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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 bi-parental 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 mapped QTL (1:2:1, 3:1 or 1:1). Our results provide information about the genetic organization of the sugarcane genome and constitute the first step toward a better dissection of complex traits. [ABSTRACT FROM AUTHOR]

When building genetic maps, it is necessary to choose from several marker ordering algorithms and criteria, and the choice is not always simple. In this study, we evaluate the efficiency of algorithms try (TRY), seriation (SER), rapid chain delineation (RCD), recombination counting and ordering (RECORD) and unidirectional growth (UG), as well as the criteria PARF (product of adjacent recombination fractions), SARF (sum of adjacent recombination fractions), SALOD (sum of adjacent LOD scores) and LHMC (likelihood through hidden Markov chains), used with the RIPPLE algorithm for error verification, in the construction of genetic linkage maps. A linkage map of a hypothetical diploid and monoecious plant species was simulated containing one linkage group and 21 markers with fixed distance of 3 cM between them. In all, 700 F2 populations were randomly simulated with 100 and 400 individuals with different combinations of dominant and co-dominant markers, as well as 10 and 20% of missing data. The simulations showed that, in the presence of co-dominant markers only, any combination of algorithm and criteria may be used, even for a reduced population size. In the case of a smaller proportion of dominant markers, any of the algorithms and criteria (except SALOD) investigated may be used. In the presence of high proportions of dominant markers and smaller samples (around 100), the probability of repulsion linkage increases between them and, in this case, use of the algorithms TRY and SER associated to RIPPLE with criterion LHMC would provide better results. [ABSTRACT FROM AUTHOR]

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