Medical Information Sciences Vortragsreihe (2 CME Punkte) Eva Holtkamp, M.Sc.

Eva Holtkamp, M.Sc.
PhD Student and Researcher at the Chair of Computational Molecular Medicine, TUM School of Computation, Information and Technology, Technical University of Munich
 
„DeepRVAT- Integration of Variant Annotations Using Deep Set Networks Boosts Rare Variant Association Genetics“
 
Rare genetic variants can strongly predispose to disease, yet accounting for rare variants is statistically challenging, and principled strategies for integrating possibly diverse types of variant annotations in a data-driven manner are lacking. Here, we present DeepRVAT (Deep Rare Variant Association Testing), a deep set model that learns gene impairment scores from rare variants, annotations, and phenotypes. DeepRVAT infers the relevance of different annotations and their combination directly from data, eliminating ad hoc modeling choices that characterize existing methods. DeepRVAT estimates a single, trait-agnostic gene impairment score for each gene in each sample, enabling both risk prediction and gene discovery in a unified framework and seamless integration into established association testing frameworks. We apply DeepRVAT on 34 quantitative and 63 binary traits across 469,382 whole-exome-sequenced individuals from the UK Biobank. We integrate state-of-the-art annotations, including AlphaMissense, PrimateAI, AbSplice, DeepRipe, and DeepSEA, and find a substantial increase in gene discoveries and improved replication rates on held-out individuals over previous methods.  We demonstrate the applicability of pre-trained DeepRVAT models to new traits, facilitating the study of disease cohorts with limited training data. Furthermore, we significantly improve the detection of individuals at high genetic risk by combining common variant polygenic risk scores with DeepRVAT
Eva Holtkamp is a PhD student in computational biology in the group of Prof. Julien Gagneur at the Technical University of Munich and the Helmholtz Center Munich. She is part of the Munich Data Science School PhD program. Previously, she earned a master’s degree in molecular biotechnology with a focus on bioinformatics from Heidelberg University. Her research focuses on using deep learning and statistical methods to understand the effects of rare genetic variants on common polygenic traits and diseases by leveraging genome sequencing data from biobank-scale cohorts.
 
Date: July 2nd 2024 (starting 5:30 pm)
Place: Lecture Hall N2045 (Faculty of Applied Informatics)
 
The lecture will be live-streamed to the conference room of the Institute for Digital Medicine (IDM, Gutenbergstr. 7, 86356 Neusäß, Room 01.B001, first floor).
Please note: If you want to attend the live-stream, please send an informal message to idm@uk-augsburg.de in advance.

 
We once again offer the chance to arrange informal individual meetings with the speaker to discuss related scientific questions. Please send an e-mail to office.bioinf@informatik.uni-augsburg.de to make an individual appointment.
The lecture will last 45 to 60 minutes and it will be followed by an open discussion. Each session will be rounded off with an informal get-together, where snacks and beverages will be served. Please feel free to join!