ArticlePLoS genetics2020
Scalable probabilistic PCA for large-scale genetic variation data.
Article in PLoS genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed, 50 citations in OpenAlex.
- Aging Trajectory Analysis of Asphalt: Differential Regulation of UV Aging Processes by Anti-Aging Agents with Varied Mechanisms.Materials (Basel, Switzerland) · 2026Article
- General, orders-of-magnitude faster whole-genome analysis with genotype representation graphs.bioRxiv : the preprint server for biology · 2026Article
- GrafAnc: Reliable and reproducible inference of continental and regional population structure.HGG advances · 2026Article
- X-LDR: an atlas of linkage disequilibrium across species.Genome biology · 2025Article
- Comprehensive gene heritability estimation reveals the genetic architecture of rare coding variants underlying complex traits.bioRxiv : the preprint server for biology · 2025Article
- Analytical and computational solution for the estimation of SNP-heritability in biobank-scale and distributed datasets.PLoS computational biology · 2025Article
- randPedPCA: rapid approximation of principal components from large pedigrees.Genetics, selection, evolution : GSE · 2025Article
- DAPCy: a Python package for the discriminant analysis of principal components method for population genetic analyses.Bioinformatics advances · 2025Article
- Principal component analysis revisited: fast multitrait genetic evaluations with smooth convergence.G3 (Bethesda, Md.) · 2024Article
- A genome-wide association study of neonatal metabolites.Cell genomics · 2024Article
- An analysis of the accuracy of retrospective birth location recall using sibling data.Nature communications · 2024Article
- Efficacy of federated learning on genomic data: a study on the UK Biobank and the 1000 Genomes Project.Frontiers in big data · 2024Article
- FiMAP: A fast identity-by-descent mapping test for biobank-scale cohorts.PLoS genetics · 2023Article
- SuSiE PCA: A scalable Bayesian variable selection technique for principal component analysis.iScience · 2023Article
- Adjusting for population stratification in polygenic risk score analyses: a guide for model specifications in the UK Biobank.Journal of human genetics · 2023Article
- Fast and accurate out-of-core PCA framework for large scale biobank data.Genome research · 2023Article
- The STROMICS genome study: deep whole-genome sequencing and analysis of 10K Chinese patients with ischemic stroke reveal complex genetic and phenotypic interplay.Cell discovery · 2023Article
- Whole genome sequencing across clinical trials identifies rare coding variants in GPR68 associated with chemotherapy-induced peripheral neuropathy.Genome medicine · 2023Article
- Limitations of principal components in quantitative genetic association models for human studies.eLife · 2023Article
- Multivariate genome-wide association analysis by iterative hard thresholding.Bioinformatics (Oxford, England) · 2023Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
Abstract
Principal component analysis (PCA) is a key tool for understanding population structure and controlling for population stratification in genome-wide association studies (GWAS). With the advent of large-scale datasets of genetic variation, there is a need for methods that can compute principal components (PCs) with scalable computational and memory requirements. We present ProPCA, a highly scalable method based on a probabilistic generative model, which computes the top PCs on genetic variation data efficiently. We applied ProPCA to compute the top five PCs on genotype data from the UK Biobank, consisting of 488,363 individuals and 146,671 SNPs, in about thirty minutes. To illustrate the utility of computing PCs in large samples, we leveraged the population structure inferred by ProPCA within White British individuals in the UK Biobank to identify several novel genome-wide signals of recent putative selection including missense mutations in RPGRIP1L and TLR4.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.