DSNetwork: An integrative approach to visualize predictions of variants’ deleteriousness
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 web 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 susceptibility loci. DSNetwork is an integrative web application implemented through the Shiny framework and available at: http://romix.genome.ulaval.ca/dsnetwork.\n\nAuthor summaryOver the past years, GWAS have enabled the identification of numerous susceptibility loci associated with complex traits (https://www.ebi.ac.uk/gwas/). However, many of those signals contain hundreds or even thousands of significantly associated variants among which only a few are really responsible of the phenotype. Substantial efforts have been made in the development of prediction methods to prioritize variants within GWAS-associated regions to go from statistical associations, to the identification of functional variants modulating gene expression, in order to ultimately gain insight into disease pathophysiology. Unfortunately, these numerous prediction tools generate contradictory predictions rendering the interpretation of results challenging. Some tools such as VEP [McLaren et al., 2016] report their scores using a color scheme, thus acknowledging the need to assist the user in the interpretation of predictor results. Nonetheless, the multiplication of approaches can often result in an extensive amount of data that is hard to synthesize. Aware of the challenge of evaluating the potential deleteriousness of variants in the context of fine mapping analyses, we created a customizable visualization approach that was implemented it in the decision support tool called DSNetwork for Decision Support Network. This tool enables quick access to gold standard and new predictors for both coding and non-coding variants through an easily interpretable visualization of these predictions for a set of variants.