Optimum Contribution Selection Research Articles

Genomic Characterization of Local Croatian Sheep Breeds-Effective Population Size, Inbreeding & Signatures of Selection.

The Istrian (IS) and the Pag sheep (PS) are local Croatian breeds which provide significant income for the regional economy and have a cultural and traditional importance for the inhabitants. The aim of this study was to estimate some important population specific genetic parameters in IS (N = 1293) and PS (N = 2637) based on genome wide SNPs. Estimates of linkage disequilibrium effective population size (Ne) evidenced more genetic variability in PS (Ne = 838) compared to IS (Ne = 197), regardless of historical time (both recent and ancient genetic variability). The discrepancy in the recent genetic variability between these breeds was additionally confirmed by the estimates of genomic inbreeding (FROH), which was estimated to be notably higher in IS (FROH>2 = 0.062) than in PS (FROH>2 = 0.029). The average FROH2-4, FROH4-8, FROH8-16, and FROH>16 were 0.26, 1.65, 2.14, and 3.72 for IS and 0.22, 0.61, 0.75, and 1.58 for PS, thus evidencing a high contribution of recent inbreeding in the overall inbreeding. One ROH island with > 30% of SNP incidence in ROHs was detected in IS (OAR6; 34,253,440-38,238,124 bp) while there was no ROH islands detected in PS. Seven genes (CCSER1, HERC3, LCORL, NAP1L5, PKD2, PYURF, and SPP1) involved in growth, feed intake, milk production, immune responses, and resistance were associated with the found autozygosity. The results of this study represent the first comprehensive insight into genomic variability of these two Croatian local sheep breeds and will serve as a baseline for setting up the most promising strategy of genomic Optimum Contribution Selection.

Elucidating population structure and genetic diversity of intercross sheep through pedigree analysis

The aim of this study was to assess the population structure and genetic diversity along with determining the accuracy of ongoing selection program in a closed flock of Intercross sheep through pedigree analysis. The pedigree records of 2245 animals maintained at CSWRI-NTRS, Garsa, Kullu during the period from 2012 to 2023 were utilized to calculate the genealogical information, genetic conservation index and other parameters based on gene origin probabilities. Lambs born during 2020 and 2023 were considered as reference population. ENDOG version 4.8 software package was used for pedigree analysis. Analysis revealed that the mean generation interval for the reference population was 3.20 years with a longer interval in dam-offspring pathways relative to sire-offspring pathways. Average inbreeding coefficients for whole and reference population were 0.21 and 0.49 % and corresponding average relatedness was 1.24 and 1.72 %, respectively. The average maximum, equivalent and completed generations were 2.50, 1.96 and 1.63 with 0.12 %, 0.17 % and 0.20 % increase in inbreeding coefficient, respectively. This resultant effective population size estimated using individual increase in inbreeding was 156.52. Effective numbers of founders (fe) and ancestors (fa) was 68 and 61, respectively for reference population with a ratio of 1.11, indicating occurrence of minor effects of bottleneck in the flock. The genetic contribution of most influent ancestors contributing 50 % of the gene pool was only 23. The estimated founder genome (fg) and non-founder genome (fng) were 35.87 and 75.91, respectively in the present population. The estimate for fg/fa ratio was 0.59 which explained that 59 % of the original genetic diversity is exit in the reference population. Genetic conservation index (GCI) ranged from 1 % to 20.28 % with a mean value of 4.91 %. Overall, males recorded a slightly higher GCI than females, i.e., 5.23 vs 4.65 %, respectively. The results revealed that, the population has augmented genetic diversity and low level of inbreeding. Small fraction of genetic diversity was vanished due to unequal contribution of founders and minor bottleneck effects occurred due to disproportionate contribution of breeding animals to gene pool of population. Use of a sufficient number of breeding rams and optimum contribution selection (OCS) is recommended to maintain the genetic diversity as well as enhancing production potential in this population.

Evolutionary computing to assemble standing genetic diversity and achieve long-term genetic gain.

Loss of genetic diversity in elite crop breeding pools can severely limit long-term genetic gains and limit ability to make gains in new traits, like heat tolerance, that are becoming important as the climate changes. Here, we investigate and propose potential breeding program applications of optimal haplotype stacking (OHS), a selection method that retains useful diversity in the population. OHS selects sets of candidates containing, between them, haplotype segments with very high segment breeding values for the target trait. We compared the performance of OHS, a similar method called optimal population value (OPV), truncation selection on genomic estimated breeding values (GEBVs), and optimal contribution selection (OCS) in stochastic simulations of recurrent selection on founder wheat genotypes. After 100 generations of intercrossing and selection, OCS and truncation selection had exhausted the genetic diversity, while considerable diversity remained in the OHS population. Gain under OHS in these simulations ultimately exceeded that from truncation selection or OCS. OHS achieved faster gains when the population size was small, with many progeny per cross. A promising hybrid strategy, involving a single cycle of OHS in the first generation followed by recurrent truncation selection, substantially improved long-term gain compared with truncation selection and performed similarly to OCS. The results of this study provide initial insights into where OHS could be incorporated into breeding programs.

Turopolje Pig: Between Conservation and Sustainability

The Turopolje pig is a fatty-type pig breed created during the Middle Ages in the Turopolje region in Central Croatia. Although the population of the Turopolje pig is stable today, this breed is still one of the endangered breeds that are severely threatened by inbreeding. The aim of this work was to evaluate the genetic diversity parameters of the recent Turopolje pig population in Croatia and to consider mating strategies to limit the increase in inbreeding in the population using algorithms within an optimal contribution selection frame. The pedigree of 1155 individuals was analysed. In addition to the basic pedigree structure, the following population parameters were analysed: average coefficient of inbreeding; mean rate of increase in coancestry; increase in coancestry between any individuals; effective population size. Based on obtained parameters, mating optimization using estimated kinship between candidates in several scenarios was carried out, restricting maximal kinship between candidates on a minimal possible value 0.07, as the least possible level of kinship, candidates on a value of 0.10, and candidates on a value of 0.25. The average inbreeding coefficient (F) in the population was 0.03, while the average F of inbred animals was 0.10. The effective population size obtained from pedigrees based on the increase in coancestry (0.17) was 29.14. Mating plans were created from 5 active boars and 120 sows in different scenarios with constraint on the level of kinship between candidates. The least possible level of kinship was 0.07, and no available solutions were under this level. With the increase in the allowed kinship between candidates, the level of inbreeding increased from 0.01 to 0.06 in the next generation. Under practical conditions, mating between ideal individuals in local pig populations is not always possible, and the use of optimisation algorithms allows alternative mating within existing populations to be considered. Finally, this approach opens up the possibility of potentially improving sustainable breeding through selection by considering breeding values for economically important traits. This approach can help to differentiate the Turopolje pig breed from other commercial and local breeds, e.g., for meat quality traits.

Efficient genomics-based ‘end-to-end’ selective tree breeding framework

Since their initiation in the 1950s, worldwide selective tree breeding programs followed the recurrent selection scheme of repeated cycles of selection, breeding (mating), and testing phases and essentially remained unchanged to accelerate this process or address environmental contingencies and concerns. Here, we introduce an “end-to-end” selective tree breeding framework that: (1) leverages strategically preselected GWAS-based sequence data capturing trait architecture information, (2) generates unprecedented resolution of genealogical relationships among tested individuals, and (3) leads to the elimination of the breeding phase through the utilization of readily available wind-pollinated (OP) families. Individuals’ breeding values generated from multi-trait multi-site analysis were also used in an optimum contribution selection protocol to effectively manage genetic gain/co-ancestry trade-offs and traits’ correlated response to selection. The proof-of-concept study involved a 40-year-old spruce OP testing population growing on three sites in British Columbia, Canada, clearly demonstrating our method’s superiority in capturing most of the available genetic gains in a substantially reduced timeline relative to the traditional approach. The proposed framework is expected to increase the efficiency of existing selective breeding programs, accelerate the start of new programs for ecologically and environmentally important tree species, and address climate-change caused biotic and abiotic stress concerns more effectively.

59. Comparing pedigree and genomic relationships to control inbreeding in optimum-contribution selection restricting the number of sires in pigs

59. Comparing pedigree and genomic relationships to control inbreeding in optimum-contribution selection restricting the number of sires in pigs

Evaluation of Linear Programming and Optimal Contribution Selection Approaches for Long-Term Selection on Beef Cattle Breeding.

The optimized selection method can maximize the genetic gain in offspring under the premise of controlling the inbreeding level of the population. At present, genetic gain has been largely improved by using genomic selection in multiple farm animals. However, the design of the optimal selection method and assessment of its effects during long-term selection in beef cattle breeding are yet to be fully explored. In this study, a simulated beef cattle population was constructed, and 15 generations of simulated breeding were carried out using the linear programming breeding strategy (LP) and optimal contribution selection strategy (OCS), respectively. The truncation selection strategy (TS-I and TS-II) was used as the control. During the breeding process, genetic parameters including genetic gain, average kinship coefficient, QTL effect variance, and average observed heterozygosity were calculated and compared across generations. Our results showed that the LP method can significantly improve the genetic gain in the population, especially the genetic performance of the traits with high heritability and the traits with high weight in the breeding process, but the inbreeding level of the population is higher under LP strategy. Although the genetic gain in the population under the OCS strategy is lower than the TS-II strategy, this method can effectively control the inbreeding level of the population. Our findings also suggest that the LP and OCS method can be used as an effective means to improve genetic gain, while the OCS method is a more ideal method to obtain sustainable genetic gain during long-term selection.

Impact of kinship matrices on genetic gain and inbreeding with optimum contribution selection in a genomic dairy cattle breeding program

BackgroundGenomic selection has increased genetic gain in dairy cattle, but in some cases it has resulted in higher inbreeding rates. Therefore, there is need for research on efficient management of inbreeding in genomically-selected dairy cattle populations, especially for local breeds with a small population size. Optimum contribution selection (OCS) minimizes the increase in average kinship while it maximizes genetic gain. However, there is no consensus on how to construct the kinship matrix used for OCS and whether it should be based on pedigree or genomic information. VanRaden’s method 1 (VR1) is a genomic relationship matrix in which centered genotype scores are scaled with the sum of 2p(1-p) where p is the reference allele frequency at each locus, and VanRaden’s method 2 (VR2) scales each locus with 2p(1-p), thereby giving greater weight to loci with a low minor allele frequency. We compared the effects of nine kinship matrices on genetic gain, kinship, inbreeding, genetic diversity, and minor allele frequency when applying OCS in a simulated small dairy cattle population. We used VR1 and VR2, each using base animals, all genotyped animals, and the current generation of animals to compute reference allele frequencies. We also set the reference allele frequencies to 0.5 for VR1 and the pedigree-based relationship matrix. We constrained OCS to select a fixed number of sires per generation for all scenarios. Efficiency of the different matrices were compared by calculating the rate of genetic gain for a given rate of increase in average kinship.ResultsWe found that: (i) genomic relationships were more efficient than pedigree-based relationships at managing inbreeding, (ii) reference allele frequencies computed from base animals were more efficient compared to reference allele frequencies computed from recent animals, and (iii) VR1 was slightly more efficient than VR2, but the difference was not statistically significant.ConclusionsUsing genomic relationships for OCS realizes more genetic gain for a given amount of kinship and inbreeding than using pedigree relationships when the number of sires is fixed. For a small genomic dairy cattle breeding program, we recommend that the implementation of OCS uses VR1 with reference allele frequencies estimated either from base animals or old genotyped animals.

Assessment of long-term trends in genetic mean and variance after the introduction of genomic selection in layers: a simulation study.

Nucleus-based breeding programs are characterized by intense selection that results in high genetic gain, which inevitably means reduction of genetic variation in the breeding population. Therefore, genetic variation in such breeding systems is typically managed systematically, for example, by avoiding mating the closest relatives to limit progeny inbreeding. However, intense selection requires maximum effort to make such breeding programs sustainable in the long-term. The objective of this study was to use simulation to evaluate the long-term impact of genomic selection on genetic mean and variance in an intense layer chicken breeding program. We developed a large-scale stochastic simulation of an intense layer chicken breeding program to compare conventional truncation selection to genomic truncation selection optimized with either minimization of progeny inbreeding or full-scale optimal contribution selection. We compared the programs in terms of genetic mean, genic variance, conversion efficiency, rate of inbreeding, effective population size, and accuracy of selection. Our results confirmed that genomic truncation selection has immediate benefits compared to conventional truncation selection in all specified metrics. A simple minimization of progeny inbreeding after genomic truncation selection did not provide any significant improvements. Optimal contribution selection was successful in having better conversion efficiency and effective population size compared to genomic truncation selection, but it must be fine-tuned for balance between loss of genetic variance and genetic gain. In our simulation, we measured this balance using trigonometric penalty degrees between truncation selection and a balanced solution and concluded that the best results were between 45° and 65°. This balance is specific to the breeding program and depends on how much immediate genetic gain a breeding program may risk vs. save for the future. Furthermore, our results show that the persistence of accuracy is better with optimal contribution selection compared to truncation selection. In general, our results show that optimal contribution selection can ensure long-term success in intensive breeding programs using genomic selection.

Management of diversity and inbreeding when importing new stock into an inbred population.

This article relates to breeding programs that seek to manage genetic diversity. The method maximizes a multicomponent objective function, applicable across breeding scenarios. However, this paper focuses on breeding decisions following immigration of 10 unrelated individuals into a highly inbred simulated population (F ≈ 0.34). We use Optimal Contribution Selection to maximize retention of genetic diversity. However, some treatments add Coancestry Assortative Mating (CAM). This helps to avoid early dilution of immigrant genetic material, maximizing its ability to contribute to genetic diversity in the longer term. After 20 generations, this resulted in considerably increased genetic diversity, with mean coancestries 59% of what random pairing gave. To manage progeny inbreeding, common practice is to reject matings above an upper limit. As a suboptimal rules-based approach, this resulted in 26% decreased genetic diversity and 8% increased inbreeding in the long term, compared with random pairing. In contrast, including mean progeny inbreeding as a continuous variable in the overall objective function decreased final inbreeding by 37% compared with random pairing. Adding some emphasis on selection for a single trait resulted in a similar pattern of effects on coancestry and inbreeding, with 12% higher trait response under CAM. Results indicate the properties of alternative methods, but we encourage users to do their own investigations of particular scenarios, such as including inbreeding depression. Practical implementation of these methods is discussed: they have been widely adopted in domestic animal breeding and are highly flexible to accommodate a wide range of technical and logistical objectives and constraints.

Accuracy of Selection in Early Generations of Field Pea Breeding Increases by Exploiting the Information Contained in Correlated Traits.

Accuracy of predicted breeding values (PBV) for low heritability traits may be increased in early generations by exploiting the information available in correlated traits. We compared the accuracy of PBV for 10 correlated traits with low to medium narrow-sense heritability (h2) in a genetically diverse field pea (Pisum sativum L.) population after univariate or multivariate linear mixed model (MLMM) analysis with pedigree information. In the contra-season, we crossed and selfed S1 parent plants, and in the main season we evaluated spaced plants of S0 cross progeny and S2+ (S2 or higher) self progeny of parent plants for the 10 traits. Stem strength traits included stem buckling (SB) (h2 = 0.05), compressed stem thickness (CST) (h2 = 0.12), internode length (IL) (h2 = 0.61) and angle of the main stem above horizontal at first flower (EAngle) (h2 = 0.46). Significant genetic correlations of the additive effects occurred between SB and CST (0.61), IL and EAngle (-0.90) and IL and CST (-0.36). The average accuracy of PBVs in S0 progeny increased from 0.799 to 0.841 and in S2+ progeny increased from 0.835 to 0.875 in univariate vs MLMM, respectively. An optimized mating design was constructed with optimal contribution selection based on an index of PBV for the 10 traits, and predicted genetic gain in the next cycle ranged from 1.4% (SB), 5.0% (CST), 10.5% (EAngle) and -10.5% (IL), with low achieved parental coancestry of 0.12. MLMM improved the potential genetic gain in annual cycles of early generation selection in field pea by increasing the accuracy of PBV.

Optimal Contribution Selection Improves the Rate of Genetic Gain in Grain Yield and Yield Stability in Spring Canola in Australia and Canada.

Crop breeding must achieve higher rates of genetic gain in grain yield (GY) and yield stability to meet future food demands in a changing climate. Optimal contributions selection (OCS) based on an index of key economic traits should increase the rate of genetic gain while minimising population inbreeding. Here we apply OCS in a global spring oilseed rape (canola) breeding program during three cycles of S0,1 family selection in 2016, 2018, and 2020, with several field trials per cycle in Australia and Canada. Economic weights in the index promoted high GY, seed oil, protein in meal, and Phoma stem canker (blackleg) disease resistance while maintaining plant height, flowering time, oleic acid, and seed size and decreasing glucosinolate content. After factor analytic modelling of the genotype-by-environment interaction for the additive effects, the linear rate of genetic gain in GY across cycles was 0.059 or 0.087 t ha-1 y-1 (2.9% or 4.3% y-1) based on genotype scores for the first factor (f1) expressed in trait units or average predicted breeding values across environments, respectively. Both GY and yield stability, defined as the root-mean-square deviation from the regression line associated with f1, were predicted to improve in the next cycle with a low achieved mean parental coancestry (0.087). These methods achieved rapid genetic gain in GY and other traits and are predicted to improve yield stability across global spring canola environments.

Kontrola uzgoja u srodstvu u populaciji banijske šare svinja pomoću optimizacijskih metoda

The aim of the study was to estimate genetic diversity parameters for Banija spotted pig population and to apply optimisation methods to control the inbreeding in the population in three different scenarios. The first scenario consisted of minimizing inbreeding by minimizing the average kinship without considering breeding values. The second scenario was to minimize inbreeding by minimizing the average kinship accounting for breeding values. The third scenario was to apply traditional optimal contribution selection by maximizing the breeding value of the offspring. A total of 5 boars and 49 sows were selected to create mating plan minimise inbreeding in the next generation. The average inbreeding coefficient in the population was 6.97, while effective population size was 8.47. In the first scenario, different kinship constrains between candidates resulted in a change in the number of selected animals and a change in the average breeding values, such that the number of selected candidates increased when the constraint on kinship was stronger, with a simultaneous decrease in breeding values. The second scenario resulted in increased inbreeding when additional weight was placed on genetic gain. The Maximisation of breeding values resulted with drastically increased average inbreeding coefficient in the next generation. Due the small total and effective population size, the application of optimisation methods demonstrated that genetic improvement is possible at a high cost of loss of the genetic variability and preservation of diversity remains the first objective in the breeding programme for Banija spotted pig.

Genomic rotational crossbreeding with advanced optimum contribution selection methods applied to simulated German Angler and German Holstein dairy cattle populations.

Many local dairy cattle breeds are facing genetic extinction due to a large proportion of foreign genes, which have been introgressed in the past. In addition, the performance gap to popular high-yielding breeds is increasing, resulting in a risk of numeric extinction. In the present simulation study, a genomic rotational crossbreeding scheme with the high-yielding German Holstein breed and the numerically small German Angler breed was analysed with the aim to utilize heterosis effects in the crossbred animals. Simultaneously inbreeding was controlled, and the amount of Holstein introgression observed in the Angler breed was reduced. Different scenarios of implementing OCS methods for Angler individuals were evaluated, which differed in their restrictions regarding kinship, native kinship, as well as the amount of genetic contributions from German Holstein. The results showed that rotational crossbreeding can result in superior crossbred offspring compared to the purebred parental lines, whereby OCS methods can simultaneously restrict the increase in inbreeding and keep the Holstein contributions at their current level. However, reducing the amount of migrant contributions while restricting the increase in the native kinship in Angler turned out to be a costly restriction. The reason was that Angler with low genetic contributions from Holsteins tended to have similar Angler ancestors. Consequently, reducing Holstein contributions would considerably increase the native kinship in Angler if it were not constrained. The constraint on the native kinship made a constraint on the conventional kinship superfluous and caused it to increase at a much lower rate than envisaged. This led to both, a high genetic diversity and a low genetic gain. The high genetic diversity in Angler also resulted in lower and oscillating heterosis effects in the crossbred animals. Thus, the reduction of migrant contribution did not increase heterosis effects in the crossbred offspring, and did not result in superior crossbred offspring in general.

New cycle, same old mistakes? Overlapping vs. discrete generations in long-term recurrent selection

BackgroundRecurrent selection is a foundational breeding method for quantitative trait improvement. It typically features rapid breeding cycles that can lead to high rates of genetic gain. Usually, generations are discrete in recurrent selection, which means that breeding candidates are evaluated and considered for selection for only one cycle. Alternately, generations can overlap, with breeding candidates considered for selection as parents for multiple cycles. With recurrent genomic selection but not phenotypic selection, candidates can be re-evaluated by using genomic estimated breeding values without additional phenotyping of the candidates themselves. Therefore, it may be that candidates with true high breeding values discarded in one cycle due to underestimation of breeding value could be identified and selected in subsequent cycles. The consequences of allowing generations to overlap in recurrent selection are unknown. We assessed whether maintaining overlapping and discrete generations led to differences in genetic gain for phenotypic, genomic truncation, and genomic optimum contribution recurrent selection by stochastic simulation.ResultsWith phenotypic selection, overlapping generations led to decreased genetic gain compared to discrete generations due to increased selection error bias. Selected individuals, which were in the upper tail of the distribution of phenotypic values, tended to also have high absolute error relative to their true breeding value compared to the overall population. Without repeated phenotyping, these individuals erroneously believed to have high value were repeatedly selected across cycles, leading to decreased genetic gain. With genomic truncation selection, overlapping and discrete generations performed similarly as updating breeding values precluded repeatedly selecting individuals with inaccurately high estimates of breeding values in subsequent cycles. Overlapping generations did not outperform discrete generations in the presence of a positive genetic trend with genomic truncation selection, as individuals from previous breeding cycles typically had truly lower breeding values than candidates from the current generation. With genomic optimum contribution selection, overlapping and discrete generations performed similarly, but overlapping generations slightly outperformed discrete generations in the long term if the targeted inbreeding rate was extremely low.ConclusionMaintaining discrete generations in recurrent phenotypic selection leads to increased genetic gain, especially at low heritabilities, by preventing selection error bias. With genomic truncation selection and genomic optimum contribution selection, genetic gain does not differ between discrete and overlapping generations assuming non-genetic effects are not present. Overlapping generations may increase genetic gain in the long term with very low targeted rates of inbreeding in genomic optimum contribution selection.

Managing genetic diversity in pig populations: implications of optimal contribution selection in the Black Slavonian pig

Implications of optimal contribution selection for litter size in the Black Slavonian pig were analysed using 23,882 litter records from 6764 sows. To obtain population parameters, pedigree analysis was performed for 7367 animals. The average inbreeding coefficient was 2.87%, the average inbreeding rate was 1.96%, and the effective population size was 25.52. The average maximum number of generations traced back was 4.12 and the average number of full generations was 1.17. Selection candidates were chosen using the following criteria: animals had no culling date in the record; animals had sufficient pedigree depth and an estimated breeding value. Using these criteria, 8 boars among 24 and 386 sows among 2028 were defined as selection candidates. Two scenarios were tested. The first scenario was based on the application of the traditional optimal contribution selection with the maximisation of genetic gain and restricting the mean kinship in the offspring accounting also for breeding values. The second scenario was based on minimising inbreeding by restricting the average kinship of the population. In the first scenario, different kinship constraints between candidates resulted in changing the number of selected animals and changed the average breeding values such that the number of selected candidates increased when the kinship constraint was stronger, with a simultaneous decrease in breeding values. The second scenario resulted in increased inbreeding when the additional weight on genetic gain was added. The results of the study showed that the use of optimal contribution selection in Black Slavonian is possible, however, with additional efforts to improve pedigree and data quality. HIGHLIGHTS Genetic diversity analysis and application of optimal contribution selection in the Black Slavonian pig were analysed using pedigree. Results indicate a recent loss of genetic variability in the Black Slavonian pig. Optimal contribution selection enabled simultaneous genetic improvement and achieving conservation goals.

Optimum contribution selection (OCS) analyses prompted successful conservation actions for Faroese Horse population

The Faroese horse, an endangered indigenous horse breed, is a part of the cultural and societal heritage of the Faroe Islands. Population history describes a severe bottleneck, prompting for quantification of the genetic diversity (level of inbreeding, probability of gene origin, effective population size) and assessment of sustainable conservation potential (Optimum Contribution Selection, OCS) of the Faroese horse population. The pedigree completeness (PCI) of the Faroese horse is adequate for a realistic estimation of the level of inbreeding (PCI5 = 0.96). In concordance with the known population history, the average inbreeding is exceptionally high; in the last cohort, it was equal to 26.8%. An estimate of the effective population size, based on individual increase in inbreeding and coancestry, accounting for the whole population history, was eight. OCS offers a tool to understand and control the increase in the average relationships in the population. Within a fixed number of matings, the repetitive use of stallions resulted in the lowest level of average relationships. Successful follow-up of mating schemes planned together with a holistic assessment of the suitability of an individual as a breeding candidate, will minimize the increase in inbreeding in future generations and maximize the possibility to increase the census size of the Faroese horse population.

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.

Estimation of genetic parameters and the effect of inbreeding on dairy traits in Istrian sheep

Pedigree analysis is an important tool for assessing genetic variability and detecting heritage patterns in domestic animals. This study aimed to (1) assess genetic variability, (2) estimate genetic parameters for daily milk yield (DMY), fat content (FC), and protein content (PC), and (3) estimate the impact of inbreeding on the aforementioned dairy traits in Istrian sheep breed. The generation interval was 3.8 years. The average family size per sire and dam was 13.5 and 1.6, respectively. There were 923 female and 282 male founders (f), 105 male and 137 female effective founders (fe ), and 72 male and 95 female effective ancestors (fa ). A total of 28 sires and 34 dams explained 50% of the genetic variability. The effective number of male and female founder genomes (fne ) was 33 and 53, respectively. Low fe/f (0.37 and 0.15), high fe/fa (1.45 and 1.44), and low fne /fe (0.45 and 0.39) for males and females indicated overuse of a small number of animals in reproduction and substantial loss of founder alleles. The heritabilities of MY, FC, and PC were 0.09, 0.03, and 0.06, respectively. Genetic correlations between DMY and FC, DMY and PC, and FC and PC were −0.22, −0.27, and 0.70, respectively. Regression of phenotypes on inbreeding coefficient () within the animal genetic model revealed negligible inbreeding depression for all traits (p<.01; a 1% increase in decreased DMY, FC, and PC by 0.002 kg/day, 0.01%, and 0.002%, respectively). Regression of breeding values on was insignificant for all traits (p>.05). HIGHLIGHTS Genetic diversity, genetic parameters and the impact of inbreeding on dairy traits were estimated using pedigree information. Results indicate recent loss of genetic variability, low heritability and negligible impact of inbreeding on dairy traits. Genomic optimum contribution selection is the best solution to achieve genetic gain and slow down the loss of genetic variability in this population.

Assessment of the Genetic Diversity of a Local Pig Breed Using Pedigree and SNP Data.

Herein, the genetic diversity of the local Přeštice Black-Pied pig breed was assessed by the simultaneous analysis of the pedigree and single nucleotide polymorphism (SNP) data. The information about sire line, dam, date of birth, sex, breeding line, and herd for 1971 individuals was considered in the pedigree analysis. The SNP analysis (n = 181) was performed using the Illumina PorcineSNP60 BeadChip kit. The quality of pedigree and SNPs and the inbreeding coefficients (F) and effective population size (Ne) were evaluated. The correlations between inbreeding based on the runs of homozygosity (FROH) and pedigree (FPED) were also calculated. The average FPED for all animals was 3.44%, while the FROH varied from 10.81% for a minimum size of 1 Mbp to 3.98% for a minimum size of 16 Mbp. The average minor allele frequency was 0.28 ± 0.11. The observed and expected within breed heterozygosities were 0.38 ± 0.13 and 0.37 ± 0.12, respectively. The Ne, obtained using both the data sources, reached values around 50 animals. Moderate correlation coefficients (0.49–0.54) were observed between FPED and FROH. It is necessary to make decisions that stabilize the inbreeding rate in the long-term using optimal contribution selection based on the available SNP data.