A bioinformatics pipeline for estimating mitochondrial DNA copy number and heteroplasmy levels from whole genome sequencing data.
A bioinformatics pipeline for estimating mitochondrial DNA copy number and heteroplasmy levels from whole genome sequencing data.
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DOI：
10.1093/nargab/lqac034
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发表时间：
2022-06
影响因子：
4.6
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中科院分区：
文献类型：
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线粒体疾病是一组异质性的疾病，可以由核或线粒体基因组中的突变引起。线粒体DNA（mtDNA）变体可以异质性状态存在，其中一定百分比的DNA分子具有变体，或同质性，其中所有DNA分子具有相同的变体。细胞中mtDNA的相对数量或拷贝数（mtDNA-CN）与线粒体功能，人类疾病和死亡率相关。为了促进异质性的准确识别和mtDNA-CN的量化，我们建立了一个生物信息学管道，该管道获取全基因组测序数据并输出线粒体变体和mtDNA-CN。我们将变异注释，以方便确定变异的意义。我们的管道通过重新映射到环状chrM并通过恢复错误映射到核编码线粒体序列的读取来产生均匀的覆盖。值得注意的是，我们为每个样品构建了一个共有的chrM序列，并回忆了样品独特的线粒体基因组的异质性。我们观察到非均聚物区域中异质性变体与年龄的关联增加了约3倍，并且与现有软件相比，能够更好地捕获chrM D环中的遗传变异。我们的生物信息学管道比现有的管道更准确地捕获线粒体遗传学的特征，这些特征对于理解线粒体功能障碍如何导致疾病非常重要。
Mitochondrial diseases are a heterogeneous group of disorders that can be caused by mutations in the nuclear or mitochondrial genome. Mitochondrial DNA (mtDNA) variants may exist in a state of heteroplasmy, where a percentage of DNA molecules harbor a variant, or homoplasmy, where all DNA molecules have the same variant. The relative quantity of mtDNA in a cell, or copy number (mtDNA-CN), is associated with mitochondrial function, human disease, and mortality. To facilitate accurate identification of heteroplasmy and quantify mtDNA-CN, we built a bioinformatics pipeline that takes whole genome sequencing data and outputs mitochondrial variants, and mtDNA-CN. We incorporate variant annotations to facilitate determination of variant significance. Our pipeline yields uniform coverage by remapping to a circularized chrM and by recovering reads falsely mapped to nuclear-encoded mitochondrial sequences. Notably, we construct a consensus chrM sequence for each sample and recall heteroplasmy against the sample's unique mitochondrial genome. We observe an approximately 3-fold increased association with age for heteroplasmic variants in non-homopolymer regions and, are better able to capture genetic variation in the D-loop of chrM compared to existing software. Our bioinformatics pipeline more accurately captures features of mitochondrial genetics than existing pipelines that are important in understanding how mitochondrial dysfunction contributes to disease.