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GEVACT : GENOMIC VARIANT CLASSIFIER TOOL Host Publication: BeSHG & NVHG First Joint Meeting "Genetics & Society" Authors: D. Daneels, G. Isel, D. Sengupta, M. Bonduelle, D. Dewan Farid, D. Croes, A. Nowé and S. Van Dooren Publication Date: Feb. 2016 Number of Pages: 2
Abstract: INTRODUCTION
With the emergence of new screening techniques, targeted, whole exome and genome
screening are becoming standard diagnostic norms in clinical settings to identify the variants
causative of a genetic disease. However, development of bioinformatics solutions for
pathogenic classification of the variants still remains a big challenge and henceforth, making
the process ponderous for geneticists and clinicians. In this work, we describe GEVACT
(Genomic Variant Classifier Tool), a tool for classification of genomic single nucleotide and
short insertion/deletion variants. The aim of this study was to design and implement a variant
classification algorithm, based on a literature review of cardiac arrhythmia syndromes and
existing knowledge of clinical geneticists.
METHODS
The algorithm we propose for GEVACT is based on a published variant classification schema
for cardiac arrhythmia syndromes (Hofman et al., 2013). It proposes two varying approaches:
one to classify missense variants and another to classify nonsense and frameshift variants.
The algorithm is implemented in two phases: pre-processing and classification.
In the pre-processing phase, an annotated tab-delimited variant file (.vcf.ann) retrieved from
Alamut-batch (Interactive Biosoftware) can be refined based on the gene list for the diseaseof-
interest, so as to reduce the number of variants for the analysis. Filters are applied to look
for variants that have already been reported in the Human Genome Mutation Database
(Stenson et al., 2003) and in ClinVar (Landrum et al., 2014), or that have previously been
detected and classified in an internal patient population. And lastly, the variants are filtered
based on their location in the genome and their coding effect, followed by the check for
minor allele frequency of the variant in a control population (Sherry ST et al. 2001).
Thereafter, in the classification phase, the filtered variants are classified as missense or
nonsense/frameshift variants. For missense variants the classification is based on the
parameters: amino acid substitution and its impact on protein function (Adzhubei et al., 2010
Kumar et al., 2009), biochemical variation (Mathe et al., 2006), conservation (Pollard et al.,
2010), frequency of variant alleles in a control population (ESP6500), effects on splicing
(Desmet et al., 2009), family and phenotype information and functional analysis. Whereas,
for the nonsense and frameshift variants, it is based on: effects on splicing, frequency of
variant alleles in a control population, family and phenotype information and functional
analysis. For each parameter, a score is given to the variant, which is subsequently
cumulated. Conclusively, based on the cumulative score each variant is classified into one of
the five categories: Class I - Non-Pathogenic Class II - VUS1 (unlikely pathogenic) Class III
- VUS2 (unclear) Class IV - VUS3 (likely pathogenic) Class V - Pathogenic (Sharon et al.,
2008).
BeSHG & NVHG First Joint Meeting 115
In this study, we report a Java based tool called GEVACT, developed for classification of
genomic variants. Input for the tool is an annotated vcf file, while the output depicts the
cumulative classification score along with the class label for a variant. The tool was tested
on a dataset of 130 cardiac arrhythmia syndrome patients, available at UZ Brussel. The
results of the variant classification made by the tool were validated by manual curation,
performed by the clinical geneticist. Definitively, the study indicates the tool to be promising
but needs to be further validated on datasets from other diseases. In addition to, we are
working on the tool to be adaptable for file inputs from other annotation software.
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