The vertical bands of high r found on some of the plots (e.g., 5A and 7A) probably arise from errors ordering markers in the consensus map and are likely a result of relatively low population size and map amalgamation between the constituent mapping populations, resulting in inaccuracies.
Marker coverage and genetic map density are influenced by many criteria such as genome length, number of markers, distribution of markers and crossovers in the genome, mapping population size and mapping strategy [ 31].
a Population size for mapping; numbers could vary according to the phenotypic assessments for late blight resistance and maturity traits.
The details of the parents used in developing the mapping population, size of the mapping population, markers used, and yield QTL identified are given in Table 1.
In some cases, the calculated CI for a single QTL did not correspond to that reported in the literature, probably due to the small population size used to map the QTL, the very low percentage of explained variability, the method used for the QTL detection, or a sparse genetic map, which thus resulted in a broader interval on the integrated map [ 74, 75].
This will in turn enable scientists to work with substantially larger effective population sizes for mapping complex agronomic traits using selective phenotyping approaches [15].
As the detection and accuracy of minor QTLs in segregating populations largely depends on map quality and population size, increasing the initial mapping population size or genome-wide analysis of expressed QTLs [ 47] may reveal other minor QTLs and will enable thorough examination of the interaction between rnpm2 and rnpm3.
Vales et al.[ 13] estimated the effect of population size on QTL mapping and concluded that a large early generation population was able to detect more QTL, including small-effect QTL, than studies that used smaller advanced generation populations.
Although bulked segregant analysis with large population size (X-QTL mapping) can identify many QTL with small effect sizes and additive effects (Lai et al. 2007; Ehrenreich et al. 2010), approaches that can increase the detection power of X-QTL have not been explored fully.
Factors influencing the statistical power of QTL mapping include mapping population size, marker density, QTL effect size, and significance level in declaring the existence of QTL.
