Marker-trait association and epistasis for brown rust resistance in sugarcane

Santos, Fernanda Raquel Camilo dos; Pinto, Luciana Rossini; Carlini-Garcia, L. A.; Gazaffi, Rodrigo; Mancini, Melina Cristina; Gonçalves, Bruna Sousa; Medeiros, Cibele Nataliane Facioli; Pérecin, Dilermando; Garcia, Antônio Augusto Franco; Souza, Anete Pereira de; Zucchi, Maria Imaculada

doi:10.1007/s10681-014-1257-3

Cited by 13 publications

(5 citation statements)

References 47 publications

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“…These latter contain large gaps due to the relative lack of SD and DD markers in genomic zones of the genome that may be redundant. Statistical methods for QTA search include: (1) single analysis of mapped or unlinked SD and DD markers and di-SD epistatic interactions through ANOVAs (Hoarau et al 2002;Reffay et al 2005;Aitken et al 2006Aitken et al , 2008Piperidis et al 2008;Nibouche et al 2012) or regression models and likelihood ratio tests (Pinto et al 2010;Santos et al 2015); (2) simple interval mapping (SIM) on linkage maps constructed for each parent with their specific SD markers (Ming et al 2002); composite interval mapping (CIM) on separate parental maps (Aljanabi et al 2007;Alwala et al 2009;Singh et al 2013;Yang et al 2018) or on a joined map (Ukoskit et al 2019) or an integrated map (Garcia et al 2006;Margarido et al 2007) of both parents (Balsalobre et al 2017;Gutierrez et al 2018) using co-factors selected outside mapping regions to increase statistical power; or (3) more sophisticated QTA search methodologies through SIM applied on an integrated map which use mixed models exploiting multiple-harvest-location (Pastina et al 2012;Costa et al 2016) or multiple-trait-environment (Margarido Sugar Tech et al 2015) datasets with appropriate (co)variances structures for modeling heterogeneity and correlation of genetic effects to limit residual variation and increase statistical power for detecting QTA effects. In contrast to diploid plants where comparison is between two alternative alleles per locus, in modern interspecific cultivars there could be about 10 to 13 segregating factors per locus (Piperidis and D'Hont 2020).…”

Section: Quantitative Trait Allele (Qta) Mappingmentioning

confidence: 99%

Applications of Quantitative Genetics and Statistical Analyses in Sugarcane Breeding

Hoarau

Dumont²,

Wei

et al. 2021

Sugar Tech

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Sugarcane breeding programs aim to deliver new high-yielding varieties, resistant to diseases and pests, which contribute to profitability and sustainability of cane industries. These programs generally mobilize significant experimental, technological and human resources on long-term basis. Their efficiency in terms of genetic gains per unit of cost and time and their ability to release new varieties rely on the development of many breeding applications based on quantitative genetics theory and on statistical analyses of numerous experimental data from selection schemes including DNA marker data developed for some genomic breeding applications. New methodological approaches and new technologies that might better guide and support breeding research in cultivars development programs are continually sought. This paper presents an overview of the main applications developed in statistical methodology in support of the efficiency of sugarcane breeding programs. For each type of application, its conceptual and methodological framework is presented. Implementation issues are reviewed as well as the main scientific and practical achievements so far obtained.

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Section: Quantitative Trait Allele (Qta) Mappingmentioning

confidence: 99%

Applications of Quantitative Genetics and Statistical Analyses in Sugarcane Breeding

Hoarau

Dumont²,

Wei

et al. 2021

Sugar Tech

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“…IACSP93-3046 is a variety with good tillering, an erect stool habit and resistance to brown rust. These parents have already been used in transcriptome 51 and mapping studies 24,52 .…”

Section: Mapping Population and Phenotypic Characterizationmentioning

confidence: 99%

Machine learning approaches reveal genomic regions associated with sugarcane brown rust resistance

Aono

Costa

Rody

et al. 2020

Preprint

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Sugarcane is an economically important crop, but its genomic complexity has hindered advances in molecular approaches for genetic breeding. New cultivars are released based on the identification of interesting traits, and for sugarcane, brown rust resistance is a desirable characteristic due to the large economic impact of the disease. Although marker-assisted selection for rust resistance has been successful, the genes involved are still unknown, and the associated regions vary among cultivars, thus restricting methodological generalization. We used genotyping by sequencing of full-sib progeny to relate genomic regions with brown rust phenotypes. We established a pipeline to identify reliable SNPs in complex polyploid data, which were used for phenotypic prediction via machine learning. We identified 14,540 SNPs, which led to a mean prediction accuracy of 50% by using different models. We also tested feature selection algorithms to increase predictive accuracy, resulting in a reduced dataset with more explanatory power for rust phenotypes. Using different feature selection techniques, we achieved accuracy of up to 95% with a dataset of 131 SNPs related to brown rust QTL regions and auxiliary genes. Therefore, our novel strategy has the potential to assist studies of the genomic organization of brown rust resistance in sugarcane. 3/154/15 10/15

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“…Data analyses of sugarcane defense-related genes from many projects worldwide identified WRKY genes ( Lambais, 2001 ; Liu et al , 2012 ; Wanderley-Nogueira et al ., 2012 ; Que et al , 2014 ; Santos et al , 2015 ). The expression of β-1,3-glucanases, chitinases, peroxidases and catalases was co-regulated with WRKY-like genes in sugarcane ( Dellagi et al ., 2000 ; Hara et al , 2000 ).…”

Section: Other Defense-related Proteins In Sugarcanementioning

confidence: 99%

“…Furthermore, specific isoforms of sugarcane WRKY-like transcription factor are associated with PR regulons ( Lambais, 2001 ). The expression analysis of WRKY genes indicates strong inductions after sugarcane interaction with U. scitaminea ( Liu et al , 2012 ), C. falcatum ( Sundar et al ., 2012 ; Muthiah et al ., 2013 ; Prathima et al ., 2013 ), S. scitamineum ( Que et al , 2014 ) and P. melanocephala ( Santos et al , 2015 ).…”

Section: Other Defense-related Proteins In Sugarcanementioning

confidence: 99%

Defense-related proteins involved in sugarcane responses to biotic stress

2017

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Sugarcane is one of the most important agricultural crops in the world. However, pathogen infection and herbivore attack cause constant losses in yield. Plants respond to pathogen infection by inducing the expression of several protein types, such as glucanases, chitinases, thaumatins, peptidase inhibitors, defensins, catalases and glycoproteins. Proteins induced by pathogenesis are directly or indirectly involved in plant defense, leading to pathogen death or inducing other plant defense responses. Several of these proteins are induced in sugarcane by different pathogens or insects and have antifungal or insecticidal activity. In this review, defense-related proteins in sugarcane are described, with their putative mechanisms of action, pathogen targets and biotechnological perspectives.

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Marker-trait association and epistasis for brown rust resistance in sugarcane

Cited by 13 publications

References 47 publications

Applications of Quantitative Genetics and Statistical Analyses in Sugarcane Breeding

Applications of Quantitative Genetics and Statistical Analyses in Sugarcane Breeding

Machine learning approaches reveal genomic regions associated with sugarcane brown rust resistance

Defense-related proteins involved in sugarcane responses to biotic stress

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