Predicted Breeding Values Research Articles

Heritability estimates and predictive ability for pig meat quality traits using identity-by-state and identity-by-descent relationships in an F2 population.

Genomic relationships can be computed with dense genome-wide genotypes through different methods, either based on identity-by-state (IBS) or identity-by-descent (IBD). The latter has been shown to increase the accuracy of both estimated relationships and predicted breeding values. However, it is not clear whether an IBD approach would achieve greater heritability ( ) and predictive ability ( ) than its IBS counterpart for data with low-depth pedigrees. Here, we compare both approaches in terms of the estimated of and , using data on meat quality and carcass traits recorded in experimental crossbred pigs, with a pedigree constrained to only three generations. Three animal models were fitted which differed on the relationship matrix: an IBS model ( ), an IBD (defined within the known pedigree) model ( ), and a pedigree model ( ). In 9 of 20 traits, the range of increase for the estimates of and was 1.2-2.9 times greater with and models than with . Whereas for all traits, both parameters were similar between genomic models. The of the genomic models was higher compared to . A scarce increment in was found with when compared to , most likely due to the former recovering sizeable relationships among founder F0 animals.

Genetic selection of Nellore cattle raised in tropical areas: Economic indexes and breeding decisions risks

Different selection indexes (Index I [Optimal], Index II [Maternal], and Index III [Terminal]) were evaluated in a full-cycle, pasture-based production system of Nellore cattle. The economic selection indexes consisted of combinations of the traits: body weight at 120 (W120), 210 (W210), 365 (W365), and 450 days (W450) of age; age at first calving (AFC); calving interval (CI); scrotal circumference at 365 (SC365) and 450 (SC450) days of age; ribeye area (REA); subcutaneous fat thickness (FAT); stayability (STAY); and accumulated productivity (ACP). Bootstrap analyses were performed to assess the distribution of the response to the optimal index by changes in the economic value of traits and the impact caused by each economic trait. The implementation of the optimal index and the adjustment of the expected return for uncertainty in the prediction of breeding values were carried out with 67 sires. The most genetic-economic important traits were W450, AFC, ACP, and REA. Index I showed the greatest economic response to selection (R$ 938.95/generation), followed by Indexes II (R$ 811.84) and III (R$ 476.26). Economic response variations of Index I ranged from R$ 100.71 to R$ 1747.43. The highest contributions and range of economic response were observed in ACP, followed by W450, and CI. Sires ranking changed ± 4.0 positions with adjustment for risk in Index I, indicating changes in breeding decisions when selecting sires. All indexes presented great efficiency and can be adapted to similar production scenarios that expect a similar overall gain. Adjustment for risk in the selection index should be considered for optimal breeding decisions.

Impact and utility of shallow pedigree using single-step genomic BLUP for prediction of unbiased genomic breeding values.

In unstructured dairy programs, pedigree is usually shallow, which leads to biased prediction of breeding values using best linear unbiased prediction (BLUP). The objective of this study was to come out with a genomic prediction strategy that can utilize shallow pedigree information and predict unbiased and more accurate GEBV for sex-limited traits in a small population using single-step GBLUP (ssGBLUP). The data and models for a population under selection were simulated. Out of current 10 generations, 10th generation with 1000 candidates served as validation population. For the complete pedigree scenario, pedigree (P)BLUP estimated breeding values (EBV) were unbiased with accuracy (r) of 0.35 ± 0.02 and 0.26 ± 0.01 for 0.3 and 0.1 h2 scenario, respectively. For the shallow pedigree, biased prediction of breeding values and low accuracies were obtained with linear decline in the accuracy of EBV for removal of information on more distant pedigree. Accuracy and bias (ρ) for scenario with removing 4 distant generations from pedigree were 0.30 ± 0.02 and 0.55 ± 0.03, respectively, in moderate h2 scenario. Use of Genomic (G)BLUP, especially with "extreme phenotypic contrast selective genotyping," (TB) resulted in higher accuracy for a small reference of females; however, GEBV were highly biased. We observed that ssGBLUPF, where the numerator relationship matrix is corrected for inbreeding, resulted in more accurate and unbiased estimates of GEBV across shallow pedigree scenario, with TB all female reference (missing 4 distant generations: r = 0.50 ± 0.02; ρ = 0.96 ± 0.02). We recommend use of ssGBLUPF with two tailed selectively genotyped all female reference in shallow pedigree scenarios, to obtain unbiased and accurate GEBV for sex-limited traits, when resources are limited.

Leveraging spatiotemporal genomic breeding value estimates of dry matter yield and herbage quality in ryegrass via random regression models.

Joint modeling of correlated multienvironment and multiharvest data of perennial crop species may offer advantages in prediction schemes and a better understanding of the underlying dynamics in space and time. The goal of the present study was to investigate the relevance of incorporating the longitudinal dimension of within-season multiple measurements of forage perennial ryegrass (Lolium perenne L.) traits in a reaction-norm model setup that additionally accounts for genotype × environment (G × E) interactions. Genetic parameters and accuracy of genomic estimated breeding value (gEBV) predictions were investigated by fitting three genomic random regression models (gRRMs) using Legendre polynomial functions to the data. Genomic DNA sequencing of family pools of diploid perennial ryegrass was performed using DNA nanoball-based technology and yielded 56,645 single-nucleotide polymorphisms, which were used to calculate the allele frequency-based genomic relationship matrix. Biomass yield's estimated additive genetic variance and heritability values were higher in later harvests. The additive genetic correlations were moderate to low in early measurements and peaked at intermediates with fairly stable values across the environmental gradient except for the initial harvest data collection. This led to the conclusion that complex (G × E) arises from spatial and temporal dimensions in the early season with lower reranking trends thereafter. In general, modeling the temporal dimension with a second-order orthogonal polynomial improved the accuracy of gEBV prediction for nutritive quality traits, but no gain in prediction accuracy was detected for dry matter yield (DMY). This study leverages the flexibility and usefulness of gRRM models for perennial ryegrass breeding and can be readily extended to other multiharvest crops.

Polymorphism in β-lactoglobulin gene and its association with dairy goats’ production traits

In the dairy industry, producing milk with greater contents of fat and protein is an important goal. In addition to the phenotypic selection, it is necessary to develop a genetic profile with several polymorphisms located at genes associated with greater percentages of fat and protein in goat milk. These polymorphisms can be used in marker-assisted selection (MAS) for the productive traits of dairy goats. We assessed two polymorphisms already described in the gene that encodes the β-lactoglobulin protein (BLG gene) in goats. One is located at the 5′ promoter region of the gene and the other one is located at the exon 7. Besides that, we associated these polymorphisms with productive characteristics of milk from animals of Saanen and Alpine breeds. A sample of 130 Saanen, Alpine and crossbreed goats were randomly chosen to be genotyped for polymorphisms in the BLG gene for PCR-RFLP. To estimate the predicted breeding values, the REMLF90 application was used, and the association between the polymorphism and the predicted breeding value from the animals. In the promoter region, the single nucleotide polymorphism (SNP) at position − 60 (C/T) was associated with the percentage of milk proteins, and the TT genotype presented higher breeding values for the protein percentage. We found in the exon 7 region, two very close polymorphisms, a base substitution at position + 4601 (S1/S2) and a variable number tandem repeat (VNTR) at position + 4641 (I2/I3). The S1S2 genotype showed a higher percentage of milk fat and dry extract when compared to the S2S2 genotype, but showed no difference compared to the S1S1 for the same trait. Our results pointed out these two polymorphisms, present in the beta-lactoglobulin gene, as candidates to be used in breeding programs to increase both the protein and fat contents of goat milk. Validation tests, however, must be done through genotyping a larger sample of animals

Adding gene transcripts into genomic prediction improves accuracy and reveals sampling time dependence.

Recent developments allowed generating multiple high-quality 'omics' data that could increase the predictive performance of genomic prediction for phenotypes and genetic merit in animals and plants. Here, we have assessed the performance of parametric and nonparametric models that leverage transcriptomics in genomic prediction for 13 complex traits recorded in 478 animals from an outbred mouse population. Parametric models were implemented using the best linear unbiased prediction, while nonparametric models were implemented using the gradient boosting machine algorithm. We also propose a new model named GTCBLUP that aims to remove between-omics-layer covariance from predictors, whereas its counterpart GTBLUP does not do that. While gradient boosting machine models captured more phenotypic variation, their predictive performance did not exceed the best linear unbiased prediction models for most traits. Models leveraging gene transcripts captured higher proportions of the phenotypic variance for almost all traits when these were measured closer to the moment of measuring gene transcripts in the liver. In most cases, the combination of layers was not able to outperform the best single-omics models to predict phenotypes. Using only gene transcripts, the gradient boosting machine model was able to outperform best linear unbiased prediction for most traits except body weight, but the same pattern was not observed when using both single nucleotide polymorphism genotypes and gene transcripts. Although the GTCBLUP model was not able to produce the most accurate phenotypic predictions, it showed the highest accuracies for breeding values for 9 out of 13 traits. We recommend using the GTBLUP model for prediction of phenotypes and using the GTCBLUP for prediction of breeding values.

Genetic parameters and accuracy of traditional and genomic breeding values for resistance to infectious bovine keratoconjunctivitis in Hereford

The objectives of this research were to estimate genetic parameters using a threshold animal model by traditional (pedigree and phenotype), single-step genomic BLUP (ssGBLUP) and Weighted ssGBLUP (WssGBLUP) methods, and assess the relative increase in accuracy with the addition of genomic information on the estimation of breeding values for the incidence of infectious bovine keratoconjunctivitis (IBK) in Hereford cattle. The data used in this study were obtained from animals belonging to four farms of Delta G breeders association and genetically evaluated by the Delta G breeding program, born between 2015 and 2017, totaling 293,716 individuals in the pedigree data and 1,579 phenotypes for IBK (1 = absence of disease or 2 = presence of disease). The genotypic data, after quality control, retained 37,505 SNP markers and 1,401 samples. A Bayesian framework and a threshold animal model were used to estimate variance components and to predict breeding values. Animals with genotype and phenotype for the traits studied were divided into 3 groups by random and k-means clustering cross-validation strategies. The estimate of direct heritability for resistance to IBK was 0.12 ± 0.01 and 0.10 ± 0.01 through PBLUP and ssGBLUP methods. The mean realized prediction accuracies based on linear regression statistics were 0.36, 0.65 and 0.70, respectively for PBLUP, ssGBLUP and WssGBLUP; therefore, the use of the genomic relationship matrices was superior to the pedigree-based method. When using the WssGBLUP method, there was an additional increase in prediction accuracy when compared to the ssGBLUP method, which was already substantially better than PBLUP. The prediction of breeding values using genomic information by the single-step methods should result in more accurate selection decisions compared to traditional pedigree evaluations and, consequently, genomic selection can be readily applied with moderately high reliability to help reduce the incidence of IBK in Hereford cattle.

Causal inference for the covariance between breeding values under identity disequilibrium

BackgroundThe covariance matrix of breeding values is at the heart of prediction methods. Prediction of breeding values can be formulated using either an “observed” or a theoretical covariance matrix, and a major argument for choosing one or the other is the reduction of the computational burden for inverting such a matrix. In this regard, covariance matrices that are derived from Markov causal models possess properties that deliver sparse inverses.ResultsBy using causal Markov models, we express the breeding value of an individual as a linear regression on ancestral breeding values, plus a residual term, which we call residual breeding value (RBV). The latter is a noise term that accounts for the uncertainty in prediction due to lack of fit of the linear regression. A notable property of these models is the parental Markov condition, through which the multivariate distribution of breeding values is uniquely determined by the distribution of the mutually independent RBV. Animal breeders have long been relying on a causal Markov model, while using the additive relationship matrix as the covariance matrix structure of breeding values, which is calculated assuming gametic equilibrium. However, additional covariances among breeding values arise due to identity disequilibrium, which is defined as the difference between the covariance matrix under the multi-loci probability of identity-by-descent (varvec{Sigma}) and its expectation under gametic phase equilibrium, i.e., A. The disequilibrium term varvec{Sigma}−A is considered in the model for predicting breeding values called the “ancestral regression” (AR), a causal Markov model. Here, we introduce the “ancestral regression to parents” (PAR) causal Markov model, which reduces the computational burden of the AR approach. By taking advantage of the conditional independence property of the PAR Markov model, we derive covariances between the breeding values of grandparents and grand-offspring and between parents and offspring. In addition, we obtain analytical expressions for the covariance between collateral relatives under the PAR model, as well as for the inbreeding coefficient.ConclusionsWe introduced the causal PAR Markov model that captures identity disequilibrium in the covariances among breeding values and produces a sparse inverse covariance matrix to build and solve a set of mixed model equations.

Neural network and Bayesian-based prediction of breeding values in Beetal goat.

The estimation of breeding values is prime concern for animal breeders in order to achieve desired genetic progress of farm animals. However, current methods for estimating BV involve simultaneous selection of animal model which are computationally intensive and time-consuming. The present attempt was made to predict breeding values of weaning trait under artificial neural networks (ANN), Bayesian technique (BT), and multiple regression (MR) methods. The data records comprising year of birth, sex, type of birth, dam's weight at kidding, birth weight, weaning weight, and estimated breeding values (BV) for weaning weight (under animal model) pertaining to 849 kids born to 37 sires and 237 dams between 2004 and 2019 were used in this study. All three methods, viz., ANN under multilayer perceptron (2 hidden layers), BT under Markov chain Monte Carlo (MCMC) approach, and MR under full model, were used for 75% training dataset initially and prediction model developed was applied on 25% test dataset. The initial analysis showed positive and significant (P < 0.01) relationship of BV with other variables which hinted that BV may be predicted with accuracy. Then, it was revealed from the results indicated that ANN, BT, and MR methods have similar accuracy (r = 0.86 to 0.87) for prediction of BV. However, ANN showed slightly higher but negligible model adequacy than BT and MR method. The prediction error under three methods was almost equal. The results indicated that these methods could be used as potential alternative for recurrent prediction of BV based on phenotypic data in order to optimize selection plans at young age in resourced population of Beetal goat.

Elucidating genotype by environment interaction over the growth trajectory of Malpura sheep in the semi-arid region of India through Random regression model

The Presence of genotype by environment (GxE) interaction over different ages across growth trajectory in different environments leads to alteration in sires ranking, it may cause problem in any breeding program by affecting efficiency of selection. The objective of the present study was to compare the random regression models (RRM) and conventional animal models and also to plot the trajectory of the growth curve using Legendre polynomial (LP) function of the RRM for better genetic evaluation for growth traits of the Malpura sheep. The data was collected from 8299 animals descended from 2529 dams and 525 sires for growth traits of Malpura sheep, selected over 45 years (1975–2019) maintained at the Animal Genetics and Breeding Division, ICAR-Central Sheep and Wool Research Institute (CSWRI), Avikanagar, Rajasthan, India. The Legendre polynomial based random regression models (LP-RRM) was more robust as compared to conventional animal model and it included direct genetic, direct maternal, maternal permanent environmental and animal permanent environmental effects as a random effects in different orders of fit. The estimates for the additive direct heritability (h2) using best LP-RRM (4444) were 0.176 ± 0.022, 0.367 ± 0.024, 0.314 ± 0.021, 0.323 ± 0.023 and 0.314 ± 0.023, respectively for live body weight at birth (BWT), three-month (WWT), six-month (6WT), nine-month (9WT) and twelve-month (12WT). The comparable trends of variances were found in LP-RRM with trends obtained from the inclusive univariate animal model that included the direct genetic, direct maternal and maternal permanent environmental components. The h2 estimates indicated the further scope of genetic improvement through selection. The maternal proportion of variance was accounting for 1–15 % variance across growth trajectory indicating the low influence of maternal effects that reduced significantly post-weaning. However, we observed that the animal permanent environment accounted for 42–58 % of variance across growth trajectory. The estimates of genetic correlation in LP-RRM were positive and higher (mostly >0.80) between most of the time points as compared to the estimates obtained from animal model, indicating less GxE when RRM is used. Currently selection of Malpura sheep is carried out at the age of six month for growth traits. The presence of genotype by environment (GxE) interaction resulted in to significant re-ranking of sires at 12WT on the basis of breeding values using animal model. However, more consistency in sires ranking were found in the reaction norms plotted for breeding values at 6WT and 12WT using LP-RRM. The RRM have property for accounting (GxE) interaction across growth trajectories more precisely. Therefore, it is recommended that the RRM approach should be used for genetic evaluation of Malpura sheep at 6-month age for unbiased, accurate, and consistent prediction of breeding values for growth traits.

Accounting for population structure in genomic predictions of Eucalyptus globulus.

Genetic groups have been widely adopted in tree breeding to account for provenance effects within pedigree-derived relationship matrices. However, provenances or genetic groups have not yet been incorporated into single-step genomic BLUP ("HBLUP") analyses of tree populations. To quantify the impact of accounting for population structure in Eucalyptus globulus, we used HBLUP to compare breeding value predictions from models excluding base population effects and models including either fixed genetic groups or the marker-derived proxies, also known as metafounders. Full-sib families from 2 separate breeding populations were evaluated across 13 sites in the "Green Triangle" region of Australia. Gamma matrices (Γ) describing similarities among metafounders reflected the geographic distribution of populations and the origins of 2 land races were identified. Diagonal elements of Γ provided population diversity or allelic covariation estimates between 0.24 and 0.56. Genetic group solutions were strongly correlated with metafounder solutions across models and metafounder effects influenced the genetic solutions of base population parents. The accuracy, stability, dispersion, and bias of model solutions were compared using the linear regression method. Addition of genomic information increased accuracy from 0.41 to 0.47 and stability from 0.68 to 0.71, while increasing bias slightly. Dispersion was within 0.10 of the ideal value (1.0) for all models. Although inclusion of metafounders did not strongly affect accuracy or stability and had mixed effects on bias, we nevertheless recommend the incorporation of metafounders in prediction models to represent the hierarchical genetic population structure of recently domesticated populations.

Estimating Heritabilities and Breeding Values From Censored Phenotypes Using a Data Augmentation Approach.

Time-dependent traits are often subject to censorship, where instead of precise phenotypes, only a lower and/or upper bound can be established for some of the individuals. Censorship reduces the precision of phenotypes but can represent compromise between measurement cost and animal ethics considerations. This compromise is particularly relevant for genetic evaluation because phenotyping initiatives often involve thousands of individuals. This research aimed to: 1) demonstrate a data augmentation approach for analysing censored phenotypes, and 2) quantify the implications of phenotype censorship on estimation of heritabilities and predictions of breeding values. First, we simulated uncensored phenotypes, representing fine-scale “age at puberty” for each individual in a population of some 5,000 animals across 50 herds. Analysis of these uncensored phenotypes provided a gold-standard control. We then produced seven “test” phenotypes by superimposing varying degrees of left, interval, and/or right censorship, as if herds were measured on only one, two or three occasions, with a binary measure categorized for animals at each visit (either pre or post pubertal). We demonstrated that our estimates of heritabilities and predictions of breeding values obtained using a data augmentation approach were remarkably robust to phenotype censorship. Our results have important practical implications for measuring time-dependent traits for genetic evaluation. More specifically, we suggest that data collection can be designed with relatively infrequent repeated measures, thereby reducing costs and increasing feasibility across large numbers of animals.

Generating Douglas-fir Breeding Value Estimates Using Airborne Laser Scanning Derived Height and Crown Metrics.

Progeny test trials in British Columbia are essential in assessing the genetic performance via the prediction of breeding values (BVs) for target phenotypes of parent trees and their offspring. Accurate and timely collection of phenotypic data is critical for estimating BVs with confidence. Airborne Laser Scanning (ALS) data have been used to measure tree height and structure across a wide range of species, ages and environments globally. Here, we analyzed a Coastal Douglas-fir [Pseudotsuga menziesii var. menziesii (Mirb.)] progeny test trial located in British Columbia, Canada, using individual tree high-density Airborne Laser Scanning (ALS) metrics and traditional ground-based phenotypic observations. Narrow-sense heritability, genetic correlations, and BVs were estimated using pedigree-based single and multi-trait linear models for 43 traits. Comparisons of genetic parameter estimates between ALS metrics and traditional ground-based measures and single- and multi-trait models were conducted based on the accuracy and precision of the estimates. BVs were estimated for two ALS models (ALSCAN and ALSACC) representing two model-building approaches and compared to a baseline model using field-measured traits. The ALSCAN model used metrics reflecting aspects of vertical distribution of biomass within trees, while ALSACC represented the most statistically accurate model. We report that the accuracy of both the ALSCAN (0.8239) and ALSACC (0.8254) model-derived BVs for mature tree height is a suitable proxy for ground-based mature tree height BVs (0.8316). Given the cost efficiency of ALS, forest geneticists should explore this technology as a viable tool to increase breeding programs’ overall efficiency and cost savings.

Comparison of Two Methods of Predicting 305-day Milk Yield for Genetic Evaluation to Design a Tunisian Holstein Reference Population

Various standardized milk yield prediction methods have been developed and used. The objective of this study was to compare two methods for the estimation of 305-day milk yield inthe Holstein breed, in terms of breeding values and their accuracy. Genetic evaluations of milk yield were compared using: 1) adjusted total lactation yield for days in milk, month, and age at calving (adjusted TY305) or 2) adjusted305-day milk yieldestimated by fitting test-day(TD) records to the Wood model(adjusted WY305).The method with better ability to predict standardized milk yield was used to identify a Tunisian cow reference population toward genomic evaluation of milk trait. Three datasets were used. The first data contains 380’709 TD records corresponding to 34’281 three first lactations of 20’758 cows collected between 2008 and 2018 in 33 herds. The second dataset contains 11’175 total first three lactation yields recorded between 2012 and 2017 from 6251 cows belonging to 33 herds.The third data is a pedigree file of 27’487 males and females. The predictive ability of the two methods was assessed by Spearman’s rank correlation between predicted breeding values for 305-day milk yield (PBV305) from the full dataset and reduced dataset in which the records from the last calving year were masked. The two methods were compared in terms of rank correlation between PBV305 and the percentage of selected animals in common when different selection intensities were applied based on PBV305.The average gain in accuracy was calculated and a Tunisian reference population was identified. The results showed that heritability estimates were 0.11 (±0.02) and 0.13 (±0.01) for adjusted WY305 and TY305, respectively. The highest correlation for PBVs between full data and reduced data was achieved in TY305 dataset. Rank correlations between PBV305 estimated for adjusted WY305 and TY305 were 0.67. The percentage of animals selected in common was 11% or 21%, respectively, when 1 or 5% of cows were chosen as future dams of bulls, according to PBVs. An average gain in accuracy of 15% was observed for cows when using adjusted TY305 to estimate PBVs for milk yield trait. The obtained results showed that adjustments applied to the total milk yield records could be appropriate for 305-day milk yield prediction and genetic evaluation of milk production in the Tunisian Holstein population. Based on two main designs (extreme yield and top accuracy), a total of 1000 cows were selected to form the Tunisian female reference population using adjusted TY305 records.

Artificial Neural Networks- An Introduction and Application in Animal Breeding and Production: A Review

With the advent of newer methods and tools for accurate analysis, the outcomes of researches have become increasingly precise. This increase in reliability of research findings and forecasting is well demanded in various fields of science including those under animal breeding as well as its allied aspects such as production. One such recent breakthrough is an artificial neural network (ANN), which is a system of hardware and/or software programmed to function like the operation of neurons in the human brain. ANNs are a variety of deep learning technology, which also falls under the umbrella of artificial intelligence. The determination of milk yield, quality of meat animals, predicting breeding values, inferring demography and recombination, genome-enabled prediction are few of the sectors where ANN has proved to be a valuable asset. The correlation coefficient between the experimental values and those predicted with the help of ANN was found to be close to 1. These studies were a significant indication of the impactful ability of ANN. The early selection of superior bulls, culling of low performing ones, frequent prediction of breeding values and taking into account the non linearity encountered in biological research are few of the many benefits of using ANN in researches of animal genetics and production. Hence, considering its prodigious role, this current review has been written to highlight the role and impact of ANN in livestock studies.

Genetic Characterization and Population Connectedness of North American and European Dairy Goats.

Genomic prediction of breeding values is routinely performed in several livestock breeding programs around the world, but the size of the training populations and the genetic structure of populations evaluated have, in many instances, limited the increase in the accuracy of genomic estimated breeding values. Combining phenotypic, pedigree, and genomic data from genetically related populations can be a feasible strategy to overcome this limitation. However, the success of across-population genetic evaluations depends on the pedigree connectedness and genetic relationship among individuals from different populations. In this context, this study aimed to evaluate the genetic connectedness and population structure of Alpine and Saanen dairy goats from four countries involved in the European project SMARTER (SMAll RuminanTs Breeding for Efficiency and Resilience), including Canada, France, Italy, and Switzerland. These analyses are paramount for assessing the potential feasibility of an across-country genomic evaluation in dairy goats. Approximately, 9,855 genotyped individuals (with 51% French genotyped animals) and 6,435,189 animals included in the pedigree files were available across all four populations. The pedigree analyses indicated that the exchange of breeding animals was mainly unilateral with flows from France to the other three countries. Italy has also imported breeding animals from Switzerland. Principal component analyses (PCAs), genetic admixture analysis, and consistency of the gametic phase revealed that French and Italian populations are more genetically related than the other dairy goat population pairs. Canadian dairy goats showed the largest within-breed heterogeneity and genetic differences with the European populations. The genetic diversity and population connectedness between the studied populations indicated that an international genomic evaluation may be more feasible, especially for French and Italian goats. Further studies will investigate the accuracy of genomic breeding values when combining the datasets from these four populations.

Genetic parameters and selection in full-sib families of tall fescue using best linear unbiased prediction (BLUP) analysis

BackgroundBetter understanding of genetic structure of economic traits is crucial for identification and selection of superior genotypes in specific breeding programs. Best linear unbiased prediction (BLUP) is the most efficient method in this regard, which is poorly used in forage plant breeding. The present study aimed to assess genetic variation, estimate genetic parameters, and predict breeding values of five essential traits in full sib families (recognized by EST-SSR markers) of tall fescue using REML/BLUP procedure.MethodForty-two full-sib families of tall fescue (included of 120 individual genotypes), recognized by EST-SSR markers along with twenty-one their corresponding parental genotypes were assessed for biomass production and agro-morphological traits at three harvests (spring, summer, and autumn) in the field during 4 years (2017–2020).ResultsConsiderable genotypic variability was observed for all traits. Low narrow-sense heritability (h2n) for dry forage yield (DFY) at three harvest indicates that non-additive gene actions may play an important role in the inheritance of this trait. Higher h2n of yield related traits and flowering time and also significant genetic correlation of these traits with forage yield, suggests that selection based on these traits may lead to indirect genetic improvement of DFY.ConclusionOur results showed the adequacy of REML/BLUP procedure for identification and selection of preferable parental genotypes and progenies with higher breeding values for future breeding programs such as variety development in tall fescue. Parental genotypes 21 M, 1 M, and 20 L were identified as superior and stable genotypes and could also produce the best hybrid combinations when they were mostly used as maternal parent.

Rare and population-specific functional variation across pig lines

BackgroundIt is expected that functional, mainly missense and loss-of-function (LOF), and regulatory variants are responsible for most phenotypic differences between breeds and genetic lines of livestock species that have undergone diverse selection histories. However, there is still limited knowledge about the existing missense and LOF variation in commercial livestock populations, in particular regarding population-specific variation and how it can affect applications such as across-breed genomic prediction.MethodsWe re-sequenced the whole genome of 7848 individuals from nine commercial pig lines (average sequencing coverage: 4.1×) and imputed whole-genome genotypes for 440,610 pedigree-related individuals. The called variants were categorized according to predicted functional annotation (from LOF to intergenic) and prevalence level (number of lines in which the variant segregated; from private to widespread). Variants in each category were examined in terms of their distribution along the genome, alternative allele frequency, per-site Wright’s fixation index (FST), individual load, and association to production traits.ResultsOf the 46 million called variants, 28% were private (called in only one line) and 21% were widespread (called in all nine lines). Genomic regions with a low recombination rate were enriched with private variants. Low-prevalence variants (called in one or a few lines only) were enriched for lower allele frequencies, lower FST, and putatively functional and regulatory roles (including LOF and deleterious missense variants). On average, individuals carried fewer private deleterious missense alleles than expected compared to alleles with other predicted consequences. Only a small subset of the low-prevalence variants had intermediate allele frequencies and explained small fractions of phenotypic variance (up to 3.2%) of production traits. The significant low-prevalence variants had higher per-site FST than the non-significant ones. These associated low-prevalence variants were tagged by other more widespread variants in high linkage disequilibrium, including intergenic variants.ConclusionsMost low-prevalence variants have low minor allele frequencies and only a small subset of low-prevalence variants contributed detectable fractions of phenotypic variance of production traits. Accounting for low-prevalence variants is therefore unlikely to noticeably benefit across-breed analyses, such as the prediction of genomic breeding values in a population using reference populations of a different genetic background.

Average semivariance directly yields accurate estimates of the genomic variance in complex trait analyses.

Many important traits in plants, animals, and microbes are polygenic and challenging to improve through traditional marker-assisted selection. Genomic prediction addresses this by incorporating all genetic data in a mixed model framework. The primary method for predicting breeding values is genomic best linear unbiased prediction, which uses the realized genomic relationship or kinship matrix (K) to connect genotype to phenotype. Genomic relationship matrices share information among entries to estimate the observed entries’ genetic values and predict unobserved entries’ genetic values. One of the main parameters of such models is genomic variance (), or the variance of a trait associated with a genome-wide sample of DNA polymorphisms, and genomic heritability (); however, the seminal papers introducing different forms of K often do not discuss their effects on the model estimated variance components despite their importance in genetic research and breeding. Here, we discuss the effect of several standard methods for calculating the genomic relationship matrix on estimates of and . With current approaches, we found that the genomic variance tends to be either overestimated or underestimated depending on the scaling and centering applied to the marker matrix (Z), the value of the average diagonal element of K, and the assortment of alleles and heterozygosity (H) in the observed population. Using the average semivariance, we propose a new matrix, , that directly yields accurate estimates of and in the observed population and produces best linear unbiased predictors equivalent to routine methods in plants and animals.

191 Improving Climatic Resilience in Pigs Through Physiological Genomics

Abstract Breeding programs have improved productive and reproductive efficiency in worldwide swine populations. However, greater performance is often accompanied by reduced climatic resilience due to greater metabolic heat production. Genomic selection is an effective tool for alleviating the negative effects of heat stress, but it requires traits that effectively capture the physiological, behavioral, and morphological mechanisms regulating the heat stress response. In this presentation we will first describe the genetic background of heat tolerance based on routinely recorded phenotypes (growth, carcass composition, and reproduction) and climatic variables obtained from public-weather-station databases. There are clear genotype-by-environment interactions across environmental conditions and the definition of the critical periods for quantifying the environmental gradient directly impacts the accuracy of genomic prediction of breeding values for heat tolerance. Secondly, we will describe the genetic background of various behavioral, physiological, and morphological indicators of heat stress in maternal line pigs, including automatically recorded vaginal temperature, skin temperature measured in different body locations and times of the day, respiration rate, panting score, body condition score, hair cortisol, hair density, and ear area. These traits are heritable and can be successfully included in genomic breeding programs. Furthermore, we will discuss the transgenerational effects of heat stress in maternal-line pigs, including an epigenomic analyses of in-utero heat stress that identified over 200 genomic regions that are differentially methylated. In summary, we will: 1) provide a comprehensive description of the environmental-gradient variables and critical periods fitted in genomic evaluations of heat tolerance; 2) describe the genetic background of various indicators of heat tolerance, including heritability, genetic correlations, candidate genes, differentially-methylated regions, and metabolic pathways associated with the significant genomic regions; 3) present the accuracies of genomic predictions for each indicator trait; and, 5) make recommendations for the implementation of genomic selection for improved heat tolerance in pigs. This research was supported by the Agriculture and Food Research Initiative (AFRI) Competitive Grants number 2020-67015-31575 and 2021-67015-34458 from the USDA National Institute of Food and Agriculture.