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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
Published on: July 3, 2020
Predicting adaptive phenotypes from multilocus genotypes in Sitka spruce (Picea sitchensis) using random forest.
Jason A Holliday1, Tongli Wang, Sally Aitken
1Department of Forest Resources and Environmental Conservation, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061-0001, USA. jah1@vt.edu
G3 (Bethesda, Md.)
|September 14, 2012
Summary
Random Forest modeling identified key single-nucleotide polymorphisms (SNPs) for Sitka spruce adaptation to climate. These SNPs, along with their interactions, explain significant variation in budset timing and cold hardiness.
Area of Science:
- Forestry and Forest Ecology
- Plant Genetics and Genomics
- Climate Change Biology
Background:
- Climate is a major factor in tree species distribution globally.
- Understanding adaptive evolution is crucial for predicting forest responses to climate change.
- Association mapping can reveal genetic bases of climate-related traits, but integrated models are needed.
Purpose of the Study:
- To utilize the Random Forest algorithm for identifying optimal single-nucleotide polymorphism (SNP) combinations predicting adaptive phenotypes in Sitka spruce.
- To investigate the role of epistasis (gene-gene interactions) in shaping adaptive traits.
Main Methods:
- Employed the Random Forest algorithm to analyze association mapping data from Sitka spruce (Picea sitchensis).
- Adjusted for population structure to accurately assess phenotypic variation.
- Developed a novel approach to quantify pairwise SNP interactions (epistasis).
Main Results:
- Random Forest successfully identified key SNPs for predicting autumn budset timing (explaining 37% variation) and cold hardiness (explaining 30% variation).
- The top five SNPs for each trait captured a substantial portion of the phenotypic variation.
- Pairwise interactions between SNPs were found to be common and significant for these adaptive traits.
Conclusions:
- Random Forest is a powerful tool for identifying marker subsets crucial for climatic adaptation in trees.
- Interactions among SNPs play a widespread role in the genetic architecture of adaptive traits.
- This approach enhances understanding of genomic underpinnings of forest adaptation to climate change.
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