| Literature DB >> 34927718 |
Dominique P Germain1,2, Thierry Levade3,4, Eric Hachulla5, Bertrand Knebelmann6, Didier Lacombe7,8, Vanessa Leguy Seguin9, Karine Nguyen10, Esther Noël11, Jean-Pierre Rabès2,12.
Abstract
Fabry disease (FD) is an X-linked genetic disease due to pathogenic variants in GLA. The phenotype varies depending on the GLA variant, alpha-galactosidase residual activity, patient's age and gender and, for females, X chromosome inactivation. Over 1000 variants have been identified, many through screening protocols more susceptible to disclose non-pathogenic variants or variants of unknown significance (VUS). This, together with the non-specificity of some FD symptoms, challenges physicians attempting to interpret GLA variants. The traditional way to interpreting pathogenicity is based on a combined approach using allele frequencies, genomic databases, global and disease-specific clinical databases, and in silico tools proposed by the American College of Medical Genetics and Genomics. Here, a panel of FD specialists convened to study how expertise may compare with the traditional approach. Several GLA VUS, highly controversial in the literature (p.Ser126Gly, p.Ala143Thr, p.Asp313Tyr), were re-analyzed through reviews of patients' charts. The same was done for pathogenic GLA variants with some specificities. Our data suggest that input of geneticists and physicians with wide expertise in disease phenotypes, prevalence, inheritance, biomarkers, alleles frequencies, disease-specific databases, and literature greatly contribute to a more accurate interpretation of the pathogenicity of variants, bringing a significant additional value over the traditional approach.Entities:
Keywords: ACMG criteria; Fabry disease; experts; genetic variants; pathogenicity interpretation; variants of unknown significance
Mesh:
Substances:
Year: 2021 PMID: 34927718 PMCID: PMC9304128 DOI: 10.1111/cge.14102
Source DB: PubMed Journal: Clin Genet ISSN: 0009-9163 Impact factor: 4.296
Revised prevalence of Fabry disease in various screening populations after re‐interpretation of pathogenicity
| Patient population | Previous prevalence | Revised prevalence | Absolute prevalence |
|---|---|---|---|
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| 0.03% | 0.014% | 1 in 6883 |
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| Hemodialysis | 0.33% | 0.21% | 1 in 476 |
| Renal transplant | 0.38% | 0.25% | 1 in 400 |
| Hypertrophic cardiomyopathy | 2.67% | 0.94% | 1 in 106 |
| Cryptogenic strokes | 4.23% | 0.13% | 1 in 769 |
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| Hemodialysis | 0.10% | 0.15% | 1 in 667 |
| Renal transplant | 0% | 0% | |
| Hypertrophic cardiomyopathy | 2.8% | 0.90% | 1 in 111 |
| Cryptogenic strokes | 2.13% | 0.14% | 1 in 714 |
FIGURE 1Main clinical manifestations of Fabry disease (FD)
FIGURE 2Most commonly reported types of GLA variants in the Human Gene Mutation Database (HGMD) and related Fabry disease (FD) phenotypes in hemizygous patients
Characteristics of common controversial GLA variants according to genetic databases and in silico prediction softwares
| c.427G>A; p.(Ala143Thr) / p.(A143T) / Thr143 | c.937G>T; p.(Asp313Tyr) / p.(D313Y) / Tyr313 | c.196G>C; p.(Glu66Gln) / p.(E66Q) / Gln66 | c.352C>T; p.(Arg118Cys) / p.(R118C) / Cys118 | c.376A>G; p.(Ser126Gly) / p.(S126G) / Gly126 | |
|---|---|---|---|---|---|
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| AF (%) in exomes, genomes (total) | 0.055, 0.018 (0.051) | 0.30, 0.31 (0.30) | 0.012, 0.0045 (0.011) | 0.022, 0.032 (0.023) | 0.033, 0.063 (0.036) |
| Highest AF (%) by population | 0.095 in European (non‐Finnish) |
0.69 in Ashkenazi Jewish 0.45 in European (non‐Finnish) | 0.15 in East Asian | 0.044 in European (non‐Finnish) | 0.074 in European (non‐Finnish) |
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| dbFGP | Benign | Benign | Benign | Benign | Likely benign |
| The Japanese Fabry Database | LO [5]; classic [4]; B [3]; VUS [1]; np [8] | Classic [5]; B [2]; LO [1]; np [9] | B [5]; classic [5]; LO [3]; np [3] | LO [2]; np [5] | LO [1]; np [6] |
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| ClinVar | VUS [10]; LP [4]; P [2] | LB [13]; VUS [3]; B [2] | VUS [4]; LB [2] | VUS [12]; LP [2]; LB [1] | LB [6]; VUS [4]; B [1] |
| LOVD | LB [2]; VUS [1] | LB [3]; B [2]; VUS [1] | np | VUS [3]; P [1] | 2 LB [2]; VUS [1] |
| OMIM | FD | VUS (recently reclassified) | Functional polymorphism and not disease causing | not provided | not provided |
| ACMG classification according to VarSome (date of query) |
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| Polyphen‐2 | Probably damaging (1) | Probably damaging (0.996) | Probably damaging (0.996) | Probably damaging (0.993) | Benign (0.043) |
| Provean | Deleterious (−3.119) | Deleterious (−3.183) | Deleterious (−2.754) | Deleterious (−4.667) | Deleterious (−2.823) |
| SIFT | Damaging (0.004) | Damaging (0.001) | Damaging (0.002) | Damaging (0.001) | Tolerated (0.060) |
| Mutation taster | Disease causing | Polymorphism | Disease causing | Polymorphism | Disease causing |
Note: Last accessed 2020‐08‐04. [ ]: The number of times referenced.
Abbreviations: AF, allele frequency; B, benign; dbFGP, International Fabry Disease Genotype–Phenotype Database; FD, Fabry disease; gnomAD, Genome Aggregation Database; LB, likely benign; LOVD, Leiden Open (source) Variation Database; LP, likely pathogenic; np, not provided; P, pathogenic; VUS, variant of unknown significance.
Available databases and in silico prediction softwares
| Name | Description |
|---|---|
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| |
| Genome Aggregation Database (gnomAD) | Includes aggregated exome and genome sequencing data. Useful to compare the frequency of a variant in the general population or in an ethnic group against the disease prevalence or against the frequency of the most common pathogenic variant. A |
| ClinVar | Collects evidence‐supported interpretations of clinical significance of variants for FD submitted by clinical testing laboratories, researchers, other databases. |
| Leiden Open (source) Variation Database (LOVD) | Displays gene variants. |
| OMIM® (Online Mendelian Inheritance in Man) | Contains information on all known Mendelian disorders and over 15,000 genes, focusing on the relationship between phenotype and genotype. |
| International Fabry Disease Genotype‐Phenotype Database (dbFGP) | Combines data from databases such as the Human Gene Mutation Database (HGMD) and The Japanese Fabry Database, diagnostic and clinical evaluations of patients with data from peer reviewed publications and input from expert FD researchers and care teams. The database provides information of the associated phenotype for a given variant. |
| The Japanese Fabry Database | Created by Meiji Pharmaceutical University and led by H. Sakuraba, this database lists |
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| VarSome | Annotation tool that classifies the genetic variant according to ACMG criteria. The verdict arises from a vast quantity of accurate curated data such as coding impact of the variant, in vitro functional studies, allele frequency, previous publications and other databases analyses. |
| PolyPhen‐2 | A tool which predicts whether an amino acid substitution or indel has an impact on the biological function of a protein. |
| Provean (Protein Variation Effect Analyzer) | A tool which predicts whether an amino acid substitution or indel has an impact on the biological function of a protein. |
| SIFT | A tool which predicts whether an amino acid substitution or indel has an impact on the biological function of a protein. |
| Mutation taster | A tool which predicts whether an amino acid substitution, insertion or deletion has an impact on the biological function of a protein. |
Practical recommendations for a more accurate diagnosis of Fabry disease
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Combine insights from disease specific databases and in vitro prediction tools with expert clinical opinion, defining the relative weight of each ACMG criterion. Consider the genetic variant as probably benign if its allele total or subpopulation frequency is: Higher than the overall prevalence of FD (0.0125%), Higher than the frequency of the most common pathogenic allele. Perform segregation analysis when additional information is required. Review the literature and databases periodically to check whether the variant has been reclassified. |
FIGURE 3Interpreting pathogenicity of genetic variants when facing uncertainty: lessons from Fabry disease (FD)