소장자료

Genome-Wide Association Studies and Genomic Prediction [electronic resource]

  • Gondro, Cedric. editor. , van der Werf, Julius. editor. , Hayes, Ben. editor.
  • 2013
Genome-Wide Association Studies and Genomic Prediction [electronic resource]
  • 자료유형
    단행본
  • 서명/저자사항
    Genome-Wide Association Studies and Genomic Prediction[electronic resource] / edited by Cedric Gondro, Julius van der Werf, Ben Hayes.
  • 개인저자
    Gondro, Cedric., editor., van der Werf, Julius., editor., Hayes, Ben., editor.
  • 단체저자
    SpringerLink (Online service)
  • 형태사항
    XI, 566 p. 67 illus., 31 illus. in color. : online resource.
  • 총서명
    Methods in Molecular Biology, Methods and Protocols,1064-3745 ; 1019
  • 내용주기
    R for Genome-Wide Association Studies -- Descriptive Statistics of Data: Understanding the Data Set and Phenotypes of Interest -- Designing a Genome-Wide Association Studies (GWAS): Power, Sample Size, and Data Structure -- Managing Large SNP Datasets with SNPpy -- Quality Control for Genome-Wide Association Studies -- Overview of Statistical Methods for Genome-Wide Association Studies (GWAS) -- Statistical Analysis of Genomic Data -- Using PLINK for Genome-Wide Association Studies (GWAS) and Data Analysis -- Genome-Wide Complex Trait Analysis (GCTA): Methods, Data Analyses, and Interpretations -- Bayesian Methods Applied to Genome-Wide Association Studies (GWAS) -- Implementing a QTL Detection Study (GWAS) Using Genomic Prediction Methodology -- Genome-Enabled Prediction Using the BLR (Bayesian Linear Regression) R-Package -- Genomic Best Linear Unbiased Prediction (gBLUP) for the Estimation of Genomic Breeding Values -- Detecting Regions of Homozygosity to Map the Cause of Recessively Inherited Disease -- Use of Ancestral Haplotypes in Genome-Wide Association Studies -- Genotype Phasing in Populations of Closely Related Individuals -- Genotype Imputation to Increase Sample Size in Pedigreed Populations -- Validation of Genome-Wide Association Studies (GWAS) Results -- Detection of Signatures of Selection Using FST -- Association Weight Matrix: A Network-Based Approach Towards Functional Genome-Wide Association Studies -- Mixed Effects Structural Equation Models and Phenotypic Causal Networks -- Epistasis, Complexity, and Multifactor Dimensionality Reduction -- Applications of Multifactor Dimensionality Reduction to Genome-Wide Data Using the R Package ?쁌DR??-- Higher Order Interactions: Detection of Epistasis Using Machine Learning and Evolutionary Computation -- Incorporating Prior Knowledge to Increase the Power of Genome-Wide Association Studies -- Genomic Selection in Animal Breeding Programs.
  • 일반주제명
    Life sciences.
    Human genetics.
    Bioinformatics.
    Life Sciences.
    Bioinformatics.
    Human Genetics.
  • 기본자료 저록
    Springer eBooks
  • 기타형태 저록
    Printed edition: 9781627034463
  • ISBN
    9781627034470
  • 언어
    영어