Meta-analysis and Combining Information in Genetics and Genomics

Note: Supplemental materials are not guaranteed with Rental or Used book purchases.

ISBN: 9781584885221 | 158488522X
Cover: Hardcover
Copyright: 7/7/2009

Rent

(Recommended)

$152.96

Term

Due

Price

Semester

Dec 12

$169.95

Quarter

Sep 4

$161.45

Short Term

Aug 13

$152.96

*This item is part of an exclusive publisher rental program and requires an additional convenience fee. This fee will be reflected in the shopping bag.
Buy New

Usually Ships in 3-5 Business Days

$217.58
eBook

Available Instantly

Online: 180 Days

Downloadable: 180 Days

*To support the delivery of the digital material to you, a digital delivery fee of $3.99 will be charged on each digital item.

$66.00*

With contributions from leading experts, Meta-analysis and Combining Information in Genetics and Genomics provides theory, methods, and guiding principles for meta-analysis and combining information for various types of genetic studies. The book begins with an introduction to meta-analysis and combining information as well as statistical genetics, bioinformatics, and proteomics. Addressing the complications inherent in comparing and replicating genetic studies, the contributors carefully examine microarrays, gene mapping, and proteomics. They detail the various techniques used to analyze and combine data from different types of studies. This is the first book devoted to meta-analysis in genetics.

Introductory Material	p. 1
A brief introduction to meta-analysis, genetics and genomics	p. 3
Introduction	p. 3
Combining information	p. 4
Genetics	p. 8
Genomics	p. 12
Combining information in genetics and genomics	p. 16
Similar Data Types I: Genotype Data	p. 21
Combining information across genome-wide linkage scans	p. 23
Introduction	p. 23
Meta-analysis of genome-wide linkage scans	p. 23
Choice of meta-analysis method	p. 28
Discussion	p. 31
Appendix A	p. 32
Genome search meta-analysis (GSMA): a nonparametric method for meta-analysis of genome-wide linkage studies Cathryn M. Lewis	p. 33
Introduction	p. 33
GSMA: Genome Search Meta-Analysis method	p. 34
Power to detect linkage using GSMA	p. 40
Extensions of GSMA	p. 42
Limitations of the GSMA	p. 43
Disease studies using GSMA	p. 44
GSMA software	p. 46
Conclusions	p. 46
Heterogeneity in meta-analysis of quantitative trait linkage studies	p. 49
Introduction	p. 49
The classical meta-analytic method	p. 51
Extracting relevant information from individual studies	p. 54
Example	p. 58
Discussion	p. 63
An empirical Bayesian framework for QTL genome-wide scans	p. 67
Introduction	p. 67
Methods	p. 69
Results	p. 72
Discussion	p. 79
Similar Data Types II: Gene Expression Data	p. 81
Composite hypothesis testing: an approach built on intersection-union tests and Bayesian posterior probabilities	p. 83
Introduction	p. 83
Composite hypothesis testing	p. 84
Assessing the significance of a composite hypothesis test	p. 86
Measuring Bayesian significance evidence in composite hypothesis testing	p. 88
Combining posterior probabilities in a Bayesian IUT	p. 90
Issues and challenges	p. 91
Summary	p. 92
Software availability	p. 93
Frequentist and Bayesian error pooling methods for enhancing statistical power in small sample microarray data analysis	p. 95
Introduction	p. 95
Local pooled error test	p. 97
Empirical Bayes heterogeneous error model (HEM)	p. 103
Conclusion	p. 112
Significance testing for small microarray experiments	p. 113
Introduction	p. 113
Methods	p. 114
Data	p. 119
Results	p. 121
Discussion	p. 131
Appendix: Array preprocessing	p. 134
Comparison of meta-analysis to combined analysis of a replicated microarray study	p. 135
Introduction	p. 135
Study description	p. 136
Statistical analyses	p. 136
Results	p. 141
Discussion	p. 153
Alternative probe set definitions for combining microarray data across studies using different versions of Affymetrix oligonucleotide arrays	p. 157
Introduction	p. 157
Combining microarray data across studies and platforms	p. 158
Meta-analysis with Affymetrix oligonucleotide arrays	p. 161
Partial probe sets method	p. 162
Example 1: CAMDA 2003 lung cancer data	p. 163
Full-length transcript-based probe sets method	p. 168
Example 2: Lung cell line data	p. 171
Discussion	p. 174
Gene ontology-based meta-analysis of genome-scale experiments	p. 175
Introduction	p. 175
Ontologies	p. 175
The Gene Ontology	p. 176
Statistical methods	p. 182
Application to stem cell data	p. 189
Conclusions	p. 197
Combining Different Data Types	p. 199
Combining genomic data in human studies	p. 201
Introduction	p. 201
Genomic data integration in cancer	p. 202
Combining data from related technologies: cDNA arrays	p. 203
Combining data from different technologies	p. 206
In vivo/in vitro genomic data integration	p. 209
Software availability	p. 210
Discussion	p. 211
An overview of statistical approaches for expression trait loci mapping	p. 213
Introduction	p. 213
ETL mapping data and methods	p. 214
Evaluation of ETL mapping methods	p. 217
Discussion	p. 222
Incorporating GO annotation information in expression trait loci mapping	p. 225
Introduction	p. 225
Expression trait loci mapping	p. 226
Data	p. 228
Methodology	p. 230
Simulations	p. 233
Results	p. 238
Conclusions	p. 241
A misclassification model for inferring transcriptional regulatory networks	p. 243
Introduction	p. 243
Methods	p. 244
Simulation results	p. 250
Application to yeast cell cycle data	p. 253
Summary	p. 255
Data integration for the study of protein interactions	p. 259
Introduction	p. 259
Data sources	p. 261
Assessing the reliability of protein interaction data	p. 262
Protein function prediction using protein interaction data	p. 266
Discussion	p. 273
Gene trees, species trees, and species networks	p. 275
Introduction	p. 275
Gene tree incongruence	p. 277
Lineage sorting	p. 281
Gene duplication and loss	p. 284
Reticulate evolution	p. 286
Distinguishing lineage sorting from HGT	p. 290
Summary	p. 292
References	p. 295
Index	p. 329
Table of Contents provided by Ingram. All Rights Reserved.

Please wait while the item is added to your bag...

FREE SHIPPING

Rent More, Save More! Use code: BBRENTAL

5% off 1 book, 7% off 2 books, 10% off 3+ books

Meta-analysis and Combining Information in Genetics and Genomics

Summary

Table of Contents

Supplemental Materials

FREE SHIPPING

Rent More, Save More! Use code: BBRENTAL

5% off 1 book, 7% off 2 books, 10% off 3+ books

Meta-analysis and Combining Information in Genetics and Genomics

Summary

Table of Contents

Supplemental Materials

Digital License