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008 130612s2013 nyu ob 001 0 eng d
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020 _a1627034471
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020 _z9781627034463
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024 7 _a10.1007/978-1-62703-447-0
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080 _a575.113 Q3
082 0 4 _a572.8
_223
049 _aMAIN
245 0 0 _aGenome-wide association studies and genomic prediction /
_cedited by Cedric Gondro, Julius van der Werf, Ben Hayes.
264 1 _aNew York :
_bHumana Press,
_c[2013]
264 4 _c©2013
300 _a1 online resource (xi, 566 pages)
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
347 _bPDF
490 1 _aMethods in molecular biology,
_x1940-6029 ;
_v1019
504 _aIncludes bibliographical references and index.
588 0 _aOnline resource; title from PDF title page (Springer, viewed June 12, 2013).
505 0 0 _tR for genome-wide association studies /
_rCedric Gondro, Laercio R. Porto-Neto, Seung Hwan Lee --
_tDescriptive statistics of data : understanding the data set and phenotypes of interest /
_rSonja Dominik --
_tDesigning a GWAS : power, sample size, and data structure /
_rRoderick D. Ball --
_tManaging large SNP datasets with SNPpy /
_rFaheem Mitha --
_tQuality control for genome-wide association studies /
_rCedric Gondro, Seung Hwan Lee, Hak Kyo Lee, Laercio R. Porto-Neto --
_tOverview of statistical methods for genome-wide association studies (GWAS) /
_rBen Hayes --
_tStatistical analysis of genomic data /
_rRoderick D. Ball --
_tUsing PLINK for genome-wide association studies (GWAS) and data analysis /
_rMiguel E. Rentería, Adrian Cortes, Sarah E. Medland --
_tGenome-wide complex trait analysis (GCTA) : Methods, data analyses, and interpretations /
_rJian Yang, Sang Hong Lee, Michael E. Goddard, Peter M. Visscher --
_tBayesian methods applied to GWAS /
_rRohan L. Fernando, Dorian Garrick --
_tImplementing a QTL detection study (GWAS) using genomic prediction methodology /
_rDorian J. Garrick, Rohan L. Fernando --
_tGenome-enabled prediction using the BLR (Bayesian Linear Regression) R-package /
_rGustavo de los Campos, Paulino Pérez, Ana I. Vazquez, José Crossa --
_tGenomic best linear unbiased prediction (gBLUP) for the estimation of genomic breeding values /
_rSamuel A. Clark, Julius van der Werf --
_tDetecting regions of homozygosity to map the cause of recessively inherited disease /
_rJames W. Kijas --
_tUse of ancestral haplotypes in genome-wide association studies /
_rTom Druet, Frédéric Farnir --
_tGenotype phasing in populations of closely related individuals /
_rJohn M. Hickey --
_tGenotype imputation to increase sample size in pedigreed populations /
_rJohn M. Hickey, Matthew A. Cleveland, Christian Maltecca, Gregor Gorjanc, Birgit Gredler, Andreas Kranis --
_tValidation of genome-wide association studies (GWAS) results /
_rJohn M. Henshall --
_tDetection of signatures of selection using FST /
_rLaercio R. Porto-Neto, Seung Hwan Lee, Hak Kyo Lee, Cedric Gondro --
_tAssociation weight matrix : a network-based approach towards functional genome-wide association studies /
_rAntonio Reverter, Marina R.S. Fortes --
_tMixed effects structural equation models and phenotypic causal networks /
_rBruno Dourado Valente, Guilherme Jordão Magalhães Rosa --
_tEpistasis, complexity, and multifactor dimensionality reduction /
_rQinxin Pan, Ting Hu, Jason H. Moore --
_tApplications of multifactor dimensionality reduction to genome-wide data using the R package 'MDR' /
_rStacey Winham --
_tHigher order interactions : detection of epistasis using machine learning and evolutionary computation /
_rRonald M. Nelson, Marcin Kierczak, Örjan Carlborg --
_tIncorporating prior knowledge to increase the power of genome-wide association studies /
_rAshley Petersen, Justin Spratt, Nathan L. Tintle --
_tGenomic selection in animal breeding programs /
_rJulius van der Werf.
520 _a"With the detailed genomic information that is now becoming available, we have a plethora of data that allows researchers to address questions in a variety of areas. Genome-wide association studies (GWAS) have become a vital approach to identify candidate regions associated with complex diseases in human medicine, production traits in agriculture, and variation in wild populations. Genomic prediction goes a step further, attempting to predict phenotypic variation in these traits from genomic information. Genome-Wide Association Studies and Genomic Prediction pulls together expert contributions to address this important area of study. The volume begins with a section covering the phenotypes of interest as well as design issues for GWAS, then moves on to discuss efficient computational methods to store and handle large datasets, quality control measures, phasing, haplotype inference, and imputation. Later chapters deal with statistical approaches to data analysis where the experimental objective is either to confirm the biology by identifying genomic regions associated to a trait or to use the data to make genomic predictions about a future phenotypic outcome (e.g. predict onset of disease). As part of the Methods in Molecular Biology series, chapters provide helpful, real-world implementation advice."--
_cBack cover
546 _aEnglish.
650 0 _aMolecular genetics.
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650 2 _aGenetic Association Studies
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655 2 _aLaboratory Manual
655 4 _aInternet Resources.
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655 7 _aLaboratory manuals.
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655 7 _aManuels de laboratoire.
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700 1 _aGondro, Cedric,
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_988833
700 1 _aWerf, Julius van der,
_eeditor.
_988834
700 1 _aHayes, Ben
_q(Ben J.),
_eeditor.
_1https://id.oclc.org/worldcat/entity/E39PCjFWWcbJBkdyXfFpHxwV4q
_988835
776 0 8 _iPrint version:
_tGenome-wide Association Studies and Genomic Prediction.
_dHumana Pr Inc 2013
_z9781627034463
_w(OCoLC)828487816
830 0 _aMethods in molecular biology (Clifton, N.J.) ;
_vv. 1019.
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