The IBS-BIR genomics event took place online on 25 February 2026 and featured presentations on methods for interpreting genetic variants associated with breast cancer risk, focusing particularly on BRCA1 and BRCA2. Kyriaki Michailidou, Associate Professor and Head of the Department of Biostatistics at The Cyprus Institute of Neurology and Genetics gave a talk titled “Utilizing large scale case-control data for variant classification in breast cancer risk genes” discussed how genetic variation contributes to breast cancer, ranging from rare high-risk mutations to common low-risk variants, and highlighted the clinical challenge of variants of uncertain significance (VUS), which create ambiguity in patient care. Their work focused on improving variant classification using large-scale case–control data, introducing a Bayesian method called the case-control likelihood ratio (ccLR). This approach overcomes limitations of traditional statistical methods for rare variants and provides substantially more evidence for classifying variants, showing strong agreement with existing clinical databases. They also described extensions of this method to detect variants with reduced penetrance and efforts to scale analyses across large biobank datasets. Bing-Jian Feng, Research Associate Professor at the Department of Dermatology and Huntsman Cancer Institute, University of Utah, gave a talk titled “Using pedigrees to interpret the pathogenicity of variants”. The talk focused on co-segregation analysis using family pedigrees, explaining how the inheritance of variants alongside disease can provide evidence for pathogenicity. They compared simpler counting methods with more robust likelihood-based approaches, emphasizing the importance of accurately modeling penetrance, which varies with age and other factors. Their talk highlighted both the strengths and limitations of pedigree-based analysis, particularly for complex diseases like cancer, and showed how incorrect assumptions can lead to misclassification. Together, the talks illustrated complementary statistical and genetic approaches aimed at improving the interpretation of rare variants and reducing uncertainty in clinical genomics.
