Integrative genetic epidemiology with OpenGWAS, OpenCRAVAT, and Bioconductor

Authors: Vincent Carey^[Harvard Medical School],
Last modified: 16 Mar, 2021.

Overview

Description

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Pre-requisites

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Participation

Students will use R to acquire variant data from resources like TCGA, select variant annotation resources available in OpenCRAVAT, and produce reports on variant function and impact.

R / Bioconductor packages used

GenomicRanges

Time outline

| Activity | Time | |------------------------------|------| | Concepts of genetic variation| 10m | | MRC Integrative Epidemiology Unit resources| 10m | | OpenCRAVAT | 10m | | Exercises | 30m |

Workshop goals and objectives

The interpretation of genetic variants is fundamental to all aspects of clinical genetics and genetic epidemiology. MRC OpenGWAS (publication https://doi.org/10.1101/2020.08.10.244293) is a data repository and API suite providing interactive access to statistics and metadata for hundreds of billions of human genetic variants. OpenCRAVAT (web site opencravat.org, publication DOI 10.1200/CCI.19.00132) is a system that amalgamates over 100 variant annotation resources and simplifies the development of rich characterizations of structural and functional contexts of genetic variants. Bioconductor (bioconductor.org) is an ecosystem of data structures and software packages that can be used in many contexts in genome biology and computational biomedicine. The gwaslake workshop adapts Bioconductor programming patterns and flexible containerization to simplify exploration, annotation, and interpretation of OpenGWAS variants assembled on a large collection of cohorts and phenotypes.

Learning goals

In this workshop, we will guide you through the assembly of variants from diverse sources, in diverse formats, for flexible annotation using OpenCRAVAT. Bioconductor data structures and app designs are used to provide high-level conveniences for representation and analysis of cohorts arising in genetic epidemiology and cancer genomics.

Through live demonstrations and interactive small-group exercises, you will learn how to:

Learning objectives

The activities undertaken in this workshop will require some familiarity with either jupyter notebooks or Rstudio, but no programming per se will be necessary to take advantage of the workshop material.

Learning Objectives:

  1. Identify the fundamental components of working with human genetic variants in a cloud-native environment, making use of diverse GWAS and PheWAS results

  2. Understand how to define a series of annotation tasks for variant data assembled on individuals or cohorts, and how to use OpenCRAVAT to execute these tasks with a Bioconductor/shiny app

  3. Use the outputs of OpenCRAVAT annotators to interpret effects of genetic variants on disease risk or other concepts of interest in genetic epidemiology.



vjcitn/gwaslake documentation built on Aug. 9, 2022, 5:45 p.m.