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rNeighborGWAS (version 1.2.4)

Testing Neighbor Effects in Marker-Based Regressions

Description

To incorporate neighbor genotypic identity into genome-wide association studies, the package provides a set of functions for variation partitioning and association mapping. The theoretical background of the method is described in Sato et al. (2021) .

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Version

Install

install.packages('rNeighborGWAS')

Monthly Downloads

562

Version

1.2.4

License

GPL-3

Maintainer

Yasuhiro Sato

Last Published

May 10th, 2021

Functions in rNeighborGWAS (1.2.4)

nei_lmm

Mixed models for testing self and neighbor effects
w

Calculating a distance decay weight
rNeighborGWAS-package

rNeigborGWAS: Testing Neighbor Effects in Marker-based Regressions
delta_PVE

Estimating the effective scale of neighbor effects
gaston2neiGWAS

Convert gaston's bed.matrix data to rNeighborGWAS genotype data.
neiGWAS

Genome-wide association mapping of neighbor effects
nei_simu

Simulating phenotypes with self and neighbor effects
nei_lm

Standard linear models for testing self and neighbor effects
nei_coval

Calculating neighbor genotypic identity
min_dist

Calculating the minimum distance
qtl_pheno_simu

Simulating phenotype values with neighbor effects.
calc_PVEnei

Calculating phenotypic variation explained by neighbor effects