netgwas: An R Package for Network-Based Genome-Wide Association Studies
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Graphical models are powerful tools for modeling and making statistical inferences regarding complex relationships among variables in multivariate data. They are widely used in statistics and machine learning, for example, to analyze biological networks. In this paper we introduce the R package netgwas, which is designed to accomplish three important and interrelated goals in genetics: linkage map construction, reconstructing intra- and inter-chromosomal interactions, and exploring high-dimensional genotype-phenotype and genotype-phenotype-environment networks. The netgwas package can deal with species of any ploidy level. The package implements recent improvements in both linkage map construction (Behrouzi and Wit, 2017a), and reconstructing conditional independence network for non-Gaussian data, discrete data, and mixed discrete and continues data (Behrouzi and Wit, 2017b). Such datasets routinely occur in genetics and genomics such as genotype data, genotype-phenotype dataset, and genotype-phenotype including environmental variables. The package uses a parallelization strategy on multi-core processors to speed-up computations for large datasets. In addition, it contains several functions for simulation and visualization, as well as three multivariate example datasets taken from the literature and used to illustrate the package capabilities. The paper includes a brief overview of the statistical methods which have been implemented in the package. The main body of the paper explains how to use the package. We also illustrate the package functionality with real examples.
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