Coverart for item
The Resource Genome-wide association studies and genomic prediction, edited by Cedric Gondro, The Center for Genetic Analysis and Applications, University of New England, Armidale, NSW, Australia, Julius van der Werf, School of Environmental and rural Science, University of New England, Armidale, NSW, Australia, Ben Hayes, Biosciences Research Division, Department of Primary Industries, Bundoora, VIC, Australia, (electronic resource)

Genome-wide association studies and genomic prediction, edited by Cedric Gondro, The Center for Genetic Analysis and Applications, University of New England, Armidale, NSW, Australia, Julius van der Werf, School of Environmental and rural Science, University of New England, Armidale, NSW, Australia, Ben Hayes, Biosciences Research Division, Department of Primary Industries, Bundoora, VIC, Australia, (electronic resource)

Label
Genome-wide association studies and genomic prediction
Title
Genome-wide association studies and genomic prediction
Statement of responsibility
edited by Cedric Gondro, The Center for Genetic Analysis and Applications, University of New England, Armidale, NSW, Australia, Julius van der Werf, School of Environmental and rural Science, University of New England, Armidale, NSW, Australia, Ben Hayes, Biosciences Research Division, Department of Primary Industries, Bundoora, VIC, Australia
Contributor
Subject
Language
eng
Summary
"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."--
Member of
Assigning source
Back cover
Cataloging source
NLM
Dewey number
572.8
LC call number
  • QH442
  • QH506
LC item number
  • .G475582 2013
  • .G46 2013
NLM call number
  • W1
  • QU 550
NLM item number
ME9616J v.1019 2013
http://library.link/vocab/relatedWorkOrContributorName
  • Gondro, Cedric
  • Werf, Julius van der
  • Hayes, Ben
Series statement
  • Methods of molecular biology,
  • Springer protocols,
Series volume
1019
http://library.link/vocab/subjectName
  • Molecular genetics
  • Variation (Biology)
  • Genome-Wide Association Study
  • Genetic Testing
  • Genomics
  • Molecular genetics
  • Variation (Biology)
Label
Genome-wide association studies and genomic prediction, edited by Cedric Gondro, The Center for Genetic Analysis and Applications, University of New England, Armidale, NSW, Australia, Julius van der Werf, School of Environmental and rural Science, University of New England, Armidale, NSW, Australia, Ben Hayes, Biosciences Research Division, Department of Primary Industries, Bundoora, VIC, Australia, (electronic resource)
Instantiates
Publication
Bibliography note
Includes bibliographical references and index
Contents
  • Quality control for genome-wide association studies
  • Cedric Gondro, Seung Hwan Lee, Hak Kyo Lee, Laercio R. Porto-Neto
  • Overview of statistical methods for genome-wide association studies (GWAS)
  • Ben Hayes
  • Statistical analysis of genomic data
  • Roderick D. Ball
  • Using PLINK for genome-wide association studies (GWAS) and data analysis
  • Miguel E. Rentería, Adrian Cortes, Sarah E. Medland
  • Genome-wide complex trait analysis (GCTA) : Methods, data analyses, and interpretations
  • Jian Yang, Sang Hong Lee, Michael E. Goddard, Peter M. Visscher
  • R for genome-wide association studies
  • Bayesian methods applied to GWAS
  • Rohan L. Fernando, Dorian Garrick
  • Implementing a QTL detection study (GWAS) using genomic prediction methodology
  • Dorian J. Garrick, Rohan L. Fernando
  • Genome-enabled prediction using the BLR (Bayesian Linear Regression) R-package
  • Gustavo de los Campos, Paulino Pérez, Ana I. Vazquez, José Crossa
  • Genomic best linear unbiased prediction (gBLUP) for the estimation of genomic breeding values
  • Samuel A. Clark, Julius van der Werf
  • Detecting regions of homozygosity to map the cause of recessively inherited disease
  • James W. Kijas
  • Cedric Gondro, Laercio R. Porto-Neto, Seung Hwan Lee
  • Use of ancestral haplotypes in genome-wide association studies
  • Tom Druet, Frédéric Farnir
  • Genotype phasing in populations of closely related individuals
  • John M. Hickey
  • Genotype imputation to increase sample size in pedigreed populations
  • John M. Hickey, Matthew A. Cleveland, Christian Maltecca, Gregor Gorjanc, Birgit Gredler, Andreas Kranis
  • Validation of genome-wide association studies (GWAS) results
  • John M. Henshall
  • Detection of signatures of selection using FST
  • Laercio R. Porto-Neto, Seung Hwan Lee, Hak Kyo Lee, Cedric Gondro
  • Descriptive statistics of data : understanding the data set and phenotypes of interest
  • Association weight matrix : a network-based approach towards functional genome-wide association studies
  • Antonio Reverter, Marina R.S. Fortes
  • Mixed effects structural equation models and phenotypic causal networks
  • Bruno Dourado Valente, Guilherme Jordão Magalhães Rosa
  • Epistasis, complexity, and multifactor dimensionality reduction
  • Qinxin Pan, Ting Hu, Jason H. Moore
  • Applications of multifactor dimensionality reduction to genome-wide data using the R package 'MDR'
  • Stacey Winham
  • Higher order interactions : detection of epistasis using machine learning and evolutionary computation
  • Ronald M. Nelson, Marcin Kierczak, Örjan Carlborg
  • Sonja Dominik
  • Incorporating prior knowledge to increase the power of genome-wide association studies
  • Ashley Petersen, Justin Spratt, Nathan L. Tintle
  • Genomic selection in animal breeding programs
  • Julius van der Werf
  • Designing a GWAS : power, sample size, and data structure
  • Roderick D. Ball
  • Managing large SNP datasets with SNPpy
  • Faheem Mitha
Control code
OCM1bookssj0001066256
Dimensions
26 cm
Dimensions
unknown
Extent
xi, 566 pages
Isbn
9781627034463
Isbn Type
(alk. paper)
Lccn
2013937825
Other physical details
illustrations
Specific material designation
remote
System control number
(WaSeSS)ssj0001066256
Label
Genome-wide association studies and genomic prediction, edited by Cedric Gondro, The Center for Genetic Analysis and Applications, University of New England, Armidale, NSW, Australia, Julius van der Werf, School of Environmental and rural Science, University of New England, Armidale, NSW, Australia, Ben Hayes, Biosciences Research Division, Department of Primary Industries, Bundoora, VIC, Australia, (electronic resource)
Publication
Bibliography note
Includes bibliographical references and index
Contents
  • Quality control for genome-wide association studies
  • Cedric Gondro, Seung Hwan Lee, Hak Kyo Lee, Laercio R. Porto-Neto
  • Overview of statistical methods for genome-wide association studies (GWAS)
  • Ben Hayes
  • Statistical analysis of genomic data
  • Roderick D. Ball
  • Using PLINK for genome-wide association studies (GWAS) and data analysis
  • Miguel E. Rentería, Adrian Cortes, Sarah E. Medland
  • Genome-wide complex trait analysis (GCTA) : Methods, data analyses, and interpretations
  • Jian Yang, Sang Hong Lee, Michael E. Goddard, Peter M. Visscher
  • R for genome-wide association studies
  • Bayesian methods applied to GWAS
  • Rohan L. Fernando, Dorian Garrick
  • Implementing a QTL detection study (GWAS) using genomic prediction methodology
  • Dorian J. Garrick, Rohan L. Fernando
  • Genome-enabled prediction using the BLR (Bayesian Linear Regression) R-package
  • Gustavo de los Campos, Paulino Pérez, Ana I. Vazquez, José Crossa
  • Genomic best linear unbiased prediction (gBLUP) for the estimation of genomic breeding values
  • Samuel A. Clark, Julius van der Werf
  • Detecting regions of homozygosity to map the cause of recessively inherited disease
  • James W. Kijas
  • Cedric Gondro, Laercio R. Porto-Neto, Seung Hwan Lee
  • Use of ancestral haplotypes in genome-wide association studies
  • Tom Druet, Frédéric Farnir
  • Genotype phasing in populations of closely related individuals
  • John M. Hickey
  • Genotype imputation to increase sample size in pedigreed populations
  • John M. Hickey, Matthew A. Cleveland, Christian Maltecca, Gregor Gorjanc, Birgit Gredler, Andreas Kranis
  • Validation of genome-wide association studies (GWAS) results
  • John M. Henshall
  • Detection of signatures of selection using FST
  • Laercio R. Porto-Neto, Seung Hwan Lee, Hak Kyo Lee, Cedric Gondro
  • Descriptive statistics of data : understanding the data set and phenotypes of interest
  • Association weight matrix : a network-based approach towards functional genome-wide association studies
  • Antonio Reverter, Marina R.S. Fortes
  • Mixed effects structural equation models and phenotypic causal networks
  • Bruno Dourado Valente, Guilherme Jordão Magalhães Rosa
  • Epistasis, complexity, and multifactor dimensionality reduction
  • Qinxin Pan, Ting Hu, Jason H. Moore
  • Applications of multifactor dimensionality reduction to genome-wide data using the R package 'MDR'
  • Stacey Winham
  • Higher order interactions : detection of epistasis using machine learning and evolutionary computation
  • Ronald M. Nelson, Marcin Kierczak, Örjan Carlborg
  • Sonja Dominik
  • Incorporating prior knowledge to increase the power of genome-wide association studies
  • Ashley Petersen, Justin Spratt, Nathan L. Tintle
  • Genomic selection in animal breeding programs
  • Julius van der Werf
  • Designing a GWAS : power, sample size, and data structure
  • Roderick D. Ball
  • Managing large SNP datasets with SNPpy
  • Faheem Mitha
Control code
OCM1bookssj0001066256
Dimensions
26 cm
Dimensions
unknown
Extent
xi, 566 pages
Isbn
9781627034463
Isbn Type
(alk. paper)
Lccn
2013937825
Other physical details
illustrations
Specific material designation
remote
System control number
(WaSeSS)ssj0001066256

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