A Guide to QTL Mapping with R/qtl (eBook)

Preface 6
Contents 9
Introduction 14
Why perform a QTL experiment? 15
Crosses and data 16
Mouse hypertension data as an example 21
Central statistical problems 22
Models for recombination 25
Models connecting genotype and phenotype 27
About R and R/qtl 30
Other software 31
Work flow 32
Further reading 33
Importing and simulating data 34
Importing data 35
Comma-delimited files 35
MapMaker/QTL 43
QTL Cartographer 44
Map Manager QTX 45
Exporting data 45
Example data 46
Data summaries 47
Simulating data 49
Additive models 50
More complex models 53
Internal data structure 55
Experimental cross 55
Genetic map 58
Further reading 59
Data checking 60
Phenotypes 60
Segregation distortion 63
Compare individuals' genotypes 65
Check marker order 66
Pairwise recombination fractions 66
Rippling marker order 73
Estimate genetic map 77
Identifying genotyping errors 79
Counting crossovers 81
Missing genotype information 83
Summary 85
Further reading 86
Single-QTL analysis 87
Marker regression 87
Interval mapping 92
Standard interval mapping 92
Haley--Knott regression 98
Extended Haley--Knott regression 100
Multiple imputation 103
Comparison of methods 106
Significance thresholds 116
The X chromosome 120
Analysis 121
Significance thresholds 125
Example 126
Interval estimates of QTL location 130
QTL effects 134
Multiple phenotypes 139
Summary 143
Further reading 144
Non-normal phenotypes 146
Nonparametric interval mapping 147
Binary traits 150
Two-part model 152
Other extensions 157
Summary 161
Further reading 161
Experimental design and power 163
Phenotypes and covariates 163
Strains and strain surveys 164
Theory 165
Variance attributable to a locus 165
Residual error variance 167
Information content 168
Examples with R/qtlDesign 169
Functions 169
Choosing a cross 170
Genotyping strategies 174
Phenotyping strategies 176
Fine mapping 177
Other experimental populations 178
Estimating power and precision by simulation 180
Summary 186
Further reading 187
Working with covariates 188
Additive covariates 188
QTL covariate interactions 199
Covariates with non-normal phenotypes 207
Composite interval mapping 214
Summary 219
Further reading 219
Two-dimensional, two-QTL scans 221
The normal model 222
Binary traits 236
The X chromosome 240
Covariates 244
Summary 247
Further reading 247
Fit and exploration of multiple-QTL models 248
Model selection 249
Class of models 251
Model fit 253
Model search 255
Model comparison 257
Further discussion 261
Bayesian QTL mapping 262
Multiple QTL mapping in R/qtl 265
makeqtl and fitqtl 266
refineqtl 270
addint 273
addqtl 274
addpair 276
Manipulating qtl objects 279
stepwiseqtl 281
Summary 288
Further reading 288
Case study I 290
Diagnostics 291
Initial cross 298
Combined data 307
Discussion 318
Case study II 320
Diagnostics 321
Initial QTL analyses 330
QTL covariate interactions 346
Discussion 360
Installing R and R/qtl 362
Installing R 362
Windows 362
Mac OS X 363
Unix/Linux 363
Installing R/qtl 364
Optimizing the R environment 365
Working directories 365
Documentation 366
Email lists 367
List of functions in R/qtl 368
QTL mapping data sets 372
Hidden Markov models for QTL mapping 377
Specification of the model 378
The backcross 379
The intercross 380
QTL genotype probabilities 380
Simulation of QTL genotypes 382
Joint QTL genotype probabilities 383
The Viterbi algorithm 384
Estimation of intermarker distances 385
Detection of genotyping errors 386
A practical issue 387
Further reading 387
References 389
Index 396