Population Genomics with R

Cover
Half Title
Title Page
Copyright Page
Dedication
Table of Contents
Preface
Symbol Description
1: Introduction
	1.1 Heredity, Genetics, and Genomics
	1.2 Principles of Population Genomics
		1.2.1 Units
		1.2.2 Genome Structures
		1.2.3 Mutations
		1.2.4 Drift and Selection
	1.3 R Packages and Conventions
	1.4 Required Knowledge and Other Readings
2: Data Acquisition
	2.1 Samples and Sampling Designs
		2.1.1 How Much DNA in a Sample?
		2.1.2 Degraded Samples
		2.1.3 Sampling Designs
	2.2 Low-Throughput Technologies
		2.2.1 Genotypes From Phenotypes
		2.2.2 DNA Cleavage Methods
		2.2.3 Repeat Length Polymorphism
		2.2.4 Sanger and Shotgun Sequencing
		2.2.5 DNA Methylation and Bisulfite Sequencing
	2.3 High-Throughput Technologies
		2.3.1 DNA Microarrays
		2.3.2 High-Throughput Sequencing
		2.3.3 Restriction Site Associated DNA
		2.3.4 RNA Sequencing
		2.3.5 Exome Sequencing
		2.3.6 Sequencing of Pooled Individuals
		2.3.7 Designing a Study With HTS
		2.3.8 The Future of DNA Sequencing
	2.4 File Formats
		2.4.1 Data Files
		2.4.2 Archiving and Compression
	2.5 Bioinformatics and Genomics
		2.5.1 Processing Sanger Sequencing Data With sangerseqR
		2.5.2 Read Mapping With Rsubread
		2.5.3 Managing Read Alignments With Rsamtools
	2.6 Simulation of High-Throughput Sequencing Data
	2.7 Exercises
3: Genomic Data in R
	3.1 What is an R Data Object?
	3.2 Data Classes for Genomic Data
		3.2.1 The Class \"loci\" (pegas)
		3.2.2 The Class \"genind\" (adegenet)
		3.2.3 The Classes \"SNPbin\" and \"genlight\" (adegenet)
		3.2.4 The Class \"SnpMatrix\" (snpStats)
		3.2.5 The Class \"DNAbin\" (ape)
		3.2.6 The Classes \"XString\" and \"XStringSet\" (Biostrings)
		3.2.7 The Package SNPRelate
	3.3 Data Input and Output
		3.3.1 Reading Text Files
		3.3.2 Reading Spreadsheet Files
		3.3.3 Reading VCF Files
		3.3.4 Reading PED and BED Files
		3.3.5 Reading Sequence Files
		3.3.6 Reading Annotation Files
		3.3.7 Writing Files
	3.4 Internet Databases
	3.5 Managing Files and Projects
	3.6 Exercises
4: Data Manipulation
	4.1 Basic Data Manipulation in R
		4.1.1 Subsetting, Replacement, and Deletion
		4.1.2 Commonly Used Functions
		4.1.3 Recycling and Coercion
		4.1.4 Logical Vectors
	4.2 Memory Management
	4.3 Conversions
	4.4 Case Studies
		4.4.1 Mitochondrial Genomes of the Asiatic Golden Cat
		4.4.2 Complete Genomes of the Fruit Fly
		4.4.3 Human Genomes
		4.4.4 Influenza H1N1 Virus Sequences
		4.4.5 Jaguar Microsatellites
		4.4.6 Bacterial Whole Genome Sequences
		4.4.7 Metabarcoding of Fish Communities
	4.5 Exercises
5: Data Exploration and Summaries
	5.1 Genotype and Allele Frequencies
		5.1.1 Allelic Richness
		5.1.2 Missing Data
	5.2 Haplotype and Nucleotide Diversity
		5.2.1 The Class \"Haplotype\"
		5.2.2 Haplotype and Nucleotide Diversity From DNA Se-quences
	5.3 Genetic and Genomic Distances
		5.3.1 Theoretical Background
		5.3.2 Hamming Distance
		5.3.3 Distances From DNA Sequences
		5.3.4 Distances From Allele Sharing
		5.3.5 Distances From Microsatellites
	5.4 Summary by Groups
	5.5 Sliding Windows
		5.5.1 DNA Sequences
		5.5.2 Summaries With Genomic Positions
		5.5.3 Package SNPRelate
	5.6 Multivariate Methods
		5.6.1 Matrix Decomposition
			5.6.1.1 Eigendecomposition
			5.6.1.2 Singular Value Decomposition
			5.6.1.3 Power Method and Random Matrices
		5.6.2 Principal Component Analysis
			5.6.2.1 Adegenet
			5.6.2.2 SNPRelate
			5.6.2.3 FlashpcaR
		5.6.3 Multidimensional Scaling
	5.7 Case Studies
		5.7.1 Mitochondrial Genomes of the Asiatic Golden Cat
		5.7.2 Complete Genomes of the Fruit Fly
		5.7.3 Human Genomes
		5.7.4 Influenza H1N1 Virus Sequences
		5.7.5 Jaguar Microsatellites
		5.7.6 Bacterial Whole Genome Sequences
		5.7.7 Metabarcoding of Fish Communities
	5.8 Exercises
6: Linkage Disequilibrium and Haplotype Structure
	6.1 Why Linkage Disequilibrium is Important?
	6.2 Linkage Disequilibrium: Two Loci
		6.2.1 Phased Genotypes
			6.2.1.1 Theoretical Background
			6.2.1.2 Implementation in Pegas
		6.2.2 Unphased Genotypes
	6.3 More Than Two Loci
		6.3.1 Haplotypes From Unphased Genotypes
			6.3.1.1 The Expectation–Maximization Algorithm
			6.3.1.2 Implementation in haplo.stats
		6.3.2 Locus-Specific Imputation
		6.3.3 Maps of Linkage Disequilibrium
			6.3.3.1 Phased Genotypes With pegas
			6.3.3.2 SNPRelate
			6.3.3.3 snpStats
	6.4 Case Studies
		6.4.1 Complete Genomes of the Fruit Fly
		6.4.2 Human Genomes
		6.4.3 Jaguar Microsatellites
	6.5 Exercises
7: Population Genetic Structure
	7.1 Hardy–Weinberg Equilibrium
	7.2 F-Statistics
		7.2.1 Theoretical Background
		7.2.2 Implementations in pegas and in mmod
		7.2.3 Implementations in snpStats and in SNPRelate
	7.3 Trees and Networks
		7.3.1 Minimum Spanning Trees and Networks
		7.3.2 Statistical Parsimony
		7.3.3 Median Networks
		7.3.4 Phylogenetic Trees
	7.4 Multivariate Methods
		7.4.1 Principles of Discriminant Analysis
		7.4.2 Discriminant Analysis of Principal Components
		7.4.3 Clustering
		7.4.4 Maximum Likelihood Methods
		7.4.5 Bayesian Clustering
	7.5 Admixture
		7.5.1 Likelihood Method
		7.5.2 Principal Component Analysis of Coancestry
		7.5.3 A Second Look at F-Statistics
	7.6 Case Studies
		7.6.1 Mitochondrial Genomes of the Asiatic Golden Cat
		7.6.2 Complete Genomes of the Fruit Fly
		7.6.3 Influenza H1N1 Virus Sequences
		7.6.4 Jaguar Microsatellites
	7.7 Exercises
8: Geographical Structure
	8.1 Geographical Data in R
		8.1.1 Packages and Classes
		8.1.2 Calculating Geographical Distances
	8.2 A Third Look at F-Statistics
		8.2.1 Hierarchical Components of Genetic Diversity
		8.2.2 Analysis of Molecular Variance
	8.3 Moran I and Spatial Autocorrelation
	8.4 Spatial Principal Component Analysis
	8.5 Finding Boundaries Between Populations
		8.5.1 Spatial Ancestry (tess3r)
		8.5.2 Bayesian Methods (Geneland)
	8.6 Case Studies
		8.6.1 Complete Genomes of the Fruit Fly
		8.6.2 Human Genomes
	8.7 Exercises
9: Past Demographic Events
	9.1 The Coalescent
		9.1.1 The Standard Coalescent
		9.1.2 The Sequential Markovian Coalescent
		9.1.3 Simulation of Coalescent Data
	9.2 Estimation of Θ
		9.2.1 Heterozygosity
		9.2.2 Number of Alleles
		9.2.3 Segregating Sites
		9.2.4 Microsatellites
		9.2.5 Trees
	9.3 Coalescent-Based Inference
		9.3.1 Maximum Likelihood Methods
		9.3.2 Analysis of Markov Chain Monte Carlo Outputs
		9.3.3 Skyline Plots
		9.3.4 Bayesian Methods
	9.4 Heterochronous Samples
	9.5 Site Frequency Spectrum Methods
		9.5.1 The Stairway Method
		9.5.2 CubSFS
		9.5.3 Popsicle
	9.6 Whole-Genome Methods (psmcr)
	9.7 Case Studies
		9.7.1 Mitochondrial Genomes of the Asiatic Golden Cat
		9.7.2 Complete Genomes of the Fruit Fly
		9.7.3 Influenza H1N1 Virus Sequences
		9.7.4 Bacterial Whole Genome Sequences
	9.8 Exercises
10: Natural Selection
	10.1 Testing Neutrality
		10.1.1 Simple Tests
		10.1.2 Selection in Protein-Coding Sequences
	10.2 Selection Scans
		10.2.1 A Fourth Look at F-Statistics
		10.2.2 Association Studies (LEA)
		10.2.3 Principal Component Analysis (pcadapt)
		10.2.4 Scans for Selection With Extended Haplotypes
		10.2.5 FST Outliers
	10.3 Time-Series of Allele Frequencies
	10.4 Case Studies
		10.4.1 Mitochondrial Genomes of the Asiatic Golden Cat
		10.4.3 Influenza H1N1 Virus Sequences
		10.4.2 Complete Genomes of the Fruit Fly
	10.5 Exercises
A: Installing R Packages
B: Compressing Large Sequence Files
C: Sampling of Alleles in a Population
D: Glossary
Bibliography
Index