Genome-wide association mapping and genomic prediction of yield-related traits and starch pasting properties in cassava

Phumichai, Chalermpol; Aiemnaka, Pornsak; Nathaisong, Piyaporn; Hunsawattanakul, Sirikan; Fungfoo, Phasakorn; Rojanaridpiched, C.; Vichukit, Vichan; Kongsil, Pasajee; Kittipadakul, Piya; Wannarat, Wannasiri; Chunwongse, Julapark; Tongyoo, Pumipat; Kijkhunasatian, Chookiat; Chotineeranat, Sunee; Piyachomkwan, Kuakoon; Wolfe, Marnin D.; Jannink, Jean‐Luc; Sorrells, Mark E.

doi:10.1007/s00122-021-03956-2

Cited by 16 publications

(11 citation statements)

References 115 publications

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“…We found predictive ability values per trait by different models varied significantly, which was opposite 36 , 42 as well as similar 35 , 51 , 137 , 138 to previous reports. For example, Bari et al (2021) 36 determined the predictive ability for six traits in a diverse pea germplasm collection using five different models.…”

Section: Discussionsupporting

confidence: 78%

“…On the other hand, Azodi et al (2019) 35 , investigated 12 linear and non-linear models for different traits in six species and concluded that predictive ability by different models varies significantly for all traits. Yu et al (2022) 51 , Phumichai et al (2022) 137 , and Roorkiwal et al (2016) 138 revealed the same phenomenon in rice, cassava, and chickpea, respectively. These reports and our findings confirmed that no single model worked best for all traits i.e., specific models are good for specific traits.…”

Section: Discussionmentioning

confidence: 86%

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Genomic prediction for agronomic traits in a diverse Flax (Linum usitatissimum L.) germplasm collection

Hoque,

Anderson,

Rahman

2024

Sci Rep

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Breeding programs require exhaustive phenotyping of germplasms, which is time-demanding and expensive. Genomic prediction helps breeders harness the diversity of any collection to bypass phenotyping. Here, we examined the genomic prediction’s potential for seed yield and nine agronomic traits using 26,171 single nucleotide polymorphism (SNP) markers in a set of 337 flax (Linum usitatissimum L.) germplasm, phenotyped in five environments. We evaluated 14 prediction models and several factors affecting predictive ability based on cross-validation schemes. Models yielded significant variation among predictive ability values across traits for the whole marker set. The ridge regression (RR) model covering additive gene action yielded better predictive ability for most of the traits, whereas it was higher for low heritable traits by models capturing epistatic gene action. Marker subsets based on linkage disequilibrium decay distance gave significantly higher predictive abilities to the whole marker set, but for randomly selected markers, it reached a plateau above 3000 markers. Markers having significant association with traits improved predictive abilities compared to the whole marker set when marker selection was made on the whole population instead of the training set indicating a clear overfitting. The correction for population structure did not increase predictive abilities compared to the whole collection. However, stratified sampling by picking representative genotypes from each cluster improved predictive abilities. The indirect predictive ability for a trait was proportionate to its correlation with other traits. These results will help breeders to select the best models, optimum marker set, and suitable genotype set to perform an indirect selection for quantitative traits in this diverse flax germplasm collection.

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Section: Discussionsupporting

confidence: 78%

Section: Discussionmentioning

confidence: 86%

Genomic prediction for agronomic traits in a diverse Flax (Linum usitatissimum L.) germplasm collection

Hoque,

Anderson,

Rahman

2024

Sci Rep

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“…Distribution of the marker allelic effects associated with increased dry matter content in (A) breeding, and (B) pre-breeding populations. Phumichai et al, 2022). To our knowledge, this is the first study to use this metric for marker validation in cassava.…”

Section: Discussionmentioning

confidence: 95%

“…The k -fold cross-validation, where the original dataset is randomly partitioned into equally sized k -subsets (a single subset is retained as the validation data for testing the model, and the remaining k - 1 subsets are used as training data), is one of the most commonly used cross-validation methods ( Refaeilzadeh et al., 2009 ; Mathew et al., 2015 ). It is routinely used to assess genomic prediction accuracies ( Okeke et al., 2017 ; de Andrade et al., 2019 ; Phumichai et al., 2022 ). To our knowledge, this is the first study to use this metric for marker validation in cassava.…”

Section: Discussionmentioning

confidence: 99%

Validation of KASP-SNP markers in cassava germplasm for marker-assisted selection of increased carotenoid content and dry matter content

et al. 2022

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Provitamin A biofortification and increased dry matter content are important breeding targets in cassava improvement programs worldwide. Biofortified varieties contribute to the alleviation of provitamin A deficiency, a leading cause of preventable blindness common among pre-school children and pregnant women in developing countries particularly Africa. Dry matter content is a major component of dry yield and thus underlies overall variety performance and acceptability by growers, processors, and consumers. Single nucleotide polymorphism (SNP) markers linked to these traits have recently been discovered through several genome-wide association studies but have not been deployed for routine marker-assisted selection (MAS). This is due to the lack of useful information on markers’ performances in diverse genetic backgrounds. To overcome this bottleneck, technical and biological validation of the loci associated with increased carotenoid content and dry matter content were carried out using populations independent of the marker discovery population. In the present study, seven previously identified markers for these traits were converted to a robust set of uniplex allele-specific polymerase chain reaction (PCR) assays and validated in two independent pre-breeding and breeding populations. These assays were efficient in discriminating marker genotypic classes and had an average call rate greater than 98%. A high correlation was observed between the predicted and observed carotenoid content as inferred by root yellowness intensity in the breeding (r = 0.92) and pre-breeding (r = 0.95) populations. On the other hand, dry matter content-markers had moderately low predictive accuracy in both populations (r< 0.40) due to the more quantitative nature of the trait. This work confirmed the markers’ effectiveness in multiple backgrounds, therefore, further strengthening their value in cassava biofortification to ensure nutritional security as well as dry matter content productivity. Our study provides a framework to guide future marker validation, thus leading to the more routine use of markers in MAS in cassava improvement programs.

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“…The k -fold cross-validation, where the original dataset is randomly partitioned into equally sized k -subsets (a single subset is retained as the validation data for testing the model, and the remaining k - 1 subsets are used as training data) is one of the most commonly used cross-validation methods (Refaeilzadeh et al, 2009; Mathew et al, 2015). It is routinely used to assess genomic prediction accuracies (Okeke et al, 2017; Andrade et al, 2019; Phumichai et al, 2022). To our knowledge, this is the first study to use this metric for marker validation in cassava.…”

Section: Discussionmentioning

confidence: 99%

Validation of SNP markers for marker-assisted selection of genotypes with increased carotenoid and dry matter contents in cassava

Ige

Olasanmi

Bauchet

et al. 2022

Preprint

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Provitamin A biofortification and increased dry matter content are important breeding targets in cassava improvement programs worldwide. Biofortified varieties contribute to the alleviation of provitamin A deficiency, a leading cause of preventable blindness in developing countries. Dry matter content is a major component of dry yield and thus underlies overall variety performance and acceptability by growers, processors, and consumers. SNP markers linked to these traits have recently been discovered through several genome-wide association studies but have not been deployed for marker-assisted selection (MAS). Assessment of marker performance in diverse genetic backgrounds is an important step towards their deployment for routine MAS. In the present study, seven previously identified markers for these traits were converted to a robust set of uniplex allele-specific PCR assays and validated in two independent pre-breeding and breeding populations. These assays were efficient in discriminating marker genotypic classes and had an average call rate greater than 98%. A high correlation was observed between the predicted and observed carotenoid content as inferred by root yellowness intensity in the breeding (r = 0.92) and pre-breeding (r = 0.95) populations. On the other hand, dry matter content-markers had moderately low predictive accuracy in both populations (r < 0.40) due to the more quantitative nature of the trait. This work confirmed marker effectiveness in multiple backgrounds, therefore, further strengthening its value in cassava biofortification to ensure nutritional security as well as dry matter content productivity. Our study provides a framework to guide future marker development, thus leading to more routine use of markers in MAS in cassava improvement programs.

show abstract

Genome-wide association mapping and genomic prediction of yield-related traits and starch pasting properties in cassava

Cited by 16 publications

References 115 publications

Genomic prediction for agronomic traits in a diverse Flax (Linum usitatissimum L.) germplasm collection

Genomic prediction for agronomic traits in a diverse Flax (Linum usitatissimum L.) germplasm collection

Validation of KASP-SNP markers in cassava germplasm for marker-assisted selection of increased carotenoid content and dry matter content

Validation of SNP markers for marker-assisted selection of genotypes with increased carotenoid and dry matter contents in cassava

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