While these recent findings may spark some concern regarding the interpretability of sequence-based genetic tests, we believe that there is ample room for hope. Improvements in genetic test interpretation are coming from data sharing, international collaborations to create gene–disease specific guidelines, and insights into pathogenic and benign variation gained from big data. Our understanding of the genome and its variation has improved immensely in recent years and that understanding can be harnessed to improve interpretation of clinical genetic tests.
Both the ACMG–AMP guidelines and the articles by Amendola et al. point out the need for gene and, in some cases, gene–disease specific guidance on elements like allele frequency thresholds for assessing rarity, clinical validity of functional assays, and the role of loss of function variants [4, 5]. Through the ClinGen project, groups comprising international experts on specific diseases are developing just this sort of guidance [3]. For example, the first initiative tackled by ClinGen’s Cardiovascular Domain Working Group has been modification of the original ACMG–AMP recommendations specifically for classification of MYH7 variants for cardiomyopathy. The majority of the needed modifications were either to remove rules not applicable to MYH7 or to refine rules to make them more specific to this gene–disease pair. Initial testing of these MYH7-cardiomyopathy specific rules shows a high level of concordance [7]. Similar efforts are underway for other disease–gene pairs [4].
Variant interpretation will continue to benefit from increased data sharing, exemplified by projects such as ExAC and databases like ClinVar [2]. Efforts are being made to resolve disagreement among laboratories submitting to ClinVar; in one such effort 15 % of differences could be resolved by sharing internal, unpublished data [8]. While these efforts are both needed and laudable, it is important that we aim not only to agree with one another in our classifications, but also to ensure that our classifications are correct and likely to stand the test of time.
We recently used sequence data on 2913 individuals with hypertrophic cardiomyopathy from the international Sarcomeric Human Cardiomyopathy Registry (SHaRe) and 103,636 individuals not selected for Mendelian disease to dissect which parts of the three-dimensional structure of the myosin heavy chain molecule are intolerant to variation [9]. The findings can help laboratories and clinicians assess the likelihood of pathogenicity of any MYH7 variant. At the gene level, Walsh et al. recently compared the burden of rare variation in 7855 cardiomyopathy cases and 60,706 ExAC exomes to assess the strength of reported associations between select genes and hereditary cardiomyopathies [10]. Analyses like these are only possible with large-scale international data sharing.
Clinicians also have to be active and critical consumers, carefully reviewing the rationale for the classifications they receive from genetic testing companies. This includes checking ClinVar, reviewing the primary data, and, if possible, making their own assessments of the appropriate classification. We have found this has become common practice among cardiovascular genetics groups like ours, with 81 % of cardiovascular genetic counselors reporting that their clinical team assesses the classification of variants they receive through clinical genetic testing. Periodic re-assessment of classifications is critical as medically impactful classification changes will occur in a subset. Outdated classifications were responsible for nearly a quarter of discordance in ClinVar in one study [8].