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

Abstract

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 noncoding 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 catalog of tissue-specific non-coding elements previously identified in the literature, we apply our method using data from 127 different cell and tissue types to present an atlas of heritability 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. CC-BY-NC-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under aThe copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/078865 doi: bioRxiv preprint first posted online Oct. 2, 2016; Author SummaryAfter years of community efforts, many experimental and computational approaches have been developed and applied for functional annotation of the human genome, yet proper annotation still remains challenging, especially in non-coding regions. As complex disease research rapidly advances, increasing evidence suggests that non-coding regulatory DNA elements may be the primary regions harboring risk variants in human complex diseases. In this paper, we introduce GenoSkyline-Plus, a principled annotation framework to identify tissue and cell type-specific functional regions in the human genome through integration of diverse high-throughput epigenomic and transcriptomic data. Through validation of known non-coding tissue-specific regulatory regions, enrichment analyses on 45 complex traits, and an in-depth case study of neurodegenerative diseases, we demonstrate the ability of GenoSkyline-Plus to accurately identify tissue-specific functionality in the human genome and provide unbiased, genome-wide insights into the genetic basis of human complex diseases.

LPM: a latent probit model to characterize the relationship among complex traits using summary statistics from multiple GWASs and functional annotations

“…LDL:β = 0.3609, se= 0.0267; TC:β = 0.3823, se= 0.0237; Triglycerides:β = 0.2478, se= 0.0293), which was consistent with findings in previous studies (16,25,17). SNPs annotated in cells of immune system were observed to be enriched for many traits, including autoimmune diseases, lipidrelated traits, hematopoietic traits, some psychiatric disorders such as SCZ and BIP.…”

A statistical framework for cross-tissue transcriptome-wide association analysis

“…To identify trait-related tissue, we first used GenoSkyline-Plus, an unsupervised learning framework trained on various epigenetic marks from the Roadmap Epigenomics Project 82 , to quantify tissue-specific functionality in the human genome 83 . We then estimated the enrichment for trait heritability in each tissue's predicted functional genome using LD score regression 34 .…”

Detecting Local Genetic Correlations with Scan Statistics

“…We used GenoSkyline-Plus tissue-specific functional annotations 41 to investigate the functional relevance of the genomic regions found to harbor local genetic correlations among five psychiatric disorders. First, we tested the five disorders' heritability enrichment in the predicted functional genome of 66 tissue and cell types ( Supplementary Table 6; Methods).…”