Building Ultra-Dense Genetic Maps in the Presence of Genotyping Errors and Missing Data

Recent advances of genomic technologies have opened unprecedented possibilities in building high-quality ultra-dense genetic maps. However, with very large numbers of markers available for a mapping population, most of the markers will remain inseparable by recombination. Real situations are also complicated by genotyping errors, which “diversify” a certain part of the markers that would be identical in error-free situations. The higher the error rate the more difficult is the problem of building a reliable map. In our algorithm, we assume that error-free markers can be selected based on the presence of “twins”. There is also a probability of an opposite effect, when non-identical markers may become “twins” because of genotyping errors. Thus, a certain threshold is introduced for the selection of markers with a sufficient number of twins. The developed algorithm (implemented in MultiPoint software) enables mapping big sets of markers (~105–106). Unlike some other algorithms used in building ultra-dense genetic maps, the proposed “twins” approach does not need any prior information (e.g., anchor markers), and hence can be applied to genetically poorly studied organisms.