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  • Book
    edited by Eleftheria Zeggini, Andrew Morris.
    Summary: 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. This book 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.

    Contents:
    Genetic architecture of complex diseases
    Population genetics and linkage disequilibrium
    Genetic association study design
    Tag SNP selection
    Genotype calling
    Data handling
    Data quality control
    Single-locus tests of association for population-based studies
    Effects of population structure in genome-wide association studies
    Genotype imputation
    Haplotype methods for population-based association studies
    Gene-Gene interaction and epistasis
    Copy number variant association studies
    Family-based association methods
    Bioinformatics approaches
    Interpreting association signals
    Delineating signals from association studies
    A genome-wide case study on obesity
    Case study on rheumatoid arthritis.
    Digital Access ScienceDirect 2011