SMAca

SMAca detects SMA carriers and estimates SMN1 copy-number from next-generation sequencing (NGS) data, resolving high sequence similarity between SMN1 and its paralog SMN2 at the SMN locus.

Key Features:

• Carrier Detection: Identifies individuals with a single copy of SMN1 indicative of SMA carrier status.
• Copy-Number Estimation: Provides absolute SMN1 copy-number estimates to distinguish genotypes associated with SMA severity.
• Silent Carrier Identification: Detects specific variants associated with SMN1 duplications to identify silent carriers with two SMN1 copies on one chromosome and none on the other.
• NGS Data Processing: Analyzes next-generation sequencing (NGS) data to address high sequence similarity between SMN1 and SMN2 and the complex architecture of the SMN locus.
• Implementation: Implemented in Python.
• Validation: Evaluated on a cohort of 326 samples from the Navarra 1000 Genomes project (NAGEN1000).

Scientific Applications:

• Population Genetics: Enables population-level estimation of SMN1 copy-number and carrier frequency.
• Clinical Diagnostics: Supports genotyping of SMN1 for diagnostic assessment of Spinal Muscular Atrophy.
• Genetic Counseling: Informs carrier status determination and reproductive risk assessment for SMA.
• Large-scale Genomic Studies: Applicable to NGS-based studies that require accurate SMN1 copy-number and carrier detection.

Methodology:

Implemented in Python, SMAca processes next-generation sequencing (NGS) data to estimate SMN1 copy-number and detect carriers, and was validated on 326 samples from the Navarra 1000 Genomes project (NAGEN1000).