Statistical Learning in Genetics by
This book provides an introduction to computer-based methods for the analysis of genomic data. Breakthroughs in molecular and computational biology have contributed to the emergence of vast data sets, where millions of genetic markers for each individual are coupled with medical records, generating an unparalleled resource for linking human genetic variation to human biology and disease. Similar developments have taken place in animal and plant breeding, where genetic marker information is combined with production traits. An important task for the statistical geneticist is to adapt, construct and implement models that can extract information from these large-scale data. An initial step is to understand the methodology that underlies the probability models and to learn the modern computer-intensive methods required for fitting these models. The objective of this book, suitable for readers who wish to develop analytic skills to perform genomic research, is to provide guidance to take this first step. This book is addressed to numerate biologists who typically lack the formal mathematical background of the professional statistician. For this reason, considerably more detail in explanations and derivations is offered. It is written in a concise style and examples are used profusely. A large proportion of the examples involve programming with the open-source package R. The R code needed to solve the exercises is provided. The MarkDown interface allows the students to implement the code on their own computer, contributing to a better understanding of the underlying theory. Part I presents methods of inference based on likelihood and Bayesian methods, including computational techniques for fitting likelihood and Bayesian models. Part II discusses prediction for continuous and binary data using both frequentist and Bayesian approaches. Some of the models used for prediction are also used for gene discovery. The challenge is to find promising genes without incurring a large proportion of false positive results. Therefore, Part II includes a detour on False Discovery Rate assuming frequentist and Bayesian perspectives. The last chapter of Part II provides an overview of a selected number of non-parametric methods. Part III consists of exercises and their solutions. Daniel Sorensen holds PhD and DSc degrees from the University of Edinburgh and is an elected Fellow of the American Statistical Association. He was professor of Statistical Genetics at Aarhus University where, at present, he is professor emeritus.
Genetic Improvement of Farmed Animals by
Comprehensive, yet concise and approachable, Genetic Improvement of Farmed Animals provides a thorough grounding in the basic sciences underpinning current farmed animal breeding practice. Relating science to practical application, it covers all the major farmed animal species: cattle, sheep, goats, poultry, pigs and fish. The book: Provides a comprehensive coverage from genetics, analysis and strategies for improvement to response to selection, new technologies and predicting breeding values; Uses examples in temperate and tropical systems, sustainability and biodiversity to turn theory into practical applications; Outlines current practice and future directions to leave readers completely up to date. Based on the previous bestseller, Genetic Improvement of Cattle and Sheep, this book has been completely revised, expanded and redesigned to be an essential textbook for undergraduate, masters and early postgraduate level students in agriculture, animal and veterinary science.
Mason's World Dictionary of Livestock Breeds, Types and Varieties by
Mason's World Dictionary of Livestock Breeds, Types and Varieties, now in its sixth edition, has a long history as a reliable and authoritative source of key livestock breed information. Intended as a list of livestock names and synonyms for breeds, groups, types and varieties worldwide, the dictionary aims to include all names found in the literature, 'defining' each breed or type with a brief indication of identifying characteristics, uses and source of origin. Expanded into a new edition, this established and trusted resource: Extends coverage to include yak and camelids in addition to the existing cattle, sheep, pigs, goats, horses, asses and water buffalo.Features well over 10,000 entries and cross-references on breeds, sub-breeds, types, varieties, strains and lines of species.Covers newly created and now-recognised local breeds documented over the past two decades. An important updated work, Mason's Dictionary forms a useful reference for livestock breeders and academics interested in breeds, as well as national and international organizations with interests in livestock.
The Value of Fibre by
Dietary fibre has been associated with impaired nutrient utilisation and reduced animal performance. A minimum amount of dietary fibre is required to maintain normal physiological functions in the gastrointestinal tract. This book reviews the latest advances in the understanding of dietary fibre for animal nutrition. Fibre clearly has more value than was once thought. This book attempts to define not only the analytical constraints but also the advances in understanding its role in intestinal development and health in both swine and poultry. It identifies how we can exploit fibre to the advantage of the host. Stimulating the gastrointestinal microbiota (often referred to as the second brain) to digest more fibre creates a more favourable environment for intestinal health. This outcome is especially important in antibiotic free diets. The type of fibre employed, the use of exogenous enzymes and the interaction between them, the gastrointestinal microbiota and the host will be covered in detail throughout the chapters. This book discusses the practical application of this research and has been written for all animal scientists, nutritionists, feed producers and anyone interested in exploring new developments in the understanding of dietary fibre.