README.md

1. code: contains all code files to get the figures/tables in the paper.

   • a_read_summarydata: read the raw summary GWAS data, then convert it to some format that can be load to GENESIS R package. Take prostate cancer as an example, the raw data can be downloaded from http://practical.icr.ac.uk/blog/?page_id=8164. The summary GWAS data is summarized into summarydata.RData.
   • b_clump_outlier: doing LD clumping for outlier SNPs with effect sizes chi-square larger than 80, i.e., take out those SNPs with chi-square larger than 80, as well as their correlated SNPs. Denote Also calculate the heritability of those outlier SNPs. The summary data after removing the outlier SNPs are stored into summarydata_RemoveOutlier.RData. The heritability that are explained by those removed outlier SNPs are stored into herit_OutlierIndep.RData.
   • b1_LDclump_gw_signif: doing LD clumping to get genome-wide significant SNPs information.
   • c_fit_GENESIS: fit_GENESIS fit GENESIS model on summarydata_RemoveOutlier.RData, and stored object into fit2_RemoveOutlier_pic*.RData, bestfit2_RemoveOutlier.RData, and fit3_RemoveOutlier.RData. If there is no outlier for a particular GWAS data, then fit_GENESIS 2 can be fitted on summarydata.RData, and the fitted object are stored in fit2_pic*.RData, bestfit2.RData, and fit3.RData.
   • d_summary_figure: contains the R code to get the figures/tables in the paper.
   • e_LifeTimeRisk_Fig5_SF567: contains the R code to get Figure 5 and Supplementary Figure 5, 6 & 7.

2. data_new contains the .RData files running from code/a_read_summarydata and code/b_clump_outlier.

3. data_files contains the REFERENCE panel, i.e., 1000 Genome Hapmap3 common SNPs information.

4. data_samplesize contains cancer_sample_size.csv file which contains the sample size information of the cancer GWAS.

5. genesis_result_new saved GENESIS results running from code/c_fit_GENESIS

6. result_png_csv_new_clump constains the summarized figures/tables running from code/d_summary_figure.

Citation

Zhang, Y.D., Hurson, A.N., Zhang, H. et al. Assessment of polygenic architecture and risk prediction based on common variants across fourteen cancers. Nat Commun 11, 3353 (2020). https://doi.org/10.1038/s41467-020-16483-3

Contact the Author

Yan Dora Zhang (doraz@hku.hk or yandorazhang@gmail.com)

(Updated on July 3, 2020)