Description Usage Arguments Details Value Examples
Runs the main MR-MtRobin algorithm: a Multi-Tissue transcriptome-wide Mendelian Randomization method ROBust to INvalid instrumental variables
1 2 | MR_MtRobin(snpID, gwas_betas, gwas_se, eqtl_betas, eqtl_se, eqtl_pvals, LD,
pval_thresh = 0.001)
|
snpID |
vector of variant identifiers to be used as instrumental variables. |
gwas_betas |
vector of coefficient estimates (betas) from GWAS study. |
gwas_se |
vector of standard errors for coefficient estimates from GWAS study. |
eqtl_betas |
matrix of coefficient estimates (betas) from eQTL study. |
eqtl_se |
matrix of standard errors for coefficient estimates from eQTL study. |
eqtl_pvals |
matrix of p-values from eQTL study. |
LD |
matrix of LD correlation coefficients (r, not r^2). |
pval_thresh |
p-value threshold for instrumental variables (IVs). |
The following are additional details describing the input arguments.
For eqtl_betas, eqtl_se, and eqtl_pvals
each row i corresponds to a SNP/variant/IV
while each column j holds the summary statistics in tissue type j.
Both the rows of the eQTL data and the order of the GWAS vectors should
match the order of snpID.
Note that the matrix LD should hold correlation coefficients
(i.e. r), not their squared values (r^2).
A list with the following elements:
lme_res | an object of class lmerMod,
returned from the reverse regression random slope
mixed model run by MR-MtRobin.. |
gwas_res | data.table of the gwas data (snpID, gwas_beta, gwas_se). |
LD | matrix of LD correlation coefficients (r, not r^2). |
The last two items are returned for use by MR_MtRobin_resample().
To conduct inference on the returned results, use function MR_MtRobin_resample.
1 2 3 4 5 6 7 | ## MR_MtRobin_input created using MR_MtRobin_setup()
## IV_gene1 created using select_IV()
MR_MtRobin(snpID=IV_gene1,
gwas_betas=MR_MtRobin_input$gwas_betas, gwas_se=MR_MtRobin_input$gwas_se,
eqtl_betas=MR_MtRobin_input$eqtl_betas, eqtl_se=MR_MtRobin_input$eqtl_se,
eqtl_pvals=MR_MtRobin_input$eqtl_pvals, LD=MR_MtRobin_input$LD)
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