次等位基因频率
基因分型
单核苷酸多态性
大规模并行测序
亲属关系
辍学(神经网络)
遗传学
DNA测序
SNP公司
SNP基因分型
等位基因频率
计算生物学
等位基因
基因型
生物
DNA
计算机科学
基因
机器学习
法学
政治学
作者
Nan Zhang,Shanshan Shi,Shaobin Lin,Zhaochen Bai,Xiaohua Ling,Jun Gao,Ruiling Yan,Xueling Ou
标识
DOI:10.1002/elps.202300111
摘要
The need to identify a missing person (MP) through kinship analysis of DNA samples found at a crime scene has become increasingly prevalent. DNA samples from MPs can be severely degraded, contain little DNA and mixed with other contributors, which often makes it difficult to apply conventional methods in practice. This study developed a massively parallel sequencing-based panel that contains 1661 single-nucleotide polymorphisms (SNPs) with low minor allele frequencies (MAFs) (averaged at 0.0613) in the Chinese Han population, and the strategy for relationship inference from DNA mixtures comprising different numbers of contributors (NOCs) and of varying allele dropout probabilities. Based on the simulated dataset and genotyping results of 42 artificial DNA mixtures (NOC = 2-4), it was observed that the present SNP panel was sufficient for balanced mixtures when referenced to the closest relatives (parents/offspring and full siblings). When the mixture profiles suffered from dropout, incorrect assignments were markedly associated with relatedness, NOC and the dropout level. We, therefore, indicate that SNPs with low MAFs could be reliably interpreted for MP identification through the kinship analysis of complex DNA mixtures. Further studies should be extended to more possible scenarios to test the feasibility of this present approach.
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