Increased Prediction Accuracy in Wheat Breeding Trials Using a Marker × Environment Interaction Genomic Selection Model

Genomic selection is an approach to select elite breeding stock based on the use of dense genetic markers and that has led to the development of various models to derive a predictive equation. However, the current genomic selection software faces several issues such as low prediction accuracy, low computational efficiency, or an inability to handle large-scale sample data. We report the development of a genomic prediction model named FMixFN with four zero-mean normal distributions as the prior distributions to optimize the predictive ability and computing efficiency. The variance of the prior distributions in our model is precisely determined based on an F2 population, and genomic estimated breeding values (GEBV) can be obtained accurately and quickly in combination with an iterative conditional expectation algorithm. We demonstrated that FMixFN improves computational efficiency and predictive ability compared to other methods, such as GBLUP, SSgblup, MIX, BayesR, BayesA, and BayesB. Most importantly, FMixFN may handle large-scale sample data, and thus should be able to meet the needs of large breeding companies or combined breeding schedules. Our study developed a Bayes genomic selection model called FMixFN, which combines stable predictive ability and high computational efficiency, and is a big data-oriented genomic selection model that has potential in the future. The FMixFN method can be freely accessed at https://zenodo.org/record/5560913 (DOI: 10.5281/zenodo.5560913).

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Genomic selection for lentil breeding: empirical evidence

10.1101/608406 ◽

2019 ◽

Cited By ~ 2

Author(s):

Teketel A. Haile ◽

Taryn Heidecker ◽

Derek Wright ◽

Sandesh Neupane ◽

Larissa Ramsay ◽

...

Keyword(s):

Genomic Selection ◽

Statistical Models ◽

Prediction Accuracy ◽

Recombinant Inbred Lines ◽

Genotype By Environment Interaction ◽

Exome Capture ◽

Environment Interaction ◽

Multiple Trait ◽

Similar Accuracy ◽

Population Prediction

AbstractGenomic selection (GS) is a type of marker-based selection which was initially suggested for livestock breeding and is being encouraged for crop breeding. Several statistical models and approaches have been developed to implement GS; however, none of these methods have been tested for use in lentil breeding. This study was conducted to evaluate different GS models and prediction scenarios based on empirical data and to make recommendations for designing genomic selection strategies for lentil breeding. We evaluated nine single-trait models, two multiple-trait models, and models that account for population structure and genotype-by-environment interaction (GEI) using a lentil diversity panel and two recombinant inbred lines (RIL) populations that were genotyped using a custom exome capture assay. Within-population, across-population and across-environment predictions were made for five phenology traits. Prediction accuracy varied among the evaluated models, populations, prediction scenarios, traits, and statistical models. Single-trait models showed similar accuracy for each trait in the absence of large effect QTL but BayesB outperformed all models when there were QTL with relatively large effects. Models that accounted for GEI and multiple-trait (MT) models increased prediction accuracy for a low heritability trait by up to 66% and 14% but accuracy did not improve for traits of high heritability. Moderate to high accuracies were obtained for within-population and across-environment predictions but across-population prediction accuracy was very low. This suggests that GS can be implemented in lentil to make predictions within populations and across environments, but across-population prediction should not be considered when the population size is small.

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Bayesian genomic models boost prediction accuracy for survival to Streptococcus agalactiae infection in Nile tilapia (Oreochromus nilioticus)

Genetics Selection Evolution ◽

10.1186/s12711-021-00629-y ◽

2021 ◽

Vol 53 (1) ◽

Author(s):

Rajesh Joshi ◽

Anders Skaarud ◽

Alejandro Tola Alvarez ◽

Thomas Moen ◽

Jørgen Ødegård

Keyword(s):

Genomic Selection ◽

Streptococcus Agalactiae ◽

Nile Tilapia ◽

Prediction Accuracy ◽

Predictive Accuracy ◽

Challenge Test ◽

Bacterial Disease ◽

Nucleotide Polymorphisms ◽

Genomic Selection Model ◽

Selection For

Abstract Background Streptococcosis is a major bacterial disease in Nile tilapia that is caused by Streptococcus agalactiae infection, and development of resistant strains of Nile tilapia represents a sustainable approach towards combating this disease. In this study, we performed a controlled disease trial on 120 full-sib families to (i) quantify and characterize the potential of genomic selection for survival to S. agalactiae infection in Nile tilapia, and (ii) identify the best genomic model and the optimal density of single nucleotide polymorphisms (SNPs) for this trait. Methods In total, 40 fish per family (15 fish intraperitoneally injected and 25 fish as cohabitants) were used in the challenge test. Mortalities were recorded every 3 h for 35 days. After quality control, genotypes (50,690 SNPs) and phenotypes (0 for dead and 1 for alive) for 2472 cohabitant fish were available. Genetic parameters were obtained using various genomic selection models (genomic best linear unbiased prediction (GBLUP), BayesB, BayesC, BayesR and BayesS) and a traditional pedigree-based model (PBLUP). The pedigree-based analysis used a deep 17-generation pedigree. Prediction accuracy and bias were evaluated using five replicates of tenfold cross-validation. The genomic models were further analyzed using 10 subsets of SNPs at different densities to explore the effect of pruning and SNP density on predictive accuracy. Results Moderate estimates of heritabilities ranging from 0.15 ± 0.03 to 0.26 ± 0.05 were obtained with the different models. Compared to a pedigree-based model, GBLUP (using all the SNPs) increased prediction accuracy by 15.4%. Furthermore, use of the most appropriate Bayesian genomic selection model and SNP density increased the prediction accuracy up to 71%. The 40 to 50 SNPs with non-zero effects were consistent for all BayesB, BayesC and BayesS models with respect to marker id and/or marker locations. Conclusions These results demonstrate the potential of genomic selection for survival to S. agalactiae infection in Nile tilapia. Compared to the PBLUP and GBLUP models, Bayesian genomic models were found to boost the prediction accuracy significantly.

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Scalable Sparse Testing Genomic Selection Strategy for Early Yield Testing Stage

Frontiers in Plant Science ◽

10.3389/fpls.2021.658978 ◽

2021 ◽

Vol 12 ◽

Author(s):

Sikiru Adeniyi Atanda ◽

Michael Olsen ◽

Jose Crossa ◽

Juan Burgueño ◽

Renaud Rincent ◽

...

Keyword(s):

Genomic Selection ◽

Prediction Accuracy ◽

Cross Validation ◽

Prediction Models ◽

Historical Data ◽

Coefficient Of Determination ◽

Selection Strategy ◽

Environment Interaction ◽

Genetic Merit ◽

Testing Stage

To enable a scalable sparse testing genomic selection (GS) strategy at preliminary yield trials in the CIMMYT maize breeding program, optimal approaches to incorporate genotype by environment interaction (GEI) in genomic prediction models are explored. Two cross-validation schemes were evaluated: CV1, predicting the genetic merit of new bi-parental populations that have been evaluated in some environments and not others, and CV2, predicting the genetic merit of half of a bi-parental population that has been phenotyped in some environments and not others using the coefficient of determination (CDmean) to determine optimized subsets of a full-sib family to be evaluated in each environment. We report similar prediction accuracies in CV1 and CV2, however, CV2 has an intuitive appeal in that all bi-parental populations have representation across environments, allowing efficient use of information across environments. It is also ideal for building robust historical data because all individuals of a full-sib family have phenotypic data, albeit in different environments. Results show that grouping of environments according to similar growing/management conditions improved prediction accuracy and reduced computational requirements, providing a scalable, parsimonious approach to multi-environmental trials and GS in early testing stages. We further demonstrate that complementing the full-sib calibration set with optimized historical data results in improved prediction accuracy for the cross-validation schemes.

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Phenomic selection in wheat breeding: identification and optimisation of factors influencing prediction accuracy and comparison to genomic selection

Theoretical and Applied Genetics ◽

10.1007/s00122-021-04005-8 ◽

2022 ◽

Author(s):

Pauline Robert ◽

Jérôme Auzanneau ◽

Ellen Goudemand ◽

François-Xavier Oury ◽

Bernard Rolland ◽

...

Keyword(s):

Genomic Selection ◽

Prediction Accuracy ◽

Wheat Breeding ◽

Factors Influencing

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Genomic Prediction and Selection for Fruit Traits in Winter Squash

G3 Genes|Genome|Genetics ◽

10.1534/g3.120.401215 ◽

2020 ◽

Vol 10 (10) ◽

pp. 3601-3610

Author(s):

Christopher O. Hernandez ◽

Lindsay E. Wyatt ◽

Michael R. Mazourek

Keyword(s):

Genomic Selection ◽

Fruit Quality ◽

Quality Index ◽

Recurrent Selection ◽

Selection Model ◽

Breeding Cycle ◽

Quality Traits ◽

Genomic Selection Model ◽

Selection For ◽

Winter Squash

Improving fruit quality is an important but challenging breeding goal in winter squash. Squash breeding in general is resource-intensive, especially in terms of space, and the biology of squash makes it difficult to practice selection on both parents. These restrictions translate to smaller breeding populations and limited use of greenhouse generations, which in turn, limit genetic gain per breeding cycle and increases cycle length. Genomic selection is a promising technology for improving breeding efficiency; yet, few studies have explored its use in horticultural crops. We present results demonstrating the predictive ability of whole-genome models for fruit quality traits. Predictive abilities for quality traits were low to moderate, but sufficient for implementation. To test the use of genomic selection for improving fruit quality, we conducted three rounds of genomic recurrent selection in a butternut squash (Cucurbita moschata) population. Selections were based on a fruit quality index derived from a multi-trait genomic selection model. Remnant seed from selected populations was used to assess realized gain from selection. Analysis revealed significant improvement in fruit quality index value and changes in correlated traits. This study is one of the first empirical studies to evaluate gain from a multi-trait genomic selection model in a resource-limited horticultural crop.

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Modeling Genotype × Environment Interaction for Genomic Selection with Unbalanced Data from a Wheat Breeding Program

Crop Science ◽

10.2135/cropsci2015.04.0207 ◽

2016 ◽

Vol 56 (5) ◽

pp. 2165-2179 ◽

Cited By ~ 50

Author(s):

Bettina Lado ◽

Pablo González Barrios ◽

Martín Quincke ◽

Paula Silva ◽

Lucía Gutiérrez

Keyword(s):

Genomic Selection ◽

Wheat Breeding ◽

Breeding Program ◽

Unbalanced Data ◽

Environment Interaction ◽

Wheat Breeding Program ◽

Genotype Environment Interaction

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Breeding Program Optimization for Genomic Selection in Winter Wheat

10.1101/2020.10.07.330415 ◽

2020 ◽

Author(s):

Megan Calvert ◽

Byron Evers ◽

Xu Wang ◽

Allan Fritz ◽

Jesse Poland

Keyword(s):

Winter Wheat ◽

Genomic Selection ◽

Economic Benefit ◽

Prediction Accuracy ◽

Wheat Breeding ◽

Breeding Program ◽

Program Optimization ◽

Crop Species ◽

Wheat Breeding Program ◽

Basic Set

AbstractDeveloping methodologies in the fields of phenomics and genomic prediction have the potential to increase the production of crop species by accelerating germplasm improvement. The integration of these technologies into germplasm improvement and breeding programs requires evidence that there will be a direct economic benefit to the program. We determined a basic set of parameters, such as prediction accuracy greater than 0.3, the ability to genotype over 7 lines for the cost of one phenotypic evaluation, and heritability levels below 0.4, at which the use of genomic selection would be of economic benefit in terms of genetic gain and operational costs to the Kansas State University (KSU) winter wheat breeding program. The breeding program was then examined to determine whether the parameters benefitting genomic selection were observed or achievable in a practical sense. Our results show that the KSU winter wheat breeding program is at a decision point with regards to their primary means of selection. A few operational changes to increase prediction accuracy would place the program in the parameter space where genomic selection is expected to outpace the current phenotypic selection methodology at a parity of the operation cost and would be of greatest benefit to the program.

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Solution of Mixed-Integer Optimization Problems in Bioinformatics with Differential Evolution Method

Mathematics ◽

10.3390/math9243329 ◽

2021 ◽

Vol 9 (24) ◽

pp. 3329

Author(s):

Sergey Salihov ◽

Dmitriy Maltsov ◽

Maria Samsonova ◽

Konstantin Kozlov

Keyword(s):

Differential Evolution ◽

Genomic Selection ◽

Optimization Problems ◽

Snp Markers ◽

Selection Model ◽

Mixed Integer ◽

Integer Optimization ◽

Mixed Integer Optimization ◽

Wide Range ◽

Genomic Selection Model

The solution of the so-called mixed-integer optimization problem is an important challenge for modern life sciences. A wide range of methods has been developed for its solution, including metaheuristics approaches. Here, a modification is proposed of the differential evolution entirely parallel (DEEP) method introduced recently that was successfully applied to mixed-integer optimization problems. The triangulation recombination rule was implemented and the recombination coefficients were included in the evolution process in order to increase the robustness of the optimization. The deduplication step included in the procedure ensures the uniqueness of individual integer-valued parameters in the solution vectors. The developed algorithms were implemented in the DEEP software package and applied to three bioinformatic problems. The application of the method to the optimization of predictors set in the genomic selection model in wheat resulted in dimensionality reduction such that the phenotype can be predicted with acceptable accuracy using a selected subset of SNP markers. The method was also successfully used to optimize the training set of samples for such a genomic selection model. According to the obtained results, the developed algorithm was capable of constructing a non-linear phenomenological regression model of gene expression in developing a Drosophila eye with almost the same average accuracy but significantly less standard deviation than the linear models obtained earlier.

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Bayesian genomic models boost prediction accuracy for resistance against Streptococcus agalactiae in Nile tilapia (Oreochromus nilioticus)

10.1101/2020.01.09.900134 ◽

2020 ◽

Author(s):

Rajesh Joshi ◽

Anders Skaaurd ◽

Alejandro Tola Alvarez ◽

Thomas Moen ◽

Jørgen Ødegård

Keyword(s):

Genomic Selection ◽

Streptococcus Agalactiae ◽

Nile Tilapia ◽

Prediction Accuracy ◽

Challenge Test ◽

Practical Implementation ◽

Bacterial Disease ◽

Genetic Strains ◽

Genomic Selection Model ◽

Selection For

AbstractStreptococcosis due to Streptococcus agalactiae is a major bacterial disease in Nile tilapia, and development of the resistant genetic strains can be a sustainable approach towards combating this problematic disease. Thus, a controlled disease trial was performed on 120 full-sib families to i) quantify and characterize the potential of genomic selection for S. agalactiae resistance in Nile tilapia and to ii) select the best genomic model and optimal SNP-chip for this trait.In total, 40 fish per family (15 fish intraperitoneally injected and 25 fish as cohabitants) were selected for the challenge test and mortalities recorded every 3 hours, until no mortalities occurred for a period of 3 consecutive days. Genotypes (50,690 SNPs) and phenotypes (0 for dead and 1 for alive) for 2472 cohabitant fish were available. The pedigree-based analysis utilized a deep pedigree, going 17 generations back in time. Genetic parameters were obtained using various genomic selection models (GBLUP, BayesB, BayesC, BayesR and BayesS) and traditional pedigree-based model (PBLUP). The genomic models were further analyzed using 10 different subsets of SNP-densities for optimum marker density selection. Prediction accuracy and bias were evaluated using 5 replicates of 10-fold cross-validation.Using an appropriate Bayesian genomic selection model and optimising it for SNP density increased prediction accuracy up to ∼71%, compared to a pedigree-based model. This result is encouraging for practical implementation of genomic selection for S. agalactiae resistance in Nile tilapia breeding programs.

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