Collapsing ROC approach for risk prediction research on both common and rare variants

TL;DR

This paper introduces a collapsing ROC (CROC) method for risk prediction that effectively incorporates both common and rare genetic variants, improving accuracy over existing methods especially when common variants are scarce.

Contribution

The paper presents a novel CROC approach extending FROC to include rare variants, enhancing risk prediction accuracy in genetic studies.

Findings

CROC outperforms FROC when common variants decrease.

Prediction accuracy improves when combining all variants.

CROC achieves higher AUC with only rare variants.

Abstract

Risk prediction that capitalizes on emerging genetic findings holds great promise for improving public health and clinical care. However, recent risk prediction research has shown that predictive tests formed on existing common genetic loci, including those from genome-wide association studies, have lacked sufficient accuracy for clinical use. Because most rare variants on the genome have not yet been studied for their role in risk prediction, future disease prediction discoveries should shift toward a more comprehensive risk prediction strategy that takes into account both common and rare variants. We are proposing a collapsing receiver operating characteristic CROC approach for risk prediction research on both common and rare variants. The new approach is an extension of a previously developed forward ROC FROC approach, with additional procedures for handling rare variants. The approach was evaluated through the use of 533 single-nucleotide polymorphisms SNPs in 37 candidate genes from the Genetic Analysis Workshop 17 mini-exome data set. We found that a prediction model built on all SNPs gained more accuracy AUC = 0.605 than one built on common variants alone AUC = 0.585. We further evaluated the performance of two approaches by gradually reducing the number of common variants in the analysis. We found that the CROC method attained more accuracy than the FROC method when the number of common variants in the data decreased. In an extreme scenario, when there are only rare variants in the data, the CROC reached an AUC value of 0.603, whereas the FROC had an AUC value of 0.524.