Analysis of missense variants in the human genome reveals widespread gene-specific clustering and improves prediction of pathogenicity

Quinodoz, Mathieu; Peter, Virginie G.; Cisarova, Katarina; Royer‐Bertrand, Béryl; Stenson, Peter D.; Cooper, David Neil; Unger, Sheila; Superti‐Furga, Andrea; Rivolta, Carlo

doi:10.1016/j.ajhg.2022.01.006

Cited by 41 publications

(29 citation statements)

References 81 publications

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“…In terms of missing value, where partly pre-existing tools fail to predict, some meta-predictors impute them using deleterious/neutral threshold ( Capriotti et al, 2013 ), average score ( Kircher et al, 2014 ; Quang et al, 2015 ), fixed score ( Quinodoz et al, 2022 ), the maximal pathogenic score ( Jagadeesh et al, 2016 ), or a flexible imputation using average value of k-nearest neighbors ( Ioannidis et al, 2016 ) and Bayesian principle component analysis (BPCA) ( Dong et al, 2015 ). There is currently no gold standard for imputation.…”

Section: Various Variant Predictorsmentioning

confidence: 99%

“…Therefore, they defined the “Type 2 circularity” as the circumstance in which all variants from the same gene are jointly labeled as pathogenic or neutral. To address this problem, it was suggested to use datasets with an appropriate pathogenic-to-neutral ratio and avoid genes with exclusive pathogenic or neutral variations when reporting performance ( Bu et al, 2022 ; Quinodoz et al, 2022 ).…”

Section: Various Variant Predictorsmentioning

confidence: 99%

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Computational approaches for predicting variant impact: An overview from resources, principles to applications

et al. 2022

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One objective of human genetics is to unveil the variants that contribute to human diseases. With the rapid development and wide use of next-generation sequencing (NGS), massive genomic sequence data have been created, making personal genetic information available. Conventional experimental evidence is critical in establishing the relationship between sequence variants and phenotype but with low efficiency. Due to the lack of comprehensive databases and resources which present clinical and experimental evidence on genotype-phenotype relationship, as well as accumulating variants found from NGS, different computational tools that can predict the impact of the variants on phenotype have been greatly developed to bridge the gap. In this review, we present a brief introduction and discussion about the computational approaches for variant impact prediction. Following an innovative manner, we mainly focus on approaches for non-synonymous variants (nsSNVs) impact prediction and categorize them into six classes. Their underlying rationale and constraints, together with the concerns and remedies raised from comparative studies are discussed. We also present how the predictive approaches employed in different research. Although diverse constraints exist, the computational predictive approaches are indispensable in exploring genotype-phenotype relationship.

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Section: Various Variant Predictorsmentioning

confidence: 99%

Section: Various Variant Predictorsmentioning

confidence: 99%

Computational approaches for predicting variant impact: An overview from resources, principles to applications

et al. 2022

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“…Methods citations (Adzhubei et al 2010 ; Alirezaie et al 2018 ; Baugh et al 2016 ; Bendl et al 2014 , 2016 ; Bromberg and Rost 2007 ; Calabrese et al 2009 ; Capriotti et al 2013 , 2006 ; Carter et al 2013 ; Chennen et al 2020 ; Choi et al 2012 ; Chun and Fay 2009 ; Dong et al 2015 ; Gonzalez-Perez and Lopez-Bigas 2011 ; Gray et al 2018 ; Gulko et al 2015 ; Hecht et al 2015 ; Ioannidis et al 2016 ; Ionita-Laza et al 2016 ; Jagadeesh et al 2016 ; Katsonis and Lichtarge 2014 ; Kircher et al 2014 ; Li et al 2009 , 2020 ; Munro and Singh 2020 ; Ng and Henikoff 2001 , 2003 ; Niroula et al 2015 ; Olatubosun et al 2012 ; Pei et al 2020 ; Pejaver et al 2020 ; Ponzoni et al 2020 ; Qi et al 2021 ; Quang et al 2015 ; Quinodoz et al 2022 ; Raimondi et al 2016 , 2017 ; Ramensky et al 2002 ; Reva et al 2011 ; Rogers et al 2018 ; Samocha et al 2017 ; Schwarz et al 2010 ; Shihab et al 2013 , 2014 , 2015 ; Stone and Sidow 2005 ; Sundaram et al 2018 ; Takeda et al 2020 ; Tavtigian et al 2006 ; Thomas et al 2003 ; Vaser et al 2016 ; Yue et al 2005 ...…”

Section: Introductionunclassified

Genome interpretation using in silico predictors of variant impact

et al. 2022

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Estimating the effects of variants found in disease driver genes opens the door to personalized therapeutic opportunities. Clinical associations and laboratory experiments can only characterize a tiny fraction of all the available variants, leaving the majority as variants of unknown significance (VUS). In silico methods bridge this gap by providing instant estimates on a large scale, most often based on the numerous genetic differences between species. Despite concerns that these methods may lack reliability in individual subjects, their numerous practical applications over cohorts suggest they are already helpful and have a role to play in genome interpretation when used at the proper scale and context. In this review, we aim to gain insights into the training and validation of these variant effect predicting methods and illustrate representative types of experimental and clinical applications. Objective performance assessments using various datasets that are not yet published indicate the strengths and limitations of each method. These show that cautious use of in silico variant impact predictors is essential for addressing genome interpretation challenges.

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“…NSD1 is characterized by low tolerance to LoF variants (pLI = 1). On the other hand, missense variants seem to be constrained to a lower extent (Z = 3.41), with pathogenic changes showing a clear positional clustering [ 8 , 9 ]. Among the 519 missense/splice site variants reported in gnomAD ( ; accessed on 5 October 2022), 396 are classified as variant of unknown significance (VoUS) or have a conflicting interpretation in ClinVar ( (accessed on 5 October 2022)).…”

Section: Introductionmentioning

confidence: 99%

“…Among the 519 missense/splice site variants reported in gnomAD ( ; accessed on 5 October 2022), 396 are classified as variant of unknown significance (VoUS) or have a conflicting interpretation in ClinVar ( (accessed on 5 October 2022)). The pathogenic missense variants largely map within functional domains implicated in chromatin regulation at the 3′ of the gene (i.e., PHD, SAC, and SET domains), and predominantly occur at the consensus cysteine and histidine residues that define these domains, with few exceptions [ 4 , 9 ]. The uncertainty of an increasing number of NSD1 variants that are routinely revealed by massive parallel sequencing in the diagnostic setting is generally solved by segregation analysis, or might require dedicated functional validation, which is further more relevant in patients with atypical or incomplete presentation of the disorder.…”

Section: Introductionmentioning

confidence: 99%

Genome-Wide DNA Methylation Profiling Solves Uncertainty in Classifying NSD1 Variants

Ferilli

Ciolfi

Pedace

et al. 2022

Genes

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Background: Inactivating NSD1 mutations causing Sotos syndrome have been previously associated with a specific genome-wide DNA methylation (DNAm) pattern. Sotos syndrome is characterized by phenotypic overlap with other overgrowth syndromes, and a definite diagnosis might not be easily reached due to the high prevalence of variants of unknown significance (VoUS) that are identified in patients with a suggestive phenotype. Objective: we performed microarray DNAm profiling in a set of 11 individuals with a clinical suspicion of Sotos syndrome and carrying an NSD1 VoUS or previously unreported variants to solve uncertainty in defining pathogenicity of the observed variants. The impact of the training cohort size on sensitivity and prediction confidence of the classifier was assessed. Results: The Sotos syndrome-specific DNAm signature was validated in six individuals with a clinical diagnosis of Sotos syndrome and carrying bona fide pathogenic NSD1 variants. Applying this approach to the remaining 11 individuals with NSD1 variants, we succeeded in confirming pathogenicity in eight subjects and excluding the diagnosis of Sotos syndrome in three. The sensitivity and prediction confidence of the classifier based on the different sizes of the training sets did not show substantial differences, though the overall performance was improved by oversampling. Conclusions: The present approach solved uncertainty in cases with NDS1 VoUS, further demonstrating the clinical utility of DNAm profiling.

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Analysis of missense variants in the human genome reveals widespread gene-specific clustering and improves prediction of pathogenicity

Cited by 41 publications

References 81 publications

Computational approaches for predicting variant impact: An overview from resources, principles to applications

Computational approaches for predicting variant impact: An overview from resources, principles to applications

Genome interpretation using in silico predictors of variant impact

Genome-Wide DNA Methylation Profiling Solves Uncertainty in Classifying NSD1 Variants

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