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• W2906954304 abstract "To the Editor: Recent work by Shah and colleagues1Shah N. Hou Y.-C.C. Yu H.-C. Sainger R. Caskey C.T. Venter J.C. Telenti A. Identification of misclassified ClinVar variants via disease population prevalence.Am. J. Hum. Genet. 2018; 102: 609-619Abstract Full Text Full Text PDF PubMed Scopus (73) Google Scholar demonstrated that many variants in the ClinVar database2Landrum M.J. Lee J.M. Riley G.R. Jang W. Rubinstein W.S. Church D.M. Maglott D.R. ClinVar: public archive of relationships among sequence variation and human phenotype.Nucleic Acids Res. 2014; 42: D980-D985Crossref PubMed Scopus (1628) Google Scholar are misclassified and that disease-specific allele frequency (AF) thresholds can identify wrongly classified alleles by flagging variants that are too prevalent in the population to be causative of rare penetrant disease. Although we agree with the main conclusions of this work, the authors compare their AF filtering approach to our recently published method3Whiffin N. Minikel E. Walsh R. O’Donnell-Luria A.H. Karczewski K. Ing A.Y. Barton P.J.R. Funke B. Cook S.A. MacArthur D. Ware J.S. Using high-resolution variant frequencies to empower clinical genome interpretation.Genet. Med. 2017; 19: 1151-1158Abstract Full Text Full Text PDF PubMed Scopus (237) Google Scholar and conclude that the method we advanced “may be prone to removing potentially pathogenic variants.” This is incorrect. Here we demonstrate that our approach is robust and further illustrate the power of disease-specific AF thresholds for investigating the genetic architecture of disease. Both methods compare the population frequency of a variant with the prevalence of a disease. Although this simple approach might be sufficient in some circumstances, such as when considering a fully penetrant disease with only a handful of causative variants, we advocate considering a fuller definition of disease architecture that explicitly incorporates penetrance and genetic heterogeneity. Using these parameters, we define the maximum AF at which a variant can be observed in the general population to be a credible candidate for causing a defined disease, under the specified genetic architecture. Importantly, we consider each ethnic sub-population separately and account for sampling variance in reference datasets.4Lek M. Karczewski K.J. Minikel E.V. Samocha K.E. Banks E. Fennell T. O’Donnell-Luria A.H. Ware J.S. Hill A.J. Cummings B.B. et al.Exome Aggregation ConsortiumAnalysis of protein-coding genetic variation in 60,706 humans.Nature. 2016; 536: 285-291Crossref PubMed Scopus (6555) Google Scholar We previously demonstrated that our framework markedly improves the signal:noise ratio for identification of penetrant Mendelian variants, without loss of sensitivity.3Whiffin N. Minikel E. Walsh R. O’Donnell-Luria A.H. Karczewski K. Ing A.Y. Barton P.J.R. Funke B. Cook S.A. MacArthur D. Ware J.S. Using high-resolution variant frequencies to empower clinical genome interpretation.Genet. Med. 2017; 19: 1151-1158Abstract Full Text Full Text PDF PubMed Scopus (237) Google Scholar It is worth restating some important caveats of filtering variants via AF. We must be vigilant for population-specific founder variants, especially in populations, such as the Finnish and Ashkenazi Jewish populations in the Genome Aggregation Database (gnomAD), where the disease architecture might be distinct. Also, because of the stochastic nature of population sampling, we do not filter variants observed as singletons. Shah et al. report that, in their hands, our method inappropriately filters 15 “high-confidence” pathogenic or likely pathogenic ClinVar variants across five cardiac phenotypes (dilated cardiomyopathy [PS115200 (MIM: 115200)], hypertrophic cardiomyopathy [PS192600 (MIM: 192600)], arrhythmogenic right ventricular cardiomyopathy [PS107970 (MIM: 107970)], long QT syndrome (LQT [PS192500 (MIM: 192500)]), and Brugada syndrome [MIM: 601144]). The work of Shah et al. cannot be directly replicated because their methods and reference dataset are not fully available. Therefore, we have assessed these 15 variants by using the larger and more comprehensive gnomAD dataset. We calculated a maximum credible population AF for a variant causative of each disease (defined as in Table 1 of Whiffin et al.; see Table S1) given a minimum penetrance of 50%.Table 1Curations and Penetrance Estimates of 15 Variants Flagged by Shah et al. and Two Additional Candidate Low-Penetrance Variants Identified in This AnalysisPhenotypeGenecDNAPenetranceACMG EvidenceACMG ClassConclusionHCMMYBPC3c.3330+5G>C12.0% (3.0%–45.0%)PVS1, PS4, PP1_strongpathogeniclikely pathogenic but with low penetranceLQTSKCNQ1c.1588C>T2.5% (0.64%–9.8%)PVS1, PS4_moderatelikely pathogeniclikely pathogenic but with low penetranceLQTSKCNQ1c.1781G>A4.1% (1.0%–16%)PS4, PM1, PP1, PS3_supportinglikely pathogeniclikely pathogenic but with low penetranceBrugadaSCN5Ac.3956G>T1.1% (0.010%–12%)PS3, PP2, PP3, PP1likely pathogeniclikely pathogenic but with low penetranceBrugadaSCN5Ac.5129C>Tethnicity-matched data not available for affected individualsPS3, PM1, PP3likely pathogeniclikely pathogenic but with low penetranceBrugada/LQTSSCN5Ac.1099C>Tethnicity-matched data not available for affected individualsPS3, PM1, PP3likely pathogeniclikely pathogenic but with low penetranceLQTSSCN5Ac.4877G>A-PS3_moderate, PP2, PP3VUSinsufficient evidence for a pathogenic assertionLQTSKCNQ1c.364dupT-PVS1VUSinsufficient evidence for a pathogenic assertionHCMMYL3c.170C>G-PS3_moderate, PP1_moderate, PP2VUSinsufficient evidence for a pathogenic assertionLQTSSCN5Ac.5872C>T-PVS1VUSinsufficient evidence for a pathogenic assertionLQTSKCNQ1c.1085A>G-PM1, PP3VUSinsufficient evidence for a pathogenic assertionDCMLMNAc.961C>T-PVS1, PP1_Strong, PM2, PS4_Moderate, PS3_Moderatepathogenicnot filtered using gnomADBrugadaSCN5Ac.4885C>T-PVS1, PS3_moderatelikely pathogenicnot filtered using gnomADLQTSKCNQ1c.573_577delG CGCT-PVS1, PP1_supporting, PS3_supportinglikely pathogenicnot filtered using gnomADHCMMYBPC3c.3181C>T-PVS1, PP1_strongpathogenicnot filtered using gnomADLQTSKCNE1c.226G>A1.7% (0.58%–5.2%)PS4, PS3pathogeniclikely pathogenic but with low penetranceaVariants identified by wider gnomAD analysisLQTSKCNQ1c.1664G>A1.6% (0.22%–12%)PM5, PM1, PS3_moderate, PP3likely pathogeniclikely pathogenic but with low penetranceaVariants identified by wider gnomAD analysisIn the Shah et al. analysis, 15 variants with “high-confidence” pathogenic assertions in ClinVar were reported to exceed the maximum credible population allele frequency for a pathogenic variant as defined by our framework. In our reanalysis, four of these are not filtered by our recommended application of this approach (using gnomAD reference populations), five do not have sufficient evidence for a pathogenic assertion, and six are likely pathogenic but with low penetrance. Full details can be found in Table S3. VUS = variant of unknown significance.a Variants identified by wider gnomAD analysis Open table in a new tab In the Shah et al. analysis, 15 variants with “high-confidence” pathogenic assertions in ClinVar were reported to exceed the maximum credible population allele frequency for a pathogenic variant as defined by our framework. In our reanalysis, four of these are not filtered by our recommended application of this approach (using gnomAD reference populations), five do not have sufficient evidence for a pathogenic assertion, and six are likely pathogenic but with low penetrance. Full details can be found in Table S3. VUS = variant of unknown significance. With proper application of our approach in this reference population, four (of 15) variants flagged by Shah et al. are not filtered (Table 1). We curated the remaining 11 variants according to contemporary guidelines from the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) guidelines5Richards S. Aziz N. Bale S. Bick D. Das S. Gastier-Foster J. Grody W.W. Hegde M. Lyon E. Spector E. et al.ACMG Laboratory Quality Assurance CommitteeStandards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology.Genet. Med. 2015; 17: 405-424Abstract Full Text Full Text PDF PubMed Scopus (14622) Google Scholar by using cardioclassifier.org,6Whiffin N. Walsh R. Govind R. Edwards M. Ahmad M. Zhang X. Tayal U. Buchan R. Midwinter W. Wilk A.E. et al.CardioClassifier: disease- and gene-specific computational decision support for clinical genome interpretation.Genet. Med. 2018; 20: 1246-1254Abstract Full Text Full Text PDF PubMed Scopus (54) Google Scholar ClinVar, and the published literature. Five did not reach a pathogenic or likely pathogenic classification (Tables 1 and S3). The remaining six variants did have sufficient evidence to be classified as (likely) pathogenic. For four of the variants, where ethnicity-specific AFs were available for the affected individuals, we estimated their penetrance by comparing the AFs for the affected individuals to data from gnomAD, as previously described.7Minikel E.V. Vallabh S.M. Lek M. Estrada K. Samocha K.E. Sathirapongsasuti J.F. McLean C.Y. Tung J.Y. Yu L.P. Gambetti P. et al.Exome Aggregation Consortium (ExAC)Quantifying prion disease penetrance using large population control cohorts.Sci. Transl. Med. 2016; 8: 322ra9Crossref PubMed Scopus (228) Google Scholar The penetrance of these variants ranged from 1.1% to 12% (Table 1). Crucially, the upper confidence intervals of all six penetrance estimates are well below the pre-specified 50% threshold. In other words, our approach appropriately filters these variants as incompatible with the specified genetic architecture. We extended our analysis to evaluate all pathogenic or likely pathogenic ClinVar variants for these five cardiac phenotypes. Starting with the same data (clinvar_20170905.vcf.gz), we annotated variants reported to cause the specified diseases with the tiering strategy outlined by Shah et al.1Shah N. Hou Y.-C.C. Yu H.-C. Sainger R. Caskey C.T. Venter J.C. Telenti A. Identification of misclassified ClinVar variants via disease population prevalence.Am. J. Hum. Genet. 2018; 102: 609-619Abstract Full Text Full Text PDF PubMed Scopus (73) Google Scholar To identify variants above the maximum credible AF for each disease, we used the highest filtering allele frequency across all gnomAD populations (“popmax”) for each variant represented, as described previously.3Whiffin N. Minikel E. Walsh R. O’Donnell-Luria A.H. Karczewski K. Ing A.Y. Barton P.J.R. Funke B. Cook S.A. MacArthur D. Ware J.S. Using high-resolution variant frequencies to empower clinical genome interpretation.Genet. Med. 2017; 19: 1151-1158Abstract Full Text Full Text PDF PubMed Scopus (237) Google Scholar These data, and the code that others can use to reproduce this analysis, are available for download (see Web Resources). This analysis flagged 47 additional variants, previously reported as pathogenic or likely pathogenic, as “insufficiently rare.” We reassessed the clinical interpretation by using contemporary ACMG/AMP5Richards S. Aziz N. Bale S. Bick D. Das S. Gastier-Foster J. Grody W.W. Hegde M. Lyon E. Spector E. et al.ACMG Laboratory Quality Assurance CommitteeStandards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology.Genet. Med. 2015; 17: 405-424Abstract Full Text Full Text PDF PubMed Scopus (14622) Google Scholar guidelines. 45/47 (95.7%) were classified as variants of unknown significance (Table 2; Table S2), and two were classified as (likely) pathogenic, but with low penetrance, clearly below our defined genetic architecture (Table 1).Table 2The Final Classifications of 15 Variants Flagged by Shah et al. and 47 Additional Variants Identified in This AnalysisVariant ClassTotal VariantsInsufficient Evidence for a Pathogenic AssertionLikely Pathogenic but with Low PenetranceNot Filtered with gnomADaEither the unpublished cohort used by Shah et al. contained additional data that were not obtainable for this work, or the framework was applied incorrectly, for example if singleton variants or known founder alleles were filtered.Flagged by Shah et al.155 (33.3%)6 (40.0%)4 (26.7%)Other set 1108 (80.0%)2 (20.0%)-Set 22626 (100.0%)0 (0.0%)-Set 31111 (100.0%)0 (0.0%)-Combined totals6250 (80.6%)8 (12.9%)4 (6.5%)a Either the unpublished cohort used by Shah et al. contained additional data that were not obtainable for this work, or the framework was applied incorrectly, for example if singleton variants or known founder alleles were filtered. Open table in a new tab Across both analyses, we identified eight (likely) pathogenic, low-penetrance variants (Table 2), two of which recapitulate a known mechanism of low penetrance. These variants are reported as causing Jervell Lange-Nielsen syndrome (a form of long QT [LQT] syndrome involving deafness [PS220400 (MIM: 220400)]) in biallelic states but have low penetrance for dominant LQT in heterozygous relatives.8Tranebjaerg L. Bathen J. Tyson J. Bitner-Glindzicz M. Jervell and Lange-Nielsen syndrome: a Norwegian perspective.Am. J. Med. Genet. 1999; 89: 137-146Crossref PubMed Scopus (72) Google Scholar As these examples demonstrate, our AF filtering framework effectively discriminates alleles that have lower penetrance and require tailored counselling. Within the ACMG/AMP framework, frequency evidence favoring a benign interpretation (BS1) is not sufficient for a (likely) benign classification in the absence of further supporting evidence. Similarly, activating BS1 does not preclude a (likely) pathogenic classification overall. For example, a common low-penetrance variant might be seen recurrently in affected individuals and show statistical enrichment in affected individuals versus controls (PS4). This contradictory evidence should trigger closer inspection and lead to consideration of a low-penetrance architecture. In other contexts, a more conservative low-penetrance architecture could be specified from the outset. It is worth highlighting a key limitation of the ACMG/AMP variant interpretation guidelines:5Richards S. Aziz N. Bale S. Bick D. Das S. Gastier-Foster J. Grody W.W. Hegde M. Lyon E. Spector E. et al.ACMG Laboratory Quality Assurance CommitteeStandards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology.Genet. Med. 2015; 17: 405-424Abstract Full Text Full Text PDF PubMed Scopus (14622) Google Scholar they were not intended to be applied to variants of very low penetrance. However, with no clear guidance on where to “draw the line,” the terminology that evolved for highly penetrant disorders is commonly also applied to low-penetrance alleles, where it may be more appropriate to describe variants as “risk factors” rather than “pathogenic.” In conclusion, we previously introduced a statistically robust, disease-specific framework with which to leverage reference-population AF for variant assessment. We show that this method does not remove true pathogenic variants, provided that they fall within the pre-defined genetic architecture. Although a specified architecture might lead to some low-penetrance variants’ being flagged with evidence in favor of benign status (BS1), this does not prevent them from achieving an actionable ACMG/AMP classification in combination with other lines of evidence. Indeed, flagging this group of variants for in-depth review enables more nuanced reporting and counselling around low-penetrance variation. The authors declare no competing interests. This work was supported by the Wellcome Trust (107469/Z/15/Z), the Medical Research Council (UK), the National Institute of Health Research (NIHR) Biomedical Research Unit in Cardiovascular Disease at Royal Brompton and Harefield National Health Services Foundation Trust and Imperial College London, the NIHR Imperial Biomedical Research Centre, the Fondation Leducq (11 CVD-01), a Health Innovation Challenge Fund award from the Wellcome Trust and Department of Health, UK (HICF-R6–373), and by the National Institute of Diabetes and Digestive and Kidney Diseases, the National Institute of General Medical Sciences, and the National Human Genome Research Institute of the NIH (awards U54DK105566, R01GM104371, and UM1HG008900). E.V.M. is supported by the National Institutes of Health under a Ruth L. Kirschstein National Research Service Award (NRSA) NIH Individual Predoctoral Fellowship (F31) (award AI122592-01A1). A.H.O.-L. is supported by National Institutes of Health under Ruth L. Kirschstein National Research Service Award 4T32GM007748. N.W. is supported by a Rosetrees and Stoneygate Imperial College Research Fellowship. This publication includes independent research commissioned by the Health Innovation Challenge Fund (HICF), a parallel funding partnership between the Department of Health and the Wellcome Trust. The views expressed in this work are those of the authors and not necessarily those of any of the funders. Download .xlsx (.15 MB) Help with xlsx files Document S1. Supplemental Tables Github, https://github.com/ImperialCardioGenetics/ResponseToShahEtAl Identification of Misclassified ClinVar Variants via Disease Population PrevalenceShah et al.The American Journal of Human GeneticsApril 05, 2018In BriefThere is a significant interest in the standardized classification of human genetic variants. We used whole-genome sequence data from 10,495 unrelated individuals to contrast population frequency of pathogenic variants to the expected population prevalence of the disease. Analyses included the ACMG-recommended 59 gene-condition sets for incidental findings and 463 genes associated with 265 OrphaNet conditions. A total of 25,505 variants were used to identify patterns of inflation (i.e., excess genetic risk and misclassification). Full-Text PDF Open ArchiveResponse to Whiffin et al.Shah et al.The American Journal of Human GeneticsJanuary 03, 2019In BriefTo the Editor: We welcome the letter by Nicola Whiffin and colleagues because it serves to frame the current debate on variant interpretation. Our goal was to highlight conditions with inflated genetic risk compared to the population prevalence of disease. As stated in their correspondence, there is no disagreement on basic concepts and key findings between their work1 and ours.2 Both studies emphasize the value of larger datasets that provide ever more precise description of allele frequencies across populations, along with better modeling of disease prevalence, penetrance, and genetic heterogeneity. Full-Text PDF Open Archive" @default.
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• W2906954304 title "Using High-Resolution Variant Frequencies Empowers Clinical Genome Interpretation and Enables Investigation of Genetic Architecture" @default.
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