Description Usage Arguments Value References See Also
Run WHT
to adjust for multiple testing
while combining two steps of the GxE interaction testing procedure. The procedure is applicable for
a multivariate phenotype, as well as a univariate phenotype.
1 | WHT(PVAL, first_bin_size = 5, FWER = 0.05)
|
PVAL |
A data.frame with three columns.
The first column (PVAL$SNP) provides the name of all SNPs or genetic variants tested.
Second column (PVAL$G.P) contains the p-values of the variants obtained from testing
an overall marginal genetic
association between the multivariate phenotype and each genetic variant individually.
And the third column (PVAL$GE.P) contains the p-values obtained from testing overall GxE effect on the
multivariate phenotype in presence of possible marginal effect due to the genetic variant and
a marginal effect
due to the environmental variable. Number of rows in PVAL is the same as the number
of genetic variants, and it has the same structure as in the output of |
first_bin_size |
A positive integer providing the number of SNPs in the top bin while ranking the SNPs or genetic variants according to their relative importance in the first step, which is evaluated with respect to the strength of overall marginal genetic association with the multivariate phenotype. Default is 5. |
FWER |
A positive real number between 0 and 1 providing the overall family wise error rate to be maintained while identifying the genetic variants having a genome-wide significant overall GxE effect on the multivariate phenotype. Default is 0.05. |
The output produced by the function is a list consisting of:
GEsnps |
Vector of SNPs/genetic variants identified to have a genome-wide significant overall GxE effect. |
adjusted.PV |
A data.frame providing the adjusted p-values with the corresponding genetic variants obtained by the weighted multiple hypothesis testing procedure. |
A Majumdar, KS Burch, S Sankararaman, B Pasaniuc, WJ Gauderman, JS Witte (2020) A two-step approach to testing overall effect of gene-environment interaction for multiple phenotypes. bioRxiv, doi: https://doi.org/10.1101/2020.07.06.190256
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