GBS-based single dosage markers for linkage and QTL mapping allow gene mining for yield-related traits in sugarcane

Abstract

Background: Sugarcane (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.

Results: We 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.

Conclusions: This 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.

Keywords: Allelic dosage; Molecular markers; Polyploidy; Quantitative traits; SNPs; Saccharum spp.

Fig. 1

Mosaic plot showing the ploidy levels that produced the highest posterior probabilities for the mapping of GBS sugarcane population data considering the following four pseudo-references: the methyl-filtered sugarcane genome, Sorghum bicolor genome, RNA-seq sugarcane transcriptome and sequences from the SUCEST project. The areas of the rectangles are proportional to the number of loci that have the same ploidy level, as indicated within each rectangle in parentheses. According to the posterior probabilities calculated for each even-numbered ploidy level within a range from 2 to 20, each locus was classified into one category using the following ad hoc criteria: Category A (green), when the highest posterior probability was greater than or equal to 0.80; Category B (yellow), when no single value of the posterior probability was higher than 0.80 but the sum of the two highest ones was greater than or equal to 0.80; and Category C (red), which included all other cases. In parentheses: the number of loci as a percentage within the given ploidy level and category

Fig. 2

Circular plot showing the redundancy between single-dose markers from four pseudo-references (methyl-filtered sugarcane genome, Sorghum bicolor genome, RNA-seq sugarcane transcriptome and SUCEST project sequences) that were used to align the GBS sugarcane tags. The red regions represent redundancy within each pseudo-reference, whereas the green, orange and blue regions represent redundancy between four, three and two pseudo-references, respectively. The remaining grey regions represent loci that are unique to each pseudo-reference

Fig. 3

Composite interval mapping (CIM) for soluble solid content (BRIX, in °Brix), sucrose content of cane (POL%C, in %), stalk diameter (SD, in mm) and fiber content (FIB, in %) from the SP80-3280 and RB835486 F1 population. Blue and yellow dotted lines indicate the LOD thresholds for Ipaussu-SP and Araras-SP, respectively, obtained after permutation tests. The portions highlighted in gray in the linkage groups show the positions of the QTLs