Genetic Data Analysis for Plant and Animal Breeding

Maltecca, Christian; Holland, James B.; Isik, Fikret

doi:10.1007/978-3-319-55177-7

Cited by 196 publications

(176 citation statements)

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“…This model was then compared against several AR1 models accounting for spatial variation (Table 2). Note that this model assumes independence between each factor (Isik et al, 2017). Note that this model assumes independence between each factor (Isik et al, 2017).…”

Section: Spatial Corrections and Heritability Estimationmentioning

confidence: 99%

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Multienvironment and Multitrait Genomic Selection Models in Unbalanced Early‐Generation Wheat Yield Trials

et al. 2019

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The majority of studies evaluating genomic selection (GS) for plant breeding have used single‐trait, single‐site models that ignore genotype × environment interaction (GEI) effects. However, such studies do not accurately reflect the complexities of many applied breeding programs, and previous papers have found that models that incorporate GEI effects and multiple traits can increase the accuracy of genomic estimated breeding values (GEBVs). This study's goal was to test GS methods for prediction in scenarios that simulate early‐generation yield testing by correcting for field spatial variation, and fitting multienvironment and multitrait models on data for 14 traits of varying heritability evaluated in unbalanced designs across four environments. Corrections for spatial variation increased across‐environment trait heritability by 25%, on average, but had little effect on model predictive ability. Results between all models were generally equivalent when predicting the performance of newly introduced genotypes. However, models incorporating GEI information and multiple traits increased prediction accuracy by up to 9.6% for low‐heritability traits when phenotypic data were sparsely collected across environments. The results suggest that GS models using multiple traits and incorporating GEI effects may best be suited to predicting line performance in new environments when phenotypic data have already been collected across a subset of the total testing environments.

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Section: Spatial Corrections and Heritability Estimationmentioning

confidence: 99%

“…Critically, an ANOVA model fit in this context ignores GEI by assuming a uniform covariance between pairs of environments (Isik et al, 2017). Early GS studies performed in plants typically used multienvironment ANOVA models to calculate adjusted means for genotypes.…”

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confidence: 99%

Multienvironment and Multitrait Genomic Selection Models in Unbalanced Early‐Generation Wheat Yield Trials

et al. 2019

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“…The ASReml Stand Alone program version 4.1 (VSN International, Hemel Hempstead, UK) was used to partition variance components in each breeding cycle. Additionally, broad-or narrow-sense heritability was calculated for each trait as appropriate, and standard errors were calculated with the Delta method [26]. Phenotypic correlations between traits were calculated using Genstat.…”

Section: Resultsmentioning

confidence: 99%

“…Because the AR1 X AR1 model requires a complete rectangular design and the fields were irregularly shaped, dummy entries containing no data were added to the data set to generate a rectangular design. Predicted values using the individual model were generated for each plant, based on the pedigree, [26] and selections were made based on these predicted values. In cases where full-sib families were planted, the interaction between mother and father was included as a random term in the model to remove non-additive variance from the predicted values.…”

Section: Resultsmentioning

confidence: 99%

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Development and Evolution of an Intermediate Wheatgrass Domestication Program

et al. 2018

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Ecological intensification of agriculture is a proposed strategy to enhance the production of food while expanding ecosystem services and reducing inputs. Perennial plants that are directly harvested for human food are a novel means of ecological intensification, by potentially providing unprecedented levels of ecological services, such as increased soil carbon and reduced nutrient leaching. However, existing herbaceous perennial plants produce low yields of harvestable seed. Therefore, we initiated a domestication program to improve the grain yield of the perennial intermediate wheatgrass (Thinopyrum intermedium [Host] Barkworth & D.R. Dewey). The breeding program has adapted to changing resources and to results from previous generations, with methods becoming more elaborate as the program has matured over six breeding cycles. Average predicted gains from selection accumulated over five cycles were 143, 181 and 60% respectively, for seed yield per head, percent naked seed and mass per seed. We did not detect negative correlations that would indicate simultaneously achieving increased grain yield and sustained perenniality would be particularly difficult. Heritability estimates based on genetic markers were the same or higher than those calculated from a pedigree, indicating that markers have potential to expedite breeding efforts.

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Modeling covariance structures for genetic and non‐genetic effects in cowpea multi‐environment trials

et al. 2023

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In cowpea breeding, multi-environment trials are conducted to select lines with high yield. The occurrence of genetic and/or statistical imbalance is common in these experiments, in addition to the possibility of (co)variance between genetic and non-genetic effects. We explore the restricted maximum likelihood/best linear unbiased prediction features to select the model with the most appropriated covariance structure and compare the results with the traditional model (homogenous variances and no covariances). Then, 17 inbred lines and three cultivars were evaluated in six experiments during two crop years in the semiarid zone of Northeast Brazil. The trait evaluated was the 100-grain weight. We selected the best model considering the Akaike Information Criterion. The model with diagonal structure for the residual effects and heterogeneous compound symmetry for the genetic effects had the best fit. The predicted genetic gain of lines selected in this model was 1.18% higher compared to the traditional model. Modeling different (co)variance structures for genetic and non-genetic effects is an efficient approach in selecting superior genotypes in multi-environment trials in cowpea breeding. INTRODUCTIONThe World Health Organization (WHO, 2021) estimated that in 2020 about 149 million children under 5 were stunted, 45 million were very thin, and 38.9 million were overweight or obese. These problems are caused by malnutrition of nutrients, for example, protein, vitamins, or minerals, which occurs

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Genetic Data Analysis for Plant and Animal Breeding

Cited by 196 publications

References 0 publications

Multienvironment and Multitrait Genomic Selection Models in Unbalanced Early‐Generation Wheat Yield Trials

Multienvironment and Multitrait Genomic Selection Models in Unbalanced Early‐Generation Wheat Yield Trials

Development and Evolution of an Intermediate Wheatgrass Domestication Program

Modeling covariance structures for genetic and non‐genetic effects in cowpea multi‐environment trials

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