title: 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.
abstract: Extensive efforts have been made to understand genomic function through both experimental and computational approaches, yet proper annotation still remains challenging, especially in non-coding regions. In this manuscript, we introduce GenoSkyline, an unsupervised learning framework to predict tissue-specific functional regions through integrating high-throughput epigenetic annotations. GenoSkyline 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 results from genome-wide association studies (GWAS) led to novel biological insights on the etiologies of a number of human complex traits. We also explored using tissue-specific functional annotations to prioritize GWAS signals and predict relevant tissue types for each risk locus. Brain and blood-specific annotations led to better prioritization performance for schizophrenia than standard GWAS p-values and non-tissue-specific annotations. As for coronary artery disease, heart-specific functional regions was highly enriched of GWAS signals, but previously identified risk loci were found to be most functional in other tissues, suggesting a substantial proportion of still undetected heart-related loci. In summary, GenoSkyline annotations can guide genetic studies at multiple resolutions and provide valuable insights in understanding complex diseases. GenoSkyline is available at http://genocanyon.med.yale.edu/GenoSkyline.
authors.affiliation: Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, United States of America.
authors.forename: Qiongshi
authors.initials: Q
authors.lastname: Lu
authors.affiliation: Program of Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America.
authors.forename: Ryan Lee
authors.initials: RL
authors.lastname: Powles
authors.affiliation: Program of Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America.
authors.forename: Qian
authors.initials: Q
authors.lastname: Wang
authors.affiliation: Division of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America.
authors.forename: Beixin Julie
authors.initials: BJ
authors.lastname: He
authors.affiliation: Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, United States of America.|Program of Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America.
authors.forename: Hongyu
authors.initials: H
authors.lastname: Zhao
mesh_terms: D003324:Coronary Artery Disease / Q000235:genetics; D015894:Genome, Human; D055106:Genome-Wide Association Study; D006801:Humans; D020641:Polymorphism, Single Nucleotide