Graphical models are powerful tools for modeling and making statistical
inferences regarding complex associations among variables in multivariate data.
In this paper we introduce the R package netgwas, which is designed based on
undirected graphical models to accomplish three important and interrelated
goals in genetics: constructing linkage map, reconstructing linkage
disequilibrium (LD) networks from multi-loci genotype data, and detecting
high-dimensional genotype-phenotype networks. The netgwas package deals with
species with any chromosome copy number in a unified way, unlike other
software. It implements recent improvements in both linkage map construction
(Behrouzi and Wit, 2018), and reconstructing conditional independence network
for non-Gaussian continuous data, discrete data, and mixed
discrete-and-continuous data (Behrouzi and Wit, 2017). Such datasets routinely
occur in genetics and genomics such as genotype data, and genotype-phenotype
data. We demonstrate the value of our package functionality by applying it to
various multivariate example datasets taken from the literature. We show, in
particular, that our package allows a more realistic analysis of data, as it
adjusts for the effect of all other variables while performing pairwise
associations. This feature controls for spurious associations between variables
that can arise from classical multiple testing approach. This 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. 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. The netgwas package is freely available at
https://cran.r-project.org/web/packages/netgwasComment: 32 pages, 9 figures; due to the limitation "The abstract field cannot
be longer than 1,920 characters", the abstract appearing here is slightly
shorter than that in the PDF fil