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Titlebook: Genome-Wide Association Studies and Genomic Prediction; Cedric Gondro,Julius van der Werf,Ben Hayes Book 2013 Springer Science+Business Me

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發(fā)表于 2025-3-21 17:45:02 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Genome-Wide Association Studies and Genomic Prediction
編輯Cedric Gondro,Julius van der Werf,Ben Hayes
視頻videohttp://file.papertrans.cn/383/382860/382860.mp4
概述Examines genome-wide association studies, from the preliminary issues to statistical approaches and more.Features detailed, step-by-step instruction.Includes tips and expert implementation advice to e
叢書名稱Methods in Molecular Biology
圖書封面Titlebook: Genome-Wide Association Studies and Genomic Prediction;  Cedric Gondro,Julius van der Werf,Ben Hayes Book 2013 Springer Science+Business Me
描述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
出版日期Book 2013
關(guān)鍵詞Computational methods; GWAS; Genome analysis; Genome-wide association study; Genomic prediction; Phenotyp
版次1
doihttps://doi.org/10.1007/978-1-62703-447-0
isbn_softcover978-1-4939-5964-8
isbn_ebook978-1-62703-447-0Series ISSN 1064-3745 Series E-ISSN 1940-6029
issn_series 1064-3745
copyrightSpringer Science+Business Media, LLC 2013
The information of publication is updating

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1064-3745 truction.Includes tips and expert implementation advice to eWith 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 iden
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https://doi.org/10.1007/978-90-481-9325-7ait variation, while avoiding spurious associations due to evidence not being well quantified or due to population structure..Single marker analysis and imputation are discussed in Sect. ., and a Bayesian multi-locus analysis using the . R package (., .) is described in Sect. .. The multi-locus anal
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https://doi.org/10.1007/978-1-62703-447-0Computational methods; GWAS; Genome analysis; Genome-wide association study; Genomic prediction; Phenotyp
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Cedric Gondro,Julius van der Werf,Ben HayesExamines genome-wide association studies, from the preliminary issues to statistical approaches and more.Features detailed, step-by-step instruction.Includes tips and expert implementation advice to e
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https://doi.org/10.1007/978-90-481-9325-7 possible effects of population structure on power are discussed in Sect. .. Section . considers analysis combining information from linkage and linkage disequilibrium when sampling from a pedigree. Section . considers combining information from two different studies—showing that data from an existi
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Descriptive Statistics of Data: Understanding the Data Set and Phenotypes of Interest,
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