Population Genomics with R

Population Genomics With R presents a multidisciplinary approach to the analysis of population genomics. The methods treated cover a large number of topics from traditional population genetics to large-scale genomics with high-throughput sequencing data. Several dozen R packages are examined and integrated to provide a coherent software environment with a wide range of computational, statistical, and graphical tools. Small examples are used to illustrate the basics and published data are used as case studies. Readers are expected to have a basic knowledge of biology, genetics, and statistical inference methods. Graduate students and post-doctorate researchers will find resources to analyze their population genetic and genomic data as well as help them design new studies. The first four chapters review the basics of population genomics, data acquisition, and the use of R to store and manipulate genomic data. Chapter 5 treats the exploration of genomic data, an important issue when analysing large data sets. The other five chapters cover linkage disequilibrium, population genomic structure, geographical structure, past demographic events, and natural selection. These chapters include supervised and unsupervised methods, admixture analysis, an in-depth treatment of multivariate methods, and advice on how to handle GIS data. The analysis of natural selection, a traditional issue in evolutionary biology, has known a revival with modern population genomic data. All chapters include exercises. Supplemental materials are available on-line (http://ape-package.ird.fr/PGR.html).

1. Introduction Heredity, Genetics, and Genomics Principles of Population Genomics Units Genome Structures Mutations Drift and Selection R Packages and Conventions Required Knowledge and Other Readings 2. Data Acquisition Samples and Sampling Designs How Much DNA in a Sample? Degraded Samples Sampling Designs Low-Throughput Technologies Genotypes From Phenotypes DNA Cleavage Methods Repeat Length Polymorphism Sanger and Shotgun Sequencing DNA Methylation and Bisulfite Sequencing High-Throughput Technologies DNA Microarrays High-Throughput Sequencing Restriction Site Associated DNA RNA Sequencing Exome Sequencing Sequencing of Pooled Individuals Designing a Study With HTS The Future of DNA Sequencing File Formats Data Files Archiving and Compression Bioinformatics and Genomics Processing Sanger Sequencing Data With sangerseqR Read Mapping With Rsubread Managing Read Alignments With Rsamtools Simulation of High-Throughput Sequencing Data Exercises 3. Genomic Data in R What is an R Data Object? Data Classes for Genomic Data The Class "loci" (pegas) The Class "genind" (adegenet) The Classes "SNPbin" and "genlight" (adegenet) The Class "SnpMatrix" (snpStats) The Class "DNAbin" (ape) The Classes "XString" and "XStringSet" (Biostrings) The Package SNPRelate Data Input and Output Reading Text Files Reading Spreadsheet Files Reading VCF Files Reading PED and BED Files Reading Sequence Files Reading Annotation Files Writing Files Internet Databases Managing Files and Projects Exercises 4. Data Manipulation Basic Data Manipulation in R Subsetting, Replacement, and Deletion Commonly Used Functions Recycling and Coercion Logical Vectors Memory Management Conversions Case Studies Mitochondrial Genomes of the Asiatic Golden Cat Complete Genomes of the Fruit Fly Human Genomes Influenza HN Virus Sequences Jaguar Microsatellites Bacterial Whole Genome Sequences Metabarcoding of Fish Communities Exercises 5. Data Exploration and Summaries Genotype and Allele Frequencies Allelic Richness Missing Data Haplotype and Nucleotide Diversity The Class "haplotype" Haplotype and Nucleotide Diversity From DNA Sequences Genetic and Genomic Distances Theoretical Background Hamming Distance Distances From DNA Sequences Distances From Allele Sharing Distances From Microsatellites Summary by Groups Sliding Windows DNA Sequences Summaries With Genomic Positions Package SNPRelate Multivariate Methods Matrix Decomposition Eigendecomposition Singular Value Decomposition Power Method and Random Matrices Principal Component Analysis adegenet SNPRelate flashpcaR Multidimensional Scaling Case Studies Mitochondrial Genomes of the Asiatic Golden Cat Complete Genomes of the Fruit Fly Human Genomes Influenza HN Virus Sequences Jaguar Microsatellites Bacterial Whole Genome Sequences Metabarcoding of Fish Communities Exercises 6. Linkage Disequilibrium and Haplotype Structure Why Linkage Disequilibrium is Important? Linkage Disequilibrium: Two Loci Phased Genotypes Theoretical Background Implementation in pegas Unphased Genotypes More Than Two Loci Haplotypes From Unphased Genotypes The Expectation–Maximization Algorithm Implementation in haplostats Locus-Specific Imputation Maps of Linkage Disequilibrium Phased Genotypes With pegas SNPRelate snpStats Case Studies Complete Genomes of the Fruit Fly Human Genomes Jaguar Microsatellites Exercises 7. Population Genetic Structure Hardy–Weinberg Equilibrium F-Statistics Theoretical Background Implementations in pegas and in mmod Implementations in snpStats and in SNPRelate Trees and Networks Minimum Spanning Trees and Networks Statistical Parsimony Median Networks Phylogenetic Trees Multivariate Methods Principles of Discriminant Analysis Discriminant Analysis of Principal Components Clustering Maximum Likelihood Methods Bayesian Clustering Admixture Likelihood Method Principal Component Analysis of Coancestry A Second Look at F-Statistics Case Studies Mitochondrial Genomes of the Asiatic Golden Cat Complete Genomes of the Fruit Fly Influenza HN Virus Sequences Jaguar Microsatellites Exercises 8. Geographical Structure Geographical Data in R Packages and Classes Calculating Geographical Distances A Third Look at F-Statistics Hierarchical Components of Genetic Diversity Analysis of Molecular Variance Moran I and Spatial Autocorrelation Spatial Principal Component Analysis Finding Boundaries Between Populations Spatial Ancestry (tessr) Bayesian Methods (Geneland) Case Studies Complete Genomes of the Fruit Fly Human Genomes Exercises 9. Past Demographic Events The Coalescent The Standard Coalescent The Sequential Markovian Coalescent Simulation of Coalescent Data Estimation of _ Heterozygosity Number of Alleles Segregating Sites Microsatellites Trees Coalescent-Based Inference Maximum Likelihood Methods Analysis of Markov Chain Monte Carlo Outputs Skyline Plots Bayesian Methods Heterochronous Samples Site Frequency Spectrum Methods The Stairway Method CubSFS Popsicle Whole-Genome Methods (psmcr) Case Studies Mitochondrial Genomes of the Asiatic Golden Cat Complete Genomes of the Fruit Fly Influenza HN Virus Sequences Bacterial Whole Genome Sequences Exercises 10. Natural Selection Testing Neutrality Simple Tests Selection in Protein-Coding Sequences Selection Scans A Fourth Look at F-Statistics Association Studies (LEA) Principal Component Analysis (pcadapt) Scans for Selection With Extended Haplotypes FST Outliers Time-Series of Allele Frequencies Case Studies Mitochondrial Genomes of the Asiatic Golden Cat Complete Genomes of the Fruit Fly Influenza HN Virus Sequences Exercises A Installing R Packages B Compressing Large Sequence Files C Sampling of Alleles in a Population

Emmanuel Paradis is senior researcher in the French Institute of Research for Development (IRD). His research focuses on evolutionary models and their applications. The development and publication of software associated to his research has been an important aspect of his activities for more than twenty years. He adopted R as his main software for data analysis in 2000 and has since published and maintained several packages, including ape since 2002 and pegas since 2009. He gives regular workshops and trainings in several countries.