Separation of the largest eigenvalues in eigenanalysis of genotype data from discrete subpopulations

TL;DR

This paper provides a mathematical framework for using principal component analysis to detect subpopulations in genetic data, emphasizing the importance of sample size over marker quantity.

Contribution

It introduces a mathematical model that justifies and quantifies the effectiveness of PCA in identifying subpopulations from genotype data.

Findings

Power depends more on number of individuals than markers

Mathematical analysis supports PCA's use in population detection

Quantitative measures of eigenvalue separation

Abstract

We present a mathematical model, and the corresponding mathematical analysis, that justifies and quantifies the use of principal component analysis of biallelic genetic marker data for a set of individuals to detect the number of subpopulations represented in the data. We indicate that the power of the technique relies more on the number of individuals genotyped than on the number of markers.