Applications

QIAGEN’s Ingenuity applications leverage the rich knowledge to help researchers and clinicians interpret the biological meaning of their data.

These questions include but certainly are not limited to:

  • What variants are associated with any type of [skeletal abnormality]?
  • What variants are associated with [breast cancer] in [african americans]?
  • What functional processes were active in these [breast cancer] samples?
  • What pathways were [down/up] regulated in these [tumor] samples?
  • What [cancer signaling pathways] have most deleterious variants in [ALL] tumor vs. normal samples?
  • What variants associated with modified [warfarin] dosing?
  • What variants are expected to [increase activity] of genes involved in [bone morphogenesis] function?
  • What regulatory networks were active in [lung] samples?
  • What is the functional effects of this compound on the cells in my experiment?
  • What variants would be expected to impact expression of [predicted] [NFkB] targets?
  • Is this variant associated with [early onset breast cancer]?  What is the literature evidence?
  • Which variants [in BRAF] lack kinase activity [in HELA cells]?
  • Which variants are observed in [>10%] of [melanomas]?
  • Which variants are deleterious in genes with [tumorigenic] mouse ortholog  KO phenotypes?
  • Which variants are associated with [elevated  CVD] risk at P<10-5 in [Framingham SHARe]?
  • Which variants are in regions [deleted] in [>20%] of [glioblastomas]?
  • What variant(s) are associated with [cystic fibrosis] patient response to [VX-770] treatment? Which are already in [clinical practice guidelines]?
  • Identify variants common in healthy individuals
  • Assess allele frequencies in relevant subpopulations
  • Identify relevant ACMG pathogenic or possibly pathogenic variants
  • Search for published variants that could explain phenotype
  • Assess coding impact of variants in all known genes/isoforms
  • Apply SIFT
  • Apply Polyphen2
  • Identify gain-of-function variants from published literature
  • Identify variants likely to disrupt splicing
  • Predict likely gain-of-function variants
  • Identify variants deleterious to known miRNAs
  • Identify variants impacting miRNA binding sites
  • Compute copy number changes if tumor/normal pairs
  • Identify gene fusion events
  • Assess promoter loss
  • Assess structural variants
  • Assess impact to known transcription factor binding sites
  • Assess impact to known enhancers
  • Identify variants impacting evolutionarily conserved sites
  • Identify variants perturbing UTRs
  • Conduct genetic analysis
  • Identify variants that likely cause gene-level loss or gain of function
  • Identify deleterious het variants in haploinsufficient genes
  • Identify deleterious homozygous and compound het variants
  • Look for variants whose inheritance pattern tracks the phenotype
  • Deprioritize variants overrepresented in unaffected controls
  • Analyze pedigree to identify transmitted, mendelian and de novo variants
  • Identify variants consistent with disease penetrance
  • Identify variants relevant to the biological context of the disease
  • Identify somatic variants for any tumor-normal pairs
  • Identify variants with published impact on drug response, toxicity or metabolism
  • Filter variants based on call quality
  • Prioritize variants consistent with linkage study outcomes
  • Compile & review literature evidence relating variants to observed phenotypes
  • Compile & review disease-model evidence linking phenotypes to relevant diseases
  • Consult OMIM for relevant genes/variants
  • Consult COSMIC for somatic variant frequencies
  • Consult TCGA for somatic variant frequencies
  • Leverage mouse model phenotypes to inform variant prioritization
  • Run statistical association gene/pathway burden test
  • Identify and assess pathways perturbed more than expected by chance
  • Identify and assess genes that are statistically significantly perturbed
  • Look for variants overrepresented in affected individuals
  • Develop networks for highest priority variants
  • Run causal network analysis to ID variants putatively linked to phenotype(s)
  • Explore the biological context of the most compelling variants
  • Compile and review relevant literature evidence for top candidates
  • Identify relevant compounds for druggable genes/pathways
  • Assess isoform-level evidence for top candidates
  • Assess protein domain impact for candidate variants
  • Overlay expression, proteomic and/or epigenetic evidence
  • Validate variants and overlay validation evidence on analysis
  • Share and work with collaborators to follow-up on insights
  • Validate prospective causal variants, overlay validation data
  • Continuously update above with emerging literature and database evidence
  • Identify causal/driver variant(s)
  • Generate publication-quality figures
  • Publish with rich documentation of methods
  • Provide online supplement for publication