{"title":"Automated variant re-evaluation is labor-balanced and gives clinically relevant results: Hereditary cardiac disease as a use case","authors":"","doi":"10.1016/j.ejmg.2024.104981","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><div>Genetic findings influence clinical care of patients suspected of hereditary cardiac diseases. As additional knowledge arises over time, the classification of genetic variants may change. The labor cost associated with systematic manual reevaluation for reported variants is substantial. We applied an automated variant classifier for reevaluation of previous reported variants to assess how such tools may assist in manual reevaluation.</div></div><div><h3>Methods</h3><div>Historically (2010–2022), patients (N = 2987) suspected of inherited cardiomyopathies or ion-channel disorders were screened for genetic variants in at least one of up to 114 genes. We had reported 1455 unique variants, of which 742 were among the 14 most relevant genes. In the 14-gene-group, we compared our reported classification to that of an autoclassifier and manually reevaluated variant classification of all variants. Among the remaining genes (N = 100), only variants where the autoclassifier predicted change of clinical impact, such as variant of uncertain significance to likely pathogenic or oppositely, were manually reevaluated.</div></div><div><h3>Results</h3><div>We identified 9% (66/742) of variants with clinical impact in the 14-gene-group. Of these, 91% could have been identified solely evaluating the 120 variants where the autoclassifier had predicted a change of clinical impact. In the 100 remaining genes, a change of clinical impact was identified in 3% (22/713) after manual reevaluation.</div></div><div><h3>Conclusion</h3><div>Using an autoclassifier reduces the workload to identify variants likely to have a change in variant class with clinical impact. Hence, we recommend using such tools to identify the variants most relevant to manually reevaluate to improve patient care.</div></div>","PeriodicalId":11916,"journal":{"name":"European journal of medical genetics","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"European journal of medical genetics","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1769721224000739","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}
引用次数: 0
Abstract
Background
Genetic findings influence clinical care of patients suspected of hereditary cardiac diseases. As additional knowledge arises over time, the classification of genetic variants may change. The labor cost associated with systematic manual reevaluation for reported variants is substantial. We applied an automated variant classifier for reevaluation of previous reported variants to assess how such tools may assist in manual reevaluation.
Methods
Historically (2010–2022), patients (N = 2987) suspected of inherited cardiomyopathies or ion-channel disorders were screened for genetic variants in at least one of up to 114 genes. We had reported 1455 unique variants, of which 742 were among the 14 most relevant genes. In the 14-gene-group, we compared our reported classification to that of an autoclassifier and manually reevaluated variant classification of all variants. Among the remaining genes (N = 100), only variants where the autoclassifier predicted change of clinical impact, such as variant of uncertain significance to likely pathogenic or oppositely, were manually reevaluated.
Results
We identified 9% (66/742) of variants with clinical impact in the 14-gene-group. Of these, 91% could have been identified solely evaluating the 120 variants where the autoclassifier had predicted a change of clinical impact. In the 100 remaining genes, a change of clinical impact was identified in 3% (22/713) after manual reevaluation.
Conclusion
Using an autoclassifier reduces the workload to identify variants likely to have a change in variant class with clinical impact. Hence, we recommend using such tools to identify the variants most relevant to manually reevaluate to improve patient care.
期刊介绍:
The European Journal of Medical Genetics (EJMG) is a peer-reviewed journal that publishes articles in English on various aspects of human and medical genetics and of the genetics of experimental models.
Original clinical and experimental research articles, short clinical reports, review articles and letters to the editor are welcome on topics such as :
• Dysmorphology and syndrome delineation
• Molecular genetics and molecular cytogenetics of inherited disorders
• Clinical applications of genomics and nextgen sequencing technologies
• Syndromal cancer genetics
• Behavioral genetics
• Community genetics
• Fetal pathology and prenatal diagnosis
• Genetic counseling.