bioRxiv · 10.1101/549154
Kevlar: a mapping-free framework for accurate discovery of de novo variants
Abstract
MotivationDiscovery of genetic variants by whole genome sequencing has proven a powerful approach to study the etiology of complex genetic disorders. Elucidation of all variants is a necessary step in identifying causative variants and disease genes. In particular, there is an increased interest in detection of de novo variation and investigation of its role in various disorders. State-of-the-art methods for variant discovery rely on mapping reads from each individual to a reference genome and predicting variants from difference observed between the mapped reads and the reference genome. This process typically results in millions of variant predictions, most of which are inherited and irrelevant to the phenotype of interest. To distinguish between inherited variation and novel variation resulting from de novo germline mutation, whole-genome sequencing of close relatives (especially parents and siblings) is commonly used. However, standard mapping-based approaches tend to have a high false-discovery rate for de novo variant prediction, which in many cases arises from problems with read mapping. This is a particular challenge in predicting de novo indels and structural variants. ResultsWe have developed a mapping-free method, Kevlar, for de novo variant discovery based on direct comparison of sequence content between related individuals. Kevlar identifies high-abundance k-mers unique to the individual of interest and retrieves the reads containing these k-mers. These reads are easily partitioned into disjoint sets by shared k-mer content for subsequent locus-by-locus processing and variant calling. Kevlar also utilizes a novel probabilistic approach to score and rank the variant predictions to identify the most likely de novo variants. We evaluated Kevlar on simulated and real pedigrees, and demonstrate its ability to detect both de novo SNVs and indels with high sensitivity and specificity. Availability: https://github.com/dib-lab/kevlar
Source connections
Explore related subjects
Keep this discovery
Explore connections, maps & timelines
Standage, D. S., Brown, C. T., Hormozdiari, F.. 2019-02-13. Kevlar: a mapping-free framework for accurate discovery of de novo variants. https://doi.org/10.1101/549154
Cite the original work for its findings. Save a collection to share your selection of sources.