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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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| Published in: | PLoS genetics 2020-05, Vol.16 (5), p.e1008773-e1008773 |
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| container_end_page | e1008773 |
| container_issue | 5 |
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| container_title | PLoS genetics |
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| creator | Agrawal, Aman Chiu, Alec M Le, Minh Halperin, Eran Sankararaman, Sriram |
| description | 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. |
| doi_str_mv | 10.1371/journal.pgen.1008773 |
| format | article |
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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. 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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. 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| 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 |
| title | Scalable probabilistic PCA for large-scale genetic variation data |
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