hbmr_ord: Hierarchical Bayesian multiple regression model incorporating...
In BMRV: Bayesian Models for Rare Variant Association Analysis

Description Usage Arguments Value Author(s) References Examples

View source: R/hbmr_ord.R

The function implements HBMR using a Gibbs sampler with probit link function for ordinal traits.

1 2	hbmr_ord(pheno, geno, qi = matrix(), fam = 0, kin = matrix(), iter = 10000, burnin = 500, gq = 20, imp = 0.1, cov = matrix(), maf = c(), pa = 1.3, pb = 0.04)

`pheno`	A phenotypic vector (N x 1). The trait must be a natural number (1, 2, 3, 4, ...).
`geno`	An N x K genotypic data matrix, where N is the number of subjects and K is the number of rare variants. Genotypic value is only for dominant coding, i.e. 0 or 1. Plug in 0 for imputed genotypes.
`qi`	An optional N x K Genotypic quality matrix, where N is the number of subjects and K is the number of rare variants. If the genotype is sequenced, this must be an integer >=1 and is its GQ score in VCF file. If the genotype is imputed, this must be a value <1, and is its expected genotypic value based on the dominant coding.
`fam`	fam=1 for family samples. In this case, a relatedness matrix should be given. See kin.
`kin`	In the case of fam=1, kin is an N x N relatedness matrix. The scale of its entries are twice the kinship coefs, i.e. the same as that in coxme.
`iter`	The number of MCMC iterations. The default value is 10000.
`burnin`	The number of burn-ins. The default value is 500.
`gq`	A cutoff for GQ score (λ_Q). It should be an positive integer. If not specified, default value is 20. See the reference for more details.
`imp`	A cutoff for imputed genotype (λ_I). It should be a real number in (0,1). If not specified, default value is 0.1. See the reference for more details.
`cov`	An optional N x M covariate data matrix, where N is the number of subjects and M is the number of covariates.
`maf`	An optional minor allele frequency information vector (K by 1). If not specified, MAF will be estimated based on the genotype data.
`pa`	The positive hyper-parameter a in the gamma distribution of Bayesian shrinkage prior. The default value is 1.3.
`pb`	The positive hyper-parameter b in the gamma distribution of Bayesian shrinkage prior. The default value is 0.04.

`BF`	The Bayes factor of δ=1 vs. δ=0
`BF_RB`	The BF estimated by using Rao-Blackwellization theorem
`p_upper`	For a BF larger than 2, we calculate p_upper that is the upper bound of the p value corresponding to the BF based on the connection BF<(-1)/(eplog(p)). The exact p value, which is smaller than p_upper, can be obtained through permutations.
`mean`	The mean of the posterior of β_0
`var`	The inverse of the mean of posterior of precision 1/σ
`est_geno`	The number of genotypes whose uncertainty are considered in estimation
`var_ran`	The estimated variance of the random effect for family design
`rv_mean_es`	The means of the posterior of γ for the K RVs
`rv_sd_es`	The standard deviations of the posterior of γ for the K RVs
`mean_cov`	The means of the posterior of for the M covariates

Liang He

He, L., Pitk<e4>niemi, J., Sarin, A. P., Salomaa, V., Sillanp<e4><e4>, M. J., & Ripatti, S. (2015). Hierarchical Bayesian Model for Rare Variant Association Analysis Integrating Genotype Uncertainty in Human Sequence Data. Genetic epidemiology, 39(2), 89-100.

K<e4>rkk<e4>inen, H. P., & Sillanp<e4><e4>, M. J. (2013). Fast Genomic Predictions via Bayesian G-BLUP and Multilocus Models of Threshold Traits Including Censored Gaussian Data. G3: Genes| Genomes| Genetics, 3(9), 1511-1523.

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data(hbmr_bin_data)
hbmr_ord(hbmr_bin_data$pheno[1:500], hbmr_bin_data$geno[1:500,1:3], fam=1, 
kin= hbmr_bin_data$kin[1:500,1:500], iter=800, burnin=200)