Recent work has demonstrated that some functional categories of the genome contribute disproportionately to the heritability of complex diseases. Here, we analyze a broad set of functional elements, including cell-type-specific elements, to estimate their polygenic contributions to heritability in genome-wide association studies (GWAS) of 17 complex diseases and traits with an average sample size of 73,599. To enable this analysis, we introduce a new method, stratified LD score regression, for partitioning heritability from GWAS summary statistics while accounting for linked markers. This new method is computationally tractable at very large sample sizes, and leverages genome-wide information. Our results include a large enrichment of heritability in conserved regions across many traits; a very large immunological disease-specific enrichment of heritability in FANTOM5 enhancers; and many cell-type-specific enrichments including significant enrichment of central nervous system cell types in body mass index, age at menarche, educational attainment, and smoking behavior.

Histones are characterized by numerous posttranslational modifications that influence gene transcription. However, because of the lack of global distribution data in higher eukaryotic systems, the extent to which gene-specific combinatorial patterns of histone modifications exist remains to be determined. Here, we report the patterns derived from the analysis of 39 histone modifications in human CD4(+) T cells. Our data indicate that a large number of patterns are associated with promoters and enhancers. In particular, we identify a common modification module consisting of 17 modifications detected at 3,286 promoters. These modifications tend to colocalize in the genome and correlate with each other at an individual nucleosome level. Genes associated with this module tend to have higher expression, and addition of more modifications to this module is associated with further increased expression. Our data suggest that these histone modifications may act cooperatively to prepare chromatin for transcriptional activation.

Histone acetyltransferases (HATs) and histone deacetylases (HDACs) function antagonistically to control histone acetylation states that are crucial to many cellular processes. We describe here genome-wide mapping experiments that reveal that both HATs (CBP, p300, PCAF, Tip60, MOF) and HDACs (HDAC1, HDAC2, HDAC3, HDAC6) on chromatin are positively correlated with gene expression and histone acetylation. We provide evidence that Tip60 and HDAC6 are targeted to transcribed regions of active genes by phosphorylated RNA Pol II. Our results indicate that MLL-mediated H3K4 methylation primes chromatin to facilitate histone acetylation. Our data suggest that the majority of HDACs in the human genome function to reset chromatin by removing acetylation in active genes; the dynamic cycle of acetylation and deacetylation by transient HAT/HDAC binding prevents Pol II from binding to the genes primed by H3K4 methylation and poises them for future activation.

SUMMARY Multipotential naïve CD4+ T cells differentiate into distinct lineages including T helper 1 (Th1), Th2, Th17, and inducible T regulatory (iTreg) cells. The remarkable diversity of CD4+ T cells begs the question whether the observed changes reflect terminal differentiation with heritable epigenetic modifications or plasticity in T cell responses. We generated genome-wide histone H3 lysine 4 (H3K4) and lysine 27 (H3K27) trimethylation maps in naïve, Th1, Th2, Th17, iTreg, and natural (n)Treg cells. We found that although modifications of signature cytokine genes (Ifng, Il4, and Il17) partially conform to the expectation of lineage commitment, critical transcription factors such as Tbx21 exhibit a broad spectrum of epigenetic states, consistent with our demonstration of T-bet and IFNγ induction in nTreg cells. Our data suggest an epigenetic mechanism underlying the specificity and plasticity of effector and regulatory T cells and also provide a framework for understanding complexity of CD4+ T helper cell differentiation.

Supplementary data are available at Bioinformatics online.