PCA-Correlated SNPs for Structure Identification in Worldwide Human Populations

Abstract

Existing methods to ascertain small sets of markers for the identification of human population structure require prior knowledge of individual ancestry. Based on Principal Components Analysis (PCA), and recent results in theoretical computer science, we present a novel algorithm that, applied on genomewide data, selects small subsets of SNPs (PCA-correlated SNPs) to reproduce the structure found by PCA on the complete dataset, without use of ancestry information. Evaluating our method on a previously described dataset (10,805 SNPs, 11 populations), we demonstrate that a very small set of PCA-correlated SNPs can be effectively employed to assign individuals to particular continents or populations, using a simple clustering algorithm. We validate our methods on the HapMap populations and achieve perfect intercontinental differentiation with 14 PCA-correlated SNPs. The Chinese and Japanese populations can be easily differentiated using less than 100 PCA-correlated SNPs ascertained after evaluating 1.7 million SNPs from HapMap. We show that, in general, structure informative SNPs are not portable across geographic regions. However, we manage to identify a general set of 50 PCA-correlated SNPs that effectively assigns individuals to one of nine different populations. Compared to analysis with the measure of informativeness, our methods, although unsupervised, achieved similar results. We proceed to demonstrate that our algorithm can be effectively used for the analysis of admixed populations without having to trace the origin of individuals. Analyzing a Puerto Rican dataset (192 individuals, 7,257 SNPs), we show that PCA-correlated SNPs can be used to successfully predict structure and ancestry proportions. We subsequently validate these SNPs for structure identification in an independent Puerto Rican dataset. The algorithm that we introduce runs in seconds and can be easily applied on large genome-wide datasets, facilitating the identification of population substructure, stratification assessment in multi-stage whole-genome association studies, and the study of demographic history in human populations.

Genome-wide patterns of population structure and admixture in West Africans and African Americans

“…2A). Our method is a generalization of the approach of Paschou et al (35) and estimates genome-wide proportion of West African ancestry for a given individual as P = b/(a + b), where b and a are the chord distances from the European and West African centroids, respectively, for the given individual along PC1. Our generalization involves undertaking the PC1 distance analysis on a grid of points along the genome (as opposed to genome-wide) centered on 15 SNP windows and using a Hidden Markov Model (HMM) for inference of ancestry state (i.e., having "0," "1," or "2" chromosomes of recent African origin; see SI Text, Fig.…”

Investigation of the fine structure of European populations with applications to disease association studies

“…Although the question of the minimum number of markers required to detect the first n components is an interesting one, 6,34 we are more concerned with samples genotyped for GWA studies, and these are typically typed, at least initially, on one of the standard panels such as the Illumina HumanHap 300 or Affymetrix Mapping 500k marker sets. The PCA that we performed with a marker set formed from the intersection between the Illumina HumanHap 300 and Affymetrix Mapping 500k panels is therefore particularly interesting as it shows that a common panel is almost equally good at detecting the first few components as the full 129 673 SNP panel; therefore, at least for purposes of identifying sample origins, samples typed on either platform can be included in the same analyses.…”

Penalized classification for optimal statistical selection of markers from high-throughput genotyping: application in sheep breeds

“…However, some methods are tractable and capable to efficiently predict breed composition using breed frequencies of thousands of markers (Kuehn et al, 2011). Therefore, it is often desirable to reduce the number of markers according to their information content, in order to create reduced panels for population genetic analysis (Paschou et al, 2007). Many clustering algorithms have been developed employing population genetic data to assign individuals to clusters (Jakobsson and Rosenberg, 2007).…”