Fine-mapping data visualization tool

Genome-wide association studies (GWAS) have identified thousands of variants in the genome that
reproducibly associate with complex disease. However, most variants uncovered in GWAS are not directly biologically causal, but rather, are correlated with the true causal variant(s) through linkage disequilibrium. Statistical fine-mapping aims to prioritize Single Nucleotide Polymorphisms (SNPs) in regions surrounding a significant association peak to discern the true causal variants. Recent integrative methods that leverage disparate sources have become important strategies in genetics research. For example, large-scale GWAS and functional annotation data derived from the ENCODE/ROADMAP project have been successfully assembled to improve power in fine-mapping, as demonstrated in methods such as PAINTOR (Kichaev et al. 2014), CAVIARBF (Chen et al. 2015) and fgwas (Pickrell 2014). However, while statistical engines that drive these methods have been well-established, principled ways of visualizing the resulting outputs of integrative fine-mapping studies is currently lacking.

The goal of this project is to create a fine-mapping tool that visually summarizes an integrative fine-mapping experiment in a publication-ready figure. As compared to existing data visualization tools, we want to specifically incorporate important visual features of fine-mapping studies at a particular locus of interest such as z-scores from GWAS, LD structure, posterior probabilities, and genomic functional annotations.

Difficulty Level: beginner friendly

Difficulty Description: This project will mostly involve using Python and its various libraries such as Matplotlib; knowledge of the command line is recommended, but not necessary. In addition, the project presents a great opportunity to get an introduction to statistical genetics.

Recommended prior knowledge:

  1. knowledge of Python or another programming language (i.e. C/C++,  Java, etc.)

Material to review before meeting:

Please read the introduction and basic method description of the following:

  1. PAINTOR fine-mapping tool: http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1004722
  2. Review paper for fine-mapping: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4572002/
  3. Play around with current data visualization tools: http://locuszoom.sph.umich.edu/

If interested, contact Ruthie Johnson (ruthjohnson@ucla.edu)

  • Date
    November 2, 2017
    Fine-mapping data visualization tool Genome-wide association studies (GWAS) have identified thousands of variants in the genome that reproducibly associate with complex disease. However, most variants uncovered […]
  • Date
    October 24, 2017
    *Image Credit: Price, A. et. al. Sensitive Detection of Chromosamal Segments of Distinct Ancestry in Admixed Populations. PLoS Genetics 2009 Haplotype-wide Association Studies? Genome-wide association studies have found […]