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.
ASHG 2016 was an exciting event for us. We loved the beautiful city of Vancouver, BC, and our calendars were packed with speakerships, poster presentations and meetings with peers and colleagues. We also announced our new Sample-to-Insight solutions for liquid biopsies and hereditary diseases — which included our bioinformatics solutions, and our booth was buzzing with people who wanted to learn more. Our public-facing ASHG activities were a germane reflection of the event’s overarching theme: “Sharing Discoveries. Shaping our Future.” Over the course of five days during ASHG, QIAGEN Bioinformatics staff delivered six separate in-booth presentations, five poster presentations and an educational workshop focused on liquid biopsy, RNA-seq, and hereditary diseases.
If you missed them, or would like to see them again, you can see Jean-Noel Billaud's presentation on an Integrative approach to biomarker discovery: Comparative analysis of two cancers using genomics and transcriptomics from RNA sequencing data here, and Helge Martens' on Rapid identification and prioritization of pathogenic variants associated with anomalies of the kidneys and urinary tract here.
We were not the only ones who were busy during ASHG. The Broad Institute’s new beta of its Genome Aggregation Database, or “gnomAD” was announced, which boasts information from 126,216 human exomes and 15,136 whole human genomes and doubles the number of exomes available from the ExAC population database. This news resonated strongly with us because we’re championing similar efforts with the Allele Frequency Community — our opt-in community resource which encourages the sharing of anonymized, pooled frequency statistics among laboratories. The industry’s continued drumbeat toward precision medicine was another recurring theme, going hand-in-hand with the strong focus on Canada’s efforts to adopt its own version of 2008’s U.S. Genetic Information Nondiscrimination Act. We also saw continued buzz around CRISPR technology, with several ASHG sessions dedicated to both the implications and obligations inherent in genome editing technology.
We hope you enjoyed your time at ASHG and we hope to see you soon. If you have questions about liquid biopsy or related solutions, do not hesitate to contact us.
Our next big event will be AMP 2016 in Charlotte, NC from Nov. 10-12 and the NGS Congress in London from Nov. 10-11. Keep an eye on this site for updates about what we’ll be doing there. We hope you enjoyed your time at ASHG and we hope to see you soon. If you have questions about liquid biopsy or related solutions, do not hesitate to contact us.
We were honored to take part in last month’s Personalized Medicine World Conference (PMWC), where speakers and attendees focused on critical areas such as liquid biopsies, pathology, immuno-oncology, next-generation sequencing, and more.
Sean Scott, VP, New Ventures here at QIAGEN Bioinformatics, presented a talk on “Enabling precision medicine in oncology through scalable NGS-based test interpretation and actionable reporting.” Sean has presented at the PWMC event for the past four years and this event continues to grow in importance. If you couldn’t make it to the event, here’s a quick recap of his presentation.
Sean reviewed current trends and shifts in NGS-based testing of cancer patients, noting the significant improvement in our ability to treat this disease by using genomic information. He walked through some of the challenges facing clinical testing labs, such as scientific and technical complexity of NGS tests, the operational scale challenges with NGS tests, the opportunity for labs to differentiate their test offerings through bioinformatics and data aggregation, as well as continued uncertainty in reimbursement hurdles.
Sean showcased the value of the QIAGEN Clinical Insight platform in enabling clinical labs in scaling their test analysis, interpretation and reporting in both somatic and inherited/germline cancers. He also presented on QIAGEN’s strategy for de-identified genomic data and progress with the Allele Frequency Community, an opt-in community co-founded by QIAGEN Bioinformatics that allows labs to share allele frequency statistics for the benefit of patients and clinical research, and the INOVA Compendium.
As always, the PMWC event was an excellent event. If you couldn’t make it this year, we hope to see you there next year!
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The 17th annual Advances in Genome Biology and Technology meeting was a whirlwind of stellar talks, great posters, and - of course - fabulous parties.
While all the talks were terrific, some really resonated with us. In the opening session, David Haussler made the case for sharing genomic data and rare variant information as much as possible, a message that we loved hearing as co-founders of the Allele Frequency Community. We believe that data sharing is essential for genomic medicine to have the kind of impact we all anticipate. In another talk, Stephan Schuster announced the launch of the GenomeAsia 100K, a new project to sequence 100,000 people in Asia for a better understanding of the genetic diversity in that population. We’re excited to see what this new trove of data contributes to the global genomics community.
Our team was busy during the conference and one of the highlights of our efforts was the lunch workshop featuring Chris Mason from Weill Cornell Medicine. He presented updates on some of his more unique projects, including efforts to sequence pathogens across New York City and more extreme environments and to unravel the biological effects of space travel through a study of twin astronauts. Mason has been beta testing the QIAseq Targeted RNA Panels, and during his presentation, he highlighted the benefits of the panels in delivering digital RNA sequencing for gene expression analysis, and he shared results from that metagenomics work as well. It was a great workshop thanks to Mason and all the attendees who took the time to join us!
We also participated in the software demo session, showcasing our QIAGEN Ingenuity® Variant Analysis™ application for sequencing data interpretation. With the connections to the Ingenuity Knowledge Base and HGMD, it relies on meticulously curated genomic and biological data to help scientists quickly home in on variants of interest to get at the root cause of disease or other phenotypes. We provided software demos at our hospitality suite as well, and we’d like to thank all the people who stopped by.
We had a great time in Orlando and we’re for sure looking forward to AGBT meeting next year – hope to see you there!
Learn more about QIAGEN Ingenuity Variant Analysis
Read more about the new QIAseq Targeted RNA Panels
The February 2016 issue of Medical Lab Observer features a story co-authored by two QIAGEN Bioinformatics executives, Ramon Felciano and Michael Hadjisavas.
The story highlights the disparity between the increasing sophistication of genetic testing and the cumbersome process of variant analysis. It outlines a few key reasons for this growing gap, including the complexity of some variants, the traditional and painstaking process by which labs categorize variants, and the lack of consistency in reporting. The authors make the case that, to keep up with demand for genetic testing, the analysis piece of the puzzle needs to be fully automated, streamlined, and scalable. They call for a community-wide approach to overcome these challenges, such as the Allele Frequency Community. By sharing their resources, communities increase the value of the repository and not only help develop the market, but also improve patient diagnostics and care.
Check out the story: "Soaring demand for genetic testing highlights need for streamlined data interpretation". The article does a great job of addressing many key considerations regarding genetic testing interpretation, the resulting glut of data, and the increasing gap between the two. We hope you enjoy reading!
Read the full story
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In this post, we share some of the trends and milestones that mattered most to us in 2015, and dust off our crystal ball for a sneak peek at 2016.
For the genomics community, 2015 kicked off with a bang: in the US, President Obama announced the Precision Medicine Initiative, a federal initiative showing buy-in at the highest level for the goal of tailoring treatments to patients. On a related note, 23andMe this year earned the first-ever approval from FDA for a direct-to-consumer genetic test. Consumer interest in genetic data is the other half of the precision medicine equation, and companies like 23andMe have demonstrated in recent years that people are fascinated by how DNA information could help them lead healthier lives.
One of our favorite stories of 2015 was the much-buzzed-about PLoS Biology publication predicting that genomics would be the biggest data generator in the world in just 10 years, eclipsing data heavyweights like astronomy and social media. We sometimes have to take a moment to consider just how quickly data production has ramped up. Where we used to mail hard drives around, we now rely on cloud-based resources to manage the petabytes of genomic data scientists work with all over the world. The PLoS publication served not only to make people aware of the rapidly increasing data production in genomics, but also to spur interest in finding ways to make sure that our data management, storage, and analysis processes are ready for the coming strain on the system.
This year also saw the launch of the Allele Frequency Community (AFC), a freely accessible data-sharing group that we are proud to have helped found. AFC is designed to help scientists and clinicians interpret DNA variants more accurately by facilitating the sharing of allele-frequency metrics from populations that may be underrepresented in public databases. In the months since AFC got started, many more members joined and the number of included datasets grew impressively.
Throughout 2015, we were honored to have the opportunity to profile the fabulous work of several of our customers. If you haven’t seen them, we encourage you to check out their stories — these scientists are responsible for some really impressive achievements:
Thomas Hampton, Dartmouth College
Rajini Haraksingh, Stanford University and Rare Genomics Institute
Yuval Itan, Rockefeller University
Alistair Pagnamenta, University of Oxford
Cliff Tepper, University of California, Davis
Hywel Williams, University College London
Looking ahead to 2016, we anticipate seeing even greater pressure to develop streamlined genomic workflows, including push-button data analysis and interpretation. There is tremendous need to help clinical geneticists and other lab members process genomic data more quickly — after all, they’re going to be seeing a lot more of it in the near future. To that end, we expanded our QIAGEN Clinical Insight (QCI™) offering this year to cover hereditary cancer as well as somatic cancer, and we plan to add more features in the coming year. In research labs, the emphasis on faster, simpler workflows comes from ever-expanding studies; whether it’s Drosophila or humans, scientists are dramatically increasing the number of organisms they include in research projects to try to gain biological insight. There will be a continuous need for better analysis solutions to make sense of all that data.
From our QIAGEN Bioinformatics team, the best wishes for a prosperous new year!
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
ASHG 2015 was everything we hoped it would be: interesting and inspiring scientific presentations and a place to reconnect with friends and customers. When we weren’t manning a busy booth, our team had a wonderful time soaking in the latest and greatest in human genetics.
Here is a recap of our scientific line up at the show:
Tuesday October 6, Dan Richards, PhD., VP of Biomedical Informatics, QIAGEN Bioinformatics, gave a presentation about “Genome-scale ACMG pathogenicity classification using comprehensive curated clinical evidence and data.”
Wednesday October 7, we hosted a workshop with two guest speakers: Yuval Itan from Rockefeller University and Ben Solomon from Inova.
Yuval Itan talked about “NGS Diagnostic Odyssey - From Bench to Beside: Join fellow investigators in an educational overview of how bioinformatic solutions transform NGS results into actionable hereditary disease insights”.
Ben Solomon gave a presentation on “Solving Diagnostic Odysseys in the Neonatal Intensive Care Unit Achieving Valuable Insight from a Single Cell Genome.”
And Thursday October 8 was the time for our three poster presentations:
How can our solutions help you?
The solutions featured in our speakers presentations may also further your NGS studies. Take a look here and get inspired:
Of course you're also more than welcome to contact us for any questions you might have. Hope to see you next year in Vancouver - we’re already looking forward to it!
This webinar highlights our highly accurate and integrated end-to-end NGS analysis solution for the discovery of novel, and clinically relevant, rare and inherited disease causing variants, from various sample types in just one step.
Dr. Anika Joecker, Global Product Manager, presents the easy to use end-to-end hereditary disease workflows in Biomedical Genomics Workbench and Ingenuity Variant Analysis, as well as the Allele Frequency Community – an extensive, high-quality, ethnically diverse collection of human allele data for use as a reference set. The presentation will also touch upon the impact of accuracy and time-savings associated with reducing the number of false positives when searching for disease causing mutations in NGS data.
If you'd like to know more about how our hereditary disease solution addresses the NGS analysis bottleneck by delivering seamless and highly accurate end-to-end workflows for the identification and interpretation of causal variants you should read our recent press release. A laboratory using this new hereditary disease solution can achieve a case solve rate as high as 100%, while significantly reducing the rate of irrelevant variants for follow-up by 98% to 100%. These close to perfect solve rates are not possible using any other bioinformatics solution available in the market today, according to the latest benchmarking study that we presented at ASHG. The solution is cost-effective and can handle a high volume of samples (for example, 18,000 whole genomes per year).
Biomedical Genomics Workbench
Ingenuity Variant Analysis
Press release: QIAGEN launches new bioinformatics solution hereditary diseases
PLoS Biology has published an interesting paper about big data in genomics from lead author Zachary Stephens, senior author Gene Robinson, and their collaborators at the University of Illinois at Urbana-Champaign and Cold Spring Harbor Laboratory.
The perspective offers a bold vision about the projected growth of data generation and management in the field. The authors compare genomic data to other areas known for their leading data production (astronomy, Twitter, and YouTube) and offer solid documentation for their theory that 10 years from now, genomics could outpace all other big data fields. Check it out: Big Data: Astronomical or Genomical?
One of the areas they focused on was data analysis, a category that’s near and dear to us. Stephens et al. call out variant interpretation as one of the most computationally intensive processes for genomic data. Projecting out to the number of genomes that could be available by 2025, they write, “Variant calling on 2 billion genomes per year, with 100,000 CPUs in parallel, would require methods that process 2 genomes per CPU-hour, three-to-four orders of magnitude faster than current capabilities.”
Another great point in the scientists’ perspective was their insistence on data sharing across labs and institutions. “For precision medicine and similar efforts to be most effective, genomes and related ’omics data need to be shared and compared in huge numbers,” the authors write. “If we do not commit as a scientific community to sharing now, we run the risk of establishing thousands of isolated, private data collections, each too underpowered to allow subtle signals to be extracted.”
We heartily support this statement, and are proud to be co-founders of a leading initiative aimed at facilitating this kind of sharing — the Allele Frequency Community. When we first conceived the community, data sharing was one of our most important goals. That’s why we adopted a share-and-share-alike approach for AFC, letting all scientists use the data as long as they share their own allele frequency data in exchange. This proviso has led to remarkable and fast growth for the community, constantly making the resource more valuable to everyone using it. We think there are opportunities to use a similar approach for other types of genomic data and hope others are inspired to try it.
Kudos to Stephens et al. for a thought-provoking commentary that has gotten the whole field talking about what the future of genomics might look like!
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