A multi-core R package that contains a set of tools based on undirected graphical
models for accomplishing three important and interrelated goals in genetics: (1)
linkage map construction, (2) reconstructing intra- and inter-chromosomal conditional
interactions (linkage disequilibrium) networks, and (3) exploring high-dimensional
genotype-phenotype network and genotype-phenotype-environment interactions network.
We use conditional independence relationships between variables.
The netgwas package can deal with biparental inbreeding and outbreeding species with
any ploidy level, namely diploid (2 sets of chromosomes), triploid (3 sets of chromosomes),
tetraploid (4 sets of chromosomes) and so on. We target on high-dimensional data where
number of variables p is larger than number of sample sizes (p >> n). The computations
is memory-optimized using the sparse matrix output. The package is implemented the recent
developments in Behrouzi and Wit (2017)