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| Content Provider | Springer Nature : BioMed Central |
|---|---|
| Author | Sikorska, Karolina Lesaffre, Emmanuel Groenen, Patrick FJ Eilers, Paul HC |
| Abstract | Background Genome-wide association studies have become very popular in identifying genetic contributions to phenotypes. Millions of SNPs are being tested for their association with diseases and traits using linear or logistic regression models. This conceptually simple strategy encounters the following computational issues: a large number of tests and very large genotype files (many Gigabytes) which cannot be directly loaded into the software memory. One of the solutions applied on a grand scale is cluster computing involving large-scale resources. We show how to speed up the computations using matrix operations in pure R code. Results We improve speed: computation time from 6 hours is reduced to 10-15 minutes. Our approach can handle essentially an unlimited amount of covariates efficiently, using projections. Data files in GWAS are vast and reading them into computer memory becomes an important issue. However, much improvement can be made if the data is structured beforehand in a way allowing for easy access to blocks of SNPs. We propose several solutions based on the R packages ff and ncdf. We adapted the semi-parallel computations for logistic regression. We show that in a typical GWAS setting, where SNP effects are very small, we do not lose any precision and our computations are few hundreds times faster than standard procedures. Conclusions We provide very fast algorithms for GWAS written in pure R code. We also show how to rearrange SNP data for fast access. |
| Related Links | https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/1471-2105-14-166.pdf |
| Ending Page | 11 |
| Page Count | 11 |
| Starting Page | 1 |
| File Format | HTM / HTML |
| ISSN | 14712105 |
| DOI | 10.1186/1471-2105-14-166 |
| Journal | BMC Bioinformatics |
| Issue Number | 1 |
| Volume Number | 14 |
| Language | English |
| Publisher | BioMed Central |
| Publisher Date | 2013-05-28 |
| Access Restriction | Open |
| Subject Keyword | Bioinformatics Microarrays Computational Biology Computer Appl. in Life Sciences Algorithms Efficient Storage Binary File Large Data File netCDF File Package SNPstats Computational Biology/Bioinformatics |
| Content Type | Text |
| Resource Type | Article |
| Subject | Molecular Biology Biochemistry Computer Science Applications Applied Mathematics Structural Biology |
| Journal Impact Factor | 2.9/2023 |
| 5-Year Journal Impact Factor | 3.6/2023 |
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