Tips in Data Visualization for Genetic Mapping

Project Summary

The aim of this Data Expedition was for students to learn hands-on data visualization techniques using a variety of data types. Students first discussed how data visualization is useful, and tips to make graphs both visually appealing and easy to understand. 

Themes and Categories
C. Ryan Campbell

Graduate Students: Jenn Coughlan, Ryan Campbell

Course: Biology 490s - Methods in Comp Bio & Genomics

Over two 70-minute class periods, the students worked through two tutorials; the first introducing them to the basics of ggplot2, a data visualization package in the free statistical interface R. Students were then given a homework assignment to visualize a simple genotype-phenotype dataset, ‘Coughlan_inversiongenopheno.csv’. In the second class, we began by discussing the homework assignment, thinking of challenges and next steps. Students were then given a much more complicated dataset, involving reduced representation whole genome data from the wildflower Senecio (from Roda et al. 2017, dataset ‘Fst_BSA_wLinkagegrp.csv’). Students used this data to associate survival with allele frequencies across different habitats to determine regions of the genome which are associated with adaptation to edaphic conditions. 

Download the course slides (PDF).


Related Projects

This data expeditions module used three full course sessions to introduce undergraduate hydrology students with minimal programming background to:

  • Public water data (water quantity and chemistry)

  • Spatial analysis of water data

  • 2 core, spatial datasets produced by the USGS that enable spatial analysis

  • The programming language R

  • R based tools for water data

  • Spatial analysis and maps in R

Exposure to local pathogens is a significant selective pressure on the human genome: the strongest selective forces identified in modern human populations are for mutations that confer increased resistance to malaria infection. Understanding how human genetic variation impacts susceptibility to pathogens can reveal important aspects of disease biology and reveal novel treatment targets. By using genome-wide association of infection-related cellular traits, we can connect human genetic variation to disease susceptibility in a controlled laboratory environment. Identification of the variants, genes, and cellular pathways involved in infectious disease pathogenesis can inform host-directed therapeutics, clinically effective risk stratification, and epidemiological prediction. This data expedition explores the effect of host genetic variation on chemokine response to Chlamydia infection.

How does human habitation relate to patterns in the natural environment? How do species respond to the presence of, and changes in, habitation? In this Data Expedition, students make use of public datasets from the Census and the Global Biodiversity Information Facility to examine relationships between individual species and human settlements. Students develop introductory skills in spatial data manipulation and visualization in R, exposure to powerful datasets and tools, and critical thinking skills in assessing dataset quality and bias.