Here at QIAGEN Silicon Valley located in Redwood City, CA where Ingenuity™ web-based apps are developed, we are always working on ways of improving the filtering, prioritizing and interpreting human genetic variation. To help us understand how this process takes place in clinical labs, we sat down with our resident expert, Tara Love, who previously spent several years at Correlagen and LabCorp as a variant analysis expert.
Tara, who is now a clinical geneticist here at QIAGEN, earned her PhD in genetics from Tufts University and completed a postdoc in cancer genetics at the Dana-Farber Cancer Institute before she moved to commercial genetic testing.
At LabCorp, Tara’s group analyzed variants from Sanger and next-generation sequencing-based gene panels related to hereditary disease that were often requested by doctors looking to confirm a diagnosis. She would receive an annotated packet of information about the sequence results pointing to regions of interest, identified variants, and other relevant data based on a comparison to the human reference genome.
That’s when Tara and her group of analysts would spring into action analyzing variants. Their first tools were Google and PubMed, which they used to search for the variants to find papers citing them. “My main objective was to gather as much information as possible on each variant,” Tara says. Next, they had to go through each paper that came back to extract useful information: for the patients reported in the paper, which disease did they have? Were they heterozygote or homozygote? “The more evidence we could gather like that could support the idea that it may be a causal or benign variant,” she adds.
A follow-up step was to visit various databases and websites that offer tools to predict the consequence of amino acid changes based on the DNA variant, as well as RNA splicing tools. These tools would provide suggestions on how that change would be expected to affect the entire protein, a clue for Tara and her team as they tried to establish a link between the variant detected and the patient’s phenotype. The drawback: “This task had to be done manually, one at a time, for the different sites where these prediction programs were housed,” Tara says.
Understandably, this hands-on process took quite a bit of time. For genetic variation that could not be found in any papers, Tara says the average time spent per variant was about half an hour. For variants that were in the literature, the average was more like two hours — with the most time ever spent on a single variant clocking in at 24 hours due to tremendous complexity. To meet clinical lab standards, every analysis had to be confirmed by another person on the team.
The process is “just not scalable” for sequencing exomes or whole genomes, Tara adds. With that much data, she says, clinical lab teams will need a reliable method for rapidly filtering out common variants to help analysts focus on variants that are more likely to be causative. Ideally, that same method would also quickly identify known pathogenic variants. “The goal should be for analysts to only have to score variants of unknown significance,” she says.
The process Tara described is the same whether it’s a major commercial testing lab or a small academic clinical lab. “Analyzing the variants in this way is a necessity whether you’re small or big,” she says. “Everybody who does this sort of testing needs help.”
We have long recognized this need and are working to help clinical geneticists with this challenge. Ingenuity Variant Analysis can already be used to identify variants likely to be drivers of disease. Also, we are developing Ingenuity Clinical, a new clinical decision support product for clinical geneticists and lab directors that aids in the interpretation of variants observed in genomic sequencing data. Ingenuity Clinical aims to provide a seamless easy-to-use solution for the clinical laboratory to rapidly assess the meaning of variants.
If you’ll be attending the American College of Medical Genetics meeting in Nashville this month, we’d be happy to provide a demo of the early-access Ingenuity Clinical decision support system. Please stop by booth #526.
If you won’t be at ACMG, please sign up here to get more information about Ingenuity Clinical.