RNA splicing analysis contributes to reclassifying variants of uncertain significance and improves the diagnosis of monogenic disorders

Variant reclassification and recontact research: A scoping review

Purpose

A primary challenge in clinical genetics is accurate interpretation of identified variants and relaying the information to patients and providers. Inconsistencies around handling variant reclassifications and notifying patients, combined with the lack of prescriptive guidelines on re-evaluation, reanalysis, and return of variants, has created practice challenges. Although relevant empirical work has emerged, the scope and outcomes of this research have not been characterized.

Methods

We conducted a systematic literature review of variant reclassification and recontact research (2013-2023) across subdisciplines of medical genetics. Of the 159 nonduplicate records screened, we summarize findings from 54 included research articles describing variant reclassification frequencies, outcomes, and stakeholder perspectives on recontact.

Results

The included articles reported on active reclassification (n = 20), passive reclassification (n = 13), stakeholder surveys (n = 11), qualitative interviews (n = 7), and reanalysis of published or ClinVar data (n = 3). On average, active and passive approaches yielded different reclassification frequencies-31% and 20%, respectively, which were considerably higher than ClinVar (<0.1%-6.4%). Despite a wealth of data on individual stakeholder perspectives and opinions on reclassification, recontact, and consensus on the need for standardization in this space, opinions differ on how to develop and implement standardized processes.

Conclusion

Many active reclassification studies reapplied standard variant classification guideline to previously reported variants-thus demonstrating the number of variants that would be successfully reclassified if reinterpretation and reanalysis were performed routinely. Research gaps identified include the need for understanding practices and opinions of nongenetics providers and engaging in deliberative democracy exercises to reach consensus on these issues.

A Mild Presentation of X-Linked Hypophosphatemia Caused by a Non-Canonical Splice Site Variant in the PHEX Gene

X-linked hypophosphatemia (XLH) is a rare inherited disorder of renal phosphate wasting with a highly variable phenotype caused by loss-of-function variants in the PHEX gene. The diagnosis of individuals with mild phenotypes can be challenging and often delayed. Here, we describe a three-generation family with a very mild clinical presentation of XLH. The diagnosis was unexpectedly found in a 39-year-old woman who was referred for genetic testing due to an unclear childhood diagnosis of a tubulopathy. Genetic testing performed by next-generation sequencing using a kidney disease gene panel identified a novel non-canonical splice site variant in the PHEX gene. Segregation analysis detected that the consultand's father, who presented with hypophosphatemia and decreased tubular phosphate reabsorption, and the consultand's son also carried this variant. RNA studies demonstrated that the non-canonical splice site variant partially altered the splicing of the PHEX gene, as both wild-type and aberrant splicing transcripts were detected in the two male members with only one copy of the PHEX gene. In conclusion, this case contributes to the understanding of the relationship between splicing variants and the variable expressivity of XLH disease. The mild phenotype of this family can be explained by the coexistence of PHEX transcripts with aberrant and wild-type splicing.

Variant reclassification and clinical implications

Genomic technologies have transformed clinical genetic testing, underlining the importance of accurate molecular genetic diagnoses. Variant classification, ranging from benign to pathogenic, is fundamental to these tests. However, variant reclassification, the process of reassigning the pathogenicity of variants over time, poses challenges to diagnostic legitimacy. This review explores the medical and scientific literature available on variant reclassification, focusing on its clinical implications.Variant reclassification is driven by accruing evidence from diverse sources, leading to variant reclassification frequency ranging from 3.6% to 58.8%. Recent studies have shown that significant changes can occur when reviewing variant classifications within 1 year after initial classification, illustrating the importance of early, accurate variant assignation for clinical care.Variants of uncertain significance (VUS) are particularly problematic. They lack clear categorisation but have influenced patient treatment despite recommendations against it. Addressing VUS reclassification is essential to enhance the credibility of genetic testing and the clinical impact. Factors affecting reclassification include standardised guidelines, clinical phenotype-genotype correlations through deep phenotyping and ancestry studies, large-scale databases and bioinformatics tools. As genomic databases grow and knowledge advances, reclassification rates are expected to change, reducing discordance in future classifications.Variant reclassification affects patient diagnosis, precision therapy and family screening. The exact patient impact is yet unknown. Understanding influencing factors and adopting standardised guidelines are vital for precise molecular genetic diagnoses, ensuring optimal patient care and minimising clinical risk.

Prioritizing de novo potential non-canonical splicing variants in neurodevelopmental disorders

BACKGROUND

Genomic variants outside of the canonical splicing site (±2) may generate abnormal mRNA splicing, which are defined as non-canonical splicing variants (NCSVs). However, the clinical interpretation of NCSVs in neurodevelopmental disorders (NDDs) is largely unknown.

METHODS

We investigated the contribution of NCSVs to NDDs from 345,787 de novo variants (DNVs) in 47,574 patients with NDDs. We performed functional enrichment and protein-protein interaction analysis to assess the association between genes carrying prioritised NCSVs and NDDs. Minigene was used to validate the impact of NCSVs on mRNA splicing.

FINDINGS

We observed significantly more NCSVs (p = 0.02, odds ratio [OR] = 2.05) among patients with NDD than in controls. Both canonical splicing variants (CSVs) and NCSVs contributed to an equal proportion of patients with NDD (0.76% vs. 0.82%). The candidate genes carrying NCSVs were associated with glutamatergic synapse and chromatin remodelling. Minigene successfully validated 59 of 79 (74.68%) NCSVs that led to abnormal splicing in 40 candidate genes, and 9 of the genes (ARID1B, KAT6B, TCF4, SMARCA2, SHANK3, PDHA1, WDR45, SCN2A, SYNGAP1) harboured recurrent NCSVs with the same variant present in more than two unrelated patients with NDD. Moreover, 36 of 59 (61.02%) NCSVs are novel clinically relevant variants, including 34 unreported and 2 clinically conflicting interpretations or of uncertain significance NCSVs in the ClinVar database.

INTERPRETATION

This study highlights the common pathology and clinical importance of NCSVs in unsolved patients with NDD.

FUNDING

The present study was funded by grants from the National Natural Science Foundation of China, China Postdoctoral Science Foundation, the Hunan Youth Science and Technology Innovation Talent Project, the Provincial Natural Science Foundation of Hunan, The Scientific Research Program of FuRong laboratory, and the Natural Science Project of the University of Anhui Province.

Performance evaluation of differential splicing analysis methods and splicing analytics platform construction

A proportion of previously defined benign variants or variants of uncertain significance in humans, which are challenging to identify, may induce an abnormal splicing process. An increasing number of methods have been developed to predict splicing variants, but their performance has not been completely evaluated using independent benchmarks. Here, we manually sourced ∼50 000 positive/negative splicing variants from > 8000 studies and selected the independent splicing variants to evaluate the performance of prediction methods. These methods showed different performances in recognizing splicing variants in donor and acceptor regions, reminiscent of different weight coefficient applications to predict novel splicing variants. Of these methods, 66.67% exhibited higher specificities than sensitivities, suggesting that more moderate cut-off values are necessary to distinguish splicing variants. Moreover, the high correlation and consistent prediction ratio validated the feasibility of integration of the splicing prediction method in identifying splicing variants. We developed a splicing analytics platform called SPCards, which curates splicing variants from publications and predicts splicing scores of variants in genomes. SPCards also offers variant-level and gene-level annotation information, including allele frequency, non-synonymous prediction and comprehensive functional information. SPCards is suitable for high-throughput genetic identification of splicing variants, particularly those located in non-canonical splicing regions.

Performance evaluation of computational methods for splice-disrupting variants and improving the performance using the machine learning-based framework

A critical challenge in genetic diagnostics is the assessment of genetic variants associated with diseases, specifically variants that fall out with canonical splice sites, by altering alternative splicing. Several computational methods have been developed to prioritize variants effect on splicing; however, performance evaluation of these methods is hampered by the lack of large-scale benchmark datasets. In this study, we employed a splicing-region-specific strategy to evaluate the performance of prediction methods based on eight independent datasets. Under most conditions, we found that dbscSNV-ADA performed better in the exonic region, S-CAP performed better in the core donor and acceptor regions, S-CAP and SpliceAI performed better in the extended acceptor region and MMSplice performed better in identifying variants that caused exon skipping. However, it should be noted that the performances of prediction methods varied widely under different datasets and splicing regions, and none of these methods showed the best overall performance with all datasets. To address this, we developed a new method, machine learning-based classification of splice sites variants (MLCsplice), to predict variants effect on splicing based on individual methods. We demonstrated that MLCsplice achieved stable and superior prediction performance compared with any individual method. To facilitate the identification of the splicing effect of variants, we provided precomputed MLCsplice scores for all possible splice sites variants across human protein-coding genes (http://39.105.51.3:8090/MLCsplice/). We believe that the performance of different individual methods under eight benchmark datasets will provide tentative guidance for appropriate method selection to prioritize candidate splice-disrupting variants, thereby increasing the genetic diagnostic yield.