Genetic diversity and phylogenetic analysis of Chinese Han and Li ethnic populations from Hainan Island by 30 autosomal insertion/deletion polymorphisms

Forensic Sci Res. 2019 Dec 13;7(2):189-195. doi: 10.1080/20961790.2019.1672933. eCollection 2022.

Abstract

With the characteristics of low mutation rate, length variation and short amplicon size, insertion/deletion polymorphisms (InDels) have the advantages of both short tandem repeats (STRs) and single nucleotide polymorphisms (SNPs). Herein, people of two ethnicities from Hainan Island were genotyped for the first time using the Investigator DIPplex kit. We investigated the forensic parameters of the 30 InDels and the phylogenetic relationships among different populations. The accumulated powers of discrimination and powers of exclusion were 0.999 999 999 9646 and 0.9897 in the Hainan Han population and 0.999 999 999 9292 and 0.9861 in the Hainan Li population, respectively. Additionally, population comparisons among geographically, ethnically and linguistically diverse populations via cluster heatmap, multidimensional scaling, principal component analysis, phylogenetic tree and STRUCTURE analyses demonstrated that the Hainan Han population had genetic similarities to the other Han, She and Tujia populations, while the Hainan Li population had close genetic relationships to the Zhuang and Miao groups; both populations had a high degree of genetic differentiation from most Turkic-speaking populations. Aforementioned results suggested that the 30 autosomal InDels are highly polymorphic and informative, which are suitable for human identification and population genetics.

Keywords: Forensic sciences; Hainan Han; Hainan Li; InDels; Investigator DIPplex kit; forensic genetics.

Grants and funding

This work was supported by the National Natural Science Foundation of China [grant numbers 81571854 and 81871532], the Open Project of Key Laboratory of Forensic Genetics in Ministry of Public Security [grant number 2017FGKFKT01] and the Fundamental Research Funds for the Central University [grant number YJ201651].