BackgroundSugarcane (Saccharum spp.) is predominantly an autopolyploid plant with a variable ploidy level, frequent aneuploidy and a large genome that hampers investigation of its organization. Genetic architecture studies are important for identifying genomic regions associated with traits of interest. However, due to the genetic complexity of sugarcane, the practical applications of genomic tools have been notably delayed in this crop, in contrast to other crops that have already advanced to marker-assisted selection (MAS) and genomic selection. High-throughput next-generation sequencing (NGS) technologies have opened new opportunities for discovering molecular markers, especially single nucleotide polymorphisms (SNPs) and insertion-deletion (indels), at the genome-wide level. The objectives of this study were to (i) establish a pipeline for identifying variants from genotyping-by-sequencing (GBS) data in sugarcane, (ii) construct an integrated genetic map with GBS-based markers plus target region amplification polymorphisms and microsatellites, (iii) detect QTLs related to yield component traits, and (iv) perform annotation of the sequences that originated the associated markers with mapped QTLs to search putative candidate genes.ResultsWe used four pseudo-references to align the GBS reads. Depending on the reference, from 3,433 to 15,906 high-quality markers were discovered, and half of them segregated as single-dose markers (SDMs) on average. In addition to 7,049 non-redundant SDMs from GBS, 629 gel-based markers were used in a subsequent linkage analysis. Of 7,678 SDMs, 993 were mapped. These markers were distributed throughout 223 linkage groups, which were clustered in 18 homo(eo)logous groups (HGs), with a cumulative map length of 3,682.04 cM and an average marker density of 3.70 cM. We performed QTL mapping of four traits and found seven QTLs. Our results suggest the presence of a stable QTL across locations. Furthermore, QTLs to soluble solid content (BRIX) and fiber content (FIB) traits had markers linked to putative candidate genes.ConclusionsThis study is the first to report the use of GBS for large-scale variant discovery and genotyping of a mapping population in sugarcane, providing several insights regarding the use of NGS data in a polyploid, non-model species. The use of GBS generated a large number of markers and still enabled ploidy and allelic dosage estimation. Moreover, we were able to identify seven QTLs, two of which had great potential for validation and future use for molecular breeding in sugarcane.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3383-x) contains supplementary material, which is available to authorized users.

Sugarcane (Saccharum spp.) is a complex autopolyploid with high potential for biomass production that can be converted into sugar and ethanol. Genetic improvement is extremely important to generate more productive and resistant cultivars. Populations of improved sugarcane are generally evaluated for several traits simultaneously and in multi-environment trials. In this study, we evaluated two full-sib families of sugarcane (SR1 and SR2) at two locations and 3 yr for stalk diameter, stalk height, stalk number, stalk weight, soluble solid content (Brix), sucrose content of cane, sucrose content of juice, fi ber, cane yield, sucrose yield, and resistance to brown rust (Puccinia melanocephala). Using a mixed model approach, we included appropriate variance-covariance (VCOV) structures for modeling heterogeneity and correlation of genetic eff ects and nongenetic residual eff ects. Th e genotypic correlations between traits were calculated across the adjusted means as the standard Pearson product-moment coeffi cient. Th rough the VCOV structures estimated for each trait, in general, the heritabilities ranged from 0.78 to 0.94. Additionally, we detected 17 and 12 signifi cant genotypic correlations between the evaluated traits for SR1 and SR2, respectively. Th e analysis of the severity data for brown rust revealed that 66 and 32% of the full-sib genotypes in SR1 and SR2, respectively, had at least 90% probability of being resistant. Abbreviations: AIC, Akaike information criterion; BIC, Bayesian information criterion; BLUP, best linear unbiased prediction; FIB, fi ber; GEI, genotype ´ environment interaction; GLMM, generalized linear mixed model; LMM, linear mixed model; METs, multi-environment trials; POL%C, sucrose content of cane in percentage; POL%J, sucrose content of juice in percentage; REML, restricted maximum likelihood; SD, stalk diameter; SH, stalk height; SN, stalk number; SR1, SP80-3280 ´ RB835486 full-sib family of sugarcane 1; SR2, SP81-3250 ´ RB925345 full-sib family of sugarcane 2; SW, stalk weight; TCH, tonnes of cane per hectare; TPH, tonnes of sucrose per hectare; VCOV, variance-covariance. core ideas• A linear mixed model is efficient in production data analysis of sugarcane.• In general, the broad-sense heritability of the traits were high, ranging from 0.78 to 0.94.• A generalized linear mixed model can be applied in brown rust analysis of sugarcane.• Multi-environment trials were applied to the genetic improvement of 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