Probabilistic ancestry maps: a method to assess and visualize population substructures in genetics

Association Studies ◽

Principal Component ◽

Generative Topographic Mapping ◽

Genome Wide ◽

Clustering And Classification ◽

Reduction Methods ◽

Linear Dimensionality Reduction ◽

Project Data

AbstractPrincipal component analysis (PCA) is a standard method to correct for population stratification in ancestry-specific genome-wide association studies (GWASs) and is used to cluster individuals by ancestry. Using the 1000 genomes project data, we examine how non-linear dimensionality reduction methods such as t-distributed stochastic neighbor embedding (t-SNE) or generative topographic mapping (GTM) can be used to provide improved ancestry maps by accounting for a higher percentage of explained variance in ancestry, and how they can help to estimate the number of principal components necessary to account for population stratification. GTM also generates posterior probabilities of class membership which can be used to assess the probability of an individual to belong to a given population - as opposed to t-SNE, GTM can be used for both clustering and classification. This paper is a first application of GTM for ancestry classification models. Our maps and software are available online.Author summaryWith this paper, we seek to encourage researchers working in genetics to use other methods than PCA to visualize ancestry and identify substructures in populations. We propose to use methods which do not only allow visualization of ancestry, but also the estimation of probabilities of belonging to different ancestry groups.

Evaluation of methods for adjusting population stratification in genome‐wide association studies: Standard versus categorical principal component analysis

Annals of Human Genetics ◽

10.1111/ahg.12339 ◽

2019 ◽

Vol 83 (6) ◽

pp. 454-464

Author(s):

Asuman S. Turkmen ◽

Yuan Yuan ◽

Nedret Billor

Keyword(s):

Principal Component Analysis ◽

Association Studies ◽

Principal Component ◽

Component Analysis ◽

Genome Wide Association ◽

Genome Wide ◽

Categorical Principal Component Analysis ◽

Evaluation Of Methods

CluStrat: a structure informed clustering strategy for population stratification

10.1101/2020.01.15.908228 ◽

2020 ◽

Cited By ~ 1

Author(s):

Aritra Bose ◽

Myson C. Burch ◽

Agniva Chowdhury ◽

Peristera Paschou ◽

Petros Drineas

Keyword(s):

Mahalanobis Distance ◽

Association Studies ◽

Principal Component ◽

Genome Wide ◽

Standard Population ◽

Leverage Scores ◽

Complex Population ◽

Causal Variants

AbstractGenome-wide association studies (GWAS) have been extensively used to estimate the signed effects of trait-associated alleles. Recent independent studies failed to replicate the strong evidence of selection for height across Europe implying the shortcomings of standard population stratification correction approaches. Here, we present CluStrat, a stratification correction algorithm for complex population structure that leverages the linkage disequilibrium (LD)-induced distances between individuals. CluStrat performs agglomerative hierarchical clustering using the Mahalanobis distance and then applies sketching-based randomized ridge regression on the genotype data to obtain the association statistics. With the growing size of data, computing and storing the genome wide covariance matrix is a non-trivial task. We get around this overhead by computing the GRM directly using a connection between statistical leverage scores and the Mahalanobis distance. We test CluStrat on a large simulation study of discrete and admixed, arbitrarily-structured sub-populations identifying two to three-fold more true causal variants when compared to Principal Component (PC) based stratification correction methods while trading off for a slightly higher spurious associations. Applying CluStrat on WTCCC2 Parkinson’s disease (PD) data, we identified loci mapped to a host of genes associated with PD such as BACH2, MAP2, NR4A2, SLC11A1, UNC5C to name a few.Availability and ImplementationCluStrat source code and user manual is available at: https://github.com/aritra90/CluStrat

PCAmatchR: a flexible R package for optimal case-control matching using weighted principal components

Bioinformatics ◽

10.1093/bioinformatics/btaa784 ◽

2020 ◽

Author(s):

Derek W Brown ◽

Timothy A Myers ◽

Mitchell J Machiela

Keyword(s):

Association Studies ◽

Principal Component ◽

R Package ◽

Case Control ◽

Supplementary Information ◽

Variation Explained ◽

And Performance ◽

Project Data

Abstract Summary A concern when conducting genome-wide association studies (GWAS) is the potential for population stratification, i.e. ancestry based genetic differences between cases and controls, that if not properly accounted for, could lead to biased association results. We developed PCAmatchR as an open source R package for performing optimal case-control matching using principal component analysis (PCA) to aid in selecting controls that are well matched by ancestry to cases. PCAmatchR takes user supplied PCA outputs and selects matching controls for cases by utilizing a weighted Mahalanobis distance metric which weights each principal component by the percent of genetic variation explained. Results from the 1000 Genomes Project data demonstrate both the functionality and performance of PCAmatchR for selecting matching controls for case populations as well as reducing inflation of association test statistics. PCAmatchR improves genomic similarity between matched cases and controls, which minimizes the effects of population stratification in GWAS analyses. Availability PCAmatchR is freely available for download on GitHub (https://github.com/machiela-lab/PCAmatchR) or through CRAN (https://cran.r-project.org/web/packages/PCAmatchR/index.html) Supplementary information Supplementary data are available at Bioinformatics online.

A practical approach to adjusting for population stratification in genome-wide association studies: principal components and propensity scores (PCAPS)

Statistical Applications in Genetics and Molecular Biology ◽

10.1515/sagmb-2017-0054 ◽

2018 ◽

Vol 17 (6) ◽

Cited By ~ 2

Author(s):

Huaqing Zhao ◽

Nandita Mitra ◽

Peter A. Kanetsky ◽

Katherine L. Nathanson ◽

Timothy R. Rebbeck

Keyword(s):

Principal Components ◽

Propensity Scores ◽

Association Studies ◽

Germ Cell Tumors ◽

Gwas Data ◽

Genome Wide Association ◽

Testicular Germ Cell ◽

Genome Wide

Abstract Genome-wide association studies (GWAS) are susceptible to bias due to population stratification (PS). The most widely used method to correct bias due to PS is principal components (PCs) analysis (PCA), but there is no objective method to guide which PCs to include as covariates. Often, the ten PCs with the highest eigenvalues are included to adjust for PS. This selection is arbitrary, and patterns of local linkage disequilibrium may affect PCA corrections. To address these limitations, we estimate genomic propensity scores based on all statistically significant PCs selected by the Tracy-Widom (TW) statistic. We compare a principal components and propensity scores (PCAPS) approach to PCA and EMMAX using simulated GWAS data under no, moderate, and severe PS. PCAPS reduced spurious genetic associations regardless of the degree of PS, resulting in odds ratio (OR) estimates closer to the true OR. We illustrate our PCAPS method using GWAS data from a study of testicular germ cell tumors. PCAPS provided a more conservative adjustment than PCA. Advantages of the PCAPS approach include reduction of bias compared to PCA, consistent selection of propensity scores to adjust for PS, the potential ability to handle outliers, and ease of implementation using existing software packages.