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An introduction to statistical genetic data analysis
Mills, Melinda
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| 자료유형 | E-BOOK |
|---|---|
| 서명/저자사항 | An introduction to statistical genetic data analysis/ Melinda C. Mills, Nicola Barban, and Felix C. Tropf. |
| 개인저자 | Mills, Melinda,author. Barban, Nicola,author, Tropf, Felix C.,1984- author, |
| 발행사항 | Cambridge, Massachusetts: The MIT Press, [2020]. |
| 형태사항 | 1 online resource: illustrations, maps. |
| 기타형태 저록 | Print version: Mills, Melinda. Introduction to statistical genetic data analysis. Cambridge, Massachusetts : The MIT Press, [2020] 9780262538381 |
| ISBN | 0262357445 9780262357449 |
| 서지주기 | Includes bibliographical references and index. |
| 내용주기 | Introduction : Fundamental Concepts and the Human Genome -- A Statistical Primer for Genetic Data Analysis -- A Primer in Human Evolution -- Genome-Wide Association Studies (GWAS) -- Introduction to Polygenic Scores and Genetic Architecture -- Gene-Environment Interplay -- Genetic data and analytical challenges -- Working with genetic data Part I : Data management, descriptive statistics and quality control -- Working with genetic data Part II : Association analysis, population stratification, and genetic relatedness -- An applied guide to creating and validating polygenic scores -- Polygenic Score and Gene-Environment Interaction (GxE) Applications -- Applying genome-wide association results -- Mendelian Randomization and instrumental variables -- Ethical Issues in Genomics Research -- Conclusions and Future Directions. |
| 요약 | "This book is truly unique in that it is the first comprehensive book that not only provides an introduction to the foundations of human genetics, but also includes a statistical primer, theoretical models and hands-on computer applications. There are many excellent introductory books on human genetics or statistical population genetics, yet most of them are written for advanced graduate and PhD students in biology or genetics. This is in stark contrast with how genetic data is being used in research today, which is increasingly across multiple scientific and research domains. Current textbooks are generally separated into distinct topics. Many focus solely on an introduction to molecular genetics and human evolution. Others provide in-depth treatments of statistical models in this area of research or bioinformatics. Few (if any) provide hands-on computer exercises. To our knowledge there is currently no comparable book on the market that spans and actively links all of these topics. Yet it is precisely these combined and interdisciplinary skills that are now required. Another unique aspect of this book is that it is written at an accessible and introductory level to reach people from a variety of backgrounds. This book is for current and aspiring students and researchers from any empirically oriented medical, biological, behavioural or social science discipline who would like to understand the main concepts of human statistical genetic data analysis, but also practitioners looking for solutions to enter and undertake this research. It is an introductory book, written for those who do not have a strong background in molecular biology, human genetics or statistical genetics, but would like to integrate genetic data into their research. We also made a concerted effort to focus on the basic terminology and practical aspects of statistical genetic data analysis rather than the math, statistics and biology behind it. The book is divided into three interdependent parts. Part I provides the foundations including: (1) fundamental concepts and the human genome, (2) a statistical primer, (3) a primer in human evolution, (4) Genome-Wide Association Studies (GWAS), (5) polygenic scores and genetic architecture; and, (6) gene-environment interplay. Part II delves into the practicalities of how to work with genetic data including: (7) genetic data and challenges, (8) data management I: descriptive statistics, quality control, (9) data management II: association analysis, population stratification and genetic relatedness; and, (10) creating and validating polygenic scores. Part III covers applications and advanced topics, namely: (11) polygenic score and gene-environment interaction applications, (12) applying GWAS results, (13) Mendelian Randomization and instrumental variables, (14) ethical issues; and, (15) conclusions and future directions. We also included two appendices which comprise of: Appendix 1: Software used in this book, Appendix 2: Data used in the book and a brief Glossary"-- |
| 해제 | Provided by publisher. |
| 일반주제명 | Genetics --Statistical methods. Genomics --Statistical methods. Genomics --Data processing. Data Interpretation, Statistical. Genomics. Genome-Wide Association Study. Gene-Environment Interaction. Models, Theoretical. Genetics --Statistical methods. Genomics --Data processing. Genomics --Statistical methods. Biostatistik Genetik Genom Humangenetik Statistische Analyse |
| 언어 | 영어 |
| 바로가기 | URL |
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