We evaluate the potential for routine WGS using ONT by sequencing the well-characterised reference sample NA12878 and the genome of an individual with ataxia-pancytopenia syndrome accompanied by severe immune dysregulation.

Whole-genome sequencing (WGS) is becoming widely used in clinical medicine in diagnostic contexts and to inform treatment choice. While current sequencing technologies have been extremely successful, their reliance on short read lengths necessarily involves some limitations: accurately assaying certain genomic regions and classes of variation can be problematic. Recent advances in throughput and cost have made WGS using the Oxford Nanopore Technologies (ONT) MinION long-read single-molecule sequencer a potential solution to these challenges. Here we evaluate the potential for routine WGS using ONT by sequencing the well-characterised reference sample NA12878 and the genome of an individual with ataxia-pancytopenia syndrome accompanied by severe immune dysregulation, to 82× and 30× respectively. For NA12878, we evaluated single-nucleotide variant (SNV) calls based on data from multiple base-calling algorithms. We demonstrate that phasing metrics from a novel, reference panel-free, long-read-based method can improve variant-calling performance from otherwise modest levels, resulting in a false discovery rate of 7.1% and false negative rate of 8.5%; remaining errors are concentrated near homopolymers and in regions of reduced sequencing coverage. In the clinical sample, we are able to identify and directly phase two non-synonymous de novo variants in SAMD9L, (OMIM #159550) inferring that they both lie on a common paternal haplotype by overlap with parental genotypes at nearby common variants. This work demonstrates that methodological innovation can substantially reduce variant-calling error rates in ONT data, and that with on-going improvements in throughput, base-calling and dedicated long-read-based SNV-calling methodology, ONT offers promise as an option for clinical WGS.

• Type: Other
• Archiver: European Genome-Phenome Archive (EGA)