scholarly journals Data-driven modelling of mutational hotspots and in-silico predictors in hypertrophic cardiomyopathy

2019 ◽  
Author(s):  
A.J. Waring ◽  
A.R. Harper ◽  
S. Salatino ◽  
C.M. Kramer ◽  
S Neubauer ◽  
...  
Keyword(s):  
Hypertrophic Cardiomyopathy ◽  
Rare Variant ◽  
In Silico ◽  
Missense Variant ◽  
Data Driven ◽  
Disease Genes ◽  
Mendelian Disease ◽  
Gene Association ◽  
Missense Variants ◽  
Mutational Hotspots

ABSTRACTBackgroundAlthough rare-missense variants in Mendelian disease-genes have been noted to cluster in specific regions of proteins, it is not clear how to consider this information when evaluating the pathogenicity of a gene or variant. Here we introduce methods for gene-association and variant-interpretation that utilise this powerful signal.MethodsWe present a case-control rare-variant association test, ClusterBurden, that combines information on both variant-burden and variant-clustering. We then introduce a data-driven modelling framework to estimate mutational hotspots in genes with missense variant-clustering and integrate further in-silico predictors into the models.ResultsWe show that ClusterBurden can increase statistical power to scan for putative disease-genes, driven by missense variants, in simulated data and a 34-gene panel dataset of 5,338 cases of hypertrophic cardiomyopathy. We demonstrate that data-driven models can allow quantitative application of the ACMG criteria PM1 and PP3, to resolve a wide range of pathogenicity potential amongst variants of uncertain significance. A web application (Pathogenicity_by_Position) is accessible for missense variant risk prediction of six sarcomeric genes and an R package is available for association testing using ClusterBurden.ConclusionThe inclusion of missense residue position enhances the power of disease-gene association and improves rare-variant pathogenicity interpretation.

scholarly journals Data-driven modelling of mutational hotspots and in silico predictors in hypertrophic cardiomyopathy

2020 ◽  
pp. jmedgenet-2020-106922
Author(s):  
Adam Waring ◽  
Andrew Harper ◽  
Silvia Salatino ◽  
Christopher Kramer ◽  
Stefan Neubauer ◽  
...  
Keyword(s):  
Hypertrophic Cardiomyopathy ◽  
In Silico ◽  
Synthetic Data ◽  
Missense Variant ◽  
Statistical Modelling ◽  
Disease Genes ◽  
Mendelian Disease ◽  
Modelling Framework ◽  
Mutational Hotspots ◽  
Anderson Darling

BackgroundAlthough rare missense variants in Mendelian disease genes often cluster in specific regions of proteins, it is unclear how to consider this when evaluating the pathogenicity of a gene or variant. Here we introduce methods for gene association and variant interpretation that use this powerful signal.MethodsWe present statistical methods to detect missense variant clustering (BIN-test) combined with burden information (ClusterBurden). We introduce a flexible generalised additive modelling (GAM) framework to identify mutational hotspots using burden and clustering information (hotspot model) and supplemented by in silico predictors (hotspot+ model). The methods were applied to synthetic data and a case–control dataset, comprising 5338 hypertrophic cardiomyopathy patients and 125 748 population reference samples over 34 putative cardiomyopathy genes.ResultsIn simulations, the BIN-test was almost twice as powerful as the Anderson-Darling or Kolmogorov-Smirnov tests; ClusterBurden was computationally faster and more powerful than alternative position-informed methods. For 6/8 sarcomeric genes with strong clustering, Clusterburden showed enhanced power over burden-alone, equivalent to increasing the sample size by 50%. Hotspot+ models that combine burden, clustering and in silico predictors outperform generic pathogenicity predictors and effectively integrate ACMG criteria PM1 and PP3 to yield strong or moderate evidence of pathogenicity for 31.8% of examined variants of uncertain significance.ConclusionGAMs represent a unified statistical modelling framework to combine burden, clustering and functional information. Hotspot models can refine maps of regional burden and hotspot+ models can be powerful predictors of variant pathogenicity. The BIN-test is a fast powerful approach to detect missense variant clustering that when combined with burden information (ClusterBurden) may enhance disease-gene discovery.

scholarly journals A combined in silico, in vitro and clinical approach to characterise novel pathogenic missense variants in PRPF31 in retinitis pigmentosa

2018 ◽  
Author(s):  
Gabrielle Wheway ◽  
Liliya Nazlamova ◽  
Nervine Meshad ◽  
Samantha Hunt ◽  
Nicola Jackson ◽  
...  
Keyword(s):  
Retinitis Pigmentosa ◽  
In Silico ◽  
Autosomal Dominant ◽  
Missense Variant ◽  
Incomplete Penetrance ◽  
Clinical Approach ◽  
Loss Of Function ◽  
Autosomal Dominant Retinitis Pigmentosa ◽  
Missense Variants

AbstractAt least six different proteins of the spliceosome, including PRPF3, PRPF4, PRPF6, PRPF8, PRPF31 and SNRNP200, are mutated in autosomal dominant retinitis pigmentosa (adRP). These proteins have recently been shown to localise to the base of the connecting cilium of the retinal photoreceptor cells, elucidating this form of RP as a retinal ciliopathy. In the case of loss-of-function variants in these genes, pathogenicity can easily be ascribed. In the case of missense variants, this is more challenging. Furthermore, the exact molecular mechanism of disease in this form of RP remains poorly understood.In this paper we take advantage of the recently published cryo EM-resolved structure of the entire human spliceosome, to predict the effect of a novel missense variant in one component of the spliceosome; PRPF31, found in a patient attending the genetics eye clinic at Bristol Eye Hospital. Monoallelic variants in PRPF31 are a common cause of autosomal dominant retinitis pigmentosa (adRP) with incomplete penetrance. We use in vitro studies to confirm pathogenicity of this novel variant PRPF31 c.341T>A, p.Ile114Asn.This work demonstrates how in silico modelling of structural effects of missense variants on cryo-EM resolved protein complexes can contribute to predicting pathogenicity of novel variants, in combination with in vitro and clinical studies. It is currently a considerable challenge to assign pathogenic status to missense variants in these proteins.

scholarly journals In Silico Analysis of Novel Titin Non-Synonymous Missense Variants Detected by Targeted Next Generation Sequencing in a Cohort of Romanian Index Patients with Hypertrophic Cardiomyopathy

2021 ◽  
Vol 31 (3) ◽  
pp. 565-571
Author(s):  
Miruna Mihaela MICHEU ◽  
◽  
Nicoleta OPRESCU ◽  
Nicoleta-Monica POPA-FOTEA ◽  
◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Hypertrophic Cardiomyopathy ◽  
In Silico ◽  
Sequence Variants ◽  
The Novel ◽  
Next Generation ◽  
Vast Number ◽  
Missense Variants ◽  
Targeted Next Generation Sequencing ◽  
Generation Sequencing

Background and aim: Most of detected variants in cardiogenetic panels are still classified as variants of unknown significance, requiring supplementary analyses for a defi nite classifi cation. Performing further in-depth studies on such vast number of candidates is unfeasible. We sought to prioritise the novel nonsynonymous missense variants identified in titin gene (TTN) in a cohort of Romanian index cases with hypertrophic cardiomyopathy (HCM). Methods: 45 unrelated probands with HCM were screened by targeted next generation sequencing (NGS) covering all TTN exons. A stepwise strategy was used to select and prioritize the candidate variants for subsequent investigation. Results: Using rigorous bioinformatic filtering, 7 novel TTN nonsynonymous missense variants were identified and were the subject of in silico sequential analysis. 4 of the 7 variants were predicted to be possibly pathogenic by the Mendelian Clinically Applicable Pathogenicity (M-CAP) algorithm. Of these, three sequence variants (c.30392G>T, c.2518G>T, and c.49G>T) were also predicted to be destabilizing according to the second computational tool (TITINdb) and were designated as likely function-impacting. Conclusions: Herein we presented our strategy to hand-pick the novel TTN missense variants to be considered for further experimental studies. By applying various in silico tools, we restricted the list of sequence variants to be investigated to those most likely to be disease-associated, and thus reducing the need to perform expensive and time-consuming additional studies.

Tumour characteristics provide evidence for germline mismatch repair missense variant pathogenicity

2019 ◽  
Vol 57 (1) ◽  
pp. 62-69 ◽  
Author(s):  
Shuwei Li ◽  
Dajun Qian ◽  
Bryony A Thompson ◽  
Stephanie Gutierrez ◽  
Sitao Wu ◽  
...  
Keyword(s):  
Mismatch Repair ◽  
In Silico ◽  
Missense Variant ◽  
High Yield ◽  
Likelihood Ratios ◽  
Family History Information ◽  
Quantitative Evidence ◽  
Missense Variants ◽  
Mmr Genes ◽  
Tumour Characteristics

BackgroundPathogenic variants in mismatch repair (MMR) genes (MLH1, MSH2, MSH6 and PMS2) increase risk for Lynch syndrome and related cancers. We quantified tumour characteristics to assess variant pathogenicity for germline MMR genes.MethodsAmong 4740 patients with cancer with microsatellite instability (MSI) and immunohistochemical (IHC) results, we tested MMR pathogenic variant association with MSI/IHC status, and estimated likelihood ratios which we used to compute a tumour characteristic likelihood ratio (TCLR) for each variant. Predictive performance of TCLR in combination with in silico predictors, and a multifactorial variant prediction (MVP) model that included allele frequency, co-occurrence, co-segregation, and clinical and family history information was assessed.ResultsCompared with non-carriers, carriers of germline pathogenic/likely pathogenic (P/LP) variants were more likely to have abnormal MSI/IHC status (p<0.0001). Among 150 classified missense variants, 73.3% were accurately predicted with TCLR alone. Models leveraging in silico scores as prior probabilities accurately classified >76.7% variants. Adding TCLR as quantitative evidence in an MVP model (MVP +TCLRPred) increased the proportion of accurately classified variants from 88.0% (MVP alone) to 98.0% and generated optimal performance statistics among all models tested. Importantly, MVP +TCLRPred resulted in the high yield of predicted classifications for missense variants of unknown significance (VUS); among 193 VUS, 62.7% were predicted as P/PL or benign/likely benign (B/LB) when assessed according to American College of Medical Genetics and Genomics/Association for Molecular Pathology guidelines.ConclusionOur study demonstrates that when used separately or in conjunction with other evidence, tumour characteristics provide evidence for germline MMR missense variant assessment, which may have important implications for genetic testing and clinical management.

An Update of In Silico Tools for the Prediction of Pathogenesis in Missense Variants

2011 ◽  
Vol 6 (2) ◽  
pp. 185-198
Author(s):  
Alejandro j. Brea-Fernandez ◽  
Marta Ferro ◽  
Ceres Fernandez-Rozadilla ◽  
Ana Blanco ◽  
Laura Fachal ◽  
...  
Keyword(s):  
In Silico ◽  
Missense Variants ◽  
In Silico Tools

scholarly journals Multiple disease genes cause hypertrophic cardiomyopathy

Heart ◽  
1994 ◽  
Vol 72 (6 Suppl) ◽  
pp. S4-S9 ◽  
Author(s):  
H. Watkins
Keyword(s):  
Hypertrophic Cardiomyopathy ◽  
Disease Genes

scholarly journals Common Treatment, Common Variant: Evolutionary Prediction of Functional Pharmacogenomic Variants

2021 ◽  
Vol 11 (2) ◽  
pp. 131
Author(s):  
Laura B. Scheinfeldt ◽  
Andrew Brangan ◽  
Dara M. Kusic ◽  
Sudhir Kumar ◽  
Neda Gharani
Keyword(s):  
In Silico ◽  
Drug Efficacy ◽  
Common Variant ◽  
Genomic Research ◽  
Whole Genome Sequencing Data ◽  
Mendelian Disease ◽  
Allele Frequency Distribution ◽  
Sequencing Data ◽  
Patient Race ◽  
The Impact

Pharmacogenomics holds the promise of personalized drug efficacy optimization and drug toxicity minimization. Much of the research conducted to date, however, suffers from an ascertainment bias towards European participants. Here, we leverage publicly available, whole genome sequencing data collected from global populations, evolutionary characteristics, and annotated protein features to construct a new in silico machine learning pharmacogenetic identification method called XGB-PGX. When applied to pharmacogenetic data, XGB-PGX outperformed all existing prediction methods and identified over 2000 new pharmacogenetic variants. While there are modest pharmacogenetic allele frequency distribution differences across global population samples, the most striking distinction is between the relatively rare putatively neutral pharmacogene variants and the relatively common established and newly predicted functional pharamacogenetic variants. Our findings therefore support a focus on individual patient pharmacogenetic testing rather than on clinical presumptions about patient race, ethnicity, or ancestral geographic residence. We further encourage more attention be given to the impact of common variation on drug response and propose a new ‘common treatment, common variant’ perspective for pharmacogenetic prediction that is distinct from the types of variation that underlie complex and Mendelian disease. XGB-PGX has identified many new pharmacovariants that are present across all global communities; however, communities that have been underrepresented in genomic research are likely to benefit the most from XGB-PGX’s in silico predictions.

Rare, Damaging DNA Variants in CORIN and Risk of Coronary Artery Disease: Insights From Functional Genomics and Large-Scale Sequencing Analyses

2021 ◽  
Author(s):  
Minxian Wang ◽  
Vivian S. Lee-Kim ◽  
Deepak S. Atri ◽  
Nadine H. Elowe ◽  
John Yu ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Association Analysis ◽  
Rare Variant ◽  
Odds Ratio ◽  
Large Scale ◽  
Missense Mutations ◽  
Rare Variant Association ◽  
Missense Variants ◽  
Artery Disease

Background: Corin is a protease expressed in cardiomyocytes that plays a key role in salt handling and intravascular volume homeostasis via activation of natriuretic peptides. It is unknown if Corin loss-of-function (LOF) is causally associated with risk of coronary artery disease (CAD). Methods: We analyzed all coding CORIN variants in an Italian case-control study of CAD. We functionally tested all 64 rare missense mutations in Western Blot and Mass Spectroscopy assays for proatrial natriuretic peptide cleavage. An expanded rare variant association analysis for Corin LOF mutations was conducted in whole exome sequencing data from 37 799 CAD cases and 212 184 controls. Results: We observed LOF variants in CORIN in 8 of 1803 (0.4%) CAD cases versus 0 of 1725 controls ( P , 0.007). Of 64 rare missense variants profiled, 21 (33%) demonstrated <30% of wild-type activity and were deemed damaging in the 2 functional assays for Corin activity. In a rare variant association study that aggregated rare LOF and functionally validated damaging missense variants from the Italian study, we observed no association with CAD—21 of 1803 CAD cases versus 12 of 1725 controls with adjusted odds ratio of 1.61 ([95% CI, 0.79–3.29]; P =0.17). In the expanded sequencing dataset, there was no relationship between rare LOF variants with CAD was also observed (odds ratio, 1.15 [95% CI, 0.89–1.49]; P =0.30). Consistent with the genetic analysis, we observed no relationship between circulating Corin concentrations with incident CAD events among 4744 participants of a prospective cohort study—sex-stratified hazard ratio per SD increment of 0.96 ([95% CI, 0.87–1.07], P =0.48). Conclusions: Functional testing of missense mutations improved the accuracy of rare variant association analysis. Despite compelling pathophysiology and a preliminary observation suggesting association, we observed no relationship between rare damaging variants in CORIN or circulating Corin concentrations with risk of CAD.

scholarly journals Rare variant pathogenicity triage and inclusion of synonymous variants improves analysis of disease associations

2018 ◽  
Author(s):  
Ridge Dershem ◽  
Raghu P.R. Metpally ◽  
Kirk Jeffreys ◽  
Sarathbabu Krishnamurthy ◽  
Diane T. Smelser ◽  
...  
Keyword(s):  
Rare Variant ◽  
Functional Characterization ◽  
Data Sets ◽  
Common Variants ◽  
Loss Of Function ◽  
Altered Expression ◽  
Missense Variants ◽  
Association Testing ◽  
Disease Associations ◽  
Causes Of Disease

AbstractMany G protein-coupled receptors (GPCRs) lack common variants that lead to reproducible genome-wide disease associations. Here we used rare variant approaches to assess the disease associations of 85 orphan or understudied GPCRs in an unselected cohort of 51,289 individuals. Rare loss-of-function variants, missense variants predicted to be pathogenic or likely pathogenic, and a subset of rare synonymous variants were used as independent data sets for sequence kernel association testing (SKAT). Strong, phenome-wide disease associations shared by two or more variant categories were found for 39% of the GPCRs. Validating the bioinformatics and SKAT analyses, functional characterization of rare missense and synonymous variants of GPR39, a Family A GPCR, showed altered expression and/or Zn2+-mediated signaling for members of both variant classes. Results support the utility of rare variant analyses for identifying disease associations for genes that lack common variants, while also highlighting the functional importance of rare synonymous variants.Author summaryRare variant approaches have emerged as a viable way to identify disease associations for genes without clinically important common variants. Rare synonymous variants are generally considered benign. We demonstrate that rare synonymous variants represent a potentially important dataset for deriving disease associations, here applied to analysis of a set of orphan or understudied GPCRs. Synonymous variants yielded disease associations in common with loss-of-function or missense variants in the same gene. We rationalize their associations with disease by confirming their impact on expression and agonist activation of a representative example, GPR39. This study highlights the importance of rare synonymous variants in human physiology, and argues for their routine inclusion in any comprehensive analysis of genomic variants as potential causes of disease.

Sign in / Sign up

Export Citation Format

Share Document