MARC Record from harvard_bibliographic_metadata

LEADER: 04052cam a2200421 a 4500
001 012846005-9
005 20110902193631.0
008 100720s2011 ne af b 001 0 eng
015 $aGBB089078$2bnb
016 7 $a101550325$2DNLM
016 7 $a015610815$2Uk
020 $a9780123751423 (hbk.)
020 $a012375142X (hbk.)
035 0 $aocn610157476
040 $aNLM$cNLM$dUKM$dYDXCP$dCDX$dVGM$dCOD
042 $apcc
050 4 $aQH
060 00 $a2010 M-946
060 10 $aQZ 50
082 04 $a616.042$222
245 00 $aAnalysis of complex disease association studies :$ba practical guide /$cedited by Eleftheria Zeggini, Andrew Morris.
260 $aAmsterdam :$bElsevier/Academic Press,$c2011.
300 $aviii, 331 p., [12] p. of plates :$bill. (some col.), col. plates. ;$c24 cm.
504 $aIncludes bibliographical references and index.
505 00 $tGenetic architecture of complex diseases --$tPopulation genetics and linkage disequilibrium --$tGenetic association study design --$tTag SNP selection --$tGenotype calling --$tData handling --$tData quality control --$tSingle-locus tests of association for population-based studies --$tEffects of population structure in genome-wide association studies --$tGenotype imputation --$tHaplotype methods for population-based association studies --$tGene-Gene interaction and epistasis --$tCopy number variant association studies --$tFamily-based association methods --$tBioinformatics approaches --$tInterpreting association signals --$tDelineating signals from association studies --$tA genome-wide case study on obesity --$tCase study on rheumatoid arthritis.
520 $aAccording 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.
650 22 $aLinkage Disequilibrium$xgenetics.
650 22 $aGenetics, Population$xmethods.
650 22 $aGenetic Predisposition to Disease$xgenetics.
650 12 $aGenome-Wide Association Study$xmethods.
650 12 $aGenetic Diseases, Inborn$xgenetics.
650 0 $aHuman genetics$xVariation.
650 0 $aGenetic disorders.
650 0 $aDiseases.
700 1 $aZeggini, Eleftheria.
700 1 $aMorris, Andrew Paul.
988 $a20110804
906 $0OCLC