Handbook of Statistical Genetics,9780470058305

Handbook of Statistical Genetics

by ; ;
Edition: 3rd
ISBN13: 9780470058305
ISBN10: 0470058307
Format: Hardcover
Pub. Date: 2007-10-01
Publisher(s): Wiley-Interscience
List Price: $531.75

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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
Table of Contents provided by Publisher. All Rights Reserved.

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