BACKGROUND: Structural variants (SVs) represent an important source of genetic variation. One of the most critical problems in their detection is breakpoint uncertainty associated with the inability to determine their exact genomic position. Breakpoint uncertainty is a characteristic issue of structural variants detected via short-read sequencing methods and complicates subsequent population analyses. The commonly used heuristic strategy reduces this issue by clustering/merging nearby structural variants of the same type before the data from individual samples are merged. RESULTS: We compared the two most used dissimilarity measures for SV clustering in terms of Mendelian inheritance errors (MIE), kinship prediction, and deviation from Hardy-Weinberg equilibrium. We analyzed the occurrence of Mendelian-inconsistent SV clusters that can be collapsed into one Mendelian-consistent SV as a new measure of dataset consistency. We also developed a new method based on constrained clustering that explicitly identifies these types of clusters. CONCLUSIONS: We found that the dissimilarity measure based on the distance between SVs breakpoints produces slightly better results than the measure based on SVs overlap. This difference is evident in trivial and corrected clustering strategy, but not in constrained clustering strategy. However, constrained clustering strategy provided the best results in all aspects, regardless of the dissimilarity measure used.

Department of Biology and Medical Genetics 1st Faculty of Medicine Charles University and General University Hospital Prague Albertov 4 128 00 Prague Czech Republic

Department of Biology and Medical Genetics 2nd Faculty of Medicine Charles University and University Hospital Motol 5 Úvalu 84 15006 Prague Czech Republic

Department of Forensic Genetics Institute of Criminalistics Strojnická 27 170 89 Prague Czech Republic

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