Evolutionary Genomics of Adaptation
Overview
Beneath the phenomena of evolutionary adaptation lies a complex lattice in which population-level processes intersect with developmental and genomic constraints. The Cresko Lab explores these multilevel interactions in the threespine stickleback fish, which offers unique advantages as a model system for understanding the genomic basis of adaptation.
The Stickleback Model System
The threespine stickleback provides unparalleled opportunities for studying evolutionary genomics:
Natural Experiments in Evolution
- Replicate adaptation: Countless independent colonizations of freshwater from marine ancestors
- Phenotypic radiation: Extensive morphological and physiological diversity within a single species
- Contemporary evolution: Examples of extremely rapid evolution on human timescales
- Extant ancestors: Marine populations representing the ancestral state still exist
Experimental Advantages
- Genetic tractability: Ability to perform controlled crosses in the laboratory
- Developmental accessibility: External fertilization and transparent embryos
- Genomic resources: High-quality reference genome and extensive population data
- CRISPR compatibility: Efficient genome editing for functional validation
Genomic Signatures of Natural Selection
By richly genotyping wild populations and generating controlled laboratory crosses, we investigate:
Population Genomic Patterns
- Selective sweeps: Identifying regions under strong positive selection
- Polygenic adaptation: Understanding selection on standing genetic variation
- Balancing selection: Detecting maintenance of adaptive variation
- Background selection: Distinguishing selection from demographic processes
Parallel Evolution at the Genomic Level
Our research reveals that a large proportion of the stickleback genome bears signatures of natural selection in replicate adaptations to freshwater: - Identification of parallel genomic regions under selection - Discovery of regulatory vs. coding sequence evolution - Understanding the role of chromosomal inversions in adaptation - Characterizing the genetic architecture of adaptive traits
RAD-seq Innovation and Applications
RAD-seq (Restriction site Associated DNA sequencing), a method pioneered at UO through a Cresko and Johnson lab collaboration, revolutionized population genomics:
Methodological Advances
- De novo genotyping: Genome-wide marker discovery without reference genomes
- Cost-effective scaling: Enabling population-level studies in non-model organisms
- Flexible resolution: Adjustable marker density for different applications
- Paired-end improvements: Building haplotype blocks for enhanced analyses
Population Genetic Applications
The method enables continuous genomic scans using population genetic statistics: - Detection of outlier loci under selection - Demographic inference and population structure analysis - Phylogeographic reconstruction - Genome-wide association studies (GWAS)
Genomic Architecture of Adaptation
We investigate how genomic organization influences evolutionary processes:
Structural Variation
- Chromosomal inversions: Their role in maintaining adaptive gene complexes
- Copy number variation: Gene duplications and deletions in adaptation
- Transposable elements: Mobile DNA as a source of adaptive variation
- Recombination landscapes: How recombination rate variation affects adaptation
Gene Regulatory Evolution
- Cis-regulatory changes: Evolution of gene expression through regulatory mutations
- Trans-acting factors: Evolution of regulatory networks
- Epigenetic variation: Heritable changes beyond DNA sequence
- Gene expression plasticity: Environmental effects on gene regulation
Phylogeography and Population History
Using paired-end RAD-seq data to build haplotype blocks, we employ coalescent approaches to investigate:
Historical Demography
- Colonization history: Timing and routes of freshwater invasions
- Population expansions: Demographic changes following colonization
- Gene flow patterns: Ongoing migration between populations
- Hybridization zones: Introgression between divergent populations
Molecular Dating
- Age of adaptive alleles: When beneficial mutations arose or were selected
- Divergence times: Dating population splits and colonization events
- Mutation rate calibration: Using known geological events to calibrate molecular clocks
- Ancestral state reconstruction: Inferring historical character states
Gene-by-Environment Interactions
Understanding how genetic variation interacts with environmental factors:
Environmental Variables
- Temperature adaptation: Thermal tolerance and performance curves
- Salinity tolerance: Osmoregulatory adaptations
- Dietary adaptations: Genetic basis of trophic specialization
- Parasite resistance: Host-pathogen coevolution
Experimental Approaches
- Common garden experiments: Separating genetic from environmental effects
- Reciprocal transplants: Testing local adaptation
- Selection experiments: Real-time evolution under controlled conditions
- Plasticity studies: Reaction norm evolution
Current Research Directions
Genomics of Rapid Evolution
- Tracking allele frequency changes in real-time
- Understanding the predictability of evolution
- Identifying constraints on adaptive evolution
- Measuring the speed of adaptation
Integrative Approaches
- Connecting genotype to phenotype to fitness
- Multi-trait analyses of correlated evolution
- Systems biology of adaptive changes
- Eco-evolutionary dynamics in nature
Conservation Applications
- Assessing adaptive potential under climate change
- Managing genetic diversity in threatened populations
- Understanding evolutionary responses to human impacts
- Predicting population resilience
Computational Tools and Resources
We develop and apply cutting-edge computational methods:
Software Development
- Population genomics pipelines
- Statistical methods for selection detection
- Visualization tools for genomic data
- Machine learning applications
Data Resources
- Population genomic datasets
- Annotated variant databases
- Expression atlases
- Phenotype-genotype maps
Collaborations
This research involves collaborations with: - Population geneticists worldwide - Computational biologists and bioinformaticians - Evolutionary ecologists - Conservation biologists
Future Directions
Our ongoing and future work aims to: - Integrate long-read sequencing for improved genome assemblies - Develop pangenome resources for stickleback - Apply machine learning to predict adaptive outcomes - Connect molecular evolution to ecosystem function
Learn More
For more information about our evolutionary genomics research: - Contact the Cresko Lab → - View our publications →