scholarly journals Combining genetic resources and elite material populations to improve the accuracy of genomic prediction in apple

2021 ◽  
Author(s):  
Xabi Cazenave ◽  
Bernard Petit ◽  
Francois Laurens ◽  
Charles-Eric Durel ◽  
Helene Muranty
Keyword(s):  
Genetic Resources ◽  
Genomic Selection ◽  
Predictive Ability ◽  
Practical Implementation ◽  
Specific Marker ◽  
Training Set ◽  
High Genetic Diversity ◽  
Breeding Programs ◽  
Breeding Cycles ◽  
Two Populations

Genomic selection is an attractive strategy for apple breeding that could reduce the length of breeding cycles. A possible limitation to the practical implementation of this approach lies in the creation of a training set large and diverse enough to ensure accurate predictions. In this study, we investigated the potential of combining two available populations, i.e. genetic resources and elite material, in order to obtain a large training set with a high genetic diversity. We compared the predictive ability of genomic predictions within-population, across-population or when combining both populations, and tested a model accounting for population-specific marker effects in this last case. The obtained predictive abilities were moderate to high according to the studied trait and were always highest when the two populations were combined into a unique training set. We also investigated the potential of such a training set to predict hybrids resulting from crosses between the two populations, with a focus on the method to design the training set and the best proportion of each population to optimize predictions. The measured predictive abilities were very similar for all the proportions, except for the extreme cases where only one of the two populations was used in the training set, in which case predictive abilities could be lower than when using both populations. Using an optimization algorithm to choose the genotypes in the training set also led to higher predictive abilities than when the genotypes were chosen at random. Our results provide guidelines to initiate breeding programs that use genomic selection when the implementation of the training set is a limitation.

scholarly journals Combining genetic resources and elite material populations to improve the accuracy of genomic prediction in apple

2021 ◽  
Author(s):  
Xabi Cazenave ◽  
Bernard Petit ◽  
Marc Lateur ◽  
Hilde Nybom ◽  
Jiri Sedlak ◽  
...  
Keyword(s):  
Genetic Resources ◽  
Genomic Selection ◽  
Predictive Ability ◽  
Practical Implementation ◽  
Specific Marker ◽  
Training Set ◽  
High Genetic Diversity ◽  
Breeding Programs ◽  
Breeding Cycles ◽  
Two Populations

Abstract Genomic selection is an attractive strategy for apple breeding that could reduce the length of breeding cycles. A possible limitation to the practical implementation of this approach lies in the creation of a training set large and diverse enough to ensure accurate predictions. In this study, we investigated the potential of combining two available populations, i.e. genetic resources and elite material, in order to obtain a large training set with a high genetic diversity. We compared the predictive ability of genomic predictions within-population, across-population or when combining both populations, and tested a model accounting for population-specific marker effects in this last case. The obtained predictive abilities were moderate to high according to the studied trait and small increases in predictive ability could be obtained for some traits when the two populations were combined into a unique training set. We also investigated the potential of such a training set to predict hybrids resulting from crosses between the two populations, with a focus on the method to design the training set and the best proportion of each population to optimize predictions. The measured predictive abilities were very similar for all the proportions, except for the extreme cases where only one of the two populations was used in the training set, in which case predictive abilities could be lower than when using both populations. Using an optimization algorithm to choose the genotypes in the training set also led to higher predictive abilities than when the genotypes were chosen at random. Our results provide guidelines to initiate breeding programs that use genomic selection when the implementation of the training set is a limitation.

scholarly journals Maximizing efficiency of genomic selection in CIMMYT’s tropical maize breeding program

2020 ◽  
Author(s):  
Sikiru Adeniyi Atanda ◽  
Michael Olsen ◽  
Juan Burgueño ◽  
Jose Crossa ◽  
Daniel Dzidzienyo ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Prediction Accuracy ◽  
Large Scale ◽  
Primary Objective ◽  
Breeding Program ◽  
Breeding Cycle ◽  
Training Set ◽  
Maize Breeding ◽  
Phenotypic Data ◽  
Breeding Programs

Abstract Key message Historical data from breeding programs can be efficiently used to improve genomic selection accuracy, especially when the training set is optimized to subset individuals most informative of the target testing set. Abstract The current strategy for large-scale implementation of genomic selection (GS) at the International Maize and Wheat Improvement Center (CIMMYT) global maize breeding program has been to train models using information from full-sibs in a “test-half-predict-half approach.” Although effective, this approach has limitations, as it requires large full-sib populations and limits the ability to shorten variety testing and breeding cycle times. The primary objective of this study was to identify optimal experimental and training set designs to maximize prediction accuracy of GS in CIMMYT’s maize breeding programs. Training set (TS) design strategies were evaluated to determine the most efficient use of phenotypic data collected on relatives for genomic prediction (GP) using datasets containing 849 (DS1) and 1389 (DS2) DH-lines evaluated as testcrosses in 2017 and 2018, respectively. Our results show there is merit in the use of multiple bi-parental populations as TS when selected using algorithms to maximize relatedness between the training and prediction sets. In a breeding program where relevant past breeding information is not readily available, the phenotyping expenditure can be spread across connected bi-parental populations by phenotyping only a small number of lines from each population. This significantly improves prediction accuracy compared to within-population prediction, especially when the TS for within full-sib prediction is small. Finally, we demonstrate that prediction accuracy in either sparse testing or “test-half-predict-half” can further be improved by optimizing which lines are planted for phenotyping and which lines are to be only genotyped for advancement based on GP.

scholarly journals Genomic Selection in Winter Wheat Breeding Using a Recommender Approach

Genes ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 779
Author(s):  
Dennis N. Lozada ◽  
Arron H. Carter
Keyword(s):  
Winter Wheat ◽  
Genomic Selection ◽  
Prediction Models ◽  
Heading Date ◽  
Predictive Ability ◽  
Wheat Breeding ◽  
Snp Markers ◽  
Bayesian Regression ◽  
Phenotypic Trait ◽  
Breeding Programs

Achieving optimal predictive ability is key to increasing the relevance of implementing genomic selection (GS) approaches in plant breeding programs. The potential of an item-based collaborative filtering (IBCF) recommender system in the context of multi-trait, multi-environment GS has been explored. Different GS scenarios for IBCF were evaluated for a diverse population of winter wheat lines adapted to the Pacific Northwest region of the US. Predictions across years through cross-validations resulted in improved predictive ability when there is a high correlation between environments. Using multiple spectral traits collected from high-throughput phenotyping resulted in better GS accuracies for grain yield (GY) compared to using only single traits for predictions. Trait adjustments through various Bayesian regression models using genomic information from SNP markers was the most effective in achieving improved accuracies for GY, heading date, and plant height among the GS scenarios evaluated. Bayesian LASSO had the highest predictive ability compared to other models for phenotypic trait adjustments. IBCF gave competitive accuracies compared to a genomic best linear unbiased predictor (GBLUP) model for predicting different traits. Overall, an IBCF approach could be used as an alternative to traditional prediction models for important target traits in wheat breeding programs.

scholarly journals Genomic predictive ability for foliar nutritive traits in perennial ryegrass

2019 ◽  
Author(s):  
Sai Krishna Arojju ◽  
Mingshu Cao ◽  
M. Z. Zulfi Jahufer ◽  
Brent A Barrett ◽  
Marty J Faville
Keyword(s):  
Genomic Selection ◽  
Genomic Prediction ◽  
Nutritive Value ◽  
Prediction Models ◽  
Genotypic Variation ◽  
Genetic Correlations ◽  
Predictive Ability ◽  
Water Soluble ◽  
Training Set ◽  
Sib Families

AbstractForage nutritive value impacts animal nutrition, which underpins livestock productivity, reproduction and health. Genetic improvement for nutritive traits has been limited, as they are typically expensive and time-consuming to measure through conventional methods. Genomic selection is appropriate for such complex and expensive traits, enabling cost-effective prediction of breeding values using genome-wide markers. The aims of the present study were to assess the potential of genomic selection for a range of nutritive traits in a multi-population training set, and to quantify contributions of genotypic, environmental and genotype-by-environment (G × E) variance components to trait variation and heritability for nutritive traits. The training set consisted of a total of 517 half-sibling (half-sib) families, from five advanced breeding populations, evaluated in two distinct New Zealand grazing environments. Autumn-harvested samples were analyzed for 18 nutritive traits and maternal parents of the half-sib families were genotyped using genotyping-by-sequencing. Significant (P<0.05) genotypic variation was detected for all nutritive traits and genomic heritability (h2g) was moderate to high (0.20 to 0.74). G × E interactions were significant and particularly large for water soluble carbohydrate (WSC), crude fat, phosphorus (P) and crude protein. GBLUP, KGD-GBLUP and BayesC genomic prediction models displayed similar predictive ability, estimated by 10-fold cross validation, for all nutritive traits with values ranging from r = 0.16 to 0.45 using phenotypes from across two environments. High predictive ability was observed for the mineral traits sulphur (0.44), sodium (0.45) and magnesium (0.45) and the lowest values were observed for P (0.16), digestibility (0.22) and high molecular weight WSC (0.23). Predictive ability estimates for most nutritive traits were retained when marker number was reduced from 1 million to as few as 50,000. The moderate to high predictive abilities observed suggests implementation of genomic selection is feasible for most of the nutritive traits examined. For traits with lower predictive ability, multi-trait genomic prediction approaches that exploit the strong genetic correlations observed amongst some nutritive traits may be useful. This appears to be particularly important for WSC, considered one of the primary constituent of nutritive value for forages.

scholarly journals Predictive ability of genomic selection models in a multi-population perennial ryegrass training set using genotyping-by-sequencing

2017 ◽  
Vol 131 (3) ◽  
pp. 703-720 ◽  
Author(s):  
Marty J. Faville ◽  
Siva Ganesh ◽  
Mingshu Cao ◽  
M. Z. Zulfi Jahufer ◽  
Timothy P. Bilton ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Perennial Ryegrass ◽  
Predictive Ability ◽  
Genotyping By Sequencing ◽  
Training Set ◽  
Selection Models

scholarly journals Reduction of genotyping marker density for genomic selection is not an affordable approach to long-term breeding in cross-pollinated crops

2021 ◽  
Author(s):  
Júlio César DoVale ◽  
Humberto Fanelli Carvalho ◽  
Felipe Sabadin ◽  
Roberto Fritsche-Neto
Keyword(s):  
Genomic Selection ◽  
Recurrent Selection ◽  
Predictive Ability ◽  
Computational Time ◽  
Marker Density ◽  
Additive Variance ◽  
Selection Scheme ◽  
Performance Accuracy ◽  
Breeding Cycles

ABSTRACTThe selection of informative markers has been studied massively as an alternative to reduce genotyping costs for the genomic selection (GS) application. Low-density marker panels are attractive for GS because they decrease computational time-consuming and multicollinearity beyond more individuals can be genotyped with the same cost. Nevertheless, these inferences are usually made empirically using “static” training sets and populations, which are adequate only to predict a breeding program’s initial cycles but might not for long-term cycles. Moreover, to the best of our knowledge, none of these inferences considered the inclusion of dominance into the GS models, which is particularly important to predict cross-pollinated crops. Therefore, that reveals an important and unexplored topic for allogamous long-term breeding. To achieve this goal, we employed two approaches: the former used empirical maize datasets, and the latter simulations of long-term breeding cycles of phenotypic and genomic recurrent selection (intrapopulation and reciprocal). Then, we observed the reducing marker density effect on populations (mean, the best genotypes performance, accuracy, additive variance) over cycles and models (additive, additive-dominance, specific combining ability (SCA)). Our results indicate that the markers reduction based on different linkage disequili brium (LD) levels is viable only within a cycle and brings a significant decrease in predictive ability over generations. Furthermore, in the long-term, regardless of the selection scheme adopted, the more makers, the better because they buffer LD losses caused by recombination over breeding cycles. Finally, regarding the accuracy, the additive-dominant models tend to outperform the additive ones and perform similar to the SCA.

scholarly journals Training Set Optimization for Sparse Phenotyping in Genomic Selection: A Conceptual Overview

2021 ◽  
Vol 12 ◽  
Author(s):  
Julio Isidro y Sánchez ◽  
Deniz Akdemir
Keyword(s):  
Genomic Selection ◽  
Lessons Learned ◽  
Crop Breeding ◽  
Training Population ◽  
Training Set ◽  
Breeding Programs ◽  
Efficiency And Effectiveness ◽  
Key Steps ◽  
The Relationship ◽  
Conceptual Overview

Genomic selection (GS) is becoming an essential tool in breeding programs due to its role in increasing genetic gain per unit time. The design of the training set (TRS) in GS is one of the key steps in the implementation of GS in plant and animal breeding programs mainly because (i) TRS optimization is critical for the efficiency and effectiveness of GS, (ii) breeders test genotypes in multi-year and multi-location trials to select the best-performing ones. In this framework, TRS optimization can help to decrease the number of genotypes to be tested and, therefore, reduce phenotyping cost and time, and (iii) we can obtain better prediction accuracies from optimally selected TRS than an arbitrary TRS. Here, we concentrate the efforts on reviewing the lessons learned from TRS optimization studies and their impact on crop breeding and discuss important features for the success of TRS optimization under different scenarios. In this article, we review the lessons learned from training population optimization in plants and the major challenges associated with the optimization of GS including population size, the relationship between training and test set (TS), update of TRS, and the use of different packages and algorithms for TRS implementation in GS. Finally, we describe general guidelines to improving the rate of genetic improvement by maximizing the use of the TRS optimization in the GS framework.

scholarly journals Frequent germplasm exchanges drive the high genetic diversity of Chinese-cultivated common apricot germplasm

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Qiuping Zhang ◽  
Diyang Zhang ◽  
Kang Yu ◽  
Jingjing Ji ◽  
Ning Liu ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Genetic Resources ◽  
Crop Breeding ◽  
High Quality ◽  
High Genetic Diversity ◽  
Breeding Programs ◽  
Germplasm Diversity ◽  
High Level ◽  
Modern Breeding ◽  
Chromosome Level

AbstractThe genetic diversity of germplasm is critical for exploring genetic and phenotypic resources and has important implications for crop-breeding sustainability and improvement. However, little is known about the factors that shape and maintain genetic diversity. Here, we assembled a high-quality chromosome-level reference of the Chinese common apricot ‘Yinxiangbai’, and we resequenced 180 apricot accessions that cover four major ecogeographical groups in China and other accessions from occidental countries. We concluded that Chinese-cultivated common apricot germplasms possessed much higher genetic diversity than those cultivated in Western countries. We also detected seven migration events among different apricot groups, where 27% of the genome was identified as being introgressed. Remarkably, we demonstrated that these introgressed regions drove the current high level of germplasm diversity in Chinese-cultivated common apricots by introducing different genes related to distinct phenotypes from different cultivated groups. Our results highlight the consideration that introgressed regions may provide an important reservoir of genetic resources that can be used to sustain modern breeding programs.

scholarly journals BWGS: a R package for genomic selection and its application to a wheat breeding programme

2019 ◽  
Author(s):  
Gilles Charmet ◽  
Louis Gautier Tran ◽  
Jérôme Auzanneau ◽  
Renaud Rincent ◽  
Sophie Bouchet
Keyword(s):  
Missing Data ◽  
Genomic Selection ◽  
Prediction Models ◽  
Predictive Accuracy ◽  
Predictive Ability ◽  
Breeding Programme ◽  
Training Set ◽  
Desktop Computer ◽  
Marker Selection ◽  
Breeding Programmes

AbstractWe developed an integrated R library called BWGS to enable easy computation of Genomic Estimates of Breeding values (GEBV) for genomic selection. BWGS relies on existing R-libraries, all freely available from CRAN servers. The two main functions enable to run 1) replicated random cross validations within a training set of genotyped and phenotyped lines and 2) GEBV prediction, for a set of genotyped-only lines. Options are available for 1) missing data imputation, 2) markers and training set selection and 3) genomic prediction with 15 different methods, either parametric or semi-parametric.The usefulness and efficiency of BWGS are illustrated using a population of wheat lines from a real breeding programme. Adjusted yield data from historical trials (highly unbalanced design) were used for testing the options of BWGS. On the whole, 760 candidate lines with adjusted phenotypes and genotypes for 47 839 robust SNP were used. With a simple desktop computer, we obtained results which compared with previously published results on wheat genomic selection. As predicted by the theory, factors that are most influencing predictive ability, for a given trait of moderate heritability, are the size of the training population and a minimum number of markers for capturing every QTL information. Missing data up to 40%, if randomly distributed, do not degrade predictive ability once imputed, and up to 80% randomly distributed missing data are still acceptable once imputed with Expectation-Maximization method of package rrBLUP. It is worth noticing that selecting markers that are most associated to the trait do improve predictive ability, compared with the whole set of markers, but only when marker selection is made on the whole population. When marker selection is made only on the sampled training set, this advantage nearly disappeared, since it was clearly due to overfitting. Few differences are observed between the 15 prediction models with this dataset. Although non-parametric methods that are supposed to capture non-additive effects have slightly better predictive accuracy, differences remain small. Finally, the GEBV from the 15 prediction models are all highly correlated to each other. These results are encouraging for an efficient use of genomic selection in applied breeding programmes and BWGS is a simple and powerful toolbox to apply in breeding programmes or training activities.

scholarly journals Genomic Predictive Ability for Foliar Nutritive Traits in Perennial Ryegrass

2019 ◽  
Vol 10 (2) ◽  
pp. 695-708 ◽  
Author(s):  
Sai Krishna Arojju ◽  
Mingshu Cao ◽  
M. Z. Zulfi Jahufer ◽  
Brent A. Barrett ◽  
Marty J. Faville
Keyword(s):  
Genomic Selection ◽  
Perennial Ryegrass ◽  
Nutritive Value ◽  
Prediction Models ◽  
Predictive Ability ◽  
Genotyping By Sequencing ◽  
Water Soluble ◽  
Soluble Carbohydrate ◽  
Training Set ◽  
Sib Families

Forage nutritive value impacts animal nutrition, which underpins livestock productivity, reproduction and health. Genetic improvement for nutritive traits in perennial ryegrass has been limited, as they are typically expensive and time-consuming to measure through conventional methods. Genomic selection is appropriate for such complex and expensive traits, enabling cost-effective prediction of breeding values using genome-wide markers. The aims of the present study were to assess the potential of genomic selection for a range of nutritive traits in a multi-population training set, and to quantify contributions of family, location and family-by-location variance components to trait variation and heritability for nutritive traits. The training set consisted of a total of 517 half-sibling (half-sib) families, from five advanced breeding populations, evaluated in two distinct New Zealand grazing environments. Autumn-harvested samples were analyzed for 18 nutritive traits and maternal parents of the half-sib families were genotyped using genotyping-by-sequencing. Significant (P < 0.05) family variance was detected for all nutritive traits and genomic heritability (h2g) was moderate to high (0.20 to 0.74). Family-by-location interactions were significant and particularly large for water soluble carbohydrate (WSC), crude fat, phosphorus (P) and crude protein. GBLUP, KGD-GBLUP and BayesCπ genomic prediction models displayed similar predictive ability, estimated by 10-fold cross validation, for all nutritive traits with values ranging from r = 0.16 to 0.45 using phenotypes from across two locations. High predictive ability was observed for the mineral traits sulfur (0.44), sodium (0.45) and magnesium (0.45) and the lowest values were observed for P (0.16), digestibility (0.22) and high molecular weight WSC (0.23). Predictive ability estimates for most nutritive traits were retained when marker number was reduced from one million to as few as 50,000. The moderate to high predictive abilities observed suggests implementation of genomic selection is feasible for most of the nutritive traits examined.

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