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. 2022 May 16:13:902309.
doi: 10.3389/fgene.2022.902309. eCollection 2022.

Determining the Area of Ancestral Origin for Individuals From North Eurasia Based on 5,229 SNP Markers

Igor Gorin  1   2   3 , Oleg Balanovsky  1   3   4 , Oleg Kozlov  1 , Sergey Koshel  3   5 , Elena Kostryukova  6 , Maxat Zhabagin  7 , Anastasiya Agdzhoyan  1   3 , Vladimir Pylev  3   4 , Elena Balanovska  1   3   4
Affiliations

Determining the Area of Ancestral Origin for Individuals From North Eurasia Based on 5,229 SNP Markers

Igor Gorin et al. Front Genet. .

Abstract

Currently available genetic tools effectively distinguish between different continental origins. However, North Eurasia, which constitutes one-third of the world's largest continent, remains severely underrepresented. The dataset used in this study represents 266 populations from 12 North Eurasian countries, including most of the ethnic diversity across Russia's vast territory. A total of 1,883 samples were genotyped using the Illumina Infinium Omni5Exome-4 v1.3 BeadChip. Three principal components were computed for the entire dataset using three iterations for outlier removal. It allowed the merging of 266 populations into larger groups while maintaining intragroup homogeneity, so 29 ethnic geographic groups were formed that were genetically distinguishable enough to trace individual ancestry. Several feature selection methods, including the random forest algorithm, were tested to estimate the number of genetic markers needed to differentiate between the groups; 5,229 ancestry-informative SNPs were selected. We tested various classifiers supporting multiple classes and output values for each class that could be interpreted as probabilities. The logistic regression was chosen as the best mathematical model for predicting ancestral populations. The machine learning algorithm for inferring an ancestral ethnic geographic group was implemented in the original software "Homeland" fitted with the interface module, the prediction module, and the cartographic module. Examples of geographic maps showing the likelihood of geographic ancestry for individuals from different regions of North Eurasia are provided. Validating methods show that the highest number of ethnic geographic group predictions with almost absolute accuracy and sensitivity was observed for South and Central Siberia, Far East, and Kamchatka. The total accuracy of prediction of one of 29 ethnic geographic groups reached 71%. The proposed method can be employed to predict ancestries from the populations of Russia and its neighbor states. It can be used for the needs of forensic science and genetic genealogy.

Keywords: ancestral origin; ancestry prediction; gene geography; human population genetics; machine learning.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
A map of the 266 populations of North Eurasia used for the analysis. Notes. Dots of different colors on the map are languages spoken by the representatives of the studied populations (the color legend is provided at the top of the map).
FIGURE 2
FIGURE 2
North Eurasia divided into genetically distinguishable ethnic geographic groups. Notes. Colored zones on the map designate areas occupied by the identified ethnic geographic groups. Groups are numbered according to their geographic coordinates. Black stars represent local populations (coincide with the populations in Figure 1).
FIGURE 3
FIGURE 3
A plot of the first and the second principal components based on the entire 4.5 M SNP panel.
FIGURE 4
FIGURE 4
Example of the map generated by the Homeland software.
See this image and copyright information in PMC

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