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.
Moghadasi et al. (Thu,) studied this question.