Kyriaki Michailidou, Associate Professor at the Department of Biostatistics, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
Kyriaki Michailidou is an Associate Professor and Head of the Biostatistics Department at the Cyprus Institute of Neurology and Genetics. Her research focuses on statistical genetics and genetic epidemiology, with an emphasis on identifying and characterizing genetic variants associated with breast and ovarian cancer risk.
“Utilizing large scale case-control data for variant classification in breast cancer risk genes”
Accurate classification of germline variants in breast cancer susceptibility genes is essential for effective genetic counseling and clinical decision-making. While rare variants are increasingly detected through widespread genetic testing, many remain as variants of uncertain significance, limiting their clinical utility. This talk will focus on how large-scale case–control datasets can be leveraged to improve variant classification in breast cancer risk genes. I will discuss statistical frameworks that integrate case–control association evidence with existing variant interpretation guidelines, highlighting their role in distinguishing pathogenic from benign variation.
Bing-Jian Feng, Research Associate Professor at the Department of Dermatology and Huntsman Cancer Institute, University of Utah, Salt Lake City, USA
Bing-Jian Feng is a Research Associate Professor in computational biology with over 20 years of experience in bioinformatics and machine learning. His research focuses on advancing precision medicine by integrating multi-omics data and developing high-performance algorithms for genetic variant interpretation and cancer risk assessment.
“Using Pedigrees to Interpret the Pathogenicity of Variants”
This presentation examines the accuracy of cosegregation analysis in germline variant classification, contrasting standard ACMG/AMP meiosis counting with Bayes factor-based quantitative methods. We will demonstrate that simple meiosis counting can lead to misclassification when disease patterns deviate from Mendelian expectations. We will also cover how to handle reduced-penetrance variants in high-risk genes (e.g., TP53 and BRCA1).