Latest improvements for Ingenuity Variant Analysis

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New features in the Winter 2017 Release

Support Copy Number Variations from VCF
Copy Number Variations have long been associated with inherited disease predisposition, as well as cancer initiation and development. With the decreasing cost of WGS and increased accuracy of secondary analysis algorithms, more NGS workflows are taking an integrated approach to variant detection that spans SNVs, small indels, and large-scale genomic alterations. Variant Analysis can now accept deletion and duplication structural variants specified in the VCF input. To assess the phenotypic effect of these variants and their role in disease, users have at their disposal a number of filtering and annotation capabilities to perform common CNV analysis tasks. These include removal of spurious calls, overlap with genes affected, comparison to known CNVs in healthy populations, and co-occurence with other variants as well as across multiple samples.

Filter and annotate variants using ClinVar
In the Predicted Deleterious Filter, in addition to leveraging QIAGEN computed ACMG classifications and presence in HGMD, users can now check for presence in ClinVar as another means to identify and retain disease-associated variants. For variants that are present in ClinVar, they will be annotated with ClinVar accession IDs and link-outs, multiple if more than one condition is associated with that variant.


  • For functional prediction of variants, complete integration of CADD scores by bringing in CADD scores for known insertion and deletion variants, in addition to SNVs.
  • Increase flexibility and readability of Phenotype-Driven Ranking Filter (PDR) network graph display by allowing users to toggle between highlighted nodes only vs. all nodes. Highlighted nodes reflect genes and diseases from the selected row in the Diseases tab, whereas related nodes reflect diseases and phenotypes connected to the gene and disease in the selected row.

  • Provide list of genes implicated when exporting filter cascade settings for terms entered into the Biological Context and Phenotype-Driven Ranking filters.

  • Use a more accurate calculation for allele fraction in the case of Ion Torrent VCFs (AF = FAO/FAO + FRO).
  • Allow special characters (' $ % ^ &) to be used in filter naming conventions, which can be part of QIAGEN KB disease names.
Content versions: CADD (v1.3), EVS (ESP6500SI-V2), Allele Frequency Community (2017-01-31), JASPAR (2013-11), Ingenuity Knowledge Base (Lorien 170127.000), Vista Enhancer (2012-07), Clinical Trials (Lorien 170127.000), BSIFT (2016-02-23), TCGA (2013-09-05), PolyPhen-2 (v2.2.2), 1000 Genome Frequency (phase3v5b), Clinvar (2017-01-04), DGV (2016-05-15), COSMIC (v79), ExAC (0.3.1), HGMD (2016.4), PhyloP (2009-11), DbSNP (149), TargetScan (6.2), SIFT4G (2016-02-23)

Note: The new features and improvements mentioned above will be available on Saturday February 25, as soon as the release-related system maintenance is completed.

Ingenuity Variant Analysis Fall 2016 Release

What’s New in the Ingenuity Variant Analysis Fall Release (2016)

Expedite disease discovery using Phenotype-Driven Ranking filter 
This new filter prioritizes and ranks variants by using user-supplied phenotype and genotype data in conjunction. This approach draws from a network of phenotype-phenotype, phenotype-disease, and disease-gene relationships established from the QIAGEN Knowledge Base, and looks for plausible diseases that can explain both the phenotypes observed as well as the genetic variations detected. For each disease, we can compute a score that represents the compatibility between the phenotype profile and disease, and this score is in turn used to rank variants which reside in disease-implicated genes. Read more about this filter in our white paper.



Evaluate variants using subpopulation Allele Frequency from ExAC

In addition to filtering with aggregate frequency, users can now filter variants by their associated frequencies in ExAC ethnic subpopulations (East Asian, South Asian, African American, European, Latino), including a max-population frequency to ensure that the variant frequency is sufficiently low in each population. The max-population frequency is also incorporated into ACMG rule calculations where ExAC is used as a basis for controls data or common/benign variants. Similarly, we have added a max-population frequency option for filtering using the Allele Frequency Community dataset. For mendelian disorders, one expects a highly-damaging, causal variant to be rare across every population, so the ability to leverage multiple ethnicities in filtering and ascribing clinical significance is of high utility.


Incorporate CADD scores for functional predictions
Based on published literature comparing score distributions of several in-silico functional prediction approaches, CADDnot only covers intronic and exonic regions, but also produces better separation between causal and benign variants than independent metrics like SIFT and PolyPhen. There are a number of well-established pathogenic variants where SIFT and PolyPhen predicts the variant to be benign, so the ability to use another prediction tool lowers the risk of discarding potentially important variants when establishing criteria for loss-of-function in the Predicted Deleterious filter.

Streamline Variant Analysis to QCI Interpret workflow
QCI Interpret is a clinical decision support solution designed for genetic testing labs using next-generation sequencing platforms, catering to both somatic and hereditary cancer interpretation needs. It provides ACMG-guided variant classifications and rationale, clinical case counts from curated literature, as well as eligible treatments and clinical trials. By offering Variant Analysis users the ability to export filtered set of variants directly to QCI Interpret along with relevant metadata, we bridge the gap between variant filtering and variant reporting for clinical test offerings that involve large panels, exomes, or genomes. Prior to QCI Interpret, users can also supply data collected from additional family members or normal tissue for comparisons between case and control samples. Using the two products in unison brings efficient reduction of the candidate variant list in Variant Analysis, followed by fine-grained assessment and configurable reporting of actionable variants in QCI Interpret.
To learn more about QCI Interpret and request a demo, please visit the QCI Interpret product page.


  • Rationale used for variant inferred activity (gain/loss/normal) is presented when users click on “Inferred Activity” in the Variant Details pane
  • If ExAC, AFC, or 1000 Genomes is used as part of Common Variants filtering, annotations providing homozygote counts (aggregate of all subpopulations) from these data sources are available
  • Lift coverage of OMIM content from 95% to 97%, resulting in more complete references to OMIM records in Variant Findings section
  • In the Biological Context filter, improved UI design for configuring inclusion of genes within 1 or more hops of the genes implicated by user-supplied terms
  • In the Genetic Disease workflow’s pre-configuration for Biological Context filter, optimized order in which upstream/downstream genes are searched for compatible variants in the sample
  • If a sample library is used for Controls input in an analysis set-up, the library can subsequently be used for allele frequency filtering in the Common Variants filter
  • Enhanced pre-configuration settings based on best-practices guidelines for single-sample Cancer workflows
  • Ability to reference existing samples in a user’s account when configuring new analyses via the API
  • Access to version numbers for public content sources and QIAGEN Knowledge Base

Ingenuity Variant Analysis version 4.2.20160927
Content versions: CADD (v1.3), SIFT (2016-02-22), EVS (ESP6500SI-V2), Allele Frequency Community (2016-08-26), JASPAR (2013-11), Ingenuity Knowledge Base (Jakku 160913.000), Vista Enhancer (2012-07), Clinical Trials (Jakku 160913.000), BSIFT (2016-02-22), TCGA (2013-09-05), PolyPhen-2 (v2.2.2), 1000 Genome Frequency (phase3v5b), Clinvar (2016-06-01), COSMIC (v77), ExAC (0.3.1), HGMD (2016.2), PhyloP (2009-11), DbSNP (147), TargetScan (6.2)

Ingenuity Variant Analysis Spring 2016 Release

What’s New in the Ingenuity Variant Analysis Release (2016)

New features

Ethnic group specific allele frequency information in Allele Frequency Community
In addition to variant composite frequency, users can now filter against East Asian, South Asian, African (American), European, and Hispanic sub-population frequencies in the Common Variants filter.

Compute allele frequencies from user-defined collection of samples
Empower users to establish their own “baseline” by computing frequencies based on a collection of samples (“My Libraries”), and annotating variants in a future analysis with library frequencies.

Make a copy of an existing analysis
Carry over analysis filter settings, samples, and sample metadata into a new analysis.

  • Added back the functionality to download original VCF
  • Added OMIM gene and phenotype ID to the variants table
  • Added GQ (Genotype Quality) to the Confidence Filter and VCF export
  • Revised compound het calculation to no longer include variants of uncertain significance
  • Retain allele depth (AD) information in exported VCF if uploaded VCF specifies the field
  • Improved handling of analyses where prefiltering produce zero variants
  • Increased numerical precision to 2 decimal points for variant call quality and allele fraction fields
  • Unified syntax for Allele Frequency Community fields across application UI and exported files (now reads as “AFC frequency” or “AFC_AF”)
  • API usage of Variant Analysis Custom Pipeline will now display the entire filter cascade, rather than only variants that survived the filter

Ingenuity Variant Analysis version 4.1.20160615
Content versions: CGI 54 Genomes (Version2.0), SIFT (2016-02-26), EVS (ESP6500SI-V2), Allele Frequency Community (2016-06-16), JASPAR (2013-11), Ingenuity Knowledge Base (Idris 160423.001), Vista Enhancer (2012-07), BSIFT (2016-02-26), TCGA (2013-09-05), PolyPhen-2 (v2.2.2), 1000 Genome Frequency (phase3v5b), Clinvar (2016-03-01), COSMIC (v76), ExAC (0.3), HGMD (2016.1), PhyloP (2019-11), DbSNP (146), TargetScan (6.2)

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