blvcm_bin {BMRV} | R Documentation |
The function implements BLVCM for binary traits using a Gibbs sampler with probit link function.
blvcm_bin(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. The trait must be 0 or 1. |
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 (must be positive). The default value is 30000. |
burnin |
The number of burn-ins (must be positive). The default value is 500. |
var |
The variance hyperparameters (must be positive) in the priors for β and γ. The default value is 1. |
lambda |
The threshold λ (must be positive) for hypothesis test. The default value is 0.2. |
cov |
A matrix of other covariates to be adjusted. |
init |
Initial values for β and γ. The default values are 0. The initial value for β must be non-negative. |
The Gibbs sampler uses the variable augmentation method for probit link described in Albert, J. H., & Chib, S. (1993). Since the variance of a binary variable is determined by its mean compared to quantitative traits, θ(s) are eliminated to avoid overfitting.
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 |
com_c |
The inverse of the posterior mean of the precision for shared environmental component |
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 |
mean_cov |
The posterior mean of the effects of covariates |
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.
Albert, J. H., & Chib, S. (1993). Bayesian analysis of binary and polychotomous response data. Journal of the American statistical Association, 88(422), 669-679.
data(blvcm_bin_data) blvcm_bin(blvcm_bin_data$pheno, blvcm_bin_data$geno[,1:3], iter=5000, burnin=500, model=2)