Skip to content

Latest commit

 

History

History
43 lines (29 loc) · 1.79 KB

README.md

File metadata and controls

43 lines (29 loc) · 1.79 KB
Raw

Genome-wide association (GWA) tutorial

Additional files

For this tutorial you will additionally need the files

  • 117Malay_282lipids.txt
  • 120Indian_282lipids.txt
  • 122Chinese_282lipids.txt
  • 105Indian_2527458snps.bed, .bim, .fam
  • 108Malay_2527458snps.bed, .bim, .fam
  • 110Chinese_2527458snps.bed, .bim, .fam

stored in the folders 'Lipidomic' and 'Genomics' contained in the following compressed file: https://sphfiles.nus.edu.sg/phg/Iomics/downloads/iOmics_data.tar.gz

UPDATE 25/06/2019: uncompressed iOmics_data.tar.gz now directly available as public/

I noticed the URL recently changed. To avoid problems with tracking the data, I have now hosted all of them in this repo. It is no longer necessary to download from the link above.

Instructions

  1. Combine the folders 'Lipidomic' and 'Genomics' and all files from this repo in your working directory.
  2. Install all packages listed on top of the scripts. snpStats and SNPRelate are deposited in BioConductor, all other packages in CRAN.

UPDATE 25/06/2019: Linux/macOS installation of GenABEL:

install.packages("GenABEL.data", repos="http://R-Forge.R-project.org")
packageurl <- "https://cran.r-project.org/src/contrib/Archive/GenABEL/GenABEL_1.8-0.tar.gz"
install.packages(packageurl, repos=NULL)
  1. Run the scripts in their exact numbered order.

Acknowledgements

This work was largely based on the following publications:

  • Establishing multiple omics baselines for three Southeast Asian populations in the Singapore Integrative Omics Study, Saw et al. (2017), Nat. Comm. (data source)
  • A guide to genome-wide association analysis and post-analytic interrogation, Reed et al. (2015), Stats. in Med. (method source)

Also, thanks to @nizzle10, @rafalcode and @bambrozio for contributing. Enjoy, all feedback is welcome!

Francisco