Quantitative Trait Loci for Resistance to Potato Dry Rot Caused by Fusarium sambucinum

Sobkowiak, S.; Janiszewska, Marta; Stefańczyk, Emil; Wasilewicz‐Flis, Iwona; Śliwka, Jadwiga

doi:10.3390/agronomy12010203

Cited by 3 publications

(1 citation statement)

References 36 publications

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“…Other algorithms for genomic prediction have been proposed that have at least two possible theoretical advantages over BLUP models, particularly for disease traits in polyploid crops. Bayesian models such as BayesC ( Habier et al., 2011 ) and BayesR ( Erbe et al., 2012 ),allow a proportion of markers to have a moderate or large effect and may have an advantage as some mutations of large effects have been reported for crop disease resistance, for example in wheat ( Thambugala et al., 2020 ; Su et al., 2021 ), potatoes ( Sobkowiak et al., 2022 ) and peanuts ( De Blas et al., 2021 ). Secondly, other methods may capture non-additive effects caused by either gene-by-gene interactions or interaction between ploidies ( Wu et al., 1992 ).…”

Section: Introductionmentioning

confidence: 99%

Genomic prediction for sugarcane diseases including hybrid Bayesian-machine learning approaches

Chen,

Bhuiyan,

Ross

et al. 2024

Front. Plant Sci.

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Sugarcane smut and Pachymetra root rots are two serious diseases of sugarcane, with susceptible infected crops losing over 30% of yield. A heritable component to both diseases has been demonstrated, suggesting selection could improve disease resistance. Genomic selection could accelerate gains even further, enabling early selection of resistant seedlings for breeding and clonal propagation. In this study we evaluated four types of algorithms for genomic predictions of clonal performance for disease resistance. These algorithms were: Genomic best linear unbiased prediction (GBLUP), including extensions to model dominance and epistasis, Bayesian methods including BayesC and BayesR, Machine learning methods including random forest, multilayer perceptron (MLP), modified convolutional neural network (CNN) and attention networks designed to capture epistasis across the genome-wide markers. Simple hybrid methods, that first used BayesR/GWAS to identify a subset of 1000 markers with moderate to large marginal additive effects, then used attention networks to derive predictions from these effects and their interactions, were also developed and evaluated. The hypothesis for this approach was that using a subset of markers more likely to have an effect would enable better estimation of interaction effects than when there were an extremely large number of possible interactions, especially with our limited data set size. To evaluate the methods, we applied both random five-fold cross-validation and a structured PCA based cross-validation that separated 4702 sugarcane clones (that had disease phenotypes and genotyped for 26k genome wide SNP markers) by genomic relationship. The Bayesian methods (BayesR and BayesC) gave the highest accuracy of prediction, followed closely by hybrid methods with attention networks. The hybrid methods with attention networks gave the lowest variation in accuracy of prediction across validation folds (and lowest MSE), which may be a criteria worth considering in practical breeding programs. This suggests that hybrid methods incorporating the attention mechanism could be useful for genomic prediction of clonal performance, particularly where non-additive effects may be important.

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Section: Introductionmentioning

confidence: 99%

Genomic prediction for sugarcane diseases including hybrid Bayesian-machine learning approaches

Chen,

Bhuiyan,

Ross

et al. 2024

Front. Plant Sci.

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Breeding New Cultivars for Biotic, Abiotic and Quality Traits

Mangal,

Luthra,

Sood

et al. 2024

Approaches for Potato Crop Improvement and Stress Management

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Resistance to biotic and abiotic stress in potato: the origin of the genes and corresponding molecular markers

Islam,

Li,

Rahman

et al. 2024

Phytopathol Res

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The integration of molecular markers in the realm of potato genetics has opened new avenues for accelerating genotype analysis and developing improved varieties. Many markers linked to important features have been discovered so far and are consistently distributed across 12 chromosomes (× = 12) of potato. Notably, the genes allied to disease resistance stand out as significant and prevalent. Molecular markers associated with these genes have revolutionized selection processes, making them faster and more effective. Besides, advanced technologies such as kompetitive allele-specific PCR, high-resolution melting assay, SNP-array, genotyping by sequencing, and genome-wide association study, are emphasizing the use of those molecular markers with greater accuracy to detect R genes aligning with the phenotypes. This review discusses advances in potato breeding for resistance against common stresses, focusing on progress made through molecular marker-assisted selection.

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Quantitative Trait Loci for Resistance to Potato Dry Rot Caused by Fusarium sambucinum

Cited by 3 publications

References 36 publications

Genomic prediction for sugarcane diseases including hybrid Bayesian-machine learning approaches

Genomic prediction for sugarcane diseases including hybrid Bayesian-machine learning approaches

Breeding New Cultivars for Biotic, Abiotic and Quality Traits

Resistance to biotic and abiotic stress in potato: the origin of the genes and corresponding molecular markers

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