blvcm {BMRV} | R Documentation |
The function implements BLVCM using a Gibbs sampler.
blvcm(pheno, geno, model = 3, iter = 30000, burnin = 500, var = -1, lambda = 0.2, cov = 0, init = c(0,0))
pheno |
An N x 3 phenotypic data matrix (trait, family number, zyg=1 for MZ, 2 for DZ), where N is the number of subjects. Please see the example data for more details. For faster convergence, it is recommanded that the phenotype should be standardized. |
geno |
An N x K genotypic data matrix, where N is the number of subjects and K is the number of rare variants. The value can be 0 or 1. A missing genotype is represented by -9, which will be imputated by BLVCM based on HWE. |
model |
Twin model: 3 for ACE model, 2 for AE model, 1 for independent subjects |
iter |
The number of MCMC iterations, which must be positive. |
burnin |
The number of burn-ins, which must be positive. |
var |
The variance hyperparameter (must be positive) in the priors for β and γ. If not specified (var=-1), the default value is the variance of the phenotype. |
lambda |
The threshold λ (must be positive) for hypothesis test. The default value is 0.2. |
cov |
A matrix of other covariates. |
init |
Initial values for β and γ (must be non-negative). The default values are 0. |
BF_main |
The Bayes factor of the main effect |
BF_int |
The Bayes factor of the interaction effect |
post_odds_beta |
The posterior odds of β |
post_odds_gamma |
The posterior odds of γ |
com_a |
The inverse of the posterior mean of the precision for additive genetic component. NA for independent samples |
com_c |
The inverse of the posterior mean of the precision for shared environmental component. NA for independent samples or AE model |
mean_mu |
The posterior mean of the intercept μ |
mean_beta |
The posterior mean of β |
mean_gamma |
The posterior mean of γ |
sd_mu |
The posterior standard deviation of the intercept μ |
sd_beta |
The posterior standard deviation of β |
sd_gamma |
The posterior standard deviation of γ |
mean_rv |
The posterior mean of α. The number of α equals the number of RVs |
mean_cov |
The posterior mean of the effects of covariates |
prior_var |
The variance hyperparameters in the priors for β and γ |
Liang He
He, L., Sillanpää, M. J., Ripatti, S., & Pitkäniemi, J. (2014). Bayesian Latent Variable Collapsing Model for Detecting Rare Variant Interaction Effect in Twin Study. Genetic epidemiology, 38(4), 310-324.
data(blvcm_data) blvcm(blvcm_data$pheno, blvcm_data$geno[,1:3], iter=10000, burnin=1000, model=3)