Use of family structure information in interaction with environments for leveraging genomic prediction models

Persa, Reyna; Iwata, Hiroyoshi; Jarquín, Diego

doi:10.1016/j.cj.2020.06.004

Cited by 8 publications

(20 citation statements)

References 20 publications

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“…For this reason, the results derived from these pipelines are briefly discussed focusing on the proposed implementation instead. These two data sets (D1: USDA soybean collection, and D2: SoyNAM) were already studied in several manuscripts [41,42]. The obtained results from our study (percentage of variability explained by the different model terms and prediction accuracy) are in line with the results of these studies.…”

Section: Discussionsupporting

confidence: 88%

Development of a Genomic Prediction Pipeline for Maintaining Comparable Sample Sizes in Training and Testing Sets across Prediction Schemes Accounting for the Genotype-by-Environment Interaction

Persa

Grondona²,

Jarquín

2021

Agriculture

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The global growing population is experiencing challenges to satisfy the food chain supply in a world that faces rapid changes in environmental conditions complicating the development of stable cultivars. Emergent methodologies aided by molecular marker information such as marker assisted selection (MAS) and genomic selection (GS) have been widely adopted to assist the development of improved genotypes. In general, the implementation of GS is not straightforward, and it usually requires cross-validation studies to find the optimum set of factors (training set sizes, number of markers, quality controls, etc.) to use in real breeding applications. In most cases, these different scenarios (combination of several factors) vary just in the levels of a single factor keeping fixed the other levels of the other factors allowing the use of previously developed routines (code reuse). In this study we present a set of structured modules than are easily to assemble for constructing complex genomic prediction pipelines from scratch. Also, we proposed a novel method for selecting training-testing sets of similar sample sizes across different cross-validation schemes (CV2, predicting tested genotypes in observed environments; CV1, predicting untested genotypes in observed environments; CV0, predicting tested genotypes in novel environments; and CV00, predicting untested genotypes in novel environments). To show how our implementation works, we considered two real data sets. These correspond to selected samples of the USDA soybean collection (D1: 324 genotypes observed in 6 environments scored for 9 traits) and of the Soybean Nested Association Mapping (SoyNAM) experiment (D2: 324 genotypes observed in 6 environments scored for 6 traits). In addition, three prediction models which consider the effect of environments and lines (M1: E + L), environments, lines and main effect of markers (M2: E + L + G), and also the inclusion of the interaction between makers and environments (M3: E + L + G + G×E) were considered. The results confirm that under CV2 and CV1 schemes, moderate improvements in predictive ability can be obtained with the inclusion of the interaction component, while for CV0 mixed results were observed, and for CV00 no improvements were shown. However, for this last scenario the inclusion of weather and soil data potentially could enhance the results of the interaction model.

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Section: Discussionsupporting

confidence: 88%

Development of a Genomic Prediction Pipeline for Maintaining Comparable Sample Sizes in Training and Testing Sets across Prediction Schemes Accounting for the Genotype-by-Environment Interaction

Persa

Grondona²,

Jarquín

2021

Agriculture

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“…The goal of considering these four prediction scenarios is to evaluate if in any of these the integration of soil-derived covariates accomplishes significant improvements in predictive ability. Persa et al (2020) provide a comprehensive review of these four cross-validation scenarios and an extension to balancing the sample sizes in training and testing sets across cross-validation schemes.…”

Section: Methodsmentioning

confidence: 99%

“…Over the last two decades (2001/2002 to 2021/2022), soybean production has nearly doubled from 182,830 to 363,860 MT ( United States Department of Agriculture, 2002 ; United States Department of Agriculture, 2022 ). The substantial increase in soybean production can be attributed to advances in agronomical practices ( Specht et al, 1999 ; Mourtzinis et al, 2018 ), faster implementation of novel technologies in farming operations ( Liu et al, 2008 ; Ainsworth et al, 2012 ; Vieira and Chen, 2021 ), and the development of improved soybean cultivars ( Salado-Navarro et al, 1993 ; Voldeng et al, 1997 ; Specht et al, 1999 ; Specht and Williams, 2015 ; Vieira and Chen, 2021 ), of which the availability of high dimensional genomic ( Song et al, 2013 , 2020 ) and phenomic data ( Moreira et al, 2019 , 2020 ; Parmley et al, 2019 ; Zhou et al, 2022 ), as well as the integration of environmental covariates (ECs) through predictive analytics, have contributed to accelerated genetic gains ( Jarquin et al, 2014a ; Jarquin et al, 2014b ; Persa et al, 2020 ; Widener et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

“…The second method utilized a training set of all remaining breeding lines except for full-sibs of the validation set to predict across populations. Persa et al (2020) expanded the reaction norm model proposed by Jarquin et al (2014b) by incorporating the interaction between genotypes’ families and the environment under the premise that the differential responses of families to environmental stimuli could be used for enhancing the selection process in target environments. The most comprehensive model improved the predictive ability by 41% (CV1) and 49% (CV2) compared to the standard GBLUP, and roughly 17% as compared to the conventional reaction norm model.…”

Section: Introductionmentioning

confidence: 99%

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Incorporation of Soil-Derived Covariates in Progeny Testing and Line Selection to Enhance Genomic Prediction Accuracy in Soybean Breeding

et al. 2022

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The availability of high-dimensional molecular markers has allowed plant breeding programs to maximize their efficiency through the genomic prediction of a phenotype of interest. Yield is a complex quantitative trait whose expression is sensitive to environmental stimuli. In this research, we investigated the potential of incorporating soil texture information and its interaction with molecular markers via covariance structures for enhancing predictive ability across breeding scenarios. A total of 797 soybean lines derived from 367 unique bi-parental populations were genotyped using the Illumina BARCSoySNP6K and tested for yield during 5 years in Tiptonville silt loam, Sharkey clay, and Malden fine sand environments. Four statistical models were considered, including the GBLUP model (M1), the reaction norm model (M2) including the interaction between molecular markers and the environment (G×E), an extended version of M2 that also includes soil type (S), and the interaction between soil type and molecular markers (G×S) (M3), and a parsimonious version of M3 which discards the G×E term (M4). Four cross-validation scenarios simulating progeny testing and line selection of tested–untested genotypes (TG, UG) in observed–unobserved environments [OE, UE] were implemented (CV2 [TG, OE], CV1 [UG, OE], CV0 [TG, UE], and CV00 [UG, UE]). Across environments, the addition of G×S interaction in M3 decreased the amount of variability captured by the environment (−30.4%) and residual (−39.2%) terms as compared to M1. Within environments, the G×S term in M3 reduced the variability captured by the residual term by 60 and 30% when compared to M1 and M2, respectively. M3 outperformed all the other models in CV2 (0.577), CV1 (0.480), and CV0 (0.488). In addition to the Pearson correlation, other measures were considered to assess predictive ability and these showed that the addition of soil texture seems to structure/dissect the environmental term revealing its components that could enhance or hinder the predictability of a model, especially in the most complex prediction scenario (CV00). Hence, the availability of soil texture information before the growing season could be used to optimize the efficiency of a breeding program by allowing the reconsideration of field experimental design, allocation of resources, reduction of preliminary trials, and shortening of the breeding cycle.

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“…In addition to the four models presented here, each model was also tested with and without a maturity covariate when predicting yield. Prior research has demonstrated that the inclusion of correlated traits as covariates in GS can lead to greatly improved prediction accuracies, especially when predicting in unknown environments and can serve as a parameter to evaluate genotype by environment effects (Jarquin et al, 2020;Michel et al, 2016;Sun et al, 2019). Furthermore, maturity is highly heritable, correlated with yield, and often evaluated early in the breeding pipeline for all candidate RILs (Ortel et al, 2020).…”

Section: Genomic Prediction Modelsmentioning

confidence: 99%

Genomic selection of soybean (Glycine max) for genetic improvement of yield and seed composition in a breeding context

Miller,

Song,

2023

The Plant Genome

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Genomic selection has been utilized for genetic improvement in both plant and animal breeding and is a favorable technique for quantitative trait development. Within this study, genomic selection was evaluated within a breeding program, using novel validation methods in addition to plant materials and data from a commercial soybean (Glycine max) breeding program. A total of 1501 inbred lines were used to test multiple genomic selection models for multiple traits. Validation included cross‐validation, inter‐environment, and empirical validation. The results indicated that the extended genomic best linear unbiased prediction (EGBLUP) model was the most effective model tested for yield, protein, and oil in cross‐validation with accuracies of 0.50, 0.68, and 0.64, respectively. Increasing marker number from 1000 to 3000 to 6000 single nucleotide polymorphism markers leads to statistically significant increases in accuracy. Cross‐environment predictions were statistically lower than cross‐validation with accuracies of 0.24, 0.54, and 0.42 for yield, protein, and oil, respectively, using the extended genomic BLUP model. Empirical validation, predicting the yield of 510 soybean lines, had a prediction accuracy of 0.34, with the inclusion of a maturity covariate leading to a notable increase in accuracy. Genomic selection identified high‐performance lines in inter‐environment predictions: 34% of lines within the upper quartile of yield, and 51% and 48% of the highest quartile protein and oil lines, respectively. Statistically similar results occurred comparing rankings in empirical validation and selection for advancements in yield trials. These results indicate that genomic selection is a useful tool for selection decisions.

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Use of family structure information in interaction with environments for leveraging genomic prediction models

Cited by 8 publications

References 20 publications

Development of a Genomic Prediction Pipeline for Maintaining Comparable Sample Sizes in Training and Testing Sets across Prediction Schemes Accounting for the Genotype-by-Environment Interaction

Development of a Genomic Prediction Pipeline for Maintaining Comparable Sample Sizes in Training and Testing Sets across Prediction Schemes Accounting for the Genotype-by-Environment Interaction

Incorporation of Soil-Derived Covariates in Progeny Testing and Line Selection to Enhance Genomic Prediction Accuracy in Soybean Breeding

Genomic selection of soybean (Glycine max) for genetic improvement of yield and seed composition in a breeding context

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