Detecting heteroplasmy from high-throughput sequencing of complete human mitochondrial DNA genomes.

Abstract

Heteroplasmy, the existence of multiple mtDNA types within an individual, has been previously detected by using mostly indirect methods and focusing largely on just the hypervariable segments of the control region. Next-generation sequencing technologies should enable studies of heteroplasmy across the entire mtDNA genome at much higher resolution, because many independent reads are generated for each position. However, the higher error rate associated with these technologies must be taken into consideration to avoid false detection of heteroplasmy. We used simulations and phiX174 sequence data to design criteria for accurate detection of heteroplasmy with the Illumina Genome Analyzer platform, and we used artificial mixtures and replicate data to test and refine the criteria. We then applied these criteria to mtDNA sequence reads for 131 individuals from five Eurasian populations that had been generated via a parallel tagged approach. We identified 37 heteroplasmies at 10% frequency or higher at 34 sites in 32 individuals. The mutational spectrum does not differ between heteroplasmic mutations and polymorphisms in the same individuals, but the relative mutation rate at heteroplasmic mutations is significantly higher than that estimated for all mutable sites in the human mtDNA genome. Moreover, there is also a significant excess of nonsynonymous mutations observed among heteroplasmies, compared to polymorphism data from the same individuals. Both mutation-drift and negative selection influence the fate of heteroplasmies to determine the polymorphism spectrum in humans. With appropriate criteria for avoiding false positives due to sequencing errors, next-generation technologies can provide novel insights into genome-wide aspects of mtDNA heteroplasmy.