An Introduction To Statistical Genetic Data Analysis

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An Introduction to Statistical Genetic Data Analysis

Melinda C. Mills,Nicola Barban,Felix C. Tropf
An Introduction to Statistical Genetic Data Analysis Book PDF

Author : Melinda C. Mills,Nicola Barban,Felix C. Tropf
Publisher : MIT Press
Page : 433 pages
File Size : 45,9 Mb
Release : 2020-02-18
Category : Science
ISBN : 9780262538381

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An Introduction to Statistical Genetic Data Analysis by Melinda C. Mills,Nicola Barban,Felix C. Tropf Pdf

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 Genetics

David J. Balding,Martin Bishop,Chris Cannings
Handbook of Statistical Genetics Book PDF

Author : David J. Balding,Martin Bishop,Chris Cannings
Publisher : John Wiley & Sons
Page : 1616 pages
File Size : 55,7 Mb
Release : 2008-06-10
Category : Science
ISBN : 0470997621

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Handbook of Statistical Genetics by David J. Balding,Martin Bishop,Chris Cannings Pdf

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.

Genetic Data Analysis for Plant and Animal Breeding

Fikret Isik,James Holland,Christian Maltecca
Genetic Data Analysis for Plant and Animal Breeding Book PDF

Author : Fikret Isik,James Holland,Christian Maltecca
Publisher : Springer
Page : 400 pages
File Size : 44,8 Mb
Release : 2017-09-09
Category : Science
ISBN : 9783319551777

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Genetic Data Analysis for Plant and Animal Breeding by Fikret Isik,James Holland,Christian Maltecca Pdf

This book fills the gap between textbooks of quantitative genetic theory, and software manuals that provide details on analytical methods but little context or perspective on which methods may be most appropriate for a particular application. Accordingly this book is composed of two sections. The first section (Chapters 1 to 8) covers topics of classical phenotypic data analysis for prediction of breeding values in animal and plant breeding programs. In the second section (Chapters 9 to 13) we provide the concept and overall review of available tools for using DNA markers for predictions of genetic merits in breeding populations. With advances in DNA sequencing technologies, genomic data, especially single nucleotide polymorphism (SNP) markers, have become available for animal and plant breeding programs in recent years. Analysis of DNA markers for prediction of genetic merit is a relatively new and active research area. The algorithms and software to implement these algorithms are changing rapidly. This section represents state-of-the-art knowledge on the tools and technologies available for genetic analysis of plants and animals. However, readers should be aware that the methods or statistical packages covered here may not be available or they might be out of date in a few years. Ultimately the book is intended for professional breeders interested in utilizing these tools and approaches in their breeding programs. Lastly, we anticipate the usage of this volume for advanced level graduate courses in agricultural and breeding courses.

Mathematical and Statistical Methods for Genetic Analysis

Kenneth Lange
Mathematical and Statistical Methods for Genetic Analysis Book PDF

Author : Kenneth Lange
Publisher : Springer Science & Business Media
Page : 376 pages
File Size : 54,9 Mb
Release : 2012-12-06
Category : Medical
ISBN : 9780387217505

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Mathematical and Statistical Methods for Genetic Analysis by Kenneth Lange Pdf

Written to equip students in the mathematical siences to understand and model the epidemiological and experimental data encountered in genetics research. This second edition expands the original edition by over 100 pages and includes new material. Sprinkled throughout the chapters are many new problems.

The Fundamentals of Modern Statistical Genetics

Nan M. Laird,Christoph Lange
The Fundamentals of Modern Statistical Genetics Book PDF

Author : Nan M. Laird,Christoph Lange
Publisher : Springer Science & Business Media
Page : 226 pages
File Size : 49,7 Mb
Release : 2010-12-13
Category : Medical
ISBN : 9781441973382

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The Fundamentals of Modern Statistical Genetics by Nan M. Laird,Christoph Lange Pdf

This book covers the statistical models and methods that are used to understand human genetics, following the historical and recent developments of human genetics. Starting with Mendel’s first experiments to genome-wide association studies, the book describes how genetic information can be incorporated into statistical models to discover disease genes. All commonly used approaches in statistical genetics (e.g. aggregation analysis, segregation, linkage analysis, etc), are used, but the focus of the book is modern approaches to association analysis. Numerous examples illustrate key points throughout the text, both of Mendelian and complex genetic disorders. The intended audience is statisticians, biostatisticians, epidemiologists and quantitatively- oriented geneticists and health scientists wanting to learn about statistical methods for genetic analysis, whether to better analyze genetic data, or to pursue research in methodology. A background in intermediate level statistical methods is required. The authors include few mathematical derivations, and the exercises provide problems for students with a broad range of skill levels. No background in genetics is assumed.

Computational Genomics with R

Altuna Akalin
Computational Genomics with R Book PDF

Author : Altuna Akalin
Publisher : CRC Press
Page : 462 pages
File Size : 49,6 Mb
Release : 2020-12-16
Category : Mathematics
ISBN : 9781498781862

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Computational Genomics with R by Altuna Akalin Pdf

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

David J. Balding,Ida Moltke,John Marioni
Handbook of Statistical Genomics Book PDF

Author : David J. Balding,Ida Moltke,John Marioni
Publisher : John Wiley & Sons
Page : 1828 pages
File Size : 40,5 Mb
Release : 2019-07-09
Category : Science
ISBN : 9781119429258

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Handbook of Statistical Genomics by David J. Balding,Ida Moltke,John Marioni Pdf

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.

Introduction to Statistical Methods in Modern Genetics

M.C. Yang
Introduction to Statistical Methods in Modern Genetics Book PDF

Author : M.C. Yang
Publisher : CRC Press
Page : 258 pages
File Size : 53,7 Mb
Release : 2000-02-23
Category : Mathematics
ISBN : 9781482287394

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Introduction to Statistical Methods in Modern Genetics by M.C. Yang Pdf

Though the basic statistical theory behind modern genetics is not that difficult, most statistical genetics papers are not easy to read for beginners, and fitting formulae to a particular area of application quickly becomes very tedious. Introduction to Statistical Methods in Modern Genetics makes a clear distinction between the necessary and unnecessary complexities. The author keeps the derivations of methods simple without losing the mathematical details. He also provides the necessary background in modern genetics for newcomers to the field, including discussion ranging from biological and molecular experiments to gene hunting and genetic engineering.

Applied Statistical Genetics with R

Andrea S. Foulkes
Applied Statistical Genetics with R Book PDF

Author : Andrea S. Foulkes
Publisher : Springer Science & Business Media
Page : 264 pages
File Size : 52,8 Mb
Release : 2009-04-28
Category : Science
ISBN : 9780387895543

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Applied Statistical Genetics with R by Andrea S. Foulkes Pdf

Statistical genetics has become a core course in many graduate programs in public health and medicine. This book presents fundamental concepts and principles in this emerging field at a level that is accessible to students and researchers with a first course in biostatistics. Extensive examples are provided using publicly available data and the open source, statistical computing environment, R.

Statistical Genetics

Benjamin Neale,Manuel Ferreira,Sarah Medland,Danielle Posthuma
Statistical Genetics Book PDF

Author : Benjamin Neale,Manuel Ferreira,Sarah Medland,Danielle Posthuma
Publisher : Garland Science
Page : 608 pages
File Size : 55,5 Mb
Release : 2007-11-30
Category : Science
ISBN : 9781134129331

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Statistical Genetics by Benjamin Neale,Manuel Ferreira,Sarah Medland,Danielle Posthuma Pdf

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.

Introduction to Statistical Methods in Modern Genetics

M.C. Yang
Introduction to Statistical Methods in Modern Genetics Book PDF

Author : M.C. Yang
Publisher : CRC Press
Page : 264 pages
File Size : 42,6 Mb
Release : 2000-02-23
Category : Mathematics
ISBN : 9056991345

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Introduction to Statistical Methods in Modern Genetics by M.C. Yang Pdf

Although the basic statistical theory behind modern genetics is not very difficult, most statistical genetics papers are not easy to read for beginners in the field, and formulae quickly become very tedious to fit a particular area of application. Introduction to Statistical Methods in Modern Genetics distinguishes between the necessary and unnecessary complexity in a presentation designed for graduate-level statistics students. The author keeps derivations simple, but does so without losing the mathematical details. He also provides the required background in modern genetics for those looking forward to entering this arena. Along with some of the statistical tools important in genetics applications, students will learn: How a gene is found How scientists have separated the genetic and environmental aspects of a person's intelligence How genetics are used in agriculture to improve crops and domestic animals What a DNA fingerprint is and why there are controversies about it Although the author assumes students have a foundation in basic statistics, an appendix provides the necessary background beyond the elementary, including multinomial distributions, inference on frequency tables, and discriminant analysis. With clear explanations, a multitude of figures, and exercise sets in each chapter, this text forms an outstanding entrée into the rapidly expanding world of genetic data analysis.

Molecular Data Analysis Using R

Csaba Ortutay,Zsuzsanna Ortutay
Molecular Data Analysis Using R Book PDF

Author : Csaba Ortutay,Zsuzsanna Ortutay
Publisher : John Wiley & Sons
Page : 354 pages
File Size : 53,5 Mb
Release : 2017-02-06
Category : Medical
ISBN : 9781119165026

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Molecular Data Analysis Using R by Csaba Ortutay,Zsuzsanna Ortutay Pdf

This book addresses the difficulties experienced by wet lab researchers with the statistical analysis of molecular biology related data. The authors explain how to use R and Bioconductor for the analysis of experimental data in the field of molecular biology. The content is based upon two university courses for bioinformatics and experimental biology students (Biological Data Analysis with R and High-throughput Data Analysis with R). The material is divided into chapters based upon the experimental methods used in the laboratories. Key features include: • Broad appeal--the authors target their material to researchers in several levels, ensuring that the basics are always covered. • First book to explain how to use R and Bioconductor for the analysis of several types of experimental data in the field of molecular biology. • Focuses on R and Bioconductor, which are widely used for data analysis. One great benefit of R and Bioconductor is that there is a vast user community and very active discussion in place, in addition to the practice of sharing codes. Further, R is the platform for implementing new analysis approaches, therefore novel methods are available early for R users.

An Introduction to Statistical Genetic Data Analysis

Melinda C. Mills,Nicola Barban,Felix C. Tropf
An Introduction to Statistical Genetic Data Analysis Book PDF

Author : Melinda C. Mills,Nicola Barban,Felix C. Tropf
Publisher : MIT Press
Page : 433 pages
File Size : 41,8 Mb
Release : 2020-02-18
Category : Science
ISBN : 9780262357449

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An Introduction to Statistical Genetic Data Analysis by Melinda C. Mills,Nicola Barban,Felix C. Tropf Pdf

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.

Analysis of Human Genetic Linkage

Jurg Ott
Analysis of Human Genetic Linkage Book PDF

Author : Jurg Ott
Publisher : JHU Press
Page : 418 pages
File Size : 50,9 Mb
Release : 1999-04-16
Category : Medical
ISBN : 0801861403

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Analysis of Human Genetic Linkage by Jurg Ott Pdf

Introduction and basic genetic principles; Genetic loci genetic polymorphisms; Aspects of statistical inference; Basics of linkage analysis; The informativeness of family data; Multipoint linkage analysis; Penetrance; Quantitative phenotypes; Numerical and computerized methods; Variability of the recombination fraction; Inconsistencies; Linkage analysis with mendelian disease loci; Nonparametric methods; Two-locus inheritance; Complex traits.

Bayesian Data Analysis, Third Edition

Andrew Gelman,John B. Carlin,Hal S. Stern,David B. Dunson,Aki Vehtari,Donald B. Rubin
Bayesian Data Analysis Third Edition Book PDF

Author : Andrew Gelman,John B. Carlin,Hal S. Stern,David B. Dunson,Aki Vehtari,Donald B. Rubin
Publisher : CRC Press
Page : 677 pages
File Size : 45,7 Mb
Release : 2013-11-01
Category : Mathematics
ISBN : 9781439840955

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Bayesian Data Analysis, Third Edition by Andrew Gelman,John B. Carlin,Hal S. Stern,David B. Dunson,Aki Vehtari,Donald B. Rubin Pdf

Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.