SeeCiTe

SeeCiTe refines copy number variant (CNV) calls from SNP genotyping arrays using parent–offspring trio data to improve specificity and sensitivity of CNV detection in biobank-scale studies.

Key Features:

• Trio-based refinement: Uses parent-offspring trio data to refine and reclassify CNV calls derived from SNP genotyping arrays.
• Post-processing of CNV caller outputs: Operates as a post-processing step on outputs from existing CNV calling tools to adjust call confidence.
• Probe-level analysis: Utilizes probe-level intensity data from trios and singletons to detect inconsistencies and potential artefacts.
• Quality categorization: Systematically assigns CNV quality tiers to distinguish high-confidence calls from potential artefacts.
• Per-call visualization: Generates visualizations of signal intensities for each putative CNV in offspring to highlight probe-level patterns and artefacts.

Scientific Applications:

• Biobank-scale CNV quality control: Improves specificity and sensitivity of CNV calls in large population cohorts such as biobanks.
• Validation and filtering of CNV calls: Reduces false positives and refines variant sets for downstream genomic analyses.
• Application to cohort studies: Applied to the Norwegian Mother, Father, and Child Cohort Study (MoBa) with reported improvements in CNV detection accuracy compared to empirical filtering.

Methodology:

Post-processes outputs from existing CNV calling tools using parent-offspring trio information and probe-level intensity data to refine initial CNV calls, systematically identify artefacts, categorize call quality, and produce per-call visualizations; implemented in R.