Optimizing GWAS Results Using an Interactive Machine Learning Approach

Fundamental Research, Laboratory Technology, Life Sciences,
  • Thursday, December 05, 2019

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

Speakers

http://Eugene%20Miloslavsky,%20DNAnexus

(Host) Eugene Miloslavsky, Product Manager, DNAnexus

Eugene Miloslavsky is a product manager for DNAnexus’s core platform and artificial intelligence and machine learning. He has a background in applying machine learning in the tech sector and has also worked to bring up production cloud platforms. Additionally, he has authored software that enables the manufacture of advanced integrated circuits.

Message Presenter
http://Peter%20Nguyen,%20DNAnexus

(Guest Presenter) Peter Nguyen, Senior Machine Learning Engineer and Data Science Lead, DNAnexus

Peter Nguyen is a machine learning engineer and project lead at DNAnexus. He has a diverse background in machine learning, computational biology and systems engineering. His research interest involves making large-scale, computational biology methods accessible to end-users to enable precision medicine.

Message Presenter

Who Should Attend?

This webinar will appeal to the following audiences:

  • Clinician Researchers, Biostatisticians, Epidemiologists within Precision Medicine programs
  • Biomarker Sciences Directors, Therapeutic Area Heads, RWD Scientists at pharmaceutical companies
  • VP/Director of Precision Medicine Initiatives or Observational Research, or Translational Medicine at Pharma or Healthcare Systems
  • Health and medical media (reporters, bloggers, etc.)

What You Will Learn

  • 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

Xtalks Partner

DNAnexus

DNAnexus, the leader in biomedical informatics and data management, has created the global network for genomics and other biomedical data, operating in 33 countries including North America, Europe, China, Australia, South America, and Africa. The secure, scalable, and collaborative DNAnexus Platform helps thousands of researchers across a spectrum of industries — biopharmaceutical, bioagricultural, sequencing services, clinical diagnostics, government, and research consortia — accelerate their genomics programs. For more information on DNAnexus, please visit www.dnanexus.com or follow the company @DNAnexus.

Media Partner

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