大葉大學圖書館 |

Analysis of complex disease association studies[electronic resource] /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	616.042
書名/作者:	Analysis of complex disease association studies/ edited 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 genetics - Variation.
標題:	Genetic disorders.
標題:	Genetic Predisposition to Disease.
標題:	Genetic Variation.
ISBN:	9780123751423 (electronic bk.)
ISBN:	012375142X (electronic bk.)
內容註:	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.
摘要、提要註:	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.
電子資源:	An electronic book accessible through the World Wide Web; click for information

Analysis of complex disease association studies[electronic resource] /
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:

427892
Human genetics
--Variation.

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

Dewey Class. No.: 616.042

Analysis of complex disease association studies[electronic resource] /
LDR:03839cam 2200337Ka 4500 001 346838
003 OCoLC
005 20110614113909.0
006 m d
007 cr cn|||||||||
008 111130s2010 enka o 000 0 eng d
020 $a 9780123751423 (electronic bk.)
020 $a 012375142X (electronic bk.)
029 1 $a AU@ $b 000046315186
029 1 $a NZ1 $b 13642884
035 $a (OCoLC)677829091
035 $a ocn677829091
037 $a 1057446:10578261 $b Elsevier Science & Technology $n http://www.sciencedirect.com
040 $a OPELS $b eng $c OPELS $d CDX $d OHS $d CUS $d OCLCQ
049 $a TEFA
050 4 $a RB155.5 $b .A53 2010
082 0 4 $a 616.042 $2 22
245 0 0 $a Analysis of complex disease association studies $h [electronic resource] / $c edited by Eleftheria Zeggini, Andrew Morris.
260 $a London : $b Academic, $c 2010.
300 $a 1 online resource.
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.
588 $a Description based on print version record.
650 0 $a Human genetics $x Variation. $3 427892
650 0 $a Genetic disorders. $3 342495
650 2 $a Genetic Predisposition to Disease. $3 427893
650 2 $a Genetic Variation. $3 427894
700 1 $a Zeggini, Eleftheria. $3 427890
700 1 $a Morris, Andrew. $3 427891
710 2 $a ScienceDirect (Online service) $3 365609
776 0 8 $i Print version: $t Analysis of complex disease association studies. $d London : Academic, 2010 $z 9780123751423 $w (OCoLC)610157476
856 4 0 $3 ScienceDirect $u http://www.sciencedirect.com/science/book/9780123751423 $z An electronic book accessible through the World Wide Web; click for information
938 $a Coutts Information Services $b COUT $n 16426419
994 $a C0 $b TEF