11.7 Evolutionary genomics

Evolutionary genomics merges molecular evolution principles with large-scale genomic data analysis. It uses bioinformatics to process vast amounts of genetic information, providing insights into species diversity and adaptation. This field examines how DNA mutations drive evolutionary changes and how genetic variation arises from these mutations and recombination events.

Comparative genomics and phylogenetic analysis are key components of evolutionary genomics. These approaches use bioinformatics tools to identify conserved elements, evolutionary patterns, and reconstruct relationships between species or genes. Genome-wide studies and population genomics further our understanding of molecular evolution and adaptation across entire genomes.

Fundamentals of evolutionary genomics

• Evolutionary genomics integrates principles of molecular evolution with large-scale genomic data analysis
• Bioinformatics plays a crucial role in processing and interpreting vast amounts of genomic sequence information
• Understanding evolutionary processes at the genomic level provides insights into species diversity and adaptation

Molecular basis of evolution

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• DNA mutations drive evolutionary changes at the molecular level
• Point mutations alter single nucleotides (transitions, transversions)
• Insertions and deletions (indels) modify gene structure and function
• Chromosomal rearrangements (inversions, translocations) impact genome organization
• Epigenetic modifications influence gene expression without altering DNA sequence

Genetic variation vs conservation

• Genetic variation arises from mutations and recombination events
• Conservation reflects evolutionary constraints on functional genomic elements
• Highly conserved regions often indicate essential biological functions
• Variable regions may represent adaptations to specific environments
• Balancing selection maintains genetic diversity in populations

Comparative genomics

• Comparative genomics examines similarities and differences between genomes of different species
• This field leverages bioinformatics tools to identify conserved elements and evolutionary patterns
• Comparative analyses reveal insights into gene function, genome structure, and species relationships

Sequence alignment methods

• Global alignment algorithms optimize similarity across entire sequences (Needleman-Wunsch)
• Local alignment algorithms identify similar regions within sequences (Smith-Waterman)
• Multiple sequence alignment tools compare more than two sequences simultaneously (ClustalW, MUSCLE)
• Profile-based methods improve alignment accuracy for distantly related sequences
• Progressive alignment strategies build alignments hierarchically based on sequence similarity

Orthology vs paralogy

• Orthologous genes derive from a common ancestor through speciation events
• Paralogous genes result from gene duplication within a species
• Orthologs often maintain similar functions across species
• Paralogs may diverge in function or acquire new roles (neofunctionalization)
• Distinguishing orthologs from paralogs crucial for accurate evolutionary inference
• Synteny analysis helps identify orthologous genomic regions

Phylogenetic analysis

• Phylogenetic analysis reconstructs evolutionary relationships between species or genes
• Bioinformatics tools enable the construction and interpretation of phylogenetic trees
• Phylogenies provide a framework for understanding patterns of genetic diversity and adaptation

Tree construction algorithms

• Distance-based methods use pairwise distances between sequences (UPGMA, Neighbor-Joining)
• Maximum parsimony seeks the tree requiring the fewest evolutionary changes
• Maximum likelihood estimates the most probable tree given a model of sequence evolution
• Bayesian inference incorporates prior probabilities into tree reconstruction
• Consensus methods combine multiple trees to represent phylogenetic uncertainty

Molecular clock hypothesis

• Assumes constant rate of molecular evolution across lineages
• Enables dating of evolutionary events using genetic differences
• Relaxed clock models allow for rate variation among branches
• Calibration points from fossil records improve molecular dating accuracy
• Tests for clocklike behavior include relative rate tests and likelihood ratio tests

Genome-wide evolutionary studies

• Genome-wide studies examine patterns of evolution across entire genomes
• Bioinformatics approaches enable large-scale analyses of genomic data
• These studies reveal global trends in molecular evolution and adaptation

Positive vs purifying selection

• Positive selection favors advantageous mutations, increasing their frequency
• Purifying selection removes deleterious mutations from populations
• Positive selection signatures include reduced genetic diversity and increased divergence
• Purifying selection maintains conserved genomic regions across species
• McDonald-Kreitman test compares polymorphism and divergence at synonymous and nonsynonymous sites
• Branch-site models detect positive selection on specific lineages

Neutral theory of evolution

• Proposes most genetic variation results from neutral mutations
• Genetic drift primarily drives allele frequency changes in populations
• Predicts constant rate of molecular evolution (molecular clock)
• Serves as null hypothesis for detecting selection
• Explains patterns of genetic diversity within and between species
• Challenges include explaining adaptive evolution and molecular function

Genomic signatures of adaptation

• Adaptation leaves distinctive patterns in genomic sequences
• Bioinformatics tools detect these signatures across genomes
• Identifying adaptive genomic regions provides insights into species' evolutionary history

Selective sweeps

• Occur when beneficial mutations rapidly increase in frequency
• Hard sweeps involve single adaptive alleles rising to fixation
• Soft sweeps result from multiple adaptive alleles or standing variation
• Genomic signatures include reduced genetic diversity and extended linkage disequilibrium
• Long-range haplotype tests detect recent selective sweeps (iHS, XP-EHH)
• Composite likelihood methods identify sweep regions (SweepFinder, SweeD)

Balancing selection

• Maintains multiple alleles in populations over long periods
• Forms of balancing selection include heterozygote advantage and frequency-dependent selection
• Genomic signatures include elevated genetic diversity and old allelic lineages
• Tajima's D test detects excess of intermediate-frequency alleles
• HKA test compares polymorphism and divergence across loci
• Trans-species polymorphisms indicate long-term balancing selection

Population genomics

• Population genomics studies genetic variation within and between populations
• Bioinformatics approaches enable analysis of large-scale population genomic data
• These studies provide insights into demographic history and adaptation

Coalescent theory

• Describes genealogical relationships of gene copies in populations
• Backward-in-time approach models ancestry of sampled sequences
• Coalescent events represent merging of lineages to common ancestors
• Time to most recent common ancestor (TMRCA) informs about population history
• Coalescent simulations generate null distributions for statistical tests
• Multispecies coalescent models account for incomplete lineage sorting

Effective population size

• Represents the size of an ideal population with equivalent genetic drift
• Smaller than census population size due to various factors (mating system, selection)
• Influences rate of genetic drift and efficacy of selection
• Estimated using genetic diversity measures (π, θ) or linkage disequilibrium patterns
• Temporal changes in Ne reflect population size changes or selective events
• Skyline plots visualize changes in effective population size over time

Horizontal gene transfer

• Horizontal gene transfer (HGT) involves genetic exchange between unrelated organisms
• Bioinformatics methods detect HGT events by identifying incongruent phylogenetic patterns
• HGT significantly impacts genome evolution, particularly in prokaryotes

Mechanisms of genetic exchange

• Transformation involves uptake of naked DNA from the environment
• Conjugation transfers genetic material through direct cell-to-cell contact
• Transduction uses bacteriophages as vectors for DNA transfer
• Gene transfer agents (GTAs) package and transfer random genomic fragments
• Nanotubes facilitate cytoplasmic bridges between cells for genetic exchange
• Membrane vesicles can carry DNA between cells

Impact on genome evolution

• HGT contributes to rapid adaptation and niche expansion
• Acquisition of antibiotic resistance genes through HGT poses clinical challenges
• Transferred genes may confer novel metabolic capabilities (photosynthesis in eukaryotes)
• HGT events can lead to the formation of mosaic genomes
• Phylogenetic incongruence serves as evidence for past HGT events
• Bioinformatics methods detect HGT using sequence composition and phylogenetic approaches

Molecular evolution rates

• Molecular evolution rates measure the pace of genetic changes over time
• Bioinformatics tools enable estimation of evolutionary rates from sequence data
• Understanding rate variation provides insights into selective pressures and mutational processes

Synonymous vs nonsynonymous changes

• Synonymous mutations do not alter amino acid sequence
• Nonsynonymous mutations change the encoded amino acid
• Synonymous changes often considered neutral, though may affect mRNA stability or translation
• Nonsynonymous changes potentially impact protein function and fitness
• Ratio of nonsynonymous to synonymous substitution rates (dN/dS) indicates selection pressure
• Codon-based models account for transition/transversion bias and codon usage

dN/dS ratio analysis

• dN/dS < 1 suggests purifying selection
• dN/dS ≈ 1 indicates neutral evolution
• dN/dS > 1 provides evidence for positive selection
• Branch-specific models allow dN/dS to vary across phylogenetic lineages
• Site-specific models detect selection acting on individual codons
• Branch-site models combine lineage and site-specific approaches
• PAML software implements various models for dN/dS analysis

Evolutionary genomics tools

• Bioinformatics tools are essential for analyzing large-scale genomic data in an evolutionary context
• These tools enable researchers to test hypotheses about evolutionary processes and patterns
• Continuous development of new algorithms and software improves our ability to interpret genomic data

PAML software suite

• Phylogenetic Analysis by Maximum Likelihood (PAML) package for molecular evolution analyses
• Implements various models for detecting selection (site, branch, and branch-site models)
• Allows estimation of divergence times using molecular clock models
• Provides tools for ancestral sequence reconstruction
• Includes programs for analyzing codon and amino acid substitutions
• Offers methods for testing evolutionary hypotheses using likelihood ratio tests

Phylogenetic databases

• TreeBASE stores published phylogenetic trees and associated data
• Ensembl Compara provides pre-computed orthology and paralogy relationships
• PhylomeDB contains genome-wide collections of gene phylogenies
• Open Tree of Life synthesizes published phylogenetic information into a comprehensive tree
• TimeTree database provides divergence time estimates for species pairs
• PANTHER classifies proteins and their genes to facilitate evolutionary analyses

Applications in bioinformatics

• Evolutionary genomics principles and tools have diverse applications in bioinformatics
• These applications range from basic research to practical applications in medicine and biotechnology
• Integration of evolutionary approaches enhances our understanding of biological systems

Ancestral sequence reconstruction

• Infers ancestral gene or protein sequences using phylogenetic information
• Maximum parsimony methods minimize the number of changes along branches
• Maximum likelihood approaches estimate the most probable ancestral states
• Bayesian inference incorporates uncertainty in ancestral reconstructions
• Applications include studying protein evolution and engineering ancient proteins
• Reconstructed ancestral sequences provide insights into molecular adaptation

Evolutionary medicine insights

• Phylogenetic analysis of pathogens informs epidemiology and vaccine development
• Evolutionary approaches help predict antibiotic resistance emergence
• Comparative genomics reveals genetic basis of human diseases
• Cancer genomics utilizes evolutionary principles to understand tumor progression
• Pharmacogenomics leverages population genomics to optimize drug treatments
• Evolutionary perspectives inform strategies for managing emerging infectious diseases

Challenges in evolutionary genomics

• Evolutionary genomics faces various challenges in data analysis and interpretation
• Bioinformatics approaches continually evolve to address these challenges
• Understanding limitations and potential biases is crucial for accurate inference

Long branch attraction

• Phylogenetic artifact where distantly related taxa incorrectly group together
• Results from rapid evolution or inadequate taxon sampling
• More likely to occur with maximum parsimony methods
• Mitigation strategies include increased taxon sampling and model-based methods
• Site-heterogeneous models can reduce long branch attraction effects
• Careful outgroup selection helps minimize long branch attraction

Incomplete lineage sorting

• Occurs when ancestral polymorphisms persist through speciation events
• Results in discordance between gene trees and species trees
• More common with rapid speciation or large ancestral population sizes
• Coalescent-based methods account for incomplete lineage sorting
• Multispecies coalescent models reconcile gene tree and species tree conflicts
• Impacts inference of species relationships and divergence times