Session 6: Integrative analyses and Mendelian Randomization

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  • Breeze, C. E., Haugen, E., Reynolds, A., Teschendorff, A., van Dongen, J., Lan, Q., Rothman, N., Bourque, G., Dunham, I., Beck, S., Stamatoyannopoulos, J., Franceschini, N., & Berndt, S. I. (2022). Integrative analysis of 3604 GWAS reveals multiple novel cell type-specific regulatory associations. Genome Biology, 23(1), 13. https://doi.org/10.1186/s13059-021-02560-3

  • PrediXcan: Gamazon, E., Wheeler, H., Shah, K. et al. A gene-based association method for mapping traits using reference transcriptome data. Nat Genet 47, 1091–1098 (2015). https://doi.org/10.1038/ng.3367

  • MetaXcan: Barbeira, A.N., Dickinson, S.P., Bonazzola, R. et al. Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics. Nat Commun 9, 1825 (2018). https://doi.org/10.1038/s41467-018-03621-1

  • FUSION: Gusev, A., Ko, A., Shi, H. et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat Genet 48, 245–252 (2016). https://doi.org/10.1038/ng.3506

  • Proteome-WAS: Brandes, N., Linial, N. & Linial, M. PWAS: proteome-wide association study—linking genes and phenotypes by functional variation in proteins. Genome Biol 21, 173 (2020). https://doi.org/10.1186/s13059-020-02089-x

  • Metabo-WAS: Ding M., Zeleznik A., Guasch-Ferre M., et al. Metabolome-Wide Association Study of the Relationship Between Habitual Physical Activity and Plasma Metabolite Levels, Am J Epid, 188, 11, [1932–1943 (2019) https://doi.org/10.1093/aje/kwz171

  • Epigenome-WAS: Campagna, M.P., Xavier, A., Lechner-Scott, J. et al. Epigenome-wide association studies: current knowledge, strategies and recommendations. Clin Epigenet 13, 214 (2021). https://doi.org/10.1186/s13148-021-01200-8

  • Regulome-WAS: Grishin, D., Gusev, A. Allelic imbalance of chromatin accessibility in cancer identifies candidate causal risk variants and their mechanisms. Nat Genet 54, 837–849 (2022). https://doi.org/10.1038/s41588-022-01075-2

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