Genetic Association Studies Background Conduct Analysis Interpretation

Genetic Association Studies is designed for students of public health, epidemiology, and the health sciecnes, covering the main principles of molecular genetics, population genetics, medical genetics, epidemiology and statistics. It presents a balanced view of genetic associations with coverage of candidate gene studies as well as genome-wide association studies. All aspects of a genetic association study are included, from the lab to analysis and interpretation of results, but also bioinformatics approaches to causality assessment. The role of the environment in genetic disease is also highlighted. Genetic Association Studies will enable readers to understand and critique genetic association studies and set them on the way to designing, executing, analyzing, interpreting, and reporting their own.

This book presents the statistical aspects of designing, analyzing and interpreting the results of genome-wide association scans (GWAS studies) for genetic causes of disease using unrelated subjects. Particular detail is given to the practical aspects of employing the bioinformatics and data handling methods necessary to prepare data for statistical analysis. The goal in writing this book is to give statisticians, epidemiologists, and students in these fields the tools to design a powerful genome-wide study based on current technology. The other part of this is showing readers how to conduct analysis of the created study. 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.

Genome-wide association studies (GWAS) for complex disorders with large case-control populations have been performed on hundreds of traits in more than 1200 published studies (http://www.genome.gov/gwastudies/) but the variants detected by GWAS account for little of the heritability of these traits, leading to an increasing interest in using family based designs. While GWAS studies are designed to find common variants with low to moderate attributable risks, family based studies are expected to find rare variants with high attributable risk. Because family-based designs can better control both genetic and environmental background, this study design is robust to heterogeneity and population stratification. Moreover, in family-based analysis, the background genetic variation can be modeled to control the residual variance which could increase the power to identify disease associated rare variants. Analysis of families can also help us gain knowledge about disease transmission and inheritance patterns. Although a family-based design has the advantage of being robust to false positives, novel and powerful methods to analyze families in genetic epidemiology continue to be needed, especially for the interaction between genetic and environmental factors associated with disease. Moreover, with the rapid development of sequencing technology, advances in approaches to the design and analysis of sequencing data in families are also greatly needed. The 11 articles in this book all introduce new methodology and, using family data, substantial new findings are presented in the areas of infectious diseases, diabetes, eye traits, autism spectrum disorder and prostate cancer.

The field of genetics is rapidly evolving and new medical breakthroughs are occuring as a result of advances in knowledge of genetics. This series continually publishes important reviews of the broadest interest to geneticists and their colleagues in affiliated disciplines. * Five sections on the latest advances in complex traits * Methods for testing with ethical, legal, and social implications * Hot topics include discussions on systems biology approach to drug discovery; using comparative genomics for detecting human disease genes; computationally intensive challenges, and more

Advances in genetics and genomics are transforming medical practice, resulting in a dramatic growth of genetic testing in the health care system. The rapid development of new technologies, however, has also brought challenges, including the need for rigorous evaluation of the validity and utility of genetic tests, questions regarding the best ways to incorporate them into medical practice, and how to weigh their cost against potential short- and long-term benefits. As the availability of genetic tests increases so do concerns about the achievement of meaningful improvements in clinical outcomes, costs of testing, and the potential for accentuating medical care inequality. Given the rapid pace in the development of genetic tests and new testing technologies, An Evidence Framework for Genetic Testing seeks to advance the development of an adequate evidence base for genetic tests to improve patient care and treatment. Additionally, this report recommends a framework for decision-making regarding the use of genetic tests in clinical care.

This text integrates the principles, methods and approaches of epidemiology and genetics in the study of disease aetiology. The authors define the central theme of genetic epidemiology as the study of the role of genetic factors and their interaction with environmental factors in the occurrence of disease in populations.