Robust genome-wide ancestry inference for heterogeneous datasets: illustrated using the 1,000 genome project with 3D facial images

Li, Jiarui; Zarzar, Tomás González; White, Julie D.; Indencleef, Karlijne; Hoskens, Hanne; Matthews, Harry R.; Nauwelaers, Nele; Zaidi, Arslan; Eller, Ryan J.; Herrick, Noah; Günther, Torsten; Svensson, Emma; Jakobsson, Mattias; Walsh, Susan; Steen, Kristel Van; Shriver, Mark D.; Claes, Peter

doi:10.1038/s41598-020-68259-w

Cited by 9 publications

(13 citation statements)

References 47 publications

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“…SUGIBS was previously proposed as a robust alternative against laboratory artifacts and outliers [14] by applying SVD on the IBS generalized genotype matrix, where IBS information corrects for potential artifacts due to errors and missingness.…”

Section: Spectral Decomposition Generalized By Identity-by-state Matrixmentioning

confidence: 99%

Hybrid Autoencoder with Orthogonal Latent Space for Robust Population Structure Inference

Yuan

Hoskens

Goovaerts

et al. 2022

Preprint

Self Cite

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Background: Analysis of population structure and genomic ancestry remains an important topic in human genetics and bioinformatics. Commonly used methods require high-quality genotype data to ensure accurate inference. However, in practice, laboratory artifacts and outliers are often present in the data. Moreover, existing methods are typically affected by the presence of related individuals in the dataset. Results: In this work, we propose a novel hybrid method, called SAE-IBS, which combines the strengths of traditional matrix decomposition-based (e.g., principal component analysis) and more recent neural network-based (e.g., autoencoders) solutions. I.e., it yields an orthogonal latent space enhancing dimensionality selection while learning non-linear transformations. The proposed approach achieves higher accuracy than existing methods for projecting poor quality target samples (genotyping errors and missing data) onto a reference ancestry space and generates a robust ancestry space in the presence of relatedness. Conclusion: We introduce a new approach and an accompanying open-source program for robust ancestry inference in the presence of missing data, genotyping errors, and relatedness. The obtained ancestry space allows for non-linear projections and exhibits orthogonality with clearly separable population groups.

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Section: Spectral Decomposition Generalized By Identity-by-state Matrixmentioning

confidence: 99%

Hybrid Autoencoder with Orthogonal Latent Space for Robust Population Structure Inference

Yuan

Hoskens

Goovaerts

et al. 2022

Preprint

Self Cite

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“…Loadings are also offered by databases like gnomAD 29 and the UK Biobank 30 . PCA serves as the primary tool to identify the origins of ancient samples in paleogenomics 14 , to identify biomarkers for forensic reconstruction in evolutionary biology 31 , and geolocalize samples 32 . As of April 2022, 32,000-216,000 genetic papers employed PC scatterplots to interpret genetic data, draw historical and ethnobiological conclusions, and describe the evolution of various taxa from prehistorical times to the present—no doubt Herculean tasks for any scatterplot.…”

Section: Introductionmentioning

confidence: 99%

Principal Component Analyses (PCA)-based findings in population genetic studies are highly biased and must be reevaluated

Elhaik

2022

Sci Rep

103

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Principal Component Analysis (PCA) is a multivariate analysis that reduces the complexity of datasets while preserving data covariance. The outcome can be visualized on colorful scatterplots, ideally with only a minimal loss of information. PCA applications, implemented in well-cited packages like EIGENSOFT and PLINK, are extensively used as the foremost analyses in population genetics and related fields (e.g., animal and plant or medical genetics). PCA outcomes are used to shape study design, identify, and characterize individuals and populations, and draw historical and ethnobiological conclusions on origins, evolution, dispersion, and relatedness. The replicability crisis in science has prompted us to evaluate whether PCA results are reliable, robust, and replicable. We analyzed twelve common test cases using an intuitive color-based model alongside human population data. We demonstrate that PCA results can be artifacts of the data and can be easily manipulated to generate desired outcomes. PCA adjustment also yielded unfavorable outcomes in association studies. PCA results may not be reliable, robust, or replicable as the field assumes. Our findings raise concerns about the validity of results reported in the population genetics literature and related fields that place a disproportionate reliance upon PCA outcomes and the insights derived from them. We conclude that PCA may have a biasing role in genetic investigations and that 32,000-216,000 genetic studies should be reevaluated. An alternative mixed-admixture population genetic model is discussed.

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“…However, high-quality genotype data is critical to the success of PCA 10 . In the presence of missing genotypes or errors in the target dataset, PCA has shown to produce patterns of misalignment during projection 14 , 15 . To overcome this problem, a robust alternative was recently proposed, known as SUGIBS, which utilizes spectral (S) decomposition of an unnormalized genomic (UG) relationship matrix generalized by an Identity-by-State (IBS) similarity matrix between the samples to be projected and individuals in the reference dataset 14 .…”

Section: Introductionmentioning

confidence: 99%

Hybrid autoencoder with orthogonal latent space for robust population structure inference

Yuan

Hoskens

Goovaerts

et al. 2023

Sci Rep

Self Cite

View full text Add to dashboard Cite

Analysis of population structure and genomic ancestry remains an important topic in human genetics and bioinformatics. Commonly used methods require high-quality genotype data to ensure accurate inference. However, in practice, laboratory artifacts and outliers are often present in the data. Moreover, existing methods are typically affected by the presence of related individuals in the dataset. In this work, we propose a novel hybrid method, called SAE-IBS, which combines the strengths of traditional matrix decomposition-based (e.g., principal component analysis) and more recent neural network-based (e.g., autoencoders) solutions. Namely, it yields an orthogonal latent space enhancing dimensionality selection while learning non-linear transformations. The proposed approach achieves higher accuracy than existing methods for projecting poor quality target samples (genotyping errors and missing data) onto a reference ancestry space and generates a robust ancestry space in the presence of relatedness. We introduce a new approach and an accompanying open-source program for robust ancestry inference in the presence of missing data, genotyping errors, and relatedness. The obtained ancestry space allows for non-linear projections and exhibits orthogonality with clearly separable population groups.

show abstract

Robust genome-wide ancestry inference for heterogeneous datasets: illustrated using the 1,000 genome project with 3D facial images

Cited by 9 publications

References 47 publications

Hybrid Autoencoder with Orthogonal Latent Space for Robust Population Structure Inference

Hybrid Autoencoder with Orthogonal Latent Space for Robust Population Structure Inference

Principal Component Analyses (PCA)-based findings in population genetic studies are highly biased and must be reevaluated

Hybrid autoencoder with orthogonal latent space for robust population structure inference

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