Dataset Information

Episignatures in practice: independent evaluation of published episignatures for the molecular diagnostics of ten neurodevelopmental disorders.

ABSTRACT: Variants of uncertain significance (VUS) are a significant issue for the molecular diagnosis of rare diseases. The publication of episignatures as effective biomarkers of certain Mendelian neurodevelopmental disorders has raised hopes to help classify VUS. However, prediction abilities of most published episignatures have not been independently investigated yet, which is a prerequisite for an informed and rigorous use in a diagnostic setting. We generated DNA methylation data from 101 carriers of (likely) pathogenic variants in ten different genes, 57 VUS carriers, and 25 healthy controls. Combining published episignature information and new validation data with a k-nearest-neighbour classifier within a leave-one-out scheme, we provide unbiased specificity and sensitivity estimates for each of the signatures. Our procedure reached 100% specificity, but the sensitivities unexpectedly spanned a very large spectrum. While ATRX, DNMT3A, KMT2D, and NSD1 signatures displayed a 100% sensitivity, CREBBP-RSTS and one of the CHD8 signatures reached <40% sensitivity on our dataset. Remaining Cornelia de Lange syndrome, KMT2A, KDM5C and CHD7 signatures reached 70-100% sensitivity at best with unstable performances, suffering from heterogeneous methylation profiles among cases and rare discordant samples. Our results call for cautiousness and demonstrate that episignatures do not perform equally well. Some signatures are ready for confident use in a diagnostic setting. Yet, it is imperative to characterise the actual validity perimeter and interpretation of each episignature with the help of larger validation sample sizes and in a broader set of episignatures.

SUBMITTER: Husson T

PROVIDER: S-EPMC10853222 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature

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Episignatures in practice: independent evaluation of published episignatures for the molecular diagnostics of ten neurodevelopmental disorders.

Husson Thomas T Lecoquierre François F Nicolas Gaël G Richard Anne-Claire AC Afenjar Alexandra A Audebert-Bellanger Séverine S Badens Catherine C Bilan Frédéric F Bizaoui Varoona V Boland Anne A Bonnet-Dupeyron Marie-Noëlle MN Brischoux-Boucher Elise E Bonnet Céline C Bournez Marie M Boute Odile O Brunelle Perrine P Caumes Roseline R Charles Perrine P Chassaing Nicolas N Chatron Nicolas N Cogné Benjamin B Colin Estelle E Cormier-Daire Valérie V Dard Rodolphe R Dauriat Benjamin B Delanne Julian J Deleuze Jean-François JF Demurger Florence F Denommé-Pichon Anne-Sophie AS Depienne Christel C Dieux Anne A Dubourg Christèle C Edery Patrick P El Chehadeh Salima S Faivre Laurence L Fergelot Patricia P Fradin Mélanie M Garde Aurore A Geneviève David D Gilbert-Dussardier Brigitte B Goizet Cyril C Goldenberg Alice A Gouy Evan E Guerrot Anne-Marie AM Guimier Anne A Harzalla Inès I Héron Delphine D Isidor Bertrand B Lacombe Didier D Le Guillou Horn Xavier X Keren Boris B Kuechler Alma A Lacaze Elodie E Lavillaureix Alinoë A Lehalle Daphné D Lesca Gaëtan G Lespinasse James J Levy Jonathan J Lyonnet Stanislas S Morel Godeliève G Jean-Marçais Nolwenn N Marlin Sandrine S Marsili Luisa L Mignot Cyril C Nambot Sophie S Nizon Mathilde M Olaso Robert R Pasquier Laurent L Perrin Laurine L Petit Florence F Pingault Veronique V Piton Amélie A Prieur Fabienne F Putoux Audrey A Planes Marc M Odent Sylvie S Quélin Chloé C Quemener-Redon Sylvia S Rama Mélanie M Rio Marlène M Rossi Massimiliano M Schaefer Elise E Rondeau Sophie S Saugier-Veber Pascale P Smol Thomas T Sigaudy Sabine S Touraine Renaud R Mau-Them Frederic Tran FT Trimouille Aurélien A Van Gils Julien J Vanlerberghe Clémence C Vantalon Valérie V Vera Gabriella G Vincent Marie M Ziegler Alban A Guillin Olivier O Campion Dominique D Charbonnier Camille C

European journal of human genetics : EJHG 20231023 2

Variants of uncertain significance (VUS) are a significant issue for the molecular diagnosis of rare diseases. The publication of episignatures as effective biomarkers of certain Mendelian neurodevelopmental disorders has raised hopes to help classify VUS. However, prediction abilities of most published episignatures have not been independently investigated yet, which is a prerequisite for an informed and rigorous use in a diagnostic setting. We generated DNA methylation data from 101 carriers o ...[more]

PMID: 37872275

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Project description:Episignatures are popular tools for the diagnosis of rare neurodevelopmental disorders. They are commonly based on a set of differentially methylated CpGs used in combination with a support vector machine model. DNA methylation (DNAm) data often include missing values due to changes in data generation technology and batch effects. While many normalization methods exist for DNAm data, their impact on episignature performance have never been assessed. In addition, technologies to quantify DNAm evolve quickly and this may lead to poor transposition of existing episignatures generated on deprecated array versions to new ones. Indeed, probe removal between array versions, technologies or during preprocessing leads to missing values. Thus, the effect of missing data on episignature performance must also be carefully evaluated and addressed through imputation or an innovative approach to episignatures design. In this paper, we used data from patients suffering from Kabuki and Sotos syndrome to evaluate the influence of normalization methods, classification models and missing data on the prediction performances of two existing episignatures. We compare how six popular normalization methods for methylarray data affect episignature classification performances in Kabuki and Sotos syndromes and provide best practice suggestions when building new episignatures. In this setting, we show that Illumina, Noob or Funnorm normalization methods achieved higher classification performances on the testing sets compared to Quantile, Raw and Swan normalization methods. We further show that penalized logistic regression and support vector machines perform best in the classification of Kabuki and Sotos syndrome patients. Then, we describe a new paradigm to build episignatures based on the detection of differentially methylated regions (DMRs) and evaluate their performance compared to classical differentially methylated cytosines (DMCs)-based episignatures in the presence of missing data. We show that the performance of classical DMC-based episignatures suffers from the presence of missing data more than the DMR-based approach. We present a comprehensive evaluation of how the normalization of DNA methylation data affects episignature performance, using three popular classification models. We further evaluate how missing data affect those models' predictions. Finally, we propose a novel methodology to develop episignatures based on differentially methylated regions identification and show how this method slightly outperforms classical episignatures in the presence of missing data.

Dataset Information

Episignatures in practice: independent evaluation of published episignatures for the molecular diagnostics of ten neurodevelopmental disorders.

Publications

Episignatures in practice: independent evaluation of published episignatures for the molecular diagnostics of ten neurodevelopmental disorders.

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