FarmCPUpp: Efficient large‐scale genomewide association studies
Abstract 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 i...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-04-01
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| Series: | Plant Direct |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/pld3.53 |
| _version_ | 1798030131388743680 |
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| author | Aaron Kusmec Patrick S. Schnable |
| author_facet | Aaron Kusmec Patrick S. Schnable |
| author_sort | Aaron Kusmec |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-11T19:35:16Z |
| format | Article |
| id | doaj.art-8a25b949a10f45b48b1e5a0ead192bb5 |
| institution | Directory Open Access Journal |
| issn | 2475-4455 |
| language | English |
| last_indexed | 2024-04-11T19:35:16Z |
| publishDate | 2018-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Plant Direct |
| spelling | doaj.art-8a25b949a10f45b48b1e5a0ead192bb52022-12-22T04:06:52ZengWileyPlant Direct2475-44552018-04-0124n/an/a10.1002/pld3.53FarmCPUpp: Efficient large‐scale genomewide association studiesAaron Kusmec0Patrick S. Schnable1Department of Agronomy Iowa State University Ames IA USADepartment of Agronomy Iowa State University Ames IA USAAbstract 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.https://doi.org/10.1002/pld3.53bioinformaticsgenomewide association studyquantitative traitsoftware |
| spellingShingle | Aaron Kusmec Patrick S. Schnable FarmCPUpp: Efficient large‐scale genomewide association studies Plant Direct bioinformatics genomewide association study quantitative trait software |
| 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 | bioinformatics genomewide association study quantitative trait software |
| url | https://doi.org/10.1002/pld3.53 |
| work_keys_str_mv | AT aaronkusmec farmcpuppefficientlargescalegenomewideassociationstudies AT patricksschnable farmcpuppefficientlargescalegenomewideassociationstudies |