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

Abstract

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 diffi cult 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 suffi cient number of twins. The developed algorithm (implemented in MultiPoint software) enables mapping big sets of markers (~10 5 -10 6 ). 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.

Low recombination rates in sexual species and sex–asex transitions

“…For similar technical issues, the 'obligate CO' rule is often difficult to evaluate with precision in most other available maps. There are many errors and filtering biases in NGSbased maps that can lead to upwards or downwards biases in map length [104,105], and in most cases it is impossible to judge this from the published data, especially as most of these studies were not conducted with the aim of precisely estimating map length. Apart from these technical issues, a biological difficulty is that in some species most COs occur at chromosome ends.…”

A High Resolution Radiation Hybrid Map of Wheat Chromosome 4A

“…The RH map was constructed using all 2687 polymorphic SNPs following Newell et al (1998) and Ronin et al (2015). In the first step, markers with missing data genotypes in more than 50 lines and markers with highly varying pA frequencies higher than 0.8 or smaller than 0.7 were temporally excluded from the analysis.…”

“…Data filtering based on the assumption that co-segregating markers (“twins”) are more reliable for mapping than singleton markers (Ronin et al, 2015) also failed because only a small number of “exact twins” of markers were found in this dataset.…”

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

“…If the error rate is low (e.g., p e 0.01-0.02), a sufficient number of such markers can be selected to build a high-quality map. Here we propose a new approach for constructing genetic maps using big genotyping data (with up to 10 3 -10 4 markers per chromosome), which extends the method by Ronin et al (2015) and includes an additional filtering step to cope with a higher level of errors (say, p e 0.02-0.04 or more). Obviously, with the higher error rates, the quality of the maps is supposed to decrease.…”

“…The efficiency of our approach for the selection of the most informative candidates was studied here on simulated and real datasets. Ordering the selected candidates, testing, and stepwise improving of the genetic map is then conducted using the effective scheme described in our previous publications (Mester et al 2003(Mester et al , 2004Korol et al 2009;Ronin et al 2010Ronin et al , 2012Ronin et al , 2015.…”