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

Description Usage Arguments Details Value Author(s) References Examples

View source: R/blvcm_bin.R

The function implements BLVCM for binary traits using a Gibbs sampler with probit link function.

1 2	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., 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.

Albert, J. H., & Chib, S. (1993). Bayesian analysis of binary and polychotomous response data. Journal of the American statistical Association, 88(422), 669-679.