But the upshot of the study was sobering for those who believe that genome sequencing is speeding to the clinic: there were far more disease-causing mutations discovered than people who actually had any disease. Of the 11 people who got reports saying their genomes harbored variants that should cause disease, only two of them actually had the disease. These conditions are not ones expected to have unusually late onset.
From our perspective, these results are not an indication that genomics has little to contribute to healthcare; instead, they are a stark reminder that efforts to accurately interpret the genome still have a long way to go. Programs like the Allele Frequency Community should help with this, but what we need most of all are more genome sequences and really strong phenotype/clinical data associated with them so that we can hone interpretation algorithms.
For the nine people who were expected to have a disease based on their genomic data but didn’t, it is likely that we will eventually discover protective mechanisms that offset the mutation, or perhaps environmental or dietary factors that explain the disconnect. For now, though, we are still at the earliest stages of truly understanding the human genome, and that’s the main message of the MedSeq results.
At QIAGEN Bioinformatics, we’re working hard to make sure that our genome analysis and interpretation tools incorporate the newest discoveries, rely on high-confidence findings, and help scientists see the big picture of how various mutations, pathways, and other factors fit together. We applaud the MedSeq team for drawing attention to this important topic.
It is not surprising to find mutations that are disease causing in healthy people because almost none of the known disease-causing mutations are 100% penetrant, or predictive of developing the resulting disease in all cases. If you get the disease depends on genomic context – some diseases are late onset and depend on your age, while others can manifest in a spectrum of how strong the effect is, and the effect may be below the threshold of calling it out as a disease.
That's why our software solutions allow for phenotype supported ranking: the ability to combine observed phenotypes of the patients with the genomic data for better interpretation results. Because of the insights we have in our QIAGEN Knowledge Base about mutations, genes, diseases, phenotypes and their relationships, we are able to prioritize mutations that are related to the individual phenotype, and we can show that this increases the rate of resolving causative mutations.
The Fall release of Ingenuity® Variant Analysis™ enriches the analysis functionality, adding speed and power, and offers new tools for the organization and management of sample analysis. We are committed to providing the most comprehensive solutions to our customers, and we’re delighted to share the highlights with you.
Analyze More Data with Greater Efficiency
Performance is important to our customers. When they pre-filter their whole genome data to exonic-only regions, they have previously been limited to data from 200 whole genome samples. We have increased that limit by 50%, so now customers can analyze up to 300 whole genome samples before the pre-filtering feature becomes mandatory. Users who are analyzing more than 300 whole genomes can either pre-filter, or bypass the pre-filter function altogether by contacting Customer Support for assistance with creating a work-around.
Better Sample Analysis and Management with New Private Control Libraries
The introduction of Private Control Libraries (PCL) delivers powerful new computation capabilities. PCLs enable users to compute and filter variant frequency from a select sample set, then compare case samples with all samples housed within the PCL. In addition, the PCL can accommodate larger volumes - up to 2000 whole genomes - when analyzing control samples, and can compare cases v. control samples using the Genetic Analysis and Statistical Analysis filters.
We have added two new tabs to PCL, which features a drag-and-drop interface to make management a snap. The first is called “My Control Libraries,” enabling you to store and easily access your PCLs. With the second tab, you can build a new library by clicking on the “New Library” tab in the “My Samples” view.
Additional Improvements
When considering other improvements that could really benefit our users, we recognized the intrinsic value of the Allele Frequency Community (AFC). We took this release as an opportunity to update the build of the AFC, which is now comprised of more than 120,000 consented exomes and genomes (about 12,000 of which are whole genomes). We have also improved integration between Ingenuity Variant Analysis and Ingenuity Pathway Analysis (the integration is in beta, with limited support), which enables Variant Analysis to export the list of gene IDs, the ACMG assessments, and the gain/loss of function information when you click on the “Export to IPA” button.
Learn more about Ingenuity Variant Analysis
