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

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

Van den Berg, B.A.
Reinders, M.J.T.
De Ridder, D.
De Beer, T.A.P.

Electrical Engineering, Mathematics and Computer Science

Intelligent Systems

2015-03-31

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.

OA-Fund TU Delft

http://resolver.tudelft.nl/uuid:fe7ea632-6e9e-42be-b1c6-124e0a9df696

Public Library of Science PLOS

1932-6203

https://doi.org/10.1371/journal.pone.0120729

PloS One, 10 (3), 2015

Institutional Repository

journal article

© 2015 van den Berg et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited