blvcm: Bayesian latent variable collapsing model (BLVCM)
In BMRV: Bayesian Models for Rare Variant Association Analysis

Description Usage Arguments Value Author(s) References Examples

View source: R/blvcm.R

The function implements BLVCM using a Gibbs sampler.

1 2	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., Sillanp<e4><e4>, M. J., Ripatti, S., & Pitk<e4>niemi, J. (2014). Bayesian Latent Variable Collapsing Model for Detecting Rare Variant Interaction Effect in Twin Study. Genetic epidemiology, 38(4), 310-324.