INTEGRATED IN SILICO AND EXPERIMENTAL ASSESSMENT OF DISEASE‐RELEVANCE OF PROTOCADHERIN 19 ( PCDH19 ) MISSENSE VARIANTS

2021 ◽  
Author(s):  
Duyen H. Pham ◽  
Melissa R. Pitman ◽  
Raman Kumar ◽  
Lachlan Jolly ◽  
Renee Schulz ◽  
...  
Keyword(s):  
In Silico ◽  
Experimental Assessment ◽  
Missense Variants ◽  
Disease Relevance

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 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 Comparison of Predictive In Silico Tools on Missense Variants in GJB2, GJB6, and GJB3 Genes Associated with Autosomal Recessive Deafness 1A (DFNB1A)

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Vera G. Pshennikova ◽  
Nikolay A. Barashkov ◽  
Georgii P. Romanov ◽  
Fedor M. Teryutin ◽  
Aisen V. Solov’ev ◽  
...  
Keyword(s):  
Clinical Significance ◽  
In Silico ◽  
Autosomal Recessive ◽  
Area Under The Curve ◽  
Roc Curves ◽  
Missense Variants ◽  
Functional Studies ◽  
Connexin Genes ◽  
Predictive Software ◽  
And Control

In silico predictive software allows assessing the effect of amino acid substitutions on the structure or function of a protein without conducting functional studies. The accuracy of in silico pathogenicity prediction tools has not been previously assessed for variants associated with autosomal recessive deafness 1A (DFNB1A). Here, we identify in silico tools with the most accurate clinical significance predictions for missense variants of the GJB2 (Cx26), GJB6 (Cx30), and GJB3 (Cx31) connexin genes associated with DFNB1A. To evaluate accuracy of selected in silico tools (SIFT, FATHMM, MutationAssessor, PolyPhen-2, CONDEL, MutationTaster, MutPred, Align GVGD, and PROVEAN), we tested nine missense variants with previously confirmed clinical significance in a large cohort of deaf patients and control groups from the Sakha Republic (Eastern Siberia, Russia): Сх26: p.Val27Ile, p.Met34Thr, p.Val37Ile, p.Leu90Pro, p.Glu114Gly, p.Thr123Asn, and p.Val153Ile; Cx30: p.Glu101Lys; Cx31: p.Ala194Thr. We compared the performance of the in silico tools (accuracy, sensitivity, and specificity) by using the missense variants in GJB2 (Cx26), GJB6 (Cx30), and GJB3 (Cx31) genes associated with DFNB1A. The correlation coefficient (r) and coefficient of the area under the Receiver Operating Characteristic (ROC) curve as alternative quality indicators of the tested programs were used. The resulting ROC curves demonstrated that the largest coefficient of the area under the curve was provided by three programs: SIFT (AUC = 0.833, p = 0.046), PROVEAN (AUC = 0.833, p = 0.046), and MutationAssessor (AUC = 0.833, p = 0.002). The most accurate predictions were given by two tested programs: SIFT and PROVEAN (Ac = 89%, Se = 67%, Sp = 100%, r = 0.75, AUC = 0.833). The results of this study may be applicable for analysis of novel missense variants of the GJB2 (Cx26), GJB6 (Cx30), and GJB3 (Cx31) connexin genes.

scholarly journals Performance of in silico prediction tools for the classification of rare BRCA1/2 missense variants in clinical diagnostics

2018 ◽  
Vol 11 (1) ◽  
Author(s):  
Corinna Ernst ◽  
Eric Hahnen ◽  
Christoph Engel ◽  
Michael Nothnagel ◽  
Jonas Weber ◽  
...  
Keyword(s):  
In Silico ◽  
Clinical Diagnostics ◽  
In Silico Prediction ◽  
Missense Variants ◽  
Prediction Tools

scholarly journals Breast cancer risks associated with missense variants in breast cancer susceptibility genes

2021 ◽  
Author(s):  
Leila Dorling ◽  
Sara Carvalho ◽  
Jamie Allen ◽  
Michael Parsons ◽  
Cristina Fortuno ◽  
...  
Keyword(s):  
Breast Cancer ◽  
In Silico ◽  
Cancer Susceptibility ◽  
Breast Cancer Susceptibility ◽  
Cancer Risks ◽  
Functional Protein ◽  
Missense Variants ◽  
Cancer Susceptibility Genes ◽  
Gene Panel Testing ◽  
Brca1 Brca2

BACKGROUND Protein truncating variants in ATM, BRCA1, BRCA2, CHEK2 and PALB2 are associated with increased breast cancer risk, but risks associated with missense variants in these genes are uncertain. METHODS Combining 59,639 breast cancer cases and 53,165 controls, we sampled training (80%) and validation (20%) sets to analyze rare missense variants in ATM (1,146 training variants), BRCA1 (644), BRCA2 (1,425), CHEK2 (325) and PALB2 (472). We evaluated breast cancer risks according to five in-silico prediction-of-deleteriousness algorithms, functional protein domain, and frequency, using logistic regression models and also mixture models in which a subset of variants was assumed to be risk-associated. RESULTS The most predictive in-silico algorithms were Helix (BRCA1, BRCA2 and CHEK2) and CADD (ATM). Increased risks appeared restricted to functional protein domains for ATM (FAT and PIK domains) and BRCA1 (RING and BRCT domains). For ATM, BRCA1 and BRCA2, data were compatible with small subsets (approximately 7%, 2% and 0.6%, respectively) of rare missense variants giving similar risk to those of protein truncating variants in the same gene. For CHEK2, data were more consistent with a large fraction (approximately 60%) of rare missense variants giving a lower risk (OR 1.75, 95% CI (1.47-2.08)) than CHEK2 protein truncating variants. There was little evidence for an association with risk for missense variants in PALB2. The best fitting models were well calibrated in the validation set. CONCLUSIONS These results will inform risk prediction models and the selection of candidate variants for functional assays, and could contribute to the clinical reporting of gene panel testing for breast cancer susceptibility.

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 Missense variants causing Wiedemann-Steiner syndrome preferentially occur in the KMT2A-CXXC domain and are accurately classified using AlphaFold2

2022 ◽  
Author(s):  
Tinna Reynisdottir ◽  
Kimberley Anderson ◽  
Leandros Boukas ◽  
Hans Bjornsson
Keyword(s):  
In Silico ◽  
Hippocampal Neurons ◽  
De Novo ◽  
Neurodevelopmental Disorder ◽  
Classification Performance ◽  
Saturation Mutagenesis ◽  
Biological Knowledge ◽  
Missense Variants ◽  
Mechanistic Basis ◽  
Insight Into

Wiedemann-Steiner syndrome (WSS) is a neurodevelopmental disorder caused by de novo variants in KMT2A, which encodes a multi–domain histone methyltransferase. To gain insight into the currently unknown pathogenesis of WSS, we examined the spatial distribution of likely WSS–causing variants across the 15 different domains of KMT2A. Compared to variants in healthy controls, WSS variants exhibit a 64.1–fold overrepresentation within the CXXC domain – which mediates binding to unmethylated CpGs – suggesting a major role for this domain in mediating the phenotype. In contrast, we find no significant overrepresentation within the catalytic SET domain. Corroborating these results, we find that hippocampal neurons from Kmt2a–deficient mice demonstrate disrupted H3K4me1 preferentially at CpG-rich regions, but this has no systematic impact on gene expression. Motivated by these results, we combine accurate prediction of the CXXC domain structure by AlphaFold2 with prior biological knowledge to develop a classification scheme for missense variants in the CXXC domain. Our classifier achieved 96.0% positive and 92.3% negative predictive value on a hold–out test set. This classification performance enabled us to subsequently perform an in silico saturation mutagenesis and classify a total of 445 variants according to their functional effects. Our results yield a novel insight into the mechanistic basis of WSS and provide an example of how AlphaFold2 can contribute to the in silico characterization of variant effects with very high accuracy, establishing a paradigm potentially applicable to many other Mendelian disorders.

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