Improving the clinical interpretation of missense variants in X linked genes using structural analysis

Sallah, Shalaw R.; Ellingford, Jamie M; Sergouniotis, Panagiotis I.; Ramsden, Simon; Lench, Nicholas J.; Lovell, Simon C.; Black, Graeme C M

doi:10.1136/jmedgenet-2020-107404

Cited by 7 publications

(11 citation statements)

References 38 publications

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“…However, no multicollinearity problems were detected between the meta-predictors included and the amino acid prediction scores. Similar approach was used previous studies 38 . Afterwards, after the outlier detection, no extreme value could be detected that would affect the result.…”

Section: Discussionmentioning

confidence: 86%

THREE STEPS NOVEL HARD MARGIN ENSEMBLE MACHINE LEARNING METHOD CLASSIFIES UNCERTAINMEFVGENE VARIANTS

Alay

Demir²,

Kirişçi

2023

Preprint

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Introduction: The International Study Group for Systemic Autoinflammatory Diseases (INSAID) consensus criteria revealed that the clinical outcomes of more than half of the MEFV gene variants are uncertain. We aimed to detect more accurate classifications of MEFV variants while simultaneously reducing MEFV variant uncertainty. Material-Methods: We extracted variants of the MEFV gene from the infevers database. We then determined the optimal number of in silico instruments for our model. On the training dataset, we implemented seven machine learning algorithms on MEFV gene variants with known clinical effects. We evaluated the effectiveness of our model in three steps: First, we performed machine-learning algorithms on the training dataset and implemented those with a prediction accuracy of greater than 90 percent. Second, we compared our gene-level and protein-level prediction results. Finally, we compared our prediction results to clinical outcomes. Results: Our analysis included 266 of 381 MEFV gene variants and four computational tools (Revel, SIFT, MetaLR, and FATHMM). In our training dataset, the accuracy of three machine learning algorithms (RF: 100%, CRAT: 100%, and KNN: 91%) exceeded the threshold value. Thus, the dataset contained 134 likely pathogenic (LP) variants and 132 likely benign (LB) variants. We found that B30.2 domain variants were 2.5 times more likely to be LP than LB (χ2:12.693, p < 0.001, OR: 2.595 [1.532-4.132]. Discussion: Considering that the clinical effects of 60% of MEFV gene variants have not yet been determined, a combined evaluation of our methods and patients' clinical manifestations significantly simplifies the interpretation of unknown variants.

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Section: Discussionmentioning

confidence: 86%

THREE STEPS NOVEL HARD MARGIN ENSEMBLE MACHINE LEARNING METHOD CLASSIFIES UNCERTAINMEFVGENE VARIANTS

Alay

Demir²,

Kirişçi

2023

Preprint

View full text Add to dashboard Cite

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“…However, no multicollinearity problems were detected between the meta-predictors included and the amino acid prediction scores. A similar approach was used in previous studies [32]. Afterwards, after the outlier detection, no extreme value could be detected that would affect the result.…”

Section: Resultsmentioning

confidence: 96%

Three Steps Novel Machine Learning Method Classifies Uncertain MEFV Gene Variants

Alay

Demir²,

Kirişçi

2023

Preprint

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Introduction: The International Study Group for Systemic Autoinflammatory Diseases (INSAID) consensus criteria revealed that the clinical outcomes of more than half of the MEFV gene variants are uncertain. In this study, we estabilished a novel approach for more accurate classification of MEFV gene variants by using the optimal number of amino acid prediction scores and machine-learning algorithms. Our goal was to determine a more accurate classification of MEFV variants while also reducing the uncertainties. Material-Methods: We extracted variants of the MEFV gene from the infevers database ,and point mutations were included, others excluded from the study. We then determined the optimal number of in silico instruments for our model. On the training dataset, we implemented seven machine learning algorithms on MEFV gene variants with known clinical effects. We evaluated the effectiveness of our model in three steps: First, we performed machine-learning algorithms on the training dataset and implemented those with a prediction accuracy of greater than 90 percent. Second, we compared our prediction results to existing algorithms and studies. Third, we evaluated our outcomes functional and clinical level. Results We included 266 of 381 MEFV gene variants and four computational tools in a study. Our algorithm classified Likely pathogenic (LP) variants with an accuracy of 96.6% while classifying 97.6% of Likely Benign (LB) variants. Among the machine learning methods used to classify MEFV variants, our classification method yielded the most accurate results on training datasets. Most of the predictors classified LB variants with higher accuracy than 90% however, LP classification showed a wide range of variety in accuracy scores between 2% − 62.5%. Disease-causing MEFV variants are frequently located in domains. Functional and clinical level evaluation compatible with our classification results. Discussion The comparison indicated that LP variant prediction is the biggest problem in variant classification, and our method might be a candidate for solving this problem with the 96.67% accuracy. Considering that 60% of the clinical effects of MEFV gene variants are unresolved, evaluating our methods in conjunction with the clinical manifestations of patients significantly simplifies the interpretation of unknown variants

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“…In-silico pathogenicity classifiers were based on output from MetaSVM and CADD. Recent research suggests that gene-specific thresholds provide better performance for missense variants in a subset of genes (Sallah et al, 2022). MetaSVM has been shown to yield high performance in benchmark studies (Anderson and Lassmann, 2018) although, some investigations showed that it can exaggerate pathogenicity of variants in specific proteins (Zaucha et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

Interpreting the spectrum of gamma-secretase complex missense variation in the context of hidradenitis suppurativa—An in-silico study

2022

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Hidradenitis suppurativa (HS) is a disease of the pilosebaceous unit characterized by recurrent nodules, abscesses and draining tunnels with a predilection to intertriginous skin. The pathophysiology of HS is complex. However, it is known that inflammation and hyperkeratinization at the hair follicle play crucial roles in disease manifestation. Genetic and environmental factors are considered the main drivers of these two pathophysiological processes. Despite a considerable proportion of patients having a positive family history of disease, only a minority of patients suffering from HS have been found to harbor monogenic variants which segregate to affected kindreds. Most of these variants are in the ɣ secretase complex (GSC) protein-coding genes. In this manuscript, we set out to characterize the burden of missense pathogenic variants in healthy reference population using large scale genomic dataset thereby providing a standard for comparing genomic variation in GSC protein-coding genes in the HS patient cohort.

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Improving the clinical interpretation of missense variants in X linked genes using structural analysis

Cited by 7 publications

References 38 publications

THREE STEPS NOVEL HARD MARGIN ENSEMBLE MACHINE LEARNING METHOD CLASSIFIES UNCERTAINMEFVGENE VARIANTS

THREE STEPS NOVEL HARD MARGIN ENSEMBLE MACHINE LEARNING METHOD CLASSIFIES UNCERTAINMEFVGENE VARIANTS

Three Steps Novel Machine Learning Method Classifies Uncertain MEFV Gene Variants

Interpreting the spectrum of gamma-secretase complex missense variation in the context of hidradenitis suppurativa—An in-silico study

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