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Abstract A major goal of modern biology is connecting phenotype with its underlying genetic basis. The Mexican cavefish (Astyanax mexicanus), a characin fish species comprised of a surface ecotype and a cave-derived ecotype, is well suited as a model to study the genetic mechanisms underlying adaptation to extreme environments. Here, we map 206 previously published quantitative trait loci (QTL) for cave-derived traits in A. mexicanus to the newest version of the surface fish genome assembly, AstMex3. These analyses revealed that QTL clusters in the genome more than expected by chance, and this clustering is not explained by the distribution of genes in the genome. To investigate whether certain characteristics of the genome facilitate phenotypic evolution, we tested whether genomic characteristics associated with increased opportunities for mutation, such as highly mutagenic CpG sites, are reliable predictors of the sites of trait evolution but did not find any significant trends. Finally, we combined the QTL map with previously collected expression and selection data to identify 36 candidate genes that may underlie the repeated evolution of cave phenotypes, including rgrb, which is predicted to be involved in phototransduction. We found this gene has disrupted exons in all non-hybrid cave populations but intact reading frames in surface fish. Overall, our results suggest specific regions of the genome may play significant roles in driving adaptation to the cave environment in A. mexicanus and demonstrate how this compiled dataset can facilitate our understanding of the genetic basis of repeated evolution in the Mexican cavefish.
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A trait‐based approach to determining principles of plant biogeography
Abstract Lineage‐specific traits determine how plants interact with their surrounding environment. Unrelated species may evolve similar phenotypic characteristics to tolerate, persist in, and invade environments with certain characteristics, resulting in some traits becoming relatively more common in certain types of habitats. Analyses of these general patterns of geographical trait distribution have led to the proposal of general principles to explain how plants diversify in space over time. Trait–environment correlation analyses quantify to what extent unrelated lineages have similar evolutionary responses to a given type of habitat. In this synthesis, I give a short historical overview on trait–environment correlation analyses, from some key observations from classic naturalists to modern approaches using trait evolution models, large phylogenies, and massive data sets of traits and distributions. I discuss some limitations of modern approaches, including the need for more realistic models, the lack of data from tropical areas, and the necessary focus on trait scoring that goes beyond macromorphology. Overcoming these limitations will allow the field to explore new questions related to trait lability and niche evolution and to better identify generalities and exceptions in how plants diversify in space over time.
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- Award ID(s):
- 1916558
- PAR ID:
- 10557030
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- American Journal of Botany
- Volume:
- 110
- Issue:
- 2
- ISSN:
- 0002-9122
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1002/ajb2.16127
