Abstract
As climate changes, understanding the genetic basis of local adaptation in plants becomes an ever more pressing issue. Combining genotype-environment association (GEA) with genotype–phenotype association (GPA) analysis has an exciting potential to uncover the genetic basis of environmental responses. We use these approaches to identify genetic variants linked to local adaptation to drought in Pinus ponderosa. Over 4 million Single Nucleotide Polymorphisms (SNPs) were identified using 223 individuals from across the Sierra Nevada of California. 927,740 (22.3%) SNPs were retained after filtering for proximity to genes and used in our association analyses. We found 1374 associated with five major climate variables, with the largest number (1151) associated with April 1st snowpack. We also conducted a greenhouse study with various drought-tolerance traits measured in first-year seedlings of a subset of the genotyped trees grown in the greenhouse. 796 SNPs were associated with control-condition trait values, while 1149 were associated with responsiveness of these traits to drought. While no individual SNPs were associated with both the environmental variables and the measured traits, several annotated genes were associated with both, particularly those involved in cell wall formation, biotic and abiotic stress responses, and ubiquitination. However, the functions of many of the associated genes have not yet been determined due to the lack of gene annotation information for conifers. Future studies are needed to assess the developmental roles and ecological significance of these unknown genes.
| Original language | English |
|---|---|
| Article number | e10620 |
| Journal | Ecology and Evolution |
| Volume | 13 |
| Issue number | 10 |
| DOIs | |
| State | Published - Oct 2023 |
| Externally published | Yes |
Funding
We thank the Forest Service's Pacific Southwest Regional Genetic Resources Program for allowing us to sample needles and collect seeds from their seed orchard, and XSEDE and UC Merced computer cluster for computational resources and support. We thank David Neale, Stephen Hart, Jason Sexton, Jeffrey Lauder, Dean Wu, and Rainbow DeSilva for their comments on the manuscript and the experimental design. Also, thank Vanessa Centeno and Shirley Calderon at UC Merced for greenhouse plant care. The sequencing was carried out at the DNA Technologies and Expression Analysis Cores at the UC Davis Genome Center, supported by NIH Shared Instrumentation Grant 1S10OD010786-01. For SNP identification, we used the MERCED computer cluster at UC Merced (funded by NSF Award ACI-1429783) and the Extreme Science and Engineering Discovery Environment (XSEDE, supported by NSF Award ACI-1548562). The funding organizations paid the experimental fees and computing resources for this research. Still, they did not play any role in the design of the study nor the collection analysis and interpretation of data, or the writing of the manuscript. We thank the Forest Service's Pacific Southwest Regional Genetic Resources Program for allowing us to sample needles and collect seeds from their seed orchard, and XSEDE and UC Merced computer cluster for computational resources and support. We thank David Neale, Stephen Hart, Jason Sexton, Jeffrey Lauder, Dean Wu, and Rainbow DeSilva for their comments on the manuscript and the experimental design. Also, thank Vanessa Centeno and Shirley Calderon at UC Merced for greenhouse plant care. The sequencing was carried out at the DNA Technologies and Expression Analysis Cores at the UC Davis Genome Center, supported by NIH Shared Instrumentation Grant 1S10OD010786‐01. For SNP identification, we used the MERCED computer cluster at UC Merced (funded by NSF Award ACI‐1429783) and the Extreme Science and Engineering Discovery Environment (XSEDE, supported by NSF Award ACI‐1548562). The funding organizations paid the experimental fees and computing resources for this research. Still, they did not play any role in the design of the study nor the collection analysis and interpretation of data, or the writing of the manuscript.
Keywords
- adaptive genetic variation
- climate change
- environmental association
- GBS
- phenotypic association
- SNP