Bioinformatics for geneticists : a bioinformatics primer for the analysis of genetic data

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 vi CONTENTS 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 CONTENTS vii 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 viii CONTENTS 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 CONTENTS ix 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 x CONTENTS 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 Ulvsb¨ack 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