Genomic prediction contributing to a promising global strategy to turbocharge gene banks

Yu, Xiaoqing; Li, Xianran; Guo, Tingting; Zhu, Chengsong; Wu, Yuye; Mitchell, Sharon E.; Roozeboom, Kraig L.; Wang, Donghai; Wang, Ming Li; Pederson, G. A.; Tesso, Tesfaye; Schnable, Patrick S.; Bernardo, Rex; Yu, Jianming

doi:10.1038/nplants.2016.150

Cited by 203 publications

(198 citation statements)

References 46 publications

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“…Heritabilities and CV1 prediction accuracy for GY and PH in our study were similar to those found in previous studies in sorghum (Fernandes, Dias, Ferreira, & Brown, 2018; Hunt et al., 2018; Yu et al., 2016). Similar heritabilities and r for flowering time, PL, PH, and GN have previously been reported in a rice diversity panel (Guo et al., 2014).…”

Section: Discussionsupporting

confidence: 91%

“…The SRT prediction method also showed smaller r for pairwise prediction results for the panicle architecture traits than other traits. Yu et al (2016) previously observed that race as a predictor explains higher variation in predicted values of biomass traits than actual phenotypic values in sorghum, suggesting that under the presence of similar racial structure in training and validation populations, the accuracy of genomic prediction might have been inflated as a result of overemphasis on racial differences (Brown, 2016). Our approach of decomposition of covariances into conditional expectations due to race could be used in dissection of impact of population structure in CV accuracy from stratified and random sampling methods in diverse as well as breeding populations.…”

Section: Crop Sciencementioning

confidence: 99%

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Impact of sorghum racial structure and diversity on genomic prediction of grain yield components

et al. 2020

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Population structure is an important factor that affects the accuracy of estimated breeding values in genomic prediction. Natural sorghum [Sorghum bicolor (L.) Moench] populations exhibit population structure resulting from genetic and morphological differentiation due to evolutionary divergence. To study the impact of sorghum racial structure and diversity in genomic prediction, we conducted two cross‐validation (CV) experiments: CV1, proportional sampling from races; and CV2, sampling from across race (AR) or within race (WR). A diversity panel with 389 individuals with 224,007 single nucleotide polymorphisms was used for genomic prediction. Genomic heritabilities for traits were positively correlated (0.63) with their mean prediction accuracy (r) from CV1, and within‐subpopulation variance accounted for ∼80% of total genetic variance. The CV1 prediction accuracy ranged from 0.52–0.69, but r declined by 39 and 54% on average for WR and AR methods, respectively. As a predictor, race explained 30–50% of covariance for grain and panicle traits, but race was a bad predictor of plant height, as expected. Grain weight was consistently the best predicted trait across CV1 and CV2 methods except in AR. Difference in average r for WR and AR was greater in durra and caudatum, small in kafir, and nonexistent in guinea and mixed subgroups. We observed higher prevalence of minor alleles among guinea and mixed subgroups, highlighting contribution of allelic diversity towards prediction accuracy. Genomic prediction in sorghum will benefit from utilization of interracial diversity, and we emphasize the need for further investigations into the role of racial structure in genomic prediction.

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Section: Discussionsupporting

confidence: 91%

Section: Crop Sciencementioning

confidence: 99%

Impact of sorghum racial structure and diversity on genomic prediction of grain yield components

et al. 2020

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“…Furthermore, our results correspond with studies by Gorjanc et al (2016) and Yu et al (2016) in that it seems possible to achieve predictive accuracies high enough to advance selection in breeding programs using diverse accessions from within genetic resource collections, but that training population sizes that are larger than those retained in many core collections are likely required to achieve useable predictions. The goal of using 10 to 20% of a collection to represent the genetic diversity of that collection for complex traits may be unrealistic.…”

Section: Discussionsupporting

confidence: 86%

Comparison of Representative and Custom Methods of Generating Core Subsets of a Carrot Germplasm Collection

et al. 2019

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Crop breeding programs are interested in using genetic resources but have difficulty identifying useful accessions from germplasm collections. To efficiently use the diversity present in large germplasm collections, breeders often identify a subset of accessions that represent the larger collection. Methods to identify these subsets, which are called core collections, do not consistently capture functional diversity, and breeders would benefit from methods that help create custom core collections using existing data from variety trials or breeding programs. Making use of high‐density genomic data and existing phenotypic data from a collection of 433 domesticated carrot (Daucus carota L.) accessions, we tested whether it is possible to develop custom subsets of accessions for specific breeding purposes. We found that for this collection, representative strategies were effective in developing core collections that capture the diversity of the collection, but they were no better than random sampling, likely because the collection itself is not strongly subdivided. Custom strategies generated subsets that differed from the total collection with altered genetic, geographic, and phenotypic compositions. When used as training populations for genomic prediction of the other accessions in the collection, however, these custom cores did not produce a substantial improvement over traditional core collections. Increasing the size of the core did improve prediction accuracy, suggesting that it is possible to improve the usefulness of core collections by identifying custom subsets that are large enough to represent the functional genetic diversity present in the collection.

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“…The other 75 additions will presumably be more unrelated to these selection candidates, leading to the intermediate average relationship (Figure 5, A and B) and often lower persistence of LD phase (Figure 5, G and H); however, these training population additions may provide information for more reliable predictions. In a study where the training population was a subset of a larger population, Yu et al (2016) found that individuals in the validation population ( i.e. , selection candidates) with the highest and lowest predicted genotypic values had the greatest upper bound for the reliability of those predictions (Karaman et al 2016).…”

Section: Discussionmentioning

confidence: 99%

Evaluating Methods of Updating Training Data in Long-Term Genomewide Selection

Neyhart

Tiede

Lorenz

et al. 2017

G3 Genes|Genomes|Genetics

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Genomewide selection is hailed for its ability to facilitate greater genetic gains per unit time. Over breeding cycles, the requisite linkage disequilibrium (LD) between quantitative trait loci and markers is expected to change as a result of recombination, selection, and drift, leading to a decay in prediction accuracy. Previous research has identified the need to update the training population using data that may capture new LD generated over breeding cycles; however, optimal methods of updating have not been explored. In a barley (Hordeum vulgare L.) breeding simulation experiment, we examined prediction accuracy and response to selection when updating the training population each cycle with the best predicted lines, the worst predicted lines, both the best and worst predicted lines, random lines, criterion-selected lines, or no lines. In the short term, we found that updating with the best predicted lines or the best and worst predicted lines resulted in high prediction accuracy and genetic gain, but in the long term, all methods (besides not updating) performed similarly. We also examined the impact of including all data in the training population or only the most recent data. Though patterns among update methods were similar, using a smaller but more recent training population provided a slight advantage in prediction accuracy and genetic gain. In an actual breeding program, a breeder might desire to gather phenotypic data on lines predicted to be the best, perhaps to evaluate possible cultivars. Therefore, our results suggest that an optimal method of updating the training population is also very practical.

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Genomic prediction contributing to a promising global strategy to turbocharge gene banks

Cited by 203 publications

References 46 publications

Impact of sorghum racial structure and diversity on genomic prediction of grain yield components

Impact of sorghum racial structure and diversity on genomic prediction of grain yield components

Comparison of Representative and Custom Methods of Generating Core Subsets of a Carrot Germplasm Collection

Evaluating Methods of Updating Training Data in Long-Term Genomewide Selection

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