Abstract
Genomic studies of human disease and drug response aim to find one or a few causal variants among millions of possible candidates. A streamlined platform for quickly and reliably assessing each variant called in such studies can save months of tedious effort, speeding discoveries for core labs' clinical and research clients, and freeing core staff to solve other data analysis challenges posed by broader clientele. Made to help cores and their clients quickly interpret human genomes, the Ingenuity® Variant Analysis™ platform (www.ingenuity.com/variants) lets users upload, annotate, and thoroughly compare whole or partial human genomes, to smartly shortlist candidate variants, genes, and pathways that may best explain user-specified phenotype(s). Leveraging Ingenuity's deep, structured knowledge base of published findings and robust predictive insight, Variant Analysis runs sophisticated tests of family- and population-scale genetic association, tailored to the user's study design and phenotype, via a simple, fluid interface that also lets one easily review, revise, and share findings. To meet the distinctive challenges of clinical genome interpretation, we have comprehensively curated published clinical assessments and population incidence of individual human sequence variants, supplementing gene- and pathway-level functional knowledge. To help researchers identify new causal candidates, we have built and validated Variant Analysis to run gene- and pathway-level burden tests ∼100x faster than conventional methods. And, by letting users easily and securely share genome data and findings with collaborators, the platform mediates efficient collaborative discovery among researchers studying similar or (as mutual controls) distinct rare diseases. Combining sophisticated functional analytics; statistically robust genetic analysis at the variant, gene, and pathway levels in an intuitive interface, the Variant Analysis platform is built to meet the varied genome interpretation needs of researchers and clinicians studying single and multiple probands, pedigrees, and case-control cohorts, to understand rare inborn diseases, common complex disease, cancers, and drug response.