A sequence-based method to predict the impact of regulatory variants using random forest

Qiao Liu,Mingxin Gan,Rui Jiang

doi:10.1186/s12918-017-0389-1

Abstract

BackgroundMost disease-associated variants identified by genome-wide association studies (GWAS) exist in noncoding regions. In spite of the common agreement that such variants may disrupt biological functions of their hosting regulatory elements, it remains a great challenge to characterize the risk of a genetic variant within the implicated genome sequence. Therefore, it is essential to develop an effective computational model that is not only capable of predicting the potential risk of a genetic variant but also valid in interpreting how the function of the genome is affected with the occurrence of the variant.ResultsWe developed a method named kmerForest that used a random forest classifier with k-mer counts to predict accessible chromatin regions purely based on DNA sequences. We demonstrated that our method outperforms existing methods in distinguishing known accessible chromatin regions from random genomic sequences. Furthermore, the performance of our method can further be improved with the incorporation of sequence conservation features. Based on this model, we assessed importance of the k-mer features by a series of permutation experiments, and we characterized the risk of a single nucleotide polymorphism (SNP) on the function of the genome using the difference between the importance of the k-mer features affected by the occurrence of the SNP. We conducted a series of experiments and showed that our model can well discriminate between pathogenic and normal SNPs. Particularly, our model correctly prioritized SNPs that are proved to be enriched for the binding sites of FOXA1 in breast cancer cell lines from previous studies.ConclusionsWe presented a novel method to interpret functional genetic variants purely base on DNA sequences. The proposed k-mer based score offers an effective means of measuring the impact of SNPs on the function of the genome, and thus shedding light on the identification of genetic risk factors underlying complex traits and diseases.

Highlights

Most disease-associated variants identified by genome-wide association studies (GWAS) exist in noncoding regions
The kmerForest model can effectively help us to discriminate accessible chromatin regions from pure genomic context. Such sequence features identified by our method can be regarded as the functional sequence elements which are the candidates for consensus motifs such as Transcription factor binding site (TFBS)
We further developed an application for evaluating the regulatory variants

Summary

Introduction

Most disease-associated variants identified by genome-wide association studies (GWAS) exist in noncoding regions. With great efforts in the past decade, genome-wide association studies (GWAS) have discovered a number of potential associations between genetic variants and human inherited diseases or traits [1, 2]. Most of such variants spread over noncoding regions of the entire. The Author(s) BMC Systems Biology 2017, 11(Suppl 2):7 Toward this goal, computational approaches have been proposed to predict functionally damaging effects of genetic variants in the whole genome level [5,6,7]. DeepBind [13] identify sequence specificities of DNA- and RNA-binding proteins from experimental data by using the deep learning technology

Results

Discussion

Conclusion