B.L. Moore, S. Aitken and C.A. Semple MRC Human Genetics Unit, Western General Hospital, Edinburgh EH4 2XU Introduction Strong concordance between cell types Prediction of higher-order structure References ‣ Histone modifications ‣ Histone variants ‣ Transcription factors ‣ Dnase hypersensitivity ‣ DNA methylation ‣ Transcription level What's the relationship between these locus-level features and higher-order chromatin? ben.moore@igmm.ed.ac.uk What determines higher-order chromatin? Identification of significantly divergent regions PC eigenvector value −2 −1 0 1 2 10 MB H1 IMR90 K562 GM06990 GM12878 "Open" "Closed" p53 0 1 2 Atf3 0 1 2 Cebpb 0 1 2 Chd1 0 1 2 Chd2 0 1 2 Cmyc 0 1 2 Ctcf 0 1 2 Egr1 0 1 2 Ezh2 0 1 2 Gabp 0 1 2 J und 0 1 2 Mafk 0 1 2 Max 0 1 2 H2az 0 1 2 H3k27ac 0 1 2 H3k27me3 0 1 2 H3k36me3 0 1 2 H3k4me1 0 1 2 H3k4me2 0 1 2 H3k4me3 0 1 2 H3k79me2 0 1 2 H3k9ac 0 1 2 H3k9me3 0 1 2 H4k20me1 0 1 2 Mxi1 0 1 2 Nrsf 0 1 2 Pol2 0 1 2 P300 0 1 2 Six5 0 1 2 Sp1 0 1 2 Taf1 0 1 2 Tbp 0 1 2 Yy1 0 1 2 Znf143 0.30 0.45 gc 1 2 3 4 Dnase −3 −1 1 0 40 80 120 P300 Chd1 Tbp Max Znf143 Mxi Six5 J unD Chd2 H3k9me3 H3k27me3 H4k20me1 Ezh2 Gabp Yy1 Sp1 Rad21 H3k36me3 H3k4me3 H3k4me2 Pol2 H3k9ac Mean decrease in accuracy 0 50 100 150 200 c−Myc P300 Ezh2 H3k9ac H3k79me2 Cebpb Six5 Chd1 Chd2 H2az H3k27me3 Pol2 Atf3 J unD H3k27ac H3k4me1 Gabp H4k20me1 H3k9me3 Mean decrease in accuracy 0 50 100 150 200 J unD Cebpb MafK Chd1 c−Myc Max H2az Egr1 Ezh2 Chd2 Ctcf Atf3 Gabp Mean decrease in accuracy 0 50 100 150 200 Conclusions: • Higher-order chromatin structure can be accurately modelled using locus-level features. • A strong correlation of chromatin structure between cell types was observed, but the determinants appear to vary greatly. • Future work will look at characterising divergent regions and identifying important variables at these sites. Box 1. Hi-C is a method of producing genome-wide measurements of the frequency that genomic regions interact in the nucleus. Hi-C builds upon existing chromosome capture techniques, but uses high- throughput sequencing to output all-vs-all measurements. See [1] for a detailed description. Recent advances in chromosome capture technology have permitted genome-wide assessment of higher-order chromatin structure in a variety of cell types. This data in conjunction with that produced by the ENCODE consortium offers an unprecedented opportunity to investigate the antecedents of higher-order chromatin structure. To address this question I used an integrative modelling approach, whereby the above input variables are considered as predictors of higher-order structure. Hi-C (see box 1) genome-wide interactions can be reduced to an eigenvector summary metric which captures compartmentalisation of the genome into two broad compartments, "open" and "closed". These have been shown to represent gene dense, transcriptionally active and relatively gene poor, inactive regions respectively [1]. Recently modelling techniques were applied to the prediction of transcription and acheived high accuracy [2] so it was of interest to see if this could be replicated with chromatin structure. The resulting model was then compared across cell types and interrogated to reveal the relative importance of the various predictor variables. An arc diagram with edges joining nodes with higher than average interaction frequency as measured by Hi-C. Edge widths are proportional to the number of pairwise interactions recorded. Nodes are coloured by eigenvector (positive or "open" are light blue) and scaled by absolute eigenvector value. Hi-C interaction matrix derived from H1 hESC [1]. Figure 1. Over-represented pairwise interactions on chromosome 1. A correlogram (A) showing scatterplots, kernel density estimates and Pearson correlation of eigenvectors of 1 MB blocks across 11 chromosomes and 5 cell types, with data coming from 3 different experiments. A line plot (B) shows the actual eigenvectors to scale on their respective chromosomes (8 s