Probabilistic graphical models for genetics, genomics and postgenomics:
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| Format: | Buch |
| Sprache: | English |
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Oxford
Oxford Univ. Press
2014
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| Ausgabe: | 1. ed. |
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| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XXVII, 449 S. Ill., graph. Darst. |
| ISBN: | 9780198709022 |
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Titel: Probabilistic graphical models for genetics, genomics and postgenomics
Autor: Sinoquet, Christine
Jahr: 2014
CONTENTS
Abbreviations xix
List of Contributors xxiii
Parti. INTRODUCTION
1. Probabilistic Graphical Models for Next-generation Genomics and Genetics 3
CHRISTINE SINOQUET
1.1. Fine-grained Description of Living Systems 4
1.1.1. DNA and the Genome 4
1.1.2. Genes and Proteins 5
1.1.3. Phenotype and Genotype 5
1.1.4. Molecular Biology, Genetics, Genomics, and Postgenomics 6
1.2. Higher Description Levels of Living Systems 6
1.2.1. Complexity in Cells 7
1.2.2. Genetics, Epigenetics, and Copy Number Polymorphism 9
1.2.3. Epigenetics with Additional Prior Knowledge on the Genome 11
1.2.4. Transcriptomics 11
1.2.5. Transcriptomics with Prior Biological Knowledge 13
1.2.6. Integrating Data from Several Levels 13
1.2.7. Recapitulation 16
1.3. An Era of High-throughput Genomic Technologies 16
1.3.1. Genotyping 16
1.3.2. Copy Number Polymorphism 19
1.3.3. DNA Methylation Measurements 19
1.3.4. Gene Expression Data 20
1.3.5. Quantitative Trait Loci 21
1.3.6. The Challenge of Handling Omics Data 23
1.4. Probabilistic Graphical Models to Infer Novel Knowledge from
Omics Data 23
1.4.1. Gene Network Inference 24
1.4.2. Causality Discovery 24
1.4.3. Association Genetics 26
1.4.4. Epigenetics 26
1.4.5. Detection of Copy Number Variations 26
1.4.6. Prediction of Outcomes from High-dimensional Genomic Data 26
2. Essentials to Understand Probabilistic Graphical Models: A Tutorial
about Inference and Learning 30
CHRISTINE SINOQUET
2.1. Introduction 32
2.2. Reminders 32
2.3. Various Classes of Probabilistic Graphical Models 38
2.3.1. Markov Chains and Hidden Markov Models 38
2.3.2. Markov Random Fields 39
2.3.3. Variants around the Concept of Markov random field 41
2.3.4. Bayesian networks 41
2.3.5. Unifying Model and Model Extension 45
2.4. Probabilistic Inference 46
2.4.1. Exact Inference 46
2.4.2. Approximate Inference 51
2.5. Learning Bayesian networks 57
2.5.1. Parameter Learning 58
2.5.2. Structure Learning 61
2.6. Learning Markov random fields 69
2.6.1. Parameter Learning 69
2.6.2. Structure Learning 72
2.7. Causal Networks 75
2.8. List of General Monographs and Focused Chapter Books 77
Part II. GENE EXPRESSION
3. Graphical Models and Multivariate Analysis of Microarray Data 85
HARRI KIIVERI
3.1. Introduction 85
3.2. The Model 87
3.3. Model Fitting 88
3.3.1. Maximum Likelihood Estimation when the Zero Pattern is Known 89
3.3.2. Determining the Pattern of Zeroes in the Inverse Covariance Matrix 90
3.4. Hypothesis Testing 92
3.4.1. Null Distributions by Permutation 92
3.4.2. A Multivariate Test Statistic 93
3.4.3. Partitioning of the Test Statistic 94
3.4.4. Testing Strategies 95
3.5. Example 96
3.6. Discussion and Conclusions 99
4. Comparison of Mixture Bayesian and Mixture Regression Approaches
to Infer Gene Networks 105
SANDRA L. RODRIGUEZ-ZAS AND BRUCE R. SOUTHEY
4.1. Introduction 106
4.2. Methods 107
4.2.1. Mixture Bayesian Network 107
4.2.2. Mixture Regression Approach 108
4.2.3. Data 110
4.3. Results 112
4.3.1. Comparison of Mixtures 112
4.3.2. Mixture Modeling of Changes in Gene Relationships 112
4.3.3. Interpretation of Mixtures 114
4.3.4. Inference of Large Networks 116
4.4. Conclusions 116
5. Network Inference in Breast Cancer with Gaussian Graphical Models and
Extensions
MARINE JEANMOUGIN, CAMILLE CHARBONNIER, MICKAËL GUEDJ,
AND JULIEN CHIQUET
5.1. Introduction
5.2. Modeling of Gene Networks by Gaussian Graphical Networks
5.2.1. Simple Gaussian graphical network
5.2.2. Extensions Motivated by Regulatory Network Modeling
5.3. Application to Estrogen Receptor Status in Breast Cancer
5.3.1. Context
5.3.2. Biological Prior Definition
5.3.3. Network Inference from Biological Prior: Application
and Interpretation
5.4. Conclusions and Discussion
Part III. CAUSALITY DISCOVERY
6. Utilizing Genotypic Information as a Prior for Learning Gene Networks
KYLE CHIPMAN AND AMBUJ SINGH
6.1. Introduction
6.2. Methods
6.2.1. eQTL Data sets
6.2.2. LCMS Method for Learning a Prior Matrix of Causal Relationships
6.2.3. Bayesian Network Structure Learning
6.2.4. Integrating the Prior Matrix
6.2.5. Stochastic Causal Tree Method
6.3. Conclusion
7. Bayesian Causal Phenotype Network Incorporating Genetic Variation
and Biological Knowledge 165
JEE YOUNG MOON, ELIAS CHAIBUB NETO, XINWEI DENG,
AND BRIAN S. YANDELL
7.1. Introduction 166
7.2. Joint Inference of Causal Phenotype Network and Causal QTLs 167
7.2.1. Standard Bayesian Network Model 168
7.2.2. HCGR Model 169
7.2.3. Systems Genetics and Causal Inference 170
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7.2.4. QTL Mapping Conditional on Phenotype Network Structure 172
7.2.5. Joint Inference of Phenotype Network and Causal QTLs 173
7.3. Causal Phenotype Network Incorporating Biological Knowledge 174
7.3.1. Model 175
7.3.2. Sketch of MCMC 178
7.3.3. Summary of Encoding of Biological Knowledge 180
7.4. Simulations 183
7.5. Analysis of Yeast Cell-Cycle Genes 185
7.6. Conclusion 188
8. Structural Equation Models for Studying Causal Phenotype Networks
in Quantitative Genetics 196
GUILHERME J. M. ROSA AND BRUNO D. VALENTE
8.1. Introduction 196
8.2. Classical Linear Mixed-effects Models in Quantitative Genetics 197
8.3. Mixed-effects Structural Equation Models 202
8.4. Data-driven Search for Phenotypic Causal Relationships 204
8.4.1. General Overview 204
8.4.2. Search Algorithms 206
8.5. Inferring Causal Structures in Genetics Applications 207
8.5.1. Genotypic information as Instrumental Variable 207
8.5.2. Accounting for Polygenic Confounding Effects 208
8.6. Concluding Remarks 210
Part IV. GENETIC ASSOCIATION STUDIES
9. Modeling Linkage Disequilibrium and Performing Association Studies
through Probabilistic Graphical Models: a Visiting Tour of Recent Advances
CHRISTINE SINOQUET AND RAPHAËL MOURAD
9.1. Introduction
9.2. Modeling Linkage Disequilibrium
9.2.1. General Panorama
9.2.2. Decomposable Markov Random Fields
9.2.3. Bayesian Network-based Approaches without Latent Variables
9.2.4. Bayesian Network-based Approaches with Latent Variables
9.2.5. Recapitulation
9.3. Single-SNP Approaches for Genome-wide Association Studies
9.3.1. Integration of Confounding Factors
9.3.2. G WAS Multilocus Approach
9.3.3. Strengths and Limitations
9.4. Identifying Epistasis at the Genome Scale
9.4.1. Bayesian Network-based Approaches
9.4.2. Markov Blanket-based Method
9.4.3. Recapitulation
9.5. Discussion
9.6. Perspectives
217
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10. Modeling Linkage Disequilibrium with Decomposable Graphical Models 247
HALEY J. ABEL AND ALUN THOMAS
10.1. Introduction
10.2. Methods
10.2.1. Decomposable Graphical Models
10.2.2. Estimating Decomposable Graphical Models
10.2.3. Application to Diploid Data by Phase Imputation
10.2.4. Estimation on the Genome-Wide Scale
10.3. Applications
10.3.1. Phasing
10.3.2. Unconditional Simulation
10.3.3. Phenotypes and Covariates
10.3.4. Admixture Mapping
10.4. Application to Sequence Data
11. Scoring, Searching and Evaluating Bayesian Network Models
of Gene-phenotype Association
XIA JIANG, SHYAM VISWESWARAN, AND RICHARD E. NEAPOLITAN
11.1. Introduction 270
11.2. Background 270
11.2.1. Epistasis 270
11.2.2. Genome-wide association studies 271
11.3. A Bayesian Network Model 272
11.4. Scoring Candidate Models 273
11.4.1. Bayesian Network Scoring Criteria 273
11.4.2. Experiments 275
11.5. Searching over the Space of Models 278
11.5.1. Experiments 280
11.6. Determining Whether a Model is Sufficiently Noteworthy 280
11.6.1. The Bayesian Network Posterior Probability (BNPP) 282
11.6.2. Prior Probabilities 285
11.6.3. Experiments 287
11.7. Discussion and Further Research 290
12. Graphical Modeling of Biological Pathways in Genome-wide Association Studies 294
MIN CHEN, JUDY CHO, AND HONGYU ZHAO
12.1. Introduction 295
12.2. MRF Modeling of Gene Pathways 296
12.3. A Bayesian Framework 300
12.3.1. Prior Specification and Likelihood Function 300
12.3.2. Posterior Distribution 302
12.3.3. Making Inference Based on the Posterior Distribution 304
12.3.4. Numerical Studies 305
12.3.5. Real Data Example—Crohn's Disease Data 309
12.4. Discussion 312
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249
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256
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258
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263
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269
13. Bayesian, Systems-based, Multilevel Analysis of Associations for Complex
Phenotypes: from Interpretation to Decision 318
PÉTER ANTAL, ANDRES MILLINGHOFFER, GABOR HULlAm,
GERGELY HAJOS, PÉTER sArKÖZY, AHORAS GÉZSI, CSABA SZALAI,
AND ANDrAs TALUS
13.1. Introduction 319
13.2. Bayesian network-based Concepts of Association and Relevance 320
13.2.1. Association and Strong Relevance 320
13.2.2. Stable Distributions, Markov Blankets and Markov Boundaries 322
13.2.3. Further relevance types 323
13.2.4. Necessary Subsets and Sufficient Supersets in Strong Relevance 326
13.2.5. Relevance for Multiple Targets 327
13.3. A Bayesian View of Relevance for Complex Phenotypes 328
13.3.1. Estimating the Posteriors of Complex Features 330
13.3.2. Sufficiency of the Data for Full Multivariate Analysis 332
13.3.3. Rate of Learning: Effect of Feature and Model Complexity 333
13.3.4. Bayesian network-based Bayesian Multilevel Analysis of Relevance 336
13.3.5. Posteriors for Multiple Target Variables 339
13.3.6. Subtypes of Strong and Weak Relevance 340
13.3.7. Interaction-redundancy Scores Based on Posteriors
of Strong Relevance 342
13.4. Bayes Optimal Decisions about Multivariate Relevance 344
13.4.1. Optimal Decision about Univariate Relevance 344
13.4.2. Optimal Bayesian Decision to Control FDR 345
13.4.3. General Bayes Optimal Decision about Multivariate Relevance 348
13.5. Knowledge Fusion: Relevance of Genes and Annotations 350
13.6. Conclusion 352
PartV. EPIGENETICS
14. Bayesian Networks in the Study of Genome-wide DNA Methylation 363
MEROMIT SINGER AND LIOR PACHTER
14.1. Introduction to Epigenetics 364
14.2. Next-generation Sequencing and DNA Methylation 365
14.2.1. Assaying Genome-wide DNA Methylation 366
14.2.2. The methyl-Seq Method 368
14.3. A Bayesian network for methyl-Seq Analysis 370
14.3.1. Notation 371
14.3.2. A Generative Model 371
14.3.3. Parameter Learning and Inference of Posterior Probabilities 372
14.4. Genomic Structure as a Prior on Methylation Status 375
14.5. Application: Methyltyping the Human Neutrophil 379
14.5.1. Unmethylated Clusters 379
14.6. Conclusions 381
15. Latent Variable Models for Analyzing DNA Methylation 387
E. ANDRÉS HOUSEMAN
15.1. Introduction 388
15.2. Latent Variable Methods for DNA Methylation in Low-dimensional Settings 390
15.2.1. Discrete Latent Variables 391
15.2.2. Continuous Latent Variables 392
15.3. Latent Variable Methods for DNA Methylation in High-dimensional Settings 396
15.3.1. Model-based Clustering: Recursively Partitioned Mixture Models 396
15.3.2. Semi-Supervised Recursively Partitioned Mixture Models 399
15.4. Conclusion 401
Part VI. DETECTION OF COPY NUMBER VARIATIONS
16. Detection of Copy Number Variations from Array Comparative Genomic
Hybridization Data Using Linear-chain Conditional Random Field Models
XIAOLIN YIN AND JING LI
16.1. Introduction
16.2. aCGH Data and Analysis
16.2.1. aCGH Data
16.2.2. Existing Algorithms
16.3. Linear-chain CRP Model for aCGH Data
16.3.1. Feature Functions
16.3.2. Parameter Estimation
16.3.3. Evaluation Methods
16.4. Experimental Results
16.4.1. A Real Example
16.4.2. Simulated Data
16.5. Conclusion
Part VII. PREDICTION OF OUTCOMES FROM
HIGH-DIMENSIONAL GENOMIC DATA
17. Prediction of Clinical Outcomes from Genome-wide Data 431
SHYAM VISWESWARAN
17.1. Introduction 431
17.2. Challenges with Genome-wide Data 432
17.3. Background 433
17.3.1. The Naive Bayes Model 433
17.3.2. Bayesian Model Averaging 434
17.3.3. Alzheimer's Disease 434
17.4. The Model-Averaged Naive Bayes (MANB) Algorithm 435
17.4.1. Overview of the MANB Algorithm 435
17.4.2. Details of the MANB Algorithm 436
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17.5. Evaluation Protocol 438
17.5.1. Data set 438
17.5.2. Protocol 438
17.6. Results 439
17.7. Conclusion 440
Index
447 |
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| spellingShingle | Probabilistic graphical models for genetics, genomics and postgenomics Graphisches Modell (DE-588)4606156-3 gnd Genetik (DE-588)4071711-2 gnd Statistisches Modell (DE-588)4121722-6 gnd Genomik (DE-588)4776397-8 gnd |
| subject_GND | (DE-588)4606156-3 (DE-588)4071711-2 (DE-588)4121722-6 (DE-588)4776397-8 |
| title | Probabilistic graphical models for genetics, genomics and postgenomics |
| title_auth | Probabilistic graphical models for genetics, genomics and postgenomics |
| title_exact_search | Probabilistic graphical models for genetics, genomics and postgenomics |
| title_full | Probabilistic graphical models for genetics, genomics and postgenomics ed. by Christine Sinoquet ... |
| title_fullStr | Probabilistic graphical models for genetics, genomics and postgenomics ed. by Christine Sinoquet ... |
| title_full_unstemmed | Probabilistic graphical models for genetics, genomics and postgenomics ed. by Christine Sinoquet ... |
| title_short | Probabilistic graphical models for genetics, genomics and postgenomics |
| title_sort | probabilistic graphical models for genetics genomics and postgenomics |
| topic | Graphisches Modell (DE-588)4606156-3 gnd Genetik (DE-588)4071711-2 gnd Statistisches Modell (DE-588)4121722-6 gnd Genomik (DE-588)4776397-8 gnd |
| topic_facet | Graphisches Modell Genetik Statistisches Modell Genomik |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027421353&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT sinoquetchristine probabilisticgraphicalmodelsforgeneticsgenomicsandpostgenomics |