Evaluation of population structure inferred by principal component analysis or the admixture model
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Evaluation of population structure inferred by principal component analysis or the admixture model. / Van Waaij, Jan; Li, Song; Garcia-Erill, Genís; Albrechtsen, Anders; Wiuf, Carsten.
In: Genetics, Vol. 225, No. 2, iyad157, 2023.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Evaluation of population structure inferred by principal component analysis or the admixture model
AU - Van Waaij, Jan
AU - Li, Song
AU - Garcia-Erill, Genís
AU - Albrechtsen, Anders
AU - Wiuf, Carsten
N1 - Publisher Copyright: © 2023 The Author(s). Published by Oxford University Press on behalf of The Genetics Society of America. All rights reserved.
PY - 2023
Y1 - 2023
N2 - Principal component analysis (PCA) is commonly used in genetics to infer and visualize population structure and admixture between populations. PCA is often interpreted in a way similar to inferred admixture proportions, where it is assumed that individuals belong to one of several possible populations or are admixed between these populations. We propose a new method to assess the statistical fit of PCA (interpreted as a model spanned by the top principal components) and to show that violations of the PCA assumptions affect the fit. Our method uses the chosen top principal components to predict the genotypes. By assessing the covariance (and the correlation) of the residuals (the differences between observed and predicted genotypes), we are able to detect violation of the model assumptions. Based on simulations and genome-wide human data, we show that our assessment of fit can be used to guide the interpretation of the data and to pinpoint individuals that are not well represented by the chosen principal components. Our method works equally on other similar models, such as the admixture model, where the mean of the data is represented by linear matrix decomposition.
AB - Principal component analysis (PCA) is commonly used in genetics to infer and visualize population structure and admixture between populations. PCA is often interpreted in a way similar to inferred admixture proportions, where it is assumed that individuals belong to one of several possible populations or are admixed between these populations. We propose a new method to assess the statistical fit of PCA (interpreted as a model spanned by the top principal components) and to show that violations of the PCA assumptions affect the fit. Our method uses the chosen top principal components to predict the genotypes. By assessing the covariance (and the correlation) of the residuals (the differences between observed and predicted genotypes), we are able to detect violation of the model assumptions. Based on simulations and genome-wide human data, we show that our assessment of fit can be used to guide the interpretation of the data and to pinpoint individuals that are not well represented by the chosen principal components. Our method works equally on other similar models, such as the admixture model, where the mean of the data is represented by linear matrix decomposition.
KW - ancient DNA
KW - PCA
KW - population structure
KW - residuals
KW - statistical fit
U2 - 10.1093/genetics/iyad157
DO - 10.1093/genetics/iyad157
M3 - Journal article
C2 - 37611212
AN - SCOPUS:85174717454
VL - 225
JO - Genetics
JF - Genetics
SN - 1943-2631
IS - 2
M1 - iyad157
ER -
ID: 371925290