{"title":"Beyond Single Diagnosis: Exploring Multidiagnostic Realities in Pediatric Patients through Genome Sequencing","authors":"Fen Guo, Ruby Liu, Yinghong Pan, Mary Colasanto, Christin Collins, Madhuri Hegde","doi":"10.1155/2024/9115364","DOIUrl":null,"url":null,"abstract":"<p>Recent advancements in the next-generation sequencing have illuminated the occurrence of multiple genetic diagnoses (MGD). While exome sequencing has provided insights, genome sequencing (GS), the most comprehensive diagnostic tool, remains underexplored for studying MGD prevalence. We retrospectively analyzed 1487 pediatric cases from our laboratory, employing GS to investigate the incidence of single definitive genetic diagnosis (SDD) and MGD in children suspected of having a genetic disease. Of these patients, 273 received at least one definitive diagnosis, including 245 with SDD (16.5%) and 28 with MGD (1.9%). Diagnostic yield was consistent across genders and unaffected by previous testing in SDD cases. Notably, prior testing significantly increased the diagnostic yield in MGD cases to 2.7% overall and 14.4% among diagnosed cases, compared to 1.1% for those with GS as a first-tier test. Age was a significant factor in diagnostic outcome for both SDD and MGD cases with neonates showing the highest diagnostic yield of 24.5% in SDD and a notably higher yield in MGD at 4.9%, representing 16.7% of the diagnosed cases. Of the 28 MGD cases, 17 exhibited distinct phenotypes, 9 had overlapping features, and 2 presented a mix, underscoring the genetic and phenotypic heterogeneity within this group. This study is the first to exclusively use GS to assess MGD prevalence. Our findings highlight the complexity of rare diseases and emphasize the importance of comprehensive, genome-level diagnostics. Clinicians must ensure that diagnoses fully account for the observed phenotypes to inform optimal therapeutic strategies and management.</p>","PeriodicalId":3,"journal":{"name":"ACS Applied Electronic Materials","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Electronic Materials","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1155/2024/9115364","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Recent advancements in the next-generation sequencing have illuminated the occurrence of multiple genetic diagnoses (MGD). While exome sequencing has provided insights, genome sequencing (GS), the most comprehensive diagnostic tool, remains underexplored for studying MGD prevalence. We retrospectively analyzed 1487 pediatric cases from our laboratory, employing GS to investigate the incidence of single definitive genetic diagnosis (SDD) and MGD in children suspected of having a genetic disease. Of these patients, 273 received at least one definitive diagnosis, including 245 with SDD (16.5%) and 28 with MGD (1.9%). Diagnostic yield was consistent across genders and unaffected by previous testing in SDD cases. Notably, prior testing significantly increased the diagnostic yield in MGD cases to 2.7% overall and 14.4% among diagnosed cases, compared to 1.1% for those with GS as a first-tier test. Age was a significant factor in diagnostic outcome for both SDD and MGD cases with neonates showing the highest diagnostic yield of 24.5% in SDD and a notably higher yield in MGD at 4.9%, representing 16.7% of the diagnosed cases. Of the 28 MGD cases, 17 exhibited distinct phenotypes, 9 had overlapping features, and 2 presented a mix, underscoring the genetic and phenotypic heterogeneity within this group. This study is the first to exclusively use GS to assess MGD prevalence. Our findings highlight the complexity of rare diseases and emphasize the importance of comprehensive, genome-level diagnostics. Clinicians must ensure that diagnoses fully account for the observed phenotypes to inform optimal therapeutic strategies and management.