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Genome-Wide Association Studies
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    Genome-Wide Association Studies
    • Online ISBN: 9781107337459
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Book description

Over the last twenty years, genome-wide association studies (GWAS) have revealed a great deal about the genetic basis of a wide range of complex diseases and they will undoubtedly continue to have a broad impact as we move to an era of personalised medicine. This authoritative text, written by leaders and innovators from both academia and industry, covers the basic science as well as the clinical, biotechnological and pharmaceutical potential of these methods. With special emphasis given to highlighting pharmacogenomics and population genomics studies using next-generation technology approaches, this is the first book devoted to combining association studies with single nucleotide polymorphisms, copy number variants, haplotypes and expressed quantitative trait loci. A reliable guide for newcomers to the field as well as for experienced scientists, this is a unique resource for anyone interested in how the revolutionary power of genomics can be applied to solve problems in complex disease.


'Genome-Wide Association Studies: From Polymorphism to Personalized Medicine, edited by Krishnarao Appasani, summarizes most elegantly the contributions of GWAS as a major discovery tool linking complex disease phenotypes to genetic variants and associated biological pathways and gene networks that were previously unknown. GWAS has transformed the genetic landscape in complex disease and has informed us more about the genetic underpinnings of common diseases and pharmacogenomics traits than any other tool to date. The present book captures this development elegantly and is a pleasure to read.'

Hakon Hakonarson - University of Pennsylvania

'From genotype to phenotype: this biological paradigm is now elucidated and extended to the vision of genomic medicine. This highly informative book combines the current knowledge of genome wide association studies with the pathophysiology, epidemiology of human disease, and health condition, especially, implicating in the development of personalized and precision medicine. The combination of technical, scientific, medical, and pharmaco-economic aspects supports the high value of this book for scientists and medical specialists working in the field.'

Christine Günther - Chief Executive Officer, apceth GmbH and Co. KG, Munich, Germany

'Through my 30 years’ experience in genetics of diabetes, I realize that now is an exciting time in the history of medical genetics thanks to successful genome-wide association studies and challenging whole-genome studies using next-generation sequencing technologies. This excellent book, covering a wide-range of topics and their practical examples in this field, is undoubtedly recommended for readers who are interested in or engaged in genomic medicine.'

Takuya Awata - International University of Health and Welfare Hospital, Tochigi, Japan

'Genome-Wide Association Studies: From Polymorphism to Personalized Medicine has an impressive and diverse list of contributors and will become a highly valuable resource for both experts and researchers entering the field.'

Jeanette Schmidt - Vice President of Informatics, Affymetrix, Inc., Santa Clara, USA

'This book details how the huge experimental efforts of GWAS can be put into both a biological and medically relevant context, indeed an excellent read for any researcher trying to understand the functional effect of genetic disease association with complex disease.'

Tara Caffrey - University of Oxford

'This volume provides a great resource for beginners to learn about the recent advances in GWAS and for domain experts to identity the gaps in the area. … The provided software and case studies can guide readers through the procedures and will easily allow a researcher to finish a project on their own. I think this book will be a reliable guide for anyone who wants to learn and understand GWAS. I hope other readers will enjoy the book as much as I did.'

Jinliang Yang Source: The Quarterly Review of Biology

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