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Analysis of complex disease associat...

Morris, Andrew.

Analysis of complex disease association studies

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Analysis of complex disease association studiesedited by Eleftheria Zeggini, Andrew Morris.
其他作者:	Zeggini, Eleftheria.
出版者:	London :Academic,2010.
面頁冊數:	1 online resource.
標題:	Human geneticsVariation.
電子資源:	An electronic book accessible through the World Wide Web; click for information
ISBN:	9780123751423 (electronic bk.)

Analysis of complex disease association studies
Analysis of complex disease association studies[electronic resource] /edited by Eleftheria Zeggini, Andrew Morris. - London :Academic,2010. - 1 online resource.

Genetic architecture of complex disease -- Population genetics and linkage disequilibrium -- Genetic association study design -- Selection of SNPs -- Genotype calling -- Data handling -- Data quality control -- Single-locus tests of association for population-based studies -- Population structure -- Haplotype-based methods -- Interaction analyses -- Copy number variant analysis -- Analysis of family-based association studies -- Bioinformatics approaches -- Interpreting association signals -- Delineating association signals -- Case study: obesity -- Case study: rheumatoid arthritis.

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. (good paragraph) 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.

ISBN: 9780123751423 (electronic bk.)

Source: 1057446:10578261Elsevier Science & Technologyhttp://www.sciencedirect.comSubjects--Topical Terms:

501825
Human genetics
--Variation.

LC Class. No.: RB155.5 / .A53 2010

Dewey Class. No.: 616.042

Analysis of complex disease association studies
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505 0 $a Genetic architecture of complex disease -- Population genetics and linkage disequilibrium -- Genetic association study design -- Selection of SNPs -- Genotype calling -- Data handling -- Data quality control -- Single-locus tests of association for population-based studies -- Population structure -- Haplotype-based methods -- Interaction analyses -- Copy number variant analysis -- Analysis of family-based association studies -- Bioinformatics approaches -- Interpreting association signals -- Delineating association signals -- Case study: obesity -- Case study: rheumatoid arthritis.
520 $a 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. (good paragraph) 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.
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