Overcoming challenges in generating GEAs
|Limitation||Relationship to GV assessments||Solutions to address limitations|
|No direct link between SNPs and phenotypes under selection.||Demographic history, rather than selection, can result in SNPs that are differentiated across populations, resulting in SNPs mistakenly identified as under selection (adaptive).||Include neutral loci in the analysis of population differentiation.|
Test assumption that the population is in migration-selection balance; decoupling of neutral and phenotypic divergence will indicate that selection contributes to divergence irrespective of migration.
|SNPs are not under selection but only connected to polymorphisms under selection indirectly, such as through linkage.||Use SNPs with known function and link to fitness (phenotype). Perform functional assays with candidate loci to confirm link.|
Test for consistency of association across multiple gradients.
Where possible, validate link between SNPs and phenotypes through reciprocal transplant or common garden experiments, functional assays, and association studies.
|Methodological: Missing SNPs.||Poor sequencing depth, poor coverage of genome.||Use high-quality, high-depth genomic data|
|Thousands of loci contribute to the traits that underpin climatic adaptation, which will not be captured by genomic data based on reduced representation sequencing methods, or low coverage sequencing.||Sequence entire genomes, include annotated reference genome in analysis, consider comparative analysis.|
Use multiple methods, including data from long read technologies, where possible, and appropriate; use multiple reference genomes.
|Copy number variants (CNVs) and structural variants such as inversions not scored despite being critical in adaptation||Explicitly consider structural variants in analyses, consider sequencing approaches that can identify structural variants (e.g., long-read sequencing for inversions).|
|Inaccurate assessment of future climate; no link with environmental variables used to identify adaptive SNPs.||Abiotic factors might not be the main drivers of species abundance, ignores biotic interactions.||Consider adding biotic interactions to initial SNP-environmental gradient associations.|
Use understanding of the natural history of the species/prior studies to formulate specific hypotheses to inform choice of variables.
|Future climate involves different selection pressures, happens faster, etc.||Test predictions experimentally, if possible in simulated contexts.|
|Genetic-environment associations are not static through time.||Consider temporal sampling (e.g., across seasons) of populations to explicitly account for temporal variation in SNP allele frequencies and genetic-environment associations.|
|The complex and polygenic nature of adaptation is ignored. Pleiotropy, where a single gene affects multiple traits, might facilitate or constrain adaptation.||Interactions among genes might limit adaptive responses. This is not captured in current assessments of genomic vulnerability.||Compare predictions of adaptive capacity based on genomic vulnerability with those estimated directly using quantitative genetic analyses that explicitly account for pleiotropic interactions between key traits.|
|The same phenotype can result from many different genotypes. This genetic redundancy is missed in current assessments of genomic vulnerability.||Assess redundancy in species, collate data across multiple populations and species. Consider experimental tests of genetic redundancy.|
|The expression of adaptive genetic variation can be environment and sex specific.||Where possible, assess adaptive capacity across multiple environments and in both sexes. Where quantitative genetic breeding designs are not possible, common garden assessments of phenotypic divergence between populations sampled from across environmental gradients can shed light on adaptive genetic diversity.|
|Genomic vulnerability approaches less effective than other approaches based on genomic variability.||Adoption of genomic vulnerability tools could conflict with those based on assessments of overall genetic variation, leading to perverse outcomes.||Compare predictions and management based on measures of genome-wide variation to assess overall adaptive capacity with those based on subsets of putatively adaptive SNPs. Test approaches in generating successful outcomes.|
Linking GV to experimental data—validating the method
Challenges of applying GV in conservation
- Hoffmann A.A.
- Miller A.D.
- Weeks A.R.
- Spatial, climate and ploidy factors drive genomic diversity and resilience in the widespread grass Themeda triandra.Mol. Ecol. 2020; 29: 3872-3888
- Polygenic adaptation: a unifying framework to understand positive selection.Nat. Rev. Genet. 2020; 21: 769-781
- Genomic signals of selection predict climate-driven population declines in a migratory bird.Science. 2018; 359: 83-86
- Ecological genomics meets community-level modelling of biodiversity: mapping the genomic landscape of current and future environmental adaptation.Ecol. Lett. 2015; 18: 1-16
- Comment on “Genomic signals of selection predict climate-driven population declines in a migratory bird”.Science. 2018; 361: eaat7279
- Finding the Genomic Basis of Local Adaptation: Pitfalls, Practical Solutions, and Future Directions.Am. Nat. 2016; 188: 379-397
- A framework for incorporating evolutionary genomics into biodiversity conservation and management.Climate Change Responses. 2015; 2: 1
- Genetic mixing for population management: From genetic rescue to provenancing.Evol. Appl. 2020; https://doi.org/10.1111/eva.13154
- Evidence of genomic adaptation to climate in Eucalyptus microcarpa: Implications for adaptive potential to projected climate change.Mol. Ecol. 2017; 26: 6002-6020
- Genomic variation predicts adaptive evolutionary responses better than population bottleneck history.PLoS Genet. 2019; 15: e1008205
- The broad footprint of climate change from genes to biomes to people.Science. 2016; 354: aaf7671
- Rapid climate change and the rate of adaptation: insight from experimental quantitative genetics.New Phytol. 2012; 195: 752-765
- Rapid repeatable phenotypic and genomic adaptation following multiple introductions.Mol. Ecol. 2020; 29: 4102-4117
- Conservation of genetic uniqueness of populations may increase extinction likelihood of endangered species: the case of Australian mammals.Front. Zool. 2016; 13: 31
- Limits to the adaptive potential of small populations.Annu. Rev. Ecol. Evol. Syst. 2006; 37: 433-458
User LicenseElsevier user license |
For non-commercial purposes:
- Read, print & download
- Text & data mine
- Translate the article
- Reuse portions or extracts from the article in other works
- Redistribute or republish the final article
- Sell or re-use for commercial purposes
Elsevier's open access license policy