Fast and robust ancestry prediction using principal component analysis

Zhang, Daiwei; Dey, Rounak; Lee, Seunggeun

doi:10.1093/bioinformatics/btaa152

Cited by 36 publications

(27 citation statements)

References 15 publications

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“…To project PCs of a reference dataset (e.g. 1000G) to a target genotype dataset, we implement the following 3 steps in function of package bigsnpr: 1) matching the variants of each dataset, including removing ambiguous alleles [A/T] and [C/G], and matching strand and direction of the alleles; 2) computing PCA of the reference dataset using the matched variants only; 3) projecting computed PCs to the target data using an optimised implementation (see Supplementary Materials) of the Online Augmentation, Decomposition, and Procrustes (OADP) transformation (Zhang et al 2019). To project individuals from the same dataset as the ones used for computing PCA, you can use function .…”

Section: Methodsmentioning

confidence: 99%

“…We implement an optimised version of the Online Augmentation, Decomposition, and Procrustes (OADP) transformation when using K′′ = K′ = K (Zhang et al 2019). We assume that the K -partial Singular Value Decomposition (SVD) of the reference matrix X (of size n × p ) has been computed as U ∆ V T .…”

Section: Supplementary Materialsmentioning

confidence: 99%

“…for detecting genetic association within these LD regions in GWAS. Second, another issue may arise when projecting PCs of a reference dataset to another study dataset: projected PCs are shrunk towards 0 in the new dataset (Lee et al 2010; Wang et al 2015; Zhang et al 2019). This shrinkage makes it potentially dangerous to use the projected PCs for analyses such as PC regression, ancestry detection and correction for ancestry.…”

Section: Introductionmentioning

confidence: 99%

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Efficient toolkit implementing best practices for principal component analysis of population genetic data

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Luu

Blum

et al. 2019

Preprint

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Principal Component Analysis (PCA) of genetic data is routinely used to infer ancestry and control for population structure in various genetic analyses. However, conducting PCA analyses can be complicated and has several potential pitfalls. These pitfalls include (1) capturing Linkage Disequilibrium (LD) structure instead of population structure, (2) projected PCs that suffer from shrinkage bias when projecting PCA from a reference dataset to another independent dataset, (3) detecting sample outliers, and (4) uneven population sizes. In this work, we explore these potential issues when using PCA, and present efficient solutions to these. Following applications to the UK Biobank and the 1000 Genomes project datasets, we make recommendations for best practices and provide efficient and user-friendly implementations of the proposed solutions in R packages bigsnpr and bigutilsr. For example, we show that PC19 to PC40 in the UK Biobank capture LD structure. Using our automatic algorithm for removing long-range LD regions, we recover 16 PCs that capture population structure only. Therefore, we recommend using only 16-18 PCs from the UK Biobank. We provide evidence for a shrinkage bias when projecting PCs computed with data from the 1000 Genomes project. Although PC1 to PC4 suffer from only moderate shrinkage (1.01-1.09), PC5 (resp. PC10) for example suffers from a shrinkage factor of 1.50 (resp. 3.14). We provide a fast way to project new individuals that is not affected by this shrinkage bias. We also show how to use PCA to restrict analyses to individuals of homogeneous ancestry. Overall, we believe this work would be of interest for anyone using PCA in their analyses of genetic data, as well as for other omics data.

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Section: Methodsmentioning

confidence: 99%

Section: Supplementary Materialsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Efficient toolkit implementing best practices for principal component analysis of population genetic data

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Luu

Blum

et al. 2019

Preprint

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“…Including PCs that capture LD as covariates in genetic analyses can lead to reduced power for detecting genetic associations within these LD regions ( Zou et al , 2010 ). Second, another issue may arise when projecting a new study dataset to the PCA space computed from a reference dataset: projected PCs are shrunk toward 0 in the new dataset ( Lee et al , 2010 ; Wang et al , 2015 ; Zhang et al , 2020 ). This shrinkage makes it potentially dangerous to use the projected PCs for analyses, such as PC regression, ancestry detection and correction for ancestry.…”

Section: Introductionmentioning

confidence: 99%

Efficient toolkit implementing best practices for principal component analysis of population genetic data

et al. 2020

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Motivation Principal Component Analysis (PCA) of genetic data is routinely used to infer ancestry and control for population structure in various genetic analyses. However, conducting PCA analyses can be complicated and has several potential pitfalls. These pitfalls include (1) capturing Linkage Disequilibrium (LD) structure instead of population structure, (2) projected PCs that suffer from shrinkage bias, (3) detecting sample outliers, and (4) uneven population sizes. In this work, we explore these potential issues when using PCA, and present efficient solutions to these. Following applications to the UK Biobank and the 1000 Genomes project datasets, we make recommendations for best practices and provide efficient and user-friendly implementations of the proposed solutions in R packages bigsnpr and bigutilsr. Results for example, we find that PC19 to PC40 in the UK Biobank capture complex LD structure rather than population structure. Using our automatic algorithm for removing long-range LD regions, we recover 16 PCs that capture population structure only. Therefore, we recommend using only 16-18 PCs from the UK Biobank to account for population structure confounding. We also show how to use PCA to restrict analyses to individuals of homogeneous ancestry. Finally, when projecting individual genotypes onto the PCA computed from the 1000 Genomes project data, we find a shrinkage bias that becomes large for PC5 and beyond. We then demonstrate how to obtain unbiased projections efficiently using bigsnpr. Conclusion Overall, we believe this work would be of interest for anyone using PCA in their analyses of genetic data, as well as for other omics data. Availability R packages bigsnpr and bigutilsr can be installed from either CRAN or GitHub (see https://github.com/privefl/bigsnpr). A tutorial on the steps to perform PCA on 1000G data is available at https://privefl.github.io/bigsnpr/articles/bedpca.html. All code used for this paper is available at https://github.com/privefl/paper4-bedpca/tree/master/code. Supplementary information Supplementary data are available at Bioinformatics online.

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“…Secondly, dimensionality reduction reserves the most contributing features of high-dimensional data, removing noise and inconsequential features, thereby achieving the goal of improving data processing speed. Principal component analysis (PCA) [35] is a widely used method of dimensionality reduction of high-dimensional data while minimizing information loss [36]. Suppose the data set is matrix X = [x 1 , x 2 , x 3 , · · · , x n ], then the process of centralizing the data set matrix would be:…”

Section: Classification Of Reconstructed Xasmentioning

confidence: 99%

Effect of Reconstruction Algorithm on the Identification of 3D Printing Polymers Based on Hyperspectral CT Technology Combined with Artificial Neural Network

Zheng

Wang

et al. 2020

Materials

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Hyperspectral X-ray CT (HXCT) technology provides not only structural imaging but also the information of material components therein. The main purpose of this study is to investigate the effect of various reconstruction algorithms on reconstructed X-ray absorption spectra (XAS) of components shown in the CT image by means of HXCT. In this paper, taking 3D printing polymer as an example, seven kinds of commonly used polymers such as thermoplastic elastomer (TPE), carbon fiber reinforced polyamide (PA-CF), acrylonitrile butadiene styrene (ABS), polylactic acid (PLA), ultraviolet photosensitive resin (UV9400), polyethylene terephthalate glycol (PETG), and polyvinyl alcohol (PVA) were selected as samples for hyperspectral CT reconstruction experiments. Seven kinds of 3D printing polymer and two interfering samples were divided into a training set and test sets. First, structural images of specimens were reconstructed by Filtered Back-Projection (FBP), Algebra Reconstruction Technique (ART) and Maximum-Likelihood Expectation-Maximization (ML-EM). Secondly, reconstructed XAS were extracted from the pixels of region of interest (ROI) compartmentalized in the images. Thirdly, the results of principal component analysis (PCA) demonstrated that the first four principal components contain the main features of reconstructed XAS, so we adopted Artificial Neural Network (ANN) trained by the reconstructed XAS expressed by the first four principal components in the training set to identify that the XAS of corresponding polymers exist in both of test sets from the training set. The result of ANN displays that FBP has the best performance of classification, whose ten-fold cross-validation accuracy reached 99%. It suggests that hyperspectral CT reconstruction is a promising way of getting image features and material features at the same time, which can be used in medical imaging and nondestructive testing.

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Fast and robust ancestry prediction using principal component analysis

Cited by 36 publications

References 15 publications

Efficient toolkit implementing best practices for principal component analysis of population genetic data

Efficient toolkit implementing best practices for principal component analysis of population genetic data

Efficient toolkit implementing best practices for principal component analysis of population genetic data

Effect of Reconstruction Algorithm on the Identification of 3D Printing Polymers Based on Hyperspectral CT Technology Combined with Artificial Neural Network

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