Assessing Predictive Properties of Genome-Wide Selection in Soybeans

Xavier, Alencar; Muir, William M.; Rainey, Katy Martin

doi:10.1534/g3.116.032268

Cited by 81 publications

(99 citation statements)

References 59 publications

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“…This result agrees with other studies, in which there have been verified the increase in PAcc as population size increases, mainly for values up to 1000 individuals (Grattapaglia and Resende, 2010). However, especially in self-pollinated species, satisfactory PAcc values have been detected, even with low to moderate population size (50-200), especially under high marker density, associated with high minor allele frequency (Xavier et al, 2016).…”

Section: Comparison Of Prediction Modelssupporting

confidence: 92%

Assessing Prediction Models for Different Traits in a Rice Population Derived from a Recurrent Selection Program

et al. 2018

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Genomic selection (GS) is a promising approach to improve rice (Oryza sativa L.) populations by using genome‐wide markers for selection prior to phenotyping to estimate breeding values. In this study, our objectives were to compare certain prediction models with different structures of genetic relationship and statistical approaches for relevant traits in rice and to discuss some implications for integrating GS into a recurrent selection program of irrigated rice. We assessed nine models in terms of predictive potential, using empirical data from S1:3 progenies phenotyped for eight traits with different heritabilities and genotyped with 6174 high‐quality single nucleotide polymorphism markers. For all traits, marker‐based models outperformed prediction based on pedigree records alone. A similar level of accuracy was observed for many models, although the level of prediction stability and prediction bias varied widely. Random forest was slightly superior for less complex traits, although with high prediction bias, whereas the semiparametric RKHS method (reproducing kernel Hilbert spaces) was superior for many traits, showing high stability and low bias. Bayesian variable selection method Bayes Cπ showed acceptable accuracy and stability for several traits and thus could be useful for genomic prediction aiming at persisting accuracy for a long‐term recurrent selection.

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Section: Comparison Of Prediction Modelssupporting

confidence: 92%

Assessing Prediction Models for Different Traits in a Rice Population Derived from a Recurrent Selection Program

et al. 2018

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“…For a detailed description of the structure of the soyNAM population the reader can refer to Xavier et al [16]. For the analysis, 39 families and 5143 RILs were kept due to uncertainty in the phenotypic and/or genotypic data.…”

Section: Soynam Phenotypic and Genotypic Datamentioning

confidence: 99%

“…For more information about the soyNAM parents, RILs, the experimental design, the collected phenotypes and corresponding genotypes, the reader can refer to Xavier et al [16].…”

Section: Soynam Phenotypic and Genotypic Datamentioning

confidence: 99%

“…For the aerial method, the highest correlations (0.33 and 0.44) were reached on days 45 and 41, respectively. To assess the usefulness of canopy coverage data in the early stages of the vegetative growth, the information from days [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] (for the four data sets) was included in the prediction models, and the results were compared with those obtained using canopy information from the whole season. …”

Section: Relationship Between Canopy Coverage Data and Grain Yieldmentioning

confidence: 99%

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Increasing Predictive Ability by Modeling Interactions between Environments, Genotype and Canopy Coverage Image Data for Soybeans

et al. 2018

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Abstract:Phenomics is a new area that offers numerous opportunities for its applicability in plant breeding. One possibility is to exploit this type of information obtained from early stages of the growing season by combining it with genomic data. This opens an avenue that can be capitalized by improving the predictive ability of the common prediction models used for genomic prediction. Imagery (canopy coverage) data recorded between days 14-71 using two collection methods (ground information in 2013 and 2014; aerial information in 2014 and 2015) on a soybean nested association mapping population (SoyNAM) was used to calibrate the prediction models together with the inclusion of several types of interactions between canopy coverage data, environments, and genomic data. Three different scenarios were considered that breeders might face testing lines in fields: (i) incomplete field trials (CV2); (ii) newly developed lines (CV1); and (iii) predicting lines in unobserved environments (CV0). Two different traits were evaluated in this study: yield and days to maturity (DTM). Results showed improvements in the predictive ability for yield with respect to those models that solely included genomic data. These relative improvements ranged 27-123%, 27-148%, and 65-165% for CV2, CV1, and CV0, respectively. No major changes were observed for DTM. Similar improvements were observed for both traits when the reduced canopy information for days 14-33 was used to build the training-testing relationships, showing a clear advantage of using phenomics in very early stages of the growing season.

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“…Although large-scale phenotyping remains time-consuming and costly, high-throughput phenotyping of the representative core collection for the traits of agronomic importance is less laborious than characterizing the entire collection (Honsdorf et al, 2014). Consequently, genomicestimated breeding values for the core collection facilitate genomic selection of superior CWR accessions (Xavier, Muir, & Rainey, 2016).…”

Section: Advanced Biotechnologies Accelerate the Use Of Wild Relatimentioning

confidence: 99%

Back into the wild—Apply untapped genetic diversity of wild relatives for crop improvement

Zhang

Mittal

Leamy

et al. 2016

Evolutionary Applications

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Deleterious effects of climate change and human activities, as well as diverse environmental stresses, present critical challenges to food production and the maintenance of natural diversity. These challenges may be met by the development of novel crop varieties with increased biotic or abiotic resistance that enables them to thrive in marginal lands. However, considering the diverse interactions between crops and environmental factors, it is surprising that evolutionary principles have been underexploited in addressing these food and environmental challenges. Compared with domesticated cultivars, crop wild relatives (CWRs) have been challenged in natural environments for thousands of years and maintain a much higher level of genetic diversity. In this review, we highlight the significance of CWRs for crop improvement by providing examples of CWRs that have been used to increase biotic and abiotic stress resistance/tolerance and overall yield in various crop species. We also discuss the surge of advanced biotechnologies, such as next‐generation sequencing technologies and omics, with particular emphasis on how they have facilitated gene discovery in CWRs. We end the review by discussing the available resources and conservation of CWRs, including the urgent need for CWR prioritization and collection to ensure continuous crop improvement for food sustainability.

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Assessing Predictive Properties of Genome-Wide Selection in Soybeans

Cited by 81 publications

References 59 publications

Assessing Prediction Models for Different Traits in a Rice Population Derived from a Recurrent Selection Program

Assessing Prediction Models for Different Traits in a Rice Population Derived from a Recurrent Selection Program

Increasing Predictive Ability by Modeling Interactions between Environments, Genotype and Canopy Coverage Image Data for Soybeans

Back into the wild—Apply untapped genetic diversity of wild relatives for crop improvement

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