Inferring Distant Relationships From Dense SNP Data Utilizing Two Genealogy Algorithms.

IF 3 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS ELECTROPHORESIS Pub Date : 2024-12-18 DOI:10.1002/elps.202400208
Xinyi Lin, Shuang Han, Nan Zhang, Xiaohua Ling, Zhaochen Bai, Xueling Ou
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Abstract

A highly esteemed method known as investigative genetic genealogy (IGG) has been developed to identify DNA samples from forensic crime scenes and human remains of disaster victims. With the advent of next-generation sequencing, it is now feasible to access information on millions of SNPs typed in a single sequencing run that fulfill the requirements for kinship inference. However, challenges such as the poor quality of forensic samples, the high cost associated with sequencing technology, and privacy concerns regarding large-scale genetic databases remain unresolved in this field. In the present study, we validated the identification of relationships up to the seventh degree using two genealogy algorithms (IBIS and KING) under various parameter settings. This was accomplished through whole genome sequencing data derived from two southern Chinese Han pedigrees during an initial phase, while also exploring workflows adapted for low-quality samples. To achieve this objective, low-coverage whole genome sequencing data were downsampled from high-coverage original datasets; additionally, mimic SNP array data-containing less information but offering greater accessibility-were prepared as reference samples. Through a series of experimental analyses, we not only validate the applicability of selected processing procedures and inference tools for low-coverage samples but also proposed that a meticulously crafted site filtering strategy can significantly improve the accuracy of kinship identification. This acknowledges the necessity for further systematic evidence in future research endeavors.

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调查遗传系谱(IGG)是一种备受推崇的方法,用于鉴定法医犯罪现场和灾民遗骸中的 DNA 样本。随着下一代测序技术的出现,现在可以在一次测序中获得数百万个 SNPs 的信息,这些 SNPs 符合亲属关系推断的要求。然而,法医样本质量差、测序技术成本高以及对大规模基因数据库隐私的担忧等挑战在该领域仍未得到解决。在本研究中,我们验证了在不同参数设置下使用两种系谱算法(IBIS 和 KING)鉴定七亲等以内的关系。在初始阶段,我们通过来自两个中国南方汉族家系的全基因组测序数据完成了这项工作,同时还探索了适用于低质量样本的工作流程。为实现这一目标,从高覆盖率原始数据集中降低了低覆盖率全基因组测序数据的采样率;此外,还准备了模拟 SNP 阵列数据作为参考样本,这些数据信息量较少,但具有更高的可访问性。通过一系列实验分析,我们不仅验证了所选处理程序和推断工具对低覆盖率样本的适用性,还提出了精心设计的位点过滤策略可以显著提高亲缘关系鉴定的准确性。这说明在未来的研究工作中需要进一步的系统证据。
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来源期刊
ELECTROPHORESIS
ELECTROPHORESIS 生物-分析化学
CiteScore
6.30
自引率
13.80%
发文量
244
审稿时长
1.9 months
期刊介绍: ELECTROPHORESIS is an international journal that publishes original manuscripts on all aspects of electrophoresis, and liquid phase separations (e.g., HPLC, micro- and nano-LC, UHPLC, micro- and nano-fluidics, liquid-phase micro-extractions, etc.). Topics include new or improved analytical and preparative methods, sample preparation, development of theory, and innovative applications of electrophoretic and liquid phase separations methods in the study of nucleic acids, proteins, carbohydrates natural products, pharmaceuticals, food analysis, environmental species and other compounds of importance to the life sciences. Papers in the areas of microfluidics and proteomics, which are not limited to electrophoresis-based methods, will also be accepted for publication. Contributions focused on hyphenated and omics techniques are also of interest. Proteomics is within the scope, if related to its fundamentals and new technical approaches. Proteomics applications are only considered in particular cases. Papers describing the application of standard electrophoretic methods will not be considered. Papers on nanoanalysis intended for publication in ELECTROPHORESIS should focus on one or more of the following topics: • Nanoscale electrokinetics and phenomena related to electric double layer and/or confinement in nano-sized geometry • Single cell and subcellular analysis • Nanosensors and ultrasensitive detection aspects (e.g., involving quantum dots, "nanoelectrodes" or nanospray MS) • Nanoscale/nanopore DNA sequencing (next generation sequencing) • Micro- and nanoscale sample preparation • Nanoparticles and cells analyses by dielectrophoresis • Separation-based analysis using nanoparticles, nanotubes and nanowires.
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