scholarly journals Genomic selection for lentil breeding: empirical evidence

2019 ◽  
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.

scholarly journals Validation of Genomic Selection Approach for Predicting Sheath Blight Resistance in Rice (Oryza sativa L.,)

2021 ◽  
pp. 158-167
Author(s):  
Mahantesh . ◽  
K. Ganesamurthy ◽  
Sayan Das ◽  
R. Saraswathi ◽  
C. Gopalakrishnan ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Prediction Accuracy ◽  
Oryza Sativa L ◽  
Hot Spot ◽  
Recombinant Inbred Lines ◽  
Sheath Blight ◽  
Blight Resistance ◽  
Illumina Platform ◽  
Selection Approach ◽  
Sheath Blight Resistance

Rice sheath blight (ShB) is one of the most serious fungal diseases caused by Rhizoctonia solani, instigating significant yield losses in many rice-growing regions of the world. Intensive studies indicated that resistance for sheath blight is controlled possibly by polygenes. Because of complex inheritance, it’s very difficult to exploit and tap all the genomic regions conferring resistance using classical approaches of QTL mapping, it’s very important to have a different strategy to harness such resistance mechanism. One promising approach that can potentially provide accurate predictions of the resistance phenotypes is genomic selection (GS). The research was undertaken with an objective to validate genomic selection approach for predicting sheath blight resistance involving 1545 Recombinant inbred lines (RILs) derived from eleven crosses between resistant and susceptible parents (Jasmine 85XTN1, Jasmine 85XSwarnaSub1, Jasmine 85XII32B, Jasmine 85XIR54, TetepXTN1, TetepXSwarna Sub1, TetepXII32B, TetepXIR54, MTU 9992XTN1, MTU 9992XII32B and MTU 9992XIRBB4). Where, Jasmine 85, Tetep & MTU 9992 were resistant parents and TN1, Swarna Sub1, II32B, IR54 & IRBB4 were susceptible parents. During rainy season (2020) the F7 RILs were screened for their reaction to sheath blight in two hot spot locations. The genotyping was done with Illumina platform having 6564 SNP markers. Bayesian B approach was used to train the statistical model for calculation of marker effects and GEBVs. The prediction accuracy of training set (data fit analysis) obtained was 0.70 and random cross validation with different approaches, the prediction accuracy ranged from 0.67 to 0.74. The results are lucrative, all in all, high prediction accuracies observed in this study suggest genomic selection as a very promising breeding strategy for predicting sheath blight resistance in Rice.

scholarly journals Scalable Sparse Testing Genomic Selection Strategy for Early Yield Testing Stage

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.

scholarly journals Increased prediction ability in Norway spruce trials using a marker x environment interaction and non-additive genomic selection model

2018 ◽  
Author(s):  
Zhi-Qiang Chen ◽  
John Baison ◽  
Jin Pan ◽  
Johan Westin ◽  
María Rosario García Gil ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Norway Spruce ◽  
Wood Quality ◽  
Genetic Parameter ◽  
Tree Height ◽  
Phenotypic Selection ◽  
Exome Capture ◽  
Parameter Estimates ◽  
Environment Interaction ◽  
Prediction Ability

AbstractA genomic selection (GS) study of growth and wood quality traits is reported based on control-pollinated Norway spruce families established in two Northern Swedish trials at two locations using exome capture as a genotyping platform. Non-additive effects including dominance and first-order epistatic interactions (including additive by additive, dominance by dominance, and additive by dominance) and marker-by-environment interaction (M×E) effects were dissected in genomic and phenotypic selection models. GS models partitioned additive and non-additive genetic variances more precisely compared with pedigree-based models. In addition, predictive ability (PA) in GS was substantially increased by including dominance and slightly increased by including M×E effects when these effects are significant. For velocity, response to GS (RGS) per year increased 91.3/43.7%, 86.9/82.9%, and 78.9/80.8% compared with response to phenotypic selection (RPS) per year when GS was based on 1) main marker effects (M), 2) M + M×E effects (A), and 3) A + dominance effects (AD) for site 1/site 2, respectively. This indicates that including M×E and dominance effects not only improves genetic parameter estimates but also may improve the genetic gain when they are significant. For tree height, Pilodyn, and modulus of elasticity (MOE), RGS per year improved up to 84.2%, 91.3%, and 92.6% compared with RPS per year, respectively.

scholarly journals Increased Prediction Ability in Norway Spruce Trials Using a Marker X Environment Interaction and Non-Additive Genomic Selection Model

2019 ◽  
Vol 110 (7) ◽  
pp. 830-843 ◽  
Author(s):  
Zhi-Qiang Chen ◽  
John Baison ◽  
Jin Pan ◽  
Johan Westin ◽  
Maria Rosario García Gil ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Norway Spruce ◽  
Genetic Parameter ◽  
Phenotypic Selection ◽  
Mendelian Inheritance ◽  
Exome Capture ◽  
Parameter Estimates ◽  
Environment Interaction ◽  
Prediction Ability ◽  
Selection Models

Abstract A genomic selection study of growth and wood quality traits is reported based on control-pollinated Norway spruce families established in 2 Northern Swedish trials at 2 locations using exome capture as a genotyping platform. Nonadditive effects including dominance and first-order epistatic interactions (including additive-by-additive, dominance-by-dominance, and additive-by-dominance) and marker-by-environment interaction (M×E) effects were dissected in genomic and phenotypic selection models. Genomic selection models partitioned additive and nonadditive genetic variances more precisely than pedigree-based models. In addition, predictive ability in GS was substantially increased by including dominance and slightly increased by including M×E effects when these effects are significant. For velocity, response to genomic selection per year increased up to 78.9/80.8%, 86.9/82.9%, and 91.3/88.2% compared with response to phenotypic selection per year when genomic selection was based on 1) main marker effects (M), 2) M + M×E effects (A), and 3) A + dominance effects (AD) for sites 1 and 2, respectively. This indicates that including M×E and dominance effects not only improves genetic parameter estimates but also when they are significant may improve the genetic gain. For tree height, Pilodyn, and modulus of elasticity (MOE), response to genomic selection per year improved up to 68.9%, 91.3%, and 92.6% compared with response to phenotypic selection per year, respectively.Subject Area: Quantitative genetics and Mendelian inheritance

scholarly journals Leveraging probability concepts for genotype by environment recommendation

2021 ◽  
Author(s):  
Kaio O.G. Dias ◽  
Jhonathan P.R. dos Santos ◽  
Matheus D. Krause ◽  
Hans-Peter Piepho ◽  
Lauro J.M. Guimarães ◽  
...  
Keyword(s):  
Stability Analysis ◽  
Statistical Models ◽  
Genotype By Environment Interaction ◽  
Decision Making Process ◽  
Environment Interaction ◽  
Informed Decision Making ◽  
Breeding Programs ◽  
Specific Adaptation ◽  
Genotype By Environment ◽  
Probability Methods

AbstractStatistical models that capture the phenotypic plasticity of a genotype across environments are crucial in plant breeding programs to potentially identify parents, generate offspring, and obtain highly productive genotypes for distinct environments. In this study, our aim is to leverage concepts of Bayesian models and probability methods of stability analysis to untangle genotype-by-environment interaction (GEI). The proposed method employs the posterior distribution obtained with the No-U-Turn sampler algorithm to get Monte Carlo estimates of adaptation and stability probabilities. We applied the proposed models in two empirical tropical datasets. Our findings provide a basis to enhance our ability to consider the uncertainty of cultivar recommendation for global or specific adaptation. We further demonstrate that probability methods of stability analysis in a Bayesian framework are a powerful tool for unraveling GEI given a defined intensity of selection that results in a more informed decision-making process towards cultivar recommendation in multi-environment trials.

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

2015 ◽  
Vol 5 (4) ◽  
pp. 569-582 ◽  
Author(s):  
Marco Lopez-Cruz ◽  
Jose Crossa ◽  
David Bonnett ◽  
Susanne Dreisigacker ◽  
Jesse Poland ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Prediction Accuracy ◽  
Wheat Breeding ◽  
Selection Model ◽  
Environment Interaction ◽  
Genomic Selection Model

scholarly journals Addressing scope of inference for global genetic evaluation of livestock

2010 ◽  
Vol 39 (suppl spe) ◽  
pp. 261-267 ◽  
Author(s):  
Robert John Tempelman
Keyword(s):  
Reference Population ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Production Traits ◽  
Multiple Trait ◽  
Heterogeneous Variances ◽  
Review Management ◽  
Dairy Bulls ◽  
Snp Panels ◽  
Genetic Evaluations

Genetic evaluations should become more accurate with the advent of whole genome selection (WGS) based on high density SNP panels. The use of WGS should then accelerate genetic gains for production traits given likely decreases in generation interval due to the greater intent to select more animals based just on their genotypes rather than phenotypes. However, past and current genetic evaluations may not generally connect well to the intended scope of inference. For example, estimating haplotype effects from the data of a single reference population does not bode well for the use of WGS in other diverse environments since the scope of inference is too narrow; conversely, WGS based on estimates, for example, derived from daughter yield deviations of dairy bulls may be too broad to infer upon genetic merit under any one particular environment. The treatment of contemporary group effects as random rather than as fixed, heterogeneous variances, genotype by environment interaction, and multiple trait analyses are all important scope of inference issues that are discussed in this review. Management systems and environments have and will continue to change; hence, it is vital that genetic evaluations are as robust and scope-appropriate as is possible in order to optimize animal adaptation to these changes.

scholarly journals Prospects for genomic selection in cassava breeding

2017 ◽  
Author(s):  
Marnin D. Wolfe ◽  
Dunia Pino Del Carpio ◽  
Olumide Alabi ◽  
Chiedozie Egesi ◽  
Lydia C. Ezenwaka ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Prediction Accuracy ◽  
Cross Validation ◽  
Prediction Models ◽  
Mosaic Disease ◽  
Cassava Mosaic Disease ◽  
Training Population ◽  
Breeding Programs ◽  
Population Prediction ◽  
Selection Of

ABSTRACTCassava (Manihot esculenta Crantz) is a clonally propagated staple food crop in the tropics. Genomic selection (GS) reduces selection cycle times by the prediction of breeding value for selection of unevaluated lines based on genome-wide marker data. GS has been implemented at three breeding programs in sub-Saharan Africa. Initial studies provided promising estimates of predictive abilities in single populations using standard prediction models and scenarios. In the present study we expand on previous analyses by assessing the accuracy of seven prediction models for seven traits in three prediction scenarios: (1) cross-validation within each population, (2) cross-population prediction and (3) cross-generation prediction. We also evaluated the impact of increasing training population size by phenotyping progenies selected either at random or using a genetic algorithm. Cross-validation results were mostly consistent across breeding programs, with non-additive models like RKHS predicting an average of 10% more accurately. Accuracy was generally associated with heritability. Cross-population prediction accuracy was generally low (mean 0.18 across traits and models) but prediction of cassava mosaic disease severity increased up to 57% in one Nigerian population, when combining data from another related population. Accuracy across-generation was poorer than within (cross-validation) as expected, but indicated that accuracy should be sufficient for rapid-cycling GS on several traits. Selection of prediction model made some difference across generations, but increasing training population (TP) size was more important. In some cases, using a genetic algorithm, selecting one third of progeny could achieve accuracy equivalent to phenotyping all progeny. Based on the datasets analyzed in this study, it was apparent that the size of a training population (TP) has a significant impact on prediction accuracy for most traits. We are still in the early stages of GS in this crop, but results are promising, at least for some traits. The TPs need to continue to grow and quality phenotyping is more critical than ever. General guidelines for successful GS are emerging. Phenotyping can be done on fewer individuals, cleverly selected, making for trials that are more focused on the quality of the data collected.Abbreviations(GS)Genomic selection(GBS)genotype-by-sequencing(IITA)International Institute of Tropical Agriculture(NRCRI)National Root Crops Research Institute(NaCRRI)National Crops Resources Research Institute(GEBVs)genomic estimated breeding values(TP)training population(RTWT)fresh root weight(RTNO)root number(SHTWT)fresh shoot weight(HI)harvest index(DM)dry matter(CMD)content cassava mosaic disease(MCMDS)mean CMD severity(VIGOR)early vigor

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