Current methods for genotyping structural variation, from high-throughput sequencing data, are generally based on comparing the reads to a linear reference genome. However, this approach is biased towards the reference, since regions which differ markedly between the individual sequenced and the reference are harder to infer, compared to regions which are more identical. Hence, prediction of structural variants is generally much harder compared to simpler SNVs. This problem can be mitigated by comparing the reads to a genome graph that contain not only the linear reference, but also the millions of variants already known. The aim of our research is to develop a method that improves discovery and genotyping of structural variation, by reducing the reference-bias using genome graphs.