Last updated: 2018-10-29

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This is my online notebook to document and share the full results of genome-wide enrichment and prioritization analyses described in the article:

Xiang Zhu and Matthew Stephens (2018). Large-scale genome-wide enrichment analyses identify new trait-associated genes and pathways across 31 human phenotypes. Nature Communications 9, 4361.

We developed a new statistical method, RSS-E, to generate the results for this study. The software that implements RSS-E is freely available at stephenslab/rss. We also provide an end-to-end example illustrating how to use RSS-E to perform the reported genome-wide enrichment and prioritization analyses of GWAS summary statistics. This software can be referenced in a journal’s “Code availability” section as DOI.

In addition, all 4,026 pre-processed gene sets used in this study (including 3,913 biological pathways and 113 tissue-based gene sets) are freely available at xiangzhu/rss-gsea. These gene sets can be referenced in a journal’s “Data availability” section as DOI.

If you find the analysis results, the pre-processed gene sets, the statistical methods, and/or the open-source software useful for your work, please kindly cite our article listed above, Zhu and Stephens (2018).

If you have any question about the notebook and/or the article, please feel free to contact me: Xiang Zhu, xiangzhu[at]uchicago[and/or]

Main results

Additional resources

How can I perform similar analyses on a new GWAS summary dataset using RSS-E?

The software that generated results of this study is freely available at stephenslab/rss. I also write a step-by-step RSS-E tutorial that illustrates how to use this software to perform genome-wide enrichment and prioritization analyses on GWAS summary statistics.

Compared with most existing enrichment methods, the most appealing feature of RSS-E is the automatic gene prioritization in light of inferred enrichments. Is this gene prioritization feature available in your software?

Yes. This feature is implemented as function compute_pip.m in RSS-E. The step-by-step RSS-E tutorial illustrates how to use this function.

There are two sanity checks for the more sophisticated RSS-E analysis in Zhu and Stephens (2018): an eyeball test and a likelihood ratio calculation. Do you have software for these sanity checks?

Yes. The eyeball test is simply plotting marginal distribution of GWAS z-scores, stratified by SNP-level annotations based on a given gene set. Here we used ggplot2::geom_density (default setting). Regarding the likelihood ratio check, I write a stand-alone script ash_lrt_31traits.R. Please carefully read the instruction in this script. For more details of these two sanity checks, please see the caption of Supplementary Figure 17 in Zhu and Stephens (2018).

Where can I download all 4,026 pre-processed gene sets used in this work?

All 4,026 gene sets used in this study are freely available at xiangzhu/rss-gsea, where the folder biological_pathway contains 3,913 biological pathways, and the folder tissue_set contains 113 GTEx tissue-based gene sets. More details about these gene sets can be found here.

Where can I find RSS-E “baseline” model fitting results of all 31 traits?

You can find summary results of “baseline” model fitting at xiangzhu/rss-gsea-baseline. For me, the baseline model fitting results are merely inferential “bases” for the enrichment model fitting results shown in the “Main results” section above. However, when I was presenting the enrichment results during my Ph.D. thesis defense, Prof. John Novembre and Prof. Xin He both pointed out these baseline results might be useful for other on-going research projects on the “fourth floor” (i.e. the fantastic computational space shared with the labs of Matthew Stephens, John Novembre and Xin He). Their comments motivated me to create a separate online notebook xiangzhu/rss-gsea-baseline to share the baseline summary results.

Where can I find “Round 1” RSS-E results of all 3,913 biological pathways?

Currently you need to contact me directly to view our “Round 1” results of all 3913 pathways. When this work was under review, one referee pointed out that our previous online results, especially our “Round 1” analysis results, were “needlessly complicated” and did not have “any obvious benefit”. Hence, I removed the “Round 1” analysis results from this notebook to simplify the presentation. I sincerely hope that this change can address this referee’s comment.

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