Session 7: GWAS, fine-mapping and PRS in diverse-genetic-ancestry and admixed samples

Lecture overview

  • Population-specific and trans-ancestry GWAS
  • Multi-population fine-mapping
  • PRS construction
  • PRS evaluation across populations
  • Absolute risk across populations

Practical:

  • Perform multi-population fine-mapping
  • Perform multi-population PRS construction using PRS-CSx
  • Perform multi-population PRS evaluation

Data Used:

Programs Used:

  • mJAM (program link and corresponding papers):
    • latest CRAN link: https://cran.r-project.org/web/packages/hJAM/index.html
    • https://www.biorxiv.org/content/10.1101/2022.12.22.521659v1
    • Lai Jiang, Shujing Xu, Nicholas Mancuso, Paul J. Newcombe, David V. Conti (2020). “A Hierarchical Approach Using Marginal Summary Statistics for Multiple Intermediates in a Mendelian Randomization or Transcriptome Analysis.” https://pubmed.ncbi.nlm.nih.gov/33404048/
    • Jiayi Shen, Lai Jiang, Kan Wang, Anqi Wang, Fei Chen, Paul J. Newcombe, Christopher A. Haiman, David V. Conti “Fine-Mapping and Credible Set Construction using a Multi-population Joint Analysis of Marginal Summary Statistics from Genome-wide Association Studies” https://www.biorxiv.org/content/10.1101/2022.12.22.521659v1.full.pdf
  • COJO:
    • https://yanglab.westlake.edu.cn/software/gcta/#COJO
    • Yang et al. (2012) Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat Genet 44(4):369-375.
  • msCAVIAR:
    • https://github.com/nlapier2/MsCAVIAR
    • Identifying Causal Variants by Fine Mapping Across Multiple Studies
    • Nathan LaPierre, Kodi Taraszka, Helen Huang, Rosemary He, Farhad Hormozdiari, Eleazar Eskin (2021) PLOS Genetics.
  • PRS-CSx
    • https://github.com/getian107/PRScsx
    • Y Ruan, YF Lin, YCA Feng, CY Chen, M Lam, Z Guo, Stanley Global Asia Initiatives, L He, A Sawa, AR Martin, S Qin, H Huang, T Ge. Improving polygenic prediction in ancestrally diverse populations. Nature Genetics, 54:573-580, 2022.

Suggested Readings

  • Zaitlen, N., Paşaniuc, B., Gur, T., Ziv, E., & Halperin, E. (2010). Leveraging genetic variability across populations for the identification of causal variants. American Journal of Human Genetics, 86(1), 23–33. https://doi.org/10.1016/j.ajhg.2009.11.016
  • Wang, A., Shen, J., Rodriguez, A. A., Saunders, E. J., Chen, F., Janivara, R., Darst, B. F., Sheng, X., Xu, Y., Chou, A. J., Benlloch, S., Dadaev, T., Brook, M. N., Plym, A., Sahimi, A., Hoffman, T. J., Takahashi, A., Matsuda, K., Momozawa, Y., … Haiman, C. A. (2023). Characterizing prostate cancer risk through multi-ancestry genome-wide discovery of 187 novel risk variants. Nature Genetics. https://doi.org/10.1038/s41588-023-01534-4
  • Martin, A. R., Kanai, M., Kamatani, Y., Okada, Y., Neale, B. M., & Daly, M. J. (2019). Clinical use of current polygenic risk scores may exacerbate health disparities. Nature Genetics, 51(4), 584–591. https://doi.org/10.1038/s41588-019-0379-x