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Unveiling the hidden: revisiting the potential of old genetic data for rare disease research

European Journal of Human Genetics volume 31, pages 1093–1094 (2023)Cite this article

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The Original Article was published on 18 July 2023

Unlike many other diagnostic tests, a negative exome sequencing does not rule out a genetic disorder; instead, it only implies that a genetic diagnosis could not be identified, given the clinical and genetic information available at the time of analysis. Reanalyzing exome data from unsolved cohorts with neurodevelopmental disorders (NDD) holds significant importance for several reasons [1]. Firstly, it allows for the identification of potentially missed or misclassified genetic variants. The initial analysis of exome data may have been limited by the available knowledge at the time, as well as the algorithms and filtering strategies employed. Revisiting the data with improved variant calling algorithms and updated databases can unmask previously concealed genomic variations and lead to the discovery of disease-causing variants that were initially overlooked. Secondly, the reanalysis provides an opportunity to evaluate diagnostic variants according to current standards and guidelines [2]. Over time, our understanding of variant pathogenicity and gene-disease associations evolves, necessitating a reassessment of previously reported variants. By applying updated classification criteria and incorporating additional evidence, such as functional studies or confirmatory publications, the pathogenicity of variants can be more accurately determined. Furthermore, reanalyzing exome data allows for the confirmation or removal of gene-disease associations (GDA). As new research emerges, some previously reported associations may be called into question or require further validation. By critically evaluating the evidence supporting the GDAs, we refine our understanding of the genetic basis of NDD and establish more reliable genotype-phenotype correlations. Lastly, reanalysis can uncover previously unknown gene associations. The constantly expanding knowledge base of gene-disease relationships, with hundreds of new associations being identified each year, provides a valuable resource for revisiting unsolved cohorts. By leveraging this wealth of information and employing advanced computational tools and filtering strategies, researchers can identify novel gene candidates and establish their role in NDD.

In this issue of the European Society of Human Genetics Bartolomaeus et al. [3] present a comprehensive study on the re-evaluation and re-analysis of published research cohorts of rare diseases. The authors revisited a cohort of 152 consanguineous families with NDD that was reported five years ago [4]. The aim of the present study [3] was to assess the diagnostic yield and validity of GDA in the cohort using updated variant classification guidelines and computational tools. The authors employed a systematic approach to re-evaluate the reported variants in the cohort. They applied diagnostic classification guidelines and their own candidate gene scoring system (AutoCaSc) [5] to assess the validity of the gene-disease associations. The sequencing data was re-processed using an up-to-date pipeline for case-level re-analysis. The results of the re-analysis revealed clinically relevant changes in 28 out of 152 families, with 10 previously reported variants being re-classified as variants of uncertain significance or benign. Additionally, the validity of three gene-disease associations was judged as limited. The study also identified 12 new disease-causing variants, highlighting the importance of re-evaluating screening studies, including those that were previously considered solved. This overlaps with previous publications arguing that 40% of older variants need reinterpretation [6, 7].

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  1. Department of Epilepsy Genetics and Personalized Medicine, Danish Epilepsy Center, Dianalund, Denmark

    Allan Bayat

  2. Department of Regional Health Research, University of Southern Denmark, Odense, Denmark

    Allan Bayat

  3. Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark

    Allan Bayat

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Correspondence to Allan Bayat.

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Bayat, A. Unveiling the hidden: revisiting the potential of old genetic data for rare disease research. Eur J Hum Genet 31, 1093–1094 (2023). https://doi.org/10.1038/s41431-023-01435-4

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