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| handbook of statistical genomics: Handbook of Statistical Genomics David J. Balding, Ida Moltke, John Marioni, 2019-07-09 A timely update of a highly popular handbook on statistical genomics This new, two-volume edition of a classic text provides a thorough introduction to statistical genomics, a vital resource for advanced graduate students, early-career researchers and new entrants to the field. It introduces new and updated information on developments that have occurred since the 3rd edition. Widely regarded as the reference work in the field, it features new chapters focusing on statistical aspects of data generated by new sequencing technologies, including sequence-based functional assays. It expands on previous coverage of the many processes between genotype and phenotype, including gene expression and epigenetics, as well as metabolomics. It also examines population genetics and evolutionary models and inference, with new chapters on the multi-species coalescent, admixture and ancient DNA, as well as genetic association studies including causal analyses and variant interpretation. The Handbook of Statistical Genomics focuses on explaining the main ideas, analysis methods and algorithms, citing key recent and historic literature for further details and references. It also includes a glossary of terms, acronyms and abbreviations, and features extensive cross-referencing between chapters, tying the different areas together. With heavy use of up-to-date examples and references to web-based resources, this continues to be a must-have reference in a vital area of research. Provides much-needed, timely coverage of new developments in this expanding area of study Numerous, brand new chapters, for example covering bacterial genomics, microbiome and metagenomics Detailed coverage of application areas, with chapters on plant breeding, conservation and forensic genetics Extensive coverage of human genetic epidemiology, including ethical aspects Edited by one of the leading experts in the field along with rising stars as his co-editors Chapter authors are world-renowned experts in the field, and newly emerging leaders. The Handbook of Statistical Genomics is an excellent introductory text for advanced graduate students and early-career researchers involved in statistical genetics. |
| handbook of statistical genomics: Handbook of Statistical Genetics David J. Balding, Martin Bishop, Chris Cannings, 2008-06-10 The Handbook for Statistical Genetics is widely regarded as the reference work in the field. However, the field has developed considerably over the past three years. In particular the modeling of genetic networks has advanced considerably via the evolution of microarray analysis. As a consequence the 3rd edition of the handbook contains a much expanded section on Network Modeling, including 5 new chapters covering metabolic networks, graphical modeling and inference and simulation of pedigrees and genealogies. Other chapters new to the 3rd edition include Human Population Genetics, Genome-wide Association Studies, Family-based Association Studies, Pharmacogenetics, Epigenetics, Ethic and Insurance. As with the second Edition, the Handbook includes a glossary of terms, acronyms and abbreviations, and features extensive cross-referencing between the chapters, tying the different areas together. With heavy use of up-to-date examples, real-life case studies and references to web-based resources, this continues to be must-have reference in a vital area of research. Edited by the leading international authorities in the field. David Balding - Department of Epidemiology & Public Health, Imperial College An advisor for our Probability & Statistics series, Professor Balding is also a previous Wiley author, having written Weight-of-Evidence for Forensic DNA Profiles, as well as having edited the two previous editions of HSG. With over 20 years teaching experience, he’s also had dozens of articles published in numerous international journals. Martin Bishop – Head of the Bioinformatics Division at the HGMP Resource Centre As well as the first two editions of HSG, Dr Bishop has edited a number of introductory books on the application of informatics to molecular biology and genetics. He is the Associate Editor of the journal Bioinformatics and Managing Editor of Briefings in Bioinformatics. Chris Cannings – Division of Genomic Medicine, University of Sheffield With over 40 years teaching in the area, Professor Cannings has published over 100 papers and is on the editorial board of many related journals. Co-editor of the two previous editions of HSG, he also authored a book on this topic. |
| handbook of statistical genomics: Handbook of Statistical Genomics David J. Balding, Ida Moltke, John Marioni, 2019-09-10 A timely update of a highly popular handbook on statistical genomics This new, two-volume edition of a classic text provides a thorough introduction to statistical genomics, a vital resource for advanced graduate students, early-career researchers and new entrants to the field. It introduces new and updated information on developments that have occurred since the 3rd edition. Widely regarded as the reference work in the field, it features new chapters focusing on statistical aspects of data generated by new sequencing technologies, including sequence-based functional assays. It expands on previous coverage of the many processes between genotype and phenotype, including gene expression and epigenetics, as well as metabolomics. It also examines population genetics and evolutionary models and inference, with new chapters on the multi-species coalescent, admixture and ancient DNA, as well as genetic association studies including causal analyses and variant interpretation. The Handbook of Statistical Genomics focuses on explaining the main ideas, analysis methods and algorithms, citing key recent and historic literature for further details and references. It also includes a glossary of terms, acronyms and abbreviations, and features extensive cross-referencing between chapters, tying the different areas together. With heavy use of up-to-date examples and references to web-based resources, this continues to be a must-have reference in a vital area of research. Provides much-needed, timely coverage of new developments in this expanding area of study Numerous, brand new chapters, for example covering bacterial genomics, microbiome and metagenomics Detailed coverage of application areas, with chapters on plant breeding, conservation and forensic genetics Extensive coverage of human genetic epidemiology, including ethical aspects Edited by one of the leading experts in the field along with rising stars as his co-editors Chapter authors are world-renowned experts in the field, and newly emerging leaders. The Handbook of Statistical Genomics is an excellent introductory text for advanced graduate students and early-career researchers involved in statistical genetics. |
| handbook of statistical genomics: Statistical Genomics Ben Hui Liu, 2017-11-22 Genomics, the mapping of the entire genetic complement of an organism, is the new frontier in biology. This handbook on the statistical issues of genomics covers current methods and the tried-and-true classical approaches. |
| handbook of statistical genomics: Handbook of Statistical Genomics David J. Balding, Ida Moltke, John Marioni, 2019-07-02 A timely update of a highly popular handbook on statistical genomics This new, two-volume edition of a classic text provides a thorough introduction to statistical genomics, a vital resource for advanced graduate students, early-career researchers and new entrants to the field. It introduces new and updated information on developments that have occurred since the 3rd edition. Widely regarded as the reference work in the field, it features new chapters focusing on statistical aspects of data generated by new sequencing technologies, including sequence-based functional assays. It expands on previous coverage of the many processes between genotype and phenotype, including gene expression and epigenetics, as well as metabolomics. It also examines population genetics and evolutionary models and inference, with new chapters on the multi-species coalescent, admixture and ancient DNA, as well as genetic association studies including causal analyses and variant interpretation. The Handbook of Statistical Genomics focuses on explaining the main ideas, analysis methods and algorithms, citing key recent and historic literature for further details and references. It also includes a glossary of terms, acronyms and abbreviations, and features extensive cross-referencing between chapters, tying the different areas together. With heavy use of up-to-date examples and references to web-based resources, this continues to be a must-have reference in a vital area of research. Provides much-needed, timely coverage of new developments in this expanding area of study Numerous, brand new chapters, for example covering bacterial genomics, microbiome and metagenomics Detailed coverage of application areas, with chapters on plant breeding, conservation and forensic genetics Extensive coverage of human genetic epidemiology, including ethical aspects Edited by one of the leading experts in the field along with rising stars as his co-editors Chapter authors are world-renowned experts in the field, and newly emerging leaders. The Handbook of Statistical Genomics is an excellent introductory text for advanced graduate students and early-career researchers involved in statistical genetics. |
| handbook of statistical genomics: Handbook of Statistical Bioinformatics Henry Horng-Shing Lu, Bernhard Schölkopf, Martin T. Wells, Hongyu Zhao, 2022-12-08 Now in its second edition, this handbook collects authoritative contributions on modern methods and tools in statistical bioinformatics with a focus on the interface between computational statistics and cutting-edge developments in computational biology. The three parts of the book cover statistical methods for single-cell analysis, network analysis, and systems biology, with contributions by leading experts addressing key topics in probabilistic and statistical modeling and the analysis of massive data sets generated by modern biotechnology. This handbook will serve as a useful reference source for students, researchers and practitioners in statistics, computer science and biological and biomedical research, who are interested in the latest developments in computational statistics as applied to computational biology. |
| handbook of statistical genomics: Statistical Population Genomics Julien Y Dutheil, 2020-10-08 This open access volume presents state-of-the-art inference methods in population genomics, focusing on data analysis based on rigorous statistical techniques. After introducing general concepts related to the biology of genomes and their evolution, the book covers state-of-the-art methods for the analysis of genomes in populations, including demography inference, population structure analysis and detection of selection, using both model-based inference and simulation procedures. Last but not least, it offers an overview of the current knowledge acquired by applying such methods to a large variety of eukaryotic organisms. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, pointers to the relevant literature, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Statistical Population Genomics aims to promote and ensure successful applications of population genomic methods to an increasing number of model systems and biological questions. This work was published by Saint Philip Street Press pursuant to a Creative Commons license permitting commercial use. All rights not granted by the work's license are retained by the author or authors. |
| handbook of statistical genomics: Computational Genomics with R Altuna Akalin, 2020-12-16 Computational Genomics with R provides a starting point for beginners in genomic data analysis and also guides more advanced practitioners to sophisticated data analysis techniques in genomics. The book covers topics from R programming, to machine learning and statistics, to the latest genomic data analysis techniques. The text provides accessible information and explanations, always with the genomics context in the background. This also contains practical and well-documented examples in R so readers can analyze their data by simply reusing the code presented. As the field of computational genomics is interdisciplinary, it requires different starting points for people with different backgrounds. For example, a biologist might skip sections on basic genome biology and start with R programming, whereas a computer scientist might want to start with genome biology. After reading: You will have the basics of R and be able to dive right into specialized uses of R for computational genomics such as using Bioconductor packages. You will be familiar with statistics, supervised and unsupervised learning techniques that are important in data modeling, and exploratory analysis of high-dimensional data. You will understand genomic intervals and operations on them that are used for tasks such as aligned read counting and genomic feature annotation. You will know the basics of processing and quality checking high-throughput sequencing data. You will be able to do sequence analysis, such as calculating GC content for parts of a genome or finding transcription factor binding sites. You will know about visualization techniques used in genomics, such as heatmaps, meta-gene plots, and genomic track visualization. You will be familiar with analysis of different high-throughput sequencing data sets, such as RNA-seq, ChIP-seq, and BS-seq. You will know basic techniques for integrating and interpreting multi-omics datasets. Altuna Akalin is a group leader and head of the Bioinformatics and Omics Data Science Platform at the Berlin Institute of Medical Systems Biology, Max Delbrück Center, Berlin. He has been developing computational methods for analyzing and integrating large-scale genomics data sets since 2002. He has published an extensive body of work in this area. The framework for this book grew out of the yearly computational genomics courses he has been organizing and teaching since 2015. |
| handbook of statistical genomics: Handbook of Statistical Analysis and Data Mining Applications Robert Nisbet, John Elder, Gary D. Miner, 2009-05-14 The Handbook of Statistical Analysis and Data Mining Applications is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers (both academic and industrial) through all stages of data analysis, model building and implementation. The Handbook helps one discern the technical and business problem, understand the strengths and weaknesses of modern data mining algorithms, and employ the right statistical methods for practical application. Use this book to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques, and discusses their application to real problems, in ways accessible and beneficial to practitioners across industries - from science and engineering, to medicine, academia and commerce. This handbook brings together, in a single resource, all the information a beginner will need to understand the tools and issues in data mining to build successful data mining solutions. - Written By Practitioners for Practitioners - Non-technical explanations build understanding without jargon and equations - Tutorials in numerous fields of study provide step-by-step instruction on how to use supplied tools to build models - Practical advice from successful real-world implementations - Includes extensive case studies, examples, MS PowerPoint slides and datasets - CD-DVD with valuable fully-working 90-day software included: Complete Data Miner - QC-Miner - Text Miner bound with book |
| handbook of statistical genomics: Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications Gary Miner, 2012-01-11 The world contains an unimaginably vast amount of digital information which is getting ever vaster ever more rapidly. This makes it possible to do many things that previously could not be done: spot business trends, prevent diseases, combat crime and so on. Managed well, the textual data can be used to unlock new sources of economic value, provide fresh insights into science and hold governments to account. As the Internet expands and our natural capacity to process the unstructured text that it contains diminishes, the value of text mining for information retrieval and search will increase dramatically. This comprehensive professional reference brings together all the information, tools and methods a professional will need to efficiently use text mining applications and statistical analysis. The Handbook of Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications presents a comprehensive how- to reference that shows the user how to conduct text mining and statistically analyze results. In addition to providing an in-depth examination of core text mining and link detection tools, methods and operations, the book examines advanced preprocessing techniques, knowledge representation considerations, and visualization approaches. Finally, the book explores current real-world, mission-critical applications of text mining and link detection using real world example tutorials in such varied fields as corporate, finance, business intelligence, genomics research, and counterterrorism activities-- |
| handbook of statistical genomics: Handbook of Quantile Regression Roger Koenker, Victor Chernozhukov, Xuming He, Limin Peng, 2017-10-12 Quantile regression constitutes an ensemble of statistical techniques intended to estimate and draw inferences about conditional quantile functions. Median regression, as introduced in the 18th century by Boscovich and Laplace, is a special case. In contrast to conventional mean regression that minimizes sums of squared residuals, median regression minimizes sums of absolute residuals; quantile regression simply replaces symmetric absolute loss by asymmetric linear loss. Since its introduction in the 1970's by Koenker and Bassett, quantile regression has been gradually extended to a wide variety of data analytic settings including time series, survival analysis, and longitudinal data. By focusing attention on local slices of the conditional distribution of response variables it is capable of providing a more complete, more nuanced view of heterogeneous covariate effects. Applications of quantile regression can now be found throughout the sciences, including astrophysics, chemistry, ecology, economics, finance, genomics, medicine, and meteorology. Software for quantile regression is now widely available in all the major statistical computing environments. The objective of this volume is to provide a comprehensive review of recent developments of quantile regression methodology illustrating its applicability in a wide range of scientific settings. The intended audience of the volume is researchers and graduate students across a diverse set of disciplines. |
| handbook of statistical genomics: Handbook of Comparative Genomics Cecilia Saccone, Graziano Pesole, 2003-02-07 This comprehensive reference covers the comparative methodology involved in studying molecular evolution. Providing a practical introduction to the role of bioinformatics in comparative genomics, this publication further discusses the basic technology used in genome sequencing projects and provides an overview of genome storage databases currently in use. This timely and cutting-edge text also: * Reviews the basic principles of genomics and gene expression analysis * Discusses analytic methods in proteomics and transcriptomics * Includes a comprehensive list of Web resource |
| handbook of statistical genomics: Handbook of Formulas and Software for Plant Geneticists and Breeders Manjit S. Kang, 2024-11-15 A simple solution to complicated statistical techniques and formulas! The Handbook of Formulas and Software for Plant Geneticists and Breeders is an up-to-date reference source that eliminates the need for hand calculations of complicated genetic formulas and equations. Contributions from members of the C1 Division of the Crop Science Society of America include computer program codes not found in Statistical Analysis System (SAS) and other commonly available statistical packages. The book provides an invaluable shortcut to sorting through piles of literature in search of programs that may have been published in abbreviated forms or never at all. The Handbook of Formulas and Software for Plant Geneticists and Breeders puts full-fledged program codes of specialized statistical and genetics-related software programs at your fingertips. It shows practicing geneticists, breeders, and students how to use specialized software through practical examples. The book is an excellent research and teaching tool in quantitative genetics and plant breeding, providing definitions of key terms and information on how to obtain desired software and key references. It also includes an extensive listing of programs available for linkage and mapping software that can be accessed through the Internet. The Handbook of Formulas and Software for Plant Geneticists and Breeders presents, among others, programs related to: genotype-by-environmental interaction (GEI) and stability analysis genetic diversity estimation best linear unbiased predictors (BLUPs) principal component and additive main effects and multiplicative interaction (AMMI) analyses quantitative trait loci -by-environment (QTL x E) analysis GGE biplot analysis diallel analyses path analysis trend analysis field plot technique The Handbook of Formulas and Software for Plant Geneticists and Breeders is essential for academics and researchers working in genetics, breeding, and genomics, and as a supplement for coursework in quantitative genetics and plant breeding. |
| handbook of statistical genomics: Scan Statistics Joseph Glaz, Vladimir Pozdnyakov, Sylvan Wallenstein, 2009-12-24 Scan statistics is currently one of the most active and important areas of research in applied probability and statistics, having applications to a wide variety of fields: archaeology, astronomy, bioinformatics, biosurveillance, molecular biology, genetics, computer science, electrical engineering, geography, material sciences, physics, reconnaissance, reliability and quality control, telecommunication, and epidemiology. Filling a gap in the literature, this self-contained volume brings together a collection of selected chapters illustrating the depth and diversity of theory, methods and applications in the area of scan statistics. |
| handbook of statistical genomics: Topological Data Analysis for Genomics and Evolution Raul Rabadan, Andrew J. Blumberg, 2019-12-19 An introduction to geometric and topological methods to analyze large scale biological data; includes statistics and genomic applications. |
| handbook of statistical genomics: Genetics and Genomics in Nursing Quannetta T Edwards, PhD, MSN, MPH, FNP-BC, WHNP, AGN-BC, FAANP, Ann Maradiegue, PhD, MSN, FNP-BC, FAANP, 2017-07-28 Delivers complex information in an easy-to-read, step-by-step format The genomic era encompasses the entire spectrum of DNA -- all of the genes, and the interaction and inter-relationship of genes (genome) to the environment. Rapidly changing research has led to numerous advances in genetic testing, diagnosis, and treatments, and it is essential that APRNs be able to integrate genetic risk assessment into clinical care. This quick reference delivers complex information in an easy-to-read, step-by-step format with bitesize info boxes and bulleted information to provide the tools necessary to understand genetics/genomics and identify red flags that can appear in patient assessments. In an age of personalized and precision medicine, genetic risk assessment has never been more important. Genetics and Genomics in Nursing begins with an overview of genetics and the science behind inheritance. Chapters then break down the processes that make up risk assessment, and walk the reader through data collection and review, identification and calculation of risk, and patient communication. Finally, the last section of this text discusses special populations and key facts nurses need to know about their risk assessment. Key Features: Provides a clear introduction to a complex topic Describes important elements of the genomic risk assessment process for use in clinical settings when evaluating patients Illustrates how to develop a three-generation pedigree Applies commonly-used standardized pedigree symbols and familial patterns to aid in risk interpretation Discusses the challenges and limitations of pedigree interpretation Explains common concepts and includes helpful genomic resources Incorporates genomic risk assessment into patient evaluation |
| handbook of statistical genomics: Handbook of Statistical Systems Biology Michael Stumpf, David J. Balding, Mark Girolami, 2011-09-09 Systems Biology is now entering a mature phase in which the key issues are characterising uncertainty and stochastic effects in mathematical models of biological systems. The area is moving towards a full statistical analysis and probabilistic reasoning over the inferences that can be made from mathematical models. This handbook presents a comprehensive guide to the discipline for practitioners and educators, in providing a full and detailed treatment of these important and emerging subjects. Leading experts in systems biology and statistics have come together to provide insight in to the major ideas in the field, and in particular methods of specifying and fitting models, and estimating the unknown parameters. This book: Provides a comprehensive account of inference techniques in systems biology. Introduces classical and Bayesian statistical methods for complex systems. Explores networks and graphical modeling as well as a wide range of statistical models for dynamical systems. Discusses various applications for statistical systems biology, such as gene regulation and signal transduction. Features statistical data analysis on numerous technologies, including metabolic and transcriptomic technologies. Presents an in-depth presentation of reverse engineering approaches. Provides colour illustrations to explain key concepts. This handbook will be a key resource for researchers practising systems biology, and those requiring a comprehensive overview of this important field. |
| handbook of statistical genomics: Oxford Handbook of Medical Statistics Janet Peacock, Philip Peacock, 2011 The majority of medical research involves quantitative methods and so it is essential to be able to understand and interpret statistics. This book shows readers how to develop the skills required to critically appraise research evidence effectively, and how to conduct research and communicate their findings. |
| handbook of statistical genomics: Conservation and the Genomics of Populations Fred W. Allendorf, W. Chris Funk, Sally N. Aitken, Margaret Byrne, Gordon Luikart, 2022-02-10 The relentless loss of biodiversity is among the greatest problems facing the world today. The third edition of this established textbook provides an updated and comprehensive overview of the essential background, concepts, and tools required to understand how genetics can be used to conserve species, reduce threat of extinction, and manage species of ecological or commercial importance. This edition is thoroughly revised to reflect the major contribution of genomics to conservation of populations and species. It includes two new chapters: Genetic Monitoring and a final Conservation Genetics in Practice chapter that addresses the role of science and policy in conservation genetics. New genomic techniques and statistical analyses are crucial tools for the conservation geneticist. This accessible and authoritative textbook provides an essential toolkit grounded in population genetics theory, coupled with basic and applied research examples from plants, animals, and microbes. The book examines genetic and phenotypic variation in natural populations, the principles and mechanisms of evolutionary change, evolutionary response to anthropogenic change, and applications in conservation and management. Conservation and the Genomics of Populations helps demystify genetics and genomics for conservation practitioners and early career scientists, so that population genetic theory and new genomic data can help raise the bar in conserving biodiversity in the most critical 20 year period in the history of life on Earth. It is aimed at a global market of applied population geneticists, conservation practitioners, and natural resource managers working for wildlife and habitat management agencies. It will be of particular relevance and use to upper undergraduate and graduate students taking courses in conservation biology, conservation genetics, and wildlife management. |
| handbook of statistical genomics: Population Genetics and Microevolutionary Theory Alan R. Templeton, 2006-09-29 The advances made possible by the development of molecular techniques have in recent years revolutionized quantitative genetics and its relevance for population genetics. Population Genetics and Microevolutionary Theory takes a modern approach to population genetics, incorporating modern molecular biology, species-level evolutionary biology, and a thorough acknowledgment of quantitative genetics as the theoretical basis for population genetics. Logically organized into three main sections on population structure and history, genotype-phenotype interactions, and selection/adaptation Extensive use of real examples to illustrate concepts Written in a clear and accessible manner and devoid of complex mathematical equations Includes the author's introduction to background material as well as a conclusion for a handy overview of the field and its modern applications Each chapter ends with a set of review questions and answers Offers helpful general references and Internet links |
| handbook of statistical genomics: Statistics in Human Genetics Pak Sham, 1997-12-08 Rigorous statistical analysis methods for human genetics application Statistics in Human Genetics explores the statistical analysis methods that are critical to good science. Beginning with a brief review of genes, gene structure, variation, and terminology, the book moves into analysis of segregation, genetic linkage, allelic associations, and continuity for a wide range of conditions. From the classic Hardy-Weinberg equation to advanced modeling, algorithms and more, this book provides authoritative guidance toward methods, analysis, and applications for anyone performing quantitative analysis of human genetics. |
| handbook of statistical genomics: Genomic Selection for Crop Improvement Rajeev K. Varshney, Manish Roorkiwal, Mark E. Sorrells, 2017-12-05 Genomic Selection for Crop Improvement serves as handbook for users by providing basic as well as advanced understandings of genomic selection. This useful review explains germplasm use, phenotyping evaluation, marker genotyping methods, and statistical models involved in genomic selection. It also includes examples of ongoing activities of genomic selection for crop improvement and efforts initiated to deploy the genomic selection in some important crops. In order to understand the potential of GS breeding, it is high time to bring complete information in the form of a book that can serve as a ready reference for geneticist and plant breeders. |
| handbook of statistical genomics: Statistical Human Genetics Robert C. Elston, Jaya M. Satagopan, Shuying Sun, 2012-02-04 Recent advances in genetics over the last quarter of a century, especially in molecular techniques, have dramatically reduced the cost of determining genetic markers and hence opened up a field of research that is increasingly helping to detect, prevent and/or cure many diseases that afflict humans. In Statistical Human Genetics: Methods and Protocols expert researchers in the field describe statistical methods and computer programs in the detail necessary to make them more easily accessible to the beginner analyzing data. Written in the highly successful Methods in Molecular BiologyTM series format, with examples of running the programs and interpreting the program outputs, the chapters include the kind of detailed description and implementation advice that is crucial for getting optimal results from human genetic data collected in the laboratory. Thorough and as much as possible intuitive, Statistical Human Genetics: Methods and Protocols aids scientists in understanding the computer programs and analytical procedures they need to use. |
| handbook of statistical genomics: Signs and Symptoms of Genetic Conditions Louanne Hudgins, Helga V. Toriello, Gregory M. Enns, H. Eugene Hoyme, 2014-05-30 Connecting an abnormal physical exam to a possible genetic condition is a daunting and inexact task for any physician, be they a primary care provider, non-geneticist specialist, or fellowship-trained geneticist. Comprising 31 clinical protocols from the world's foremost clinical geneticists, Signs and Symptoms of Genetic Conditions provides a practical manual for the diagnosis and management of common human genetic conditions based on their presenting signs and/or symptoms. Each chapter examines a specific clinical finding and leads the user through a step-by-step approach to a differential diagnosis. To maximize clinical utility, this handbook features: · Prominent flow chart diagrams that graphically depict the diagnostic approach · Concise recommendations for laboratory and/or imaging studies · Health supervision and management strategies for the most common conditions associated with each presenting sign or symptom Whether for the student, resident, or seasoned clinician, Signs and Symptoms of Genetic Conditions will serve as a frontline resource for navigating differential diagnosis. |
| handbook of statistical genomics: Handbook of Bayesian Variable Selection Mahlet Tadesse, Marina Vannucci, 2021-12 Bayesian variable selection has experienced substantial developments over the past 30 years with the proliferation of large data sets. Identifying relevant variables to include in a model allows simpler interpretation, avoids overfitting and multicollinearity, and can provide insights into the mechanisms underlying an observed phenomenon. Variable selection is especially important when the number of potential predictors is substantially larger than the sample size and sparsity can reasonably be assumed. The Handbook of Bayesian Variable Selection provides a comprehensive review of theoretical, methodological and computational aspects of Bayesian methods for variable selection. The topics covered include spike-and-slab priors, continuous shrinkage priors, Bayes factors, Bayesian model averaging, partitioning methods, as well as variable selection in decision trees and edge selection in graphical models. The handbook targets graduate students and established researchers who seek to understand the latest developments in the field. It also provides a valuable reference for all interested in applying existing methods and/or pursuing methodological extensions-- |
| handbook of statistical genomics: Statistical Genetics Benjamin Neale, Manuel Ferreira, Sarah Medland, Danielle Posthuma, 2007-11-30 Statistical Genetics is an advanced textbook focusing on conducting genome-wide linkage and association analysis in order to identify the genes responsible for complex behaviors and diseases. Starting with an introductory section on statistics and quantitative genetics, it covers both established and new methodologies, providing the genetic and statistical theory on which they are based. Each chapter is written by leading researchers, who give the reader the benefit of their experience with worked examples, study design, and sources of error. The text can be used in conjunction with an associated website (www.genemapping.org) that provides supplementary material and links to downloadable software. |
| handbook of statistical genomics: Population Genomics with R Emmanuel Paradis, 2020-03-13 Population Genomics With R presents a multidisciplinary approach to the analysis of population genomics. The methods treated cover a large number of topics from traditional population genetics to large-scale genomics with high-throughput sequencing data. Several dozen R packages are examined and integrated to provide a coherent software environment with a wide range of computational, statistical, and graphical tools. Small examples are used to illustrate the basics and published data are used as case studies. Readers are expected to have a basic knowledge of biology, genetics, and statistical inference methods. Graduate students and post-doctorate researchers will find resources to analyze their population genetic and genomic data as well as help them design new studies. The first four chapters review the basics of population genomics, data acquisition, and the use of R to store and manipulate genomic data. Chapter 5 treats the exploration of genomic data, an important issue when analysing large data sets. The other five chapters cover linkage disequilibrium, population genomic structure, geographical structure, past demographic events, and natural selection. These chapters include supervised and unsupervised methods, admixture analysis, an in-depth treatment of multivariate methods, and advice on how to handle GIS data. The analysis of natural selection, a traditional issue in evolutionary biology, has known a revival with modern population genomic data. All chapters include exercises. Supplemental materials are available on-line (http://ape-package.ird.fr/PGR.html). |
| handbook of statistical genomics: Molecular Plant Breeding Yunbi Xu, 2010 Recent advances in plant genomics and molecular biology have revolutionized our understanding of plant genetics, providing new opportunities for more efficient and controllable plant breeding. Successful techniques require a solid understanding of the underlying molecular biology as well as experience in applied plant breeding. Bridging the gap between developments in biotechnology and its applications in plant improvement, Molecular Plant Breeding provides an integrative overview of issues from basic theories to their applications to crop improvement including molecular marker technology, gene mapping, genetic transformation, quantitative genetics, and breeding methodology. |
| handbook of statistical genomics: Applied Statistics in Agricultural, Biological, and Environmental Sciences Barry Glaz, Kathleen M. Yeater, 2020-01-22 Better experimental design and statistical analysis make for more robust science. A thorough understanding of modern statistical methods can mean the difference between discovering and missing crucial results and conclusions in your research, and can shape the course of your entire research career. With Applied Statistics, Barry Glaz and Kathleen M. Yeater have worked with a team of expert authors to create a comprehensive text for graduate students and practicing scientists in the agricultural, biological, and environmental sciences. The contributors cover fundamental concepts and methodologies of experimental design and analysis, and also delve into advanced statistical topics, all explored by analyzing real agronomic data with practical and creative approaches using available software tools. IN PRESS! This book is being published according to the “Just Published” model, with more chapters to be published online as they are completed. |
| handbook of statistical genomics: The Phylogenetic Handbook Marco Salemi, Anne-Mieke Vandamme, Philippe Lemey, 2009-03-26 A broad, hands on guide with detailed explanations of current methodology, relevant exercises and popular software tools. |
| handbook of statistical genomics: Public Health Genomics Claudia N. Mikail, 2008-11-03 The Centers for Disease Control (CDC) has recognized genomics as a priority area in public health education. To help public health students and professionals achieve proficiency in the language of genetics and attain genomics competencies delineated by the CDC, this book offers an introduction to basic molecular genetics and discusses the relevance of genomics to such key public health issues as environmental health, ethnic health disparities, health policy and law, research ethics, maternal and child health, clinical preventive medicine, health behavior, health economics, and communicable disease control. Presented in a context that is easy to understand, the book serves as an accessible portal of entry into the world of public health genomics. |
| handbook of statistical genomics: The Handbook of Metabolic Phenotyping John C. Lindon, Jeremy K. Nicholson, Elaine Holmes, 2018-10-04 The Handbook of Metabolic Phenotyping is the definitive work on the rapidly developing subject of metabolic phenotyping. It explores in detail the wide array of analytical chemistry and statistical modeling techniques used in the field, coupled with surveys of the various application areas in human development, nutrition, disease, therapy, and epidemiology to create a comprehensive exploration of the area of study. It covers recent studies that integrate the various -omics data sets to derive a systems biology view. It also addresses current issues on standardization, assay and statistics validation, and data storage and sharing. Written by experts with many years of practice in the field who pioneered many of the approaches widely used today, The Handbook of Metabolic Phenotyping is a valuable resource for postgrads and research scientists studying and furthering the field of metabolomics. - Contains theoretical and practical explanations of all the main analytical chemistry techniques used in metabolic phenotyping - Explores, in detail, the many diverse statistical approaches used in the field - Offers practical tips for successfully conducting metabolic phenotyping studies - Features reviews of all of the various fields of activity relating to human studies |
| handbook of statistical genomics: Interpreting Basic Statistics Zealure C. Holcomb, Keith S. Cox, 2017-08-09 Interpreting Basic Statistics gives students valuable practice in interpreting statistical reporting as it actually appears in peer-reviewed journals. New to the eighth edition: A broader array of basic statistical concepts is covered, especially to better reflect the New Statistics. Journal excerpts have been updated to reflect current styles in statistical reporting. A stronger emphasis on data visualizations has been added. The statistical exercises have been re-organized into units to facilitate ease of use and understanding. About this book Each of the 64 exercises gives a brief excerpt of statistical reporting from a published research article, and begins with guidelines for interpreting the statistics in the excerpt. The questions on the excerpts promote learning by requiring students to interpret information in tables and figures, perform simple calculations to further their interpretations, critique data-reporting techniques, and evaluate procedures used to collect data. Each exercise covers a limited number of statistics, making it easy to coordinate the exercises with lectures and a main textbook. The questions in each exercise are divided into two parts: (1) Factual Questions and (2) Questions for Discussion. The factual questions require careful reading for details, while the discussion questions show that interpreting statistics is more than a mathematical exercise. These questions require students to apply good judgment as well as statistical reasoning in arriving at appropriate interpretations. |
| handbook of statistical genomics: Statistical Methods in Water Resources D.R. Helsel, R.M. Hirsch, 1993-03-03 Data on water quality and other environmental issues are being collected at an ever-increasing rate. In the past, however, the techniques used by scientists to interpret this data have not progressed as quickly. This is a book of modern statistical methods for analysis of practical problems in water quality and water resources.The last fifteen years have seen major advances in the fields of exploratory data analysis (EDA) and robust statistical methods. The 'real-life' characteristics of environmental data tend to drive analysis towards the use of these methods. These advances are presented in a practical and relevant format. Alternate methods are compared, highlighting the strengths and weaknesses of each as applied to environmental data. Techniques for trend analysis and dealing with water below the detection limit are topics covered, which are of great interest to consultants in water-quality and hydrology, scientists in state, provincial and federal water resources, and geological survey agencies.The practising water resources scientist will find the worked examples using actual field data from case studies of environmental problems, of real value. Exercises at the end of each chapter enable the mechanics of the methodological process to be fully understood, with data sets included on diskette for easy use. The result is a book that is both up-to-date and immediately relevant to ongoing work in the environmental and water sciences. |
| handbook of statistical genomics: Big Data in Omics and Imaging Momiao Xiong, 2017-12-01 Big Data in Omics and Imaging: Association Analysis addresses the recent development of association analysis and machine learning for both population and family genomic data in sequencing era. It is unique in that it presents both hypothesis testing and a data mining approach to holistically dissecting the genetic structure of complex traits and to designing efficient strategies for precision medicine. The general frameworks for association analysis and machine learning, developed in the text, can be applied to genomic, epigenomic and imaging data. FEATURES Bridges the gap between the traditional statistical methods and computational tools for small genetic and epigenetic data analysis and the modern advanced statistical methods for big data Provides tools for high dimensional data reduction Discusses searching algorithms for model and variable selection including randomization algorithms, Proximal methods and matrix subset selection Provides real-world examples and case studies Will have an accompanying website with R code The book is designed for graduate students and researchers in genomics, bioinformatics, and data science. It represents the paradigm shift of genetic studies of complex diseases– from shallow to deep genomic analysis, from low-dimensional to high dimensional, multivariate to functional data analysis with next-generation sequencing (NGS) data, and from homogeneous populations to heterogeneous population and pedigree data analysis. Topics covered are: advanced matrix theory, convex optimization algorithms, generalized low rank models, functional data analysis techniques, deep learning principle and machine learning methods for modern association, interaction, pathway and network analysis of rare and common variants, biomarker identification, disease risk and drug response prediction. |
| handbook of statistical genomics: Handbook of Mixture Analysis Sylvia Fruhwirth-Schnatter, Gilles Celeux, Christian P. Robert, 2019-01-04 Mixture models have been around for over 150 years, and they are found in many branches of statistical modelling, as a versatile and multifaceted tool. They can be applied to a wide range of data: univariate or multivariate, continuous or categorical, cross-sectional, time series, networks, and much more. Mixture analysis is a very active research topic in statistics and machine learning, with new developments in methodology and applications taking place all the time. The Handbook of Mixture Analysis is a very timely publication, presenting a broad overview of the methods and applications of this important field of research. It covers a wide array of topics, including the EM algorithm, Bayesian mixture models, model-based clustering, high-dimensional data, hidden Markov models, and applications in finance, genomics, and astronomy. Features: Provides a comprehensive overview of the methods and applications of mixture modelling and analysis Divided into three parts: Foundations and Methods; Mixture Modelling and Extensions; and Selected Applications Contains many worked examples using real data, together with computational implementation, to illustrate the methods described Includes contributions from the leading researchers in the field The Handbook of Mixture Analysis is targeted at graduate students and young researchers new to the field. It will also be an important reference for anyone working in this field, whether they are developing new methodology, or applying the models to real scientific problems. |
| handbook of statistical genomics: The Oxford Handbook of Bayesian Econometrics John Geweke, Gary Koop, Herman van Dijk, 2011-09-29 Bayesian econometric methods have enjoyed an increase in popularity in recent years. Econometricians, empirical economists, and policymakers are increasingly making use of Bayesian methods. This handbook is a single source for researchers and policymakers wanting to learn about Bayesian methods in specialized fields, and for graduate students seeking to make the final step from textbook learning to the research frontier. It contains contributions by leading Bayesians on the latest developments in their specific fields of expertise. The volume provides broad coverage of the application of Bayesian econometrics in the major fields of economics and related disciplines, including macroeconomics, microeconomics, finance, and marketing. It reviews the state of the art in Bayesian econometric methodology, with chapters on posterior simulation and Markov chain Monte Carlo methods, Bayesian nonparametric techniques, and the specialized tools used by Bayesian time series econometricians such as state space models and particle filtering. It also includes chapters on Bayesian principles and methodology. |
| handbook of statistical genomics: The Oxford Handbook of Political Methodology Janet M. Box-Steffensmeier, Henry E. Brady, David Collier, 2008-08-21 Political methodology has changed dramatically over the past thirty years, and many new methods and techniques have been developed. Both the Political Methodology Society and the Qualitative/Multi-Methods Section of the American Political Science Association have engaged in ongoing research and training programs that have advanced quantitative and qualitative methodology. The Oxford Handbook of Political Methodology presents and synthesizes these developments. The Handbook provides comprehensive overviews of diverse methodological approaches, with an emphasis on three major themes. First, specific methodological tools should be at the service of improved conceptualization, comprehension of meaning, measurement, and data collection. They should increase analysts' leverage in reasoning about causal relationships and evaluating them empirically by contributing to powerful research designs. Second, the authors explore the many different ways of addressing these tasks: through case-studies and large-n designs, with both quantitative and qualitative data, and via techniques ranging from statistical modelling to process tracing. Finally, techniques can cut across traditional methodological boundaries and can be useful for many different kinds of researchers. Many of the authors thus explore how their methods can inform, and be used by, scholars engaged in diverse branches of methodology. |
| handbook of statistical genomics: An Introduction to Statistical Genetic Data Analysis Melinda C. Mills, Nicola Barban, Felix C. Tropf, 2020-02-18 A comprehensive introduction to modern applied statistical genetic data analysis, accessible to those without a background in molecular biology or genetics. Human genetic research is now relevant beyond biology, epidemiology, and the medical sciences, with applications in such fields as psychology, psychiatry, statistics, demography, sociology, and economics. With advances in computing power, the availability of data, and new techniques, it is now possible to integrate large-scale molecular genetic information into research across a broad range of topics. This book offers the first comprehensive introduction to modern applied statistical genetic data analysis that covers theory, data preparation, and analysis of molecular genetic data, with hands-on computer exercises. It is accessible to students and researchers in any empirically oriented medical, biological, or social science discipline; a background in molecular biology or genetics is not required. The book first provides foundations for statistical genetic data analysis, including a survey of fundamental concepts, primers on statistics and human evolution, and an introduction to polygenic scores. It then covers the practicalities of working with genetic data, discussing such topics as analytical challenges and data management. Finally, the book presents applications and advanced topics, including polygenic score and gene-environment interaction applications, Mendelian Randomization and instrumental variables, and ethical issues. The software and data used in the book are freely available and can be found on the book's website. |
| handbook of statistical genomics: Bioinformatics and Functional Genomics Jonathan Pevsner, 2005-03-04 Wiley is proud to announce the publication of the first ever broad-based textbook introduction to Bioinformatics and Functional Genomics by a trained biologist, experienced researcher, and award-winning instructor. In this new text, author Jonathan Pevsner, winner of the 2001 Johns Hopkins University Teacher of the Year award, explains problem-solving using bioinformatic approaches using real examples such as breast cancer, HIV-1, and retinal-binding protein throughout. His book includes 375 figures and over 170 tables. Each chapter includes: Problems, discussion of Pitfalls, Boxes explaining key techniques and math/stats principles, Summary, Recommended Reading list, and URLs for freely available software. The text is suitable for professionals and students at every level, including those with little to no background in computer science. |
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