Dataset Information

Pervasive within-Mitochondrion Single-Nucleotide Variant Heteroplasmy as Revealed by Single-Mitochondrion Sequencing.

ABSTRACT: A number of mitochondrial diseases arise from single-nucleotide variant (SNV) accumulation in multiple mitochondria. Here, we present a method for identification of variants present at the single-mitochondrion level in individual mouse and human neuronal cells, allowing for extremely high-resolution study of mitochondrial mutation dynamics. We identified extensive heteroplasmy between individual mitochondrion, along with three high-confidence variants in mouse and one in human that were present in multiple mitochondria across cells. The pattern of variation revealed by single-mitochondrion data shows surprisingly pervasive levels of heteroplasmy in inbred mice. Distribution of SNV loci suggests inheritance of variants across generations, resulting in Poisson jackpot lines with large SNV load. Comparison of human and mouse variants suggests that the two species might employ distinct modes of somatic segregation. Single-mitochondrion resolution revealed mitochondria mutational dynamics that we hypothesize to affect risk probabilities for mutations reaching disease thresholds.

SUBMITTER: Morris J

PROVIDER: S-EPMC5771502 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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Publications

Pervasive within-Mitochondrion Single-Nucleotide Variant Heteroplasmy as Revealed by Single-Mitochondrion Sequencing.

Morris Jacqueline J Na Young-Ji YJ Zhu Hua H Lee Jae-Hee JH Giang Hoa H Ulyanova Alexandra V AV Baltuch Gordon H GH Brem Steven S Chen H Isaac HI Kung David K DK Lucas Timothy H TH O'Rourke Donald M DM Wolf John A JA Grady M Sean MS Sul Jai-Yoon JY Kim Junhyong J Eberwine James J

Cell reports 20171201 10

A number of mitochondrial diseases arise from single-nucleotide variant (SNV) accumulation in multiple mitochondria. Here, we present a method for identification of variants present at the single-mitochondrion level in individual mouse and human neuronal cells, allowing for extremely high-resolution study of mitochondrial mutation dynamics. We identified extensive heteroplasmy between individual mitochondrion, along with three high-confidence variants in mouse and one in human that were present ...[more]

PMID: 29212019

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Project description:BackgroundThe targeted capture and sequencing of genomic regions has rapidly demonstrated its utility in genetic studies. Inherent in this technology is considerable heterogeneity of target coverage and this is expected to systematically impact our sensitivity to detect genuine polymorphisms. To fully interpret the polymorphisms identified in a genetic study it is often essential to both detect polymorphisms and to understand where and with what probability real polymorphisms may have been missed.ResultsUsing down-sampling of 30 deeply sequenced exomes and a set of gold-standard single nucleotide variant (SNV) genotype calls for each sample, we developed an empirical model relating the read depth at a polymorphic site to the probability of calling the correct genotype at that site. We find that measured sensitivity in SNV detection is substantially worse than that predicted from the naive expectation of sampling from a binomial. This calibrated model allows us to produce single nucleotide resolution SNV sensitivity estimates which can be merged to give summary sensitivity measures for any arbitrary partition of the target sequences (nucleotide, exon, gene, pathway, exome). These metrics are directly comparable between platforms and can be combined between samples to give "power estimates" for an entire study. We estimate a local read depth of 13X is required to detect the alleles and genotype of a heterozygous SNV 95% of the time, but only 3X for a homozygous SNV. At a mean on-target read depth of 20X, commonly used for rare disease exome sequencing studies, we predict 5-15% of heterozygous and 1-4% of homozygous SNVs in the targeted regions will be missed.ConclusionsNon-reference alleles in the heterozygote state have a high chance of being missed when commonly applied read coverage thresholds are used despite the widely held assumption that there is good polymorphism detection at these coverage levels. Such alleles are likely to be of functional importance in population based studies of rare diseases, somatic mutations in cancer and explaining the "missing heritability" of quantitative traits.

Dataset Information

Pervasive within-Mitochondrion Single-Nucleotide Variant Heteroplasmy as Revealed by Single-Mitochondrion Sequencing.

Publications

Pervasive within-Mitochondrion Single-Nucleotide Variant Heteroplasmy as Revealed by Single-Mitochondrion Sequencing.

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