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

Sep 22, 2026

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Variants of uncertain significance (VUS) complicate genetic testing because their clinical impact remains unclear, making co-segregation analysis in families a key classification strategy. CAL-Leiden (Co-segregation Analysis via Likelihood ratio analysis-Leiden) provides a comprehensive model for BRCA1, BRCA2, and PALB2, aligned with ACMG/AMP guidance. It expands cancer types, uses a multiple-cancers-per-individual framework, integrates population incidence from multiple countries, and incorporates penetrance from recent literature. A web platform supports diagnostic labs, enabling validation on pedigrees and performance comparison to alternative approaches.

LanguageEnglish

CategoryResearch & Report

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Abstract

Introduction

Variants of uncertain significance and co-segregation analysis

Prior pedigree likelihood models and limitations

Survival-based pedigree models and web-based tools

Human Genetics (2026) 145:34 [https://doi.org/10.1007/s00439-025-02816-z](https://doi.org/10.1007/s00439-025-02816-z) ORIGINAL INVESTIGATION A comprehensive and accessible model for co-segregation analysis in BRCA1, BRCA2, and PALB2 variant classification Setareh Moghadasi1 · Ramin Monajemi2 · Merel E. Braspenning3 · Maaike P. G. Vreeswijk3 · Mar Rodríguez-Girondo2 Received: 29 April 2025 / Accepted: 30 December 2025 © The Author(s) 2026, modified publication 2026 Abstract 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 webbased platform has been developed, making the model accessible and practical for use in diagnostic labs: [https://bioex](https://bioex)[p.net/cosegregation/](p.net/cosegregation/. We demonstrate)[. We demonstrate](p.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. Introduction The broad application of sequencing technologies in DNA diagnostic laboratories and the use of extensive cancer related gene panels have resulted in the identification of a growing number of sequence variants in major cancer-predisposition genes such as BRCA1, BRCA2 and PALB2, of which the clinical importance remains uncertain, referred to as Variants of Uncertain Significance (VUS) . A variety of approaches have been used to assess the clinical relevance of these VUS, among which co-segregation analysis. 􀀍 Mar Rodríguez-Girondo [M.Rodriguez_Girondo@lumc.nl](M.Rodriguez_Girondo@lumc.nl) 1 Department of Clinical Genetics, Leiden University Medical Center, Leiden, The Netherlands 2 Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands 3 Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands Co-segregation analysis studies the inheritance of a genetic variant within a family, particularly its presence in affected or unaffected family members, to determine the likelihood of its pathogenicity or association with a specific disease. Different statistical approaches have been described to determine the likelihood of pathogenicity using co-segregation data which can then be integrated together with supplementary evidence for the application inACMG/ AMP-based classification in which all the available data against or in favor of pathogenicity are combined (Tavtigian et al. 2018) . The major contribution in the early 2000s was the introduction of the full pedigree likelihood model (Thompson et al. 2003) . This method proposed the concept of assessing the probability that observed genetic pattern

FAQs

What problem does CAL-Leiden address in variant classification?

CAL-Leiden addresses the challenge of classifying variants of uncertain significance (VUS) by using co-segregation information from families to estimate likelihood of pathogenicity.

Which genes and cancers are included in the CAL-Leiden model?

The model is designed for BRCA1, BRCA2, and PALB2 variant classification and incorporates an expanded set of cancer types, including pancreatic cancer, breast, ovarian, and contralateral breast cancer.

How does CAL-Leiden handle multiple diagnoses within a single individual?

CAL-Leiden uses a multiple-cancers-per-individual framework so that likelihood contributions account for all relevant cancers observed in a person, including contralateral breast cancer.

Sep 22, 2026

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