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. 2010 Aug 13;87(2):237-49.
doi: 10.1016/j.ajhg.2010.07.014.

Detecting Heteroplasmy From High-Throughput Sequencing of Complete Human Mitochondrial DNA Genomes

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Free PMC article

Detecting Heteroplasmy From High-Throughput Sequencing of Complete Human Mitochondrial DNA Genomes

Mingkun Li et al. Am J Hum Genet. .
Free PMC article

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.

Figures

Figure 1
Figure 1
False-Negative Error Rate and False-Positive Error Rate in Detecting Heteroplasmy Inferred from Simulation False-negative error rate and false-positive error rate calculated under different error rates (1%, 0.5%, 0.3%, 0.1%), coverage (36×, 76×), heteroplasmy levels (minor allele frequencies of 10%, 20%, 30%, 40%), and frequency thresholds used to define heteroplasmy (40%, 30%, 20%, 10%). For each setting, the simulation was repeated 100 times. FP denotes the false-positive error rate, FN denotes the false-negative error rate.
Figure 2
Figure 2
Heteroplasmy Level Estimated from Sequencing Reads for the Expected Heteroplasmic Positions in the Artificially Mixed Samples Horizontal lines indicate the expected heteroplasmy levels. The rightmost column gives the mean and standard error of the mean value of all heteroplasmic positions under each mixture ratio.
Figure 3
Figure 3
Distribution of Frequency Differences between Replicates Only positions with a frequency difference greater than 0.01 are shown.
Figure 4
Figure 4
Mutation Spectrum for the Heteroplasmy and Polymorphism Data

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