Heart enhancers with deeply conserved regulatory activity are established early in zebrafish development.

During the phylotypic period, embryos from different genera show similar gene expression patterns, implying common regulatory mechanisms. Here we set out to identify enhancers involved in the initial events of cardiogenesis, which occurs during the phylotypic period. We isolate early cardiac progenitor cells from zebrafish embryos and characterize 3838 open chromatin regions specific to this cell population. Of these regions, 162 overlap with conserved non-coding elements (CNEs) that also map to open chromatin regions in human. Most of the zebrafish conserved open chromatin elements tested drive gene expression in the developing heart. Despite modest sequence identity, human orthologous open chromatin regions recapitulate the spatial temporal expression patterns of the zebrafish sequence, potentially providing a basis for phylotypic gene expression patterns. Genome-wide, we discover 5598 zebrafish-human conserved open chromatin regions, suggesting that a diverse repertoire of ancient enhancers is established prior to organogenesis and the phylotypic period

EpiAlign: an alignment-based bioinformatic tool for comparing chromatin state sequences.

The availability of genome-wide epigenomic datasets enables in-depth studies of epigenetic modifications and their relationships with chromatin structures and gene expression. Various alignment tools have been developed to align nucleotide or protein sequences in order to identify structurally similar regions. However, there are currently no alignment methods specifically designed for comparing multi-track epigenomic signals and detecting common patterns that may explain functional or evolutionary similarities. We propose a new local alignment algorithm, EpiAlign, designed to compare chromatin state sequences learned from multi-track epigenomic signals and to identify locally aligned chromatin regions. EpiAlign is a dynamic programming algorithm that novelly incorporates varying lengths and frequencies of chromatin states. We demonstrate the efficacy of EpiAlign through extensive simulations and studies on the real data from the NIH Roadmap Epigenomics project. EpiAlign is able to extract recurrent chromatin state patterns along a single epigenome, and many of these patterns carry cell-type-specific characteristics. EpiAlign can also detect common chromatin state patterns across multiple epigenomes, and it will serve as a useful tool to group and distinguish epigenomic samples based on genome-wide or local chromatin state patterns

Genome-wide compendium and functional assessment of in vivo heart enhancers

Whole-genome sequencing is identifying growing numbers of non-coding variants in human disease studies, but the lack of accurate functional annotations prevents their interpretation. We describe the genome-wide landscape of distant-acting enhancers active in the developing and adult human heart, an organ whose impairment is a predominant cause of mortality and morbidity. Using integrative analysis of >35 epigenomic data sets from mouse and human pre- and postnatal hearts we created a comprehensive reference of >80,000 putative human heart enhancers. To illustrate the importance of enhancers in the regulation of genes involved in heart disease, we deleted the mouse orthologs of two human enhancers near cardiac myosin genes. In both cases, we observe in vivo expression changes and cardiac phenotypes consistent with human heart disease. Our study provides a comprehensive catalogue of human heart enhancers for use in clinical whole-genome sequencing studies and highlights the importance of enhancers for cardiac function

The Pharmacoepigenomics Informatics Pipeline and H-GREEN Hi-C Compiler: Discovering Pharmacogenomic Variants and Pathways with the Epigenome and Spatial Genome

Over the last decade, biomedical science has been transformed by the epigenome and spatial genome, but the discipline of pharmacogenomics, the study of the genetic underpinnings of pharmacological phenotypes like drug response and adverse events, has not. Scientists have begun to use omics atlases of increasing depth, and inferences relating to the bidirectional causal relationship between the spatial epigenome and gene expression, as a foundational underpinning for genetics research. The epigenome and spatial genome are increasingly used to discover causative regulatory variants in the significance regions of genome-wide association studies, for the discovery of the biological mechanisms underlying these phenotypes and the design of genetic tests to predict them. Such variants often have more predictive power than coding variants, but in the area of pharmacogenomics, such advances have been radically underapplied. The majority of pharmacogenomics tests are designed manually on the basis of mechanistic work with coding variants in candidate genes, and where genome wide approaches are used, they are typically not interpreted with the epigenome. This work describes a series of analyses of pharmacogenomics association studies with the tools and datasets of the epigenome and spatial genome, undertaken with the intent of discovering causative regulatory variants to enable new genetic tests. It describes the potent regulatory variants discovered thereby to have a putative causative and predictive role in a number of medically important phenotypes, including analgesia and the treatment of depression, bipolar disorder, and traumatic brain injury with opiates, anxiolytics, antidepressants, lithium, and valproate, and in particular the tendency for such variants to cluster into spatially interacting, conceptually unified pathways which offer mechanistic insight into these phenotypes. It describes the Pharmacoepigenomics Informatics Pipeline (PIP), an integrative multiple omics variant discovery pipeline designed to make this kind of analysis easier and cheaper to perform, more reproducible, and amenable to the addition of advanced features. It described the successes of the PIP in rediscovering manually discovered gene networks for lithium response, as well as discovering a previously unknown genetic basis for warfarin response in anticoagulation therapy. It describes the H-GREEN Hi-C compiler, which was designed to analyze spatial genome data and discover the distant target genes of such regulatory variants, and its success in discovering spatial contacts not detectable by preceding methods and using them to build spatial contact networks that unite disparate TADs with phenotypic relationships. It describes a potential featureset of a future pipeline, using the latest epigenome research and the lessons of the previous pipeline. It describes my thinking about how to use the output of a multiple omics variant pipeline to design genetic tests that also incorporate clinical data. And it concludes by describing a long term vision for a comprehensive pharmacophenomic atlas, to be constructed by applying a variant pipeline and machine learning test design system, such as is described, to thousands of phenotypes in parallel. Scientists struggled to assay genotypes for the better part of a century, and in the last twenty years, succeeded. The struggle to predict phenotypes on the basis of the genotypes we assay remains ongoing. The use of multiple omics variant pipelines and machine learning models with omics atlases, genetic association, and medical records data will be an increasingly significant part of that struggle for the foreseeable future.PHDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145835/1/ariallyn_1.pd

Discovery and functional characterization of cardiovascular long noncoding RNAs

Recent advances in sequencing and genomic technologies have resulted in the discovery of thousands of previously unannotated long noncoding RNAs (lncRNAs). However, their function in the cardiovascular system remains elusive. Here we review and discuss considerations for cardiovascular lncRNA discovery, annotation and functional characterization. Although we primarily focus on the heart, the proposed pipeline should foster functional and mechanistic exploration of these transcripts in various cardiovascular pathologies. Moreover, these insights could ultimately lead to novel therapeutic approaches targeting lncRNAs for the amelioration of cardiovascular diseases including heart failure

Epigenomics of Cell Fate in Development and Disease

Epigenetic features at regulatory elements provide instructive cues for transcriptional regulation during development. However, the particular epigenetic alterations necessary for proper cell fate acquisition and differentiation are not well understood. This dissertation explores the epigenetic dynamics of regulatory elements during development and uses epigenome annotations to document inappropriate transcriptional regulation in disease. First, I summarize my contributions to developing a new algorithm for detecting differential DNA methylation, M&M. I report the application of the M&M algorithm to identify distinct classes of DNA methylation dynamics in surface ectoderm (SE) progenitor cells and SE-derived lineages: epigenome alterations, and differential DNA methylation in particular, that are present in progenitor cells are transmitted to daughter cells and consequently observed in differentiated cells. I exploit this property of DNA methylation to characterize DNA methylation dynamics in surface ectoderm embryonic tissue and SE-derived cells. Next, I use zebrafish to investigate the biological relevance of the classes of DNA methylation dynamics described in the SE context. In zebrafish, I use the pigment cell development system to understand the contribution of DNA methylation to a particular cell fate choice: melanocyte or iridophore cell fate. Next, I investigate the consequence of somatic mutations in primary liver cancer by utilizing epigenomic annotations of human tissues to distinguish putatively functional mutations from passenger mutations. Here I present support for the hypothesis that transcriptional regulatory instructions for heterologous cell types are co-opted by cancer cells during malignant tumorigenesis. Finally I present a review of the evolution of epigenetic regulation over regulatory elements. Altogether, this dissertation advances our understanding of epigenetic regulation in cell fate decisions by integrating functional genomics with developmental biology and cancer genetics

Integration of Functional Genomic Data in Genetic Analysis

Identifying disease risk genes is a central topic of human genetics. Cost-effective exome and whole genome sequencing enabled large-scale discovery of genetic variations. However, the statistical power of finding new risk genes through rare genetic variation is fundamentally limited by sample sizes. As a result, we have an incomplete understanding of genetic architecture and molecular etiology of most of human conditions and diseases. In this thesis, I developed new computational methods that integrate functional genomics data sets, such as epigenomic profiles and single-cell transcriptomics, to improve power for identifying genetic risks and gain more insights on etiology of developmental disorders. The overall hypothesis that disease risk genes contributing to developmental disorders are bottleneck genes under normal development and subject to precise transcriptional regulations to maintain spatiotemporal specific expression during development. In this thesis I describe two major research projects. The first project, Episcore, predicts haploinsufficient genes based on a large integrated epigenomic profiles from multiple tissues and cell lines by supervised machine learning methods. The second one, A-risk, predicts plausibility of being risk genes of autism spectrum disorder based on single-cell RNA-seq data collected in human fetal midbrain and prefrontal cortex. Both methods were shown to be able to improve gene discovery in analysis of de novo mutations in developmental disorders. Overall, my thesis represents an effort to integrate functional genomics data by machine learning to facilitate both discovery and interpretation of genetic studies of human diseases. We believe that such integrative analysis can help us better understand genetic variants and disease etiology