An invaluable student-tested study aid, this primer, first published in 2007, provides guided instruction for the analysis and interpretation of genetic principles and practice in problem solving. Each section is introduced with a summary of useful hints for problem solving and an overview of the topic with key terms. A series of problems, generally progressing from simple to more complex, then allows students to test their understanding of the material. Each question and answer is accompanied by detailed explanation. This third edition includes additional problems in basic areas that often challenge students, extended coverage in molecular biology and development, an expanded glossary of terms, and updated historical landmarks. Students at all levels, from beginning biologists and premedical students to graduates seeking a review of basic genetics, will find this book a valuable aid. It will complement the formal presentation in any genetics textbook or stand alone as a self-paced review manual.

This third edition of a student-tested primer provides guided instruction in the analysis and interpretation of genetic principles and problem solving. All core areas of genetics are covered. Each section is introduced with a summary of key concepts and terms. A series of problems, graded from simple to more complex, then allows students to test their understanding of the material. Each question is accompanied by a detailed explanation. This new edition includes additional problems, extensively expanded coverage of molecular biology throughout, new overview chapters, and an expanded glossary.

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 covers basic concepts in population and quantitative genetics, including measuring selection on phenotypic traits. The emphasis is on material applicable to field studies of evolution focusing on ecologically important traits. Topics addressed are critical for training students in ecology, evolution, conservation biology, agriculture, forestry, and wildlife management. Many texts in this field are too complex and mathematical to allow the average beginning student to readily grasp the key concepts. A Primer of Ecological Genetics, in contrast, employs mathematics and statistics-fully explained, but at a less advanced level-as tools to improve understanding of biological principles. The main goal is to enable students to understand the concepts well enough that they can gain entry into the primary literature. Integration of the different chapters of the book shows students how diverse concepts relate to each other.

This timely book illustrates the value of bioinformatics, not simply as a set of tools but rather as a science increasingly essential to navigate and manage the host of information generated by genomics and the availability of completely sequenced genomes. Bioinformatics can be used at all stages of genetics research: to improve study design, to assist in candidate gene identification, to aid data interpretation and management and to shed light on the molecular pathology of disease-causing mutations. Written specifically for geneticists, this book explains the relevance of bioinformatics showing how it may be used to enhance genetic data mining and markedly improve genetic analysis.

A Primer of Human Genetics is an introductory textbook designed to give students the foundation they need to understand and appreciate the extraordinary shifts in human genetics that have accompanied the arrival of genomics. The book lays out the key concepts of human evolution, quantitative genetics, and personalized medicine before describing the tools that are missing from most contemporary textbooks: genome-wide association studies, whole-genome resequencing, gene expression and epigenome profiling, and integrative genomics. The final section provides an up-to-date survey of specific findings in six major domains of human disease: immunological, metabolic, cardiovascular, cancer, neuropsychological, and aging disorders. After reading this textbook, not only will students be better equipped to read current literature, they will gain a sense of the impact that the revolution in genomics has had for our understanding of the human condition, as well as of the major trends in human genetics research. Students are assumed to have a core understanding of genetics such as would be obtained in a general genetics class. Each chapter is approximately 20 pages long, and organized under up to ten sub-headings, most of which incorporate an illustrative figure. Chapter summary points recap the key messages, and references point students to key reviews and contemporary highlights. Several of the chapters contain a single box, which provides extra material on topics such as 'Consent and Responsible Conduct of Research' and 'Methylation Analysis in Biology.'

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.

This concise, entry level text provides an introduction to the importance of genetic studies in conservation and presents the essentials of the discipline in an easy-to-follow format, with main points and terms clearly highlighted. The authors assume only a basic knowledge of Mendelian genetics and simple statistics, making the book accessible to those with a limited background in these areas. Connections between conservation genetics and the wider field of conservation biology are interwoven throughout the book. Worked examples are provided throughout to help illustrate key equations and glossary and suggestions for further reading provide additional support for the reader. Many beautiful pen and ink portraits of endangered species are included to enhance the text. Written for short, introductory level courses in genetics, conservation genetics and conservation biology, this book will also be suitable for practising conservation biologists, zoo biologists and wildlife managers.

A student-tested study aid, this primer provides guided instruction to the analysis and interpretation of genetic principles and problem solving.

What are the genomic signatures of adaptations in DNA? How often does natural selection dictate changes to DNA? How does the ebb and flow in the abundance of individuals over time get marked onto chromosomes to record genetic history? Molecular population genetics seeks to answer such questions by explaining genetic variation and molecular evolution from micro-evolutionary principles. It provides a way to learn about how evolution works and how it shapes species by incorporating molecular details of DNA as the heritable material. It enables us to understand the logic of how mutations originate, change in abundance in populations, and become fixed as DNA sequence divergence between species. With the revolutionary advances in genomic data acquisition, understanding molecular population genetics is now a fundamental requirement for today's life scientists. These concepts apply in analysis of personal genomics, genome-wide association studies, landscape and conservation genetics, forensics, molecular anthropology, and selection scans. This book introduces, in an accessible way, the bare essentials of the theory and practice of molecular population genetics.