Looking back at ACMG 2016 where speakers looked ahead to improved variant analysis
Inspiring talks at ACMG
We recently attended the annual meeting of the American College of Medical Genetics and Genomics in Tampa, Fla., where we enjoyed meeting and speaking with new people at our booth. One of our executives, Dr. Dan Richards, VP of Informatics, gave a well-attended presentation entitled “Genome-scale ACMG Pathogenicity Predictions Using Comprehensively-Curated Clinical Genetics Disease Models.” We also attended two similarly compelling talks at the event, which resulted in GenomeWeb coverage that resonated strongly with us.
The first GenomeWeb report focused on a lecture by Stanford University genetic counselor Mitchel Pariani, whose talk was entitled “Lessons Learned from a Re-review of FBN1 Variants Utilizing New Tools in Variant Review.” Pariani discussed the gap in classifications and recommendations from clinical centers and testing laboratories when they conduct predictive testing on a variant in a disease-related gene. Given that this gap could have a significant impact on treatment, Pariani’s team tested the consistency of variant interpretation classification to the well-understood FBN1 gene, which resulted in discordant variant classifications. This led him to stress the importance of ongoing variant classification review for genetics counselors and to point out the need for strong communications between testing and clinical labs to maintain the most relevant, up-to-date information on variants that may have been reclassified.
We couldn’t agree more with Pariani’s assertions. We built the QIAGEN Clinical Insight (QCI™) Interpret solution to closely follow ACMG guidelines, enabling clinical geneticists to feel confident in their interpretations. We also work to ensure that gaps like the one described by Pariani are increasingly rare by continuously updating our QIAGEN Knowledge Base of manually curated publications. This gives our users the strongest possible foundation for building their analyses and interpretations of clinically relevant variants.
GenomeWeb also reported on an ACMG presentation by Baylor and Johns Hopkins researchers who reported that the diagnostic yield rate for clinical exome sequencing studies of hereditary disease reached only 25%. To improve this rate, one of the presenters suggested reanalyzing variants with better bioinformatics solutions, and using matchmaking tools to find similar patient cases that might establish a causative candidate variant. ACMG recommends that researchers retain raw genomic data and variant files — especially since new disease genes continue to be discovered at a rapid pace.
Improve the case solve rate
In support of solving more of these cases, we offers an Advanced Testing Solution for NGS applications. Biomedical Genomics Workbench and Biomedical Genomics Server Solution enable variant identification and filtering, QC reporting, and result validation/visualization, enabling variant discovery across all samples with a low false positive rate. Ingenuity Variant Analysis combines analytical tools and integrated content to rapidly identify and prioritize variants from human sequencing data. It offers variant interpretation, filtering, and prioritization to generate answers for more cases where routine testing failed. Here’s a little benchmarking data that illustrates how our solution can improve the case solve rate.
It was great to hear these speakers touching on many of the points we advocate on a daily basis. This is an exciting time for genetics and genomics, and we are proud to provide comprehensive solutions that have an impact on how progressive healthcare decisions can be informed and made.