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.) has a complex genome with variable ploidy and frequent aneuploidy, which hampers the understanding of phenotype and genotype relations. Despite this complexity, genome-wide association studies (GWAS) may be used to identify favorable alleles for target traits in core collections and then assist breeders in better managing crosses and selecting superior genotypes in breeding populations. Therefore, in the present study, we used a diversity panel of sugarcane, called the Brazilian Panel of Sugarcane Genotypes (BPSG), with the following objectives: (i) estimate, through a mixed model, the adjusted means and genetic parameters of the five yield traits evaluated over two harvest years; (ii) detect population structure, linkage disequilibrium (LD) and genetic diversity using simple sequence repeat (SSR) markers; (iii) perform GWAS analysis to identify marker-trait associations (MTAs); and iv) annotate the sequences giving rise to SSR markers that had fragments associated with target traits to search for putative candidate genes. The phenotypic data analysis showed that the broad-sense heritability values were above 0.48 and 0.49 for the first and second harvests, respectively. The set of 100 SSR markers produced 1,483 fragments, of which 99.5% were polymorphic. These SSR fragments were useful to estimate the most likely number of subpopulations, found to be four, and the LD in BPSG, which was stronger in the first 15 cM and present to a large extension (65 cM). Genetic diversity analysis showed that, in general, the clustering of accessions within the subpopulations was in accordance with the pedigree information. GWAS performed through a multilocus mixed model revealed 23 MTAs, six, three, seven, four and three for soluble solid content, stalk height, stalk number, stalk weight and cane yield traits, respectively. These MTAs may be validated in other populations to support sugarcane breeding programs with introgression of favorable alleles and marker-assisted selection.

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.

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