Integrating Next-Generation Sequencing with Deep Phenotyping: Enhancing Prenatal Diagnostic Precision and Unveiling the Comprehensive Phenotypic Spectrum of Skeletal Disorders
Prenatal Genetics
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Primary Categories:
- Prenatal Genetics
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Secondary Categories:
- Prenatal Genetics
Introduction:
Fetal skeletal disorders (FSD) present diagnostic challenges due to their heterogeneous manifestations. Our retrospective cohort study aimed to assess the diagnostic yield of FSD through integration of next-generation sequencing (NGS) with deep phenotyping (DP). Furthermore, we conducted a comprehensive analysis of the genotype-phenotype correlations of prenatal hotspot genes and their variants in both prenatal and postnatal cohorts. The primary aim was to elucidate the full phenotypic spectrum associated with prenatal hotspot gene variants implicated in skeletal disorders, thereby improving our understanding of skeletal disorders.
Methods:
This study employed a two-pronged approach. Initially, a phenotype-driven strategy was applied to classify and diagnose FSD cases identified through DP during routine prenatal care, utilizing standardized Human Phenotype Ontology (HPO) terms. We included 152 Chinese families with FSD without informative results from karyotype and chromosome microarray analysis (CMA). Next-generation sequencing (NGS), including exome sequencing (ES) and genome sequencing (GS), was performed, and the molecular diagnostic outcomes were analyzed. Additionally, to explore the phenotypic spectrum of prenatal gene variants linked to skeletal disorders, we focused on four high-frequency genes identified in the prenatal cohort. A genotype-driven strategy was adopted under the Deciphering disorders Involving Scoliosis and COmorbidities (DISCO) study framework, recruiting 1000 Chinese individuals with skeletal disorders or as healthy family members. Individuals carrying variants in these four genes underwent deep phenotyping, and the genotype-phenotype correlations were investigated across both prenatal and postnatal cohorts to clarify the phenotypic spectrum of these prenatal hotspot gene variants in skeletal disorders.
Results:
The FSD cohort exhibited diverse manifestations. Diagnostic results revealed a positive yield of 57.2%, with de novo variants being the most common (61/152, 40.1%). Follow-up visits revealed varied pregnancy outcomes, with a positive result proportion of 36.8% for delivered fetuses and 60.8% for termination of pregnancy (TOP) cases. Notably, four hotspot genes (FGFR3, COL1A1, COL1A2, and COL2A1) were identified. Upon integrating the genotypic and phenotypic data from the postnatal cohort for these four hotspot genes, we observed a complete lack of overlap in the variant sites for the three fibrillar structural domain genes: COL1A1, COL1A2, and COL2A1, between the prenatal and postnatal periods. In contrast, for the FGFR3, a significant overlap in variants was observed, particularly at the classic variant within exon 8 (G1138A). The phenotypic manifestations of the FGFR3 gene at the classic variant site (G1138A) were found to be considerably diverse within the postnatal cohort.
Conclusion:
Our study demonstrates the effectiveness of integrating NGS with DP in enhancing the diagnostic precision of FSD. The identification of FSD hotspot genes has shed light on the genetic architecture of the disease, offering a foundation for personalized genetic counseling and potential therapeutic targets. Our findings underscore the significance of integrating both genotype-driven and phenotype-driven approaches into clinical practice, paving the way for improved patient management and the advancement of precision medicine in skeletal disorders.
Fetal skeletal disorders (FSD) present diagnostic challenges due to their heterogeneous manifestations. Our retrospective cohort study aimed to assess the diagnostic yield of FSD through integration of next-generation sequencing (NGS) with deep phenotyping (DP). Furthermore, we conducted a comprehensive analysis of the genotype-phenotype correlations of prenatal hotspot genes and their variants in both prenatal and postnatal cohorts. The primary aim was to elucidate the full phenotypic spectrum associated with prenatal hotspot gene variants implicated in skeletal disorders, thereby improving our understanding of skeletal disorders.
Methods:
This study employed a two-pronged approach. Initially, a phenotype-driven strategy was applied to classify and diagnose FSD cases identified through DP during routine prenatal care, utilizing standardized Human Phenotype Ontology (HPO) terms. We included 152 Chinese families with FSD without informative results from karyotype and chromosome microarray analysis (CMA). Next-generation sequencing (NGS), including exome sequencing (ES) and genome sequencing (GS), was performed, and the molecular diagnostic outcomes were analyzed. Additionally, to explore the phenotypic spectrum of prenatal gene variants linked to skeletal disorders, we focused on four high-frequency genes identified in the prenatal cohort. A genotype-driven strategy was adopted under the Deciphering disorders Involving Scoliosis and COmorbidities (DISCO) study framework, recruiting 1000 Chinese individuals with skeletal disorders or as healthy family members. Individuals carrying variants in these four genes underwent deep phenotyping, and the genotype-phenotype correlations were investigated across both prenatal and postnatal cohorts to clarify the phenotypic spectrum of these prenatal hotspot gene variants in skeletal disorders.
Results:
The FSD cohort exhibited diverse manifestations. Diagnostic results revealed a positive yield of 57.2%, with de novo variants being the most common (61/152, 40.1%). Follow-up visits revealed varied pregnancy outcomes, with a positive result proportion of 36.8% for delivered fetuses and 60.8% for termination of pregnancy (TOP) cases. Notably, four hotspot genes (FGFR3, COL1A1, COL1A2, and COL2A1) were identified. Upon integrating the genotypic and phenotypic data from the postnatal cohort for these four hotspot genes, we observed a complete lack of overlap in the variant sites for the three fibrillar structural domain genes: COL1A1, COL1A2, and COL2A1, between the prenatal and postnatal periods. In contrast, for the FGFR3, a significant overlap in variants was observed, particularly at the classic variant within exon 8 (G1138A). The phenotypic manifestations of the FGFR3 gene at the classic variant site (G1138A) were found to be considerably diverse within the postnatal cohort.
Conclusion:
Our study demonstrates the effectiveness of integrating NGS with DP in enhancing the diagnostic precision of FSD. The identification of FSD hotspot genes has shed light on the genetic architecture of the disease, offering a foundation for personalized genetic counseling and potential therapeutic targets. Our findings underscore the significance of integrating both genotype-driven and phenotype-driven approaches into clinical practice, paving the way for improved patient management and the advancement of precision medicine in skeletal disorders.