Impact of Gene-Disease Relationship Curation on Patient Diagnostics and Panel Design
Laboratory Genetics and Genomics
-
Primary Categories:
- Laboratory Genetics
-
Secondary Categories:
- Laboratory Genetics
Introduction:
One of the major challenges for diagnostic laboratories is the evaluation of the clinical impact of genetic variants, with an important step being the establishment of gene-disease relationship (GDR). GDR is not only essential for the accurate interpretation of genetic findings during the analysis of exome and genome data, but also for variant classification and it is an important component in the design of diagnostic gene panels in terms of clinical sensitivity, specificity, and validity. In fact, despite the widely recognized benefits of exome and genome sequencing, gene panels may still be the preferred option in cases where cost-effectiveness and targeted analysis are prioritized while minimizing secondary findings. Here, we summarize our experience over the last 2 years following the Clinical Genome Resource (ClinGen) framework in the daily work of our diagnostic laboratory and its application in the selection of genes for both our designed and customized diagnostic panels.
Methods:
The ClinGen Clinical Validity Framework for evaluation of GDR was applied. We systematically evaluated genes without OMIM entries or with entries indicating that the relationship between the phenotype and gene was provisional. Gene selection depended on the specific variants identified during the diagnostic evaluation of patients’ genomic data. Our laboratory’s internal biodatabank with exome/genome data from previously tested individuals, in addition to data available in the literature or public databases were used for gene classification. According to the framework, definitive, strong, moderate, limited, or no known disease relationship can be reached. Disputed or refuted classification can also be reached, as non-supportive evidence for GDR.
In accordance with the ACMG Technical Standards for diagnostic gene sequencing panels, genes that reached moderate or higher level of evidence of GDR were considered eligible candidates for inclusion in diagnostic gene panels.
Results:
In this period more than 500 genes were evaluated, and a strong/definitive level of evidence has been reached for 24% of the genes, moderate for 27%, limited for 43%, and no known disease relationship or disputed for 5% of the genes. A total of 86 genes (14.7%) without OMIM entry reached moderate or strong GDR. The latter together with all known genes with well-established GDR (with moderate, strong, or definitive GDR in GenCC) were included in the pool of genes which are eligible for inclusion in pre-designed or customized gene panels.
Conclusion:
Our results demonstrate the importance of careful assessment of gene clinical validity data, along with the use of genetic data repositories. Implementation of the ClinGen standardized scoring system for gene-disease association assessment is feasible and relevant in the routine clinical diagnostic setting and can definitively contribute to maximizing clinical utility in diagnostic test design.
References:
1. Strande, N. T. et al., Am J Hum Genet 100, 6 (2017)
2. Bean, L. et al., Genet Med 22, 3 (2020)
3. Zonic, E. et al., Genet Med Open 1 (2023)
One of the major challenges for diagnostic laboratories is the evaluation of the clinical impact of genetic variants, with an important step being the establishment of gene-disease relationship (GDR). GDR is not only essential for the accurate interpretation of genetic findings during the analysis of exome and genome data, but also for variant classification and it is an important component in the design of diagnostic gene panels in terms of clinical sensitivity, specificity, and validity. In fact, despite the widely recognized benefits of exome and genome sequencing, gene panels may still be the preferred option in cases where cost-effectiveness and targeted analysis are prioritized while minimizing secondary findings. Here, we summarize our experience over the last 2 years following the Clinical Genome Resource (ClinGen) framework in the daily work of our diagnostic laboratory and its application in the selection of genes for both our designed and customized diagnostic panels.
Methods:
The ClinGen Clinical Validity Framework for evaluation of GDR was applied. We systematically evaluated genes without OMIM entries or with entries indicating that the relationship between the phenotype and gene was provisional. Gene selection depended on the specific variants identified during the diagnostic evaluation of patients’ genomic data. Our laboratory’s internal biodatabank with exome/genome data from previously tested individuals, in addition to data available in the literature or public databases were used for gene classification. According to the framework, definitive, strong, moderate, limited, or no known disease relationship can be reached. Disputed or refuted classification can also be reached, as non-supportive evidence for GDR.
In accordance with the ACMG Technical Standards for diagnostic gene sequencing panels, genes that reached moderate or higher level of evidence of GDR were considered eligible candidates for inclusion in diagnostic gene panels.
Results:
In this period more than 500 genes were evaluated, and a strong/definitive level of evidence has been reached for 24% of the genes, moderate for 27%, limited for 43%, and no known disease relationship or disputed for 5% of the genes. A total of 86 genes (14.7%) without OMIM entry reached moderate or strong GDR. The latter together with all known genes with well-established GDR (with moderate, strong, or definitive GDR in GenCC) were included in the pool of genes which are eligible for inclusion in pre-designed or customized gene panels.
Conclusion:
Our results demonstrate the importance of careful assessment of gene clinical validity data, along with the use of genetic data repositories. Implementation of the ClinGen standardized scoring system for gene-disease association assessment is feasible and relevant in the routine clinical diagnostic setting and can definitively contribute to maximizing clinical utility in diagnostic test design.
References:
1. Strande, N. T. et al., Am J Hum Genet 100, 6 (2017)
2. Bean, L. et al., Genet Med 22, 3 (2020)
3. Zonic, E. et al., Genet Med Open 1 (2023)