| hJAM_lnreg {hJAM} | R Documentation |
Fit hJAM with linear regression
Description
The hJAM function is to get the results from the hJAM model using input data
Usage
hJAM_lnreg(betas.Gy, N.Gy, Gl, A, ridgeTerm = FALSE)
Arguments
betas.Gy |
The betas in the paper: the marginal effects of SNPs on the phenotype (Gy) |
N.Gy |
The sample size of Gy |
Gl |
The reference panel (Gl), such as 1000 Genome |
A |
The A matrix in the paper: the marginal/conditional effects of SNPs on the exposures (Gx) |
ridgeTerm |
ridgeTerm = TRUE when the matrix L is singular. Matrix L is obtained from the cholesky decomposition of G0'G0. Default as FALSE. |
Value
An object of the hJAM with linear regression results.
- Exposure
The intermediates, such as the modifiable risk factors in Mendelian Randomization and gene expression in transcriptome analysis.
- numSNP
The number of SNPs that the user use in the instrument set.
- Estimate
The conditional estimates of the associations between intermediates and the outcome.
- StdErr
The standard error of the conditional estimates of the associations between intermediates and the outcome.
- Lower.CI
The lower bound of the 95% confidence interval of the estimates.
- Upper.CI
The upper bound of the 95% confidence interval of the estimates.
- Pvalue
The p value of the estimates with a type-I error equals 0.05.
Author(s)
Lai Jiang
References
Lai Jiang, Shujing Xu, Nicholas Mancuso, Paul J. Newcombe, David V. Conti (2020). A Hierarchical Approach Using Marginal Summary Statistics for Multiple Intermediates in a Mendelian Randomization or Transcriptome Analysis. bioRxiv https://doi.org/10.1101/2020.02.03.924241.
Examples
data(Gl)
data(betas.Gy)
data(conditional_A)
hJAM_lnreg(betas.Gy = betas.Gy, Gl = Gl, N.Gy = 459324, A = conditional_A, ridgeTerm = TRUE)