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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

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