par Versbraegen, Nassim 
Président du jury Defrance, Matthieu
Promoteur Lenaerts, Tom
Publication Non publié, 2024-06-24
Président du jury Defrance, Matthieu
Promoteur Lenaerts, Tom
Publication Non publié, 2024-06-24
Thèse de doctorat
| Résumé : | Increasing evidence points to the complex interplay of multiple genetic variants as a major contributing factor in many human diseases. Oligogenic diseases, in which a small set of genes collaborate to cause a pathology, present a compelling example of this phenomenon and necessitate a shift away from traditional single-gene inheritance models. Our work aimed to develop robust methods for pinpointing pathogenic combinations of genetic variants across patient cohorts, ultimately improving disease understanding and potentially guiding future diagnostic approaches.We began by developing a novel machine learning framework that integrates explainable AI (XAI) techniques and game-theoretic concepts. This framework allows us to classify and characterise different types of oligogenic effects, providing insights into the specific mechanisms by which multiple genes interact to drive disease. Next, we focused on refining existing computational methods used to predict the pathogenicity of variant combinations. Our emphasis was two-fold: improving computational efficiency for handling the expansive datasets associated with cohort analysis, and critically, reducing false-positive rates to ensure the reliability of our results. With these tools in hand, we developed a specialised cohort analysis approach tailored to investigating diseases with complex genetic origins. To demonstrate the capabilities of our methodology, we delved into a Marfan syndrome cohort. Marfan syndrome is a hereditary condition affecting the body's connective tissue. Our analysis successfully uncovered potential modifier mutations that appear to interact with the primary disease-causing variant, offering new clues about the intricate genetic landscape of this condition. |
