Pedigree analysis in R /

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Vigeland, Magnus Dehli
Formato: Electrónico eBook
Idioma:Inglés
Publicado: London : Academic Press, 2021.
Temas:
Forensic genetics > Data processing.
Medical genetics > Data processing.
R (Computer program language)
G�en�etique l�egale > Informatique.
G�en�etique m�edicale > Informatique.
R (Langage de programmation)
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 4.2.3 Recombination Maps in ibdsim2
  • Built-in maps
  • Uniform maps
  • 4.3 Variation in Realised Coefficients
  • 4.3.1 First-Cousin Inbreeding
  • 4.3.2 Some Siblings Are More Equal Than Others
  • 4.4 Distributions of IBD Segments
  • 4.4.1 The Importance of Sex
  • 4.4.2 Separating the Inseparable: H-U-G
  • 4.5 The Probability of No IBD Sharing
  • Chapter 5: Probabilities on Pedigrees
  • 5.1 Computing Pedigree Likelihoods
  • 5.1.1 Basic Likelihood Calculations
  • 5.1.2 The General Likelihood Expression
  • 5.1.3 The Elston-Stewart Algorithm
  • 5.1.4 Likelihoods With pedprobr
  • 5.2 Factors Affecting Performance
  • 5.2.1 Allele Lumping
  • 5.2.2 Pedigree Loops
  • 5.3 Likelihoods With Linked Markers
  • 5.3.1 Linked Markers in R: Two Markers
  • 5.3.2 Linked Markers in R: More Than Two Markers
  • 5.4 Modelling Mutations
  • 5.4.1 Properties of Mutation Models
  • Reversibility
  • Lumpability
  • 5.5 Modelling Deviation From HWE
  • 5.5.1 Founder Inbreeding
  • 5.5.2 Theta Correction
  • Chapter 6: Kinship Testing
  • 6.1 Theory and Methods
  • 6.1.1 Kinship Testing vs. Classical Hypothesis Testing
  • 6.1.2 The Likelihood Ratio
  • 6.1.3 Alternatives to LR
  • 6.1.4 Kinship Testing With forrel
  • 6.2 Paternity Testing
  • 6.2.1 Manual Calculation of \LR
  • 6.2.2 Paternity Testing With forrel
  • 6.2.3 Direct Computation of \LR
  • 6.3 A Relationship Riddle
  • 6.4 Missing Person Identification
  • 6.4.1 Terminology and Hypotheses
  • 6.4.2 A Case Study
  • 6.5 Power Analysis for Kinship Testing
  • 6.5.1 Exclusion Power
  • 6.5.2 Inclusion Power
  • 6.5.3 Power Analysis for Missing Person Cases
  • 6.6 Dealing With Mutations
  • Chapter 7: Inference of Pairwise Relatedness
  • 7.1 Maximum-Likelihood Estimation of Kappa
  • 7.1.1 The Likelihood Function
  • 7.1.2 The Maximum-Likelihood Estimate
  • 7.1.3 Inadmissible Estimates?
  • 7.1.4 What About the Kinship Coefficient?
  • 7.2 Estimation of Identity Coefficients
  • 7.3 ML Estimation in forrel
  • 7.3.1 Example: The Relationship Riddle
  • 7.3.2 Confidence and Uncertainty
  • 7.4 Quality Control of Pedigree Data
  • 7.5 Violating the Assumptions
  • 7.5.1 Inaccurate Allele Frequencies
  • 7.5.2 Linkage Between Markers
  • 7.5.3 Inbreeding
  • Chapter 8: Pedigree Reconstruction
  • 8.1 Reconstruction by Pairwise Inference
  • 8.2 Maximum-Likelihood Pedigree Reconstruction
  • 8.2.1 Restrictions on the Space of Pedigrees
  • 8.2.2 Solving the Relationship Riddle
  • Chapter 9: Linkage Analysis
  • 9.1 Theoretical Background
  • 9.1.1 Hypothesis Testing and the LOD Score
  • Example
  • 9.1.2 Unknown Phase
  • 9.1.3 Parametric Disease Models
  • 9.1.4 What Is a Significant LOD Score?
  • 9.1.5 Multipoint Analysis
  • 9.1.6 Power Assessment
  • 9.2 LOD Scores in paramlink2
  • 9.2.1 An X-Linked Example
  • 9.2.2 More About the lod() Function
  • Disease Models
  • 9.3 A Case Study
  • 9.3.1 Power Assessment
  • 9.3.2 Loading the Data
  • 9.3.3 Quality Control
  • 9.3.4 Disease Model and Preliminary Analysis
  • 9.3.5 Multipoint Analysis
  • 9.3.6 Liability Classes
  • 9.3.7 Summarising LOD Peaks
  • Chapter 10: Segregation Analysis for Variant Interpretation
  • 10.1 Background
  • 10.2 The Bayes Factor
  • 10.2.1 Using the Bayes Factor in the ACMG Framework
  • 10.3 Bayes Factors With the segregatr Package
  • 10.4 A Case Study
  • Bibliography.

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