Advanced Genetic Analysis brings a state-of-the-art,exciting new approach to genetic analysis. Focusing on theunderlying principles of modern genetic analysis, this bookprovides the 'how' and 'why' of the essential analytical toolsneeded. The author's vibrant, accessible style provides an easyguide to difficult genetic concepts, from mutation and genefunction to gene mapping and chromosome segregation. Throughout, abalanced range of model organisms and timely examples are used toillustrate the theoretical basics. Basic principles - Focuses students attention on the 'how' and'why' of the essential analytical tools. Vibrant, accessible style provides an easy guide throughdifficult genetic concepts and techniques. Text boxes highlight key questions and timely examples. Boxes of key information in each chapter, chapter summaries andextensive references - prompt the student to synthesise andreinforce the chapter material. Special reference section addressing a range of model organismsto help provide a particularly relevant context for students'research interests.

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.

With its unique integration of genetics and molecular biology, this text probes fascinating questions that explore how our understanding of key genetic phenomena can be used to understand biological systems. Opening with a brief overview of key genetic principles, model organisms, and epigenetics, the book goes on to explore the use of gene mutations, the analysis of gene expression and activity, a discussion of the genetic structure of natural populations, and more.

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.

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.

Professors Lynch and Walsh bring together the diverse array of theoretical and empirical applications of quantitative genetics in a work that is comprehensive and accessible to anyone with a rudimentary understanding of statistics and genetics.

Molecular Genetic Analysis is an advanced textbook to teach the theory and practice of molecular genetic analysis to senior undergraduates and graduates studying genetics, molecular biology and cell biology. This book uses a case study approach, with the yeast Saccharomyces as the model genetic organism, to explain the theory and practice of molecular genetic analysis. It provides enough information so readers will be able to apply the approach to their own research project.