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