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

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].

The findings of this study have important implications for the field of rare disease research and diagnostics. The authors emphasize the need for re-evaluation and re-analysis of published cohorts, not only for negative cases but also for supposedly solved ones. They argue that the complexity of computational re-analysis should be balanced against the decreasing costs of re-testing. The study recommends a screening procedure that can quickly identify the majority (83%) of new variants, as extensive re-analysis per case may not be feasible for most institutions.

The study also emphasizes the importance of assessing the validity of GDAs and distinguishing between established genes, published candidate genes, and candidate genes. Through a rigorous evaluation process, the authors downgraded certain genes from established to candidate status and vice versa based on the available evidence. This critical assessment helps refine our understanding of the genetic basis of NDD and highlights the need for continuous updates in gene classifications as new evidence emerges.

Although Bartolomaeus et al. [3] conclude that it is generally prudent to re-evaluate all data older than five years, they raise three key questions [3]:

1. “Should this be done for all cases, or should certain levels of analysis be prioritized?”

2. “Is it worthwhile to completely re-calculate old data, or should new data be generated?”

3. “What timeframes are adequate for the different levels?”

According to the manuscript, re-analysis of data older than five years should be considered for all cases and that re-calculating old data can be worthwhile [3]. While the study did not directly investigate re-sequencing, it mentions that re-sequencing, particularly using genome sequencing, would have provided additional benefits such as high-quality copy number variation analysis and detection of non-coding variants. Finally, the manuscript does not explicitly mention specific timeframes for the different levels of analysis. However, it suggests that a sensible timeframe for iterative re-analysis in research cohorts could be about one to two years.

Overall, this study contributes to our understanding of the importance of iterative re-analysis of next-generation sequencing data in rare disease cohorts. The findings underscore the need to re-evaluate and re-analyze published data to uncover missed variants, update variant classifications, and improve the validity of gene-disease associations. Finally, the study provides valuable insights and recommendations for future research screening studies in the field of neurodevelopmental disorders.

Funding

There was no specific funding.

Competing interests

The author declares no competing interests.