hbmr_ord {BMRV} | R Documentation |
The function implements HBMR using a Gibbs sampler with probit link function for ordinal traits.
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)/(e*p*log(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., Pitkäniemi, J., Sarin, A. P., Salomaa, V., Sillanpää, 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.
Kärkkäinen, H. P., & Sillanpää, 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.
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)