/MGflashfm-GLGC-analysis

The MGflashfm-GLGC analysis scripts and instructions

Primary LanguageRMIT LicenseMIT

MGflashfm-GLGC-analysis

DOI

The MGflashfm-Global Lipids Genetic Consortium (GLGC) analysis scripts and instructions

Replication

This repository holds the scripts and instructions used to generate the results for the MGflashfm fine-mapping paper (cited below).

For the repository containing the source code of MGflashfm R package, see: https://github.com/jennasimit/MGflashfm

Paper citation:

Leveraging information between multiple population groups and traits improves fine-mapping resolution
F Zhou, O Soremekun, T Chikowore, S Fatumo, I Barroso, AP Morris, JL Asimit
The paper and its supplementary material are published in Nature Communications
doi: https://doi.org/10.1038/s41467-023-43159-5

For more details, see the folder (GLGC_data_scripts/) and the paper (Methods and Supplementary Materials)

Background

Global Lipids Genetic Consortium (GLGC) performed a multi-group genome-wide meta-analysis of lipid levels in 1.65 million people1. Their multi-group meta-analysis included five genetically similar groups that are labelled by continent. For consistency, we have kept the same group labels as those published: admixed African or African (AFR, N=99,432, 6.0% of the sample); East Asian (EAS, N=146,492, 8.9%); European (EUR, N=1,320,016, 79.8%); Hispanic (HIS, N=48,057, 2.9%); and South Asian (SAS, N=40,963, 2.5%). We consider four of their five blood lipids traits: low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), triglycerides (TG), and total cholesterol (TC); non-high-density lipoprotein cholesterol (nonHDL-C) is excluded due to its higher number of missing variants in any given region, compared to the other four traits.

We used MGflashfm and MGfm to fine-map signals among HDL, LDL, TG, and TC in the five groups, and provide the following scripts and data:

  • GLGC_analysis.R: the main analysis script that prepares the data for fine-mapping analysis and runs the analyses
  • QCfunctions.R: a dependency in the GLGC_analysis.R script, here we provide functions that we used to perform quality control on the GWAS summary data from each population group
  • GLGC_input_50regions_chr_pos_index_100k.csv: Details of fine-mapped regions

Reference

Footnotes

  1. Graham, S. E. et al. (2021) The power of genetic diversity in genome-wide association studies of lipids.. Nature 600, 675–679. https://www.nature.com/articles/s41467-020-14791-2. ↩