Genomic Estimated Breeding Values Research Articles

Short Communication: Genomic estimated breeding values for bovine respiratory disease resistance in Angus feedlot cattle.

Bovine respiratory disease (BRD) causes major losses in feedlot cattle worldwide. A genetic component for BRD resistance in feedlot cattle and calves has been reported in a number of studies, with heritabilities ranging from 0.04 to 0.2. These results suggest selection could be used to reduce the incidence of BRD. Genomic selection could be an attractive approach for breeding for BRD resistance, given the phenotype is not likely to be recorded on breeding animals. In this study, we derived GEBVs for BRD resistance and assessed their accuracy in a reasonably large data set recorded for feedlot treatment of BRD (1213 Angus steers, in two feedlots). In fivefold cross validation, genomic predictions were moderately accurate (0.23±0.01) when a BayesR approach was used. Expansion of this approach to include more animals and a diversity of breeds is recommended to successfully develop a GEBV for BRD resistance in feedlots for the beef industry.

Genomic Prediction of Growth Traits in Yorkshire Pigs of Different Reference Group Sizes Using Different Estimated Breeding Value Models.

The need for sufficient reference population data poses a significant challenge in breeding programs aimed at improving pig farming on a small to medium scale. To overcome this hurdle, investigating the advantages of combing reference populations of varying sizes is crucial for enhancing the accuracy of the genomic estimated breeding value (GEBV). Genomic selection (GS) in populations with limited reference data can be optimized by combining populations of the same breed or related breeds. This study focused on understanding the effect of combing different reference group sizes on the accuracy of GS for determining the growth effectiveness and percentage of lean meat in Yorkshire pigs. Specifically, our study investigated two important traits: the age at 100 kg live weight (AGE100) and the backfat thickness at 100 kg live weight (BF100). This research assessed the efficiency of genomic prediction (GP) using different GEBV models across three Yorkshire populations with varying genetic backgrounds. The GeneSeek 50K GGP porcine high-density array was used for genotyping. A total of 2295 Yorkshire pigs were included, representing three Yorkshire pig populations with different genetic backgrounds-295 from Danish (small) lines from Huaibei City, Anhui Province, 500 from Canadian (medium) lines from Lixin County, Anhui Province, and 1500 from American (large) lines from Shanghai. To evaluate the impact of different population combination scenarios on the GS accuracy, three approaches were explored: (1) combining all three populations for prediction, (2) combining two populations to predict the third, and (3) predicting each population independently. Five GEBV models, including three Bayesian models (BayesA, BayesB, and BayesC), the genomic best linear unbiased prediction (GBLUP) model, and single-step GBLUP (ssGBLUP) were implemented through 20 repetitions of five-fold cross-validation (CV). The results indicate that predicting one target population using the other two populations yielded the highest accuracy, providing a novel approach for improving the genomic selection accuracy in Yorkshire pigs. In this study, it was found that using different populations of the same breed to predict small- and medium-sized herds might be effective in improving the GEBV. This investigation highlights the significance of incorporating population combinations in genetic models for predicting the breeding value, particularly for pig farmers confronted with resource limitations.

Genetic parameters for mesenteric torsion in a pig maternal line

Mesenteric torsion (MT) is a condition characterized by the twisting of intestinal loops, leading to a disruption in blood flow and abdominal distension. Several factors can predispose animals to this condition, which primarily affects healthy, fast-growing pigs and often results in fatal outcomes. Given the absence of studies on the genetic factors associated with this condition, our research aimed to estimate the heritability coefficient for MT and explore the genetic correlations between MT and performance traits in pigs. Our dataset comprised of 43,384 records of pigs from a maternal line from a nucleus farm. These pigs were born between 2010 and 2022 and had recorded information regarding MT status (0 for healthy and 1 for affected animals), weaning weight (WW), as well as pedigree, genotypes, and genomic estimated breeding values (GEBVs) for the following traits: MT status, days to reach 110 kg (D110), feed conversion adjusted to 110 kg (FC110), and backfat thickness adjusted to 110 kg (BT110). In our statistical model, the effects of contemporary group (CG: sex, year, and week of weaning) and the linear covariate WW were considered fixed effects, while the direct additive genetic effect was accounted as random. On average, pigs affected by MT had an age of 104.35 ± 37.75 days, and the condition had an overall herd frequency of 2.46 %. Mesenteric torsion occurred more frequently in female pigs raised in collective pens compared to non-castrated males raised in individual pens. Heritability estimates for MT, using both pedigree (0.13 ± 0.02) and genomic (0.12 ± 0.02) data, were of low magnitude. This suggests that while there is genetic variability associated with MT, a significant portion of this variation is due to environmental variance. The correlation between MT and BT110 GEBVs was of low magnitude (0.05). In contrast, the correlations between GEBVs for MT and both D110 and FC110 were moderate in magnitude (0.22 for both traits). This implies that pigs taking longer to reach 110 kg and those with poorer feed conversion are more likely to develop MT. There is genetic variability for mesenteric torsion in pigs. Selection aimed at improving feed conversion and reducing the age at which pigs reach 110 kg could lead to a decrease in the occurrence of MT within pig populations.

A large population study to assess the magnitude of prenatal programming in dairy cattle

The performance of an adult dairy cow may be influenced by events that occur before her birth. The present study investigated potential effects of 2 prenatal groups of factors, Assisted Reproductive Techniques (ART) and maternal characteristics (e.g., dam parity), on offspring performance during their first lactation, in populations of 2 dairy cow breeds: French Holstein and Montbéliarde. The different ART studied included the type of semen (conventional or X-sorted) used for Artificial Insemination (AI) and the technology of conception used (AI, embryo transfer, or in vitro fertilization). Three maternal characteristics were considered: (1) the dam age at first calving, (2) dam parity number, and (3) indicators of dam udder health during gestation (somatic cell score and events of clinical mastitis). First, we investigated whether heifer survival from 3d to 18 mo old was associated with any of the prenatal factors considered. We then estimated the associations of these prenatal factors with 8 traits of commercial interest: (1) stature, (2-4) milk, fat, and protein yields, (5) somatic cell score, (6) clinical mastitis, and (7-8) heifer and cow conception rate, all measured on genotyped cows. Linear models were used for this study with the prenatal factors as covariates in the model, and for the 8 traits, phenotypes were adjusted for their corresponding genomic estimated breeding value. The results indicated that the survival rate of heifers born from embryo transfer was significantly higher than that of heifers born from AI (probably due to preferential management practices), while the other prenatal factors did not explain differences in heifer survival. Among the Montbéliarde cows born from AI, those born from X-sorted semen showed a lightly but significantly lower milk yield than those born without X-sorting of the semen (-52 kg of milk in the first lactation). Among the Holstein cows, those born from embryo transfer presented significantly lower milk performance than cows born from AI. Regarding the maternal characteristics, none or very weak associations were found between the dam age at first calving and the offspring performance in both breeds. Dam parity, on the other hand, was associated with offspring performance for milk, fat, and protein yield in both breeds, however not in the same direction. In the Holstein breed, an increase in dam parity was favorable for offspring performance for milk, fat, and protein yield, whereas in the Montbéliarde breed, an increase in dam parity was associated with lower milk and protein yield and no association was found for fat yield. The udder health of the dam during gestation was not or only weakly associated with the traits studied in the offspring. Although some significant associations were identified due to the large sample size, the effects were modest, typically less than 1% of the phenotypic mean, and were not consistently observed across the 2 breeds.

Developing a liquid capture chip to accelerate the genetic progress of cattle

AbstractLarge‐scale genotyping at a low cost is crucial for molecular breeding of livestock. In this study, the Cattle110K capture chip was developed, based on the genotyping by target sequencing system. The chip panel included 112,180 single necleotide polymorphisms (SNPs), from potential functional regions screened by genome‐wide associated study, BayesB, expression quantitative trait loci‐mapping, ATAC‐seq, and reported functional markers. All the SNPs on the panel were distributed evenly on the cattle genome, with more than 99% of the markers having a minor allele frequency greater than 0.05. Assessment results indicated that a total of 1.2 M high‐quality SNPs were identified in the 110 K regions, averaging approximately 10 SNPs per target sequence. The genotype consistency for the repetitive samples using the Cattle110K liquid chip was 99.21% while the concordance between the Illumina BovineHD BeadChip and this chip averaged 98.17%. A significant association signal for slaughter weight and carcass length was identified on 37.3–41.5 Mb of chromosome 6, pinpointing the NCAPG‐LCORL locus. This locus has previously been associated with meat and carcass traits in cattle. Additionally, novel candidate regions were identified around 3.4 Mb of chromosome 13 and 73.5 Mb of chromosome 8, significantly correlated with hip height and marbling score, respectively. We compared the accuracy of genomic estimated breeding values between the Illumina BovineHD BeadChip and this chip. The results demonstrated that the Cattle110K capture chip had a comparable ability in genomic prediction to the Illumina BovineHD BeadChip. Advances in using the cost‐effective liquid capture chip are expected to accelerate the genetic progress of cattle in the coming years.

Prediction accuracy of genomic estimated breeding values for fruit traits in cultivated tomato (Solanum lycopersicum L.)

BackgroundGenomic selection (GS) is an efficient breeding strategy to improve quantitative traits. It is necessary to calculate genomic estimated breeding values (GEBVs) for GS. This study investigated the prediction accuracy of GEBVs for five fruit traits including fruit weight, fruit width, fruit height, pericarp thickness, and Brix. Two tomato germplasm collections (TGC1 and TGC2) were used as training populations, consisting of 162 and 191 accessions, respectively.ResultsLarge phenotypic variations for the fruit traits were found in these collections and the 51K Axiom™ SNP array generated confident 31,142 SNPs. Prediction accuracy was evaluated using different cross-validation methods, GS models, and marker sets in three training populations (TGC1, TGC2, and combined). For cross-validation, LOOCV was effective as k-fold across traits and training populations. The parametric (RR-BLUP, Bayes A, and Bayesian LASSO) and non-parametric (RKHS, SVM, and random forest) models showed different prediction accuracies (0.594–0.870) between traits and training populations. Of these, random forest was the best model for fruit weight (0.780–0.835), fruit width (0.791–0.865), and pericarp thickness (0.643–0.866). The effect of marker density was trait-dependent and reached a plateau for each trait with 768−12,288 SNPs. Two additional sets of 192 and 96 SNPs from GWAS revealed higher prediction accuracies for the fruit traits compared to the 31,142 SNPs and eight subsets.ConclusionOur study explored several factors to increase the prediction accuracy of GEBVs for fruit traits in tomato. The results can facilitate development of advanced GS strategies with cost-effective marker sets for improving fruit traits as well as other traits. Consequently, GS will be successfully applied to accelerate the tomato breeding process for developing elite cultivars.

Including microbiome information in a multi-trait genomic evaluation: a case study on longitudinal growth performance in beef cattle

BackgroundGrowth rate is an important component of feed conversion efficiency in cattle and varies across the different stages of the finishing period. The metabolic effect of the rumen microbiome is essential for cattle growth, and investigating the genomic and microbial factors that underlie this temporal variation can help maximize feed conversion efficiency at each growth stage.ResultsBy analysing longitudinal body weights during the finishing period and genomic and metagenomic data from 359 beef cattle, our study demonstrates that the influence of the host genome on the functional rumen microbiome contributes to the temporal variation in average daily gain (ADG) in different months (ADG1, ADG2, ADG3, ADG4). Five hundred and thirty-three additive log-ratio transformed microbial genes (alr-MG) had non-zero genomic correlations (rg) with at least one ADG-trait (ranging from |0.21| to |0.42|). Only a few alr-MG correlated with more than one ADG-trait, which suggests that a differential host-microbiome determinism underlies ADG at different stages. These alr-MG were involved in ribosomal biosynthesis, energy processes, sulphur and aminoacid metabolism and transport, or lipopolysaccharide signalling, among others. We selected two alternative subsets of 32 alr-MG that had a non-uniform or a uniform rg sign with all the ADG-traits, regardless of the rg magnitude, and used them to develop a microbiome-driven breeding strategy based on alr-MG only, or combined with ADG-traits, which was aimed at shaping the rumen microbiome towards increased ADG at all finishing stages. Combining alr-MG information with ADG records increased prediction accuracy of genomic estimated breeding values (GEBV) by 11 to 22% relative to the direct breeding strategy (using ADG-traits only), whereas using microbiome information, only, achieved lower accuracies (from 7 to 41%). Predicted selection responses varied consistently with accuracies. Restricting alr-MG based on their rg sign (uniform subset) did not yield a gain in the predicted response compared to the non-uniform subset, which is explained by the absence of alr-MG showing non-zero rg at least with more than one of the ADG-traits.ConclusionsOur work sheds light on the role of the microbial metabolism in the growth trajectory of beef cattle at the genomic level and provides insights into the potential benefits of using microbiome information in future genomic breeding programs to accurately estimate GEBV and increase ADG at each finishing stage in beef cattle.

Optimal open nucleus breeding system for long-term genetic gain in the Pacific white shrimp using genomic selection

The open nucleus breeding system (ONBS) has been widely employed in livestock breeding and has demonstrated a desirable genetic gain compared to the traditional closed nucleus breeding system (CNBS). However, no related study and application for ONBS exist on aquatic animal breeding. The present investigation simulates the family-based ONBS with different proportions of introduced multiplier individuals used as parents of the nucleus population (NP) and CNBS schemes for the harvest body weight of the Pacific white shrimp (Litopenaeus vannamei). The multiplier population (MP) comprised progenies from eight families with high estimated breeding value (EBV) or genomic EBV (GEBV) of NP of the previous generation. ONBS was assigned three proportions (10%, 30%, and 50%) of the introduced MP individuals. The short-term selection (G0 to G10) and long-term selection (G0 to G20) were performed for CNBS and ONBS schemes using pedigree-based best linear unbiased prediction (PBLUP) with the A relationship matrix from the pedigree or single-step genomic BLUP (ssGBLUP) with the H relationship matrix integrating SNP markers and pedigree. The genetic gain of body weight and inbreeding coefficient of NP for eight breeding schemes with CNBS_PBLUP (CP), CNBS_ssGLUP (CS), ONBS_PBLUP (OP10, OP30, OP50), and ONBS_ssGBLUP (OS10, OS30, OS50) with three proportions of the introduced MP individuals were calculated. Compared to CP, genetic gains for the three OP schemes increased by 7.68 to 11.27% after short-term selection, and by 2.51 to 5.79% after long-term selection. In comparison to CS, genetic gains for the three OS schemes increased by 8.21 to 8.96% after short-term selection, and from 4.46 to 5.87% after long-term selection. The ONBS schemes achieved higher genetic gains than the corresponding CNBS schemes whether using genomic or pedigree information. The advantage of ONBS over CNBS is reduced after long-term selection. Genetic gains for the three OS schemes increased by 9.77 to 14.06% after long-term selection compared with the corresponding OP schemes. Furthermore, no significant difference (P > 0.05) in genetic gain was revealed among the ONBS schemes after either pedigree-based or genomic selection. Inbreeding coefficients for the three OP schemes increased from 59.45 to 70.98% compared with CP, whereas inbreeding coefficients for the three OS schemes increased from 24.72 to 32.22% compared with CS. Besides, after long-term selection, inbreeding coefficients for the three OS schemes decreased from 13.13 to 14.79% compared with the corresponding OP schemes. In a nutshell, ONBS using introduced MP individuals has great promise in the selective breeding program of L. vannamei. Genomic information has more potential to improve selection response and control inbreeding level than pedigree information when applying ONBS.

Genetic Basis of Potato Tuber Defects and Identification of Heat-Tolerant Clones.

Heat stress during the potato growing season reduces tuber marketable yield and quality. Tuber quality deterioration includes external (heat sprouts, chained tubers, knobs) and internal (vascular discoloration, hollow heart, internal heat necrosis) tuber defects, as well as a reduction in their specific gravity and increases in reducing sugars that result in suboptimal (darker) processed products (french fries and chips). Successfully cultivating potatoes under heat-stress conditions requires planting heat-tolerant varieties that can produce high yields of marketable tubers, few external and internal tuber defects, high specific gravity, and low reducing sugars (in the case of processing potatoes). Heat tolerance is a complex trait, and understanding its genetic basis will aid in developing heat-tolerant potato varieties. A panel of 217 diverse potato clones was evaluated for yield and quality attributes in Dalhart (2019 and 2020) and Springlake (2020 and 2021), Texas, and genotyped with the Infinium 22 K V3 Potato Array. A genome-wide association study was performed to identify genomic regions associated with heat-tolerance traits using the GWASpoly package. Quantitative trait loci were identified on chromosomes 1, 3, 4, 6, 8, and 11 for external defects and on chromosomes 1, 2, 3, 10, and 11 for internal defects. Yield-related quantitative trait loci were detected on chromosomes 1, 6, and 10 pertaining to the average tuber weight and tuber number per plant. Genomic-estimated breeding values were calculated using the StageWise package. Clones with low genomic-estimated breeding values for tuber defects were identified as donors of good traits to improve heat tolerance. The identified genomic regions associated with heat-tolerance attributes and the genomic-estimated breeding values will be helpful to develop new potato cultivars with enhanced heat tolerance in potatoes.

Improving Genomic Predictions in Multi-Breed Cattle Populations: A Comparative Analysis of BayesR and GBLUP Models.

Numerous studies have shown that combining populations from similar or closely related genetic breeds improves the accuracy of genomic predictions (GP). Extensive experimentation with diverse Bayesian and genomic best linear unbiased prediction (GBLUP) models have been developed to explore multi-breed genomic selection (GS) in livestock, ultimately establishing them as successful approaches for predicting genomic estimated breeding value (GEBV). This study aimed to assess the effectiveness of using BayesR and GBLUP models with linkage disequilibrium (LD)-weighted genomic relationship matrices (GRMs) for genomic prediction in three different beef cattle breeds to identify the best approach for enhancing the accuracy of multi-breed genomic selection in beef cattle. Additionally, a comparison was conducted to evaluate the predictive precision of different marker densities and genetic correlations among the three breeds of beef cattle. The GRM between Yunling cattle (YL) and other breeds demonstrated modest affinity and highlighted a notable genetic concordance of 0.87 between Chinese Wagyu (WG) and Huaxi (HX) cattle. In the within-breed GS, BayesR demonstrated an advantage over GBLUP. The prediction accuracies for HX cattle using the BayesR model were 0.52 with BovineHD BeadChip data (HD) and 0.46 with whole-genome sequencing data (WGS). In comparison to the GBLUP model, the accuracy increased by 26.8% for HD data and 9.5% for WGS data. For WG and YL, BayesR doubled the within-breed prediction accuracy to 14.3% from 7.1%, outperforming GBLUP across both HD and WGS datasets. Moreover, analyzing multiple breeds using genomic selection showed that BayesR consistently outperformed GBLUP in terms of predictive accuracy, especially when using WGS. For instance, in a mixed reference population of HX and WG, BayesR achieved a significant accuracy of 0.53 using WGS for HX, which was a substantial enhancement over the accuracies obtained with GBLUP models. The research further highlights the benefit of including various breeds in the reference group, leading to enhanced accuracy in predictions and emphasizing the importance of comprehensive genomic selection methods. Our research findings indicate that BayesR exhibits superior performance compared to GBLUP in multi-breed genomic prediction accuracy, achieving a maximum improvement of 33.3%, especially in genetically diverse breeds. The improvement can be attributed to the effective utilization of higher single nucleotide polymorphism (SNP) marker density by BayesR, resulting in enhanced prediction accuracy. This evidence conclusively demonstrates the significant impact of BayesR on enhancing genomic predictions in diverse cattle populations, underscoring the crucial role of genetic relatedness in selection methodologies. In parallel, subsequent studies should focus on refining GRM and exploring alternative models for GP.

Genomic selection accelerates genetic improvement of resistance to Vibriosis in the Pacific oyster, Crassostrea gigas

Due to lack of acquired immune system, the oysters cultured along coasts are subject to frequent pathogen threats, which leads to severe disease outbreaks around the world. It's well recognized that the selection breeding of aquatic animals can be accelerated via the harnessing of genomic tools to increase genetic gain and shorten the breeding time. In this work, we carried out genomic selection breeding in the Pacific oysters (Crassostrea gigas) for genetic improvement of resistance to Vibriosis. The genome-wide variations were genotyped by ddRAD-seq from 295 oysters with contrasted resistance to Vibrio infection. Based on genome-wide SNPs, we performed an estimation of genomic heritability and prediction accuracy for resistance to Vibrio alginolyticus in C. gigas. The genomic heritability of resistance to V. alginolyticus was low to moderate, ranging from 0.1405 to 0.2730. Four genomic selection models including rrBLUP, Bayes A, Bayes B and Bayesian Lasso were evaluated, of which Bayes A showed superior prediction accuracy and computational speed. The genomic estimated breeding value (GEBV) calculated by genomic selection model can effectively distinguish the resistance or susceptibility of oysters to Vibriosis. Selection of individuals with high GEBV as broodstock greatly improved the resistance to Vibriosis of their progeny, resulting in 18.42% increase in relative survival rate and 12.73% increase in relative survival time compared to the control population. For the first time, this work reported the efficiency of genomic selection breeding for genetic improvement for resistance trait to Vibriosis in the C. gigas, which would greatly accelerate the cultivation of Vibriosis resistant oyster strains to support the healthy and sustainable development of aquaculture.

Impacts of reference population size and methods on the accuracy of genomic prediction for fleece traits in Inner Mongolia Cashmere Goats.

Inner Mongolia Cashmere Goats (IMCGs) are famous for its cashmere quality and it's a unique genetic resource in China. Therefore, it is necessary to use genomic selection to improve the accuracy of selection for fleece traits in Inner Mongolia cashmere goats. The aim of this study was to determine the effect of methods (GBLUP, BayesA, BayesB, Bayesian LASSO, Bayesian Ridge Region) and the reference population size on accuracy of genomic selection in IMCGs. This study fully utilizes the pedigree and phenotype records of fleece traits in 2255 individuals, genotype of 50794 SNPs after quality control, and environmental data to perform genomic selection of fleece traits. Then GBLUP and Bayes series methods (BayesA, BayesB, Bayesian LASSO, Bayesian Ridge Region) were used to perform estimates of genetic parameter and genomic breeding value. And the accuracy of genomic estimated breeding value (GEBV) is evaluated using the five-fold cross validation method. And the analysis of variance and multiple comparison methods were used to determine the best method for genomic selection in fleece traits of IMCGs. Further the different reference population sizes (500, 1000, 1500, and 2000) was set. Then the best method was applied to estimate genome breeding values, and evaluate the impact of reference population sizes on the accuracy of genome selection for fleece traits in IMCGs. It was found that the genomic prediction accuracy for each fleece trait in IMCGs by GBLUP method is highest, and it is significantly higher than that obtained by Bayesian method. The accuracy of breeding value estimation is 58.52% -68.49%. Also, it was found that the size of the reference population has a significant impact on the accuracy of genome prediction of fleece traits. When the reference population size is 2000, the accuracy of genomic prediction for each fleece trait is significantly higher than other levels, with accuracy of 55.47% -67.87%. This provides a theoretical basis for design a reasonable genome selection plan for Inner Mongolia cashmere goats in the later stag.

Modelling missing pedigree with metafounders and validating single-step genomic predictions in a small dairy cattle population with a great influence of foreign genetics

Genetic improvement in small countries rely heavily on foreign genetics. In an importing country such as Uruguay, consideration of unknown parent groups (UPG) for foreign sires is essential. However, the use of UPG in genomic model evaluations may lead to bias in genomic estimated breeding values. The objective of this study was to study different models including UPG or Metafounders (MF) in the Uruguayan Holstein evaluation and to analyze bias, dispersion, and accuracy of (G)EBV predictions in BLUP and ssGBLUP. Gamma matrix (Γ) was estimated either by using base allele population frequencies obtained by bounded linear regression (MFbounded), or by using 2 values to design Γ, i.e., a single value for the diagonal and a different value for the off-diagonal (MFrobust). Both Γ estimators performed well in terms of GEBV predictions, but MFbounded was the best option. There is, however, some bias whose origin was not completely understood. UPG or MF seem to model correctly genetic progress for unknown parents except for the very first groups (earlier time period). As for validation bulls, bias was observed across all models, whereas for validation cows it was only observed with UPG in BLUP. Overdispersion was found in all models, but it was mostly detected in validation bulls. Ratio of accuracies indicated that ssGBLUP gave better predictions than BLUP.

Genomic prediction based on preselected single-nucleotide polymorphisms from genome-wide association study and imputed whole-genome sequence data annotation for growth traits in Duroc pigs.

The use of whole-genome sequence (WGS) data is expected to improve genomic prediction (GP) power of complex traits because it may contain mutations that in strong linkage disequilibrium pattern with causal mutations. However, a few previous studies have shown no or small improvement in prediction accuracy using WGS data. Incorporating prior biological information into GP seems to be an attractive strategy that might improve prediction accuracy. In this study, a total of 6334 pigs were genotyped using 50K chips and subsequently imputed to the WGS level. This cohort includes two prior discovery populations that comprise 294 Landrace pigs and 186 Duroc pigs, as well as two validation populations that consist of 3770 American Duroc pigs and 2084 Canadian Duroc pigs. Then we used annotation information and genome-wide association study (GWAS) from the WGS data to make GP for six growth traits in two Duroc pig populations. Based on variant annotation, we partitioned different genomic classes, such as intron, intergenic, and untranslated regions, for imputed WGS data. Based on GWAS results of WGS data, we obtained trait-associated single-nucleotide polymorphisms (SNPs). We then applied the genomic feature best linear unbiased prediction (GFBLUP) and genomic best linear unbiased prediction (GBLUP) models to estimate the genomic estimated breeding values for growth traits with these different variant panels, including six genomic classes and trait-associated SNPs. Compared with 50K chip data, GBLUP with imputed WGS data had no increase in prediction accuracy. Using only annotations resulted in no increase in prediction accuracy compared to GBLUP with 50K, but adding annotation information into the GFBLUP model with imputed WGS data could improve the prediction accuracy with increases of 0.00%-2.82%. In conclusion, a GFBLUP model that incorporated prior biological information might increase the advantage of using imputed WGS data for GP.

The Effects of Genetic Distance and Genetic Diversity on Genomic Prediction Accuracy for Soybean Quantitative Disease Resistance to Phytophthora sojae

Assessing quantitative disease resistance and other complex traits can be time- and space-consuming, limiting the number of lines that can be evaluated in a breeding program. A potential improvement is the application of genomic prediction models, where genotypic data are used to calculate genomic estimated breeding values, referred to as genomic selection. Genomic prediction uses training datasets, where a germplasm panel is phenotyped and genotyped to calculate genomic estimated breeding values in a validation panel of lines based solely on genotypic data. To develop an initial phenotypic dataset, breeders may consider utilizing previously phenotyped lines in a publicly available dataset, such as plant introductions (PIs) from the USDA Soybean collection. A relevant question is, how effective is genomic prediction across diverse training and prediction panels? To answer this, we used previously collected phenotypic data for quantitative disease resistance toward Phytophthora sojae. Diverse germplasm panels were represented by sets of PIs originating from the United States, the Republic of Korea, and worldwide, for a total collection of 1,768 PIs. The accuracy of the prediction model was significantly influenced by population differentiation between panels, as well as genetic and phenotypic diversity. Low prediction accuracy resulted when the panel used to create the prediction model was highly differentiated from the validation panel. However, the addition of a small number of accessions to the training panel that were more closely related to the validation panel resulted in increased accuracy. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Genomic prediction for root and yield traits of barley under a water availability gradient: a case study comparing different spatial adjustments

BackgroundIn drought periods, water use efficiency depends on the capacity of roots to extract water from deep soil. A semi-field phenotyping facility (RadiMax) was used to investigate above-ground and root traits in spring barley when grown under a water availability gradient. Above-ground traits included grain yield, grain protein concentration, grain nitrogen removal, and thousand kernel weight. Root traits were obtained through digital images measuring the root length at different depths. Two nearest-neighbor adjustments (M1 and M2) to model spatial variation were used for genetic parameter estimation and genomic prediction (GP). M1 and M2 used (co)variance structures and differed in the distance function to calculate between-neighbor correlations. M2 was the most developed adjustment, as accounted by the Euclidean distance between neighbors.ResultsThe estimated heritabilities (h^2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\widehat{h}}^{2}$$\\end{document}) ranged from low to medium for root and above-ground traits. The genetic coefficient of variation (GCV\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$GCV$$\\end{document}) ranged from 3.2 to 7.0% for above-ground and 4.7 to 10.4% for root traits, indicating good breeding potential for the measured traits. The highest GCV\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$GCV$$\\end{document} observed for root traits revealed that significant genetic change in root development can be achieved through selection. We studied the genotype-by-water availability interaction, but no relevant interaction effects were detected. GP was assessed using leave-one-line-out (LOO) cross-validation. The predictive ability (PA) estimated as the correlation between phenotypes corrected by fixed effects and genomic estimated breeding values ranged from 0.33 to 0.49 for above-ground and 0.15 to 0.27 for root traits, and no substantial variance inflation in predicted genetic effects was observed. Significant differences in PA were observed in favor of M2.ConclusionsThe significant GCV\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$GCV$$\\end{document} and the accurate prediction of breeding values for above-ground and root traits revealed that developing genetically superior barley lines with improved root systems is possible. In addition, we found significant spatial variation in the experiment, highlighting the relevance of correctly accounting for spatial effects in statistical models. In this sense, the proposed nearest-neighbor adjustments are flexible approaches in terms of assumptions that can be useful for semi-field or field experiments.

Genomic selection for target traits in the Australian lentil breeding program.

Genomic selection (GS) uses associations between markers and phenotypes to predict the breeding values of individuals. It can be applied early in the breeding cycle to reduce the cross-to-cross generation interval and thereby increase genetic gain per unit of time. The development of cost-effective, high-throughput genotyping platforms has revolutionized plant breeding programs by enabling the implementation of GS at the scale required to achieve impact. As a result, GS is becoming routine in plant breeding, even in minor crops such as pulses. Here we examined 2,081 breeding lines from Agriculture Victoria's national lentil breeding program for a range of target traits including grain yield, ascochyta blight resistance, botrytis grey mould resistance, salinity and boron stress tolerance, 100-grain weight, seed size index and protein content. A broad range of narrow-sense heritabilities was observed across these traits (0.24-0.66). Genomic prediction models were developed based on 64,781 genome-wide SNPs using Bayesian methodology and genomic estimated breeding values (GEBVs) were calculated. Forward cross-validation was applied to examine the prediction accuracy of GS for these targeted traits. The accuracy of GEBVs was consistently higher (0.34-0.83) than BLUP estimated breeding values (EBVs) (0.22-0.54), indicating a higher expected rate of genetic gain with GS. GS-led parental selection using early generation breeding materials also resulted in higher genetic gain compared to BLUP-based selection performed using later generation breeding lines. Our results show that implementing GS in lentil breeding will fast track the development of high-yielding cultivars with increased resistance to biotic and abiotic stresses, as well as improved seed quality traits.

Genomic Selection as an Approach to Select for Reduced Aflatoxin Contamination in Peanut Under Terminal Drought Stress

Pre-harvest aflatoxin contamination (PAC) occurs when the peanut is exposed to severe drought and heat stress prior to harvest. Development of genotypes that are either resistant or limiting to Aspergillus infection and/or that can curtail aflatoxin production is a key objective in peanut breeding. Using SNP markers and phenotype data on aflatoxin resistance collected in previous years, we utilized genomic selection (GS) to study PAC resistance in peanut. The technique was validated in the Florida-07 x GP-NC WS 16 recombinant inbred population by assessing high heritability traits. GS was then deployed to study PAC resistance in the Tifrunner x C76-16 and Florida-07 x C76-16 populations. The resultant models yielded prediction accuracy values of 0.24 and 0.23 which while low, were comparable to the heritability values of 0.31 and 0.1 for each population, respectively. Using genomic estimated breeding values (GEBVs), entries were selected for two drought shelter studies. Manual scoring and multispectral imaging were used to acquire end season drought stress data. Low correlation values (-0.15 for shelter A and 0.32 for shelter B) were observed between the GEBVs and actual end season aflatoxin content. Correlations between visual drought ratings and PAC were also low (0.23 and 0.16 for shelter A and B respectively). While strong inverse correlations were observed between GNDVI and visual drought ratings (-0.74 and -0.73 for shelters A and B respectively), GNDVI did not confer a clear advantage for selection of PAC resistance. Ideally, the use of genome-spanning markers in GS may enable selection for a difficult to phenotype trait like PAC resistance. Although this study did not show clear advantages of the method over conventional selection, it is an important step for implementation of GS and the use of GEBVs for trait selection in peanut.

Effect of population structure on the accuracy of genomic prediction for carcass traits in Hanwoo cattle: a simulation study

Improvement of quantitative traits in livestock is an essential goal of animal breeding. For the achievement of this goal, an accurate breeding value estimation is needed. The genetic relationship between reference and test groups is a key factor in determining the accuracy of genomic estimated breeding value (GEBV) thus, the structure of the reference population is crucial for efficient genomic selection. By the number of sharing parents, the population structure can be divided into half-sibling and full-sibling families. Also, the population structure of Hanwoo cattle primarily consists of half-sibling families because of the production system. Therefore, comparing half-sibling and full-sibling families is challenging in the Hanwoo cattle population, yet an important issue in the direction of breeding strategy in Korea. The objective of this study was to compare the accuracy of GEBV between different family structures and investigate efficient family size in the reference population using simulated data. 6 different populations were simulated using QMSim software, and the individuals in the last generations were separated into reference and test groups. The GEBV was calculated using BLUPF90 software. Practical accuracy was between 0.36-0.52 in half-sibling families and 0.55-0.77 in full-sibling families. The increase rate of accuracy was highest at the sibling size of 20, with practical accuracy of 0.52 in half-sibling families and 0.77 in full-sibling families. As a result, the most efficient population structure for genomic prediction was a sibling size of 20 in a full-sibling family.

Effect of the number of QTL of carcass weight on the accuracy of genomic estimated breeding values in Hanwoo

The objective of this study was to investigate the relationship between the number of QTL (Quantitative Trait Loci) affecting the carcass weight of Hanwoo and the accuracy of GEBV (Genomic Estimated Breeding Values). The study was performed simulation study to generate three populations comprising 100, 500 and 1,000 QTL. Each population consisted of 13,500 individuals which were selected from the 6th to 10th generations of all 10 generations, while keeping litter size constant (N=1) in population parameters. The genetic parameters for these populations were set at 0.45 trait heritability, 0.3 QTL heritability and 48,578 total markers. In order to confirm the number of significant QTL under the threshold of Bonferroni correction, both GWAS (Genome-wide association study) and GWAS based on Random Forest (RF) were performed. In each population, there were 33, 50 and 32 QTL identified by only GWAS, with 100, 500 and 1,000 QTL. Additionally, RF-based GWAS detected 65, 260 and 491. These results showed that the number of significant QTL is not related to the overall number of QTL in the population. Furthermore, machine learning based GWAS is more accurate than solely using GWAS. To estimate the accuracy of GEBV (Genomic Best Linear Unbiased Prediction) in each population, 5-fold cross-validation was employed using SNP effect of the QTL over p-value. In the result of the estimation, GEBV rose with an increase in the number of QTL in the population, and the correlation between TBV (True Breeding Values) followed the same tendency. As a result, this study suggested that the number of QTL related to carcass weight in Hanwoo could exert a significant influence on genetic improvement.