Bioinformatics for Geneticists: A Bioinformatics Primer for the Analysis of Genetic Data 2nd edition [Hardback]

Foreword xi

Preface xv

Contributors xvii

Glossary xix

Section I An Introduction to Bioinformatics for The Geneticist 1

1 Bioinformatics challenges for the geneticist 3
Michael R. Barnes

1.1 Introduction 3

1.2 The role of bioinformatics in genetics research 4

1.3 Genetics in the post-genome era 5

1.4 Conclusions 12

References 15

2 Managing and manipulating genetic data 17
Karl W. Broman and Simon C. Heath

2.1 Introduction 17

2.2 Basic principles 18

2.3 Data entry and storage 20

2.4 Data manipulation 21

2.5 Examples of code 22

2.6 Resources 30

2.7 Summary 31

References 31

Section II Mastering Genes, Genomes and Genetic Variation Data 33

3 The HapMap A haplotype map of the human genome 35
Ellen M. Brown and Bryan J. Barratt

3.1 Introduction 35

3.2 Accessing the data 38

3.3 Application of HapMap data in association studies 42

3.4 Future perspectives 54

References 54

4 Assembling a view of the human genome 59
Colin A. M. Semple

4.1 Introduction 59

4.2 Genomic sequence assembly 60

4.3 Annotation from a distance: the generalities 64

4.4 Annotation up close and personal: the specifics 70

4.5 Annotation: the next generation 78

References 80

5 Finding, delineating and analysing genes 85
Christopher Southan and Michael R. Barnes

5.1 Introduction 85

5.2 Why learn to predict and analyse genes in the complete genome era? 86

5.3 The evidence cascade for gene products 88

5.4 Dealing with the complexities of gene models 95

5.5 Locating known genes in the human genome 97

5.6 Genome portal inspection 100

5.7 Analysing novel genes 101

5.8 Conclusions and prospects 102

References 103

6 Comparative genomics 105
Martin S. Taylor and Richard R. Copley

6.1 Introduction 105

6.2 The genomic landscape 106

6.3 Concepts 109

6.4 Practicalities 113

6.5 Technology 118

6.6 Applications 132

6.7 Challenges and future directions 137

6.8 Conclusion 138

References 139

Section III Bioinformatics for Genetic Study Design and Analysis 145

7 Identifying mutations in single gene disorders 147
David P. Kelsell, Diana Blaydon and Charles A. Mein

7.1 Introduction 147

7.2 Clinical ascertainment 147

7.3 Genome-wide mapping of monogenic diseases 148

7.4 The nature of mutation in monogenic diseases 152

7.5 Considering epigenetic effects in mendelian traits 160

7.6 Summary 162

References 162

8 From Genome Scan to Culprit Gene 165
Ian C. Gray

8.1 Introduction 165

8.2 Theoretical and practical considerations 166

8.3 A stepwise approach to locus refinement and candidate gene
identification 176

8.4 Conclusion 180

8.5 A list of the software tools and Web links mentioned in this chapter
181

References 182

9 Integrating Genetics, Genomics and Epigenomics to Identify Disease Genes
185
Michael R. Barnes

9.1 Introduction 185

9.2 Dealing with the (draft) human genome sequence 186

9.3 Progressing loci of interest with genomic information 187

9.4 In silico characterization of the IBD5 locus a case study 191

9.5 Drawing together biological rationale hypothesis building 209

9.6 Identification of potentially functional polymorphisms 211

9.7 Conclusions 212

References 213

10 Tools for statistical genetics 217
Aruna Bansal, Charlotte Vignal and Ralph McGinnis

10.1 Introduction 217

10.2 Linkage analysis 217

10.3 Association analysis 223

10.4 Linkage disequilibrium 229

10.5 Quantitative trait locus (QTL) mapping in experimental crosses 235

10.6 Closing remarks 239

References 241

Section IV Moving From Associated Genes to Disease Alleles 247

11 Predictive functional analysis of polymorphisms: An overview 249
Mary Plumpton and Michael R. Barnes

11.1 Introduction 249

11.2 Principles of predictive functional analysis of polymorphisms 252

11.3 The anatomy of promoter regions and regulatory elements 256

11.4 The anatomy of genes 258

11.5 Pseudogenes and regulatory mRNA 266

11.6 Analysis of novel regulatory elements and motifs in nucleotide
sequences 266

11.7 Functional analysis of non-synonymous coding polymorphisms 268

11.8 Integrated tools for functional analysis of genetic variation 273

11.9 A note of caution on the prioritization of in silico predictions for
further laboratory investigation 275

11.10 Conclusions 275

References 276

12 Functional in silico analysis of gene regulatory polymorphism 281
Chaolin Zhang, Xiaoyue Zhao, Michael Q. Zhang

12.1 Introduction 281

12.2 Predicting regulatory regions 282

12.3 Modelling and predicting transcription factor-binding sites 288

12.4 Predicting regulatory elements for splicing regulation 295

12.5 Evaluating the functional importance of regulatory polymorphisms 300

References 302

13 Amino-acid properties and consequences of substitutions 311
Matthew J. Betts and Robert B. Russell

13.1 Introduction 311

13.2 Protein features relevant to amino-acid behaviour 312

13.3 Amino-acid classifications 316

13.4 Properties of the amino acids 318

13.5 Amino-acid quick reference 321

13.6 Studies of how mutations affect function 334

13.7 A summary of the thought process 339

References 340

14 Non-coding RNA bioinformatics 343
James R. Brown, Steve Deharo, Barry Dancis, Michael R. Barnes and Philippe
Sanseau

14.1 Introduction 343

14.2 The non-coding (nc) RNA universe 344

14.3 Computational analysis of ncRNA 349

14.4 ncRNA variation in disease 356

14.5 Assessing the impact of variation in ncRNA 362

14.6 Data resources to support small ncRNA analysis 363

14.7 Conclusions 363

References 364

Section V Analysis at the Genetic and Genomic Data Interface 369

15 What are microarrays? 371
Catherine A. Ball and Gavin Sherlock

15.1 Introduction 371

15.2 Principles of the application of microarray technology 373

15.3 Complementary approaches to microarray analysis 377

15.4 Differences between data repository and research database 377

15.5 Descriptions of freely available research database packages 377

References 385

16 Combining quantitative trait and gene-expression data 389
Elissa J. Chesler

16.1 Introduction: the genetic regulation of endophenotypes 389

16.2 Transcript abundance as a complex phenotype 390

16.3 Scaling up genetic analysis and mapping models for microarrays 394

16.4 Genetic correlation analysis 397

16.5 Systems genetic analysis 400

16.6 Using expression QTLs to identify candidate genes for the regulation of
complex phenotypes 403

16.7 Conclusions 408

References 408

17 Bioinformatics and cancer genetics 413
Joel Greshock

17.1 Introduction 413

17.2 Cancer genomes 414

17.3 Approaches to studying cancer genetics 415

17.4 General resources for cancer genetics 418

17.5 Cancer genes and mutations 420

17.6 Copy number alterations in cancer 425

17.7 Loss of heterozygosity in cancer 431

17.8 Gene-expression data in cancer 432

17.9 Multiplatform gene target identification 435

17.10 The epigenetics of cancer 438

17.11 Tumour modelling 438

17.12 Conclusions 439

References 439

18 Needle in a haystack? Dealing with 500 000

SNP genome scans 447
Michael R. Barnes and Paul S. Derwent

18.1 Introduction 447

18.2 Genome scan analysis issues 449

18.3 Ultra-high-density genome-scanning technologies 459

18.4 Bioinformatics for genome scan analysis 469

18.5 Conclusions 489

References 490

19 A bioinformatics perspective on genetics in drug discovery and
development 495
Christopher Southan, Magnus Ulvsbäck and Michael R. Barnes

19.1 Introduction 495

19.2 Target genetics 498

19.3 Pharmacogenetics (PGx) 508

19.4 Conclusions: toward personalized medicine 525

References 525

Appendix I 529

Appendix II 531

Index 537

Michael R. Barnes: Bioinformatics, GlaxoSmithKline Pharmaceuticals, UK