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Keywords {🔍}

plink, data, article, pubmed, bit, google, scholar, table, test, variants, population, likelihoods, exact, algorithm, analysis, format, file, software, tables, association, matrix, central, cas, linkage, size, variant, information, genomic, genotype, number, reference, allele, ibs, alleles, genetic, datasets, full, hardyweinberg, fishers, set, genomewide, studies, tests, pairs, permutation, genet, disequilibrium, major, equilibrium, operations,

Topics {✒️}

write-var-ranges simplifies splitting time/constant-space hardy-weinberg equilibrium 5}{6}\begin{aligned} \frac{[\text{sum early-termination snp-hwe algorithm gene-trait mapping genome-wide case-control studies = 2pq\qquad\\ &\text{freq} multiple-testing adjusted p-values modern l2/l3 caches early-termination snp-hwe /writings/diary/archive/2011/11/02/faster_popcount_update major/minor-allele based design genome-wide association studies genome-wide interaction analysis reduce disk-caching variance genome-wide epistasis detection enables detailed study fast-epistasis variance correction distance/relationship partial sums gplv3-licensed open source population-based linkage analyses high-accuracy haplotype phasing floating point adds/multiplies high-end computing resources detecting gene-gene interactions likelihoods decay super-geometrically likelihood-based qtdt enjoyed laurent cam tellier ieee-754 double-precision numbers longest base-pair spans article download pdf population-genetic studies }\cdot \frac{\frac{n_{1} }\cdot \frac{\frac{n_{2} ^{2} \qquad \text{freq} improved sse2-based implementation genetic studies tend genome sequencing studies extreme tables}]} {[\text{sum detect genotyping error prints mini-manual entries full size image genomic relationship matrix hardy-weinberg equilibrium population genetic assumptions distance ibs square open access license heavy-duty operations default privacy choices/manage cookies sequence alignment/map format

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WebPage:
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         headline:Second-generation PLINK: rising to the challenge of larger and richer datasets
         description:PLINK 1 is a widely used open-source C/C++ toolset for genome-wide association studies (GWAS) and research in population genetics. However, the steady accumulation of data from imputation and whole-genome sequencing studies has exposed a strong need for faster and scalable implementations of key functions, such as logistic regression, linkage disequilibrium estimation, and genomic distance evaluation. In addition, GWAS and population-genetic data now frequently contain genotype likelihoods, phase information, and/or multiallelic variants, none of which can be represented by PLINK 1’s primary data format. To address these issues, we are developing a second-generation codebase for PLINK. The first major release from this codebase, PLINK 1.9, introduces extensive use of bit-level parallelism, $O\left (\sqrt {n}\right)$ -time/constant-space Hardy-Weinberg equilibrium and Fisher’s exact tests, and many other algorithmic improvements. In combination, these changes accelerate most operations by 1-4 orders of magnitude, and allow the program to handle datasets too large to fit in RAM. We have also developed an extension to the data format which adds low-overhead support for genotype likelihoods, phase, multiallelic variants, and reference vs. alternate alleles, which is the basis of our planned second release (PLINK 2.0). The second-generation versions of PLINK will offer dramatic improvements in performance and compatibility. For the first time, users without access to high-end computing resources can perform several essential analyses of the feature-rich and very large genetic datasets coming into use.
         datePublished:2015-02-25T00:00:00Z
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            High-density SNP genotyping
            Computational statistics
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            Computer Appl. in Life Sciences
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      headline:Second-generation PLINK: rising to the challenge of larger and richer datasets
      description:PLINK 1 is a widely used open-source C/C++ toolset for genome-wide association studies (GWAS) and research in population genetics. However, the steady accumulation of data from imputation and whole-genome sequencing studies has exposed a strong need for faster and scalable implementations of key functions, such as logistic regression, linkage disequilibrium estimation, and genomic distance evaluation. In addition, GWAS and population-genetic data now frequently contain genotype likelihoods, phase information, and/or multiallelic variants, none of which can be represented by PLINK 1’s primary data format. To address these issues, we are developing a second-generation codebase for PLINK. The first major release from this codebase, PLINK 1.9, introduces extensive use of bit-level parallelism, $O\left (\sqrt {n}\right)$ -time/constant-space Hardy-Weinberg equilibrium and Fisher’s exact tests, and many other algorithmic improvements. In combination, these changes accelerate most operations by 1-4 orders of magnitude, and allow the program to handle datasets too large to fit in RAM. We have also developed an extension to the data format which adds low-overhead support for genotype likelihoods, phase, multiallelic variants, and reference vs. alternate alleles, which is the basis of our planned second release (PLINK 2.0). The second-generation versions of PLINK will offer dramatic improvements in performance and compatibility. For the first time, users without access to high-end computing resources can perform several essential analyses of the feature-rich and very large genetic datasets coming into use.
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      dateModified:2015-02-25T00:00:00Z
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         GWAS
         Population genetics
         Whole-genome sequencing
         High-density SNP genotyping
         Computational statistics
         Bioinformatics
         Computational Biology/Bioinformatics
         Computer Appl. in Life Sciences
         Proteomics
         Data Mining and Knowledge Discovery
         Human Genetics
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               name:Mathematical Biology Section, NIDDK/LBM, National Institutes of Health, Bethesda, USA
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      name:Complete Genomics, Mountain View, USA
      name:BGI Cognitive Genomics Lab, Shenzhen, China
      name:Mathematical Biology Section, NIDDK/LBM, National Institutes of Health, Bethesda, USA
      name:BGI Cognitive Genomics Lab, Shenzhen, China
      name:Bioinformatics Centre, University of Copenhagen, Copenhagen, Denmark
      name:Mathematical Biology Section, NIDDK/LBM, National Institutes of Health, Bethesda, USA
      name:Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, USA
      name:Division of Psychiatric Genomics, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, USA
      name:Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, USA
      name:Analytic and Translational Genetics Unit, Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, USA
      name:Mathematical Biology Section, NIDDK/LBM, National Institutes of Health, Bethesda, USA
      name:Department of Psychology, University of Minnesota Twin Cities, Minneapolis, USA

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