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Molnos, S. ; Baumbach, C. ; Wahl, S. ; Müller-Nurasyid, M. ; Strauch, K. ; Wang-Sattler, R. ; Waldenberger, M. ; Meitinger, T. ; Adamski, J. ; Kastenmüller, G. ; Suhre, K. ; Peters, A. ; Grallert, H. ; Theis, F.J. ; Gieger, C.

pulver: An R package for parallel ultra-rapid p-value computation for linear regression interaction terms.

BMC Bioinformatics 18:429 (2017)
Publ. Version/Full Text DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
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/.  
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Publication type Article: Journal article
Document type Scientific Article
Keywords Algorithm Linear regression interaction term SNP–CpG interaction Software; Genome-wide Association; Human Metabolism; Metabolomics; Genetics; Loci
Language english
Publication Year 2017
HGF-reported in Year 2017
ISSN (print) / ISBN 1471-2105
e-ISSN 1471-2105
Journal BMC Bioinformatics
Quellenangaben Volume: 18, Issue: 1, Pages: , Article Number: 429 Supplement: ,
Publisher Biomed Central Ltd
Publishing Place London
Reviewing status Peer reviewed
Institute(s) Institute of Epidemiology (EPI)
Institute of Genetic Epidemiology (IGE)
Institute of Human Genetics (IHG)
Molekulare Endokrinologie und Metabolismus (MEM)
Institute of Bioinformatics and Systems Biology (IBIS)
Institute of Computational Biology (ICB)
POF-Topic(s) 30202 - Environmental Health
30501 - Systemic Analysis of Genetic and Environmental Factors that Impact Health
30201 - Metabolic Health
30505 - New Technologies for Biomedical Discoveries
30205 - Bioengineering and Digital Health
Research field(s) Genetics and Epidemiology
Enabling and Novel Technologies
PSP Element(s) G-504091-004
G-504100-001
G-504091-003
G-504091-001
G-504091-002
G-504000-001
G-500700-001
G-505600-003
G-503700-001
G-503800-001
G-504090-001
PubMed ID 28962546
DOI 10.1186/s12859-017-1838-y
WOS ID WOS:000412133900002
Scopus ID 85030241332
Erfassungsdatum 2017-10-11
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