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As researchers continue to make enormous progress in mapping disease genes, exciting, novel, and complex analyses have emerged. In Linkage Disequilibrium and Association Mapping: Analysis and Applications, scientists from around the world, who are leaders in this field, contribute their vast experience and expertise to produce a comprehensive and fascinating text for researchers and clinicians alike. The volume comprises four general sections: the first presents an overview and historical basis of the subject. The second section considers the developing methodology and recent findings from studies which have characterized the genome-wide linkage disequilibrium structure in enormous detail. The following section examines all aspects of disease association mapping methodology, and the final two chapters review the early successes in mapping genes involved in two of the most important human diseases: asthma and type 2 diabetes.

Linkage Disequilibrium. --- Chromosome Mapping. --- Genomics --- Gene mapping. --- Génomique --- Cartes chromosomiques --- Statistical methods. --- Méthodes statistiques --- Gene mapping --- Linkage Disequilibrium --- Chromosome Mapping --- Statistical methods --- Electronic books. -- local. --- Genomics -- Statistical methods. --- Genetic Techniques --- Genetic Linkage --- Investigative Techniques --- Genetic Phenomena --- Phenomena and Processes --- Analytical, Diagnostic and Therapeutic Techniques and Equipment --- Genetics --- Biology --- Health & Biological Sciences --- Chromosome mapping --- Genetic mapping --- Genome mapping --- Linkage mapping (Genetics) --- Mapping, Gene --- Genome research --- Genomes --- Research --- Molecular genetics --- Technique --- Human genetics. --- Cytology. --- Human Genetics. --- Cell Biology. --- Cell biology --- Cellular biology --- Cells --- Cytologists --- Heredity, Human --- Human biology --- Physical anthropology --- Genomics - Statistical methods

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Genomics --- Meta-Analysis --- Statistical methods --- Meta-analysis. --- Computational Biology --- Statistics as Topic --- Meta-Analysis as Topic. --- Medicine --- Psychometrics --- Social sciences --- Genome research --- Genomes --- Molecular genetics --- Statistical methods. --- methods. --- Research --- Evaluation --- Biomathematics. Biometry. Biostatistics --- Overviews, Clinical Trial --- Clinical Trial Overviews --- Data Pooling --- Clinical Trial Overview --- Data Poolings --- Meta Analysis as Topic --- Overview, Clinical Trial --- Clinical Trials as Topic --- Review Literature as Topic --- Models, Statistical --- Meta-analysis --- Meta-Analysis as Topic --- methods --- Genomics - Statistical methods

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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. Karl W. Broman is Professor in the Department of Biostatistics and Medical Informatics at the University of Wisconsin-Madison, and is the chief developer of R/qtl. Saunak Sen is Associate Professor in Residence in the Department of Epidemiology and Biostatistics and the Center for Bioinformatics and Molecular Biostatistics at the University of California, San Francisco.

Gene mapping. --- Genomics -- Statistical methods. --- Genomics --- Gene mapping --- Genetic Techniques --- Biology --- Genetic Loci --- Investigative Techniques --- Genome Components --- Biological Science Disciplines --- Analytical, Diagnostic and Therapeutic Techniques and Equipment --- Natural Science Disciplines --- Genome --- Disciplines and Occupations --- Genetic Structures --- Genetic Phenomena --- Phenomena and Processes --- Chromosome Mapping --- Genetics --- Methods --- Quantitative Trait Loci --- Health & Biological Sciences --- Statistical methods --- Statistical methods. --- Genome research --- Genomes --- Chromosome mapping --- Genetic mapping --- Genome mapping --- Linkage mapping (Genetics) --- Mapping, Gene --- Research --- Life sciences. --- Biochemistry. --- Animal genetics. --- Statistics. --- Life Sciences. --- Biochemistry, general. --- Statistics for Life Sciences, Medicine, Health Sciences. --- Animal Genetics and Genomics. --- Statistical analysis --- Statistical data --- Statistical science --- Mathematics --- Econometrics --- Biological chemistry --- Chemical composition of organisms --- Organisms --- Physiological chemistry --- Chemistry --- Medical sciences --- Biosciences --- Sciences, Life --- Science --- Composition --- Molecular genetics --- Technique --- Statistics . --- R (Computer program language). --- GNU-S (Computer program language) --- Domain-specific programming languages