Optimising desired gain indices to maximise selection response.

Introduction: In plant breeding, we often aim to improve multiple traits at once. However, without knowing the economic value of each trait, it is hard to decide which traits to focus on. This is where "desired gain selection indices" come in handy, which can yield optimal gains in each trait based on the breeder's prioritisation of desired improvements when economic weights are not available. However, they lack the ability to maximise the selection response and determine the correlation between the index and net genetic merit. Methods: Here, we report the development of an iterative desired gain selection index method that optimises the sampling of the desired gain values to achieve a targeted or a user-specified selection response for multiple traits. This targeted selection response can be constrained or unconstrained for either a subset or all the studied traits. Results: We tested the method using genomic estimated breeding values (GEBVs) for seven traits in a bread wheat (Triticum aestivum) reference breeding population comprising 3,331 lines and achieved prediction accuracies ranging between 0.29 and 0.47 across the seven traits. The indices were validated using 3,005 double haploid lines that were derived from crosses between parents selected from the reference population. We tested three user-specified response scenarios: a constrained equal weight (INDEX1), a constrained yield dominant weight (INDEX2), and an unconstrained weight (INDEX3). Our method achieved an equivalent response to the user-specified selection response when constraining a set of traits, and this response was much better than the response of the traditional desired gain selection indices method without iteration. Interestingly, when using unconstrained weight, our iterative method maximised the selection response and shifted the average GEBVs of the selection candidates towards the desired direction. Discussion: Our results show that the method is an optimal choice not only when economic weights are unavailable, but also when constraining the selection response is an unfavourable option.

A comparison of genetic and genomic breeding values in Saanen and Alpine goats.

Nowadays, several countries are developing or adopting genomic selection in the dairy goat sector. The most used method to estimate breeding values is Single-Step Genomic Best Linear Unbiased Prediction (ssGBLUP) which offers several advantages in terms of computational process and accuracy of the estimated breeding values (EBVs). Saanen and Alpine are the predominant dairy goat breeds in Italy, and both have similar breeding programs where EBVs for productive traits are currently calculated using BLUP. This work describes the implementation of genomic selection for these two breeds in Italy, aligning with the selection practices already carried out in the international landscape. The available dataset included 3 611 genotyped animals, 11 470 lactation records, five traits (milk, protein and fat yields, and fat and protein percentages), and three-generation pedigrees. EBVs were estimated using BLUP, GBLUP, and ssGBLUP both with single and multiple trait approaches. The methods were compared in terms of correlation between EBVs and genetic trends. Results were also validated with the linear regression method excluding part of the phenotypic data. In both breeds, EBVs and GEBVs were strongly correlated and the trend of each trait was similar comparing the three methods. The average increase in accuracy across traits and methods amounted to +13 and +10% from BLUP to ssGBLUP for Alpine and Saanen breeds, respectively. Results indicated higher prediction accuracy and correlation for GBLUP and ssGBLUP compared to BLUP, implying that the use of genotypes increases the accuracy of EBVs, particularly in the absence of phenotypic data. Therefore, ssGBLUP is likely to be the most effective method to enhance genetic gain in Italian Saanen and Alpine goats.

Impact of heat stress on genetic evaluation of oocyte and embryo production in Gir dairy cattle.

Identifying and selecting genotypes tolerant to heat stress might improve reproductive traits in dairy cattle, including oocyte and embryo production. The temperature-humidity index (THI) was used, via random regression models, to investigate the impact of heat stress on genetic parameters and breeding values of oocyte and embryo production in Gir dairy cattle. We evaluated records of total oocytes (TO), viable oocytes (VO), cleaved embryos (CE), and viable embryos (VE) from dairy Gir donors. Twenty-four models were tested, considering age at ovum pick-up (AOPU) and THI means as a regressor in the genetic evaluation. We computed THI in eight periods, from 0 to 112 days before ovum pick-up, which were adjusted by different orders of Legendre polynomials (second, third, and fourth). The best-fit model according to Akaike's information criterion (AIC) and Model Posterior Probabilities (MPP) considered Legendre polynomials of third order and THI means of 112 days for TO, fourth order and 56 days for VO, second order and 28 days for CE, and second order and 42 days for VE, respectively. The heritability (h2) estimates across AOPU and THI scales ranged from 0.34 to 0.62 for TO, 0.31 to 0.58 for VO, 0.26 to 0.39 for CE, and 0.15 to 0.26 for VE, respectively. The fraction of the phenotypic variance explained by the permanent environment in different AOPU and THI scales ranged from 0.03 to 0.25 for TO, 0.05 to 0.26 for VO, 0.09 to 0.36 for CE, and 0.15 to 0.27 for VE, respectively. Spearman's rank correlation between the estimated breeding values in different AOPU and THI scale from the top 5% sires and females ranged from 0.18 to 0.90 for TO, 0.31 to 0.95 for VO, 0.14 to 0.85 for CE, and 0.47 to 0.94 for VE, respectively. The h2 estimates for all evaluated traits varied from moderate to high magnitude across AOPU and THI scales, indicating that genetic selection can result in rapid genetic progress for the evaluated traits. There was a reranking among the best animals in different AOPU and THI. It is possible to select dairy Gir cattle tolerant to heat stress to improve oocyte and embryo production.

Ridge regression and deep learning models for genome-wide selection of complex traits in New Mexican Chile peppers.

BACKGROUND: Genomewide prediction estimates the genomic breeding values of selection candidates which can be utilized for population improvement and cultivar development. Ridge regression and deep learning-based selection models were implemented for yield and agronomic traits of 204 chile pepper genotypes evaluated in multi-environment trials in New Mexico, USA. RESULTS: Accuracy of prediction differed across different models under ten-fold cross-validations, where high prediction accuracy was observed for highly heritable traits such as plant height and plant width. No model was superior across traits using 14,922 SNP markers for genomewide selection. Bayesian ridge regression had the highest average accuracy for first pod date (0.77) and total yield per plant (0.33). Multilayer perceptron (MLP) was the most superior for flowering time (0.76) and plant height (0.73), whereas the genomic BLUP model had the highest accuracy for plant width (0.62). Using a subset of 7,690 SNP loci resulting from grouping markers based on linkage disequilibrium coefficients resulted in improved accuracy for first pod date, ten pod weight, and total yield per plant, even under a relatively small training population size for MLP and random forest models. Genomic and ridge regression BLUP models were sufficient for optimal prediction accuracies for small training population size. Combining phenotypic selection and genomewide selection resulted in improved selection response for yield-related traits, indicating that integrated approaches can result in improved gains achieved through selection. CONCLUSIONS: Accuracy values for ridge regression and deep learning prediction models demonstrate the potential of implementing genomewide selection for genetic improvement in chile pepper breeding programs. Ultimately, a large training data is relevant for improved genomic selection accuracy for the deep learning models.

Genomic prediction of pig growth traits based on machine learning.

This study aimed to assess and compare the performance of different machine learning models in predicting selected pig growth traits and genomic estimated breeding values (GEBV) using automated machine learning, with the goal of optimizing whole-genome evaluation methods in pig breeding. The research employed genomic information, pedigree matrices, fixed effects, and phenotype data from 9968 pigs across multiple companies to derive four optimal machine learning models: deep learning (DL), random forest (RF), gradient boosting machine (GBM), and extreme gradient boosting (XGB). Through 10-fold cross-validation, predictions were made for GEBV and phenotypes of pigs reaching weight milestones (100 kg and 115 kg) with adjustments for backfat and days to weight. The findings indicated that machine learning models exhibited higher accuracy in predicting GEBV compared to phenotypic traits. Notably, GBM demonstrated superior GEBV prediction accuracy, with values of 0.683, 0.710, 0.866, and 0.871 for B100, B115, D100, and D115, respectively, slightly outperforming other methods. In phenotype prediction, GBM emerged as the best-performing model for pigs with B100, B115, D100, and D115 traits, achieving prediction accuracies of 0.547, followed by DL at 0.547, and then XGB with accuracies of 0.672 and 0.670. In terms of model training time, RF required the most time, while GBM and DL fell in between, and XGB demonstrated the shortest training time. In summary, machine learning models obtained through automated techniques exhibited higher GEBV prediction accuracy compared to phenotypic traits. GBM emerged as the overall top performer in terms of prediction accuracy and training time efficiency, while XGB demonstrated the ability to train accurate prediction models within a short timeframe. RF, on the other hand, had longer training times and insufficient accuracy, rendering it unsuitable for predicting pig growth traits and GEBV.

Understanding the underlying genetic mechanisms for age at first calving, inter-calving period and scrotal circumference in Bonsmara cattle.

BACKGROUND: Reproduction is a key feature of the sustainability of a species and thus represents an important component in livestock genetic improvement programs. Most reproductive traits are lowly heritable. In order to gain a better understanding of the underlying genetic basis of these traits, a genome-wide association was conducted for age at first calving (AFC), first inter-calving period (ICP) and scrotal circumference (SC) within the South African Bonsmara breed. Phenotypes and genotypes (120,692 single nucleotide polymorphisms (SNPs) post editing) were available on 7,128 South African Bonsmara cattle; the association analyses were undertaken using linear mixed models. RESULTS: Genomic restricted maximum likelihood analysis of the 7,128 SA Bonsmara cattle yielded genomic heritability's of 0.183 (SE = 0.021) for AFC, 0.207 (SE = 0.022) for ICP and 0.209 (SE = 0.019) for SC. A total of 16, 23 and 51 suggestive (P ≤ 4 × 10-6) SNPs were associated with AFC, ICP and SC, while 11, 11 and 44 significant (P ≤ 4 × 10-7) SNPs were associated with AFC, ICP and SC respectively. A total of 11 quantitative trait loci (QTL) and 11 candidate genes were co-located with these associated SNPs for AFC, with 10 QTL harbouring 11 candidate genes for ICP and 41 QTL containing 40 candidate genes for SC. The QTL identified were close to genes previously associated with carcass, fertility, growth and milk-related traits. The biological pathways influenced by these genes include carbohydrate catabolic processes, cellular development, iron homeostasis, lipid metabolism and storage, immune response, ovarian follicle development and the regulation of DNA transcription and RNA translation. CONCLUSIONS: This was the first attempt to study the underlying polymorphisms associated with reproduction in South African beef cattle. Genes previously reported in cattle breeds for numerous traits bar AFC, ICP or SC were detected in this study. Over 20 different genes have not been previously reported in beef cattle populations and may have been associated due to the unique genetic composite background of the SA Bonsmara breed.

Efficient Genomic Prediction of Yield and Dry Matter in Hybrid Potato.

There is an ongoing endeavor within the potato breeding sector to rapidly adapt potato from a clonal polyploid crop to a diploid hybrid potato crop. While hybrid breeding allows for the efficient generation and selection of parental lines, it also increases breeding program complexity and results in longer breeding cycles. Over the past two decades, genomic prediction has revolutionized hybrid crop breeding through shorter breeding cycles, lower phenotyping costs, and better population improvement, resulting in increased genetic gains for genetically complex traits. In order to accelerate the genetic gains in hybrid potato, the proper implementation of genomic prediction is a crucial milestone in the rapid improvement of this crop. The authors of this paper set out to test genomic prediction in hybrid potato using current genotyped material with two alternative models: one model that predicts the general combining ability effects (GCA) and another which predicts both the general and specific combining ability effects (GCA+SCA). Using a training set comprising 769 hybrids and 456 genotyped parental lines, we found that reasonable a prediction accuracy could be achieved for most phenotypes with both zero common parents (ρ=0.36-0.61) and one (ρ=0.50-0.68) common parent between the training and test sets. There was no benefit with the inclusion of non-additive genetic effects in the GCA+SCA model despite SCA variance contributing between 9% and 19% of the total genetic variance. Genotype-by-environment interactions, while present, did not appear to affect the prediction accuracy, though prediction errors did vary across the trial's targets. These results suggest that genomically estimated breeding values on parental lines are sufficient for hybrid yield prediction.

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.

Association between body condition genomic values and feed intake, milk production, and body weight in French Holstein cows.

Body condition score (BCS) offers a good estimate of the amount of stored fat on the body, and its variations can be used as a proxy for energy balance. Many countries have implemented a genomic evaluation of BCS, including France, where estimated breeding values are based on an individual BCS determination during the first lactation. In this article, we investigate the degree to which this genomic estimated breeding value based on a single phenotype record per cow might reflect different profiles of body reserves throughout lactation and be used to predict, and perhaps limit, their mobilization during early lactation. We also investigate whether selection on BCS affects other traits. A data set including 686 lactations of 435 Holstein cows from 3 experimental farms not used in the reference population for genomic evaluation was used to estimate the effects of the BCS direct genomic value (iBCS) on BCS, body weight, feed intake, milk production, and fat and protein contents throughout the lactation period. For each trait, the model included different iBCS regressions and an effect of the direct genomic value of the trait itself when available. It thus appeared that cows with a positive iBCS always had a higher BCS than negative iBCS cows, whatever the lactation stage, and that this difference increased during the first 6 mo to reach a difference of 0.8 point. A similar effect was seen regarding body weight, but it was the opposite for milk production, with negative iBCS cows producing slightly more milk (difference of about 3% over lactation). Feed intake increased slightly faster at the beginning of lactation for cows with positive iBCS. Therefore, iBCS is a promising tool that could help to limit intense mobilization during early lactation. Should feed efficiency be included in the breeding goal, greater attention should be paid to BCS to avoid further body mobilization in early lactation.

Invited review: Reliability computation from the animal model era to the single-step genomic model era.

The calculation of exact reliabilities involving the inversion of mixed model equations poses a heavy computational challenge when the system of equations is large. This has prompted the development of different approximation methods. We give an overview of the various methods and computational approaches in calculating reliability from the era before the animal model to the era of single-step genomic models. The different methods are discussed in terms of modeling, development, and applicability in large dairy cattle populations. The paper also describes the problems faced in reliability computation. Many details dispersed throughout the literature are presented in this paper. It is clear that a universal solution applicable to every model and input data may not be possible, but we point out several efficient and accurate algorithms developed recently for a variety of very large genomic evaluations.

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.

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

BACKGROUND: The 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. RESULTS: Population 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. CONCLUSION: We 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.

Weak genotype x environment interaction suggests that measuring scrotal circumference at 12 and 18 mo of age is helpful to select precocious Brahman cattle.

The aim of this study was to evaluate the genotype x environment interaction (GxE) for scrotal circumference (SC) measured at different ages using pedigree-based (A-1) and pedigree and genomic-based (H-1) relationship matrices. Data from 1,515 Brahman bulls, from the Cooperative Research Centre for Beef Genetic Technologies (Beef CRC) experimental dataset, were used in this study. SC was adjusted to age and body weight measured at 6 mo (SC6), 12 mo (SC12), 18 mo (SC18), and 24 mo of age (SC24). Body weight (BW) measured at 6 mo (BW6), 12 mo (BW12), 18 mo (BW18), and 24 mo of age (BW24) was used as criteria to describe the environment for SC in each age. All the animals measured were genotyped using medium-density SNP chips ("50k" or "70k" SNP) and their genotype were imputed using a reference panel with 729,068 SNP. The environment gradient (EG) was obtained by standardizing the solutions of the contemporary groups obtained by Animal Model with BW as the dependent variable. Then, the reaction norms (RN) were determined through a Random Regression Model. The breeding values (EBV) were estimated using either A-1 or H-1. The rank correlation was obtained using Spearman's correlation among the EBV estimated for the traits in analysis. For SC6 and SC24, higher estimates of heritability (h²) were obtained using A-1, when compared with those observed with H-1. In those ages, the improvement of the environment decreases the h² coefficient. On the other hand, the h² for SC12 and SC18 increased as the environment became more favorable, regardless of the matrix used. The RN for SC6 and SC24 estimated using A-1 and H-1 showed a decrease of variance from the worst to the best environment, an indication of existence of GxE. On the other hand, for SC12 and SC18, there were no significant differences between the EBV estimated in the lower and in the higher environments, regardless of the kinship matrix used, suggesting absence of GxE on those ages. Spearman's correlation among EBV estimated using A-1 and H-1 in different EG was practically equal to unity for all traits evaluated. In our study, there was weak evidence of GxE effect on SC in ages suitable for selection for sexual precocity. So, the absence of GxE at 12 and 18 mo means that these ages are advantageous for measuring SC to selection for sexual precocity. The advantage is that no changes in classification were observed when the sires were evaluated in different environments.

Beef production systems rely on efficient cow-calf operations, that is, farms where the cow herd has a high level of fertility and pregnancies are common. Bull fertility also plays an important role in terms of pregnancy rates. To increase herd fertility, cattle breeders and genetic selection programs use some indicator traits that are related to fertility. A common indicator trait used is scrotal circumference (SC), which like most reproduction traits are influenced by the animal's genetics and its environment. For some traits, when the environment has a large effect and it interacts with the genetics of the animals, selection might be less successful. Therefore, it is important to investigate genotype by environment interactions and their effect on reproduction traits used for selection. In this study, SC was measured at four different ages in Brahman cattle. We found weak evidence of genotype by environment effect on SC measured at 12 and 18 mo. In short, SC measured at these ages can be a good indicator of sexual precocity. No changes in sire rankings were observed when SC was measured at those ages, meaning that selecting the best sire is more straightforward than if the environment was playing a bigger role.

Methodology for the identification of relevant loci for milk traits in dairy cattle, using machine learning algorithms.

Machine learning methods were considered efficient in identifying single nucleotide polymorphisms (SNP) underlying a trait of interest. This study aimed to construct predictive models using machine learning algorithms, to identify loci that best explain the variance in milk traits of dairy cattle. Further objectives involved validating the results by comparison with reported relevant regions and retrieving the pathways overrepresented by the genes flanking relevant SNPs. Regression models using XGBoost (XGB), LightGBM (LGB), and Random Forest (RF) algorithms were trained using estimated breeding values for milk production (EBVM), milk fat content (EBVF) and milk protein content (EBVP) as phenotypes and genotypes on 40417 SNPs as predictor variables. To evaluate their efficiency, metrics for actual vs. predicted values were determined in validation folds (XGB and LGB) and out-of-bag data (RF). Less than 4500 relevant SNPs were retrieved for each trait. Among the genes flanking them, signaling and transmembrane transporter activities were overrepresented. The models trained:•Predicted breeding values for animals not included in the dataset.•Were efficient in identifying a subset of SNPs explaining phenotypic variation. The results obtained using XGB and LGB algorithms agreed with previous results. Therefore, the method proposed could be applied for future association studies on milk traits.

Response of Rambouillet Lambs to an Artificial Gastrointestinal Nematode Infection.

Gastrointestinal nematodes (GIN) threaten the productivity and health of sheep worldwide, prompting the need for genetic selection to reduce GIN susceptibility. Fecal egg count (FEC), packed-cell volume (PCV), and various production traits were examined in parasitized Rambouillet sheep and compared to sire FEC estimated breeding value (EBV). Rambouillet lambs (n = 77) were inoculated with 10,000 H. contortus L3 larvae. Subsequently, FEC, PCV, and body weight (BW) were captured at seven-day intervals for six weeks. Lambs were sired by one of two rams with post-weaning FEC EBV of −9% or +9%. Mean FEC differed (p = 0.0132) with lambs from the lower EBV sire ("Sire L") being reduced, versus those from the higher EBV sire ("Sire H"), being 2135 ± 211 vs. 2912 ± 207 eggs per gram, respectively. Males and females did not differ for FEC, but females exhibited a higher mean PCV than males, (33.74 vs. 29.65%, p < 0.0001). Lambs were shorn ~120 d post artificial infection and wool measurements were captured. A negative correlation between FEC and grease fleece weight was observed. Our results describe the response of Rambouillet lambs to artificial H. contortus infection and suggest FEC EBV can reduce susceptibility to GIN in this breed.

Combining canopy reflectance spectrometry and genome-wide prediction to increase response to selection for powdery mildew resistance in cultivated strawberry.

High-throughput phenotyping is an emerging approach in plant science, but thus far only a few applications have been made in horticultural crop breeding. Remote sensing of leaf or canopy spectral reflectance can help breeders rapidly measure traits, increase selection accuracy, and thereby improve response to selection. In the present study, we evaluated the integration of spectral analysis of canopy reflectance and genomic information for the prediction of strawberry (Fragaria × ananassa) powdery mildew disease. Two multi-parental breeding populations of strawberry comprising a total of 340 and 464 pedigree-connected seedlings were evaluated in two separate seasons. A single-trait Bayesian prediction method using 1001 spectral wavebands in the ultraviolet-visible-near infrared region (350-1350 nm wavelength) combined with 8552 single nucleotide polymorphism markers showed up to 2-fold increase in predictive ability over models using markers alone. The integration of high-throughput phenotyping was further validated independently across years/trials with improved response to selection of up to 90%. We also conducted Bayesian multi-trait analysis using the estimated vegetative indices as secondary traits. Three vegetative indices (Datt3, REP_Li, and Vogelmann2) had high genetic correlations (rA) with powdery mildew visual ratings with average rA values of 0.76, 0.71, and 0.71, respectively. Increasing training population sizes by incorporating individuals with only vegetative index information yielded substantial increases in predictive ability. These results strongly indicate the use of vegetative indices as secondary traits for indirect selection. Overall, combining spectrometry and genome-wide prediction improved selection accuracy and response to selection for powdery mildew resistance, demonstrating the power of an integrated phenomics-genomics approach in strawberry breeding.

Genomic prediction models for traits differing in heritability for soybean, rice, and maize.

BACKGROUND: Genomic selection is a powerful tool in plant breeding. By building a prediction model using a training set with markers and phenotypes, genomic estimated breeding values (GEBVs) can be used as predictions of breeding values in a target set with only genotype data. There is, however, limited information on how prediction accuracy of genomic prediction can be optimized. The objective of this study was to evaluate the performance of 11 genomic prediction models across species in terms of prediction accuracy for two traits with different heritabilities using several subsets of markers and training population proportions. Species studied were maize (Zea mays, L.), soybean (Glycine max, L.), and rice (Oryza sativa, L.), which vary in linkage disequilibrium (LD) decay rates and have contrasting genetic architectures. RESULTS: Correlations between observed and predicted GEBVs were determined via cross validation for three training-to-testing proportions (90:10, 70:30, and 50:50). Maize, which has the shortest extent of LD, showed the highest prediction accuracy. Amongst all the models tested, Bayes B performed better than or equal to all other models for each trait in all the three crops. Traits with higher broad-sense and narrow-sense heritabilities were associated with higher prediction accuracy. When subsets of markers were selected based on LD, the accuracy was similar to that observed from the complete set of markers. However, prediction accuracies were significantly improved when using a subset of total markers that were significant at P ≤ 0.05 or P ≤ 0.10. As expected, exclusion of QTL-associated markers in the model reduced prediction accuracy. Prediction accuracy varied among different training population proportions. CONCLUSIONS: We conclude that prediction accuracy for genomic selection can be improved by using the Bayes B model with a subset of significant markers and by selecting the training population based on narrow sense heritability.

Cluster analysis to explore additive-genetic patterns for the identification of sheep resistant, resilient and susceptible to gastrointestinal nematodes.

Infection caused by gastrointestinal nematodes is an important issue for animal health and production. Controlling worm infections improves the sustainability of the sheep industry. Genetic selection of animals that are resistant to gastrointestinal nematodes is another strategy to render sheep production more sustainable by decreasing the use of anthelmintics. The aims of this study were (1) to explore the additive-genetic pattern of EBVs for Famacha© (FAM), packed-cell volume (PVC), and fecal egg counts (FEC) of Santa Ines sheep, (2) to propose a classification of animals that are resistant, resilient and susceptible to gastrointestinal nematodes based on their additive-genetic patterns, and (3) to identify the most suitable animals for selection based on their genetic pattern. A dataset of 2,241 records from 747 animals was used to predict the breeding values for indicator traits of resistance to gastrointestinal nematodes with THRGIBBS1F90 and to carry out cluster analyses was used R software. Three clusters of animals were found in the population using hierarchical cluster analysis of the breeding values for FAM, PCV and FEC. Each cluster was characterized by different additive-genetic patterns identified by k-means non-hierarchical cluster analysis. Among a total of 747 animals, 196 were classified as resistant, 288 as resilient, and 263 as susceptible. Cluster analysis is a valuable tool for data screening that permits to evaluate only selection candidates based on their additive-genetic pattern for gastrointestinal nematode resistance. EBVs for FEC were decisive to divide the population into resilient, resistant and susceptible animals. It is also important to include the EBVs for PCV and FAM to adequately distinguish resistant from resilient animals. Finally, the resistant cluster consisted of the most desirable animals to be used as selection candidates in order to genetically improve resistance to infection with gastrointestinal nematodes. This cluster contained animals with the most appropriate additive-genetic pattern to achieve the breeding goal, with positive breeding values for PCV and negative breeding values for FAM and FEC.

The impact of direct-maternal genetic correlations on international beef cattle evaluations for Limousin weaning weight.

In beef cattle maternally influenced traits, estimates of direct-maternal genetic correlations (rdm) are usually reported to be negative. In international evaluations, rdm can differ both within countries (rdm_WC) and between countries (rdm_BC). The rdm_BC are difficult to estimate and are assumed to be zero in the current model for international beef cattle evaluations (Interbeef). Our objective was to investigate re-ranking of international estimated breeding values (IEBVs) in international beef cattle evaluations between models that either used estimated values for rdm or assumed them to be 0. Age-adjusted weaning weights and pedigree data were available for Limousin beef cattle from ten European countries. International EBVs were obtained using a multi-trait animal model with countries modeled as different traits. We compared IEBVs from a model that uses estimated rdm_BC (ranging between -0.14 and +0.14) and rdm_WC (between -0.33 and +0.40) with IEBVs obtained either from the current model that assumes rdm_BC to be 0, or from an alternative model that assumes both rdm_BC and rdm_WC to be 0. Direct and maternal IEBVs were compared across those three scenarios for different groups of animals. The ratio of population accuracies from the linear regression method was used to further investigate the impact of rdm on international evaluations, for both the whole set of animals in the evaluation and the domestic ones. Ignoring rdm_BC, i.e., replacing estimated values with 0, resulted in no (rank correlations > 0.99) or limited (between 0.98 and 0.99) re-ranking for direct and maternal IEBVs, respectively. Both rdm_BC and rdm_WC had less impact on direct IEBVs than on maternal IEBVs. Re-ranking of maternal IEBVs decreased with increasing reliability. Ignoring rdm_BC resulted in no re-ranking for sires with IEBVs that might be exchanged across countries and limited re-ranking for the top 100 sires. Using estimated rdm_BC values instead of considering them to be 0 resulted in null to limited increases in population accuracy. Ignoring both rdm_BC and rdm_WC resulted in considerable re-ranking of animals' IEBVs in all groups of animals evaluated. This study showed the limited impact of the current practice of ignoring rdm_BC in international evaluations for Limousin weaning weight, most likely because the estimated rdm_BC was close to 0. We expect that these conclusions can be extended to other traits that have reported rdm values in the range of rdm_WC values for weaning weight in Limousin.