Systematic ancestry difference among samples can cause spurious associations and can lead to invalid PRS results. Similarly, power of the PRS analysis can be improved by controlling for other confounders. To account for that, PRSice allow the incorporation of covariates into the analysis.
When large number of covariates are included in the model, missing data might poses a problem. PRSice will automatically exclude any samples with missing covariates from the regression model but will still calculate the PRS for that sample. The In_Regression column in the best score output is used to indicate whether the sample is included in the regression model (Yes for included; No for excluded)
PRSice currently only support numeric covariates. To include non-numeric covariates, dummy variable must be generated beforehand.
-cHeader of covariates. If not provided, will use all variables in the covariate file. By adding
@in front of the string, any numbers within
]will be parsed. E.g.
@PC[1-3]will be read as PC1,PC2,PC3. Discontinuous input are also supported:
@cov[1.3-5]will be parsed as cov1,cov3,cov4,cov5
-CCovariate file. First column should be FID and the second column should be IID. If
--ignore-fidis set, first column should be IID