A Bayesian method to estimate variant-induced disease penetrance

Kroncke, Brett M.; Zuo, Yi; Glazer, Andrew M.; Roden, Dan M.; Blume, Jeffrey D.

doi:10.1371/journal.pgen.1008862

Cited by 13 publications

(19 citation statements)

References 41 publications

(61 reference statements)

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“…Evaluation and Performance of KCNQ1 Variant Penetrance Model. We implemented a Bayesian penetrance model using all available data for the KCNQ1-LQT1 genotype-phenotype pair (Figure 1) 17,18 . We curated >2,000 manuscripts from the literature, and in combination with phenotyped heterozygotes from international arrhythmia clinics and putative heterozygote controls from gnomAD 19 , found 9,969 KCNQ1 heterozygotes harboring 630 unique missense and in-frame insertion/deletion variants.…”

Section: Resultsmentioning

confidence: 99%

“…In contrast to LQT1, the LQT3 phenotype arises from a specific loss of channel inactivation, which may arise through late current, window current, or altered kinetics of inactivation 25 . We previously curated a dataset to evaluate this relationship empirically 18 , which includes a total of 86,118 SCN5A heterozygotes with 1,243 manifesting the LQT3 phenotype.…”

Section: Resultsmentioning

confidence: 99%

“…Implementing two instances of this approach, we develop penetrance models for KCNQ1-LQT1 and SCN5A-LQT3, incorporate data from KCNH2-LQT2 17 and SCN5A-BrS1 18 , and investigate penetrance across these four genotype-phenotype relationships. We demonstrate how penetrance varies among these genotype-phenotype relationships and correlate penetrance with structural features of each gene product.…”

Section: Introductionmentioning

confidence: 99%

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Continuous Bayesian Variant Interpretation Accounts for Incomplete Penetrance among Mendelian Cardiac Channelopathies

O’Neill

Sala

Denjoy

et al. 2022

Preprint

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Background: The congenital Long QT Syndrome (LQTS) and Brugada Syndrome (BrS) are Mendelian autosomal dominant diseases which frequently precipitate fatal cardiac arrhythmias. Incomplete penetrance is a barrier to clinical management of heterozygotes harboring variants in the major implicated disease genes KCNQ1, KCNH2, and SCN5A. We apply and evaluate a Bayesian penetrance estimation strategy that accounts for this phenomenon and evaluate penetrance distributions and rationalize their structural underpinnings across four genotype-phenotype pairs. Methods: We generated Bayesian penetrance estimation models for KCNQ1-LQT1 and SCN5A-LQT3 using variant-specific features and clinical data from the literature, international arrhythmia genetic centers, and population controls. We analyzed the distribution of posterior penetrance estimates across four-genotype phenotype relationships and compared continuous estimates to ClinVar annotations. Posterior estimates were mapped onto protein structure. Results: Bayesian models of KCNQ1-LQT1 and SCN5A-LQT3 are well-calibrated to clinical observations. Variant-informed penetrance estimates of KCNQ1-LQT1 and SCN5A-LQT3 are empirically equivalent to 10 and 5 heterozygote clinical phenotypes, respectively. Posterior penetrance estimates were bimodal for KCNQ1-LQT1 and KCNH2-LQT2, with a higher fraction of missense variants with high penetrance among KCNQ1 variants. SCN5A-LQT3 and SCN5A-BrS had comparatively more variants with predicted low penetrance. There was a wide distribution of variant penetrance estimates among similar categories of ClinVar annotations. Structural mapping revealed heterogeneity among hot spot regions and featured high penetrance estimates for KCNQ1 variants in contact with calmodulin and the S6 domain. Conclusions: Bayesian penetrance estimates provide a continuous framework for variant interpretation, provide higher resolution within hot spot domains, and facilitate prospective clinical management of variant heterozygotes.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Continuous Bayesian Variant Interpretation Accounts for Incomplete Penetrance among Mendelian Cardiac Channelopathies

O’Neill

Sala

Denjoy

et al. 2022

Preprint

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“…Other tools have leveraged large functional datasets toward gene-specific predictors. For example, a Bayesian method was conditioned on various Brugada Syndrome variant attributes curated from over 700 publications to generate SCN5A penetrance probabilities ( 152 ). Clerx and co-authors combed the literature and applied machine learning on I Na data for over 200 variants and 20 variant-associated features (e.g., location, physicochemical properties) to improve predictions ( 153 ).…”

Section: Computational and Predictive Datamentioning

confidence: 99%

How Functional Genomics Can Keep Pace With VUS Identification

Anderson

Munawar

Reilly

et al. 2022

Front. Cardiovasc. Med.

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Over the last two decades, an exponentially expanding number of genetic variants have been identified associated with inherited cardiac conditions. These tremendous gains also present challenges in deciphering the clinical relevance of unclassified variants or variants of uncertain significance (VUS). This review provides an overview of the advancements (and challenges) in functional and computational approaches to characterize variants and help keep pace with VUS identification related to inherited heart diseases.

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“…In past work, we described an algorithm for estimating the probability of a diagnosis of Brugada syndrome given the presence of a variant in the cardiac sodium channel gene SCN5A . 11 Although we incorporated variant-specific covariates (eg, sequence conservation, functional perturbation, structural location, etc), Brugada syndrome is likely oligogenic and the clinical phenotype is sometimes difficult to assess. In this article, we develop a similar algorithm for estimating the probability of long QT syndrome type 2 (LQT2), a well-characterized and monogenic disorder induced by variants in the cardiac potassium channel gene KCNH2 (also called the human Ether-a-go-go-Related Gene, or hERG ).…”

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confidence: 99%

Estimating the Posttest Probability of Long QT Syndrome Diagnosis for Rare KCNH2 Variants

Kozek

Wada

Sala

et al. 2021

Circ: Genomic and Precision Medicine

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Background - The proliferation of genetic profiling has revealed many associations between genetic variations and disease. However, large-scale phenotyping efforts in largely healthy populations, coupled with DNA sequencing, suggest variants currently annotated as pathogenic are more common in healthy populations than previously thought. In addition, novel and rare variants are frequently observed in genes associated with disease both in healthy individuals and those under suspicion of disease. This raises the question of whether these variants can be useful predictors of disease. To answer this question, we assessed the degree to which the presence of a variant in the cardiac potassium channel gene KCNH2 was diagnostically predictive for the autosomal dominant long QT syndrome. Methods - We estimated the probability of a long QT diagnosis given the presence of each KCNH2 variant using Bayesian methods that incorporated variant features such as changes in variant function, protein structure, and in silico predictions. We call this estimate the post-test probability of disease. Our method was applied to over 4,000 individuals heterozygous for 871 missense or in-frame insertion/deletion variants in KCNH2 and validated against a separate international cohort of 933 individuals heterozygous for 266 missense or in-frame insertion/deletion variants. Results - Our method was well-calibrated for the observed fraction of heterozygotes diagnosed with Long QT. Heuristically, we found that the innate diagnostic information one learns about a variant from three-dimensional variant location, in vitro functional data, and in silico predictors is equivalent to the diagnostic information one learns about that same variant by clinically phenotyping 10 heterozygotes. Most importantly, these data can be obtained in the absence of any clinical observations. Conclusions - We show how variant-specific features can inform a prior probability of disease for rare variants even in the absence of clinically-phenotyped heterozygotes.

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A Bayesian method to estimate variant-induced disease penetrance

Cited by 13 publications

References 41 publications

Continuous Bayesian Variant Interpretation Accounts for Incomplete Penetrance among Mendelian Cardiac Channelopathies

Continuous Bayesian Variant Interpretation Accounts for Incomplete Penetrance among Mendelian Cardiac Channelopathies

How Functional Genomics Can Keep Pace With VUS Identification

Estimating the Posttest Probability of Long QT Syndrome Diagnosis for Rare KCNH2 Variants

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