PCA-Correlated SNPs for Structure Identification in Worldwide Human Populations

By Peristera Paschou, E. Ziv, E. Burchard, S. Choudhry, W. Rodriguez-Cintron, Michael W. Mahoney, Petros Drineas. PLoS Genetics, 2007

308 citations recorded by the source corpus.

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.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/c8d3af7a05a09e1d27297a70400b314543bcbd44

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Citations in the corpus

An improved approximation algorithm for the column subset selection problem (2008, methodology): “In addition, applications of such ideas include: (i) [34], where a “compact CUR matrix decomposition” was applied to static and dynamic data analysis in large sparse graphs; (ii) [25, 24, 13], where these ideas were used for compression and classification of hyperspectral medical data and the recons”
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Summer–Fall Distribution of Stocks of Immature Sockeye Salmon in the Bering Sea as Revealed by Single-Nucleotide Polymorphisms (2010, background): “Single-nucleotide polymorphisms have been shown to be especially effective in studies of population genetics such as this one ( Paschou et al. 2007 ; Narum et al.”
Unsupervised feature selection for principal components analysis (2008, methodology): “Several approaches have been used to this end; see [35] for a detailed discussion.”
Discovery of multi-dimensional modules by integrative analysis of cancer genomic data (2012, methodology): “If we do not have enough memory space, we can consider reducing the dimension of input data by data-reduction techniques such as the PCA-select tool used for decreasing the feature number in population structure studies (20).”
Revisiting the Nystrom Method for Improved Large-scale Machine Learning (2013, background): “, 2012), while other work has demonstrated that leverage scores are often very nonuniform in ways that render uniform sampling inappropriate and that can be essential to highlight properties of downstream interest (Paschou et al., 2007; Mahoney & Drineas, 2009).”
Additive Approximations in High Dimensional Nonparametric Regression via the SALSA (2016, methodology): “The datasets were taken from the UCI repository, Bristol Multilevel Modeling and the following sources: (Guillame-Bert et al., 2014; Just et al., 2010; Paschou, 2007; Tegmark et al, 2006; Tu, 2012; Wehbe et al., 2014).”
Handbook of Big Data (2016, background): “In order to simply see some AN running, the reader may paste the following code into an R interpreter: source("http://stat.wharton.upenn.edu/~buja/association-navigator.R") p -200 mymatrix -matrix(rnorm(20000),ncol=p) colnames(mymatrix) - paste("V", 1:p, "_", c(rep("A",p/2),rep("B",p/2)), sep="")…”
Fast approximation of matrix coherence and statistical leverage (2011, background): “, 2009) norm, or we might be seeking useful features or data points in downstream data analysis applications (Paschou et al., 2007; Mahoney and Drineas, 2009), or we might be seeking to develop high-quality numerical implementations of low-rank matrix approximation algorithms (Halko et al.”