pulver: an R package for parallel ultra-rapid p-value computation for linear regression interaction terms
Abstract Background Genome-wide association studies allow us to understand the genetics of complex diseases. Human metabolism provides information about the disease-causing mechanisms, so it is usual to investigate the associations between genetic variants and metabolite levels. However, only consid...
| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2017-09-01
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| Series: | BMC Bioinformatics |
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| Online Access: | http://link.springer.com/article/10.1186/s12859-017-1838-y |
| _version_ | 1819145192679145472 |
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| author | Sophie Molnos Clemens Baumbach Simone Wahl Martina Müller-Nurasyid Konstantin Strauch Rui Wang-Sattler Melanie Waldenberger Thomas Meitinger Jerzy Adamski Gabi Kastenmüller Karsten Suhre Annette Peters Harald Grallert Fabian J. Theis Christian Gieger |
| author_facet | Sophie Molnos Clemens Baumbach Simone Wahl Martina Müller-Nurasyid Konstantin Strauch Rui Wang-Sattler Melanie Waldenberger Thomas Meitinger Jerzy Adamski Gabi Kastenmüller Karsten Suhre Annette Peters Harald Grallert Fabian J. Theis Christian Gieger |
| author_sort | Sophie Molnos |
| collection | DOAJ |
| description | Abstract Background Genome-wide association studies allow us to understand the genetics of complex diseases. Human metabolism provides information about the disease-causing mechanisms, so it is usual to investigate the associations between genetic variants and metabolite levels. However, only considering genetic variants and their effects on one trait ignores the possible interplay between different “omics” layers. Existing tools only consider single-nucleotide polymorphism (SNP)–SNP interactions, and no practical tool is available for large-scale investigations of the interactions between pairs of arbitrary quantitative variables. Results We developed an R package called pulver to compute p-values for the interaction term in a very large number of linear regression models. Comparisons based on simulated data showed that pulver is much faster than the existing tools. This is achieved by using the correlation coefficient to test the null-hypothesis, which avoids the costly computation of inversions. Additional tricks are a rearrangement of the order, when iterating through the different “omics” layers, and implementing this algorithm in the fast programming language C++. Furthermore, we applied our algorithm to data from the German KORA study to investigate a real-world problem involving the interplay among DNA methylation, genetic variants, and metabolite levels. Conclusions The pulver package is a convenient and rapid tool for screening huge numbers of linear regression models for significant interaction terms in arbitrary pairs of quantitative variables. pulver is written in R and C++, and can be downloaded freely from CRAN at https://cran.r-project.org/web/packages/pulver/ . |
| first_indexed | 2024-12-22T12:54:08Z |
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| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-22T12:54:08Z |
| publishDate | 2017-09-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-5050b8deacdc406baf1a8ec8049548a92022-12-21T18:25:10ZengBMCBMC Bioinformatics1471-21052017-09-011811810.1186/s12859-017-1838-ypulver: an R package for parallel ultra-rapid p-value computation for linear regression interaction termsSophie Molnos0Clemens Baumbach1Simone Wahl2Martina Müller-Nurasyid3Konstantin Strauch4Rui Wang-Sattler5Melanie Waldenberger6Thomas Meitinger7Jerzy Adamski8Gabi Kastenmüller9Karsten Suhre10Annette Peters11Harald Grallert12Fabian J. Theis13Christian Gieger14Research Unit of Molecular Epidemiology, Helmholtz Zentrum MünchenResearch Unit of Molecular Epidemiology, Helmholtz Zentrum MünchenResearch Unit of Molecular Epidemiology, Helmholtz Zentrum MünchenDepartment of Medicine I, University Hospital Grosshadern, Ludwig-Maximilians-UniversitätInstitute of Genetic Epidemiology, Helmholtz Zentrum MünchenResearch Unit of Molecular Epidemiology, Helmholtz Zentrum MünchenResearch Unit of Molecular Epidemiology, Helmholtz Zentrum MünchenInstitute of Human Genetics, Helmholtz Zentrum MünchenGerman Center for Diabetes Research (DZD)Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum MünchenInstitute of Bioinformatics and Systems Biology, Helmholtz Zentrum MünchenResearch Unit of Molecular Epidemiology, Helmholtz Zentrum MünchenResearch Unit of Molecular Epidemiology, Helmholtz Zentrum MünchenInstitute of Computational Biology, Helmholtz Zentrum MünchenResearch Unit of Molecular Epidemiology, Helmholtz Zentrum MünchenAbstract Background Genome-wide association studies allow us to understand the genetics of complex diseases. Human metabolism provides information about the disease-causing mechanisms, so it is usual to investigate the associations between genetic variants and metabolite levels. However, only considering genetic variants and their effects on one trait ignores the possible interplay between different “omics” layers. Existing tools only consider single-nucleotide polymorphism (SNP)–SNP interactions, and no practical tool is available for large-scale investigations of the interactions between pairs of arbitrary quantitative variables. Results We developed an R package called pulver to compute p-values for the interaction term in a very large number of linear regression models. Comparisons based on simulated data showed that pulver is much faster than the existing tools. This is achieved by using the correlation coefficient to test the null-hypothesis, which avoids the costly computation of inversions. Additional tricks are a rearrangement of the order, when iterating through the different “omics” layers, and implementing this algorithm in the fast programming language C++. Furthermore, we applied our algorithm to data from the German KORA study to investigate a real-world problem involving the interplay among DNA methylation, genetic variants, and metabolite levels. Conclusions The pulver package is a convenient and rapid tool for screening huge numbers of linear regression models for significant interaction terms in arbitrary pairs of quantitative variables. pulver is written in R and C++, and can be downloaded freely from CRAN at https://cran.r-project.org/web/packages/pulver/ .http://link.springer.com/article/10.1186/s12859-017-1838-yAlgorithmLinear regression interaction termSNP–CpG interactionSoftware |
| spellingShingle | Sophie Molnos Clemens Baumbach Simone Wahl Martina Müller-Nurasyid Konstantin Strauch Rui Wang-Sattler Melanie Waldenberger Thomas Meitinger Jerzy Adamski Gabi Kastenmüller Karsten Suhre Annette Peters Harald Grallert Fabian J. Theis Christian Gieger pulver: an R package for parallel ultra-rapid p-value computation for linear regression interaction terms BMC Bioinformatics Algorithm Linear regression interaction term SNP–CpG interaction Software |
| title | pulver: an R package for parallel ultra-rapid p-value computation for linear regression interaction terms |
| title_full | pulver: an R package for parallel ultra-rapid p-value computation for linear regression interaction terms |
| title_fullStr | pulver: an R package for parallel ultra-rapid p-value computation for linear regression interaction terms |
| title_full_unstemmed | pulver: an R package for parallel ultra-rapid p-value computation for linear regression interaction terms |
| title_short | pulver: an R package for parallel ultra-rapid p-value computation for linear regression interaction terms |
| title_sort | pulver an r package for parallel ultra rapid p value computation for linear regression interaction terms |
| topic | Algorithm Linear regression interaction term SNP–CpG interaction Software |
| url | http://link.springer.com/article/10.1186/s12859-017-1838-y |
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