Prior Knowledge based mutation prioritization towards causal variant finding in rare disease

TL;DR

This paper introduces a structure-based mutation prioritization method that leverages protein structural features to improve the identification of pathogenic variants in rare diseases, especially when statistical evidence is limited.

Contribution

It proposes a novel approach using protein structural features for mutation effect prediction, enhancing accuracy over sequence-only methods in rare disease contexts.

Findings

Structure-based features achieve high discernibility of pathogenic vs benign mutations.

Combining structure- and sequence-based features improves prediction accuracy.

Structure features are crucial for assessing rare variants in rare diseases.

Abstract

How do we determine the mutational effects in exome sequencing data with little or no statistical evidence? Can protein structural information fill in the gap of not having enough statistical evidence? In this work, we answer the two questions with the goal towards determining pathogenic effects of rare variants in rare disease. We take the approach of determining the importance of point mutation loci focusing on protein structure features. The proposed structure-based features contain information about geometric, physicochemical, and functional information of mutation loci and those of structural neighbors of the loci. The performance of the structure-based features trained on 80\% of HumDiv and tested on 20\% of HumDiv and on ClinVar datasets showed high levels of discernibility in the mutation's pathogenic or benign effects: F score of 0.71 and 0.68 respectively using multi-layer perceptron. Combining structure- and sequence-based feature further improve the accuracy: F score of 0.86 (HumDiv) and 0.75 (ClinVar). Also, careful examination of the rare variants in rare diseases cases showed that structure-based features are important in discerning importance of variant loci.