RHE-mc is no longer actively maintained. Please switch to GENIE, which estimates additive, GxE, or both types of heritability. You can download GENIE here: https://github.com/sriramlab/GENIE/.
Randomized Haseman–Elston regression for Multi-variance Components
The following packages are required on a Linux machine to compile and use the software package.
g++
cmake
make
git clone https://github.com/sriramlab/RHE-mc.git
cd RHE-mc
mkdir build
cd build/
cmake ..
make
An executable file named RHEmc will be in the build folder after the installation steps. Run RHE-mc as follows:
./RHEmc <command_line arguments>
To run the high memory efficient version : (-jn 1000 is recommended when there are many overlapping annotations or small annotations)
The memory usage of RHEmc_mem does not depend on the number of jackknife blocks.
./RHEmc_mem <command_line arguments>
To estimate dominance heritability and additive heritability jointly run :
./RHEmc_dom <command_line arguments>
To run the multiple phenotypes version:
./RHEmc_mp <command_line arguments>
genotype (-g): The path of genotype file
phenotype (-p): The path of phenotype file
covariate (-c): The path of covariate file
annotation (-annot): The path of annotation file.
num_vec (-k): The number of random vectors (10 is recommended).
num_block (-jn): The number of jackknife blocks. (100 is recommended). The higher number of jackknife blocks the higher the memory usage.
out_put (-o): The path of the output file.
Genotype: It must be in bed format.
Phenotype: It must have a header in the following format: FID IID name_of_phenotype. In case of multiple phenotypes: FID IID name_of_phen_1 name_of_phen_2 . . . name_of_phen_n
Covariate: It must have a header in the following format: FID IID name_of_cov_1 name_of_cov_2 . . . name_of_cov_n
Annotation: It has M rows (M=number of SNPs) and K columns (K=number of annotations). If SNP i belongs to annotation j, then there is "1" in row i and column j. Otherwise, there is "0". (delimiter is " ")
1) Number and order of individuals must be the same in phenotype, genotype, and covariate files.
2) Number and order of SNPs must be the same in the bim file and annotation file.
3) Annotation file does not have a header. The code supports overlapping annotations (e.g : functional annotation)
4) SNPs with MAF=0 must be excluded from the genotype file.
5) RHE-mc excludes individuals with NA values in the phenotype file from the analysis.
To make sure that everything works well, sample files are provided in the example directory. Look at test.sh file and run it :
chmod +x test.sh
./test.sh
Pazokitoroudi, A. et al. Efficient variance components analysis across millions of genomes. Nat Commun 11, 4020 (2020). https://doi.org/10.1038/s41467-020-17576-9