Handbook of Statistical Genomics (4TH ed.)
Jacket Description/Back: A timely update of a highly popular handbook on statistical genomics This new, two-volume edition of a classic text provides a thorough introduction to statistical genomics, a vital resource for advanced graduate students,...
|
Jacket Description/Back: A timely update of a highly popular handbook on statistical genomics This new, two-volume edition of a classic text provides a thorough introduction to statistical genomics, a vital resource for advanced graduate students, early-career researchers and new entrants to the field. It introduces new and updated information on developments that have occurred since the 3rd edition. Widely regarded as the reference work in the field, it features new chapters focusing on statistical aspects of data generated by new sequencing technologies, including sequence-based functional assays. It expands on previous coverage of the many processes between genotype and phenotype, including gene expression and epigenetics, as well as metabolomics. It also examines population genetics and evolutionary models and inference, with new chapters on the multi-species coalescent, admixture and ancient DNA, as well as genetic association studies including causal analyses and variant interpretation. The Handbook of Statistical Genomics focuses on explaining the main ideas, analysis methods and algorithms, citing key recent and historic literature for further details and references. It also includes a glossary of terms, acronyms and abbreviations, and features extensive cross-referencing between chapters, tying the different areas together. With heavy use of up-to-date examples and references to web-based resources, this continues to be a must-have reference in a vital area of research.
The Handbook of Statistical Genomics is an excellent introductory text for advanced graduate students and early-career researchers involved in statistical genetics. Biographical Note: DAVID J. BALDING, PhD, is Professor of Statistical Genetics at the University of Melbourne and holds an honorary appointment at University College London. IDA MOLTKE, PhD, is an Assistant Professor at the Department of Biology, University of Copenhagen. JOHN MARIONI, PhD, is a Group Leader at the European Bioinformatics Institute and the Cancer Research UK Cambridge Institute. Table of Contents: Volume 1 List of Contributors xxiii Editors' Preface to the Fourth Edition xxvii Glossary xxix Abbreviations and Acronyms xxxix 1 Statistical Modeling and Inference in Genetics 1 2 Linkage Disequilibrium, Recombination and Haplotype Structure 51 3 Haplotype Estimation and Genotype Imputation 87 4 Mathematical Models in Population Genetics 115 5 Coalescent Theory 145 6 Phylogeny Estimation Using Likelihood-Based Methods 177 7 The Multispecies Coalescent 219 8 Population Structure, Demography and Recent Admixture 247 9 Statistical Methods to Detect Archaic Admixture and Identify Introgressed Sequences 275 10 Population Genomic Analyses of DNA from Ancient Remains 295 11 Sequence Covariation Analysis in Biological Polymers 325 12 Probabilistic Models for the Study of Protein Evolution 347 13 Adaptive Molecular Evolution 369 14 Detecting Natural Selection 397 15 Evolutionary Quantitative Genetics 421 16 Conservation Genetics 457 17 Statistical Methods for Plant Breeding 501 18 Forensic Genetics 531 Volume 2 19 Ethical Issues in Statistical Genetics 551 20 Descent-Based Gene Mapping in Pedigrees and Populations 573 21 Genome-Wide Association Studies 597 22 Replication and Meta-analysis of Genome-Wide Association Studies 631 23 Inferring Causal Relationships between Risk Factors and Outcomes Using Genetic Variation 651 24 Improving Genetic Association Analysis through Integration of Functional Annotations of the Human Genome 679 25 Inferring Causal Associations between Genes and Disease via the Mapping of Expression Quantitative Trait Loci 697 26 Statistical Methods for Single-Cell RNA-Sequencing 735 27 Variant Interpretation and Genomic Medicine 761 28 Prediction of Phenotype from DNA Variants 799 29 Disease Risk Models 815 30 Bayesian Methods for Gene Expression Analysis 843 31 Modelling Gene Expression Dynamics with Gaussian Process Inference 879 32 Modelling Non-homogeneous Dynamic Bayesian Networks with Piecewise Linear Regression Models 899 33 DNA Methylation 933 34 Statistical Methods in Metabolomics 949 35 Statistical and Computational Methods in Microbiome and Metagenomics 977 36 Bacterial Population Genomics 997 Reference Author Index 1021 Subject Index 1109 Publisher Marketing: A timely update of a highly popular handbook on statistical genomics This new, two-volume edition of a classic text provides a thorough introduction to statistical genomics, a vital resource for advanced graduate students, early-career researchers and new entrants to the field. It introduces new and updated information on developments that have occurred since the 3rd edition. Widely regarded as the reference work in the field, it features new chapters focusing on statistical aspects of data generated by new sequencing technologies, including sequence-based functional assays. It expands on previous coverage of the many processes between genotype and phenotype, including gene expression and epigenetics, as well as metabolomics. It also examines population genetics and evolutionary models and inference, with new chapters on the multi-species coalescent, admixture and ancient DNA, as well as genetic association studies including causal analyses and variant interpretation. The Handbook of Statistical Genomics focuses on explaining the main ideas, analysis methods and algorithms, citing key recent and historic literature for further details and references. It also includes a glossary of terms, acronyms and abbreviations, and features extensive cross-referencing between chapters, tying the different areas together. With heavy use of up-to-date examples and references to web-based resources, this continues to be a must-have reference in a vital area of research.
The Handbook of Statistical Genomics is an excellent introductory text for advanced graduate students and early-career researchers involved in statistical genetics. |