Meta-analysis and Combining Information in Genetics and Genomics (Chapman & Hall/CRC Computational Biology Series)

Content: Pt. 0. Introductory Material -- 1. brief introduction to meta-analysis, genetics, and genomics / Darlene R. Goldstein and Rudy Guerra -- Pt I. Similar Data Types I: Genotype Data -- 2. Combining information across genome-wide linkage scans / Carol J. Etzel and Tracy J. Costello -- 3. Genome search meta-analysis (GSMA): a nonparametric method for meta-analysis of genome-wide linkage studies / Cathryn M. Lewis -- 4. Heterogeneity in meta-analysis of quantitative trait linkage studies / Hans C. van Houwelingen and Jeremie J. P. Lebrec -- 5. empirical Bayesian framework for QTL genome-wide scans / Kui Zhang ... [et al.] -- Pt. II. Similar Data Types II: Gene Expression Data -- 6. Composite hypothesis testing: an approach built on intersection-union tests and Bayesian posterior probabilities / Stephen Erickson, Kyoungmi Kim and David B. Allison -- 7. Frequentist and Bayesian error pooling methods for enhancing statistical power in small sample microarray data analysis / Jae K. Lee, Hyung Jun Cho and Michael O'Connell -- 8. Significance testing for small microarray experiments / Charles Kooperberg ... [et al.] -- 9. Comparison of meta-analysis to combined analysis of a replicated microarray study / Darlene R. Goldstein ... [et al.] -- 10. Alternative probe set definitions for combining microarray data across studies using different versions of Affymetrix oligonucleotide arrays / Jeffrey S. Morris ... [et al.] -- 11. Gene ontology-based meta-analysis of genome-scale experiments / Chad A. Shaw -- Pt. III. Combining Different Data Types -- 12. Combining genomic data in human studies / Debashis Ghosh, Daniel Rhodes and Arul Chinnaiyan -- 13. overview of statistical approaches for expression trait loci mapping / Christina Kendziorski and Meng Chen -- 14. Incorporating GO annotation information in expression trait loci mapping / J. Blair Christian and Rudy Guerra -- 15. misclassification model for inferring transcriptional regulatory networks / Ning Sun and Hongyu Zhao -- 16. Data integration for the study of protein interactions / Fengzhu Sun ... [et al.] -- 17. Gene trees, species trees, and species networks / Luay Nakhleh, Derek Ruths and Hideki Innan. Published Titles......Page 4 Dedication......Page 8 Contents......Page 10 Contributors......Page 16 Preface......Page 22 Part 0. Introductory Material......Page 26 CHAPTER 1: A brief introduction to meta-analysis,genetics and genomics......Page 28 Part I. Similar Data Types I: Genotype Data......Page 46 CHAPTER 2: Combining information across genome-wide linkage scans......Page 48 CHAPTER 3: Genome search meta-analysis (GSMA):a nonparametric method formeta-analysis of genome-wide linkage studies......Page 58 CHAPTER 4: Heterogeneity in meta-analysis of quantitative trait linkage studies......Page 74 CHAPTER 5: An empirical Bayesian framework for QTL genome-wide scans......Page 92 Part II. Similar Data Types II: Gene Expression Data......Page 106 CHAPTER 6: Composite hypothesis testing: anapproach built on intersection-uniontests and Bayesian posterior probabilities......Page 108 CHAPTER 7: Frequentist and Bayesian error pooling methods for enhancing statistical powerin small sample microarray data analysis......Page 120 CHAPTER 8: Significance testing for small microarray experiments......Page 138 CHAPTER 9: Comparison of meta-analysis tocombined analysis of a replicated microarray study......Page 160 CHAPTER 10: Alternative probe set definitions for combining microarray data across studies using different versions of Affymetrix oligonucleotide arrays......Page 182 CHAPTER 11: Gene ontology-based meta-analysis ofgenome-scale experiments......Page 200 Part III. Combining Different DataTypes......Page 224 CHAPTER 12: Combining genomic data in human studies......Page 226 CHAPTER 13: An overview of statistical approachesfor expression trait loci mapping......Page 238 CHAPTER 14: Incorporating GO annotation information in expression trait loci mapping......Page 250 CHAPTER 15: A misclassification model for inferring transcriptional regulatory networks......Page 268 CHAPTER 16: Data integration for the study of protein interactions......Page 284 CHAPTER 17: Gene trees, species trees, and species networks......Page 300 References......Page 320 Back cover......Page 354

Novel Techniques for Analyzing and Combining Data from Modern Biological Studies

Broadens the Traditional Definition of Meta-Analysis

With the diversity of data and meta-data now available, there is increased interest in analyzing multiple studies beyond statistical approaches of formal meta-analysis. Covering an extensive range of quantitative information combination methods, Meta-analysis and Combining Information in Genetics and Genomics looks at how to analyze multiple studies from a broad perspective.

After presenting the basic ideas and tools of meta-analysis, the book addresses the combination of similar data types: genotype data from genome-wide linkage scans and data derived from microarray gene expression experiments. The expert contributors show how some data combination problems can arise even within the same basic framework and offer solutions to these problems. They also discuss the combined analysis of different data types, giving readers an opportunity to see data combination approaches in action across a wide variety of genome-scale investigations.

As heterogeneous data sets become more common, biological understanding will be significantly aided by jointly analyzing such data using fundamentally sound statistical methodology. This book provides many novel techniques for analyzing data from modern biological studies that involve multiple data sets, either of the same type or multiple data sources.

With the diversity of data and meta-data now available, there is increased interest in analyzing multiple studies beyond statistical approaches of formal meta-analysis. Covering an extensive range of quantitative information combination methods, this book looks at how to analyze multiple studies from a broad perspective. As heterogeneous data sets become more common, biological understanding will be significantly aided by jointly analyzing such data using fundamentally sound statistical methodology. This book provides many novel techniques for analyzing data from modern biological studies that involve multiple data sets, either of the same type or multiple data sources