Genome interpretation using in silico predictors of variant impact

Katsonis, Panagiotis; Wilhelm, Kevin; Williams, Amanda; Lichtarge, Olivier

doi:10.1007/s00439-022-02457-6

Cited by 39 publications

(33 citation statements)

References 374 publications

(456 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While a plethora of in silico prediction algorithms of variant impact are available and useful for assessment of great numbers of uncharacterized variants [ 29 ], clinical decision making is still based on additional markers of pathogenicity or neutrality. The International Society for Gastrointestinal Hereditary Tumours (InSiGHT) has developed criteria for the interpretation of MMR gene variants.…”

Section: Introductionmentioning

confidence: 99%

Functional characterization of MLH1 missense variants unveils mechanisms of pathogenicity and clarifies role in cancer

et al. 2022

View full text Add to dashboard Cite

Lynch syndrome is a heritable condition caused by a heterozygous germline inactivating mutation of the DNA mismatch repair (MMR) genes, most commonly the MLH1 gene. However, one third of the identified alterations are missense variants, for which the clinical significance is unclear in many cases. We have identified three MLH1 missense alterations (p.(Glu736Lys), p.(Pro640Thr) and p.(Leu73Pro)) in six individuals from large Tunisian families. For none of these alterations, a classification of pathogenicity was available, consequently diagnosis, predictive testing and targeted surveillance in affected families was impossible. We therefore performed functional laboratory testing using a system testing stability as well as catalytic activity that includes clinically validated reference variants. Both p.(Leu73Pro) and p.(Pro640Thr) were found to be non-functional due to severe defects in protein stability and catalytic activity. In contrast, p.(Glu736Lys) was comparable to the wildtype protein and therefore considered a neutral substitution. Analysis of residue conservation and of the structural roles of the substituted residues corroborated these findings. In conjunction with the available clinical data, two variants fulfil classification criteria for class 4 “likely pathogenic”. The findings of this work clarify the mechanism of pathogenicity of two unclear MLH1 variants and enables predictive testing and targeted surveillance in members of carrier families worldwide.

show abstract

Section: Introductionmentioning

confidence: 99%

Functional characterization of MLH1 missense variants unveils mechanisms of pathogenicity and clarifies role in cancer

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Databases can be categorized according to their scope, purpose, and scale. Several reviews ( Thorisson et al, 2009 ; Brookes and Robinson, 2015 ; Zhang et al, 2019 ; Banck et al, 2021 ; Katsonis et al, 2022 ) provided comprehensive details of the content, usage, comparisons, and limitations for those databases. In this section, we briefly review the most frequently used databases ( Table 1 ) containing sequence information, population-scale data, phenotype ontology, clinical and experimental evidence.…”

Section: Database Resources For Variant Predictorsmentioning

confidence: 99%

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

et al. 2022

View full text Add to dashboard Cite

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.

show abstract

“…Advances in technologies for whole-genome or exome sequencing and high-throughput functional assays have increased our knowledge on the consequences of the genetic variability in humans and the relationship to disease ( McInnes et al, 2021 ; Arnedo-Pac et al, 2022 ; Høie et al, 2022 ; Katsonis et al, 2022 ). However, our capacity to predict the pathogenicity of single amino acid variants is still limited, with some approaches providing good overall results but failing to predict correlation for some individual mutations or phenotypes ( Katsonis et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Effect of naturally-occurring mutations on the stability and function of cancer-associated NQO1: Comparison of experiments and computation

Pacheco-García

Cagiada

Tienne-Matos

et al. 2022

Front. Mol. Biosci.

View full text Add to dashboard Cite

Recent advances in DNA sequencing technologies are revealing a large individual variability of the human genome. Our capacity to establish genotype-phenotype correlations in such large-scale is, however, limited. This task is particularly challenging due to the multifunctional nature of many proteins. Here we describe an extensive analysis of the stability and function of naturally-occurring variants (found in the COSMIC and gnomAD databases) of the cancer-associated human NAD(P)H:quinone oxidoreductase 1 (NQO1). First, we performed in silico saturation mutagenesis studies (>5,000 substitutions) aimed to identify regions in NQO1 important for stability and function. We then experimentally characterized twenty-two naturally-occurring variants in terms of protein levels during bacterial expression, solubility, thermal stability, and coenzyme binding. These studies showed a good overall correlation between experimental analysis and computational predictions; also the magnitude of the effects of the substitutions are similarly distributed in variants from the COSMIC and gnomAD databases. Outliers in these experimental-computational genotype-phenotype correlations remain, and we discuss these on the grounds and limitations of our approaches. Our work represents a further step to characterize the mutational landscape of NQO1 in the human genome and may help to improve high-throughput in silico tools for genotype-phenotype correlations in this multifunctional protein associated with disease.

show abstract

Genome interpretation using in silico predictors of variant impact

Cited by 39 publications

References 374 publications

Functional characterization of MLH1 missense variants unveils mechanisms of pathogenicity and clarifies role in cancer

Functional characterization of MLH1 missense variants unveils mechanisms of pathogenicity and clarifies role in cancer

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

Effect of naturally-occurring mutations on the stability and function of cancer-associated NQO1: Comparison of experiments and computation

Contact Info

Product

Resources

About