Prescreening of large-effect markers with multiple strategies improves the accuracy of genomic prediction
Keanning LiBingxing AnMang LiangTianpeng ChangTianyu DengLili DuSheng CaoYueying DuHongyan LiLingyang XuLupei ZhangXue GaoJunya LiHuijiang Gao
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Presently, integrating multi-omics information into a prediction model has become a ameliorate strategy for genomic selection to improve genomic prediction accuracy. Here, we set the genomic and transcriptomic data as the training population data, using BSLMM, TWAS, and eQTL mapping to prescreen features according to |β∘b|>0, top1% percent of phenotypic variation explained (PVE), expression-associated single nucleotide polymorphisms (eSNPs), and egenes (false discovery rate (FDR) < 0.01), where these loci were set as extra fixed effects (named GBLUP-Fix) and random effects (GFBLUP) to improve the prediction accuracy in the validation population, respectively. The results suggested that both GBLUP-Fix and GFBLUP models could improve the accuracy of longissimus dorsi muscle (LDM), water holding capacity WHC, shear force SF, and pH in Huaxi cattle on average from 2.14% to 8.69%, especially the improvement of GFBLUP-TWAS over GBLUP was 13.66% for SF. These methods also captured more genetic variance than GBLUP. Our study confirmed that multi-omics-assisted large-effects loci prescreening could improve the accuracy of genomic prediction.Keywords:
Genomic Selection
Recently, a selection index called Valor Económico Lechero (VEL) was developed for Chilean dairy cattle under pasture. However, a specific selection scheme has not yet been implemented. This study aimed to estimate genetic progress from selection on the VEL selection index based on selection schemes using progeny testing (PT) and genomic selection (GS). Under a PT-scheme, estimated genetic progress was 41.50, 3.44, and 2.33 kg/year for milk, fat, and protein yield, respectively. The realised genetic gain takes eight-year after the PT-scheme implementation, which may be a disincentive for implementing a PT-scheme, suggesting that importing frozen semen of proven bulls could be a preferred alternative. In this case, an option may be to conduct the genetic evaluation of those bulls using their progeny in Chile for the traits included in VEL selection index. In the case of implementing a specific selection scheme, compared to PT, a more profitable alternative might be the implementation of a GS-scheme, that would result in a faster genetic gain in the aggregate breeding value or merit for all the traits included in the selection objective (0.323-0.371
Genetic gain
Genomic Selection
Progeny testing
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Genomic selection is particularly beneficial for dairy cattle breeding programs, because it allows to significantly reduce generation interval, and cheaply increase selection intensity, while the accuracy of selection is only marginally lower compared to progeny testing schemes. It relaxes the requirement of traditional dairy cattle breeding schemes to measure phenotypes from progeny groups for each male selection candidate. Therefore, genomic selection holds the promise to allow selection for new traits, that are difficult or expensive to measure.
Genomic Selection
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The availability of genomic marker panels has made possible more precise estimates of breeding values. Sheep breeding programs are implementing genomic selection. In Latxa dairy sheep breed, a previous study using pre-corrected data and a small number of genotyped animals did not show a clear advantage of genomic selection. The objective of the present study was to ascertain the possible benefits of GS for the Latxa breed based on more data than before and using better tools, in particular single-step genomic BLUP using metafounders to model missing pedigree. Goodness of prediction of pedigree and genomic evaluations was analyzed by cross-validation comparing predictions of young rams using whole and partial (truncated) data sets. The results showed that with the current available data, genetic and genomic evaluations have the same accuracy. Contrary to the previous study, predictions were nearly unbiased, which shows the advantage of using single-step genomic BLUP. However, genomic information did not yield more precise evaluations. This could be explained by the small number of sibs in the young rams.
Genomic Selection
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Genomic Selection
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Summary Using Monte Carlo simulation, two schemes of restricted selection were compared under various combinations of genetic parameters and constraints on the genetic gains. The first selection scheme is the combination of best linear unbiased prediction (BLUP) evaluation and linear programming technique (BLUP + LP), and the second one is based on the restricted BLUP selection (RBLUP). Selection for two traits was supposed, in which animals were selected to maximize the genetic gain in trait 2 (Δ g 2 ) under a proportional restriction on the genetic gain in trait 1 (Δ g 1 ) to satisfy the intended ratio (Δ g 1 :Δ g 2 ). Little differences were found between the two selection schemes with respect to the genetic gains averaged over replicates. However, in all the cases studied, the variance of genetic gains among replicates under BLUP + LP selection was smaller and less sensitive to the genetic parameters and the intended restriction than RBLUP selection. Under the situations of antagonistic selection, the difference tended to be larger. When the heritabilities of the two traits were different, RBLUP selection remarkably increased the variance of genetic gain in a trait with a higher heritability. These results suggest that BLUP + LP selection should always be preferable to RBLUP selection because of the smaller risk of selection. This choice is especially important for the situation where the genetic parameters act as limiting factors for the achievement of intended genetic gains.
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Truncation selection
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Abstract Family selection is an important procedure to be considered in the early stage of sugarcane ( Saccharum spp.) breeding. Different approaches are available, but few comparative studies are performed in practice. The aim of this study was to evaluate the potential genetic gain when different selection strategies at early sugarcane breeding stages are considered. Two experiments involving the first and second selection stages of the Sugarcane Breeding Program of RIDESA/UFSCar were performed. In the first stage, three selection methods based on the concept of selection between and within families were applied to predict the highest genetic gain, that is, BLUPi: simultaneously contemplates family and individual information for selection; BLUPis: promotes the dynamic allocation of individuals to be selected in each family; BLUP AUS : identifies high potential families and establishes differentiated selection intensities; additionally, mass and random selection methods were also performed. In the second stage, the selected clones were evaluated to compare the realized genetic gain. In the first stage, BLUP AUS had the highest predicted gain from selection ( P GS; 12.7%) in tonnes of Pol per hectare (TPH). The BLUPis was highly correlated with BLUP AUS and was efficient. Moreover, BLUPi proved to be economically impracticable since phenotypic evaluations must be performed at the individual level. Family selection via BLUP AUS was equivalent to mass selection probably due to the low coefficient of genetic variation (CV g ≤ 15) among the families. However, the family selection strategy provides extra information for breeders that cannot be ignored; the possibility of studying the combining ability of genotypes for identifying promising parents for future cross combinations.
Genetic gain
Hectare
Progeny testing
Breeding program
Saccharum
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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
Beef Cattle
Linkage Disequilibrium
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With the fast and wide development of genomic evaluations in dairy cattle, the design of breeding schemes has been modified and the long process of progeny testing is being replaced by an early and accurate genomic selection step.In the future, only selected candidates will get performances to be evaluated by the classical method of Best Linear Unbiased Prediction (BLUP). After a genomic selection step, information about the selection process is no longer complete, BLUP assumptions are violated and solutions, i.e., estimated breeding values, are feared to be incorrect.The aim of the thesis study was to consider the consequences of genomic selection on the classical genetic evaluations at the national and international levels.First, bias in national breeding values was assessed by repeated simulations. Estimated breeding values were systematically underestimated and less accurate after a genomic selection step not accounted for in genetic evaluation models.Secondly, a statistical procedure, a BLUP model with genomic pseudo-performances, was investigated to eliminate, with success, bias in estimated breeding values.In a third part, the consequences of genomic selection on international evaluations were studied by simulations. Bulls from the country sending incomplete and/or biased breeding values were the most penalized in international rankings.In conclusion, it is not only necessary but also urgent to prevent from bias in classical evaluations and therefore avoid harmful impacts on international comparisons, on future genomic evaluations, and more generally on selection efficiency. Alternative approaches were thus discussed to propose short and long term strategies for routine evaluations. Nevertheless, a main consequence of bias corrected breeding values is that all genetic predictions will include some genomic information in a near future: adaptations of evaluation methods are still required to optimally benefit from all types of information.
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Genetic gain
Genomic information
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Abstract Popularly known as juçaizeiro, Euterpe edulis has been gaining prominence in the fruit growing sector and has demanded the development of superior genetic materials. As it is a native species and still little studied, the application of more sophisticated techniques can result in higher gains with less time. Until now, there are no studies that apply genomic prediction for this crop, especially in multi-trait analysis. In this sense, this study aimed to apply new methods and breeding techniques for the juçaizeiro, to optimize this breeding program through the application of genomic selection. This data consisted of 275 juçaizeiro genotypes from a population of Rio Novo do Sul-ES, Brazil. The genomic prediction was performed using the multi-trait (G-BLUP MT) and single-trait (G-BLUP ST) models and the selection of superior matrices was based on the selection index of Mulamba and Mock. Similar results for predictive ability were observed for both models. However, the G-BLUP ST model provided greater selection gains when compared to the G-BLUP MT. For this reason, the genomic estimated breeding values (GEBVs) from the G-BLUP ST were used to select the six superior genotypes (UFES.A.RN.390, UFES.A.RN.386, UFES.A.RN.080, UFES.A.RN.383, UFES.S.RN.098, and UFES.S.RN.093), to provide superior genetic materials for the development of seedlings and implantation of productive orchards, which will meet the demands of the productive, industrial and consumer market. Key message In the first genomic selection study for Euterpe edulis , substantial gains for multiple traits of fruit production was reported. This is a key factor for the sustainable use of the species in the Atlantic Forest.
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Genomic Selection
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