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.


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.