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The biobank era of genomics has ushered in a multitude of opportunities for precision medicine research. In particular, biobanks connected to electronic health records (EHR) provide rich phenotype information used to study to clinical phenome. First, I describe two computational methods designed to infer the genetic architecture of complex traits using biobank-scale data. Both methods are based on Markov Chain Monte Carlo techniques. Next, I provide an overview of the UCLA ATLAS Community Health Initiative (ATLAS), an EHR-linked biobank embedded within UCLA Health. Using this data set, I explore the role of genetic ancestry in common disease risk across the UCLA patient population. Next, I include a review of how race, ethnicity, and genetic ancestry are utilized in the field of EHR- linked biobanks. Finally, I propose an EHR-based algorithm, called PheNet, which identifies undiagnosed patients with Common Variable Immunodeficiency Disorders and demonstrate its application across a total of 5 University of California Health systems.
For recent advancements in sequencing technologies, genetic information can be obtained from a large population at a relatively low cost. This provides an unprecedented opportunity to understand the role of genetic variability in association with complex human traits. One common strategy is to conduct genome-wide association studies to identify loci significantly associated with phenotypes of interest. However, the findings are usually limited to common variants with small effect sizes. Collectively, these identified loci can not fully explain the observed heritability, which is a problem commonly referred to as "the missing heritability." To uncover this problem, human genetic research has shifted more focus to other types of genetic variations, including rare variants, which is further capacitated and facilitated by the next-generation sequencing technique. These rare mutations are believed to harbor large effect sizes and, therefore to be one of the major contributors to complex traits.Here, we describe our effort in analyzing the effect of rare variants in two complex human traits, Alzheimer's Disease and Tourette Syndrome, followed by conducting a genome-wide association study on human blood lipids. Exploring large whole-genome sequencing datasets, we have first demonstrated that rare variants were strongly associated with Alzheimer's Disease, neurofibrillary tangles, and age-related phenotypes within the endocytic pathway using a gene-set burden analysis framework. Subsequent gene-based analyses identified one AD-associated gene, ANKRD13D, and two e-Genes, HLA-A and SLC26A7. Leveraging bulk and scRNA-Seq data, we observed significant differential expression patterns in all three implicated genes. Secondly, we have explored a specific type of rare variants, de novo mutations, within Tourette Syndrome patients using a whole-exome sequencing trio dataset and identified a recurrent mutation in one gene, FBN2, previously implicated in TS. Comparing to the expected mutation rate, we demonstrated that the protein-truncating variants were enriched in probands. In addition, gene-set analysis displayed differential expression patterns across different tissue types and brain developmental stages. Lastly, we have performed a multi-population meta-analysis on blood lipid levels using electronic health records and genotyping information from the UCLA ATLAS database. We have observed genetic effects both specific to and shared across five different populations. Compared to previous large-scale GWASes, our results demonstrated consistent effect estimates while identifying one novel locus, rs72552763.
The practice of modern medicine and biomedical research requires sophisticated information technologies with which to manage patient information, plan diagnostic procedures, interpret laboratory results, and carry out investigations. Biomedical Informatics provides both a conceptual framework and a practical inspiration for this swiftly emerging scientific discipline at the intersection of computer science, decision science, information science, cognitive science, and biomedicine. Now revised and in its third edition, this text meets the growing demand by practitioners, researchers, and students for a comprehensive introduction to key topics in the field. Authored by leaders in medical informatics and extensively tested in their courses, the chapters in this volume constitute an effective textbook for students of medical informatics and its areas of application. The book is also a useful reference work for individual readers needing to understand the role that computers can play in the provision of clinical services and the pursuit of biological questions. The volume is organized so as first to explain basic concepts and then to illustrate them with specific systems and technologies.
During the last five years, clinical research and development costs have risen exponentially without a proportionate increase in the number of new medications. While patient recruitment for clinical studies is only one component in the development of a new medicine or treatment, it is one of the most significant bottlenecks in the overall drug development process. Now it is imperative that industry leaders see beyond reactive measures and recognize that advancing their approach to patient recruitment is absolutely essential to advancing medicine and continuing the stability of their corporate brand across the globe. Reinventing Patient Recruitment: Revolutionary Ideas for Clinical Trial Success is a definitive guide to planning, implementing and evaluating recruitment strategies and campaigns globally. The combined experience of the authors provides a depth of perspective and boldness of innovative leadership to set the standards for future patient recruitment programs and practices. This book is a must-have for pharmaceutical, biotechnology and medical device industry professionals concerned with enrolling for domestic and multinational clinical studies and remaining on time and on budget.
This User’s Guide is intended to support the design, implementation, analysis, interpretation, and quality evaluation of registries created to increase understanding of patient outcomes. For the purposes of this guide, a patient registry is an organized system that uses observational study methods to collect uniform data (clinical and other) to evaluate specified outcomes for a population defined by a particular disease, condition, or exposure, and that serves one or more predetermined scientific, clinical, or policy purposes. A registry database is a file (or files) derived from the registry. Although registries can serve many purposes, this guide focuses on registries created for one or more of the following purposes: to describe the natural history of disease, to determine clinical effectiveness or cost-effectiveness of health care products and services, to measure or monitor safety and harm, and/or to measure quality of care. Registries are classified according to how their populations are defined. For example, product registries include patients who have been exposed to biopharmaceutical products or medical devices. Health services registries consist of patients who have had a common procedure, clinical encounter, or hospitalization. Disease or condition registries are defined by patients having the same diagnosis, such as cystic fibrosis or heart failure. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews.
This fourth edition of the best-selling textbook, Human Genetics and Genomics, clearly explains the key principles needed by medical and health sciences students, from the basis of molecular genetics, to clinical applications used in the treatment of both rare and common conditions. A newly expanded Part 1, Basic Principles of Human Genetics, focuses on introducing the reader to key concepts such as Mendelian principles, DNA replication and gene expression. Part 2, Genetics and Genomics in Medical Practice, uses case scenarios to help you engage with current genetic practice. Now featuring full-color diagrams, Human Genetics and Genomics has been rigorously updated to reflect today’s genetics teaching, and includes updated discussion of genetic risk assessment, “single gene” disorders and therapeutics. Key learning features include: Clinical snapshots to help relate science to practice 'Hot topics' boxes that focus on the latest developments in testing, assessment and treatment 'Ethical issues' boxes to prompt further thought and discussion on the implications of genetic developments 'Sources of information' boxes to assist with the practicalities of clinical research and information provision Self-assessment review questions in each chapter Accompanied by the Wiley E-Text digital edition (included in the price of the book), Human Genetics and Genomics is also fully supported by a suite of online resources at www.korfgenetics.com, including: Factsheets on 100 genetic disorders, ideal for study and exam preparation Interactive Multiple Choice Questions (MCQs) with feedback on all answers Links to online resources for further study Figures from the book available as PowerPoint slides, ideal for teaching purposes The perfect companion to the genetics component of both problem-based learning and integrated medical courses, Human Genetics and Genomics presents the ideal balance between the bio-molecular basis of genetics and clinical cases, and provides an invaluable overview for anyone wishing to engage with this fast-moving discipline.
Clinical Genomics provides an overview of the various next-generation sequencing (NGS) technologies that are currently used in clinical diagnostic laboratories. It presents key bioinformatic challenges and the solutions that must be addressed by clinical genomicists and genomic pathologists, such as specific pipelines for identification of the full range of variants that are clinically important. This book is also focused on the challenges of diagnostic interpretation of NGS results in a clinical setting. Its final sections are devoted to the emerging regulatory issues that will govern clinical use of NGS, and reimbursement paradigms that will affect the way in which laboratory professionals get paid for the testing. Simplifies complexities of NGS technologies for rapid education of clinical genomicists and genomic pathologists towards genomic medicine paradigm Tried and tested practice-based analysis for precision diagnosis and treatment plans Specific pipelines and meta-analysis for full range of clinically important variants
This book provides a comprehensive overview of deep learning (DL) in medical and healthcare applications, including the fundamentals and current advances in medical image analysis, state-of-the-art DL methods for medical image analysis and real-world, deep learning-based clinical computer-aided diagnosis systems. Deep learning (DL) is one of the key techniques of artificial intelligence (AI) and today plays an important role in numerous academic and industrial areas. DL involves using a neural network with many layers (deep structure) between input and output, and its main advantage of is that it can automatically learn data-driven, highly representative and hierarchical features and perform feature extraction and classification on one network. DL can be used to model or simulate an intelligent system or process using annotated training data. Recently, DL has become widely used in medical applications, such as anatomic modelling, tumour detection, disease classification, computer-aided diagnosis and surgical planning. This book is intended for computer science and engineering students and researchers, medical professionals and anyone interested using DL techniques.