Insight into neutral and disease-associated human genetic variants through interpretable predictors.

Abstract

A variety of methods that predict human nonsynonymous single nucleotide polymorphisms (SNPs) to be neutral or disease-associated have been developed over the last decade. These methods are used for pinpointing disease-associated variants in the many variants obtained with next-generation sequencing technologies. The high performances of current sequence-based predictors indicate that sequence data contains valuable information about a variant being neutral or disease-associated. However, most predictors do not readily disclose this information, and so it remains unclear what sequence properties are most important. Here, we show how we can obtain insight into sequence characteristics of variants and their surroundings by interpreting predictors. We used an extensive range of features derived from the variant itself, its surrounding sequence, sequence conservation, and sequence annotation, and employed linear support vector machine classifiers to enable extracting feature importance from trained predictors. Our approach is useful for providing additional information about what features are most important for the predictions made. Furthermore, for large sets of known variants, it can provide insight into the mechanisms responsible for variants being disease-associated.