GenoSkyline is an unsupervised learning framework to predict tissue-specific functional regions through integrating high-throughput epigenomic annotations. Our method successfully identified a variety of non-coding regulatory machinery including enhancers, regulatory miRNA, and hypomethylated transposable elements in extensive case studies. Integrative analysis of GenoSkyline annotations and a collection of genome-wide association studies showed novel biological insight of human complex traits. In summary, GenoSkyline annotations can guide genetic studies at multiple resolutions and provide valuable insights in understanding complex diseases.
Citations:Lu Q, Yao X, Hu Y, and Zhao H. (2016).
GenoWAP: GWAS signal prioritization through integrated analysis of genomic functional annotation.
Bioinformatics, 32(4): 542-548.
Lu Q.*, Powles R.*, Wang Q., He B., and 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)
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).
We recently developed a breast-specific custom track of GenoSkyline annotation for the NCI Up for a Challenge. The BED file for breast-specific annotation is now available for download.
GenoSkyline should not be used for commercial purpose without our permission.
Version 1.0.1 fixed some minor issues in the bed files to make it compatible with our annotation extraction script on GitHub.
|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.
|LDSC files||Version 1.0.0||Download|
|Sample code||Version 1.0.0||Download|
Last updated on 2016-03-21
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.
|Source Code and User Manual||Available on GitHub||Link|
Last updated on 2015-11-02
Qiongshi Lu is a doctoral student in Biostatistics at Yale School of Public Health. 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.
Qian Wang is a doctoral student in Computational Biology and Bioinformatics Program at Yale University. Her research interest is in post-GWAS analysis, especially in exploring the interaction effects of genetic and environmental factors on diseases, as well as in studying the shared genetic factors of multiple traits. She is also interested in various applications of NGS.
B. Julie He is a clinical fellow in Cardiovascular Medicine at the Yale University School of Medicine. She has research interests in studying genetic predisposition to cardiac diseases.
Hongyu Zhao is Ira V. Hiscock Professor of Public Health (Biostatistics) and Professor of Genetics and of Statistics at Yale University.