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

Beyond genome wide discovery : an exploration of novel genetic variants for coronary heart disease

Recent developments spurred on by the Human Genome Project have for the first time permitted genome wide association studies leading to identification of multiple novel variants for complex diseases. This thesis consists of a series of studies exploring recent genetic findings for coronary heart disease (CHD) within the broader context of the promises of the genomic era that new findings would ultimately lead to 1) Identification of new disease mechanisms 2) Permit genotype based risk prediction and 3) Promote development of novel and targeted therapies based on genotype. We sought to address these questions, using the Emory Genebank, a collection of angiographically phenotyped subjects with stored blood samples and long-term follow up. We first refined the phenotype for CHD to help understand underlying mechanism and demonstrated differential associations between 8 novel risk variants including 9p21, and sub-phenotypes of CHD and thereby proposed differing mechanisms of risk for these loci. With two non-CHD cohorts we then demonstrated further association between one particular risk variant at 6p24 and the intermediate phenotype of arterial elasticity and related this to a potential novel mechanism of risk. Despite significant association with first events in population cohorts, we showed that these risk variants including 9p21 have limited value in secondary risk prediction, failing to demonstrate any association with prospective events in our cohort as single markers or when combined into a cumulative genetic risk score. Finally in subjects carrying leukotriene pathway CHD risk variants, we administered an oral leukotriene synthesis inhibitor and after just 4 week of therapy observed significant improvement in their endothelial function. In summary, these studies demonstrate the value of refining the phenotype to understand potential mechanisms, the complexities of genetic risk prediction and the feasibility and benefit of targeting therapy based on risk genotype.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Doctor of Philosophy

dissertationWith the growing national dissemination of the electronic health record (EHR), there are expectations that the public will benefit from biomedical research and discovery enabled by electronic health data. Clinical data are needed for many diseases and conditions to meet the demands of rapidly advancing genomic and proteomic research. Many biomedical research advancements require rapid access to clinical data as well as broad population coverage. A fundamental issue in the secondary use of clinical data for scientific research is the identification of study cohorts of individuals with a disease or medical condition of interest. The problem addressed in this work is the need for generalized, efficient methods to identify cohorts in the EHR for use in biomedical research. To approach this problem, an associative classification framework was designed with the goal of accurate and rapid identification of cases for biomedical research: (1) a set of exemplars for a given medical condition are presented to the framework, (2) a predictive rule set comprised of EHR attributes is generated by the framework, and (3) the rule set is applied to the EHR to identify additional patients that may have the specified condition. iv Based on this functionality, the approach was termed the ‘cohort amplification' framework. The development and evaluation of the cohort amplification framework are the subject of this dissertation. An overview of the framework design is presented. Improvements to some standard associative classification methods are described and validated. A qualitative evaluation of predictive rules to identify diabetes cases and a study of the accuracy of identification of asthma cases in the EHR using frameworkgenerated prediction rules are reported. The framework demonstrated accurate and reliable rules to identify diabetes and asthma cases in the EHR and contributed to methods for identification of biomedical research cohorts

Beyond genome wide discovery: an exploration of novel genetic variants for coronary heart disease

Recent developments spurred on by the Human Genome Project have for the first time permitted genome wide association studies leading to identification of multiple novel variants for complex diseases. This thesis consists of a series of studies exploring recent genetic findings for coronary heart disease (CHD) within the broader context of the promises of the genomic era that new findings would ultimately lead to 1) Identification of new disease mechanisms 2) Permit genotype based risk prediction and 3) Promote development of novel and targeted therapies based on genotype. We sought to address these questions, using the Emory Genebank, a collection of angiographically phenotyped subjects with stored blood samples and long-term follow up. We first refined the phenotype for CHD to help understand underlying mechanism and demonstrated differential associations between 8 novel risk variants including 9p21, and sub-phenotypes of CHD and thereby proposed differing mechanisms of risk for these loci. With two non-CHD cohorts we then demonstrated further association between one particular risk variant at 6p24 and the intermediate phenotype of arterial elasticity and related this to a potential novel mechanism of risk. Despite significant association with first events in population cohorts, we showed that these risk variants including 9p21 have limited value in secondary risk prediction, failing to demonstrate any association with prospective events in our cohort as single markers or when combined into a cumulative genetic risk score. Finally in subjects carrying leukotriene pathway CHD risk variants, we administered an oral leukotriene synthesis inhibitor and after just 4 week of therapy observed significant improvement in their endothelial function. In summary, these studies demonstrate the value of refining the phenotype to understand potential mechanisms, the complexities of genetic risk prediction and the feasibility and benefit of targeting therapy based on risk genotype

Precision Medicine

This colligated Special Issue of Pharmaceutics on Precision Medicine: Applied Concepts of Pharmacogenomics in Patients with Various Diseases and Polypharmacy offers to the reader a series of articles that describe the concept of Precision Medicine, discuss its implementation process and limitations, demonstrate its value by illustrating some clinical cases, and open the door to new and more sophisticated techniques and applications

PREDICT: a method for inferring novel drug indications with application to personalized medicine

The authors present a new method, PREDICT, for the large-scale prediction of drug indications, and demonstrate its use on both approved drugs and novel molecules. They also provide a proof-of-concept for its potential utility in predicting patient-specific medications

Artificial Intelligence in Oncology Drug Discovery and Development

There exists a profound conflict at the heart of oncology drug development. The efficiency of the drug development process is falling, leading to higher costs per approved drug, at the same time personalised medicine is limiting the target market of each new medicine. Even as the global economic burden of cancer increases, the current paradigm in drug development is unsustainable. In this book, we discuss the development of techniques in machine learning for improving the efficiency of oncology drug development and delivering cost-effective precision treatment. We consider how to structure data for drug repurposing and target identification, how to improve clinical trials and how patients may view artificial intelligence

Kernel machine methods for analysis of genomic data from different sources

Comprehensive understanding of complex trait etiology requires examination of multiple sources of genomic variability. Recent advances in high-throughput biotechnology, especially sequencing technology, have enabled multiple platform genomic profile of biological samples. In this dissertation, we consider using the kernel machine regression (KMR) framework to analyze data from different genetic data sources. In the first part of this dissertation, we develop a new strategy for identification of large scale, global changes in methylation that are associated with environmental variables or clinical outcomes via a functional regression approach. The density or the cumulative distribution function of the methylation values for each individual can be approximated using B-spline basis functions with the spline coefficients to summarize the individual's overall methylation profile. A variance component score test is proposed to test for association between the overall distribution and a continuous or dichotomous outcome and applied to two real studies. In the second part, we construct a microbiome regression-based kernel association test (MiRKAT) for testing the association between microbial community profiles and a continuous or dichotomous variable of interest such as an environmental exposure or disease status. This method regresses the outcome on the covariates (including potential confounders) and the microbiome compositional profiles through kernel functions. We demonstrate the improved control of type I error and superior power of MiRKAT compared to existing methods through simulations and real studies. In the final part, we focus on integrative analysis of genome wide association studies (GWAS) and methylation studies. We propose to use the KMR for first testing the cumulative genetic/epigenetic effect on a trait and for subsequent mediation analysis to understand the mechanisms by which the genomic data influence the trait. In particular, we develop an approach that works at the gene level (to allow for a common analysis unit across data types). We compare pair-wise similarity in trait values between individuals to pair-wise similarity in methylation and genotype values, with correspondence suggestive of association. For a significant gene, we develop a causal steps approach to mediation analysis which enables elucidation of the manner in which the different data types work, or do not work, together.Doctor of Philosoph

Creating Persian-like music using computational intelligence

Dastgāh are modal systems in traditional Persian music. Each Dastgāh consists of a group of melodies called Gushé, classified in twelve groups about a century ago (Farhat, 1990). Prior to that time, musical pieces were transferred through oral tradition. The traditional music productions revolve around the existing Dastgāh, and Gushe pieces. In this thesis computational intelligence tools are employed in creating novel Dastgāh-like music.There are three types of creativity: combinational, exploratory, and transformational (Boden, 2000). In exploratory creativity, a conceptual space is navigated for discovering new forms. Sometimes the exploration results in transformational creativity. This is due to meaningful alterations happening on one or more of the governing dimensions of an item. In combinational creativity new links are established between items not previously connected. Boden stated that all these types of creativity can be implemented using artificial intelligence.Various tools, and techniques are employed, in the research reported in this thesis, for generating Dastgāh-like music. Evolutionary algorithms are responsible for navigating the space of sequences of musical motives. Aesthetical critics are employed for constraining the search space in exploratory (and hopefully transformational) type of creativity. Boltzmann machine models are applied for assimilating some of the mechanisms involved in combinational creativity. The creative processes involved are guided by aesthetical critics, some of which are derived from a traditional Persian music database.In this project, Cellular Automata (CA) are the main pattern generators employed to produce raw creative materials. Various methodologies are suggested for extracting features from CA progressions and mapping them to musical space, and input to audio synthesizers. The evaluation of the results of this thesis are assisted by publishing surveys which targeted both public and professional audiences. The generated audio samples are evaluated regarding their Dastgāh-likeness, and the level of creativity of the systems involved