Identification of Long-Range Regulatory Elements in the Human Genome

Genome-wide association studies have shown that the majority of disease-associated genetic variants lie within non-coding regions of the human genome. Subsequently, a challenge following these discoveries is to identify how these variants modulate the risk of disease. Enhancers are non-coding regulatory elements that can be bound by proteins to activate the expression of a gene that may be linearly distant. Experimentally probing all possible enhancer–target gene pairs can be laborious. Hi-C, a technique developed by Job Dekker’s group in 2009, combines high-throughput sequencing with chromosome conformation capture to detect DNA interactions genome-wide and thereby reveals the three-dimensional architecture of chromatin in the nucleus. However, the utility of the datasets produced by this technique for discovering long-range regulatory interactions is largely unexplored. In this thesis, we develop novel approaches to identify DNA-interacting units and their interactions in Hi-C datasets with the goal of uncovering all enhancer–target gene interactions. We began by identifying significantly interacting regions in these datasets, subsequently focusing on candidate enhancer–gene pairs. We found that the identified putative enhancers are enriched for p300 binding activity, while their target promoters are likely to be cell-type-specific. Furthermore, we revealed that enhancers and target genes often interact in many-to-many relationships and the majority of enhancer–target gene interactions are intra-chromosomal and within 1 Mb of each other. Next, we refined our analytical approach to identify physically-interacting DNA regions at ~1 kb resolution and better define the boundaries of likely enhancer elements. By searching for over-represented sequences (motifs) in these putative promoter-interacting enhancers, we were then able to identify bound transcription factors. This newer approach provides the potential to identify protein complexes involved in enhancer–promoter interactions, which can be verified in future experiments. We implemented a high-throughput identification pipeline for promoter-interacting enhancer elements (HIPPIE) using both of the above described approaches. HIPPIE can be run efficiently on typical Linux servers and grid computing environments and is available as open-source software. In summary, our findings demonstrate the potential utility of Hi-C technologies for elucidating the mechanisms by which long-range enhancers regulate gene expression and ultimately result in human disease phenotypes

The Expanding Landscape of Alternative Splicing Variation in Human Populations.

Alternative splicing is a tightly regulated biological process by which the number of gene products for any given gene can be greatly expanded. Genomic variants in splicing regulatory sequences can disrupt splicing and cause disease. Recent developments in sequencing technologies and computational biology have allowed researchers to investigate alternative splicing at an unprecedented scale and resolution. Population-scale transcriptome studies have revealed many naturally occurring genetic variants that modulate alternative splicing and consequently influence phenotypic variability and disease susceptibility in human populations. Innovations in experimental and computational tools such as massively parallel reporter assays and deep learning have enabled the rapid screening of genomic variants for their causal impacts on splicing. In this review, we describe technological advances that have greatly increased the speed and scale at which discoveries are made about the genetic variation of alternative splicing. We summarize major findings from population transcriptomic studies of alternative splicing and discuss the implications of these findings for human genetics and medicine

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

Interpretation of psychiatric genome-wide association studies with multispecies heterogeneous functional genomic data integration.

Genome-wide association studies and other discovery genetics methods provide a means to identify previously unknown biological mechanisms underlying behavioral disorders that may point to new therapeutic avenues, augment diagnostic tools, and yield a deeper understanding of the biology of psychiatric conditions. Recent advances in psychiatric genetics have been made possible through large-scale collaborative efforts. These studies have begun to unearth many novel genetic variants associated with psychiatric disorders and behavioral traits in human populations. Significant challenges remain in characterizing the resulting disease-associated genetic variants and prioritizing functional follow-up to make them useful for mechanistic understanding and development of therapeutics. Model organism research has generated extensive genomic data that can provide insight into the neurobiological mechanisms of variant action, but a cohesive effort must be made to establish which aspects of the biological modulation of behavioral traits are evolutionarily conserved across species. Scalable computing, new data integration strategies, and advanced analysis methods outlined in this review provide a framework to efficiently harness model organism data in support of clinically relevant psychiatric phenotypes

Genome architecture: from linear organisation of chromatin to the 3D assembly in the nucleus

The genetic information is stored in the eukaryotic nucleus in the form of chromatin. This is a macromolecular entity that includes genomic DNA and histone proteins that form nucleosomes, plus a large variety of chromatin-associated non-histone proteins. Chromatin is structurally and functionally organised at various levels. One reveals the linear topography of DNA, histones and their post-translational modifications and non-histone proteins along each chromosome. This level provides regulatory information about the association of genomic elements with particular signatures that have been used to define chromatin states. Importantly, these chromatin states correlate with structural and functional genomic features. Another regulatory layer is established at the level of the 3D organisation of chromatin within the nucleus, which has been revealed clearly as non-random. Instead, a variety of intra- and inter-chromosomal genomic domains with specific epigenetic and functional properties has been identified. In this review, we discuss how the recent advances in genomic approaches have contributed to our understanding of these two levels of genome architecture. We have emphasised our analysis with the aim of integrating information available for yeast, Arabidopsis, Drosophila, and mammalian cells. We consider that this comparative study helps define common and unique features in each system, providing a basis to better understand the complexity of genome organisation.Ministerio de Economía y Competitividad (grant BFU2012–34821) and an institutional grant of Fundación Ramón Areces to the Centro de Biología Molecular Severo Ochoa.Peer Reviewe

Computational mapping of regulatory domains of human genes

Das menschliche Genom enthält Millionen von regulatorischen Elementen - Enhancern -, die die Genexpression quantitativ regulieren. Trotz des enormen Fortschritts beim Verständnis, wie Enhancer die Genexpression steuern, fehlt es in diesem Bereich immer noch an einem systematischen, integrativen und zugänglichen Ansatz zur Entdeckung und Dokumentation von cis-regulatorischen Beziehungen im gesamten Genom. Wir haben eine neuartige Methode - reg2gene - entwickelt, die Genexpression~Enhancer-Aktivität modelliert und integriert. reg2gene besteht aus drei Hauptschritten: 1) Datenquantifizierung, 2) Datenmodellierung und Signifikanzbewertung und 3) Datenintegration, die in dem R-Paket reg2gene zusammengefasst sind. Als Ergebnis haben wir zwei Sätze von Enhancer-Gen-Assoziationen (EGAs) identifiziert: den flexiblen Satz von ~230K EGAs (flexibleC) und den stringenten Satz von ~60K EGAs (stringentC). Wir haben große Unterschiede zwischen den bisher veröffentlichten Berechnungsmodellen für Enhancer-Gene-Assoziationen festgestellt, vor allem in Bezug auf die Lage, die Anzahl und die Eigenschaften der definierten Enhancer-Regionen und EGAs. Wir führten ein detailliertes Benchmarking von sieben Sets von rechnerisch modellierten EGAs durch, zeigten jedoch, dass keiner der derzeit verfügbaren Benchmark-Datensätze als "goldener Standard" verwendet werden kann. Wir definierten einen zusätzlichen Benchmark-Datensatz mit positiven und negativen EGAs, mit dem wir zeigten, dass das stringentC-Modell den höchsten positiven Vorhersagewert (PPV) hatte. Wir haben das Potenzial von EGAs zur Identifizierung von Genzielen von nicht-kodierenden SNP-Gene-Assoziationen nachgewiesen. Schließlich führten wir eine funktionelle Analyse durch, um neue Genziele, Enhancer-Pleiotropie und Mechanismen der Enhancer-Aktivität zu ermitteln. Insgesamt bringt diese Arbeit unser Verständnis der durch Enhancer vermittelten Regulierung der Genexpression in Gesundheit und Krankheit voran.Human genome contains millions of regulatory elements - enhancers - that quantitatively regulate gene expression. Multiple experimental and computational approaches were developed to associate enhancers with their gene targets. Despite the tremendous progress in understanding how enhancers tune gene expression, the field still lacks an approach that is systematic, integrative and accessible for discovering and documenting cis-regulatory relationships across the genome. We developed a novel computational approach - reg2gene- that models and integrates gene expression ~ enhancer activity. reg2gene consists of three main steps: 1) data quantification, 2) data modelling and significance assessment, and 3) data integration gathered in the reg2gene R package. As a result we identified two sets of enhancer-gene associations (EGAs): the flexible set of ~230K EGAs (flexibleC), and the stringent set of ~60K EGAs (stringentC). We identified major differences across previously published computational models of enhancer-gene associations; mostly in the location, number and properties of defined enhancer regions and EGAs. We performed detailed benchmarking of seven sets of computationally modelled EGAs, but showed that none of the currently available benchmark datasets could be used as a “golden-standard” benchmark dataset. To account for that observation, we defined an additional benchmark set of positive and negative EGAs with which we showed that the stringentC model had the highest positive predictive value (PPV) across all analyzed computational models. We reviewed the influence of EGA sets on the functional analysis of risk SNPs and demonstrated the potential of EGAs to identify gene targets of non-coding SNP-gene associations. Lastly, we performed a functional analysis to detect novel gene targets, enhancer pleiotropy, and mechanisms of enhancer activity. Altogether, this work advances our understanding of enhancer-mediated gene expression regulation in health and disease.Ljudski genom sadrži milijune regulatornih elemenata - enhancera - koji kvantitativno reguliraju ekspresiju gena. Unatoč ogromnom napretku u razumijevanju načina na koji enhanceri reguliraju ekspresiju gena, području još uvijek nedostaje pristup koji je sustavan, integrativan i dostupan za otkrivanje i dokumentiranje cis-regulatornih odnosa u cijelom genomu. Razvili smo novu računalnu metodu - reg2gene - koja modelira i integrira aktivnost enhancera~ekspresije gena. reg2gene sastoji se od tri glavna koraka: 1) kvantifikacija podataka, 2) modeliranje podataka i procjena značaja, i 3) integracija podataka prikupljenih u reg2gene R paketu. Kao rezultat toga, identificirali smo dva skupa enhancer-gen interakcija (EGA): fleksibilni skup od ~ 230K EGA (flexibleC) i strogi skup od ~ 60K EGA (stringentC). Utvrdili smo velike razlike u prethodno objavljenim računalnim modelima enhancer-gen interakcija; uglavnom u lokaciji, broju i svojstvima definiranih enhancera i EGA. Izveli smo detaljno mjerenje performansi sedam skupova računalno modeliranih EGA-a, ali smo pokazali da se niti jedan od trenutno dostupnih skupova referentnih podataka ne može koristiti kao referentni skup podataka "zlatnI standard". Definirali smo dodatni referentni skup pozitivnih i negativnih EGA -a pomoću kojih smo pokazali da stringentC ima najveću pozitivnu prediktivnu vrijednost (PPV). Pokazali smo potencijal EGA-a za identifikaciju genskih meta nekodirajucih SNP-ova. Proveli smo funkcionalnu analizu kako bismo otkrili nove genske mete, pleiotropiju enhancera i mehanizme aktivnosti enhancera. Ovaj rad poboljšava naše razumijevanje regulacije ekspresije gena posredovane enhancerima

A User's Guide to the Encyclopedia of DNA Elements (ENCODE)

The mission of the Encyclopedia of DNA Elements (ENCODE) Project is to enable the scientific and medical communities to interpret the human genome sequence and apply it to understand human biology and improve health. The ENCODE Consortium is integrating multiple technologies and approaches in a collective effort to discover and define the functional elements encoded in the human genome, including genes, transcripts, and transcriptional regulatory regions, together with their attendant chromatin states and DNA methylation patterns. In the process, standards to ensure high-quality data have been implemented, and novel algorithms have been developed to facilitate analysis. Data and derived results are made available through a freely accessible database. Here we provide an overview of the project and the resources it is generating and illustrate the application of ENCODE data to interpret the human genome