Description Usage Arguments Details Value Author(s) Examples
The sparse Bayesian learning (SBL) method for quantitative trait locus (QTL) mapping and genome-wide association studies (GWAS) deals with a linear mixed model. This is also a multiple locus model that includes all markers (random effects) in a single model and detect significant markers simultaneously. SBL method adopts coordinate descent algorithm to update parameters by estimating one parameter at a time conditional on the posterior modes of all other parameters. The parameter estimation process requires multiple iterations and the final estimated parameters take the values when the entire program converges.
1 |
x |
a design matrix for fixed effects |
y |
a vector of response variables |
z |
a design matrix for random effects |
t |
a number between [-2,0] to control sparseness of the model, default is -1. |
max.iter |
maximum number of iterations set by user to stop the program, default is 200. |
min.err |
minimum threshold of mean squared error of random effects estimated from the current and the previous iteration to stop the program, default is 1e-6. |
The multiple locus hierarchical linear mixed model of SBL is
y=Xβ+Zγ+ε
where y is an n*1 vector of response variables (observations of the trait); X is an n*p design matrix for fixed effects; β is a p*1 vector of fixed effect; Z is an n*m genotype indicator matrix; γ is an m*1 vector of marker effects and ε is an n*1 vector of residual errors with an aassumed ε~N(0,Σ) distribution. Each marker effect, γ[k] for marker k, is treated as a random variable following N(0,Φ[k]) distribution, where Φ[k] is the prior variance. The estimate of γ[k] is best linear unbiased prediciton (BLUP). The estimate of Φ[k] is miximum likelihood estimate (MLE).
iteration |
a matrix storing intermediate results of each iteration before
the entire program converges, including " " " "beta[1]… beta[p]" estimates of fixed effects "gamma[1]… gamma[m]" estimates of random effects |
parm |
a vector containing 5 elements: " " " " " " |
blup |
a matrix containing 4 columns: " " " " " |
Meiyue Wang and Shizhong Xu
Maintainer: Meiyue Wang mwang024@ucr.edu
1 2 3 4 5 6 7 8 9 10 11 12 13 | # Load example data from sbl package
data(gen)
data(phe)
data(intercept)
# Run sblgwas() to perform association study of example data
# setting t = 0 leads to the most sparse model
fit<-sblgwas(x=intercept, y=phe, z=gen, t=0)
my.blup<-fit$blup
# setting t = -2 leads to the least sparse model
fit<-sblgwas(x=intercept, y=phe, z=gen, t=-2)
my.blup<-fit$blup
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.