Integrative Analysis of DNA Methylation and Gene Expression Patterns in Tissues from Hepatocellular Carcinoma Patients Compared to European Americans (EAs), the incidence of Hepatocellular Carcinoma (HCC) is higher in African Americans (AAs) and is associated with more advanced tumor stage at diagnosis and lower survival rates. Despite the importance of DNA methylation in neoplastic transformation, the mechanisms of epigenetic disparity between AAs and EAs are yet to be fully realized in HCC. In this paper we present an integrated analysis of genomewide DNA methylation and gene expression in a racially diverse cohort of 16 HCC patients using the Illumina MethylationEPIC BeadChip and HiSeq 4000 platforms. A mixedeffects ANOVA model was applied to compare molecular changes between tumors and adjacent noncancerous liver tissues. In an effort to control for underlying variation in tumor composition, we used DNA methylation to assess measurements of tumor purity and adjusted our analysis to account for potential bias due to cell mixture effects. As a result, we were able to identify 17,249 differentially methylated regions (DMRs) and 981 differentially methylated and expressed genes (DMDE) associated with HCC. Further, 14,912 DMRs and 637 DMDE genes were identified as being associated with the EA race and 3,257 DMRs and 156 DMDE genes identified as being associated with the AA race in HCC. We were able to identify 20 candidate genes as potential tumor suppressors in HCC, including TBX15, IGF1R, WDR66, ITPKB, CFTR, KCNA3, CXCL12, EAspecific MT1L, PIK23R5, and AAspecific GHR and ADRA2B. In summary, we have identified differentially methylated and expressed markers of HCC that are specific to AA and EA racial groups. These findings offer insights into the molecular mechanisms of epigenetic regulation in HCC progression as well as identify potential markers to address racial disparity in precision therapy and diagnosis.

Systems Biology in Heterogenous Tissues: Integrating Multiple *Omics Datasets to Understand Hematopoietic Differentiation Motivation: Systems biology integrates expression, methylation, transcription factor binding and histone modification profiles with other physiological characteristics of a specific organ. Repositories that provide the required data, like ENCODE, generally work on a high level and do not take the heterogeneity of cell types within an organ into consideration. The hematopoietic system allows the characterization and study of each cell type involved in the generation of blood cells from bone marrow stem cells and thus provides a good foundation for systems biology studies. Here we compare RNA expression, DNA methylation, chromatin accessibility, DNA binding proteins and histone modification profiles in seven different hematopoietic populations using a Bayesian nonparametric hierarchical latentclass mixedeffect model known as IDEAS to characterize epigenetic changes associated with hematopoietic differentiation. Unlike other existing approaches IDEAS considers various cell types of a biological systems in concert instead of disjointly. Results: Using the VISION database and the IDEAS toolkit we provide insights into the transcriptional, epigenetic and regulatory programs governing the hematopoietic differentiation process. The characterization of the different hematopoietic components and their interactions provide the foundations for a systems biology model of hematopoiesis. Previous hematopoietic epigenome segmentation studies have focused on histone modifications, chromatin accessibility and DNA binding protein profiles. DNA methylation has been shown to vary markedly in hematopoietic populations. Inclusion of DNA methylation in these segmentation studies increased the original 36state model of regulatory interactions to 41 states. These new DNA methylationrelated states were associated with repressive marks, active RNA transcription, and a novel state regulated by DNA methylation alone. Imputing epigenetic models on inputs systematically perturbed for hematopoietic populations resulted in models of varying degrees of overlap, which were quantified and set in context with underlying biological processes. Conclusion: Our data show that methylation has a strong impact on functional genomic modeling and can be used to discern cell type specific epigenetic regulatory behavior by leveraging imputation for missing cell type data.