With the detailed genomic information that is now becoming available, we have a plethora of data that allows researchers to address questions in a variety of areas. Genome-wide association studies (GWAS) have become a vital approach to identify candidate regions associated with complex diseases in human medicine, production traits in agriculture, and variation in wild populations. Genomic prediction goes a step further, attempting to predict phenotypic variation in these traits from genomic information. Genome-Wide Association Studies and Genomic Prediction pulls together expert contributions to address this important area of study. The volume begins with a section covering the phenotypes of interest as well as design issues for GWAS, then moves on to discuss efficient computational methods to store and handle large datasets, quality control measures, phasing, haplotype inference, and imputation. Later chapters deal with statistical approaches to data analysis where the experimental objective is either to confirm the biology by identifying genomic regions associated to a trait or to use the data to make genomic predictions about a future phenotypic outcome (e.g. predict onset of disease). As part of the Methods in Molecular Biology series, chapters provide helpful, real-world implementation advice.

Fitness and adaptation are fundamental characteristics of plant and animal species, enabling them to survive in their environment and to adapt to the inevitable changes in this environment. This is true for both the genetic resources of natural ecosystems as well as those used in agricultural production.

Extensive genetic variation exists between varieties/breeds in a species and amongst individuals within breeds. This variation has developed over very long periods of time. A major ongoing challenge is how to best utilize this variation to meet short-term demands whilst also conserving it for longer-term possible use.

Many animal breeding programs have led to increased performance for production traits but this has often been accompanied by reduced fitness. In addition, the global use of genetic resources prompts the question whether introduced genotypes are adapted to local production systems. Understanding the genetic nature of fitness and adaptation will enable us to better manage genetic resources allowing us to make efficient and sustainable decisions for the improvement or breeding of these resources.

This book had an ambitious goal in bringing together a sample of the world s leading scientists in animal breeding and evolutionary genetics to exchange knowledge to advance our understanding of these vital issues.

With the detailed genomic information that is now becoming available, we have a plethora of data that allows researchers to address questions in a variety of areas. Genome-wide association studies (GWAS) have become a vital approach to identify candidate regions associated with complex diseases in human medicine, production traits in agriculture, and variation in wild populations. Genomic prediction goes a step further, attempting to predict phenotypic variation in these traits from genomic information. Genome-Wide Association Studies and Genomic Prediction pulls together expert contributions to address this important area of study. The volume begins with a section covering the phenotypes of interest as well as design issues for GWAS, then moves on to discuss efficient computational methods to store and handle large datasets, quality control measures, phasing, haplotype inference, and imputation. Later chapters deal with statistical approaches to data analysis where the experimental objective is either to confirm the biology by identifying genomic regions associated to a trait or to use the data to make genomic predictions about a future phenotypic outcome (e.g. predict onset of disease). As part of the Methods in Molecular Biology series, chapters provide helpful, real-world implementation advice.

Through this book, researchers and students will learn to use R for analysis of large-scale genomic data and how to create routines to automate analytical steps. The philosophy behind the book is to start with real world raw datasets and perform all the analytical steps needed to reach final results. Though theory plays an important role, this is a practical book for graduate and undergraduate courses in bioinformatics and genomic analysis or for use in lab sessions. How to handle and manage high-throughput genomic data, create automated workflows and speed up analyses in R is also taught. A wide range of R packages useful for working with genomic data are illustrated with practical examples. The key topics covered are association studies, genomic prediction, estimation of population genetic parameters and diversity, gene expression analysis, functional annotation of results using publically available databases and how to work efficiently in R with large genomic datasets. Important principles are demonstrated and illustrated through engaging examples which invite the reader to work with the provided datasets. Some methods that are discussed in this volume include: signatures of selection, population parameters (LD, FST, FIS, etc); use of a genomic relationship matrix for population diversity studies; use of SNP data for parentage testing; snpBLUP and gBLUP for genomic prediction. Step-by-step, all the R code required for a genome-wide association study is shown: starting from raw SNP data, how to build databases to handle and manage the data, quality control and filtering measures, association testing and evaluation of results, through to identification and functional annotation of candidate genes. Similarly, gene expression analyses are shown using microarray and RNAseq data. At a time when genomic data is decidedly big, the skills from this book are critical. In recent years R has become the de facto< tool for analysis of gene expression data, in addition to its prominent role in analysis of genomic data. Benefits to using R include the integrated development environment for analysis, flexibility and control of the analytic workflow. Included topics are core components of advanced undergraduate and graduate classes in bioinformatics, genomics and statistical genetics. This book is also designed to be used by students in computer science and statistics who want to learn the practical aspects of genomic analysis without delving into algorithmic details. The datasets used throughout the book may be downloaded from the publisher's website.

Fitness and adaptation are fundamental characteristics of plant and animal species, enabling them to survive in their environment and to adapt to the inevitable changes in this environment. This is true for both the genetic resources of natural ecosystems as well as those used in agricultural production.

Extensive genetic variation exists between varieties/breeds in a species and amongst individuals within breeds. This variation has developed over very long periods of time. A major ongoing challenge is how to best utilize this variation to meet short-term demands whilst also conserving it for longer-term possible use.

Many animal breeding programs have led to increased performance for production traits but this has often been accompanied by reduced fitness. In addition, the global use of genetic resources prompts the question whether introduced genotypes are adapted to local production systems. Understanding the genetic nature of fitness and adaptation will enable us to better manage genetic resources allowing us to make efficient and sustainable decisions for the improvement or breeding of these resources.

This book had an ambitious goal in bringing together a sample of the world s leading scientists in animal breeding and evolutionary genetics to exchange knowledge to advance our understanding of these vital issues.