The Omicia Blog

Automated, Algorithmic Approaches to
Variant Interpretation

Genomics provides the opportunity to realize the promise of personalized medicine, with tests scaling from single genes to comprehensive panels, exomes and whole genomes. Whole genome and whole exome sequencing provide a wealth of information to potentially identify the cause of disease in an individual. In fact, a single exome has tens of thousands of variants.

Traditional variant interpretation methods of prioritization and filtering are often underpowered to effectively analyze these large sets of variants, however, and when analyzing individuals and nuclear families many cases still go undiagnosed.

Variant Prioritization and Filtering

Filtering techniques apply hard cut-offs to data. Often applied progressively or in parallel workflows, filters can be time consuming and difficult to track. Filtering can result in premature or inadvertent removal of causative candidates from the consideration set, and at the same time may also generate large numbers of false positive candidates.

Prioritization algorithms typically evaluate variants based on predicted deleteriousness. Prioritization is driven by individual variants, and may be limited to coding regions resulting in incomplete coverage. Indeed, SIFT only scores 60% of the human proteome1. Bioinformatic variant prioritization algorithms often only look at amino acid impact or conservation at a specific position, and do not look at allele frequency or aggregate impact at a gene level.

A More Comprehensive Approach to Variant Analysis

A more comprehensive approach is required to accurately interpret whole exome and genome data from individuals and nuclear families. This is why the University of Utah and Omicia co-developed the VAAST and Phevor algorithms.

VAAST ranks variants and their associated genes by their likelihood to cause disease in a specific individual, family or cohort. VAAST evaluates predicted impact on protein function, allele frequency as well as evolutionary conservation in its statistical ranking process. This is superior to traditional techniques that look at individual variants only or that only look at one or a subset of these factors using filtering approaches.

VAAST was first published in 2011 when it was used to discover the genetic cause for Ogden Syndrome2. This was one of the first disease gene discoveries using next-generation sequencing (NGS). VAAST has since been used in numerous cases to identify the underlying genetic cause of disease, for both known and novel disease genes.

Last October, Omicia’s Founder and CSO, Martin Reese, presented a first glimpse of Phevor at VentureBeat’s Healthbeat Innovation Showcase. Now, we are excited to announce that we have officially released Phevor within our Opal platform. Phevor is currently available to all users within Opal, in both the research and clinical applications.

Algorithmically Integrating a Patient’s Phenotype Data to Refine Prioritization

Phevor (Phenotype Driven Variant Ontological Re-ranking tool) integrates a patient’s or a cohort’s phenotypic information into a comprehensive clinical bioinformatics genome analysis.

Phevor, published in 2014, uses a novel algorithmic approach to directly integrate clinical phenotype information with gene function and disease information – bridging the gap between clinicians and computational biologists3. Phevor starts by mapping phenotype terms to the Human Phenotype Ontology4, Gene Ontology and other ontologies then uses a unique network propagation approach to identify additional gene candidates. This process creates a ranked list of genes ordered by the specific phenotype provided. Phevor then combines this prioritized list of genes with the VAAST analysis to produce a combined ranking of candidate genes based on deleteriousness and the specific phenotype or phenotypes in question.

The Phevor paper outlines three cases where Phevor was used to ascertain the genetic cause of disease in three undiagnosed children, including identification of a novel disease gene in a 6-month old infant with idiopathic liver disease.The integration of VAAST and Phevor within the Opal platform provides intuitive access to these advanced algorithms, and usage within Opal Clinical’s clinical interpretation workflows . Opal also allows users to combine these advanced algorithms with traditional filtering techniques, accelerating and improving the accuracy of interpretation while providing flexibility. Omicia is the exclusive licensee of VAAST and Phevor.

This combined algorithmic variant interpretation approach significantly increases the power and likelihood for diagnosis in individual patients or patients with two or three other family members, the most commonly occurring clinical scenarios.

Opal’s clinical workflows with VAAST and Phevor will be used for clinical interpretation for the 100,000 Genomes Project. After a rigorous evaluation process, Omicia was just selected as an interpretation provider for rare disease cases in the project’s pilot phase.

Opal users may access Phevor now within Clinical Reporter single exome and family workflows.

 

References

[1] Adzhubei et al, Nat Methods 2010.

[2] Rope et al, Am J Hum Genet. 2011, Yandell et al, Genome Res. 2011

[3] Singleton et al, Am. J. Hum. Gen. 2014

[4] Köhler, S., et al. The Human Phenotype Ontology project: linking molecular biology and disease through phenotype data. Nucleic Acids Res. 42, D966-74 (2014).

Automated, Algorithmic Approaches to Variant Interpretation

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