Genomic Landscape and Diagnostic Yield of Chromosomal Microarray Analysis in 471 Turkish Children with Neurodevelopmental Disorders and Congenital Anomalies

Why It Matters

The study quantifies CMA’s value in a developing‑country setting, guiding clinicians to prioritize testing for patients with cardiac anomalies or seizures, and supporting health‑system investment in genomic diagnostics.

Key Takeaways

• 24.2% of Turkish NDD patients had detectable CNVs via CMA
• Pathogenic detection rate reached 16.5%, driven by deletions on chromosomes 5, 22
• Cardiac anomalies and seizures markedly increased diagnostic yield
• VUS variants were smaller, often located on chromosome X
• Mosaic abnormalities identified, underscoring CMA’s sensitivity

Pulse Analysis

Chromosomal microarray analysis (CMA) has become the cornerstone of first‑tier genetic testing for neurodevelopmental disorders (NDDs) worldwide, offering a cost‑effective alternative to whole‑genome sequencing in many health systems. In emerging markets, where budget constraints limit access to high‑throughput technologies, CMA provides a pragmatic balance of diagnostic power and affordability. The Turkish cohort underscores how a well‑implemented CMA program can deliver clinically actionable results at scale, reinforcing its role in public‑health genetics strategies.

The study’s 16.5% pathogenic/likely‑pathogenic (P/LP) detection rate aligns with global benchmarks, yet the concentration of deletions on chromosomes 5 and 22 highlights regional genomic hotspots that may inform future diagnostic panels. Notably, variants of uncertain significance (VUS) clustered on chromosome X, reflecting the X‑linked complexity of many NDD phenotypes. The strong association between multiple congenital anomalies—particularly cardiac defects—and higher diagnostic yields suggests that phenotype‑driven triage can optimize resource allocation, directing CMA toward patients most likely to benefit.

From a business perspective, these findings validate continued investment in CMA platforms for hospitals and diagnostic labs serving heterogeneous populations. As precision medicine gains traction, integrating CMA data with emerging sequencing and AI‑driven interpretation tools will enhance variant classification, reducing VUS rates and improving patient outcomes. Policymakers and payers can leverage this evidence to justify reimbursement models that prioritize CMA for high‑risk pediatric cohorts, ultimately driving cost‑effective care while expanding the genomic knowledge base in under‑represented regions.