimisation and random optimisation. Results: The results show that Bayesian optimisation indeed finds breeding scheme parametrisations that provide good breeding improvement with regard to the entire parameter space and outperforms random optimisation. Moreover, the results also show that the optimised parameter distributions differ according to breeder constraints. Discussion: This study is one of the first to apply Bayesian optimisation to the design of breeding schemes while considering constraints. The presented approach has some limitations and should be considered as a first proof of concept that demonstrates the potential of Bayesian optimisation when applied to breeding schemes. Determining a general "rule of thumb" for breeding optimisation may be difficult and considering the specific constraints of each breeding campaign is important for finding an optimal breeding scheme.

Optimizing the Construction and Update Strategies for the Genomic Selection of Pig Reference and Candidate Populations in China.

Optimizing the construction and update strategies for reference and candidate populations is the basis of the application of genomic selection (GS). In this study, we first simulated1200-purebred-pigs population that have been popular in China for 20 generations to study the effects of different population sizes and the relationship between individuals of the reference and candidate populations. The results showed that the accuracy was positively correlated with the size of the reference population within the same generation (r = 0.9366, p < 0.05), while was negatively correlated with the number of generation intervals between the reference and candidate populations (r = -0.9267, p < 0.01). When the reference population accumulated more than seven generations, the accuracy began to decline. We then simulated the population structure of 1200 purebred pigs for five generations and studied the effects of different heritabilities (0.1, 0.3, and 0.5), genotyping proportions (20, 30, and 50%), and sex ratios on the accuracy of the genomic estimate breeding value (GEBV) and genetic progress. The results showed that if the proportion of genotyping individuals accounts for 20% of the candidate population, the traits with different heritabilities can be genotyped according to the sex ratio of 1:1male to female. If the proportion is 30% and the traits are of low heritability (0.1), the sex ratio of 1:1 male to female is the best. If the traits are of medium or high heritability, the male-to-female ratio is 1:1, 1:2, or 2:1, which may achieve higher genetic progress. If the genotyping proportion is up to 50%, for low heritability traits (0.1), the proportion of sows from all genotyping individuals should not be less than 25%, and for the medium and high heritability traits, the optimal choice for the male-to-female ratio is 1:1, which may obtain the greatest genetic progress. This study provides a reference for determining a construction and update plan for the reference population of breeding pigs.

Breeding Schemes: What Are They, How to Formalize Them, and How to Improve Them?

Formalized breeding schemes are a key component of breeding program design and a gateway to conducting plant breeding as a quantitative process. Unfortunately, breeding schemes are rarely defined, expressed in a quantifiable format, or stored in a database. Furthermore, the continuous review and improvement of breeding schemes is not routinely conducted in many breeding programs. Given the rapid development of novel breeding methodologies, it is important to adopt a philosophy of continuous improvement regarding breeding scheme design. Here, we discuss terms and definitions that are relevant to formalizing breeding pipelines, market segments and breeding schemes, and we present a software tool, Breeding Pipeline Manager, that can be used to formalize and continuously improve breeding schemes. In addition, we detail the use of continuous improvement methods and tools such as genetic simulation through a case study in the International Institute of Tropical Agriculture (IITA) Cassava east-Africa pipeline. We successfully deploy these tools and methods to optimize the program size as well as allocation of resources to the number of parents used, number of crosses made, and number of progeny produced. We propose a structured approach to improve breeding schemes which will help to sustain the rates of response to selection and help to deliver better products to farmers and consumers.

Strategies for Effective Use of Genomic Information in Crop Breeding Programs Serving Africa and South Asia.

Much of the world's population growth will occur in regions where food insecurity is prevalent, with large increases in food demand projected in regions of Africa and South Asia. While improving food security in these regions will require a multi-faceted approach, improved performance of crop varieties in these regions will play a critical role. Current rates of genetic gain in breeding programs serving Africa and South Asia fall below rates achieved in other regions of the world. Given resource constraints, increased genetic gain in these regions cannot be achieved by simply expanding the size of breeding programs. New approaches to breeding are required. The Genomic Open-source Breeding informatics initiative (GOBii) and Excellence in Breeding Platform (EiB) are working with public sector breeding programs to build capacity, develop breeding strategies, and build breeding informatics capabilities to enable routine use of new technologies that can improve the efficiency of breeding programs and increase genetic gains. Simulations evaluating breeding strategies indicate cost-effective implementations of genomic selection (GS) are feasible using relatively small training sets, and proof-of-concept implementations have been validated in the International Maize and Wheat Improvement Center (CIMMYT) maize breeding program. Progress on GOBii, EiB, and implementation of GS in CIMMYT and International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) breeding programs are discussed, as well as strategies for routine implementation of GS in breeding programs serving Africa and South Asia.

Favorable Conditions for Genomic Evaluation to Outperform Classical Pedigree Evaluation Highlighted by a Proof-of-Concept Study in Poplar.

Forest trees like poplar are particular in many ways compared to other domesticated species. They have long juvenile phases, ongoing crop-wild gene flow, extensive outcrossing, and slow growth. All these particularities tend to make the conduction of breeding programs and evaluation stages costly both in time and resources. Perennials like trees are therefore good candidates for the implementation of genomic selection (GS) which is a good way to accelerate the breeding process, by unchaining selection from phenotypic evaluation without affecting precision. In this study, we tried to compare GS to pedigree-based traditional evaluation, and evaluated under which conditions genomic evaluation outperforms classical pedigree evaluation. Several conditions were evaluated as the constitution of the training population by cross-validation, the implementation of multi-trait, single trait, additive and non-additive models with different estimation methods (G-BLUP or weighted G-BLUP). Finally, the impact of the marker densification was tested through four marker density sets. The population under study corresponds to a pedigree of 24 parents and 1,011 offspring, structured into 35 full-sib families. Four evaluation batches were planted in the same location and seven traits were evaluated on 1 and 2 years old trees. The quality of prediction was reported by the accuracy, the Spearman rank correlation and prediction bias and tested with a cross-validation and an independent individual test set. Our results show that genomic evaluation performance could be comparable to the already well-optimized pedigree-based evaluation under certain conditions. Genomic evaluation appeared to be advantageous when using an independent test set and a set of less precise phenotypes. Genome-based methods showed advantages over pedigree counterparts when ranking candidates at the within-family levels, for most of the families. Our study also showed that looking at ranking criteria as Spearman rank correlation can reveal benefits to genomic selection hidden by biased predictions.

Review: Towards the agroecological management of ruminants, pigs and poultry through the development of sustainable breeding programmes. II. Breeding strategies.

Agroecology uses ecological processes and local resources rather than chemical inputs to develop productive and resilient livestock and crop production systems. In this context, breeding innovations are necessary to obtain animals that are both productive and adapted to a broad range of local contexts and diversity of systems. Breeding strategies to promote agroecological systems are similar for different animal species. However, current practices differ regarding the breeding of ruminants, pigs and poultry. Ruminant breeding is still an open system where farmers continue to choose their own breeds and strategies. Conversely, pig and poultry breeding is more or less the exclusive domain of international breeding companies which supply farmers with hybrid animals. Innovations in breeding strategies must therefore be adapted to the different species. In developed countries, reorienting current breeding programmes seems to be more effective than developing programmes dedicated to agroecological systems that will struggle to be really effective because of the small size of the populations currently concerned by such systems. Particular attention needs to be paid to determining the respective usefulness of cross-breeding v. straight breeding strategies of well-adapted local breeds. While cross-breeding may offer some immediate benefits in terms of improving certain traits that enable the animals to adapt well to local environmental conditions, it may be difficult to sustain these benefits in the longer term and could also induce an important loss of genetic diversity if the initial pure-bred populations are no longer produced. As well as supporting the value of within-breed diversity, we must preserve between-breed diversity in order to maintain numerous options for adaptation to a variety of production environments and contexts. This may involve specific public policies to maintain and characterize local breeds (in terms of both phenotypes and genotypes), which could be used more effectively if they benefited from the scientific and technical resources currently available for more common breeds. Last but not least, public policies need to enable improved information concerning the genetic resources and breeding tools available for the agroecological management of livestock production systems, and facilitate its assimilation by farmers and farm technicians.