Optimum Contribution Selection Research Articles

A chickpea genetic variation map based on the sequencing of 3,366 genomes

Zero hunger and good health could be realized by 2030 through effective conservation, characterization and utilization of germplasm resources1. So far, few chickpea (Cicerarietinum) germplasm accessions have been characterized at the genome sequence level2. Here we present a detailed map of variation in 3,171 cultivated and 195 wild accessions to provide publicly available resources for chickpea genomics research and breeding. We constructed a chickpea pan-genome to describe genomic diversity across cultivated chickpea and its wild progenitor accessions. A divergence tree using genes present in around 80% of individuals in one species allowed us to estimate the divergence of Cicer over the last 21 million years. Our analysis found chromosomal segments and genes that show signatures of selection during domestication, migration and improvement. The chromosomal locations of deleterious mutations responsible for limited genetic diversity and decreased fitness were identified in elite germplasm. We identified superior haplotypes for improvement-related traits in landraces that can be introgressed into elite breeding lines through haplotype-based breeding, and found targets for purging deleterious alleles through genomics-assisted breeding and/or gene editing. Finally, we propose three crop breeding strategies based on genomic prediction to enhance crop productivity for 16 traits while avoiding the erosion of genetic diversity through optimal contribution selection (OCS)-based pre-breeding. The predicted performance for 100-seed weight, an important yield-related trait, increased by up to 23% and 12% with OCS- and haplotype-based genomic approaches, respectively.

Long-term impact of conventional and optimal contribution conservation methods on genetic diversity and genetic gain in local pig breeds.

There are rich and vast genetic resources of indigenous pig breeds in the world. Currently, great attention is paid to either crossbreeding or conservation of these indigenous pig breeds, and insufficient attention is paid to the combination of conservation and breeding along with their long-term effects on genetic diversity. Therefore, the objective of this study is to compare the long-term effects of using conventional conservation and optimal contribution selection methods on genetic diversity and genetic gain. A total of 11 different methods including conventional conservation and optimal contribution selection methods were investigated using stochastic simulations. The long-term effects of using these methods were evaluated in terms of genetic diversity metrices such as expected heterozygosity (He) and the rate of genetic gain. The results indicated that the rates of true inbreeding in these conventional conservation methods were maintained at around 0.01. The optimal contribution selection methods based either on the pedigree (POCS) or genome (GOCS) information showed more genetic gain than conventional methods, and POCS achieved the largest genetic gain. Furthermore, the effect of using GOCS methods on most of the genetic diversity metrics was slightly better than the conventional conservation methods when the rate of true inbreeding was the same, but this also required more sires used in OCS methods. According to the rate of true inbreeding, there was no significant difference among these conventional methods. In conclusion, there is no significant difference in different ways of selecting sows on inbreeding when we use different conventional conservation methods. Compared with conventional methods, POCS method could achieve the most genetic gain. However, GOCS methods can not only achieve higher genetic gain, but also maintain a relatively high level of genetic diversity. Therefore, GOCS is a better choice if we want to combine conservation and breeding in actual production in the conservation farms.

Pre-selection against a lethal recessive allele in breeding schemes with optimum-contribution selection or truncation selection

BackgroundWe tested the hypothesis that breeding schemes with a pre-selection step, in which carriers of a lethal recessive allele (LRA) were culled, and with optimum-contribution selection (OCS) reduce the frequency of a LRA, control rate of inbreeding, and realise as much genetic gain as breeding schemes without a pre-selection step.MethodsWe used stochastic simulation to estimate true genetic gain realised at a 0.01 rate of true inbreeding (ΔFtrue) by breeding schemes that combined one of four pre-selection strategies with one of three selection strategies. The four pre-selection strategies were: (1) no carriers culled, (2) male carriers culled, (3) female carriers culled, and (4) all carriers culled. Carrier-status was known prior to selection. The three selection strategies were: (1) OCS in which Delta {text{F}}_{{{text{true}}}} was predicted and controlled using pedigree relationships (POCS), (2) OCS in which Delta {text{F}}_{{{text{true}}}} was predicted and controlled using genomic relationships (GOCS), and (3) truncation selection of parents. All combinations of pre-selection strategies and selection strategies were tested for three starting frequencies of the LRA (0.05, 0.10, and 0.15) and two linkage statuses with the locus that has the LRA being on a chromosome with or without loci affecting the breeding goal trait. The breeding schemes were simulated for 10 discrete generations (t = 1, …, 10). In all breeding schemes, ΔFtrue was calibrated to be 0.01 per generation in generations t = 4, …, 10. Each breeding scheme was replicated 100 times.ResultsWe found no significant difference in true genetic gain from generations t = 4, …, 10 between breeding schemes with or without pre-selection within selection strategy. POCS and GOCS schemes realised similar true genetic gains from generations t = 4, …, 10. POCS and GOCS schemes realised 12% more true genetic gain from generations t = 4, …, 10 than truncation selection schemes.ConclusionsWe advocate for OCS schemes with pre-selection against the LRA that cause animal suffering and high costs. At LRA frequencies of 0.10 or lower, OCS schemes in which male carriers are culled reduce the frequency of LRA, control rate of inbreeding, and realise no significant reduction in true genetic gain compared to OCS schemes without pre-selection against LRA.

How to achieve a higher selection plateau in forest tree breeding? Fostering heterozygote×homozygote relationships in optimal contribution selection in the case study of Populus nigra.

In breeding, optimal contribution selection (OCS) is one of the most effective strategies to balance short‐ and long‐term genetic responses, by maximizing genetic gain and minimizing global coancestry. Considering genetic diversity in the selection dynamic—through coancestry—is undoubtedly the reason for the success of OCS, as it avoids preliminary loss of favorable alleles. Originally formulated with the pedigree relationship matrix, global coancestry can nowadays be assessed with one of the possible formulations of the realized genomic relationship matrix. Most formulations were optimized for genomic evaluation, but few for the management of coancestry. We introduce here an alternative formulation specifically developed for genomic OCS (GOCS), intended to better control heterozygous loci, and thus better account for Mendelian sampling. We simulated a multigeneration breeding program with mate allocation and under GOCS for twenty generations, solved with quadratic programming. With the case study of Populus nigra, we have shown that, although the dynamic was mainly determined by the trade‐off between genetic gain and genetic diversity, better formulations of the genomic relationship matrix, especially those fostering individuals carrying multiple heterozygous loci, can lead to better short‐term genetic gain and a higher selection plateau.

Statistical analysis in support of maintaining a healthy traditional Siamese cat population

BackgroundFor many years, breeders of companion animals have applied inbreeding or line breeding to transfer desirable genetic traits from parents to their offspring. Simultaneously, this resulted in a considerable spread of hereditary diseases and phenomena associated with inbreeding depression.ResultsOur cluster analysis of kinship and inbreeding coefficients suggests that the Thai or traditional Siamese cat could be considered as a subpopulation of the Siamese cat, which shares common ancestors, although they are considered as separate breeds. In addition, model-based cluster analysis could detect regional differences between Thai subpopulations. We show that by applying optimal contribution selection and simultaneously limiting the contributions by other breeds, the genetic diversity within subpopulations can be improved.ConclusionIn principle, the European mainland Thai cat population can achieve a genetic diversity of about 26 founder genome equivalents, a value that could potentially sustain a genetically diverse population. However, reaching such a target will be difficult in the absence of a supervised breeding program. Suboptimal solutions can be obtained by minimisation of kinships within regional subpopulations. Exchanging animals between different regions on a small scale might be already quite useful to reduce the kinship, by achieving a potential diversity of 23 founder genome equivalents. However, contributions by other breeds should be minimised to preserve the original Siamese gene pool.

The Modular Breeding Program Simulator (MoBPS) allows efficient simulation of complex breeding programs

Context Breeding programs aim at improving the genetic characteristics of livestock populations with respect to productivity, fitness and adaptation, while controlling negative effects such as inbreeding or health and welfare issues. As breeding is affected by a variety of interdependent factors, the analysis of the effect of certain breeding actions and the optimisation of a breeding program are highly complex tasks. Aims This study was conducted to display the potential of using stochastic simulation to analyse, evaluate and compare breeding programs and to show how the Modular Breeding Program Simulator (MoBPS) simulation framework can further enhance this. Methods In this study, a simplified version of the breeding program of Göttingen Minipigs was simulated to analyse the impact of genotyping and optimum contribution selection in regard to both genetic gain and diversity. The software MoBPS was used as the backend simulation software and was extended to allow for a more realistic modelling of pig breeding programs. Among others, extensions include the simulation of phenotypes with discrete observations (e.g. teat count), variable litter sizes, and a breeding value estimation in the associated R-package miraculix that utilises a graphics processing unit. Key results Genotyping with the subsequent use of genomic best linear unbiased prediction (GBLUP) led to substantial increases in genetic gain (15.3%) compared with a pedigree-based BLUP, while reducing the increase of inbreeding by 24.8%. The additional use of optimum genetic selection was shown to be favourable compared with the plain selection of top boars. The use of graphics processing unit-based breeding value estimation with known heritability was ~100 times faster than the state-of-the-art R-package rrBLUP. Conclusions The results regarding the effect of both genotyping and optimal contribution selection are in line with well established results. Paired with additional new features such as the modelling of discrete phenotypes and adaptable litter sizes, this confirms MoBPS to be a unique tool for the realistic modelling of modern breeding programs. Implications The MoBPS framework provides a powerful tool for scientists and breeders to perform stochastic simulations to optimise the practical design of modern breeding programs to secure standardised breeding of high-quality animals and answer associated research questions.

The maintenance of genetic diversity in a local cattle breed through optimal contribution selection

The maintenance of genetic diversity in a local cattle breed through optimal contribution selection

Optimal Contribution Selection in Highly Fecund Species With Overlapping Generations.

Optimal contributions approaches to parental selection in closed breeding populations aim to maximize genetic gains, while restraining long-term inbreeding. The adoption of optimal contribution selection (OCS) in highly fecund outcrossing species presents a number of challenges not applicable to species of low fecundity (e.g., livestock) for which they were developed. This is particularly true if overlapping-generations or rolling-front breeding strategies are applied, in which case the number of individuals per family in juvenile (i.e., sexually immature) age groups is not necessarily known but is likely to be large. In these circumstances, conventional OCS procedures must be modified or a large number of dummy individuals defined, making computations onerous. Here, an approach to OCS is presented that involves the use of “between-family relationship matrices” instead of “between-individual relationship matrices.” The method is applicable to breeding programs involving highly fecund outcrossing species with overlapping generations, including circumstances where the number of juveniles per family is unknown but large.

Genomic mating as sustainable breeding for Chinese indigenous Ningxiang pigs.

An important economic reason for the loss of local breeds is that they tend to be less productive, and hence having less market value than commercial breeds. Nevertheless, local breeds often have irreplaceable values, genetically and sociologically. In the breeding programs with local breeds, it is crucial to balance the selection for genetic gain and the maintaining of genetic diversity. These two objectives are often conflicting, and finding the optimal point of the trade-off has been a challenge for breeders. Genomic selection (GS) provides a revolutionary tool for the genetic improvement of farm animals. At the same time, it can increase inbreeding and produce a more rapid depletion of genetic variability of the selected traits in future generations. Optimum-contribution selection (OCS) represents an approach to maximize genetic gain while constraining inbreeding within a targeted range. In the present study, 515 Ningxiang pigs were genotyped with the Illumina Porcine SNP60 array or the GeneSeek Genomic Profiler Porcine 50K array. The Ningxiang pigs were found to be highly inbred at the genomic level. Average locus-wise inbreeding coefficients were 0.41 and 0.37 for the two SNP arrays used, whereas genomic inbreeding coefficients based on runs of homozygosity were 0.24 and 0.25, respectively. Simulated phenotypic data were used to assess the utility of genomic OCS (GOCS) in comparison with GS without inbreeding control. GOCS was conducted under two scenarios, selecting sires only (GOCS_S) or selecting sires and dams (GOCS_SD), while kinships were constrained on selected parents. The genetic gain for average daily body weight gain (ADG) per generation was between 18.99 and 20.55 g with GOCS_S, and between 23.20 and 28.92 with GOCS_SD, and it varied from 25.38 to 48.38 g under GS without controlling inbreeding. While the rate of genetic gain per generation obtained using GS was substantially larger than that obtained by the two scenarios of genomic OCS in the beginning generations of selection, the difference in the genetic gain of ADG between GS and GOCS reduced quickly in latter generations. At generation ten, the difference in the realized rates of genetic gain between GS and GOCS_SD diminished and ended up with even a slightly higher genetic gain with GOCS_SD, due to the rapid loss of genetic variance with GS and fixation of causative genes. The rate of inbreeding was mostly maintained below 5% per generation with genomic OCS, whereas it increased to between 10.5% and 15.3% per generation with GS. Therefore, genomic OCS appears to be a sustainable strategy for the genetic improvement of local breeds such as Ningxiang pigs, but keeping mind that a variety of GOCS methods exist and the optimal forms remain to be exploited further.

Management of Genetic Diversity in the Era of Genomics.

Management of genetic diversity aims to (i) maintain heterozygosity, which ameliorates inbreeding depression and loss of genetic variation at loci that may become of importance in the future; and (ii) avoid genetic drift, which prevents deleterious recessives (e.g., rare disease alleles) from drifting to high frequency, and prevents random drift of (functional) traits. In the genomics era, genomics data allow for many alternative measures of inbreeding and genomic relationships. Genomic relationships/inbreeding can be classified into (i) homozygosity/heterozygosity based (e.g., molecular kinship matrix); (ii) genetic drift-based, i.e., changes of allele frequencies; or (iii) IBD-based, i.e., SNPs are used in linkage analyses to identify IBD segments. Here, alternative measures of inbreeding/relationship were used to manage genetic diversity in genomic optimal contribution (GOC) selection schemes. Contrary to classic inbreeding theory, it was found that drift and homozygosity-based inbreeding could differ substantially in GOC schemes unless diversity management was based upon IBD. When using a homozygosity-based measure of relationship, the inbreeding management resulted in allele frequency changes toward 0.5 giving a low rate of increase in homozygosity for the panel used for management, but not for unmanaged neutral loci, at the expense of a high genetic drift. When genomic relationship matrices were based on drift, following VanRaden and as in GCTA, drift was low at the expense of a high rate of increase in homozygosity. The use of IBD-based relationship matrices for inbreeding management limited both drift and the homozygosity-based rate of inbreeding to their target values. Genetic improvement per percent of inbreeding was highest when GOC used IBD-based relationships irrespective of the inbreeding measure used. Genomic relationships based on runs of homozygosity resulted in very high initial improvement per percent of inbreeding, but also in substantial discrepancies between drift and homozygosity-based rates of inbreeding, and resulted in a drift that exceeded its target value. The discrepancy between drift and homozygosity-based rates of inbreeding was caused by a covariance between initial allele frequency and the subsequent change in frequency, which becomes stronger when using data from whole genome sequence.

Quantitative genetic analyses provide parameters for selection and conservation of captive Great-billed Seed-finches (Sporophila maximiliani).

The Great-billed Seed-finch (Sporophila maximiliani) is an endangered South American bird that has suffered from trafficking and the destruction of its natural habitat. In contrast, there are over 180,000 Great-billed Seed-finches legally raised in captivity in Brazil. The interest as a pet for Great-billed Seed-finches is due to their exceptional ability to sing. In the present research, the unknown genetic structure of the Great-billed Seed-finch captive population was investigated by quantitative analysis of 6,226 pedigree records. Additionally, 7,671 phenotypic records were available to estimate genetic parameters such as heritability and evolvability of a song-related trait of these birds for competitions. The captive Great-billed Seed-Finch population faces many of the problems commonly encountered in domestic animal populations such as a high level of inbreeding (average of 8.26%, 70.47% of birds were inbred), pedigree bottlenecks, unbalanced contribution of breeding animals and structuring (equivalent number of subpopulations of 2.91). Despite this, most genetic diversity remains preserved within aviaries. The high generation interval (5.74 years) found for this population should help to prevent a rapid increase in inbreeding and genetic drift. These results should serve as strong motivation and support for urgent actions to manage the genetic diversity of captive Great-billed Seed-Finches. From the viewpoint of genetic improvement for singing time in tournaments (STT), this trait presents sufficient variability to allow response to artificial selection given the heritability of 18.7% and evolvability of 2,447%. In contrast, inbreeding and high generation interval appear to be the most considerable barriers that may limit the genetic gain for STT. Widespread adoption of techniques such as optimal genetic contribution selection and implementation of routine genetic diversity monitoring via pedigree analysis and molecular tools can be crucial both in terms of breeding and conservation of genetic diversity of captive Great-billed Seed-Finches.

Controlling Coancestry and Thereby Future Inbreeding by Optimum-Contribution Selection Using Alternative Genomic-Relationship Matrices.

We tested the consequences of using alternative genomic relationship matrices to predict genomic breeding values (GEBVs) and control of coancestry in optimum contribution selection, where the relationship matrix used to calculate GEBVs was not necessarily the same as that used to control coancestry. A stochastic simulation study was carried out to investigate genetic gain and true genomic inbreeding in breeding schemes that applied genomic optimum contribution selection (GOCS) with different genomic relationship matrices. Three genomic-relationship matrices were used to predict the GEBVs based on three information sources: markers (GM), QTL (GQ), and markers and QTL (GA). Strictly, GQ is not possible to implement in practice since we do not know the quantitative trait loci (QTL) positions, but more and more information is becoming available especially about the largest QTL. Two genomic-relationship matrices were used to control coancestry: GM and GA. Three genetic architectures were simulated: with 7702, 1000, and 500 QTLs together with 54,218 markers. Selection was for a single trait with heritability 0.2. All selection candidates were phenotyped and genotyped before selection. With 7702 QTL, there were no significant differences in rates of genetic gain at the same rate of true inbreeding using different genomic relationship matrices in GOCS. However, as the number of QTLs was reduced to 1000, prediction of GEBVs using a genomic relationship matrix constructed based on GQ and control of coancestry using GM realized 29.7% higher genetic gain than using GM for both prediction and control of coancestry. Forty-three percent of this increased rate of genetic gain was due to increased accuracies of GEBVs. These findings indicate that with large numbers of QTL, it is not critical what information, i.e., markers or QTL, is used to construct genomic-relationship matrices. However, it becomes critical with small numbers of QTL. This highlights the importance of using genomic-relationship matrices that focus on QTL regions for GEBV estimation when the number of QTL is small in GOCS. Relationships used to control coancestry are preferably based on marker data.

Mate selection provides similar genetic progress and average inbreeding than optimum contribution selection in the long-term

Optimum contribution selection (OCS) and mate selection (MS) are alternative strategies to maximize genetic gain under controlled rates of inbreeding. There is evidence in the literature that MS outperforms OCS in controlling inbreeding under the same expected genetic gain in the short-term. It is unclear, however, if the same would occur in the long-term. This study aimed to compare OCS and MS regarding short- and long-term genetic progress and inbreeding, using simulated data. The structure of the simulated population aimed to mimic an aquaculture breeding program. Twenty discrete generations were simulated, considering 50 families and 2000 offspring per generation, and a trait with a heritability of 0.3. OCS and MS were applied using a differential evolution (DE) algorithm, under an objective function that accounted for genetic merit, coancestry among selection candidates and inbreeding of the future progeny. For OCS, the optimization process consisted of selection based on optimum contribution followed by minimum inbreeding mating. Objective functions using different weights on coancestry were tested. For each application, 20 replicates were simulated and the results were compared based on their average. Both strategies, OCS and MS, were very effective in controlling inbreeding over the generations. In the short-term, MS was more efficient than OCS in controlling inbreeding under the same genetic gain. In the long-term, OCS and MS resulted in similar genetic progress and average inbreeding, under the same penalty on coancestry.

An endangered horse breed can be conserved by using optimum contribution selection and preselection of stallions

The Jutland Horse is an original Danish horse breed and is regarded as Denmark’s national draft horse. It was previously used as a draught horse in the farming industry. The studbook dates back to ...

Advanced optimum contribution selection as a tool to improve regional cattle breeds: a feasibility study for Vorderwald cattle

In the middle of the 20th century, increasing inbreeding rates were identified as a threat to livestock breeding. Consequences include reduced fertility, fitness and phenotypic expression of lethal alleles. An important step in mitigating this inbreeding was the introduction of optimum contribution selection (OCS). OCS facilitates the simultaneous management of genetic gain and inbreeding rates. However, using a standard OCS methodology for regional breeds with historical introgression for upgrading reasons could lead to reinforced selection on introgressed genetic material since those alleles improve the rate of genetic gain and reduce the average kinship in the population. Consequently, regional breeds may become genetically extinct if a standard OCS approach is used. Thus, the advanced OCS (aOCS) approach takes introgressed genetic material into account. The major goals of this study were to (i) gather key information on the feasibility of aOCS under practical conditions of the actual breeding scheme of Vorderwald cattle, (ii) identify superior strategies for implementing the actual scheme and (iii) examine whether historical breeding decisions to increase genetic gain by introgression from commercial breeds could have been avoided by using aOCS. Stochastic simulations were designed in this study to create populations from the historical gene pool by using aOCS. Simultaneously, all practical constraints of a breeding scheme were met. Thus, the simulated populations were comparable with real data. The annual genetic gain was higher in reality (1.56) than in the simulation scenarios (1.12–1.40). The introgressed genetic material increased to 61.3% in reality but was conserved at a final value of 15.3% (±0.78) across simulations. The classical rate of inbreeding and rate of native inbreeding were constrained to 0.092% on an annual basis. This value is equal to an effective population size of 100. The observed values for rates of inbreeding were 0.082–0.087% and 0.087–0.088% for classical and native kinship, respectively. The corresponding figures in reality were 0.067% and 0.184%, respectively. This study suggests that aOCS is feasible for Vorderwald cattle. Strategies for implementation are identified. Finally, we conclude that historical breeding decisions could have been avoided by using aOCS. The genetic gain would have been reduced by at least 12.2%, but the introgressed genetic material, genetic diversity and native genetic diversity would have been more desirable for a breed under conservation.

Implementation of advanced Optimum Contribution Selection in small-scale breeding schemes: prospects and challenges in Vorderwald cattle

Vorderwald cattle are a regional cattle breed from the Black Forest in south western Germany. In recent decades, commercial breeds have been introgressed to upgrade the breed in performance traits. On one hand, native genetic diversity of the breed should be conserved. On the other hand, moderate rates of genetic gain are needed to satisfy breeders to keep the breed. These goals are antagonistic, since the native proportion of the gene pool is negatively correlated to performance traits and the carriers of introgressed alleles are less related to the population. Thus, a standard Optimum Contribution Selection (OCS) approach would lead to reinforced selection on migrant contributions (MC). Our objective was the development of strategies for practical implementation of an OCS approach to manage the MC and native genetic diversity of regional breeds. Additionally, we examined the organisational efforts and the financial impacts on the breeding scheme of Vorderwald cattle. We chose the advanced Optimum Contribution Selection (aOCS) to manage the breed in stochastic simulations based on real pedigree data. In addition to standard OCS approaches, aOCS facilitates the management of the MC and the rate of inbreeding at native alleles. We examined two aOCS strategies. Both strategies maximised genetic gain, while strategy (I) conserved the MC in the breeding population and strategy (II) reduced the MC at a predefined annual rate. These two approaches were combined with one of three flows of replacement of sires (FoR strategies). Additionally, we compared breeding costs to clarify about the financial impact of implementing aOCS in a young sire breeding scheme. According to our results, conserving the MC in the population led to significantly (P < 0.01) higher genetic gain (1.16 ± 0.13 points/year) than reducing the MC (0.88 ± 0.10 points/year). In simulation scenarios that conserved the MC, the final value of MC was 57.6% ± 0.004, while being constraint to 58.2%. However, reducing the MC is only partially feasible based on pedigree data. Additionally, this study proves that the classical rate of inbreeding can be managed by constraining only the rate of inbreeding at native alleles within the aOCS approach. The financial comparison of the different breeding schemes proved the feasibility of implementing aOCS in Vorderwald cattle. Implementing the modelled breeding scheme would reduce costs by 1.1% compared with the actual scheme. Reduced costs were underpinned by additional genetic gain in superior simulation scenarios compared to expected genetic gain in reality (+4.85%).

Characterization of Genetic Diversity Conserved in the Gene Bank for Dutch Cattle Breeds

In this study, we characterized genetic diversity in the gene bank for Dutch native cattle breeds. A total of 715 bulls from seven native breeds and a sample of 165 Holstein Friesian bulls were included. Genotype data were used to calculate genetic similarities. Based on these similarities, most breeds were clearly differentiated, except for two breeds (Deep Red and Improved Red and White) that have recently been derived from the MRY breed, and for the Dutch Friesian and Dutch Friesian Red, which have frequently exchanged bulls. Optimal contribution selection (OCS) was used to construct core sets of bulls with a minimized similarity. The composition of the gene bank appeared to be partly optimized in the semen collection process, i.e., the mean similarity within breeds based on the current number of straws per bull was 0.32% to 1.49% lower than when each bull would have contributed equally. Mean similarity could be further reduced within core sets by 0.34% to 2.79% using OCS. Material not needed for the core sets can be made available for supporting in situ populations and for research. Our findings provide insight in genetic diversity in Dutch cattle breeds and help to prioritize material in gene banking.

POLYHEDRAL-BASED METHODS FOR MIXED-INTEGER SOCP IN TREE BREEDING

Optimal contribution selection (OCS) is a mathematical optimization problem that aims to maximize the total benefit from selecting a group of individuals under a constraint on genetic diversity. We are specifically focused on OCS as applied to forest tree breeding, when selected individuals will contribute equally to the gene pool. Since the diversity constraint in OCS can be described with a second-order cone, equal deployment in OCS can be mathematically modeled as mixed-integer second-order cone programming (MI-SOCP). If we apply a general solver for MI-SOCP, non-linearity embedded in OCS requires a heavy computation cost. To address this problem, we propose an implementation of lifted polyhedral programming (LPP) relaxation and a cone-decomposition method (CDM) to generate effective linear approximations for OCS. In particular, CDM successively solves OCS problems much faster than generic approaches for MI-SOCP. The approach of CDM is not limited to OCS, so that we can also apply the approach to other MI-SOCP problems.

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

In this study, we compared 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 have shown 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 (1) the method of sire selection and their use on cows or bull-dams, and (2) 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 scenarios in terms of genetic gain, selection accuracy, generation interval, genetic and genic variance, rate of coancestry, effective population size, and conversion efficiency. The results showed that early use of genomically tested sires increased genetic gain compared with 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. Although maximizing intensity gave the lowest conversion efficiency, faster turnover 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 with truncation selection, optimizing sire selection and their usage increased the conversion efficiency by achieving either comparable genetic gain for a smaller loss of genetic variation or higher genetic gain for a comparable loss of genetic variation. Our results will help breeding organizations implement sustainable genomic selection.

Genetic parameters and selection response for the harvest body weight of the giant freshwater prawn (Macrobrachium rosenbergii) in a breeding program in China.

A multi-trait selective breeding program of Macrobrachium rosenbergii was initiated in China in 2015. In this program, the M. rosenbergii resources were widely collected from four countries, the origin of the founders was verified with 16 microsatellites and the pedigree was reconstructed, and the optimum contribution selection was used to make the mating design. In this study, we evaluated the genetic parameters and selection response for the harvest body weight (HBW) of M. rosenbergii after being communally reared for 95–109 days. The data were collected from two generations that comprised 25,212 progenies from 150 sires and 198 dams. The residual maximum-likelihood methodology was employed to evaluate the variance components, by fitting an animal model. The accuracy of estimated breeding values increased by 0.38% after pedigree reconstruction using microsatellite markers. The estimated heritability (h2) for HBW was moderate (0.212 ± 0.049) and the common environmental coefficient (c2) was low (0.063 ± 0.017) when all the data were used for the analysis. Within generations, h2 was moderate to high (0.198 ± 0.080 to 0.338 ± 0.049). c2 could only be estimated in G1, which was 0.055 ± 0.030. The average HBW of males was significantly larger than that of females (P < 0.01). h2 estimated for female HBWs were higher than that for males within generations, while h2 estimated for female HBWs were lower than that for males across generations. But they were not significantly different (P > 0.05). The genetic correlations between sexes were moderate to high within each generation (0.529 to 0.763). Two methods were used to estimate the realized response. One method was calculated from the differences between the least squares means of the selected population HBW and that of control population HBW, which was 14.01%. The other method was calculated from the differences between the EBVs of the selected population HBW and that of control population HBW, which was 11.52%. The predicted responses derived from two sets of genetic parameters acquired from within- and across- generation datasets were 11.68% and 10.67%, respectively. The present study provides valuable information for breeding programs of M. rosenbergii.