Genome-wide association study (GWAS) has been a great success in the past decade, with tens of thousands of loci identified associated with many complex diseases in humans. However, challenges still remain in both identifying new risk loci and interpreting results. Bonferroni-corrected significance level is a conservative threshold for high dimensional hypothesis testing, leading to insufficient statistical power when the effect size is moderate at each risk locus. Complex structure of linkage disequilibrium also makes it challenging to distinguish causal variants from large haplotype blocks. We propose GenoWAP (Genome-Wide Association Prioritizer), a post-GWAS prioritization method that integrates genomic functional annotation and GWAS test statistics. After prioritization, real disease-associated loci become easier to be identified. Within each locus, GenoWAP is also able to identify real functional spots among correlated markers. GenoWAP has the potential to be widely used to reveal functional spots at disease-associated risk loci and guide further studies such as resequencing analysis.
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)
Please read our paper for more details.
In order to fully utilize the GenoWAP algorithm, we suggest you to use the source code. However, frozen versions for multiple platforms are also available for download. We recently released GenoWAP V1.2, in which we implemented the new feature of integrating GenoSkyline tissue-specific functional annotations. Check out our GenoSkyline paper for more details.
GenoWAP should not be used for commercial purpose without our permission.
Last updated on 2015-11-02
|Source Code and User Manual||Available on GitHub||Link|
Qiongshi Lu is a doctoral student in Biostatistics at Yale University. His research interest is in predicting disease-specific and tissue-specific functional non-coding regions in the human genome. He is also interested in the application of next generation sequencing and statistical graphical models in genomic epidemiology.
Xinwei (David) Yao is an undergraduate majoring in Intensive Mathematics at Yale College (Jonathan Edwards). He is interested in combining his knowledge in Math and Computer Science in interesting applications and is passionate about technology and software development.
Yiming Hu is a doctoral student in Biostatistics at Yale University. His research interest is in developing statistical and computational method in genetics. Specifically, he is interested in developing similarity measure for clustering using gene expression data, and studying tumor heterogeneity using DNA sequencing and single-cell RNA-seq data.
Hongyu Zhao is Ira V. Hiscock Professor of Public Health (Biostatistics) and Professor of Genetics and of Statistics at Yale University.