Faculty Opinions recommendation of Interpreting principal component analyses of spatial population genetic variation.

By Yoav Gilad. 2008

245 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/9349c2d9b7e8e3d5c7a089aa8f2f27453bd9e131

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

fastSTRUCTURE: Variational Inference of Population Structure in Large SNP Data Sets (2014, background): “…while these low-dimensional projections allow for straightforward visualization of the underlying population structure, it is not always straightforward to derive and interpret estimates for global ancestry of sample individuals from their projection coordinates (Novembre and Stephens 2008).”
Inference of Population Splits and Mixtures from Genome-Wide Allele Frequency Data (2012, unknown): “In application to dogs, we show that the boxer and basenji breeds have a considerable component of ancestry from grey wolves subsequent to domestication.”
Ecotypes and the controversy over stages in the formation of new species (2012, methodology): “…coupled with new methodologies to analyze geographical patterns of population structure (Pritchard, Stephens & Donnelly, 2000; Dyer & Nason, 2004; Novembre & Stephens, 2008; Novembre et al., 2008; Novembre & Di Rienzo, 2009) has revived interest in understanding how genetic variation is…”
Inference of Population Structure using Dense Haplotype Data (2012, background): “[3]–[5]) whose entries quantify the genetic similarity between pairs of individuals.”
Fast and Efficient Estimation of Individual Ancestry Coefficients (2014, background): “But a drawback of PCA is that interpretation in terms of ancestry is often diﬃcult, as it can be confounded by demographic factors or irregular sampling designs (Novembre and Stephens 2008; Mc Vean 2009; Fran¸cois et al. 2010).”
Population Structure and Cryptic Relatedness in Genetic Association Studies (2009, background): “As for any other regression covariate, there is an argument for only including a PC in the model if it shows an association with the phenotype (Novembre and Stephens, 2008; Lee, Wright and Zou, 2010).”
Ancient human genomes suggest three ancestral populations for present-day Europeans (2013, background): “PCA gradients of genetic variation may arise under very different histories 20 .”
Revealing multi-scale population structure in large cohorts (2018, background): “, in (20)): visualization is not a replacement for statistical testing.”
Variance component model to account for sample structure in genome-wide association studies (2010, unknown): “Some principal components may represent broad differences across individuals within a given data set, effectively capturing a few major axes of population structure, but it is unclear how to interpret the rest of the principal components as surrogates of sample structur”
Application of t-SNE to Human Genetic Data (2017, methodology): “The reduced dimensions can be directly visualized and, in the case of PCA, can be used as covariates in the association analysis [20, 21, 22, 23, 24, 25, 26].”
Visualizing spatial population structure with estimated effective migration surfaces (2014, background): “For example, PCA is heavily inﬂuenced by sampling biases: more data being collected preferentially from some regions than others [21, 22, 23].”