Setting the standard: Invitae helps adapt the ACMG/AMP variant classification framework for inherited cardiomyopathies

Invitae cardiology genetics expert John Garcia on the importance of consistent variant classification and a collaboration to make recommended modifications to the ACMG variant classification framework.

Sequencing DNA isn’t enough. A genetic sequence must also be translated into medically actionable information, in a manner that is both accurate and consistent across testing laboratories.

In 2015, the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) published guidelines to standardize evidence assessment (Richards et al., Genet Med 2015). The resulting framework was a great first step in leveling the playing field for clinical interpretation. However, the guidelines were knowingly written to have general applicability across all Mendelian disorders, and many of the criteria allow for significant subjectivity.

Recognizing these limitations, the NIH-backed ClinGen Genome Resource is adapting the framework for specific genes and diseases by assembling an international panel of clinical and laboratory experts to make specific recommendation and modifications. The first gene to be analyzed in this way was MYH7, a key contributor to inherited cardiomyopathies.

The expert panel, which includes two Invitae scientists, discussed adjustments to the ACMG framework and tested those adjustments on a series of 60 representative variants.

Adapted framework
The resulting consensus decisions affected nearly all of the original ACMG guidelines, including specific recommendations for how to use many of the rules.

One of the more important introductions to the official ACMG framework was the concept of using the increased prevalence of a variant in probands compared to controls as evidence for pathogenicity. For variants that are rare or absent from population databases, the panel recommended that the observation of increasing numbers of well-phenotyped probands with the variant should increase the confidence that the variant itself is causative. Several academic institutions and Invitae shared available phenotypic data with the expert panel curators, and this combination of evidence upgraded several variants that would have remained VUS if only the primary literature was taken into consideration.

In all instances, the recommendations were consistent with Sherloc, Invitae’s effort to refine the ACMG guidelines on a gene- and condition-specific basis.

Sharing > Silos
One of Invitae’s core principles is that information is more valuable when shared. This applies to both the genotypes and phenotypes we observe as well as sharing how we approach difficult problems like variant interpretation. We are committed to continuing our involvement in the greater scientific community through data sharing efforts through ClinVar and collaborations such as ClinGen Expert Panels, so that the whole community is improved and a greater understanding is reached by all.

The results of this exercise were recently published in Genetics in Medicine.

Kelly MA, Caleshu C, Morales A et al. Adaptation and validation of the ACMG/AMP variant classification framework for MYH7-associated inherited cardiomyopathies: recommendations by ClinGen’s Inherited Cardiomyopathy Expert Panel. Genet Med. January 2018. [Epub ahead of print]. doi:10.1038/gim.2017.218
Nykamp K, Anderson M, Powers M et al. Sherloc: a comprehensive refinement of the ACMG-AMP variant classification criteria. Genet Med. 2017;19(10):1105-17. doi:10.1038/gim.2017.37
Kobayashi Y, Yang S, Nykamp K, Garcia J, Lincoln SE, Topper SE. Pathogenic variant burden in the ExAC database: an empirical approach to evaluating population data for clinical variant interpretation. Genome Med. 2017;9(1):13. doi:10.1186/s13073-017-0403-7
Walsh R, Thomson KL, Ware JS et al. Reassessment of Mendelian gene pathogenicity using 7,855 cardiomyopathy cases and 60,706 reference samples. Genet Med. 2016;19(2):192-203. doi:10.1038/gim.2016.90
Whiffin N, Minikel E, Walsh R et al. Using high-resolution variant frequencies to empower clinical genome interpretation. Genet Med. 2017;19(10):1151-8. doi:10.1038/gim.2017.26
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