Design and Analysis of Genome-Wide Association Studies provides a compendium of well-established statistical methods based upon single SNP associations. It also provides an introduction to more advanced statistical methods and issues. Knowing that technology, for instance large scale SNP arrays, is quickly changing, this text has significant lessons for future use with sequencing data. Emphasis on statistical concepts that apply to the problem of finding disease associations irrespective of the technology ensures its future applications. The author includes current bioinformatics tools while outlining the tools that will be required for use with extensive databases from future large scale sequencing projects. The author includes current bioinformatics tools while outlining additional issues and needs arising from the extensive databases from future large scale sequencing projects.
This burgeoning science merges the principles of statistics and genetics studies to make sense of the vast amounts of information available with the mapping of genomes. In order to make the most of the information available, statistical tools must be tailored and translated for the analytical issues which are original to large-scale association studies. Analysis of Complex Disease Association Studies will provide researchers with advanced biological knowledge who are entering the field of genome-wide association studies with the groundwork to apply statistical analysis tools appropriately and effectively. With the use of consistent examples throughout the work, chapters will provide readers with best practice for getting started (design), analyzing, and interpreting data according to their research interests. Frequently used tests will be highlighted and a critical analysis of the advantages and disadvantage complimented by case studies for each will provide readers with the information they need to make the right choice for their research. Additional tools including links to analysis tools, tutorials, and references will be available electronically to ensure the latest information is available.Easy access to key information including advantages and disadvantage of tests for particular applications, identification of databases, languages and their capabilities, data management risks, frequently used testsExtensive list of references including links to tutorial websitesCase studies and Tips and Tricks
Genetic association studies have in the last few years substantially enhanced our understanding of factors underlying traits of high medical importance, such as body mass index, lipid levels, blood pressure and many others. There is growing empirical evidence that low-frequency and rare variants play an important role in complex human phenotypes. This book covers multiple aspects of study design, analysis and interpretation for complex trait studies focusing on rare sequence variation. In many areas of genomic research, including complex trait association studies, technology is in danger of outstripping our capacity to analyse and interpret the vast amounts of data generated. The field of statistical genetics in the whole-genome sequencing era is still in its infancy, but powerful methods toanalyse the aggregation of low-frequency and rare variants are now starting to emerge.
About the editors:
Somnath Datta is Professor and Vice Chair of Bioinformatics and Biostatistics at the University of Louisville. He is Fellow of the American Statistical Association, Fellow of the Institute of Mathematical Statistics and Elected Member of the International Statistical Institute. He has contributed to numerous research areas in Statistics, Biostatistics and Bioinformatics.
Dan Nettleton is Professor and Laurence H. Baker Endowed Chair of Biological Statistics in the Department of Statistics at Iowa State University. He is Fellow of the American Statistical Association and has published research on a variety of topics in statistics, biology and bioinformatics.
Population neuroscience endeavors to identify environmental and genetic factors that shape the function and structure of the human brain; it uses the tools and knowledge of genetics (and the “omics” sciences), epidemiology and neuroscience. This text attempts to provide a bridge spanning these three disciplines so that their practitioners can communicate easily with each other when working together on large-scale imaging studies of the developing, mature and aging brain.
By understanding the processes driving variations in brain function and structure across individuals, we will also be able to predict an individual’s risk of (or resilience against) developing a brain disorder. In the long term, the hope is that population neuroscience will lay the foundation for personalized preventive medicine and, in turn, reduce the burden associated with complex, chronic disorders of brain and body.
Full, 4-color illustration program enhances and reinforces key concepts and themes
Uniform organization of chapters includes interest boxes that focus on human health and disease, chapter-opening case studies, and concept statements to engage non-specialist readers
This textbook provides graduate students with a working knowledge of genetic epidemiology research methods. Following an overview of the field, the book reviews key genetic concepts, provides an update on relevant genomic technology, including genome-wide chips and DNA sequencing, and describes methods for assessing the magnitude of genetic influences on diseases and risk factors. The book focuses on research study designs for discovering disease susceptibility genes, including family-based linkage analysis, candidate gene and genome-side association studies, assessing gene-environment interactions and epistasis, studies of Non-Mendelian inheritance, and statistical analyses of data from these studies. Specific applications of each research method are illustrated using a variety of diseases and risk factors relevant to public health, and useful web-based genetic analysis software, human reference panels, and repositories, that can greatly facilitate this work, are described.