| Type: | Package |
| Title: | Sparse Bayesian Learning for QTL Mapping and Genome-Wide Association Studies |
| Version: | 0.1.0 |
| Author: | Meiyue Wang and Shizhong Xu |
| Maintainer: | Meiyue Wang <mwang024@ucr.edu> |
| Description: | Implements sparse Bayesian learning method for QTL mapping and genome-wide association studies. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| LazyData: | true |
| Depends: | stats, R (≥ 2.10) |
| RoxygenNote: | 6.1.0 |
| Date: | 2018-10-17 |
| Suggests: | knitr, rmarkdown |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2018-10-17 19:29:53 UTC; Dell |
| Repository: | CRAN |
| Date/Publication: | 2018-10-26 15:50:03 UTC |
Genotype
Description
A matrix with dimension of 30*50 containing the numeric genotype
indicator for 30 simulated individuals of an F2 family generated from the cross
of two inbred lines according to map provided in the package.
Usage
gen
Format
A matrix with 30 rows and 50 columns:
- ID
individual ID
- marker
name of marker
Intercept
Description
Design vector of the intercept for the 30 simulated individuals.
Usage
intercept
Format
A sequence of number containing 30 replicates of number 1.
Genetic map
Description
The genetic map used in the generation of numeric genotype indicators for 30 simulated individuals of an F2 family generated from the cross of two inbred lines.
Usage
map
Format
A data frame with 50 rows and 4 variables:
- chr
chromosome
- marker
name of marker
- cm
genetic distance measured in centi-Morgan
- effect
simulated effect assigned to marker in the simulation experiment
Phenotype
Description
Observations of the phenotype for 30 simulated individuals.
Usage
phe
Format
A sequence of number with length equal to 30.
Sparse Bayesian Learning for QTL Mapping and Genome-Wide Association Studies
Description
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.
Usage
sblgwas(x, y, z, t = -1, max.iter = 200, min.err = 1e-06)
Arguments
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. |
Details
The multiple locus hierarchical linear mixed model of SBL is
y=X\beta+Z\gamma+\epsilon
where y is an n*1 vector of response variables (observations of the trait);
X is an n*p design matrix for fixed effects; \beta is a p*1
vector of fixed effect; Z is an n*m genotype indicator matrix;
\gamma is an m*1 vector of marker effects and \epsilon is
an n*1 vector of residual errors with an aassumed
\epsilon~N(0,\Sigma) distribution. Each marker effect, \gamma[k]
for marker k, is treated as a random variable following N(0,\Phi[k])
distribution, where \Phi[k] is the prior variance. The estimate of
\gamma[k] is best linear unbiased prediciton (BLUP). The estimate of
\Phi[k] is miximum likelihood estimate (MLE).
Value
iteration |
a matrix storing intermediate results of each iteration before
the entire program converges, including " " " " " |
parm |
a vector containing 5 elements: " " " " " " |
blup |
a matrix containing 4 columns: " " " " " |
Author(s)
Meiyue Wang and Shizhong Xu
Maintainer: Meiyue Wang mwang024@ucr.edu
Examples
# 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