Graphical models are a powerful tool in modelling and analysing complex biological associations in high-dimensional data. The R-package netgwas implements the recent methodological development on copula graphical models to (i) construct linkage maps, (ii) infer linkage disequilibrium networks from genotype data, and (iii) detect high-dimensional genotype-phenotype networks. The netgwas learns the structure of networks from ordinal data and mixed ordinal-and-continuous data. Here, we apply the functionality in netgwas to various multivariate example datasets taken from the literature to demonstrate the kind of insight that can be obtained from the package. We show that our package offers a more realistic association analysis than the classical approaches, as it discriminates between direct and induced correlations by adjusting for the effect of all other variables while performing pairwise associations. This feature controls for spurious interactions between variables that can arise from conventional approaches in a biological sense. The netgwas package uses a parallelization strategy on multi-core processors to speed-up computations.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2023-011.zip
Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".
For attribution, please cite this work as
Behrouzi, et al., "The R Journal: netgwas: An R Package for Network-Based Genome Wide Association Studies", The R Journal, 2023
BibTeX citation
@article{RJ-2023-011,
author = {Behrouzi, Pariya and Arends, Danny and Wit, Ernst C.},
title = {The R Journal: netgwas: An R Package for Network-Based Genome Wide Association Studies},
journal = {The R Journal},
year = {2023},
note = {https://doi.org/10.32614/RJ-2023-011},
doi = {10.32614/RJ-2023-011},
volume = {14},
issue = {4},
issn = {2073-4859},
pages = {18-37}
}