PGS-based Phenotype Prediction Interval (PredInterval)

PredInterval is a statistical method that quantifies polygenic score (PGS)-based phenotype prediction uncertainty through the construction of well-calibrated prediction intervals. PredInterval is non-parametric in natural and extracts information based on quantiles of phenotypic residuals through cross-validations, thus achieving well-calibrated coverage of true phenotypic values. In addition, the PredInterval framework is general and can be paired with any PGS method or pre-computed SNP effect sizes obtained from publicly available resources.

How to use PredInterval

Example codes for the construction of 95% prediction interval for PGS-based phenotype prediction based on 5-fold cross-validations

workdir=/your/data/directory
pheno_train=${workdir}/pheno_train.txt
PGS_train_prefix=${workdir}/train_PGS_subset
test_fam=${workdir}/test_set.fam
PGS_test_prefix=${workdir}/test_PGS_subset
cv_fold=5
output=${workdir}/CI_output.txt
conf_level=0.95
Rscript PredInterval.R ${pheno_train} ${PGS_train_prefix} ${test_fam} ${PGS_test_prefix} ${cv_fold} ${output} ${conf_level} 

Citations

Chang Xu, Santhi K. Ganesh, and Xiang Zhou (2024). Statistical construction of calibrated prediction intervals for polygenic score based phenotype prediction.

Questions

If you have any questions on PredInterval software, please email to Chang Xu (xuchang@umich.edu).