mity: A Highly Sensitive Mitochondrial Variant Analysis Pipeline for Whole Genome Sequencing Data

Author(s): Clare Puttick, Ryan L Davis, Kishore R Kumar, Julian MW Quinn, Trent Zeng, Christian Fares, Mark Pinese, David M Thomas, Marcel E Dinger, Carolyn M Sue, Mark J Cowley

Mitochondrial diseases (MDs) are the most common group of inherited metabolic disorders and are often challenging to diagnose due to extensive genotype-phenotype heterogeneity. MDs are caused by mutations in the nuclear or mitochondrial genome, where pathogenic mitochondrial variants are usually heteroplasmic and typically at much lower allelic fraction in the blood than affected tissues. Both genomes can now be readily analyzed using whole genome sequencing (WGS), but most nuclear variant detection methods fail to detect low heteroplasmy variants in the mitochondrial genome. We developed mity, a bioinformatics pipeline for detecting, annotating, and interpreting heteroplasmic single nucleotide variants and insertion/deletion variants in the mitochondrial genome from WGS data. We optimized mity to accurately detect variants from high mitochondrial DNA sequencing depth (>3000x) obtained by WGS of blood from 13 control cell line replicates, 10 patients, and 2,570 healthy controls. mity can detect pathogenic mitochondrial variants, with heteroplasmy ranging from <1% to 100%. Through extensive variant annotations, mity enables easy interpretation of mitochondrial variants and can be incorporated into existing diagnostic WGS pipelines. WGS combined with mity could simplify the diagnostic pathway for MDs, avoid invasive tissue biopsies and increase the diagnostic rate for mitochondrial diseases and other conditions caused by impaired mitochondrial function.