CLAMMS: a scalable algorithm for calling common and rare copy number variants from exome sequencing data.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Existing algorithms for copy number variant (CNV) detection from exome sequencing are not optimized for large-scale population studies.
• Limitations include difficulties in automated pipeline integration and poor performance in detecting common variants.

Purpose of the Study:

• Introduce Copy number estimation using Lattice-Aligned Mixture Models (CLAMMS), a novel algorithm designed for scalable CNV detection.
• Address the limitations of previous tools in large population cohorts and across the allele frequency spectrum.

Main Methods:

• CLAMMS algorithm implemented in C.
• Validation through Mendelian inheritance checks on pedigrees.
• Comparison with SNP genotyping arrays on 3164 samples.
• TaqMan quantitative polymerase chain reaction for locus-specific validation.

Main Results:

• CLAMMS demonstrates high scalability for large exome sequencing datasets.
• 95% validation rate for rare variants and high precision (99%) and recall (94%) for common variants.
• Adherence to Mendelian inheritance patterns observed.

Conclusions:

• CLAMMS is a robust and scalable solution for CNV detection in large human exome sequencing studies.
• The algorithm is suitable for identifying variants across the entire allele frequency spectrum.
• CLAMMS integrates well into automated variant-calling pipelines.