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<p><b>Volume 1</b></p> <p>List of Contributors xxiii</p> <p>Editors’ Preface to the Fourth Edition xxvii</p> <p>Glossary xxix</p> <p>Abbreviations and Acronyms xxxix</p> <p>1 Statistical Modeling and Inference in Genetics 1<br /><i>Daniel Wegmann and Christoph Leuenberger</i></p> <p>2 Linkage Disequilibrium, Recombination and Haplotype Structure 51<br /><i>Gil McVean and Jerome Kelleher</i></p> <p>3 Haplotype Estimation and Genotype Imputation 87<br /><i>Jonathan Marchini</i></p> <p>4 Mathematical Models in Population Genetics 115<br /><i>Nick Barton and Alison Etheridge</i></p> <p>5 Coalescent Theory 145<br /><i>Magnus Nordborg</i></p> <p>6 Phylogeny Estimation Using Likelihood-Based Methods 177<br /><i>John P. Huelsenbeck</i></p> <p>7 The Multispecies Coalescent 219<br /><i>Laura Kubatko</i></p> <p>8 Population Structure, Demography and Recent Admixture 247<br /><i>G. Hellenthal</i></p> <p>9 Statistical Methods to Detect Archaic Admixture and Identify Introgressed Sequences 275<br /><i>Liming Li and Joshua M. Akey</i></p> <p>10 Population Genomic Analyses of DNA from Ancient Remains 295<br /><i>Torsten Gunther and Mattias Jakobsson</i></p> <p>11 Sequence Covariation Analysis in Biological Polymers 325<br /><i>William R. Taylor, Shaun Kandathil, and David T. Jones</i></p> <p>12 Probabilistic Models for the Study of Protein Evolution 347<br /><i>Umberto Perron, Iain H. Moal, Jeffrey L. Thorne, and Nick Goldman</i></p> <p>13 Adaptive Molecular Evolution 369<br /><i>Ziheng Yang</i></p> <p>14 Detecting Natural Selection 397<br /><i>Aaron J. Stern and Rasmus Nielsen</i></p> <p>15 Evolutionary Quantitative Genetics 421<br /><i>Bruce Walsh and Michael B. Morrissey</i></p> <p>16 Conservation Genetics 457<br /><i>Mark Beaumont and Jinliang Wang</i></p> <p>17 Statistical Methods for Plant Breeding 501<br /><i>Ian Mackay, Hans-Peter Piepho, and Antonio Augusto Franco Garcia</i></p> <p>18 Forensic Genetics 531<br /><i>B.S.Weir</i></p> <p><b>Volume 2</b></p> <p>19 Ethical Issues in Statistical Genetics 551<br /><i>Susan E. Wallace and Richard Ashcroft</i></p> <p>20 Descent-Based Gene Mapping in Pedigrees and Populations 573<br /><i>E.A. Thompson</i></p> <p>21 Genome-Wide Association Studies 597<br /><i>Andrew P. Morris and Lon R. Cardon</i></p> <p>22 Replication and Meta-analysis of Genome-Wide Association Studies 631<br /><i>Frank Dudbridge and Paul Newcombe</i></p> <p>23 Inferring Causal Relationships between Risk Factors and Outcomes Using Genetic Variation 651<br /><i>Stephen Burgess, Christopher N. Foley, and Verena Zuber</i></p> <p>24 Improving Genetic Association Analysis through Integration of Functional Annotations of the Human Genome 679<br /><i>Qiongshi Lu and Hongyu Zhao</i></p> <p>25 Inferring Causal Associations between Genes and Disease via the Mapping of Expression Quantitative Trait Loci 697<br /><i>Solveig K. Sieberts and Eric E. Schadt</i></p> <p>26 Statistical Methods for Single-Cell RNA-Sequencing 735<br /><i>Tallulah S. Andrews, Vladimir Yu. Kiselev, and Martin Hemberg</i></p> <p>27 Variant Interpretation and Genomic Medicine 761<br /><i>K. Carss, D. Goldstein, V. Aggarwal, and S. Petrovski</i></p> <p>28 Prediction of Phenotype from DNA Variants 799<br /><i>M.E. Goddard, T.H.E. Meuwissen, and H.D. Daetwyler</i></p> <p>29 Disease Risk Models 815<br /><i>Allison Meisner and Nilanjan Chatterjee</i></p> <p>30 Bayesian Methods for Gene Expression Analysis 843<br /><i>Alex Lewin, Leonardo Bottolo, and Sylvia Richardson</i></p> <p>31 Modelling Gene Expression Dynamics with Gaussian Process Inference 879<br /><i>Magnus Rattray, Jing Yang, Sumon Ahmed, and Alexis Boukouvalas</i></p> <p>32 Modelling Non-homogeneous Dynamic Bayesian Networks with Piecewise Linear Regression Models 899<br /><i>Marco Grzegorczyk and Dirk Husmeier</i></p> <p>33 DNA Methylation 933<br /><i>Kasper D. Hansen, Kimberly D. Siegmund, and Shili Lin</i></p> <p>34 Statistical Methods in Metabolomics 949<br /><i>Timothy M.D. Ebbels, Maria De Iorio, and David A. Stephens</i></p> <p>35 Statistical and Computational Methods in Microbiome and Metagenomics 977<br /><i>Hongzhe Li</i></p> <p>36 Bacterial Population Genomics 997<br /><i>Jukka Corander, Nicholas J. Croucher, Simon R. Harris, John A. Lees, and Gerry Tonkin-Hill</i></p> <p>Reference Author Index 1021</p> <p>Subject Index 1109</p>

<p><b>DAVID J. BALDING, PhD,</b> is Professor of Statistical Genetics at the University of Melbourne and holds an honorary appointment at University College London. <p><b>IDA MOLTKE, PhD,</b> is an Assistant Professor at the Department of Biology, University of Copenhagen. <p><b>JOHN MARIONI, PhD,</b> is a Group Leader at the European Bioinformatics Institute and the Cancer Research UK Cambridge Institute.

<p><b>A timely update of a highly popular handbook on statistical genomics</b> <p>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. <p><i>The Handbook of Statistical Genomics</i> 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. <ul> <li>Provides much-needed, timely coverage of new developments in this expanding area of study</li> <li>Numerous, brand new chapters, for example covering bacterial genomics, microbiome and metagenomics</li> <li>Detailed coverage of application areas, with chapters on plant breeding, conservation and forensic genetics</li> <li>Extensive coverage of human genetic epidemiology, including ethical aspects</li> <li>Edited by one of the leading experts in the field along with rising stars as his co-editors</li> <li>Chapter authors are world-renowned experts in the field, and newly emerging leaders.</li> </ul> <p><i>The Handbook of Statistical Genomics</i> is an excellent introductory text for advanced graduate students and early-career researchers involved in statistical genetics.

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