bayesR

Bayesian hierarchical model for complex trait analysis

(https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1004969)

Update 01/04/2021

Various new features and improvements:

further reduced memory requirements
inclusion of covariates
grouped effects models to fit more complex models (e.g. partioning of variance)
flat input files (not supported in bayesRv2)
fitted values
prediction of phenotypes
unified source code

Previous release moved to folder /old

Quick start

Clone:

git clone https://github.com/syntheke/bayesR.git

Compile:

in the src folder

gfortran -o bayesR -O2 -cpp RandomDistributions.f90 baymods.f90 bayesR.f90
gfortran -o bayesRv2 -O2 -cpp -Dblock -fopenmp RandomDistributions.f90 baymods.f90 bayesR.f90
ifort -o bayesR -O3 -fpp RandomDistributions.f90 baymods.f90 bayesR.f90
ifort -o bayesRv2 -O3 -fpp -Dblock -openmp -static RandomDistributions.f90 baymods.f90 bayesR.f90

Run:

bayesR -bfile simdata -out simout

Example1

Example from the 14th QTL-MAS workshop.

bayesR -bfile example/simdata -out simout -numit 10000 -burnin 5000 -seed 333

Example2

Genome position specific priors

bayesR -bfile simdata2 -out simout2 -numit 10000 -burnin 5000 -seed 333 -n 2 -snpmodel mod2 -segment seg

Example3

Grouped effects with mixture priors

bayesR -bfile simdata2 -out simout3 -numit 10000 -burnin 5000 -seed 333 -n 2 -snpmodel mod3 -segments seg -varcomp var3

Help:

bayesR -help

Tell me more:

BayesRManual.pdf

ccmucdenver/bayesR

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