Session 2: Basic GWAS analyses

Basic GWAS analyses

  • Basic GWAS testing.
  • Meta-analyses.
  • Visualizations.
  • Annotating GWAS results.

Practical

  • To perform a basic GWAS using linear and logistic regression.
  • To display simple visualization of GWAS results and post GWAS QC.
  • To provide additional considerations and for deploying GWAS in real studies.
  • To provide a simple meta-analysis example and results

Data Used

GWAS: Reilly MP, Li M, He J, Ferguson JF, Stylianou IM, Mehta NN, et al. Identification of ADAMTS7 as a novel locus for coronary atherosclerosis and association of ABO with myocardial infarction in the presence of coronary atherosclerosis: two genome-wide association studies. Lancet. 2011;377:383–392.

Meta-Analysis: Zhang et al. Genome-wide association study identifies 32 novel breast cancer susceptibility loci from overall and subtype-specific analyses. Nat Genet. 2020 Jun;52(6):572-581. doi: 10.1038/s41588-020-0609-2

Programs Used

Purcell S, Neale B, Todd-Brown K, et al. PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses. Am J Hum Genet. 2007;81(3):559-575.

Turner, (2018). qqman: an R package for visualizing GWAS results using Q-Q and manhattan plots. Journal of Open Source Software, 3(25), 731, https://doi.org/10.21105/joss.00731.

Buhm Han and Eleazar Eskin, “Random-Effects Model Aimed at Discovering Associations in Meta-Analysis of Genome-wide Association Studies”, The American Journal of Human Genetics (2011) 88, 586-598.

K. Watanabe, E. Taskesen, A. van Bochoven and D. Posthuma. Functional mapping and annotation of genetic associations with FUMA. Nat. Commun. 8:1826. (2017). https://www.nature.com/articles/s41467-017-01261-5

Machiela MJ, Chanock SJ. LDassoc: an online tool for interactively exploring genome-wide association study results and prioritizing variants for functional investigation. Bioinformatics. 2017 Sept 12.

  • Main reading on Genome Wide Association Studies and Analysis
    • Uffelmann, E., Huang, Q. Q., Munung, N. S., de Vries, J., Okada, Y., Martin, A. R., Martin, H. C., & Lappalainen, T. (2021). Genome-wide association studies. Nature Reviews Methods Primers, 1(1), 1–21. https://doi.org/10.1038/s43586-021-00056-9
  • Overview of findings from genome-wide association studies
    • Penney, K. L., Michailidou, K., Carere, D. A., Zhang, C., Pierce, B., Lindström, S., & Kraft, P. (2017). Genetic Epidemiology of Cancer. https://doi.org/10.1093/oso/9780190238667.003.0005
    • Visscher, P. M., Wray, N. R., Zhang, Q., Sklar, P., McCarthy, M. I., Brown, M. A., & Yang, J. (2017). 10 Years of GWAS Discovery: Biology, Function, and Translation. American Journal of Human Genetics, 101(1), 5–22. https://doi.org/10.1016/j.ajhg.2017.06.005