hJAM_lnreg: Fit hJAM with linear regression

In hJAM: Hierarchical Joint Analysis of Marginal Summary Statistics

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)

hJAM documentation built on March 26, 2020, 8:13 p.m.