Technology-driven and evidence-based genomic analysis for integrated pediatric and prenatal genetics evaluation

Area of Science:

• Genomics and Genetics
• Clinical Diagnostics
• Pediatric Medicine
• Prenatal Diagnosis

Background:

• The decade post-Human Genome Project saw rapid advancements in genomic technologies.
• Genomic analysis is increasingly applied to pediatric patients with developmental and intellectual disabilities (DD/ID), multiple congenital anomalies (MCA), and autistic spectrum disorders (ASD).
• This technology has also been extended to prenatal diagnosis.

Purpose of the Study:

• To evaluate the analytical and clinical validity of genomic analysis, specifically oligonucleotide array comparative genomic hybridization (aCGH) and single nucleotide polymorphism (SNP) chips.
• To assess the diagnostic yield and types of genomic abnormalities in pediatric and prenatal cases.
• To review the establishment of guidelines and resources for genomic analysis implementation.

Main Methods:

• Utilized oligonucleotide array comparative genomic hybridization (aCGH) and single nucleotide polymorphism (SNP) chips for genomic analysis.
• Evaluated analytical and clinical validities through case series, multi-center comparisons, and large patient-control studies.
• Reviewed published reports on prenatal diagnosis in pregnancies with ultrasound-detected anomalies and normal cytogenetics.

Main Results:

• aCGH demonstrated >99% sensitivity and specificity with enhanced analytical resolution.
• Diagnostic yield in pediatric patients with DD/ID, MCA, and ASD was 12%-20%, with ~60% being recurrent genomic disorders.
• In prenatal cases with ultrasound anomalies and normal cytogenetics, genomic abnormalities were found in ~10%, with 30% being syndromic.

Conclusions:

• Genomic analysis, particularly aCGH, is a powerful tool for diagnosing genetic disorders in pediatric and prenatal settings.
• Established guidelines and knowledge resources support the integration of genomic analysis into clinical practice.
• This technology-driven approach facilitates understanding disease mechanisms and developing targeted therapies.