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Systematic tissue-specific functional annotation of the human genome highlights immune-related DNA elements for late-onset Alzheimer's disease

By Qiongshi Lu, Ryan L Powles, Sarah Abdallah, Derek Ou, Qian Wang, Yiming Hu, Yisi Lu, Wei Liu, Boyang Li, Shubhabrata Mukherjee, Paul K Crane, Hongyu Zhao

Posted 02 Oct 2016
bioRxiv DOI: 10.1101/078865 (published DOI: 10.1371/journal.pgen.1006933)

Continuing efforts from large international consortia have made genome-wide epigenomic and transcriptomic annotation data publicly available for a variety of cell and tissue types. However, synthesis of these datasets into effective summary metrics to characterize the functional non-coding genome remains a challenge. Here, we present GenoSkyline-Plus, an extension of our previous work through integration of an expanded set of epigenomic and transcriptomic annotations to produce high-resolution, single tissue annotations. After validating our annotations with a large catalog of known tissue-specific non-coding elements, we apply our method using data from 127 different cell and tissue types to present an atlas of enrichment across 45 different GWAS traits. We show that broader organ system categories (e.g. immune system) increase statistical power in identifying biologically relevant tissue types for complex diseases while annotations of individual cell types (e.g. monocytes or B-cells) provide deeper insights into disease etiology. Additionally, we use our GenoSkyline-Plus annotations in an in-depth case study of late-onset Alzheimer's disease (LOAD). Our analyses suggest a strong connection between LOAD heritability and genetic variants contained in regions of the genome functional in monocytes. Furthermore, we show that the localization of SNPs to monocyte-functional regions is a pattern of inheritance shared with Parkinson's disease. Overall, we show that integrated genome annotations at the single tissue level may be a valuable tool for understanding the etiology of complex human diseases. Our expanded GenoSkyline-Plus annotations are freely available at http://genocanyon.med.yale.edu/GenoSkyline.

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