Autosomal deletion/insertion polymorphisms for global stratification analyses and ancestry origin inferences of different continental populations by machine learning methods

Abstract

A lot of population data of 30 deletion/insertion polymorphisms (DIPs) of the Investigator DIPplex kit in different continental populations have been reported. Here, we assessed genetic distributions of these 30 DIPs in different continental populations to pinpoint candidate ancestry informative DIPs. Besides, the effectiveness of machine learning methods for ancestry analysis was explored. Pairwise informativeness (In) values of 30 DIPs revealed that six loci displayed relatively high In values (>0.1) among different continental populations. Besides, more loci showed high population‐specific divergence (PSD) values in African population. Based on the pairwise In and PSD values of 30 DIPs, 17 DIPs in the Investigator DIPplex kit were selected to ancestry analyses of African, European, and East Asian populations. Even though 30 DIPs provided better ancestry resolution of these continental populations based on the results of PCA and population genetic structure, we found that 17 DIPs could also distinguish these continental populations. More importantly, these 17 DIPs possessed more balanced cumulative PSD distributions in these populations. Six machine learning methods were used to perform ancestry analyses of these continental populations based on 17 DIPs. Obtained results revealed that naïve Bayes manifested the greatest performance; whereas, k nearest neighbor showed relatively low performance. To sum up, these machine learning methods, especially for naïve Bayes, could be used as the valuable tool for ancestry analysis.

Forensic efficiencies of individual identification, kinship testing and ancestral inference in three Yunnan groups based on a self-developed multiple DIP panel

“…So far, a series of DIP multiplex amplification systems have been developed to meet different forensic objectives like individual identification and kinship testing over the past decade. The Investigator ® DIPplex kit was the first commercial DIPplex panel for forensic individual identification, but the 30 A-DIPs included in this kit were low polymorphic in Chinese different groups in several forensic genetic studies ( He et al, 2019 ; Jian et al, 2019 ; Zou et al, 2020 ; Jin X. et al, 2021b ). Subsequently, several in-house panels including 35 A-DIPs ( Jin et al, 2019 ), and 43 A-DIPs in this study ( Jin R. et al, 2021a ), and commercial kits including 32 X-DIPs ( Gomes et al, 2020 ), 38 X-DIPs ( Chen et al, 2021 ), AGCU DIP 50 kit ( Chen et al, 2019 ), and AGCU DIP 60 kit ( Fan et al, 2021 ) were developed.…”

Systematic selection of ancestry informative SNPs for differentiating Han, Japanese, Dai, and Kinh populations

“…However, some admixed individuals may situate in the intermediate between major populations, which are hard to infer their population origins. In a previous study, Jin et al explored the power of six machine learning methods to the ancestral resolution of three major continental populations, and obtained results showed that these artificial intelligent methods could be viewed as promising tools for unveiling ancestral origins of unknown individuals [19]. Accordingly, we can make full of the advantages of machine learning to develop a high-efficient, convenient, and pragmatic tool to infer the biogeographical origins of different populations, especially for those within the same continents or admixed populations.…”