Choosing the right tool: Leveraging of plant genetic resources in wheat (Triticum aestivum L.) benefits from selection of a suitable genomic prediction model

Berkner, Marcel O.; Schulthess, Albert W.; Zhao, Yusheng; Jiang, Yong; Oppermann, Markus; Reif, Jochen C.

doi:10.1007/s00122-022-04227-4

Cited by 3 publications

(4 citation statements)

References 64 publications

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“…This is consistent with the findings of previous genomic prediction studies in wheat. EG-BLUP resulted in more accurate predictions compared with G-BLUP for the prediction of TGW, plant height, and yellow rust resistance ( Berkner et al., 2022 ). The particular advantage of this model is its ability to account for additive effect but also for additive-by-additive epistasis ( Jiang and Reif, 2015 ).…”

Section: Discussionmentioning

confidence: 99%

“…Genomic prediction could be used to characterize these new non-phenotyped parts of the collection as well as those parts without reliable phenotypic data. The power of targeted genomic prediction has recently been shown by many studies in the context of genebanks ( Yu et al., 2016 ; Gonzalez et al., 2021 ; Berkner et al., 2022 ; Schulthess et al., 2022 ). Finally, informing the interested public on the newly generated information according to the FAIR (Findable, Accessible, Interoperable and Reusable) ( Wilkinson et al., 2016 ) principles will further activate genebanks.…”

Section: Introductionmentioning

confidence: 99%

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Genomic prediction reveals unexplored variation in grain protein and lysine content across a vast winter wheat genebank collection

Berkner,

Weise,

Reif

et al. 2024

Front. Plant Sci.

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Globally, wheat (Triticum aestivum L.) is a major source of proteins in human nutrition despite its unbalanced amino acid composition. The low lysine content in the protein fraction of wheat can lead to protein-energy-malnutrition prominently in developing countries. A promising strategy to overcome this problem is to breed varieties which combine high protein content with high lysine content. Nevertheless, this requires the incorporation of yet undefined donor genotypes into pre-breeding programs. Genebank collections are suspected to harbor the needed genetic diversity. In the 1970s, a large-scale screening of protein traits was conducted for the wheat genebank collection in Gatersleben; however, this data has been poorly mined so far. In the present study, a large historical dataset on protein content and lysine content of 4,971 accessions was curated, strictly corrected for outliers as well as for unreplicated data and consolidated as the corresponding adjusted entry means. Four genomic prediction approaches were compared based on the ability to accurately predict the traits of interest. High-quality phenotypic data of 558 accessions was leveraged by engaging the best performing prediction model, namely EG-BLUP. Finally, this publication incorporates predicted phenotypes of 7,651 accessions of the winter wheat collection. Five accessions were proposed as donor genotypes due to the combination of outstanding high protein content as well as lysine content. Further investigation of the passport data suggested an association of the adjusted lysine content with the elevation of the collecting site. This publicly available information can facilitate future pre-breeding activities.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Genomic prediction reveals unexplored variation in grain protein and lysine content across a vast winter wheat genebank collection

Berkner,

Weise,

Reif

et al. 2024

Front. Plant Sci.

Self Cite

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show abstract

“…This is consistent with the findings of previous genomic prediction studies in wheat. EG-BLUP resulted in more accurate predictions compared with G-BLUP for the prediction of TGW, plant height, and yellow rust resistance (Berkner et al, 2022). The particular advantage of this model is its ability to account for additive effect but also for additive x additive epistasis (Jiang and Reif, 2015).…”

Section: Eg-blup With High Potential For Genomic Predictionmentioning

confidence: 99%

“…Genomic prediction could be used to characterize these new non-phenotyped parts of the collection as well as those parts without reliable phenotypic data. The power of targeted genomic prediction has recently been shown by many studies in the context of genebanks (Yu et al, 2016;Gonzalez et al, 2021;Berkner et al, 2022;Schulthess et al, 2022). Finally, informing the interested public on the newly generated information according to the FAIR (Findable, Accessible, Interoperable and Reusable) (Wilkinson et al, 2016) principles will further activate genebanks.…”

Section: Introductionmentioning

confidence: 99%

Genomic unveiling of the diversity in grain protein and lysine content throughout a genebank collection of winter wheat

Berkner

Weise

Reif

et al. 2023

Preprint

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Globally, wheat (Triticum aestivum L.) is a major source of proteins in human nutrition despite its unbalanced amino acid composition. The low lysine content in the protein fraction of wheat can lead to protein-energy-malnutrition prominently in developing countries. A promising strategy to overcome this problem is to breed varieties which combine high protein content with high lysine content. Nevertheless, this requires the incorporation of yet undefined donor genotypes into pre-breeding programs. Genebank collections are suspected to harbor the needed genetic diversity. In the 1970s, a large-scale screening of protein traits was conducted for the wheat genebank collection in Gatersleben; however, this data has been poorly mined so far. In the present study, a large historical dataset on protein content and lysine content was curated and the corresponding adjusted entry means were calculated. High-quality phenotypic data of 558 accessions was leveraged by engaging four genomic prediction approaches. Based on the predicted phenotypes of 7,651 winter wheat accessions, few of them were recommended as donor genotypes due to suitable protein characteristic. Further investigation of the passport data suggested an association of the adjusted lysine content with the elevation of the collecting site. This publicly available information can facilitate future pre-breeding activities.

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The BLUP method in evaluation of breeding values of Russian spring wheat lines using micro- and macroelements in seeds

Potapova,

Zlobin,

Leonova

et al. 2024

Vestn. VOGiS

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Genomic selection is a technology that allows for the determination of the genetic value of varieties of agricultural plants and animal breeds, based on information about genotypes and phenotypes. The measured breeding value (BV) for varieties and breeds in relation to the target trait allows breeding stages to be thoroughly planned and the parent forms suitable for crossing to be chosen. In this work, the BLUP method was used to assess the breeding value of 149 Russian varieties and introgression lines (4 measurements for each variety or line, 596 phenotypic points) of spring wheat according to the content of seven chemical elements in the grain – K, Ca, Mg, Mn, Fe, Zn, Cu. The quality of the evaluation of breeding values was assessed using cross-validation, when the sample was randomly divided into five parts, one of which was chosen as a test population. The following average values of the Pearson correlation were obtained for predicting the concentration of trace elements: K – 0.67, Ca – 0.61, Mg – 0.4, Mn – 0.5, Fe – 0.38, Zn – 0.46, Cu – 0.48. Out of the 35 models studied, the p-value was below the nominal significant threshold (p-value < 0.05) for 28 models. For 11 models, the p-value was significant after correction for multiple testing (p-value < 0.001). For Ca and K, four out of five models and for Mn two out of five models had a p-value below the threshold adjusted for multiple testing. For 30 varieties that showed the best varietal values for Ca, K and Mn, the average breeding value was 296.43, 785.11 and 4.87 mg/kg higher, respectively, than the average breeding value of the population. The results obtained show the relevance of the application of genomic selection models even in such limited-size samples. The models for K, Ca and Mn are suitable for assessing the breeding value of Russian wheat varieties based on these characteristics.

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Choosing the right tool: Leveraging of plant genetic resources in wheat (Triticum aestivum L.) benefits from selection of a suitable genomic prediction model

Cited by 3 publications

References 64 publications

Genomic prediction reveals unexplored variation in grain protein and lysine content across a vast winter wheat genebank collection

Genomic prediction reveals unexplored variation in grain protein and lysine content across a vast winter wheat genebank collection

Genomic unveiling of the diversity in grain protein and lysine content throughout a genebank collection of winter wheat

The BLUP method in evaluation of breeding values of Russian spring wheat lines using micro- and macroelements in seeds

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