Despite the benefits of quantitative data generated by massively parallel sequencing, resolving mitotypes from mixtures occurring in certain ratios remains challenging. In this study, a bioinformatic mixture deconvolution method centered on population-based phasing was developed and validated. The method was first tested on 270 in silico two-person mixtures varying in mixture proportions. An assortment of external reference panels containing information on haplotypic variation (from similar and different haplogroups) was leveraged to assess the effect of panel composition on phasing accuracy. Building on these simulations, mitochondrial genomes from the Human Mitochondrial DataBase were sourced to populate the panels and key parameter values were identified by deconvolving an additional 7290 in silico two-person mixtures. Finally, employing an optimized reference panel and phasing parameters, the approach was validated with in vitro two-person mixtures with differing proportions. Deconvolution was most accurate when the haplotypes in the mixture were similar to haplotypes present in the reference panel and when the mixture ratios were neither highly imbalanced nor subequal (e.g., 4:1). Overall, errors in haplotype estimation were largely bounded by the accuracy of the mixture’s genotype results. The proposed framework is the first available approach that automates the reconstruction of complete individual mitotypes from mixtures, even in ratios that have traditionally been considered problematic.

中文翻译：

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用于法医线粒体 DNA 混合物分析的连续统计定相框架


尽管大规模并行测序产生的定量数据有很多好处，但从以特定比例出现的混合物中解析线粒体型仍然具有挑战性。在本研究中，开发并验证了一种以基于群体的定相为中心的生物信息学混合反卷积方法。该方法首先在 270 个不同比例的计算机模拟两人混合物上进行了测试。利用包含单倍型变异信息（来自相似和不同单倍群）的各种外部参考面板来评估面板组成对定相准确性的影响。在这些模拟的基础上，从人类线粒体数据库中获取线粒体基因组来填充面板，并通过在计算机两人混合物中对另外 7290 个进行去卷积来确定关键参数值。最后，采用优化的参考面板和定相参数，用不同比例的体外两人混合物对该方法进行了验证。当混合物中的单倍型与参考组中存在的单倍型相似并且混合比例既不高度不平衡也不低于相等（例如，4:1）时，反卷积是最准确的。总体而言，单倍型估计的误差很大程度上受到混合物基因型结果准确性的限制。所提出的框架是第一个可用的方法，可以自动从混合物中重建完整的个体有丝分裂型，即使是传统上被认为有问题的比例。