Predicted Breeding Values Research Articles

Correlations of goat milk coagulation properties between dams and daughters

Goat milk coagulation properties (gMCP) are important trait for the cheesemaking industry but are rarely included in national breeding programs. In the present study, we monitored goat milk quality traits and gMCP of Slovenian Alpine goats within a single flock (N = 77), reared in the same common environment over three-year period. In addition, using pedigree data, we looked for correlations among dams, daughters (N = 32 pairs) and gMCP. Traditional gMCP measurements were introduced into a 4-parameter curd firmness over time (CFt) model. Several gMCP parameters were associated with parity and stage of lactation. Most notably, estimated rennet coagulation time (RCTeq), curd firmness at 15 (a15eq) and 20 min (a20eq) after rennet addition were strongly correlated between dams and their daughters. These results support further research of genetic parameters for gMCP and the inclusion of breeding value predictions for gMCP in national breeding programs.

Genetic Control of Pitch Canker Response in Southern Pine and Southern Pine Hybrids

Fusarium circinatum causes pine pitch canker (PPC) disease and associated symptoms such as resinous lesions, mechanical weakness, and crown dieback that may lead to mortality in Pinus and Pseudotsuga spp. There are no ameliorative techniques available for the disease, and the genetic resistance among populations to support commercial plantation deployment has not been well characterized. In this study, we characterize the genetic control of PPC disease tolerance (and/or resistance) and predict the tolerance of families in existing breeding populations: open-pollinated (OP) half-sib and control-pollinated full-sib (FS) slash pine (Pinus elliottii var. elliottii Engelm.), OP loblolly pine (Pinus taeda L.), and advanced-generation OP hybrid slash × P. caribaea (Pinus elliottii var. elliottii Engelm. × Pinus caribaea var. bahamensis, caribaea, and hondurensis) using F. circinatum isolates obtained from three locations in Georgia and FL, USA. We describe a new experimental design that improves the accuracy of breeding value predictions, provides more precise genetic parameter estimates, and facilitates comparisons within and among taxa as well as comparisons among isolates. We found strong evidence for genetic control of the ratio of stem damage by F. circinatum, especially in slash pine and slash × P. caribaea hybrids. Loblolly and slash × P. caribaea hybrids exhibited less damage than slash pine. We observed a spectrum of virulence among F. circinatum isolate sources, which were not equally virulent in different pine taxa.

Training set designs for prediction of yield and moisture of maize test cross hybrids with unreplicated trials.

Unreplicated field trials and genomic prediction are both used to enhance the efficiency in early selection stages of a hybrid maize breeding program. No results are available on the optimal experimental design when combining both approaches. Our objectives were to investigate the effect of the training set design on the accuracy of genomic prediction in unreplicated maize test crosses. We carried out a cross validation study on basis of an experimental data set consisting of 1436 hybrids evaluated for yield and moisture for which genotyping information of 461 SNP markers were available. Training set designs of different size, implementing within environment prediction, within year prediction, across year prediction, and combinations of data sources across years and environments were compared with respect to their prediction accuracy. Across year prediction did not reach prediction accuracies that are useful for genomic selection. Within year prediction across environments provided useful correlations between observed and predicted breeding values. The prediction accuracies did not improve when adding to the training set data from previous years. We conclude that using all data available from unreplicated tests of the current breeding cycle provides a good accuracy of predicting test crosses, whereas adding data from previous breeding cycles, in which the genotypes are less related to the tested material, has only limited value for increasing the prediction accuracy.

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.

Reliabilities of estimated breeding values in models with metafounders

BackgroundReliabilities of best linear unbiased predictions (BLUP) of breeding values are defined as the squared correlation between true and estimated breeding values and are helpful in assessing risk and genetic gain. Reliabilities can be computed from the prediction error variances for models with a single base population but are undefined for models that include several base populations and when unknown parent groups are modeled as fixed effects. In such a case, the use of metafounders in principle enables reliabilities to be derived.MethodsWe propose to compute the reliability of the contrast of an individual’s estimated breeding value with that of a metafounder based on the prediction error variances of the individual and the metafounder, their prediction error covariance, and their genetic relationship. Computation of the required terms demands only little extra work once the sparse inverse of the mixed model equations is obtained, or they can be approximated. This also allows the reliabilities of the metafounders to be obtained. We studied the reliabilities for both BLUP and single-step genomic BLUP (ssGBLUP), using several definitions of reliability in a large dataset with 1,961,687 dairy sheep and rams, most of which had phenotypes and among which 27,000 rams were genotyped with a 50K single nucleotide polymorphism (SNP) chip. There were 23 metafounders with progeny sizes between 100,000 and 2000 individuals.ResultsIn models with metafounders, directly using the prediction error variance instead of the contrast with a metafounder leads to artificially low reliabilities because they refer to a population with maximum heterozygosity. When only one metafounder is fitted in the model, the reliability of the contrast is shown to be equivalent to the reliability of the individual in a model without metafounders. When there are several metafounders in the model, using a contrast with the oldest metafounder yields reliabilities that are on a meaningful scale and very close to reliabilities obtained from models without metafounders. The reliabilities using contrasts with ssGBLUP also resulted in meaningful values.ConclusionsThis work provides a general method to obtain reliabilities for both BLUP and ssGBLUP when several base populations are included through metafounders.

The effect of new and ancestral inbreeding on milk production traits in Iranian Holstein cattle.

Inbreeding depression, the reduction of fitness and performance, is due to an increase in the mating of related individuals. Based on the purge hypothesis, inbreeding and breeding over generations reduce the effect of deleterious alleles responsible for inbreeding depression. Thus, recent inbreeding is assumed to be more harmful than ancestral inbreeding. This study aimed at evaluating the effects of new and ancestral inbreeding on milk, fat and protein production in Iranian Holstein cattle. The secondary objective was to examine the changes in predicted breeding values when the inbreeding effect was included in the model's analysis. To this end, inbreeding coefficients were calculated using the pedigree of 2,394,517 Holstein cattle to achieve these goals. In addition, 419,132 records of milk, fat and protein yields of first parity cows were collected to assess inbreeding depression and breeding values. The average inbreeding coefficients were 0.83% and 1.68% for the whole population and the inbred animals, respectively. A 1% increase in classical pedigree-based inbreeding coefficient was associated with a decrease of 11.99 kg in milk, 0.39 kg in fat and 0.29 kg in protein. The effect of ancestral inbreeding was more detrimental to performance traits than the effect of new inbreeding. This result contradicted the hypothesis of purging. By including the inbreeding coefficient in the model, the rank of animals remained unchanged, but the average predicted breeding values increased. In general, inbreeding depression was observed in Iranian Holstein cows; however, no evidence of purging was observed. The average of inbreeding coefficients was not high in this population, although accounting for inbreeding coefficients in the analytical model did significantly increase the predicted breeding values. It is recommended that the analytical model incorporate the inbreeding coefficient to improve the accuracy of genetic evaluation. In future studies, inbreeding depression should be assessed using genomic data for performance and reproduction traits.

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.

Differential sensitivity of climate conditions on birth weight genetic values in mice divergently selected for birth weight residual variance.

After 32 generations of a divergent selection experiment for residual variance of birth weight in mice, two divergent lines were thus obtained: the heterogeneous line (H-line) and the homogeneous line (L-line). Throughout the generations, differences were observed between the two lines in traits such as litter size, survival at weaning and birth weight variability caused by unidentified environmental conditions. The L-line exhibited advantages in terms of higher survival rates, larger litter sizes, and less sensitivity to changes in food intake. The study is an examination of the effects of climate as an environmental factor on the performance of these animals. Climate factors including maximum, minimum, and mean temperature (T), humidity (H), and TH index; at three stages (the fecundation, a week before the parturition and the parturition), were linked to a birth weight dataset consisting of 22,614 records distributed as follows: 8,853 corresponding to the H-line, 12,649 to the L-line and 1,112 to the initial population. Out of the 27 analysed climatic variables, the maximum temperature one week before parturition (MXTW) was identified as the most influential when comparing heteroscedastic models with the deviance information criterion. The order of Legendre polynomial to apply in the following random regression model was tested by a cross-validation using homoscedastic models. Finally, MXTW was compared on how it affected the two divergent lines by analyzing predicted breeding values (PBV) obtained from a random regression heterosdedastic model. The mean PBV of the H-line in the first generation showed a range of 0.070g with a negative slope, which was 35 times higher than the range obtained for the L-line, which varied within 0.002g. In the last generation of selection, the H-line exhibited greater instability of PBV across temperatures, with a difference of 0.101g between the maximum and minimum mean PBV, compared to 0.017g for the L-line. The standard deviations of the slopes in the H-line were more dispersed than in the L-line. Unlike the H-line, the L-line had slopes that were not significantly different from 0 throughout the generations of selection, indicating greater stability in response to MXTW variations. The H-line exhibited a higher sensitivity to changes in MXTW, particularly in birth weight, with the L-line being more stable. The selection for uniformity of birth weight could lead to less sensitive animals under environmental changes.

Boundaries for genotype, phenotype, and pedigree truncation in genomic evaluations in pigs.

Historical data collection for genetic evaluation purposes is a common practice in animal populations; however, the larger the dataset, the higher the computing power needed to perform the analyses. Also, fitting the same model to historical and recent data may be inappropriate. Data truncation can reduce the number of equations to solve, consequently decreasing computing costs; however, the large volume of genotypes is responsible for most of the increase in computations. This study aimed to assess the impact of removing genotypes along with phenotypes and pedigree on the computing performance, reliability, and inflation of GEBV from ssGBLUP for selection candidates. Data from two pig lines, a terminal sire (L1) and a maternal line (L2), were analyzed in this study. Four analyses were implemented: growth and "weaning to finish" mortality on L1, pre-weaning and reproductive traits on L2. Four genotype removal scenarios were proposed: removing genotyped animals without phenotypes and progeny (noInfo), removing genotyped animals based on birth year (Age), the combination of noInfo and Age scenarios (noInfo + Age), and no genotype removal (AllGen). In all scenarios, phenotypes were removed, based on birth year, and three pedigree depths were tested: two and three generations traced back and using the entire pedigree. The full dataset contained 1,452,257 phenotypes for growth traits, 324,397 for weaning to finish mortality, 517,446 for pre-weaning traits, and 7,853,629 for reproductive traits in pure and crossbred pigs. Pedigree files for lines L1 and L2 comprised 3,601,369 and 11,240,865 animals, of which 168,734 and 170,121 were genotyped, respectively. In each truncation scenario, the linear regression method was used to assess the reliability and dispersion of GEBV for genotyped parents (born after 2019). The number of years of data that could be removed without harming reliability depended on the number of records, type of analyses (multitrait vs. single trait), the heritability of the trait, and data structure. All scenarios had similar reliabilities, except for noInfo, which performed better in the growth analysis. Based on the data used in this study, considering the last ten years of phenotypes, tracing three generations back in the pedigree, and removing genotyped animals not contributing own or progeny phenotypes, increases computing efficiency with no change in the ability to predict breeding values.

DIGITAL REVOLUTION IN PLANT BREEDING: A COMPREHENSIVE REVIEW OF METHODOLOGIES, TOOLS, APPLICATIONS, AND FUTURE PERSPECTIVES

Digital breeding integrates modern technologies like genomics, bioinformatics, and data analytics into conventional plant breeding. It accelerates the breeding process, improves selection effectiveness, and enhances crop yield and quality. High-throughput genotyping and phenotyping technologies enable efficient analysis of large populations and rapid characterization of plant traits. Genomic selection and data analytics aid in predicting breeding values and analyzing extensive data for trait improvement. Digital breeding applications include accelerated breeding cycles, trait-based breeding, disease resistance, stress tolerance, nutritional quality enhancement, remote sensing, yield prediction, multi-environment testing, and precision breeding and prospects involve integrating multiple omics technologies, developing precise phenotypic prediction models, and fostering data sharing and collaboration. Digital breeding can greatly improve breeding programs and address global food security challenges.Digital breeding integrates modern technologies like genomics, bioinformatics, and data analytics into conventional plant breeding. It accelerates the breeding process, improves selection effectiveness, and enhances crop yield and quality. High-throughput genotyping and phenotyping technologies enable efficient analysis of large populations and rapid characterization of plant traits. Genomic selection and data analytics aid in predicting breeding values and analyzing extensive data for trait improvement. Digital breeding applications include accelerated breeding cycles, trait-based breeding, disease resistance, stress tolerance, nutritional quality enhancement, remote sensing, yield prediction, multi-environment testing, and precision breeding and prospects involve integrating multiple omics technologies, developing precise phenotypic prediction models, and fostering data sharing and collaboration. Digital breeding can greatly improve breeding programs and address global food security challenges.

Heat stress on breeding value prediction for milk yield and composition of a Brazilian Holstein cattle population

Due to the high milk production of Holstein cows, many countries have chosen to import semen to improve local dairy herds. This strategy would be more effective if this semen was used in the same environment conditions in which the bulls were selected. If the effect of genotype by environment (G × E) interaction is not considered, the estimated breeding values (EBVs) may vary, potentially reducing the selection response. We evaluate the impact of heat stress on selection for milk yield and composition of Holstein cows using random regression models. To verify the interference of heat stress in milk yield (MY) and composition traits (fat, protein, total saturated, and total unsaturated fatty acids content in milk), temperature-humidity index (THI) on test-day milk records was used. The threshold value to divide the environments using test-day information from Brazilian Holstein cows was 72 units of THI, i.e., < 72 represented no heat stress and > 72 represented heat stress. Legendre polynomials of second-order (Leg 2) model and two lactation points (33 and 122 DIM) were used to estimate heritabilities and EBVs for five important dairy traits. The heritabilities of milk components and fatty acids were low (0.09-0.29), regardless of lactation period and degree of heat stress, with the exception of protein content (0.30-0.35). Fat content was the only milk component that was reduced according to the degree of heat stress and lactation period. The EBVs tended to decrease in heat stress conditions, thus animals with high genetic potential demonstrated evidence of G × E interaction. However, acclimatization of dairy cows to heat stress in the farm production systems may have been responsible for the low differences among genetic parameters and EBVs with and without heat stress found in this study.

Increasing cassava root yield: Additive-dominant genetic models for selection of parents and clones.

Genomic selection has been promising in situations where phenotypic assessments are expensive, laborious, and/or inefficient. This work evaluated the efficiency of genomic prediction methods combined with genetic models in clone and parent selection with the goal of increasing fresh root yield, dry root yield, as well as dry matter content in cassava roots. The bias and predictive ability of the combinations of prediction methods Genomic Best Linear Unbiased Prediction (G-BLUP), Bayes B, Bayes Cπ, and Reproducing Kernel Hilbert Spaces with additive and additive-dominant genetic models were estimated. Fresh and dry root yield exhibited predominantly dominant heritability, while dry matter content exhibited predominantly additive heritability. The combination of prediction methods and genetic models did not show significant differences in the predictive ability for dry matter content. On the other hand, the prediction methods with additive-dominant genetic models had significantly higher predictive ability than the additive genetic models for fresh and dry root yield, allowing higher genetic gains in clone selection. However, higher predictive ability for genotypic values did not result in differences in breeding value predictions between additive and additive-dominant genetic models. G-BLUP with the classical additive-dominant genetic model had the best predictive ability and bias estimates for fresh and dry root yield. For dry matter content, the highest predictive ability was obtained by G-BLUP with the additive genetic model. Dry matter content exhibited the highest heritability, predictive ability, and bias estimates compared with other traits. The prediction methods showed similar selection gains with approximately 67% of the phenotypic selection gain. By shortening the breeding cycle time by 40%, genomic selection may overcome phenotypic selection by 10%, 13%, and 18% for fresh root yield, dry root yield, and dry matter content, respectively, with a selection proportion of 15%. The most suitable genetic model for each trait allows for genomic selection optimization in cassava with high selection gains, thereby accelerating the release of new varieties.

Genome-wide association mapping and genomic prediction of agronomical traits and breeding values in Iranian wheat under rain-fed and well-watered conditions

BackgroundThe markers detected by genome-wide association study (GWAS) make it possible to dissect genetic structure and diversity at many loci. This can enable a wheat breeder to reveal and used genomic loci controlling drought tolerance. This study was focused on determining the population structure of Iranian 208 wheat landraces and 90 cultivars via genotyping-by-sequencing (GBS) and also on detecting marker-trait associations (MTAs) by GWAS and genomic prediction (GS) of wheat agronomic traits for drought-tolerance breeding. GWASs were conducted using both the original phenotypes (pGWAS) and estimated breeding values (eGWAS). The bayesian ridge regression (BRR), genomic best linear unbiased prediction (gBLUP), and ridge regression-best linear unbiased prediction (rrBLUP) approaches were used to estimate breeding values and estimate prediction accuracies in genomic selection.ResultsPopulation structure analysis using 2,174,975 SNPs revealed four genetically distinct sub-populations from wheat accessions. D-Genome harbored the lowest number of significant marker pairs and the highest linkage disequilibrium (LD), reflecting different evolutionary histories of wheat genomes. From pGWAS, BRR, gBLUP, and rrBLUP, 284, 363, 359 and 295 significant MTAs were found under normal and 195, 365, 362 and 302 under stress conditions, respectively. The gBLUP with the most similarity (80.98 and 71.28% in well-watered and rain-fed environments, correspondingly) with the pGWAS method in the terms of discovered significant SNPs, suggesting the potential of gBLUP in uncovering SNPs. Results from gene ontology revealed that 29 and 30 SNPs in the imputed dataset were located in protein-coding regions for well-watered and rain-fed conditions, respectively. gBLUP model revealed genetic effects better than other models, suggesting a suitable tool for genome selection in wheat.ConclusionWe illustrate that Iranian landraces of bread wheat contain novel alleles that are adaptive to drought stress environments. gBLUP model can be helpful for fine mapping and cloning of the relevant QTLs and genes, and for carrying out trait introgression and marker-assisted selection in both normal and drought environments in wheat collections.

Changes in genetic trends in US dairy cattle since the implementation of genomic selection

Genomic selection increases accuracy and decreases generation interval, accelerating genetic changes in populations. Assumptions of genetic improvement must be addressed to quantify the magnitude and direction of change. Genetic trends of US dairy cattle breeds were examined to determine the genetic gain since the implementation of genomic evaluations in 2009. Inbreeding levels and generation intervals were also investigated. Breeds included Ayrshire, Brown Swiss, Guernsey, Holstein (HO), and Jersey (JE), which were characterized by the evaluation breed the animal received. Mean genomic predicted breeding values (PBV¯) were analyzed per year to calculate genetic trends for bulls and cows. The data set contained 154,008 bulls and 33,022,242 cows born since 1975. Breakpoints were estimated using linear regression, and nonlinear regression was used to fit the piecewise model for the small sample number in some years. Generation intervals and inbreeding levels were also investigated since 1975. Milk, fat, and protein yields, somatic cell score, productive life, daughter pregnancy rate, and livability PBV¯ were documented. In 2017, 100% of bulls in this data set were genotyped. The percentage of genotyped cows has increased 23 percentage points since 2010. Overall, production traits have increased steadily over time, as expected. The HO and JE breeds have benefited most from genomics, with up to 192% increase in genetic gain since 2009. Due to the low number of observations, trends for Ayrshire, Brown Swiss, and Guernsey are difficult to infer from. Trends in fertility are most substantial; particularly, most breeds are trending downwards and daughter pregnancy rate for JE has been decreasing steadily since 1975 for bulls and cows. Levels of genomic inbreeding are increasing in HO bulls and cows. In 2017, genomic inbreeding levels were 12.7% for bulls and 7.9% for cows. A suggestion to control this is to include the genomic inbreeding coefficient with a negative weight to the selection index of bulls with high future genomic inbreeding levels. For sires of bulls, the current generation intervals are 2.2 yr in HO, 3.2 in JE, 4.4 in Brown Swiss, 5.1 in Ayrshire, and 4.3 in Guernsey. The number of colored breed bulls in the United States is currently at an extremely low level, and this number will only increase with a market incentive or additional breed association involvement. Increased education and extension could be beneficial to increase knowledge about inbreeding levels, use of genomics and genetic improvement, and genetic diversity in the genomic selection era.

A Comparison between Three Tuning Strategies for Gaussian Kernels in the Context of Univariate Genomic Prediction.

Genomic prediction is revolutionizing plant breeding since candidate genotypes can be selected without the need to measure their trait in the field. When a reference population contains both phenotypic and genotypic information, it is trained by a statistical machine learning method that is subsequently used for making predictions of breeding or phenotypic values of candidate genotypes that were only genotyped. Nevertheless, the successful implementation of the genomic selection (GS) methodology depends on many factors. One key factor is the type of statistical machine learning method used since some are unable to capture nonlinear patterns available in the data. While kernel methods are powerful statistical machine learning algorithms that capture complex nonlinear patterns in the data, their successful implementation strongly depends on the careful tuning process of the involved hyperparameters. As such, in this paper we compare three methods of tuning (manual tuning, grid search, and Bayesian optimization) for the Gaussian kernel under a Bayesian best linear unbiased predictor model. We used six real datasets of wheat (Triticum aestivum L.) to compare the three strategies of tuning. We found that if we want to obtain the major benefits of using Gaussian kernels, it is very important to perform a careful tuning process. The best prediction performance was observed when the tuning process was performed with grid search and Bayesian optimization. However, we did not observe relevant differences between the grid search and Bayesian optimization approach. The observed gains in terms of prediction performance were between 2.1% and 27.8% across the six datasets under study.

Local breed proportions and local breed heterozygosity in genomic predictions for crossbred dairy cows.

For genomic prediction of crossbred animals, models that account for the breed origin of alleles (BOA) in marker genotypes can allow the effects of marker alleles to differ depending on their ancestral breed. Previous studies have shown that genomic estimated breeding values for crossbred cows can be calculated using the marker effects that are estimated in the contributing pure breeds and combined based on estimated BOA in the genotypes of the crossbred cows. In the presented study, we further exploit the BOA information for improving the prediction of genomic breeding values of crossbred dairy cows. We investigated 2 types of BOA-derived breed proportions: global breed proportions, defined as the proportion of marker alleles assigned to each breed across the whole genome; and local breed proportions (LBP), defined as the proportions of alleles on chromosome segments which were assigned to each breed. Further, we investigated 2 BOA-derived measures of heterozygosity for the prediction of total genetic value. First, global breed heterozygosity, defined as the proportion of marker loci that have alleles originating in 2 different breeds over the whole genome. Second, local breed heterozygosity (LBH), defined as proportions of marker loci on chromosome segments that had alleles originating in 2 different breeds. We estimated variance related to LBP and LBH on the remaining variation after accounting for prediction with solutions from the genomic evaluations of the pure breeds and validated alternative models for production traits in 5,214 Danish crossbred dairy cows. The estimated LBP variances were 0.9, 1.2, and 1.0% of phenotypic variance for milk, fat, and protein yield, respectively. We observed no clear LBH effect. Cross-validation showed that models with LBP effects had a numerically small but statistically significantly higher predictive ability than models only including global breed proportions. We observed similar improvement in accuracy by the model having an across crossbred residual additive genetic effect, accounting for the additive genetic variation that was not accounted for by the solutions from purebred. For genomic predictions of crossbred animals, estimated BOA can give useful information on breed proportions, both globally in the genome and locally in genome regions, and on breed heterozygosity.

Deciphering the genetic basis and prediction genomic estimated breeding values of heat tolerance in Zhikong scallop Chlamys farreri

The changes in seawater temperature due to global warming have produced profound influences on the growth, survival and production of aquatic species. Therefore, improving the performance of these species under heat stress is crucial for the sustainable development of aquaculture. Genomic selection, as a method of genetic improvement of economic traits, has great potential and value in cultivation of new strains with adaptation advantages. In present study, 1060 Zhikong scallops Chlamys farreri, an economically important species, were exposed to a gradient heating experiment to obtain individuals with heat tolerance. Among them, 154 individuals died firstly during experiment were considered as heat-sensitive group (HS), and 156 still alive after recovering for 7 days were taken as heat-tolerant group (HT). Then, these 310 individuals were sequenced, and 35,507 single nucleotide polymorphisms (SNPs) of 289 scallops were genotyped for genome wide association study (GWAS) and genomic prediction analysis. The results showed that seven SNPs and two genes (PNC1 and RASD1) were significant associated with heat tolerance trait in C. farreri. In addition, the heat tolerance trait of C. farreri was moderately heritable, and the heritability of survival status and survival time was 0.52 ± 0.04 and 0.24 ± 0.03, respectively. Furthermore, genomic best linear unbiased prediction (GBLUP) and Bayes B models were proved to be more suitable for genomic breeding value predictions of heat tolerance trait in C. farreri. The accuracy of genomic estimated breeding value could be stabilized when the number of markers reached 8 k. Population size could improve the accuracy of breeding value predictions and the accuracy has not reach a plateau yet in our study. Taken together, the findings obtained in this study provide insights into the genetic basis of heat tolerance in C. farreri and contribute to the genetic improvement of scallop and other related species.

SNP- and haplotype-based single-step genomic predictions for body weight, wool, and reproductive traits in North American Rambouillet sheep.

Rambouillet sheep are commonly raised in extensive grazing systems in the US, mainly for wool and meat production. Genomic evaluations in US sheep breeds, including Rambouillet, are still incipient. Therefore, we aimed to evaluate the feasibility of performing genomic prediction of breeding values for various traits in Rambouillet sheep based on single nucleotide polymorphisms (SNP) or haplotypes (fitted as pseudo-SNP) under a single-step GBLUP approach. A total of 28,834 records for birth weight (BWT), 23,306 for postweaning weight (PWT), 5,832 for yearling weight (YWT), 9,880 for yearling fibre diameter (YFD), 11,872 for yearling greasy fleece weight (YGFW), and 15,984 for number of lambs born (NLB) were used in this study. Seven hundred forty-one individuals were genotyped using a moderate (50 K; n=677) or high (600 K; n=64) density SNP panel, in which 32 K SNP in common between the two SNP panels (after genotypic quality control) were used for further analyses. Single-step genomic predictions using SNP (H-BLUP) or haplotypes (HAP-BLUP) from blocks with different linkage disequilibrium (LD) thresholds (0.15, 0.35, 0.50, 0.65, and 0.80) were evaluated. We also considered different blending parameters when constructing the genomic relationship matrix used to predict the genomic-enhanced estimated breeding values (GEBV), with alpha equal to 0.95 or 0.50. The GEBV were compared to the estimated breeding values (EBV) obtained from traditional pedigree-based evaluations (A-BLUP). The mean theoretical accuracy ranged from 0.499 (A-BLUP for PWT) to 0.795 (HAP-BLUP using haplotypes from blocks with LD threshold of 0.35 and alpha equal to 0.95 for YFD). The prediction accuracies ranged from 0.143 (A-BLUP for PWT) to 0.330 (A-BLUP for YGFW) while the prediction bias ranged from -0.104 (H-BLUP for PWT) to 0.087 (HAP-BLUP using haplotypes from blocks with LD threshold of 0.15 and alpha equal to 0.95 for YGFW). The GEBV dispersion ranged from 0.428 (A-BLUP for PWT) to 1.035 (A-BLUP for YGFW). Similar results were observed for H-BLUP or HAP-BLUP, independently of the LD threshold to create the haplotypes, alpha value, or trait analysed. Using genomic information (fitting individual SNP or haplotypes) provided similar or higher prediction and theoretical accuracies and reduced the dispersion of the GEBV for body weight, wool, and reproductive traits in Rambouillet sheep. However, there were no clear improvements in the prediction bias when compared to pedigree-based predictions. The next step will be to enlarge the training populations for this breed to increase the benefits of genomic predictions.

Single-step genomic BLUP with many metafounders.

Single-step genomic BLUP (ssGBLUP) model for routine genomic prediction of breeding values is developed intensively for many dairy cattle populations. Compatibility between the genomic (G) and the pedigree (A) relationship matrices remains an important challenge required in ssGBLUP. The compatibility relates to the amount of missing pedigree information. There are two prevailing approaches to account for the incomplete pedigree information: unknown parent groups (UPG) and metafounders (MF). unknown parent groups have been used routinely in pedigree-based evaluations to account for the differences in genetic level between groups of animals with missing parents. The MF approach is an extension of the UPG approach. The MF approach defines MF which are related pseudo-individuals. The MF approach needs a Γ matrix of the size number of MF to describe relationships between MF. The UPG and MF can be the same. However, the challenge in the MF approach is the estimation of Γ having many MF, typically needed in dairy cattle. In our study, we present an approach to fit the same amount of MF as UPG in ssGBLUP with Woodbury matrix identity (ssGTBLUP). We used 305-day milk, protein, and fat yield data from the DFS (Denmark, Finland, Sweden) Red Dairy cattle population. The pedigree had more than 6 million animals of which 207,475 were genotyped. We constructed the preliminary gamma matrix (Γ pre ) with 29MF which was expanded to 148MF by a covariance function (Γ 148). The quality of the extrapolation of the Γ pre matrix was studied by comparing average off-diagonal elements between breed groups. On average relationships among MF in were 1.8% higher than in Γ pre . The use of Γ 148 increased the correlation between the G and A matrices by 0.13 and 0.11 for the diagonal and off-diagonal elements, respectively. [G]EBV were predicted using the ssGTBLUP and Pedigree-BLUP models with the MF and UPG. The prediction reliabilities were slightly higher for the ssGTBLUP model using MF than UPG. The ssGBLUP MF model showed less overprediction compared to other models.

Comparison of different models for the estimation of genetic parameters in tropical goats.

This study aimed to estimate the variance components and genetic parameters for body weight in tropical goats testing different models using Bayesian approach and investigate the effectiveness of fitting the effects of maternal genetic, permanent environmental, and covariance between direct and maternal effects. Records from 1980 to 2010 of 1453 Anglo-Nubian goats' herd were used. Six performance growth traits: birth weight (BW, kg), at 28 (W28, kg), 56 (W56, kg), 112 (W112, kg), 140 (W140, kg), and 196 (W196; kg) days of age, were evaluated. There was a negative covariance between direct genetic effects and maternal additive for all weights. The effect of maternal permanent environment is an important source of variation for performance characteristics in goats until the 196days, and must be considered in genetic evaluation models in order to obtain accurate predictions of breeding values of individuals. The importance of inclusion of the additive maternal effect appears to be more dependent on the structure of the data set under evaluation. Given the structure of the data, the described management and criteria for choosing the best model (deviance information criterion and the Bayes factor) should make the estimation of parameters for weights at birth and at 28 and 56days using model IV, since that will provide more consistent results than the type I (less complex), without the need of accurate representations of knowledge prior to data collection. Over time, the breeding program will have more data and thereby increase the possibility of building a prior distribution confident that would enable the inference of parameters for more complex models. However, these are preferable components for the estimation of the characteristics and weights to 112 at 140 and at 196days, using model I (less complex).