Combined genotype and phenotype analyses reveal patterns of genomic adaptation to local environments in the subtropical oak Quercus acutissima
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Understanding the effects of the demographic dynamics and environmental heterogeneity on the genomic variation of forest species is important not only for uncovering the evolutionary history of the species but also for predicting their ability to adapt to climate change. In this study, we combined a common garden experiment with range-wide population genomics analyses to infer the demographic history and characterize patterns of local adaptation in a subtropical oak species, Quercus acutissima. We scanned about 8% of the oak genome using a balanced representation of both genic and non-genic regions and identified a total of 55,361 SNPs in 167 trees. Genomic diversity analyses revealed an east-west split in the species distribution range. Coalescent-based model simulations inferred a late Pleistocene divergence in Q. acutissima between the east and west groups as well as subsequent pre-glaciation population expansion events. Consistent with observed genetic differentiation, morphological traits also showed east-west differentiation and the biomass allocation in seedlings was significantly associated with precipitation. Environment was found to have a significant and stronger impact on the non-neutral than the neutral SNPs, and also significantly associated with the phenotypic differentiation, suggesting that apart from the geography, environment had played a role in determining non-neutral and phenotypic variation. Our approach, which combined a common garden experiment with landscape genomics data, validated the hypothesis of local adaptation of this long-lived oak tree of subtropical China. Our study joins the small number of studies that have combined genotypic and phenotypic data to detect patterns of local adaptation.
Citations (7)
- https://doi.org/10.1111/pce.15603OpenAlex
Cited on 11 May 2025
Weight: 1.59
- https://doi.org/10.1111/mec.17483OpenAlex
Cited on 26 July 2024
Weight: 1.53
Cited on 27 November 2023
Weight: 1.46
Cited on 01 September 2023
Weight: 1.46
- https://doi.org/10.1111/nph.17793OpenAlex
Cited on 13 October 2021
Weight: 1.23
- https://doi.org/10.1002/hyp.14091OpenAlex
Cited on 15 February 2021
Weight: 1.23
- https://doi.org/10.1111/jse.12568DataCite MDC
Cited on 02 March 2020
Weight: 1.00
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Publication Details
Subfield
Molecular Biology
Field
Biochemistry, Genetics and Molecular Biology
Domain
Life Sciences
Confidence Score
61%
Source
Scholar Data Model