DSNetwork: An Integrative Approach to Visualize Predictions of Variants' Deleteriousness.

Audrey Lemaçon,Marie-Pier Scott-Boyer,Régis Ongaro-Carcy,Penny Soucy,Jacques Simard,Arnaud Droit

doi:10.3389/fgene.2019.01349

Abstract

One of the most challenging tasks of the post-genome-wide association studies (GWAS) research era is the identification of functional variants among those associated with a trait for an observed GWAS signal. Several methods have been developed to evaluate the potential functional implications of genetic variants. Each of these tools has its own scoring system, which forces users to become acquainted with each approach to interpret their results. From an awareness of the amount of work needed to analyze and integrate results for a single locus, we proposed a flexible and versatile approach designed to help the prioritization of variants by aggregating the predictions of their potential functional implications. This approach has been made available through a graphical user interface called DSNetwork, which acts as a single point of entry to almost 60 reference predictors for both coding and non-coding variants and displays predictions in an easy-to-interpret visualization. We confirmed the usefulness of our methodology by successfully identifying functional variants in four breast cancer and nine schizophrenia susceptibility loci.

Highlights

Since 2006, thousands of susceptibility loci have been identified through Genome-Wide Association Studies (GWAS) for numerous traits and complex diseases, including breast cancer (MacArthur et al, 2017)
Statistical fine-mapping analyses combined with the functional annotation of genetic variants can help pinpoint the genetic variant responsible for complex traits, or at least narrow down the number of variants underlying the observed association for further functional studies
Tremendous efforts have been put forth to assist the functional assessment of variants at DSNetwork: Integration of Deleteriousness Predictions risk loci and numerous scoring methods and tools have been developed to predict the deleteriousness of variants based on a number of characteristics such as sequence conservation, characteristics of amino acid substitution, and location of the variant within protein domains or three-dimensional protein structure

Summary

Introduction

Since 2006, thousands of susceptibility loci have been identified through Genome-Wide Association Studies (GWAS) for numerous traits and complex diseases, including breast cancer (MacArthur et al, 2017). Statistical fine-mapping analyses combined with the functional annotation of genetic variants can help pinpoint the genetic variant (or variants) responsible for complex traits, or at least narrow down the number of variants underlying the observed association for further functional studies. In this regard, tremendous efforts have been put forth to assist the functional assessment of variants at DSNetwork: Integration of Deleteriousness Predictions risk loci and numerous scoring methods and tools have been developed to predict the deleteriousness of variants based on a number of characteristics such as sequence conservation, characteristics of amino acid substitution, and location of the variant within protein domains or three-dimensional protein structure

Methods

Results

Conclusion