Journal Article

Ancestry inference using principal component analysis and spatial analysis: a distance-based analysis to account for population substructure

Byun, Jinyoung, Seldin, Michael F., Amos, Christopher I., Busam, Jonathan A., Han, Younghun, Gorlov, Ivan P.

2017

Overview

Background Accurate inference of genetic ancestry is of fundamental interest to many biomedical, forensic, and anthropological research areas. Genetic ancestry memberships may relate to genetic disease risks. In a genome association study, failing to account for differences in genetic ancestry between cases and controls may also lead to false-positive results. Although a number of strategies for inferring and taking into account the confounding effects of genetic ancestry are available, applying them to large studies (tens thousands samples) is challenging. The goal of this study is to develop an approach for inferring genetic ancestry of samples with unknown ancestry among closely related populations and to provide accurate estimates of ancestry for application to large-scale studies. Methods In this study we developed a novel distance-based approach, Ancestry Inference using Principal component analysis and Spatial analysis (AIPS) that incorporates an Inverse Distance Weighted (IDW) interpolation method from spatial analysis to assign individuals to population memberships. Results We demonstrate the benefits of AIPS in analyzing population substructure, specifically related to the four most commonly used tools EIGENSTRAT, STRUCTURE, fastSTRUCTURE, and ADMIXTURE using genotype data from various intra-European panels and European-Americans. While the aforementioned commonly used tools performed poorly in inferring ancestry from a large number of subpopulations, AIPS accurately distinguished variations between and within subpopulations. Conclusions Our results show that AIPS can be applied to large-scale data sets to discriminate the modest variability among intra-continental populations as well as for characterizing inter-continental variation. The method we developed will protect against spurious associations when mapping the genetic basis of a disease. Our approach is more accurate and computationally efficient method for inferring genetic ancestry in the large-scale genetic studies.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Analysis / Ancestry inference / Animal Genetics and Genomics / Biomedical and Life Sciences / Decomposition / Disease / Eigenvalues / Forensic science / Gene mapping / Genome-wide association studies / Genomes / Genomics / Genotypes / Health risks / Human and rodent genomics / Inference / Interpolation / Inverse distance weighted interpolation / Life Sciences / Mapping / Methodology / Methodology Article / Methods / Microarrays / Microbial Genetics and Genomics / Plant Genetics and Genomics / Population / Populations / Precipitation / Principal component analysis / Principal components analysis / Proteomics / Spatial analysis / Spatial analysis (Statistics) / Subpopulations / Substructures