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Ancestry analysis using a self-developed 56 AIM-InDel loci and machine learning methods.

Liu Liu¹, Shuanglin Li², Wei Cui¹

¹Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, 1838 Guangzhou Avenue North, Guangzhou, Guangdong, PR China.

Forensic Science International

|June 18, 2024

Summary

A new panel of 56 insertion/deletion (InDel) polymorphisms aids ancestry analysis. This tool accurately predicts continental ancestry and reveals the Inner Mongolia Manchu group

Keywords:

Ancestry analysis Genetic relationship Inner Mongolia Manchu Insertion/deletion polymorphism Machine learning

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Area of Science:

Population genetics
Human ancestry analysis
Forensic genetics

Background:

Insertion/deletion (InDel) polymorphisms are valuable ancestry-informative markers.
Understanding genetic structures and relationships is crucial for population studies.

Purpose of the Study:

To develop and evaluate a novel panel of 56 ancestry-informative InDels (AIM-InDels).
To investigate the genetic relationships of the Chinese Inner Mongolia Manchu group with other populations.
To assess the efficacy of machine learning models for ancestry prediction using AIM-InDels.

Main Methods:

Development of a 56 AIM-InDel panel.
Analysis of genetic structures and relationships among 27 populations.
Application of four machine learning models (including random forest) for ancestry prediction.

Main Results:

The Inner Mongolia Manchu group shows close genetic ties to East Asian populations, particularly Han Chinese in Beijing.
The AIM-InDel panel effectively distinguishes between northern and southern East Asian populations.
The random forest model achieved high accuracy (91.87% for 5 continents, 99.73% for 3 continents) in ancestry prediction.
The model assigned Inner Mongolia Manchus to East Asian populations with closer affinities to northern groups, distinguishing 87.18% of them.

Conclusions:

The 56 AIM-InDel panel is effective for population genetic studies and ancestry analysis.
Machine learning models, especially random forest, show strong potential for accurate ancestry prediction using AIM-InDels.
This panel and model can contribute to understanding the genetic landscape of East Asian populations.