Handbook of Statistical Genomics

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

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 Daniel Wegmann and Christoph Leuenberger 2 Linkage Disequilibrium, Recombination and Haplotype Structure 51 Gil McVean and Jerome Kelleher 3 Haplotype Estimation and Genotype Imputation 87 Jonathan Marchini 4 Mathematical Models in Population Genetics 115 Nick Barton and Alison Etheridge 5 Coalescent Theory 145 Magnus Nordborg 6 Phylogeny Estimation Using Likelihood-Based Methods 177 John P. Huelsenbeck 7 The Multispecies Coalescent 219 Laura Kubatko 8 Population Structure, Demography and Recent Admixture 247 G. Hellenthal 9 Statistical Methods to Detect Archaic Admixture and Identify Introgressed Sequences 275 Liming Li and Joshua M. Akey 10 Population Genomic Analyses of DNA from Ancient Remains 295 Torsten Gunther and Mattias Jakobsson 11 Sequence Covariation Analysis in Biological Polymers 325 William R. Taylor, Shaun Kandathil, and David T. Jones 12 Probabilistic Models for the Study of Protein Evolution 347 Umberto Perron, Iain H. Moal, Jeffrey L. Thorne, and Nick Goldman 13 Adaptive Molecular Evolution 369 Ziheng Yang 14 Detecting Natural Selection 397 Aaron J. Stern and Rasmus Nielsen 15 Evolutionary Quantitative Genetics 421 Bruce Walsh and Michael B. Morrissey 16 Conservation Genetics 457 Mark Beaumont and Jinliang Wang 17 Statistical Methods for Plant Breeding 501 Ian Mackay, Hans-Peter Piepho, and Antonio Augusto Franco Garcia 18 Forensic Genetics 531 B.S.Weir Volume 2 19 Ethical Issues in Statistical Genetics 551 Susan E. Wallace and Richard Ashcroft 20 Descent-Based Gene Mapping in Pedigrees and Populations 573 E.A. Thompson 21 Genome-Wide Association Studies 597 Andrew P. Morris and Lon R. Cardon 22 Replication and Meta-analysis of Genome-Wide Association Studies 631 Frank Dudbridge and Paul Newcombe 23 Inferring Causal Relationships between Risk Factors and Outcomes Using Genetic Variation 651 Stephen Burgess, Christopher N. Foley, and Verena Zuber 24 Improving Genetic Association Analysis through Integration of Functional Annotations of the Human Genome 679 Qiongshi Lu and Hongyu Zhao 25 Inferring Causal Associations between Genes and Disease via the Mapping of Expression Quantitative Trait Loci 697 Solveig K. Sieberts and Eric E. Schadt 26 Statistical Methods for Single-Cell RNA-Sequencing 735 Tallulah S. Andrews, Vladimir Yu. Kiselev, and Martin Hemberg 27 Variant Interpretation and Genomic Medicine 761 K. Carss, D. Goldstein, V. Aggarwal, and S. Petrovski 28 Prediction of Phenotype from DNA Variants 799 M.E. Goddard, T.H.E. Meuwissen, and H.D. Daetwyler 29 Disease Risk Models 815 Allison Meisner and Nilanjan Chatterjee 30 Bayesian Methods for Gene Expression Analysis 843 Alex Lewin, Leonardo Bottolo, and Sylvia Richardson 31 Modelling Gene Expression Dynamics with Gaussian Process Inference 879 Magnus Rattray, Jing Yang, Sumon Ahmed, and Alexis Boukouvalas 32 Modelling Non-homogeneous Dynamic Bayesian Networks with Piecewise Linear Regression Models 899 Marco Grzegorczyk and Dirk Husmeier 33 DNA Methylation 933 Kasper D. Hansen, Kimberly D. Siegmund, and Shili Lin 34 Statistical Methods in Metabolomics 949 Timothy M.D. Ebbels, Maria De Iorio, and David A. Stephens 35 Statistical and Computational Methods in Microbiome and Metagenomics 977 Hongzhe Li 36 Bacterial Population Genomics 997 Jukka Corander, Nicholas J. Croucher, Simon R. Harris, John A. Lees, and Gerry Tonkin-Hill Reference Author Index 1021 Subject Index 1109

About the Author :
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.