Scalable probabilistic PCA for large-scale genetic variation data

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 compon...

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Bibliographic Details
Published in:PLoS genetics 2020-05, Vol.16 (5), p.e1008773-e1008773
Main Authors: Agrawal, Aman, Chiu, Alec M, Le, Minh, Halperin, Eran, Sankararaman, Sriram
Format: Article
Language:English
Subjects:
Accuracy
Adaptor Proteins, Signal Transducing - genetics
Algorithms
Biobanks
Biological Specimen Banks
Biology and Life Sciences
Computational Biology - methods
Computer applications
Datasets
European Continental Ancestry Group - genetics
Genetic analysis
Genetic diversity
Genetic variation
Genetics, Population
Genome-wide association studies
Genome-Wide Association Study - methods
Genomes
Genotype & phenotype
Genotypes
Heritability
Humans
Linkage disequilibrium
Methods
Models, Genetic
Mutation, Missense
Physical Sciences
Polymorphism, Single Nucleotide
Population
Population genetics
Population structure
Positive selection
Principal Component Analysis
Principal components analysis
Research and Analysis Methods
Single-nucleotide polymorphism
Toll-Like Receptor 4 - genetics
United Kingdom - ethnology
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Summary: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.
ISSN:1553-7404
1553-7390
1553-7404
DOI:10.1371/journal.pgen.1008773