A comprehensive and accessible model for co-segregation analysis in BRCA1, BRCA2, and PALB2 variant classification.

Variants of uncertain significance (VUS) are genetic variations with unclear clinical implications, often complicating clinical management in genetic testing. The analysis of co-segregation of the variant with the disease in families has been shown to be a powerful tool for the classification of these variants. We present CAL-Leiden (Co-segregation Analysis via Likelihood ratio analysis-Leiden), a comprehensive co-segregation model facilitating the classification of variants in BRCA1, BRCA2 and PALB2 genes, which can be used as an important component of the ACMG/AMP (the American College of Medical Genetics and Genomics/ the Association for Molecular Pathology) classification guideline. CAL-Leiden includes an expanded range of cancer types, including pancreatic cancer, in addition to breast and ovarian cancer. The model operates on a multiple-cancers-per-individual framework, so that likelihood contributions account for all relevant cancers observed in a person, including contralateral breast cancer. The model integrates population incidence rates from the Netherlands, the United Kingdom and United States, along with penetrance data from the latest literature. A web-based platform has been developed, making the model accessible and practical for use in diagnostic labs: https://bioexp.net/cosegregation/ . We demonstrate the functionality of the tool with multiple pedigrees and compare its performance with alternative approaches. The features in CAL-Leiden collectively contribute to a more comprehensive and accurate assessment of variant pathogenicity, helping clinical laboratory specialists and researchers in classification of the variants of uncertain significance.