Population Structure and Eigenanalysis

By Nick Patterson, Alkes L Price, David Reich. PLoS Genetics, 2006

5,166 citations recorded by the source corpus.

Current methods for inferring population structure from genetic data do not provide formal significance tests for population differentiation. We discuss an approach to studying population structure (principal components analysis) that was first applied to genetic data by Cavalli-Sforza and colleagues. We place the method on a solid statistical footing, using results from modern statistics to develop formal significance tests. We also uncover a general “phase change” phenomenon about the ability to detect structure in genetic data, which emerges from the statistical theory we use, and has an important implication for the ability to discover structure in genetic data: for a fixed but large dataset size, divergence between two populations (as measured, for example, by a statistic like FST) below a threshold is essentially undetectable, but a little above threshold, detection will be easy. This means that we can predict the dataset size needed to detect structure.

Subfields: Genetics

View Paper: https://www.semanticscholar.org/paper/198a1a291115a70c2241174cbcec38465efbe740

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In Genetics

Citations in the corpus

What can genetics tell us about population connectivity? (2010, neutral): “In general, dataset size and choice of statistical methods appear to strongly affect detection of departures from panmixia (Patterson et al. 2006; Reich et al. 2008).”
Random matrix theory in statistics: A review (2014, methodology): “Patterson et al. (2006) used the phase transition behavior to explain the nature of difficulties of inferring the population structure from genetic data.”
LEA: An R package for landscape and ecological association studies (2015, methodology): “3 Analysis of population structure and imputation of missing data The R package LEA implements two classical approaches for the estimation of population genetic structure: principal component analysis (pca) and admixture analysis (Patterson et al., 2006; Pritchard et al., 2000) using sparse nonnegat”
Pcadapt: An R Package to Perform Genome Scans for Selection Based on Principal Component Analysis (2016, methodology): “114 Then, we use the normalized genotype matrix ̃ G to ascertain population structure 115 with PCA (Patterson et al., 2006).”
Association Mapping for Grain Quality in a Diverse Sorghum Collection (2012, methodology): “Principal component analysis was conducted to construct a plot of the most signii cant axes for grouping pattern variation and to obtain axes for further model testing and association mapping (Patterson et al., 2006; Price et al., 2006; Zhu and Yu, 2009).”
Inference of Population Structure using Dense Haplotype Data (2012, result): “This helps explain previous observations [50] that structure is frequently detectable using both types of approach, or neither.”
Asymptotic power of sphericity tests for high-dimensional data (2013, background): “With the important exception of Srivastava (2005), (5.4)–(5.6) are the first results on the asymptotic power of those tests against contiguous alternatives. Srivastava (2005) analyzes the asymptotic power of tests similar to those in Examples 1 and 2.”
Signals of polygenic adaptation on height have been overestimated due to uncorrected population structure in genome-wide association studies (2018, neutral): “628 [19] Patterson N, Price AL, Reich D.”
Discriminant analysis of principal components: a new method for the analysis of genetically structured populations (2010, positive): “Multivariate analyses have been used for decades to extract various types of information from genetic data and have attracted renewed interest in the field [ 6-12 ].”
Inference of Population Splits and Mixtures from Genome-Wide Allele Frequency Data (2012, background): “Another plausible transformation would be to scale all allele frequencies by m̂(1{m̂), where m̂ is the mean allele frequency across populations [19,33].”