genomic prediction
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2022 ◽  
Vol 293 ◽  
pp. 110677
Author(s):  
Ce Liu ◽  
Xiaoxiao Liu ◽  
Xi'ao Wang ◽  
Yike Han ◽  
Huanwen Meng ◽  
...  

2022 ◽  
Author(s):  
Annika Perry ◽  
Witold Wachowiak ◽  
Joan Beaton ◽  
Glenn Iason ◽  
Joan Cottrell ◽  
...  

2022 ◽  
Vol 12 ◽  
Author(s):  
Philomin Juliana ◽  
Xinyao He ◽  
Felix Marza ◽  
Rabiul Islam ◽  
Babul Anwar ◽  
...  

Wheat blast is an emerging threat to wheat production, due to its recent migration to South Asia and Sub-Saharan Africa. Because genomic selection (GS) has emerged as a promising breeding strategy, the key objective of this study was to evaluate it for wheat blast phenotyped at precision phenotyping platforms in Quirusillas (Bolivia), Okinawa (Bolivia) and Jashore (Bangladesh) using three panels: (i) a diversity panel comprising 172 diverse spring wheat genotypes, (ii) a breeding panel comprising 248 elite breeding lines, and (iii) a full-sibs panel comprising 298 full-sibs. We evaluated two genomic prediction models (the genomic best linear unbiased prediction or GBLUP model and the Bayes B model) and compared the genomic prediction accuracies with accuracies from a fixed effects model (with selected blast-associated markers as fixed effects), a GBLUP + fixed effects model and a pedigree relationships-based model (ABLUP). On average, across all the panels and environments analyzed, the GBLUP + fixed effects model (0.63 ± 0.13) and the fixed effects model (0.62 ± 0.13) gave the highest prediction accuracies, followed by the Bayes B (0.59 ± 0.11), GBLUP (0.55 ± 0.1), and ABLUP (0.48 ± 0.06) models. The high prediction accuracies from the fixed effects model resulted from the markers tagging the 2NS translocation that had a large effect on blast in all the panels. This implies that in environments where the 2NS translocation-based blast resistance is effective, genotyping one to few markers tagging the translocation is sufficient to predict the blast response and genome-wide markers may not be needed. We also observed that marker-assisted selection (MAS) based on a few blast-associated markers outperformed GS as it selected the highest mean percentage (88.5%) of lines also selected by phenotypic selection and discarded the highest mean percentage of lines (91.8%) also discarded by phenotypic selection, across all panels. In conclusion, while this study demonstrates that MAS might be a powerful strategy to select for the 2NS translocation-based blast resistance, we emphasize that further efforts to use genomic tools to identify non-2NS translocation-based blast resistance are critical.


2022 ◽  
Author(s):  
Jian Cheng ◽  
Francesco Tiezzi ◽  
Jeremy Howard ◽  
Christian Maltecca ◽  
Jicai Jiang

Abstract Background: Genomic selection has been implemented in livestock genetic evaluations for years. However, currently most genomic selection models only consider the additive effects associated with SNP markers and nonadditive genetic effects have been for the most part ignored. Methods: Production traits for 26,735 to 27,647 Duroc pigs and reproductive traits for 5,338 sows were used, including off-test body weight (WT), off-test back fat (BF), off-test loin muscle depth (MS), number born alive (NBA), number born dead (NBD), and number weaned (NW). All animals were genotyped with the PorcineSNP60K Bead Chip. Variance components were estimated using a linear mixed model that includes inbreeding coefficient, additive, dominance, additive-by-additive, additive-by-dominance, dominance-by-dominance effect, and common litter environmental effect. Genomic prediction performance, including all nonadditive genetic effects, was compared with a reduced model that included only additive genetic effect. Results: Significant estimates of additive-by-additive effect variance were observed for NBA, BF, and WT (31%, 9%, and 10%, respectively). Production traits showed significant large estimates of additive-by-dominance variance (9%-23%). MS also showed large estimate of dominance-by-dominance variance (10%). Dominance effect variance estimates were low for all traits (0%-2%). Compared to the reduced model, prediction accuracies using the full model, including nonadditive effects, increased significantly by 12%, 12%, and 1% for NBA, WT, and MS, respectively. A strong dominance association signal with BF was identified near AK5.Conclusions: Sizable estimates of epistatic effects were found for the reproduction and production traits, while the dominance effect was relatively small for all traits yet significant for all production traits. Including nonadditive effects, especially epistatic effects in the genomic prediction model, significantly improved prediction accuracy for NBA, WT, and MS.


Author(s):  
Osval Antonio Montesinos López ◽  
Abelardo Montesinos López ◽  
Jose Crossa

AbstractWe give a detailed description of random forest and exemplify its use with data from plant breeding and genomic selection. The motivations for using random forest in genomic-enabled prediction are explained. Then we describe the process of building decision trees, which are a key component for building random forest models. We give (1) the random forest algorithm, (2) the main hyperparameters that need to be tuned, and (3) different splitting rules that are key for implementing random forest models for continuous, binary, categorical, and count response variables. In addition, many examples are provided for training random forest models with different types of response variables with plant breeding data. The random forest algorithm for multivariate outcomes is provided and its most popular splitting rules are also explained. In this case, some examples are provided for illustrating its implementation even with mixed outcomes (continuous, binary, and categorical). Final comments about the pros and cons of random forest are provided.


Author(s):  
Ana Guillenea ◽  
Guosheng Su ◽  
Mogens Sandø Lund ◽  
Emre Karaman

2022 ◽  
Author(s):  
Sang He ◽  
Hongyan Liu ◽  
Junhui Zhan ◽  
Yun Meng ◽  
Yamei Wang ◽  
...  

Author(s):  
Osval Antonio Montesinos López ◽  
Abelardo Montesinos López ◽  
Jose Crossa

AbstractIn this chapter, the support vector machines (svm) methods are studied. We first point out the origin and popularity of these methods and then we define the hyperplane concept which is the key for building these methods. We derive methods related to svm: the maximum margin classifier and the support vector classifier. We describe the derivation of the svm along with some kernel functions that are fundamental for building the different kernels methods that are allowed in svm. We explain how the svm for binary response variables can be expanded for categorical response variables and give examples of svm for binary and categorical response variables with plant breeding data for genomic selection. Finally, general issues for adopting the svm methodology for continuous response variables are provided, and some examples of svm for continuous response variables for genomic prediction are described.


2022 ◽  
Vol 2 (0) ◽  
pp. 1-12
Author(s):  
Wanfang Fu ◽  
◽  
Cassia da Silva Linge ◽  
John Mark Lawton ◽  
Ksenija Gasic
Keyword(s):  

Author(s):  
Miguel Angel Raffo ◽  
Pernille Sarup ◽  
Xiangyu Guo ◽  
Huiming Liu ◽  
Jeppe Reitan Andersen ◽  
...  

Abstract Key message Including additive and additive-by-additive epistasis in a NOIA parametrization did not yield orthogonal partitioning of genetic variances, nevertheless, it improved predictive ability in a leave-one-out cross-validation for wheat grain yield. Abstract Additive-by-additive epistasis is the principal non-additive genetic effect in inbred wheat lines and is potentially useful for developing cultivars based on total genetic merit; nevertheless, its practical benefits have been highly debated. In this article, we aimed to (i) evaluate the performance of models including additive and additive-by-additive epistatic effects for variance components (VC) estimation of grain yield in a wheat-breeding population, and (ii) to investigate whether including additive-by-additive epistasis in genomic prediction enhance wheat grain yield predictive ability (PA). In total, 2060 sixth-generation (F6) lines from Nordic Seed A/S breeding company were phenotyped in 21 year-location combinations in Denmark, and genotyped using a 15 K-Illumina-BeadChip. Three models were used to estimate VC and heritability at plot level: (i) “I-model” (baseline), (ii) “I + GA-model”, extending I-model with an additive genomic effect, and (iii) “I + GA + GAA-model”, extending I + GA-model with an additive-by-additive genomic effects. The I + GA-model and I + GA + GAA-model were based on the Natural and Orthogonal Interactions Approach (NOIA) parametrization. The I + GA + GAA-model failed to achieve orthogonal partition of genetic variances, as revealed by a change in estimated additive variance of I + GA-model when epistasis was included in the I + GA + GAA-model. The PA was studied using leave-one-line-out and leave-one-breeding-cycle-out cross-validations. The I + GA + GAA-model increased PA significantly (16.5%) compared to the I + GA-model in leave-one-line-out cross-validation. However, the improvement due to including epistasis was not observed in leave-one-breeding-cycle-out cross-validation. We conclude that epistatic models can be useful to enhance predictions of total genetic merit. However, even though we used the NOIA parameterization, the variance partition into orthogonal genetic effects was not possible.


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