Building Ultra-High-Density Linkage Maps Based on Efficient Filtering of Trustable Markers.

The study is focused on addressing the problem of building genetic maps in the presence of ∼103-104 of markers per chromosome. We consider a spectrum of situations with intrachromosomal heterogeneity of recombination rate, different level of genotyping errors, and missing data. In the ideal scenario of the absence of errors and missing data, the majority of markers should appear as groups of cosegregating markers ("twins") representing no challenge for map construction. The central aspect of the proposed approach is to take into account the structure of the marker space, where each twin group (TG) and singleton markers are represented as points of this space. The confounding effect of genotyping errors and missing data leads to reduction of TG size, but upon a low level of these effects surviving TGs can still be used as a source of reliable skeletal markers. Increase in the level of confounding effects results in a considerable decrease in the number or even disappearance of usable TGs and, correspondingly, of skeletal markers. Here, we show that the paucity of informative markers can be compensated by detecting kernels of markers in the marker space using a clustering procedure, and demonstrate the utility of this approach for high-density genetic map construction on simulated and experimentally obtained genotyping datasets.