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Ancestry inference using principal component analysis and spatial analysis: a distance-based analysis to account for
Jinyoung Byun1, Younghun Han1, Ivan P Gorlov1
1Department of Biomedical Data Science, Dartmouth Geisel School of Medicine, One Medical Center Drive, Lebanon, NH, 03756, USA.
BMC Genomics
|October 18, 2017
Summary
A new method, Ancestry Inference using Principal component analysis and Spatial analysis (AIPS), accurately infers genetic ancestry in large populations. This approach improves upon existing tools for population substructure analysis and disease association studies.
Area of Science:
- Genetics
- Population Genetics
- Bioinformatics
Background:
- Accurate genetic ancestry inference is crucial for biomedical, forensic, and anthropological research.
- Genetic ancestry can influence disease risk and confound genome-wide association studies.
- Current methods struggle with large-scale studies involving closely related populations.
Purpose of the Study:
- To develop a novel, scalable approach for inferring genetic ancestry in large datasets.
- To accurately estimate ancestry for individuals with unknown origins among related populations.
- To provide a robust method for large-scale genetic studies.
Main Methods:
- Developed Ancestry Inference using Principal component analysis and Spatial analysis (AIPS), a novel distance-based approach.
- Incorporated Inverse Distance Weighted (IDW) interpolation from spatial analysis.
- Applied AIPS to genotype data from intra-European panels and European-Americans.
Main Results:
- AIPS accurately distinguished genetic variations between and within subpopulations.
- Outperformed commonly used tools like EIGENSTRAT, STRUCTURE, fastSTRUCTURE, and ADMIXTURE in inferring ancestry.
- Demonstrated effectiveness in analyzing population substructure.
Conclusions:
- AIPS is applicable to large-scale datasets for both intra- and inter-continental variation.
- The method protects against spurious associations in genetic disease studies.
- AIPS offers a more accurate and computationally efficient solution for genetic ancestry inference.
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