Broman / Sen A Guide to QTL Mapping with R/qtl

1. Auflage 2009
ISBN: 978-0-387-92125-9
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 400 Seiten

Reihe: Statistics for Biology and Health

Quantitative trait locus (QTL) mapping is used to discover the genetic and molecular architecture underlying complex quantitative traits. It has important applications in agricultural, evolutionary, and biomedical research. R/qtl is an extensible, interactive environment for QTL mapping in experimental crosses. It is implemented as a package for the widely used open source statistical software R and contains a diverse array of QTL mapping methods, diagnostic tools for ensuring high-quality data, and facilities for the fit and exploration of multiple-QTL models, including QTL x QTL and QTL x environment interactions. This book is a comprehensive guide to the practice of QTL mapping and the use of R/qtl, including study design, data import and simulation, data diagnostics, interval mapping and generalizations, two-dimensional genome scans, and the consideration of complex multiple-QTL models. Two moderately challenging case studies illustrate QTL analysis in its entirety.

The book alternates between QTL mapping theory and examples illustrating the use of R/qtl. Novice readers will find detailed explanations of the important statistical concepts and, through the extensive software illustrations, will be able to apply these concepts in their own research. Experienced readers will find details on the underlying algorithms and the implementation of extensions to R/qtl. There are 150 figures, including 90 in full color.

Autoren/Hrsg.

Broman, Karl W.

Sen, Saunak

Inhaltsverzeichnis

1;Preface;6
2;Contents;9
3;Introduction;14
3.1;Why perform a QTL experiment?;15
3.2;Crosses and data;16
3.2.1;Mouse hypertension data as an example;21
3.3;Central statistical problems;22
3.3.1;Models for recombination;25
3.3.2;Models connecting genotype and phenotype;27
3.4;About R and R/qtl;30
3.5;Other software;31
3.6;Work flow;32
3.7;Further reading;33
4;Importing and simulating data;34
4.1;Importing data;35
4.1.1;Comma-delimited files;35
4.1.2;MapMaker/QTL;43
4.1.3;QTL Cartographer;44
4.1.4;Map Manager QTX;45
4.2;Exporting data;45
4.3;Example data;46
4.4;Data summaries;47
4.5;Simulating data;49
4.5.1;Additive models;50
4.5.2;More complex models;53
4.6;Internal data structure;55
4.6.1;Experimental cross;55
4.6.2;Genetic map;58
4.7;Further reading;59
5;Data checking;60
5.1;Phenotypes;60
5.2;Segregation distortion;63
5.3;Compare individuals' genotypes;65
5.4;Check marker order;66
5.4.1;Pairwise recombination fractions;66
5.4.2;Rippling marker order;73
5.4.3;Estimate genetic map;77
5.5;Identifying genotyping errors;79
5.6;Counting crossovers;81
5.7;Missing genotype information;83
5.8;Summary;85
5.9;Further reading;86
6;Single-QTL analysis;87
6.1;Marker regression;87
6.2;Interval mapping;92
6.2.1;Standard interval mapping;92
6.2.2;Haley--Knott regression;98
6.2.3;Extended Haley--Knott regression;100
6.2.4;Multiple imputation;103
6.2.5;Comparison of methods;106
6.3;Significance thresholds;116
6.4;The X chromosome;120
6.4.1;Analysis;121
6.4.2;Significance thresholds;125
6.4.3;Example;126
6.5;Interval estimates of QTL location;130
6.6;QTL effects;134
6.7;Multiple phenotypes;139
6.8;Summary;143
6.9;Further reading;144
7;Non-normal phenotypes;146
7.1;Nonparametric interval mapping;147
7.2;Binary traits;150
7.3;Two-part model;152
7.4;Other extensions;157
7.5;Summary;161
7.6;Further reading;161
8;Experimental design and power;163
8.1;Phenotypes and covariates;163
8.2;Strains and strain surveys;164
8.3;Theory;165
8.3.1;Variance attributable to a locus;165
8.3.2;Residual error variance;167
8.3.3;Information content;168
8.4;Examples with R/qtlDesign;169
8.4.1;Functions;169
8.4.2;Choosing a cross;170
8.4.3;Genotyping strategies;174
8.4.4;Phenotyping strategies;176
8.4.5;Fine mapping;177
8.5;Other experimental populations;178
8.6;Estimating power and precision by simulation;180
8.7;Summary;186
8.8;Further reading;187
9;Working with covariates;188
9.1;Additive covariates;188
9.2;QTL covariate interactions;199
9.3;Covariates with non-normal phenotypes;207
9.4;Composite interval mapping;214
9.5;Summary;219
9.6;Further reading;219
10;Two-dimensional, two-QTL scans;221
10.1;The normal model;222
10.2;Binary traits;236
10.3;The X chromosome;240
10.4;Covariates;244
10.5;Summary;247
10.6;Further reading;247
11;Fit and exploration of multiple-QTL models;248
11.1;Model selection;249
11.1.1;Class of models;251
11.1.2;Model fit;253
11.1.3;Model search;255
11.1.4;Model comparison;257
11.1.5;Further discussion;261
11.2;Bayesian QTL mapping;262
11.3;Multiple QTL mapping in R/qtl;265
11.3.1;makeqtl and fitqtl;266
11.3.2;refineqtl;270
11.3.3;addint;273
11.3.4;addqtl;274
11.3.5;addpair;276
11.3.6;Manipulating qtl objects;279
11.3.7;stepwiseqtl;281
11.4;Summary;288
11.5;Further reading;288
12;Case study I;290
12.1;Diagnostics;291
12.2;Initial cross;298
12.3;Combined data;307
12.4;Discussion;318
13;Case study II;320
13.1;Diagnostics;321
13.2;Initial QTL analyses;330
13.3;QTL covariate interactions;346
13.4;Discussion;360
14;Installing R and R/qtl;362
14.1;Installing R;362
14.1.1;Windows;362
14.1.2;Mac OS X;363
14.1.3;Unix/Linux;363
14.2;Installing R/qtl;364
14.3;Optimizing the R environment;365
14.4;Working directories;365
14.5;Documentation;366
14.6;Email lists;367
15;List of functions in R/qtl;368
16;QTL mapping data sets;372
17;Hidden Markov models for QTL mapping;377
17.1;Specification of the model;378
17.1.1;The backcross;379
17.1.2;The intercross;380
17.2;QTL genotype probabilities;380
17.3;Simulation of QTL genotypes;382
17.4;Joint QTL genotype probabilities;383
17.5;The Viterbi algorithm;384
17.6;Estimation of intermarker distances;385
17.7;Detection of genotyping errors;386
17.8;A practical issue;387
17.9;Further reading;387
18;References;389
19;Index;396