Introduction
GenoSkyline is a principled framework to predict tissue-specific functional regions through integrating high-throughput epigenomic annotations. Integrative analysis of GenoSkyline annotations with GWAS summary statistics could systematically identify biologically relevant tissue types and provide novel insights into the genetic basis of human complex traits.
Citations:
Lu Q*, Powles R*, Abdallah S, Ou D, Wang Q, Hu Y, Lu Y, Liu W, Li B, Mukherjee S, Crane P, Zhao H. (2017). Systematic tissue-specific functional annotation of the human genome highlights immune-related DNA elements for late-onset Alzheimer's disease. PLOS Genetics, 13(7): e1006933. (* Equal Contribution)Lu Q*, Powles R*, Wang Q, He B, Zhao H. (2016). Integrative tissue-specific functional annotations in the human genome provide novel insights on many complex traits and improve signal prioritization in genome wide association studies. PLOS Genetics, 12(4): e1005947. (* Equal Contribution)
Lu Q, Yao X, Hu Y, Zhao H. (2016). GenoWAP: GWAS signal prioritization through integrated analysis of genomic functional annotation. Bioinformatics, 32(4): 542-548.
GenoSkyline-Plus Annotations
GenoSkyline-Plus is a comprehensive update of GenoSkyline that incorporates more annotation data into the framework and extends to 127 integrated annotation tracks covering a spectrum of human tissue and cell types. Pre-calculated hg19 GenoSkyline-Plus scores are freely available. Annotation tracks can be visualized on UCSC genome browser. BED files for all 127 GenoSkyline-Plus tracks are now available for download.
GenoSkyline-Plus annotations should not be used for commercial purpose without our permission.
| GenoSkyline Files | Version 1.0.0 | Download via Google Drive | |
| GenoSkylinePlus Files | Version 1.0.0 | Download via Google Drive | |
| Annotation scores (.bed) | Version 1.0.0 | Visualize Data | Download Instruction |
| LD score files (1KG_phase1) | Version 1.0.0 | User Manual | Download (500Mb) |
| LD score files (1KG_phase3) | Version 1.0.0 | User Manual | Download (570Mb) |
Last updated on 2017-06-10
GenoSkyline Annotations
Pre-calculated GenoSkyline scores for the hg19 genome are available for download. Custom tracks on UCSC genome browser for data visualization are also available. Click the "Visualize" button to see the instructions for visualizing GenoSkyline in the genome browser. Click the "Download" button to download GenoSkyline annotations in BED format (60~120 Mb for each track; the 5th column in each file is the GenoSkyline score).
GenoSkyline annotations should not be used for commercial purpose without our permission.
| Brain | Version 1.0.1 | Visualize | Download |
| GI | Version 1.0.1 | Visualize | Download |
| Lung | Version 1.0.1 | Visualize | Download |
| Heart | Version 1.0.1 | Visualize | Download |
| Blood | Version 1.0.1 | Visualize | Download |
| Muscle | Version 1.0.1 | Visualize | Download |
| Epithelium | Version 1.0.1 | Visualize | Download |
| ESC | Version 1.0.1 | Visualize | Download |
| Fetal Cells | Version 1.0.1 | Visualize | Download |
Last updated on 2016-04-05
We also provide the required files for using GenoSkyline in LD score regression. Details about LDSC software can be accessed on its Github page.
| LD score files | Version 1.0.0 | Download |
| Sample code | Version 1.0.0 | Download |
Last updated on 2016-03-21
GenoWAP Software
The new feature of integrating tissue-specific functional annotation for GWAS signal prioritization has been implemented in GenoWAP Version 1.2. In order to fully utilize the GenoWAP algorithm, we suggest you to use the source code. Frozen versions for mac and windows are also readily available for download.
GenoWAP should not be used for commercial purpose without our permission.
| Mac | Version 1.2.1 | Download |
| Windows | Version 1.2.1 | Download |
| Simulated Sample Data | Download | |
| Source Code and User Manual | Available on GitHub | Link |
Last updated on 2015-11-02
About Us
Qiongshi Lu is an Assistant Professor of Biostatistics at University of Wisconsin-Madison. His research focuses on integrative genomic functional annotations and their applications in statistical genetics. More specifically, he is interested in utilizing functional annotation to enhance the performance of GWAS signal prioritization and functional variant fine-mapping.
Ryan Powles is a doctoral student in Computational Biology and Bioinformatics Program at Yale University. He is interested in the use of statistical methods to effectively characterize genetic variation through functional genomics data. He hopes to apply these techniques in a variety of contexts across the non-coding regions of the human genome.
Hongyu Zhao is Ira V. Hiscock Professor of Public Health (Biostatistics) and Professor of Genetics and of Statistics at Yale University.
Links
Zhao Lab | Yale Center for Statistical Genomics and Proteomics
Yale Computational Biology and Bioinformatics