Computational Methods for Causal Variant Prioritization

This session showcases state-of-the-art methods for prioritizing causal risk variants in complex trait genetic studies.

Learning Objectives:
1. Identify genetic variants associated with transcription factor binding in liver.
2. Examine using machine learning to predict and prioritize the effects of non-coding variants on diseases, leveraging functional annotations and GWAS data to identify causal variants and gain biological insights.
3. Manage robustness of summary statistics-based methods under LD mismatch.
4. Evaluate whether current genome language and other deep learning models can help pinpoint causal variants in statistical fine-mapping

Please note: This item is only available as part of the ASHG 2024 Annual Meeting Digital Pass.