CluStrat: A Structure Informed Clustering Strategy for Population Stratification

Bose, Aritra; Burch, Myson C.; Chowdhury, Agniva; Paschou, Peristera; Drineas, Petros

doi:10.1007/978-3-030-45257-5_19

Cited by 4 publications

(4 citation statements)

References 9 publications

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“…The simulated datasets included a wide array of configurations and were generated using the data simulator in a previous work. 22 Additionally, UK Biobank enhanced PRS (ePRS-UKB) for multiple phenotypes were used to further evaluate the model in real-world scenarios across different disease outcomes.…”

Section: Methodsmentioning

confidence: 99%

FairPRS: adjusting for admixed populations in polygenic risk scores using invariant risk minimization

et al. 2022

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Polygenic risk scores (PRS) are increasingly used to estimate the personal risk of a trait based on genetics. However, most genomic cohorts are of European populations, with a strong under-representation of non-European groups. Given that PRS poorly transport across racial groups, this has the potential to exacerbate health disparities if used in clinical care. Hence there is a need to generate PRS that perform comparably across ethnic groups. Borrowing from recent advancements in the domain adaption field of machine learning, we propose - an Invariant Risk Minimization (IRM) approach for estimating fair PRS or debiasing a pre-computed PRS. We test our method on both a diverse set of synthetic data and real data from the UK Biobank. We show our method can create ancestry-invariant PRS distributions that are both racially unbiased and largely improve phenotype prediction. We hope that will contribute to a fairer characterization of patients by genetics rather than by race.

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Section: Methodsmentioning

confidence: 99%

FairPRS: adjusting for admixed populations in polygenic risk scores using invariant risk minimization

et al. 2022

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“…It is worth noting that there is a long line of research on matrix sketching methods, including gaussian sketching, the use of the subsampled randomized hadamard transforms, the count-min sketch, etc. and its application in human genetics [1][2][3]. In our work, we evaluated both the count-min sketch and the gaussian sketch.…”

Section: Mask-lmmmentioning

confidence: 99%

MaSk-LMM: A Matrix Sketching Framework for Linear Mixed Models in Association Studies

Burch,

Bose,

Dexter

et al. 2023

Preprint

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Linear mixed models (LMMs) have been widely used in genome-wide association studies (GWAS) to control for population stratification and cryptic relatedness. Unfortunately, estimating LMM parameters is computationally expensive, necessitating large-scale matrix operations to build the genetic relatedness matrix (GRM). Over the past 25 years, Randomized Linear Algebra has provided alternative approaches to such matrix operations by leveragingmatrix sketching, which often results in provably accurate fast and efficient approximations. We leveragematrix sketchingto develop a fast and efficient LMM method calledMatrix-SketchingLMM(MaSk-LMM) by sketching the genotype matrix to reduce its dimensions and speed up computations. Our framework comes with both theoretical guarantees and a strong empirical performance compared to current state-of-the-art.

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“…We generated simulated data emulating real-world populations to evaluate whether ThreSPCA can correctly identify markers which contribute to the genetic differences between and within the populations. Based on previous work [32], we simulated two datasets varying m = {5000, 10000} SNPs genotyped across n = {500, 1000} individuals based on the Pritchard-Stephens-Donelly (PSD) model [33] with the mixing parameter between populations, α = 0.01. The allele frequencies were simulated based on real-world data from three divergent populations, namely CEU (Utah residents with Northern and Western European ancestry), ASW (African ancestry in Southwestern US), and MXL (Mexican ancestry in California) from the HapMap Phase 3 data [34].…”

Section: Datamentioning

confidence: 99%

A Fast, Provably Accurate Approximation Algorithm for Sparse Principal Component Analysis Reveals Human Genetic Variation Across the World

Chowdhury

Bose

Zhou

et al. 2022

Preprint

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Principal component analysis (PCA) is a widely used dimensionality reduction technique in machine learning and multivariate statistics. To improve the interpretability of PCA, various approaches to obtain sparse principal direction loadings have been proposed, which are termed Sparse Principal Component Analysis (SPCA). In this paper, we present ThreSPCA, a provably accurate algorithm based on thresholding the Singular Value Decomposition for the SPCA problem, without imposing any restrictive assumptions on the input covariance matrix. Our thresholding algorithm is conceptually simple; much faster than current state-of-the-art; and performs well in practice. When applied to genotype data from the 1000 Genomes Project, ThreSPCA is faster than previous benchmarks, at least as accurate, and leads to a set of interpretable biomarkers, revealing genetic diversity across the world. Code available at https://github.com/aritra90/ThreSPCA

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CluStrat: A Structure Informed Clustering Strategy for Population Stratification

Cited by 4 publications

References 9 publications

FairPRS: adjusting for admixed populations in polygenic risk scores using invariant risk minimization

FairPRS: adjusting for admixed populations in polygenic risk scores using invariant risk minimization

MaSk-LMM: A Matrix Sketching Framework for Linear Mixed Models in Association Studies

A Fast, Provably Accurate Approximation Algorithm for Sparse Principal Component Analysis Reveals Human Genetic Variation Across the World

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