Eleftheria Zeggini – författare

According to the National Institute of Health, a genome-wide association study is defined as any study of genetic variation across the entire human genome that is designed to identify genetic associations with observable traits (such as blood pressure or weight), or the presence or absence of a disease or condition. Whole genome information, when combined with clinical and other phenotype data, offers the potential for increased understanding of basic biological processes affecting human health, improvement in the prediction of disease and patient care, and ultimately the realization of the promise of personalized medicine. In addition, rapid advances in understanding the patterns of human genetic variation and maturing high-throughput, cost-effective methods for genotyping are providing powerful research tools for identifying genetic variants that contribute to health and disease.

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 tests Extensive list of references including links to tutorial websites Case studies and Tips and Tricks

This book is unique in covering a wide range of design and analysis issues in genetic studies of rare variants, taking advantage of collaboration of the editors with many experts in the field through large-scale international consortia including the UK10K Project, GO-T2D and T2D-GENES. Chapters provide details of state-of-the-art methodology for rare variant detection and calling, imputation and analysis in samples of unrelated individuals and families. The book also covers analytical issues associated with the study of rare variants, such as the impact of fine-scale population structure, and with combining information on rare variants across studies in a meta-analysis framework.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 animportant 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 to analyse the aggregation of low-frequency and rare variants are now starting to emerge.The chapter Functional Annotation of Rare Genetic Variants is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.