Statistical Methodology
- Newcombe, P. J., Conti, D. V., & Richardson, S. (2016). JAM: A
Scalable Bayesian Framework for Joint Analysis of Marginal SNP Effects.
Genetic epidemiology, 40(3), 188–201. https://doi.org/10.1002/gepi.21953
- Shen, J., Jiang, L., Wang, K., Wang, A., Chen, F., Haiman, C.A.
& Conti, D.V. (2022). Multiethnic Joint Analysis of Marginal Summary
Statistics from Genome-wide Association Studies. Manuscript in
preparation.
Applications on Prostate Cancer
- Conti, D. V., Darst, B. F., Moss, L. C., Saunders, E. J., Sheng, X.,
Chou, A., Schumacher, F. R., Olama, A., Benlloch, S., Dadaev, T., Brook,
M. N., Sahimi, A., Hoffmann, T. J., Takahashi, A., Matsuda, K.,
Momozawa, Y., Fujita, M., Muir, K., Lophatananon, A., Wan, P., … Haiman,
C. A. (2021). Trans-ancestry genome-wide association meta-analysis of
prostate cancer identifies new susceptibility loci and informs genetic
risk prediction. Nature genetics, 53(1), 65–75. https://doi.org/10.1038/s41588-020-00748-0
- Wang, A., Shen, J., Rodriguez, A., Saunders, E., Chen, F., Janivara,
R., Darst, B., Sheng, X., Xu, Y., Chou, A., Nakagama, H., Witte, J.,
Gaziano, J., Justice, A., Mancuso, N., Terao, C., Eeles, R., Kote-Jarai,
Z., Madduri, R., Conti, D.V. & Haiman, A.C. (2022). Characterizing
prostate cancer risk through multi-ancestry genome-wide discovery of 187
novel risk variants. Manuscript submitted for publication.
Tutorial:
Check out some examples of running mJAM (here).