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Quantitative approaches to variant classification increase the yield and precision of genetic testing in Mendelian diseases: the case of hypertrophic cardiomyopathy


Journal article


R. Walsh, F. Mazzarotto, N. Whiffin, R. Buchan, W. Midwinter, Alicja E Wilk, Nicholas Li, L. Felkin, N. Ingold, Risha Govind, Mian Ahmad, Erica Mazaika, M. Allouba, Xiaolei Zhang, A. de Marvao, S. Day, E. Ashley, S. Colan, M. Michels, A. Pereira, D. Jacoby, Carolyn Y. Ho, K. Thomson, H. Watkins, P. Barton, I. Olivotto, S. Cook, J. Ware
Genome Medicine, 2018

Semantic Scholar DOI PubMedCentral PubMed
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APA   Click to copy
Walsh, R., Mazzarotto, F., Whiffin, N., Buchan, R., Midwinter, W., Wilk, A. E., … Ware, J. (2018). Quantitative approaches to variant classification increase the yield and precision of genetic testing in Mendelian diseases: the case of hypertrophic cardiomyopathy. Genome Medicine.


Chicago/Turabian   Click to copy
Walsh, R., F. Mazzarotto, N. Whiffin, R. Buchan, W. Midwinter, Alicja E Wilk, Nicholas Li, et al. “Quantitative Approaches to Variant Classification Increase the Yield and Precision of Genetic Testing in Mendelian Diseases: the Case of Hypertrophic Cardiomyopathy.” Genome Medicine (2018).


MLA   Click to copy
Walsh, R., et al. “Quantitative Approaches to Variant Classification Increase the Yield and Precision of Genetic Testing in Mendelian Diseases: the Case of Hypertrophic Cardiomyopathy.” Genome Medicine, 2018.


BibTeX   Click to copy

@article{r2018a,
  title = {Quantitative approaches to variant classification increase the yield and precision of genetic testing in Mendelian diseases: the case of hypertrophic cardiomyopathy},
  year = {2018},
  journal = {Genome Medicine},
  author = {Walsh, R. and Mazzarotto, F. and Whiffin, N. and Buchan, R. and Midwinter, W. and Wilk, Alicja E and Li, Nicholas and Felkin, L. and Ingold, N. and Govind, Risha and Ahmad, Mian and Mazaika, Erica and Allouba, M. and Zhang, Xiaolei and de Marvao, A. and Day, S. and Ashley, E. and Colan, S. and Michels, M. and Pereira, A. and Jacoby, D. and Ho, Carolyn Y. and Thomson, K. and Watkins, H. and Barton, P. and Olivotto, I. and Cook, S. and Ware, J.}
}

Abstract

International guidelines for variant interpretation in Mendelian disease set stringent criteria to report a variant as (likely) pathogenic, prioritising control of false-positive rate over test sensitivity and diagnostic yield. Genetic testing is also more likely informative in individuals with well-characterised variants from extensively studied European-ancestry populations. Inherited cardiomyopathies are relatively common Mendelian diseases that allow empirical calibration and assessment of this framework. We compared rare variants in large hypertrophic cardiomyopathy (HCM) cohorts (up to 6179 cases) to reference populations to identify variant classes with high prior likelihoods of pathogenicity, as defined by etiological fraction (EF). We analysed the distribution of variants using a bespoke unsupervised clustering algorithm to identify gene regions in which variants are significantly clustered in cases. Analysis of variant distribution identified regions in which variants are significantly enriched in cases and variant location was a better discriminator of pathogenicity than generic computational functional prediction algorithms. Non-truncating variant classes with an EF ≥ 0.95 were identified in five established HCM genes. Applying this approach leads to an estimated 14–20% increase in cases with actionable HCM variants, i.e. variants classified as pathogenic/likely pathogenic that might be used for predictive testing in probands’ relatives. When found in a patient confirmed to have disease, novel variants in some genes and regions are empirically shown to have a sufficiently high probability of pathogenicity to support a “likely pathogenic” classification, even without additional segregation or functional data. This could increase the yield of high confidence actionable variants, consistent with the framework and recommendations of current guidelines. The techniques outlined offer a consistent and unbiased approach to variant interpretation for Mendelian disease genetic testing. We propose adaptations to ACMG/AMP guidelines to incorporate such evidence in a quantitative and transparent manner.


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