Exploration of the ancestral inference effectiveness of 126 AI-SNPs and the genetic feature of Inner Mongolian Manchu group

Abstract

In addition to the validated ancestry-informative single nucleotide polymorphisms (AI-SNPs) in classic panels, there are many new potential AI-SNPs yet to be explored. Moreover, the search for AI-SNPs with highly discriminative power for ancestry inference in inter- and intra-continental populations has become a realistic need. In this study, 126 novel AI-SNPs were selected to distinguish the African, European, Central/South Asian and East Asian populations, and a random forest model was introduced to assess the performance of the AI-SNP set. This panel was further used in the genetic analysis of the Manchu group in Inner Mongolia, China, based on 79 reference populations from seven continental regions. Results showed that the 126 AI-SNPs were able to achieve the ancestry informative inference for African, East Asian, European, and Central/South Asian populations. Population genetic analyses indicated that the Manchu group in Inner Mongolia was genetically typical of East Asian populations and was more closely related to the northern Han Chinese and Japanese than to other Altaic-speaking populations. Overall, this study provided a selection of new promising loci of ancestry inference for major intercontinental populations and intracontinental subgroups, as well as genetic insights and valuable data for dissecting the genetic structure of the Inner Mongolian Manchu group.

Keywords: Ancestry inference; Inner Mongolian Manchu; Population genetic analysis; Random forest; Single nucleotide polymorphism.