Genome-Wide Association Studies and Genomic Prediction /

Genome-Wide Association Studies and Genomic Prediction /

Detalles Bibliográficos
Autor Corporativo: SpringerLink (Online service)
Otros Autores: Gondro, Cedric. (Editor ), van der Werf, Julius. (Editor ), Hayes, Ben. (Editor )
Formato: eBook
Lenguaje:English
Publicado: Totowa, NJ : Humana Press : Imprint: Humana, 2013.
Edición:1st ed. 2013.
Colección:Methods in Molecular Biology, 1019
Materias:
Bioinformatics.
Human genetics.
Human Genetics.
Tabla de Contenidos:
  • 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 ‘MDR’
  • 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.

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