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.

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.

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.

Examines a number of novel techniques used to study populations, phenotypes, and gene expression in organisms as diverse as humans and plants. The book ends with three case studies applying statistical genetics to identify genes associated with human diseases.

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.

This Book Describes The Methodology Of A Field Of Biology That Weds Statistics And Genetics And Goes By The Name Of Statistical Genetics. The Relevant Principles Of Genetics, Statistics And Breeding Are Discussed Together At One Place. The Emphasis Is On The Important Applications Of The Statisticogenetic Principles To The Plant And Animal Breeding Problems. It Has Been Written For The Benefit Of Post- Graduate Students Majoring In The Fields Of Either Genetics And Breeding Or Statistics Applied To Agriculture. Also, Since Advanced Researches In This Field Which Have Taken Place Over The Years Are Scattered In Various Journals, This Book Is Equally Addressed To Researchers In The Fields Of Statistics, Genetics And Breeding.The Book Is Unique In Several Respects And Sets Itself Apart When Compared With Other Published Books On The Subject. The Basic Elements Of Mendelian Genetics Which Is Very Crucial To The Understanding Of The Subject Have Been Included Right In The Beginning. The Relevant Topics Of Population Genetics Essential For Understanding The Principles Of Quantitative Inheritance Have Also Been Dealt With. Special Treatment Has Been Given To The Topics Dealing With The Methods Of Selection And Crossbreeding Which Can Bring About Desired Genetic Improvement. Diallel And Partial Diallel Crosses, Both The Design As Well As The Analysis Aspects Have Been Included.Because Of The Inter-Disciplinary Nature Of The Subject, Bringinng In So To Say Biology And Mathematics Together, The Book Has Adopted An Integrated Approach Placing Due Emphasis On Both The Fields. Appropriate Mathematical And Statistical Derivations For Mathematically Oriented Readers As Also Concepts In Non- Mathematical Terms For Biologist Friends Have Been Presented In A Judicious Manner.

Conventional statistical methods have a very serious flaw. They routinely miss differences among groups or associations among variables that are detected by more modern techniques, even under very small departures from normality. Hundreds of journal articles have described the reasons standard techniques can be unsatisfactory, but simple, intuitive explanations are generally unavailable. Situations arise where even highly nonsignificant results become significant when analyzed with more modern methods. Without assuming the reader has any prior training in statistics, Part I of this book describes basic statistical principles from a point of view that makes their shortcomings intuitive and easy to understand. The emphasis is on verbal and graphical descriptions of concepts. Part II describes modern methods that address the problems covered in Part I. Using data from actual studies, many examples are included to illustrate the practical problems with conventional procedures and how more modern methods can make a substantial difference in the conclusions reached in many areas of statistical research. The second edition of this book includes a number of advances and insights that have occurred since the first edition appeared. Included are new results relevant to medians, regression, measures of association, strategies for comparing dependent groups, methods for dealing with heteroscedasticity, and measures of effect size.

From the Reviews of the First Edition: "This magnificent book attempts to catalogue and introduce all aspects of modern statistical genetics...I can thoroughly recommend it." Short Book Reviews of ISI "...extremely well written and referenced work, which must come highly recommended..." Statistical Methods in Medical Research Provides comprehensive coverage of a thriving area of research Features nine newly commissioned chapters All existing chapters have been fully updated with new advances in the field and new references Now includes a glossary of terms, and a list of acronyms and abbreviations. Features extensive cross-referencing between chapters. Each chapter is written by a leading international authority from the field. Complemented by examples, case studies, and references to useful resources on the web.