4.5 Article

Forensic genetic informativeness of an SNP panel consisting of 19 multi-allelic SNPs

期刊

FORENSIC SCIENCE INTERNATIONAL-GENETICS
卷 34, 期 -, 页码 49-56

出版社

ELSEVIER IRELAND LTD
DOI: 10.1016/j.fsigen.2018.01.006

关键词

Non-binary SNP; Tetra-allelic SNP; AIM-SNP; Forensic genetics

资金

  1. National Natural Science Foundation of China [81571861, 81630054]

向作者/读者索取更多资源

Current research focusing on forensic personal identification, phenotype inference and ancestry information on single-nucleotide polymorphisms (SNPs) has been widely reported. In the present study, we focused on tetra-allelic SNPs in the Chinese Han population. A total of 48 tetra-allelic SNPs were screened out from the Chinese Han population of the 1000 Genomes Database, including Chinese Han in Beijing (CHB) and Chinese Han South (CHS). Considering the forensic genetic requirement for the polymorphisms, only 11 tetra-allelic SNPs with a heterozygosity > 0.06 were selected for further multiplex panel construction. In order to meet the demands of personal identification and parentage identification, an additional 8 tri-allelic SNPs were combined into the final multiplex panel. To ensure application in the degraded DNA analysis, all the PCR products were designed to be 87-188 bp. Employing multiple PCR reactions and SNaPshot minisequencing, 511 unrelated Chinese Han individuals from Sichuan were genotyped. The combined match probability (CMP), combined discrimination power (CDP), and cumulative probability of exclusion (CPE) of the panel were 6.07x10(-11), 0.9999999999393 and 0.996764, respectively. Based on the population data retrieved from the 1000 Genomes Project, Fst values between Chinese Han in Sichuan (SCH) and all the populations included in the 1000 Genomes Project were calculated. The results indicated that two SNPs in this panel may contain ancestry information and may be used as markers of forensic biogeographical ancestry inference.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.5
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据