FarmCPUpp: Efficient large‐scale genomewide association studies

Genomewide association studies (GWAS) are computationally demanding analyses that use large sample sizes and dense marker sets to discover associations between quantitative trait variation and genetic variants. FarmCPU is a powerful new method for performing GWAS. However, its performance is hampere...

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Detalles Bibliográficos
Autores principales: Kusmec, Aaron, Schnable, Patrick S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2018
Materias:
Original Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6508500/
https://www.ncbi.nlm.nih.gov/pubmed/31245719
http://dx.doi.org/10.1002/pld3.53
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author Kusmec, Aaron
Schnable, Patrick S.
author_facet Kusmec, Aaron
Schnable, Patrick S.
author_sort Kusmec, Aaron
collection PubMed
description Genomewide association studies (GWAS) are computationally demanding analyses that use large sample sizes and dense marker sets to discover associations between quantitative trait variation and genetic variants. FarmCPU is a powerful new method for performing GWAS. However, its performance is hampered by details of its implementation and its reliance on the R programming language. In this paper, we present an efficient implementation of FarmCPU, called FarmCPUpp, that retains the R user interface but improves memory management and speed through the use of C++ code and parallel computing.
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spelling pubmed-65085002019-06-26 FarmCPUpp: Efficient large‐scale genomewide association studies Kusmec, Aaron Schnable, Patrick S. Plant Direct Original Research Genomewide association studies (GWAS) are computationally demanding analyses that use large sample sizes and dense marker sets to discover associations between quantitative trait variation and genetic variants. FarmCPU is a powerful new method for performing GWAS. However, its performance is hampered by details of its implementation and its reliance on the R programming language. In this paper, we present an efficient implementation of FarmCPU, called FarmCPUpp, that retains the R user interface but improves memory management and speed through the use of C++ code and parallel computing. John Wiley and Sons Inc. 2018-04-10 /pmc/articles/PMC6508500/ /pubmed/31245719 http://dx.doi.org/10.1002/pld3.53 Text en © 2018 The Authors. Plant Direct published by American Society of Plant Biologists, Society for Experimental Biology and John Wiley & Sons Ltd. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Research
Kusmec, Aaron
Schnable, Patrick S.
FarmCPUpp: Efficient large‐scale genomewide association studies
title FarmCPUpp: Efficient large‐scale genomewide association studies
title_full FarmCPUpp: Efficient large‐scale genomewide association studies
title_fullStr FarmCPUpp: Efficient large‐scale genomewide association studies
title_full_unstemmed FarmCPUpp: Efficient large‐scale genomewide association studies
title_short FarmCPUpp: Efficient large‐scale genomewide association studies
title_sort farmcpupp: efficient large‐scale genomewide association studies
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6508500/
https://www.ncbi.nlm.nih.gov/pubmed/31245719
http://dx.doi.org/10.1002/pld3.53
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AT schnablepatricks farmcpuppefficientlargescalegenomewideassociationstudies

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