{"id":20074714,"url":"https://github.com/greenelab/tad_pathways","last_synced_at":"2026-05-31T20:31:27.715Z","repository":{"id":79359964,"uuid":"65410058","full_name":"greenelab/tad_pathways","owner":"greenelab","description":"DEPRECATED: Integrating topologically associating domains (TADs) to prioritize GWAS signal","archived":false,"fork":false,"pushed_at":"2017-04-21T17:37:07.000Z","size":1822,"stargazers_count":1,"open_issues_count":3,"forks_count":2,"subscribers_count":6,"default_branch":"master","last_synced_at":"2025-05-23T00:26:52.301Z","etag":null,"topics":["analysis","deprecated","methodology","pathway"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"bsd-3-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/greenelab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2016-08-10T19:21:20.000Z","updated_at":"2020-09-29T19:50:09.000Z","dependencies_parsed_at":"2023-03-09T04:15:32.342Z","dependency_job_id":null,"html_url":"https://github.com/greenelab/tad_pathways","commit_stats":null,"previous_names":[],"tags_count":1,"template":false,"template_full_name":null,"purl":"pkg:github/greenelab/tad_pathways","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/greenelab%2Ftad_pathways","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/greenelab%2Ftad_pathways/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/greenelab%2Ftad_pathways/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/greenelab%2Ftad_pathways/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/greenelab","download_url":"https://codeload.github.com/greenelab/tad_pathways/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/greenelab%2Ftad_pathways/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":33748607,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-05-31T02:00:06.040Z","response_time":95,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["analysis","deprecated","methodology","pathway"],"created_at":"2024-11-13T14:53:52.532Z","updated_at":"2026-05-31T20:31:27.697Z","avatar_url":"https://github.com/greenelab.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"**IMPORTANT: This repository is no longer maintained. In addition to running a TAD_Pathways\nanalysis for Bone Mineral Density GWAS, this analysis pipeline downloads genomic data and\nexplores distributions across TADs. A more streamlined analysis pipeline that does not require\ndata preprocessing is available at https://github.com/greenelab/tad_pathways_pipeline**\n\n**NOTE: Several files built from this pipeline are used in the above repository including the\nTAD based gene index and the hg19 converted NHGRI-EBI GWAS Catalog.**\n\n# Incorporating TADs into GWAS Analysis - TAD_Pathways\n\nGregory P. Way and Casey S. Greene 2016\n\n[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.163950.svg)](https://doi.org/10.5281/zenodo.163950)\n\n## Summary\n\nThe repository contains methods for manipulating, observing, and visualizing\ntopologically associating domains (TADs) in the context of SNPs, genes, and \nrepeat elements for human (hg19) and mouse (mm9) genomes.\n\nThe repository also proposes methods and tools for the incorporation of TAD\ndomains into the prioritization of GWAS signals through the investigation of\npublicly available GWAS data. We introduce **TAD pathways** as a method to\nidentify the likely causal genes from GWAS independent of distance to sentinel\nSNP.\n\nA preprint of our method is available [here on bioRxiv](http://dx.doi.org/10.1101/087718)\n\n## Contact\n\nFor all questions and bug reporting please file a\n[GitHub issue](https://github.com/greenelab/tad_pathways/issues)\n\nFor all other questions contact Casey Greene at csgreene@mail.med.upenn.edu or\nStruan Grant at grants@email.chop.edu\n\n## Usage\n\nThere are two ways to implement a TAD_Pathways analysis:\n\n1. Disease/Trait Specific - Uses GWAS identified SNPs\n2. Custom - Uses custom SNP list\n\n### Disease/Trait Specific\n\nCurates the GWAS catalog and TAD boundaries to visualize TADs and generate\nTAD based gene lists. This will also perform a TAD pathways analysis for\nBone Mineral Density GWAS. This will reproduce the analysis and figures used\nin the paper.\n\n```sh\n# Using python dependencies\nconda env create --quiet --force --file environment.yml\nsource activate tad_pathways\n\nbash scripts/run_pipeline.sh\n```\n\nThis will download data, perform analyses, and output several genomic figures.\nThe command will also output TAD based genes for 299 different GWAS traits.\nOur TAD_Pathways method can be applied directly using these gene lists.\n\n### Custom\n\nTAD_Pathways is customizable and allows a user to prespecify any SNP list of\ninterest to test TAD based pathway associations. To perform a custom analysis\ncreate a comma separated file where the first row of each column names the list\nof snps below in subsequent rows.\n\nE.g.: `custom_example.csv`\n\n| Group 1 | Group 2 |\n| ------- | ------- |\n| rs12345 | rs67891 |\n| rs19876 | rs54321 |\n\nThen, perform the following steps:\n\n```bash\n# Extract locations for SNP list\nRscript --vanilla scripts/tad_util/build_snp_list.R \\\n        --snp_file \"custom_example.csv\" \\\n        --output_file \"mapped_results.tsv\"\n\n# Build TAD based genelists for each group\npython scripts/build_custom_TAD_genelist.py \\\n       --snp_data_file \"mapped_results.tsv\" \\\n       --output_file \"custom_tad_genelist.tsv\"\n\n# The output file is then ready for the manual \"TAD_Pathways\" steps below\n```\n\n\n## TAD_Pathways\n\nAs a case study to demonstrate the utility of a TAD based approach,\ninput the TAD based gene list for the Bone Mineral Density (1,297 genes) into a\npathway analysis:\n\nNext, run a\n[WebGestalt](http://bioinfo.vanderbilt.edu/webgestalt/ \"Pathway Analysis\")\npathway analysis on the gene list.\n\n### WebGestalt Parameters\n\n| Parameter | Input |\n| --------- | ----- |\n| Select gene ID type | *hsapiens__gene_symbol* |\n| Enrichment Analysis | *GO Analysis* |\n| GO Slim Classification | Yes |\n| Reference Set | *hsapiens__genome* |\n| Statistical Method | *Hypergeometric* |\n| Multiple Test Adjustment | *BH* |\n| Significance Level | *Top10* |\n| Minimum Number of Genes for a Category | *4*\n\nNote - The output of `scripts/run_pipeline.sh` in *data/TAD_based_genes/* for\nall traits is ready for TAD Pathway Analysis.\n\nAfter performing the WebGestalt analysis, click `Export TSV Only` and save the\nfile in `data/gestalt/\u003cTRAIT\u003e_gestalt.tsv` where `\u003cTRAIT\u003e` is \"BMD\" for the\nexample.\n\n## GWAS/eQTL Integration\n\n### Data Access\n\n* GWAS Catalog (2016-02-25)\n* eQTL (2016-05-09)\n[eQTL Browser](http://www.ncbi.nlm.nih.gov/projects/gap/eqtl/index.cgi \"eQTL\")\n\n### Nearest BMD gene GWAS reports\n\n* [Richards _et al._ 2008 Lancet](http://doi.org/10.1016/S0140-6736(08)60599-1)\n* [Rivadeneira _et al._ 2009 Nature Genetics](http://doi.org/10.1038/ng.446)\n* [Estrada _et al._ 2012 Nature Genetics](http://doi.org/10.1038/ng.2249)\n* [Styrkarsdottir _et al._ 2013 Nature](http://doi.org/10.1038/nature12124)\n\n### eQTL Browser Parameters\n\n* Analysis ID (All)\n* Association Test Significance Filters (p-value 1 x 10^-1)\n* Phenotype Traits  (*Bone Mineral Density*)\n\n## Dependencies\n\nAll analyses were performed in the Anaconda python distribution (3.5.1). For\nspecific package versions please refer to `environment.yml`. R version 3.3.0 was\nused for visualization. For more specific environment dependencies refer to our\naccompanying docker file at `docker/Dockerfile`\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgreenelab%2Ftad_pathways","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgreenelab%2Ftad_pathways","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgreenelab%2Ftad_pathways/lists"}