Optimizing GWAS Results Using an Interactive Machine Learning Approach

Genome-wide association studies (GWAS) present a viable approach for researchers to identify genetic variations associated with a particular trait. GWAS have already identified several single nucleotide polymorphisms associated with diabetes, Parkinson’s disease, amongst others. However, these comprehensive studies frequently identify large numbers of genetic variants associated with the phenotypes, not all of which are causal. Researchers seek to understand which variants narrowly missed the threshold but actually are causal. Fine mapping, a statistical process in which additional data are introduced to the GWAS dataset, enables researchers to prioritize those variants that warrant additional examination, but assembling the data and building the proper computing environment to perform fine mapping is time-consuming and challenging.

In this free webinar, the featured speakers will provide an overview of how to optimize GWAS results using fine mapping. Specifically, by borrowing from Bayesian statistical methods, they present an interactive approach for applying machine learning-based models in fine mapping. Real-life examples will be demonstrated using UK Biobank data on the DNAnexus platform.

By attending this webinar, attendees will:

• Understand the applications and benefits of fine mapping for refining GWAS results
• Learn about the computing environment needed for fine mapping
• Gain strategies for building and deploying interactive machine learning models
• See the DNAnexus platform and how it enables machine learning in a secure and scalable environment