ArticleG3 (Bethesda, Md.)2012
Predicting adaptive phenotypes from multilocus genotypes in Sitka spruce (Picea sitchensis) using random forest.
Article in G3 (Bethesda, Md.), 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- Machine learning to predict genotypes and genotype-environment interaction associated with complex traits for genomic selection.Plant phenomics (Washington, D.C.) · 2026Article
- Climate change and crop resilience: harnessing metabolomics for predicting stress tolerance.The New phytologist · 2026Review
- In Situ GWAS in Two Range-Edge Populations of European Beech (Fagus sylvatica L.) Identify Genetic Factors of Adaptation to Climate Change.Molecular ecology · 2026Article
- Robustly enhancing crop genomic prediction accuracy through ensemble learning and iterative optimization.Nature communications · 2026Article
- High-Accuracy Chicken Breed Identification Using Microsatellite Genotype Data and AutoGluon Framework.Biology · 2025Article
- GPS: Harnessing data fusion strategies to improve the accuracy of machine learning-based genomic and phenotypic selection.Plant communications · 2025Article
- Population structure limits the use of genomic data for predicting phenotypes and managing genetic resources in forest trees.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Considerations in the search for epistasis.Genome biology · 2024Review
- Dual-extraction modeling: A multi-modal deep-learning architecture for phenotypic prediction and functional gene mining of complex traits.Plant communications · 2024Article
- Prediction accuracy of genomic estimated breeding values for fruit traits in cultivated tomato (Solanum lycopersicum L.).BMC plant biology · 2024Article
- Low-density SNP markers with high prediction accuracy of genomic selection for bacterial wilt resistance in tomato.Frontiers in plant science · 2024Article
- FSF-GA: A Feature Selection Framework for Phenotype Prediction Using Genetic Algorithms.Genes · 2023Article
- Digitalizing breeding in plants: A new trend of next-generation breeding based on genomic prediction.Frontiers in plant science · 2023Review
- Harnessing tree-ring phenotypes to disentangle gene by environment interactions and their climate dependencies in a circum-Mediterranean pine.Annals of botany · 2022Article
- Article
- IIb-RAD-sequencing coupled with random forest classification indicates regional population structuring and sex-specific differentiation in salmon lice (Ecology and evolution · 2022Article
- Population genomics and geographic dispersal in Chagas disease vectors: Landscape drivers and evidence of possible adaptation to the domestic setting.PLoS genetics · 2022Article
- Genomic Selection: A Tool for Accelerating the Efficiency of Molecular Breeding for Development of Climate-Resilient Crops.Frontiers in genetics · 2022Review
- Whole-Genome Resequencing to Evaluate Life History Variation in Anadromous Migration ofFrontiers in genetics · 2022Article
- Crop genomic selection with deep learning and environmental data: A survey.Frontiers in artificial intelligence · 2022Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Climate is the primary driver of the distribution of tree species worldwide, and the potential for adaptive evolution will be an important factor determining the response of forests to anthropogenic climate change. Although association mapping has the potential to improve our understanding of the genomic underpinnings of climatically relevant traits, the utility of adaptive polymorphisms uncovered by such studies would be greatly enhanced by the development of integrated models that account for the phenotypic effects of multiple single-nucleotide polymorphisms (SNPs) and their interactions simultaneously. We previously reported the results of association mapping in the widespread conifer Sitka spruce (Picea sitchensis). In the current study we used the recursive partitioning algorithm 'Random Forest' to identify optimized combinations of SNPs to predict adaptive phenotypes. After adjusting for population structure, we were able to explain 37% and 30% of the phenotypic variation, respectively, in two locally adaptive traits--autumn budset timing and cold hardiness. For each trait, the leading five SNPs captured much of the phenotypic variation. To determine the role of epistasis in shaping these phenotypes, we also used a novel approach to quantify the strength and direction of pairwise interactions between SNPs and found such interactions to be common. Our results demonstrate the power of Random Forest to identify subsets of markers that are most important to climatic adaptation, and suggest that interactions among these loci may be widespread.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.