A Complete Pedigree-Based Graph Workflow for Rare Candidate Variant Analysis

Traditional methods that use a linear reference genome for analyses of whole genome sequencing data have been found to be inadequate for detection of structural variants, rare variation and variants that originate in high-complexity or repetitive regions of the human genome. Genome graphs help to systematically embed genetic variation from a population of samples into one reference structure. Though genome graphs have helped to reduce this mapping bias, there are still performance improvements that can be made. Here we present a workflow that uses population and pedigree genetic information to reduce reference bias and improve variant detection sensitivity as well as to generate a small list of candidate variants that are causal to rare genetic disorders at the genome scale.

• Health & Bioscience