ORPP logo
Image from Google Jackets

Handbook of Statistical Genomics.

By: Contributor(s): Material type: TextTextPublisher: Newark : John Wiley & Sons, Incorporated, 2019Copyright date: ©2019Edition: 4th edDescription: 1 online resource (1218 pages)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781119429227
Subject(s): Genre/Form: Additional physical formats: Print version:: Handbook of Statistical GenomicsLOC classification:
  • QH438.4.S73 .H363 2019
Online resources:
Contents:
Intro -- Handbook of Statistical Genomics -- Contents -- List of Contributors -- Editors' Preface to the Fourth Edition -- Glossary -- Abbreviations and Acronyms -- 1 Statistical Modeling and Inference in Genetics -- 1.1 Statistical Models and Inference -- 1.1.1 Statistical Models -- 1.1.2 Inference Methods and Algorithms -- 1.2 Maximum Likelihood Inference -- 1.2.1 Properties of Maximum Likelihood Estimators -- 1.2.2 Quantifying Confidence: the Fisher Information Matrix -- 1.2.3 Newton's Method -- 1.2.4 Latent Variable Problems: the EM Algorithm -- 1.2.5 Approximate Techniques -- 1.3 Bayesian Inference -- 1.3.1 Choice of Prior Distributions -- 1.3.2 Bayesian Point Estimates and Confidence Intervals -- 1.3.3 Markov Chain Monte Carlo -- 1.3.4 Empirical Bayes for Latent Variable Problems -- 1.3.5 Approximate Bayesian Computation -- 1.4 Model Selection -- 1.4.1 Likelihood Ratio Statistic -- 1.4.2 Bayesian Model Choice -- 1.5 Hidden Markov Models -- 1.5.1 Bayesian Inference of Hidden States Using Forward-Backward Algorithm -- 1.5.2 Finding the Most Likely Hidden Path (Viterbi Algorithm) -- 1.5.3 MLE Inference of Hierarchical Parameters (Baum-Welch Algorithm) -- Acknowledgements -- References -- 2 Linkage Disequilibrium, Recombination and Haplotype Structure -- 2.1 What Is Linkage Disequilibrium? -- 2.2 Measuring Linkage Disequilibrium -- 2.2.1 Single-Number Summaries of LD -- 2.2.2 The Spatial Distribution of LD -- 2.2.3 Various Extensions of Two-Locus LD Measures -- 2.3 Modelling Linkage Disequilibrium and Genealogical History -- 2.3.1 A Historical Perspective -- 2.3.2 Coalescent Modelling -- 2.3.3 Relating Genealogical History to LD -- 2.4 Data Analysis -- 2.4.1 Estimating Recombination Rates -- 2.4.2 Methods Exploiting Haplotype Structure -- 2.5 Prospects -- Acknowledgements -- References -- 3 Haplotype Estimation and Genotype Imputation.
3.1 Haplotype Estimation -- 3.1.1 A Simple Haplotype Frequency Model -- 3.1.2 Hidden Markov Models for Phasing -- 3.1.3 Phasing in Related Samples -- 3.1.4 Phasing Using Sequencing Data -- 3.1.5 Phasing from a Reference Panel -- 3.1.6 Measuring Phasing Performance -- 3.2 Genotype Imputation -- 3.2.1 Uses of Imputation in GWASs -- 3.2.2 Haploid Imputation -- 3.2.3 Imputation Methods -- 3.2.4 Testing Imputed Genotypes for Association -- 3.2.5 Summary Statistic Imputation -- 3.2.6 Factors Affecting Accuracy -- 3.2.7 Quality Control for Imputed Data -- 3.3 Future Directions -- References -- 4 Mathematical Models in Population Genetics -- 4.1 Introduction -- 4.2 Single-Locus Models -- 4.2.1 Random Drift and the Kingman Coalescent -- 4.2.2 Diffusion Approximations -- 4.2.3 Spatially Structured Populations -- 4.3 Multiple Loci -- 4.3.1 Linkage Equilibrium -- 4.3.2 Beyond Linkage Equilibrium -- 4.4 Outlook -- References -- 5 Coalescent Theory -- 5.1 Introduction -- 5.2 The Coalescent -- 5.2.1 The Fundamental Insights -- 5.2.2 The Coalescent Approximation -- 5.3 Generalizing the Coalescent -- 5.3.1 Robustness and Scaling -- 5.3.2 Variable Population Size -- 5.3.3 Population Structure on Different Time-Scales -- 5.4 Geographical Structure -- 5.4.1 The Structured Coalescent -- 5.4.2 The Strong-Migration Limit -- 5.5 Diploidy and Segregation -- 5.5.1 Hermaphrodites -- 5.5.2 Males and Females -- 5.6 Recombination -- 5.6.1 The Ancestral Recombination Graph -- 5.6.2 Properties and Effects of Recombination -- 5.7 Selection -- 5.7.1 Balancing Selection -- 5.7.2 Selective Sweeps -- 5.7.3 Background Selection -- 5.8 Neutral Mutations -- 5.9 Concluding Remarks -- 5.9.1 The Coalescent and 'Classical' Population Genetics -- 5.9.2 The Coalescent and Phylogenetics -- 5.9.3 Prospects -- Acknowledgements -- References -- 6 Phylogeny Estimation Using Likelihood-Based Methods.
6.1 Introduction -- 6.1.1 Statistical Phylogenetics -- 6.1.2 Chapter Outline -- 6.2 Maximum Likelihood and Bayesian Estimation -- 6.2.1 Maximum Likelihood -- 6.2.2 Bayesian Inference -- 6.3 Choosing among Models Using Likelihood Ratio Tests and Bayes Factors -- 6.4 Calculating the Likelihood for a Phylogenetic Model -- 6.4.1 Character Matrices and Alignments -- 6.4.2 The Phylogenetic Model -- 6.4.3 Calculating the Probability of a Character History -- 6.4.4 Continuous-Time Markov Model -- 6.4.5 Marginalizing over Character Histories -- 6.5 The Mechanics of Maximum Likelihood and Bayesian Inference -- 6.5.1 Maximum Likelihood -- 6.5.2 Bayesian Inference and Markov Chain Monte Carlo -- 6.6 Applications of Likelihood-Based Methods in Molecular Evolution -- 6.6.1 A Taxonomy of Commonly Used Substitution Models -- 6.6.2 Expanding the Model around Groups of Sites -- 6.6.3 Rate Variation across Sites -- 6.6.4 Divergence Time Estimation -- 6.7 Conclusions -- References -- 7 The Multispecies Coalescent -- 7.1 Introduction -- 7.2 Probability Distributions under the Multispecies Coalescent -- 7.2.1 Gene Tree Probabilities -- 7.2.2 Site Pattern Probabilities -- 7.2.3 Species Tree Likelihoods under the Multispecies Coalescent -- 7.2.4 Model Assumptions and Violations -- 7.3 Species Tree Inference under the Multispecies Coalescent -- 7.3.1 Summary Statistics Methods -- 7.3.2 Bayesian Full-Data Methods -- 7.3.3 Site Pattern-Based Methods -- 7.3.4 Multilocus versus SNP Data -- 7.3.5 Empirical Examples -- 7.4 Coalescent-Based Estimation of Parameters at the Population and Species Levels -- 7.4.1 Speciation Times and Population Sizes -- 7.4.2 Hybridization and Gene Flow -- 7.4.3 Species Delimitation -- 7.4.4 Future Prospects -- Acknowledgements -- References -- 8 Population Structure, Demography and Recent Admixture -- 8.1 Introduction.
8.1.1 'Admixture' versus 'Background' Linkage Disequilibrium -- 8.2 Spatial Summaries of Genetic Variation Using Principal Components Analysis -- 8.3 Clustering Algorithms -- 8.3.1 Defining 'Populations' -- 8.3.2 Clustering Based on Allele Frequency Patterns -- 8.3.3 Incorporating Admixture -- 8.3.4 Incorporating Admixture Linkage Disequilibrium -- 8.3.5 Incorporating Background Linkage Disequilibrium: Using Haplotypes to Improve Inference -- 8.3.6 Interpreting Genetic Clusters -- 8.4 Inferring Population Size Changes and Split Times -- 8.4.1 Allele Frequency Spectrum Approaches -- 8.4.2 Approaches Using Whole-Genome Sequencing -- 8.5 Identifying/Dating Admixture Events -- 8.5.1 Inferring DNA Segments Inherited from Different Sources -- 8.5.2 Measuring Decay of Linkage Disequilibrium -- 8.6 Conclusion -- Acknowledgements -- References -- 9 Statistical Methods to Detect Archaic Admixture and Identify Introgressed Sequences -- 9.1 Introduction -- 9.2 Methods to Test Hypotheses of Archaic Admixture and Infer Admixture Proportions -- 9.2.1 Genetic Drift and Allele Frequency Divergence in Genetically Structured Populations -- 9.2.2 Three-Population Test -- 9.2.3 D-Statistic -- 9.2.4 F4-Statistic -- 9.3 Methods to Identify Introgressed Sequences -- 9.3.1 S*-Statistic -- 9.3.2 Hidden Markov and Conditional Random Field Models -- 9.3.3 Relative Advantages and Disadvantages of Approaches to Detect Introgressed Sequences -- 9.4 Summary and Perspective -- References -- 10 Population Genomic Analyses of DNA from Ancient Remains -- 10.1 Introduction -- 10.2 Challenges of Working with and Analyzing Ancient DNA Data -- 10.2.1 Sequence Degradation -- 10.2.2 Contamination -- 10.2.3 Handling Sequence Data from Ancient Material -- 10.2.4 Different Sequencing Approaches and the Limitations in their Resulting Data.
10.2.5 Effects of Limited Amounts of Data on Downstream Analysis -- 10.3 Opportunities of Ancient DNA -- 10.3.1 Population Differentiation in Time and Space -- 10.3.2 Continuity -- 10.3.3 Migration and Admixture over Time -- 10.3.4 Demographic Inference Based on High-Coverage Ancient Genomes -- 10.3.5 Allele Frequency Trajectories -- 10.4 Some Examples of How Genetic Studies of Ancient Remains Have Contributed to a New Understanding of the Human Past -- 10.4.1 Archaic Genomes and the Admixture with Modern Humans -- 10.4.2 Neolithic Revolution in Europe and the Bronze Age Migrations -- 10.5 Summary and Perspective -- Acknowledgements -- References -- 11 Sequence Covariation Analysis in Biological Polymers -- 11.1 Introduction -- 11.2 Methods -- 11.2.1 DCA Method -- 11.2.2 PSICOV -- 11.2.3 plmDCA, GREMLIN and CCMpred -- 11.3 Applications -- 11.3.1 Globular Protein Fold Prediction -- 11.3.2 Transmembrane Protein Prediction -- 11.3.3 RNA Structure Prediction -- 11.3.4 Protein Disordered Regions -- 11.3.5 Protein-Protein Interactions -- 11.3.6 Allostery and Dynamics -- 11.3.7 CASP -- 11.4 New Developments -- 11.4.1 Sequence Alignment -- 11.4.2 Comparison to Known Structures -- 11.4.3 Segment Parsing -- 11.4.4 Machine Learning -- 11.4.5 Deep Learning Methods -- 11.4.6 Sequence Pairing -- 11.4.7 Phylogeny Constraints -- 11.5 Outlook -- Acknowledgements -- References -- 12 Probabilistic Models for the Study of Protein Evolution -- 12.1 Introduction -- 12.2 Empirically Derived Models of Amino Acid Replacement -- 12.2.1 The Dayhoff and Eck Model -- 12.2.2 Descendants of the Dayhoff Model -- 12.3 Heterogeneity of Replacement Rates among Sites -- 12.4 Protein Structural Environments -- 12.5 Variation of Preferred Residues among Sites -- 12.6 Models with a Physicochemical Basis -- 12.7 Codon-Based Models -- 12.8 Dependence among Positions.
12.9 Stochastic Models of Structural Evolution.
Tags from this library: No tags from this library for this title. Log in to add tags.
Star ratings
    Average rating: 0.0 (0 votes)
No physical items for this record

Intro -- Handbook of Statistical Genomics -- Contents -- List of Contributors -- Editors' Preface to the Fourth Edition -- Glossary -- Abbreviations and Acronyms -- 1 Statistical Modeling and Inference in Genetics -- 1.1 Statistical Models and Inference -- 1.1.1 Statistical Models -- 1.1.2 Inference Methods and Algorithms -- 1.2 Maximum Likelihood Inference -- 1.2.1 Properties of Maximum Likelihood Estimators -- 1.2.2 Quantifying Confidence: the Fisher Information Matrix -- 1.2.3 Newton's Method -- 1.2.4 Latent Variable Problems: the EM Algorithm -- 1.2.5 Approximate Techniques -- 1.3 Bayesian Inference -- 1.3.1 Choice of Prior Distributions -- 1.3.2 Bayesian Point Estimates and Confidence Intervals -- 1.3.3 Markov Chain Monte Carlo -- 1.3.4 Empirical Bayes for Latent Variable Problems -- 1.3.5 Approximate Bayesian Computation -- 1.4 Model Selection -- 1.4.1 Likelihood Ratio Statistic -- 1.4.2 Bayesian Model Choice -- 1.5 Hidden Markov Models -- 1.5.1 Bayesian Inference of Hidden States Using Forward-Backward Algorithm -- 1.5.2 Finding the Most Likely Hidden Path (Viterbi Algorithm) -- 1.5.3 MLE Inference of Hierarchical Parameters (Baum-Welch Algorithm) -- Acknowledgements -- References -- 2 Linkage Disequilibrium, Recombination and Haplotype Structure -- 2.1 What Is Linkage Disequilibrium? -- 2.2 Measuring Linkage Disequilibrium -- 2.2.1 Single-Number Summaries of LD -- 2.2.2 The Spatial Distribution of LD -- 2.2.3 Various Extensions of Two-Locus LD Measures -- 2.3 Modelling Linkage Disequilibrium and Genealogical History -- 2.3.1 A Historical Perspective -- 2.3.2 Coalescent Modelling -- 2.3.3 Relating Genealogical History to LD -- 2.4 Data Analysis -- 2.4.1 Estimating Recombination Rates -- 2.4.2 Methods Exploiting Haplotype Structure -- 2.5 Prospects -- Acknowledgements -- References -- 3 Haplotype Estimation and Genotype Imputation.

3.1 Haplotype Estimation -- 3.1.1 A Simple Haplotype Frequency Model -- 3.1.2 Hidden Markov Models for Phasing -- 3.1.3 Phasing in Related Samples -- 3.1.4 Phasing Using Sequencing Data -- 3.1.5 Phasing from a Reference Panel -- 3.1.6 Measuring Phasing Performance -- 3.2 Genotype Imputation -- 3.2.1 Uses of Imputation in GWASs -- 3.2.2 Haploid Imputation -- 3.2.3 Imputation Methods -- 3.2.4 Testing Imputed Genotypes for Association -- 3.2.5 Summary Statistic Imputation -- 3.2.6 Factors Affecting Accuracy -- 3.2.7 Quality Control for Imputed Data -- 3.3 Future Directions -- References -- 4 Mathematical Models in Population Genetics -- 4.1 Introduction -- 4.2 Single-Locus Models -- 4.2.1 Random Drift and the Kingman Coalescent -- 4.2.2 Diffusion Approximations -- 4.2.3 Spatially Structured Populations -- 4.3 Multiple Loci -- 4.3.1 Linkage Equilibrium -- 4.3.2 Beyond Linkage Equilibrium -- 4.4 Outlook -- References -- 5 Coalescent Theory -- 5.1 Introduction -- 5.2 The Coalescent -- 5.2.1 The Fundamental Insights -- 5.2.2 The Coalescent Approximation -- 5.3 Generalizing the Coalescent -- 5.3.1 Robustness and Scaling -- 5.3.2 Variable Population Size -- 5.3.3 Population Structure on Different Time-Scales -- 5.4 Geographical Structure -- 5.4.1 The Structured Coalescent -- 5.4.2 The Strong-Migration Limit -- 5.5 Diploidy and Segregation -- 5.5.1 Hermaphrodites -- 5.5.2 Males and Females -- 5.6 Recombination -- 5.6.1 The Ancestral Recombination Graph -- 5.6.2 Properties and Effects of Recombination -- 5.7 Selection -- 5.7.1 Balancing Selection -- 5.7.2 Selective Sweeps -- 5.7.3 Background Selection -- 5.8 Neutral Mutations -- 5.9 Concluding Remarks -- 5.9.1 The Coalescent and 'Classical' Population Genetics -- 5.9.2 The Coalescent and Phylogenetics -- 5.9.3 Prospects -- Acknowledgements -- References -- 6 Phylogeny Estimation Using Likelihood-Based Methods.

6.1 Introduction -- 6.1.1 Statistical Phylogenetics -- 6.1.2 Chapter Outline -- 6.2 Maximum Likelihood and Bayesian Estimation -- 6.2.1 Maximum Likelihood -- 6.2.2 Bayesian Inference -- 6.3 Choosing among Models Using Likelihood Ratio Tests and Bayes Factors -- 6.4 Calculating the Likelihood for a Phylogenetic Model -- 6.4.1 Character Matrices and Alignments -- 6.4.2 The Phylogenetic Model -- 6.4.3 Calculating the Probability of a Character History -- 6.4.4 Continuous-Time Markov Model -- 6.4.5 Marginalizing over Character Histories -- 6.5 The Mechanics of Maximum Likelihood and Bayesian Inference -- 6.5.1 Maximum Likelihood -- 6.5.2 Bayesian Inference and Markov Chain Monte Carlo -- 6.6 Applications of Likelihood-Based Methods in Molecular Evolution -- 6.6.1 A Taxonomy of Commonly Used Substitution Models -- 6.6.2 Expanding the Model around Groups of Sites -- 6.6.3 Rate Variation across Sites -- 6.6.4 Divergence Time Estimation -- 6.7 Conclusions -- References -- 7 The Multispecies Coalescent -- 7.1 Introduction -- 7.2 Probability Distributions under the Multispecies Coalescent -- 7.2.1 Gene Tree Probabilities -- 7.2.2 Site Pattern Probabilities -- 7.2.3 Species Tree Likelihoods under the Multispecies Coalescent -- 7.2.4 Model Assumptions and Violations -- 7.3 Species Tree Inference under the Multispecies Coalescent -- 7.3.1 Summary Statistics Methods -- 7.3.2 Bayesian Full-Data Methods -- 7.3.3 Site Pattern-Based Methods -- 7.3.4 Multilocus versus SNP Data -- 7.3.5 Empirical Examples -- 7.4 Coalescent-Based Estimation of Parameters at the Population and Species Levels -- 7.4.1 Speciation Times and Population Sizes -- 7.4.2 Hybridization and Gene Flow -- 7.4.3 Species Delimitation -- 7.4.4 Future Prospects -- Acknowledgements -- References -- 8 Population Structure, Demography and Recent Admixture -- 8.1 Introduction.

8.1.1 'Admixture' versus 'Background' Linkage Disequilibrium -- 8.2 Spatial Summaries of Genetic Variation Using Principal Components Analysis -- 8.3 Clustering Algorithms -- 8.3.1 Defining 'Populations' -- 8.3.2 Clustering Based on Allele Frequency Patterns -- 8.3.3 Incorporating Admixture -- 8.3.4 Incorporating Admixture Linkage Disequilibrium -- 8.3.5 Incorporating Background Linkage Disequilibrium: Using Haplotypes to Improve Inference -- 8.3.6 Interpreting Genetic Clusters -- 8.4 Inferring Population Size Changes and Split Times -- 8.4.1 Allele Frequency Spectrum Approaches -- 8.4.2 Approaches Using Whole-Genome Sequencing -- 8.5 Identifying/Dating Admixture Events -- 8.5.1 Inferring DNA Segments Inherited from Different Sources -- 8.5.2 Measuring Decay of Linkage Disequilibrium -- 8.6 Conclusion -- Acknowledgements -- References -- 9 Statistical Methods to Detect Archaic Admixture and Identify Introgressed Sequences -- 9.1 Introduction -- 9.2 Methods to Test Hypotheses of Archaic Admixture and Infer Admixture Proportions -- 9.2.1 Genetic Drift and Allele Frequency Divergence in Genetically Structured Populations -- 9.2.2 Three-Population Test -- 9.2.3 D-Statistic -- 9.2.4 F4-Statistic -- 9.3 Methods to Identify Introgressed Sequences -- 9.3.1 S*-Statistic -- 9.3.2 Hidden Markov and Conditional Random Field Models -- 9.3.3 Relative Advantages and Disadvantages of Approaches to Detect Introgressed Sequences -- 9.4 Summary and Perspective -- References -- 10 Population Genomic Analyses of DNA from Ancient Remains -- 10.1 Introduction -- 10.2 Challenges of Working with and Analyzing Ancient DNA Data -- 10.2.1 Sequence Degradation -- 10.2.2 Contamination -- 10.2.3 Handling Sequence Data from Ancient Material -- 10.2.4 Different Sequencing Approaches and the Limitations in their Resulting Data.

10.2.5 Effects of Limited Amounts of Data on Downstream Analysis -- 10.3 Opportunities of Ancient DNA -- 10.3.1 Population Differentiation in Time and Space -- 10.3.2 Continuity -- 10.3.3 Migration and Admixture over Time -- 10.3.4 Demographic Inference Based on High-Coverage Ancient Genomes -- 10.3.5 Allele Frequency Trajectories -- 10.4 Some Examples of How Genetic Studies of Ancient Remains Have Contributed to a New Understanding of the Human Past -- 10.4.1 Archaic Genomes and the Admixture with Modern Humans -- 10.4.2 Neolithic Revolution in Europe and the Bronze Age Migrations -- 10.5 Summary and Perspective -- Acknowledgements -- References -- 11 Sequence Covariation Analysis in Biological Polymers -- 11.1 Introduction -- 11.2 Methods -- 11.2.1 DCA Method -- 11.2.2 PSICOV -- 11.2.3 plmDCA, GREMLIN and CCMpred -- 11.3 Applications -- 11.3.1 Globular Protein Fold Prediction -- 11.3.2 Transmembrane Protein Prediction -- 11.3.3 RNA Structure Prediction -- 11.3.4 Protein Disordered Regions -- 11.3.5 Protein-Protein Interactions -- 11.3.6 Allostery and Dynamics -- 11.3.7 CASP -- 11.4 New Developments -- 11.4.1 Sequence Alignment -- 11.4.2 Comparison to Known Structures -- 11.4.3 Segment Parsing -- 11.4.4 Machine Learning -- 11.4.5 Deep Learning Methods -- 11.4.6 Sequence Pairing -- 11.4.7 Phylogeny Constraints -- 11.5 Outlook -- Acknowledgements -- References -- 12 Probabilistic Models for the Study of Protein Evolution -- 12.1 Introduction -- 12.2 Empirically Derived Models of Amino Acid Replacement -- 12.2.1 The Dayhoff and Eck Model -- 12.2.2 Descendants of the Dayhoff Model -- 12.3 Heterogeneity of Replacement Rates among Sites -- 12.4 Protein Structural Environments -- 12.5 Variation of Preferred Residues among Sites -- 12.6 Models with a Physicochemical Basis -- 12.7 Codon-Based Models -- 12.8 Dependence among Positions.

12.9 Stochastic Models of Structural Evolution.

Description based on publisher supplied metadata and other sources.

Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

There are no comments on this title.

to post a comment.

© 2024 Resource Centre. All rights reserved.