Although the genetic map has DNA marker density well-suited to quantitative trait loci mapping and samples most of the genome, our previous observations that sorghum pericentromeric heterochromatin is recalcitrant to recombination is highlighted by the finding that the vast majority of recombination in sorghum is concentrated in small regions of euchromatin that are distal to most chromosomes.
Besides, with increased marker density as well as allele frequency spectrum, the chance of having higher LD among markers and QTL increases.
The large gaps in some linkage groups, together with low marker density as well as the small population size may all have contributed to inability in detecting some QTLs with small effects.
However, it is generally difficult to compare the level of LD obtained in different studies because of differences in sample size, measures of LD, type of markers and marker density, as well as because of the recent history of the population [ 11].
Accuracy of IBD-GS was stable across marker densities performing well even down to 10 SNPs/M (2.5 to 6.1% reduction in accuracy compared to ~1200 SNPs/M).
These studies have used different marker platforms and marker densities, as well as different parametric and nonparametric statistical models (e.g., de los Campos et al. 2009, 2010; Heffner et al. 2009, 2010; Crossa et al. 2010, 2011, 2013a, b; Heslot et al. 2012; Pérez-Rodríguez et al. 2010, 2012).
Successful genetic anchoring of a plant genome sequence assembly with the use of maps developed in the reference-sequenced genotype depends on marker density and distribution, as well as map accuracy and resolution.
As well as increased marker density in the WGS data, there is also a shift in the minor allele frequency (MAF) spectrum of the component markers (Fig. 4).
We investigated the extent to which population sample size within the WGS datasets impacts the marker density available for map generation, as well as the length of the final LD maps.
To improve the efficiency of CNV discovery with SNP arrays, manufacturers have included nongenotyping, nonpolymorphic markers in their designs, which are specifically designed to detect CNVs with greater performance, as well as increasing marker density in CNV regions (Supplementary Table S1).
Although our study is also well-powered, the extremely high marker density of our QTL analysis should predict QTL locations with much greater accuracy.
