Meta-analysis and Combining Information in Genetics and Genomics Book

Part 0 Introductory Material 1

1 A brief introduction to meta-analysis, genetics and genomics Darlene R. Goldstein Rudy Guerra 3

1.1 Introduction 3

1.2 Combining information 4

1.3 Genetics 8

1.4 Genomics 12

1.5 Combining information in genetics and genomics 16

Part I Similar Data Types I: Genotype Data 21

2 Combining information across genome-wide linkage scans Carol J. Etzel Tracy J. Costello 23

2.1 Introduction 23

2.2 Meta-analysis of genome-wide linkage scans 23

2.3 Choice of meta-analysis method 28

2.4 Discussion 31

2.5 Appendix A 32

3 Genome search meta-analysis (GSMA): a nonparametric method for meta-analysis of genome-wide linkage studies Cathryn M. Lewis 33

3.1 Introduction 33

3.2 GSMA: Genome Search Meta-Analysis method 34

3.3 Power to detect linkage using GSMA 40

3.4 Extensions of GSMA 42

3.5 Limitations of the GSMA 43

3.6 Disease studies using GSMA 44

3.7 GSMA software 46

3.8 Conclusions 46

4 Heterogeneity in meta-analysis of quantitative trait linkage studies Hans C. van Houwelingen Jérémie J. P. Lebrec 49

4.1 Introduction 49

4.2 The classical meta-analytic method 51

4.3 Extracting relevant information from individual studies 54

4.4 Example 58

4.5 Discussion 63

5 An empirical Bayesian framework for QTL genome-wide scans Kui Zhang Howard Wiener T. Mark Beasley Christopher I. Amos David B. Allison 67

5.1 Introduction 67

5.2 Methods 69

5.3 Results 72

5.4 Discussion 79

Part II Similar Data Types II: Gene Expression Data 81

6 Composite hypothesis testing: an approach built on intersection-union tests and Bayesian posterior probabilities Stephen Erickson Kyoungmi Kim David B.Allison 83

6.1 Introduction 83

6.2 Composite hypothesis testing 84

6.3 Assessing the significance of a composite hypothesis test 86

6.4 Measuring Bayesian significance evidence in composite hypothesis testing 88

6.5 Combining posterior probabilities in a Bayesian IUT 90

6.6 Issues and challenges 91

6.7 Summary 92

6.8 Software availability 93

7 Frequentist and Bayesian error pooling methods for enhancing statistical power in small sample microarray data analysis Jae K. Lee HyungJun Cho Michael O'Connell 95

7.1 Introduction 95

7.2 Local pooled error test 97

7.3 Empirical Bayes heterogeneous error model (HEM) 103

7.4 Conclusion 112

8 Significance testing for small microarray experiments Charles Kooperberg Aaron Aragaki Charles C. Carey Suzannah Rutherford 113

8.1 Introduction 113

8.2 Methods 114

8.3 Data 119

8.4 Results 121

8.5 Discussion 131

8.6 Appendix: Array preprocessing 134

9 Comparison of meta-analysis to combined analysis of a replicated microarray study Darlene R. Goldstein Mauro Delorenzi Ruth Luthi-Carter Thierry Sengstag 135

9.1 Introduction 135

9.2 Study description 136

9.3 Statistical analyses 136

9.4 Results 141

9.5 Discussion 153

10 Alternative probe set definitions for combining microarray data across studies using different versions of Affymetrix oligonucleotide arrays Jeffrey S. Morris Chunlei Wu Kevin R. Coombes Keith A. Baggerly Jing Wang Li Zhang 157

10.1 Introduction 157

10.2 Combining microarray data across studies and platforms 158

10.3 Meta-analysis with Affymetrix oligonucleotide arrays 161

10.4 Partial probe sets method 162

10.5 Example 1: CAMDA 2003 lung cancer data 163

10.6 Full-length transcript-based probe sets method 168

10.7 Example 2: Lung cell line data 171

10.8 Discussion 174

11 Gene ontology-based meta-analysis of genome-scale experiments Chad A. Shaw 175

11.1 Introduction 175

11.2 Ontologies 175

11.3 The Gene Ontology 176

11.4 Statistical methods 182

11.5 Application to stem cell data 189

11.6 Conclusions 197

Part III Combining Different Data Types 199

12 Combining genomic data in human studies Debashis Ghosh Daniel Rhodes Arul Chinnaiyan 201

12.1 Introduction 201

12.2 Genomic data integration in cancer 202

12.3 Combining data from related technologies: cDNA arrays 203

12.4 Combining data from different technologies 206

12.5 In vivo/in vitro genomic data integration 209

12.6 Software availability 210

12.7 Discussion 211

13 An overview of statistical approaches for expression trait loci mapping Christina Kendziorski Meng Chen 213

13.1 Introduction 213

13.2 ETL mapping data and methods 214

13.3 Evaluation of ETL mapping methods 217

13.4 Discussion 222

14 Incorporating GO annotation information in expression trait loci mapping J. Blair Christian Rudy Guerra 225

14.1 Introduction 225

14.2 Expression trait loci mapping 226

14.3 Data 228

14.4 Methodology 230

14.5 Simulations 233

14.6 Results 238

14.7 Conclusions 241

15 A misclassification model for inferring transcriptional regulatory networks Ning Sun Hongyu Zhao 243

15.1 Introduction 243

15.2 Methods 244

15.3 Simulation results 250

15.4 Application to yeast cell cycle data 253

15.5 Summary 255

16 Data integration for the study of protein interactions Fengzhu Sun Ting Chen Minghua Deng Hyunju Lee Zhidong Tu 259

16.1 Introduction 259

16.2 Data sources 261

16.3 Assessing the reliability of protein interaction data 262

16.4 Protein function prediction using protein interaction data 266

16.5 Discussion 273

17 Gene trees, species trees, and species networks Luay Nakhleh Derek Ruths Hideki Innan 275

17.1 Introduction 275

17.2 Gene tree incongruence 277

17.3 Lineage sorting 281

17.4 Gene duplication and loss 284

17.5 Reticulate evolution 286

17.6 Distinguishing lineage sorting from HGT 290

17.7 Summary 292

References 295

Index 329