Investigating copy number variants in schizophrenia pedigrees using a new consensus pipeline called PECAN

Area of Science:

• Genomics
• Human Genetics
• Bioinformatics

Background:

• Copy number variants (CNVs) are associated with various human diseases, including psychiatric disorders.
• Whole genome sequencing (WGS) provides superior copy number variant (CNV) detection compared to array-based methods.
• Accurate CNV identification is crucial for understanding disease mechanisms.

Purpose of the Study:

• To introduce PECAN (PEdigree Copy number vAriaNt calling), a robust and transparent CNV calling pipeline for short-read WGS data.
• To enhance CNV calling by integrating multiple methods and pedigree information for improved accuracy and scalability.
• To identify disease-associated CNVs in individuals with schizophrenia.

Main Methods:

• PECAN combines four CNV calling algorithms and structural variant genotyping for comprehensive analysis.
• The pipeline incorporates pedigree data to retain lower-confidence CNVs.
• Performance was benchmarked against gold-standard CNV calls for NA12878 and HG002 reference samples.

Main Results:

• PECAN demonstrated high precision and recall in benchmarking, outperforming existing pedigree-based CNV callers.
• A curated list of high-confidence gold standard CNVs for the NA12878 sample is provided.
• A rare deletion co-segregating with schizophrenia was identified in a family, overlapping the PITRM1 gene.

Conclusions:

• PECAN is a scalable and accurate tool for CNV detection in WGS data, particularly valuable for family-based studies.
• The identified deletion in PITRM1 provides a potential genetic link to schizophrenia and associated neurological phenotypes.
• This work contributes a valuable resource for CNV calling and highlights a novel candidate gene for schizophrenia.