Handbook of Statistical Genomics (4^th Ed.)

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.

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

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.