Detecting inherited and novel structural variants in low-coverage parent-child sequencing data

Structural variants (SVs) are a class of genomic variation shared by members of the same species. Though relatively rare, they represent an increasingly important class of variation, as SVs have been associated with diseases and susceptibility to some types of cancer. Common approaches to SV detection require the sequencing and mapping of fragments from a test genome to a high-quality reference genome. Candidate SVs correspond to fragments with discordant mapped configurations. However, because errors in the sequencing and mapping will also create discordant arrangements, many of these predictions will be spurious. When sequencing coverage is low, distinguishing true SVs from errors is even more challenging. In recent work, we have developed SV detection methods that exploit genome information of closely related individuals - parents and children. Our previous approaches were based on the assumption that any SV present in a child's genome must have come from one of their parents. However, using this strict restriction may have resulted in failing to predict any rare but novel variants present only in the child. In this work, we generalize our previous approaches to allow the child to carry novel variants. We consider a constrained optimization approach where variants in the child are of two types either inherited - and therefore must be present in a parent - or novel. For simplicity, we consider only a single parent and single child each of which have a haploid genome. However, even in this restricted case, our approach has the power to improve variant prediction. We present results on both simulated candidate variant regions, parent-child trios from the 1000 Genomes Project, and a subset of the 17 Platinum Genomes.