Handbook of Statistical Genetics

by Balding, David J.; Bishop, Martin; Cannings, Chris

• ISBN13: 9780470058305
• ISBN10: 0470058307
• Edition: 3rd
• Format: Hardcover
• Copyright: 2007-10-01
• Publisher: Wiley-Interscience

Summary

The Handbook for Statistical Genetics is widely regarded as the reference work in the field. However, the field has developed considerably over the past three years. In particular the modeling of genetic networks has advanced considerably via the evolution of microarray analysis. As a consequence the 3rd edition of the handbook contains a much expanded section on Network Modeling, including 5 new chapters covering metabolic networks, graphical modeling and inference and simulation of pedigrees and genealogies. Other chapters new to the 3rd edition include Human Population Genetics, Genome-wide Association Studies, Family-based Association Studies, Pharmacogenetics, Epigenetics, Ethic and Insurance. As with the second Edition, the Handbook includes a glossary of terms, acronyms and abbreviations, and features extensive cross-referencing between the chapters, tying the different areas together. With heavy use of up-to-date examples, real-life case studies and references to web-based resources, this continues to be must-have reference in a vital area of research.

Author Biography

D. J. Balding

Imperial College of Science, Technology and Medicine, London, UK

M. Bishop

CNR-ITB, Milan, Italy

C. Cannings

University of Sheffield, UK

Table of Contents

List of Contributors

Editor's Preface to the Third Edition

Glossary of Terms

Abbreviations and Acronyms

Genomes

Chromosome Maps

Introduction

Genetic Maps

Physical Maps

Radiation Hybrid Mapping

Other Physical Mapping Approaches

Gene Maps

Acknowledgments

References

Statistical Significance in Biological Sequence Comparison

Introduction

Statistical Significance and Biological Significance

Estimating Statistical Significance for Local Similarity Searches

Summary: Exploiting Statistical Estimates

Acknowledgments

References

Bayesian Methods in Biological Sequence Analysis

Introduction

Overview of the Bayesian Methodology

Hidden Markov Model: A General Introduction

Pairwise Alignment of Biological Sequences

Multiple Sequence Alignment

Finding Recurring Patterns in Biological Sequences

Joint Analysis of Sequence Motifs and Expression Microarrays

Summary

Acknowledgments

Markov Chain Monte Carlo Methods

References

Statistical Approaches in Eukaryotic Gene Prediction

Structural Organization and Expression of Eukaryotic Genes

Methods of Functional Signal Recognition

Linear Discriminant Analysis

Prediction of Donor and Acceptor Splice Junctions

Identification of Promoter Regions in Human DNA

Recognition of PolyA Sites

Characteristics for Recognition of 3-Processing Sites

Identification of Multiple Genes in Genomic Sequences

Discriminative and Probabilistic Approaches for Multiple Gene Prediction

Internal Exon Recognition

Recognition of Flanking Exons

Performance of Gene Identification Programs

Using Protein Similarity Information to Improve Gene Prediction

Genome Annotation Assessment Project (EGASP)

Annotation of Sequences from Genome Sequencing Projects

Characteristics and Computational Identification of miRNA genes

Prediction of microRNA Targets

Internet Resources for Gene Finding and Functional Site Prediction

Acknowledgments

References

Comparative Genomics

Introduction

Homology

Genomic Mutation

Comparative Maps

Gene Order and Content

Whole Genome Sequences

Conclusions and Future Research

Acknowledgments

References

Beyond the Genome

Analysis of Microarray Gene Expression Data

Introduction

Data Visualization and Quality Control

Error Models, Calibration and Measures of Differential Expression

Identification of Differentially Expressed Genes

Pattern Discovery

Conclusions

Acknowledgments

References

Statistical Inference for Microarray Studies

Introduction

Initial Data Processing

Testing the Association of Phenotype with Expression

Multiple Testing

Annotation Analysis

Validation Analysis

Study Design and Sample Size

Discussion Related Chapters

References

Bayesian Methods for Microarray Data

Introduction

Extracting Signal From Observed Intensi

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