Last updated: 2018-09-16
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Xiang Zhu
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2018-09-16
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wflow_publish(“analysis/index.Rmd”)
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Xiang Zhu
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2018-09-16
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Build site.
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Xiang Zhu
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2018-09-16
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Xiang Zhu
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2018-07-02
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Xiang Zhu
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2018-07-02
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Xiang Zhu
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2018-07-02
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2018-07-02
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2018-07-02
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2018-07-02
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2018-07-02
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Xiang Zhu
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2018-07-01
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2018-06-30
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2018-06-27
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2018-06-27
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2018-06-27
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2018-06-26
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2018-06-26
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2018-06-26
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2018-06-26
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Xiang Zhu
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2018-06-26
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Start workflowr project.
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Overview
This is my online notebook to document and share the full results of genome-wide enrichment analyses described in the manuscript:
Xiang Zhu and Matthew Stephens (2017). A large-scale genome-wide enrichment analysis identifies new trait-associated genes, pathways and tissues across 31 human phenotypes. bioRxiv. https://doi.org/10.1101/160770.
The software that generated these results is freely available at stephenslab/rss
. We also provide an end-to-end example illustrating how to use this software to perform genome-wide enrichment analyses of GWAS summary statistics. This software can be referenced in a journal’s “Code availability” section as [][zenodo-software].
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 .
If you find the analysis results, the pre-processed gene sets, the statistical methods, and/or the software useful for your work, please kindly cite our manuscript listed above, Zhu and Stephens (2017).
If you have any question about the notebook and/or the manuscript, please feel free to contact me: Xiang Zhu, xiangzhu@uchicago.edu
or xiangzhu@stanford.edu
.
Main results
Anthropometric phenotypes
Additional resources
- How can I perform similar analyses on a new dataset of GWAS summary statistics?
The software that generated results of this study is available at stephenslab/rss
. I also write a step-by-step tutorial illustrating how to use this software to perform genome-wide enrichment and prioritization analyses on GWAS summary statistics.
- Where can I download all 4,026 pre-processed gene sets?
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 “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 projects on the “fourth floor” (i.e. the great 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” 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 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 hope that this change can address the referee’s comment.
- Where can I find the gene prioritization results?
Currently you need to contact me directly to view our full prioritization results. Unlike the gene set enrichment results, the gene prioritization results cannot be easily tabulated, and thus I have not displayed them in this notebook yet.
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