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利用低覆盖度测序数据进行关系推断的改进计算。

Improved computations for relationship inference using low-coverage sequencing data.

机构信息

Mathematical Sciences, Chalmers University of Technology and the University of Gothenburg, Göteborg, Sweden.

Department of Forensic Genetics and Toxicology, National Board of Forensic Medicine, Linköping, Sweden.

出版信息

BMC Bioinformatics. 2023 Mar 9;24(1):90. doi: 10.1186/s12859-023-05217-z.

Abstract

Pedigree inference, for example determining whether two persons are second cousins or unrelated, can be done by comparing their genotypes at a selection of genetic markers. When the data for one or more of the persons is from low-coverage next generation sequencing (lcNGS), currently available computational methods either ignore genetic linkage or do not take advantage of the probabilistic nature of lcNGS data, relying instead on first estimating the genotype. We provide a method and software (see familias.name/lcNGS) bridging the above gap. Simulations indicate how our results are considerably more accurate compared to some previously available alternatives. Our method, utilizing a version of the Lander-Green algorithm, uses a group of symmetries to speed up calculations. This group may be of further interest in other calculations involving linked loci.

摘要

例如,系谱推断(确定两个人是否是表亲或无血缘关系)可以通过比较他们在一系列遗传标记上的基因型来完成。当一个或多个人的数据来自低覆盖度的下一代测序(lcNGS)时,目前可用的计算方法要么忽略遗传连锁,要么不利用 lcNGS 数据的概率性质,而是依赖于首先估计基因型。我们提供了一种方法和软件(见 familias.name/lcNGS)来弥合上述差距。模拟表明,与一些先前可用的替代方法相比,我们的结果要准确得多。我们的方法利用了 Lander-Green 算法的一个版本,利用一组对称性来加速计算。这个群组可能在涉及连锁基因座的其他计算中具有进一步的意义。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6142/9999603/8ecbfe80b97e/12859_2023_5217_Fig1_HTML.jpg

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