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% when 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. As a result of this approach, we achieved an 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.

Quantitative trait locus (QTL) mapping contributes to sugarcane (Saccharum spp.) breeding programs by providing information about the genetic effects, positioning and number of QTLs. Combined with marker-assisted selection, it can help breeders reduce the time required to develop new sugarcane varieties. We performed a QTL mapping study for important agronomic traits in sugarcane using the composite interval mapping method for outcrossed species. A new approach allowing the 1:2:1 segregation ratio and different ploidy levels for SNP markers was used to construct an integrated genetic linkage map that also includes AFLP and SSR markers. Were used 688 molecular markers with 1:1, 3:1 and 1:2:1 segregation ratios. A total of 187 individuals from a biparental cross (IACSP95-3018 and IACSP93-3046) were assayed across multiple harvests from two locations. The evaluated yield components included stalk diameter (SD), stalk weight (SW), stalk height (SH), fiber percentage (Fiber), sucrose content (Pol) and soluble solid content (Brix). The genetic linkage map covered 4512.6 cM and had 118 linkage groups corresponding to 16 putative homology groups. A total of 25 QTL were detected for SD (six QTL), SW (five QTL), SH (four QTL), Fiber (five QTL), Pol (two QTL) and Brix (three QTL). The percentage of phenotypic variation explained by each QTL ranged from 0.069 to 3.87 %, with a low individual effect because of the high ploidy level. The mapping model provided estimates of the segregation ratio of each E. A. Costa, C. O. Anoni, and M. C. Mancini have contributed equally to this work.Electronic supplementary material The online version of this article

RESUMO

Bru1 is currently the major gene conferring brown rust resistance in sugarcane, and diagnostic markers are available. A survey for the presence of this gene was conducted on 391 genotypes including Brazilian cultivars, clones and basic germplasm. The efficiency of these markers for identifying resistant cultivars and artificially inoculated basic germplasm was also evaluated. The Bru1 frequency among cultivars (73.5%) suggests this gene is the prevalent source of brown rust resistance in Brazilian sugarcane breeding programmes. Most of the cultivars known to be resistant were positive for Bru1, although other genes for resistance could be present in lines not having Bru1. Only 17.8% of the basic germplasm accessions were positive for the Bru1 gene, and a low correlation between Bru1 diagnostic markers and brown rust severity was observed for basic germplasm accessions. Overall, Bru1 diagnostic markers proved to be efficient identifying resistant cultivars and clones and have potential to be in screening brown rust resistance in Brazilian breeding programmes.

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