Predicting Adaptive Phenotypes From Multilocus Genotypes in Sitka Spruce (Picea sitchensis) Using Random Forest

G3: Genes, Genomes, Genetics

Sally Aitken²  Jason A. Holliday¹  Tongli Wang² 

[1] Department of Forest Resources and Environmental Conservation, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061Department of Forest Resources and Environmental Conservation, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061Department of Forest Resources and Environmental Conservation, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061;Department of Forest Sciences, University of British Columbia, Vancouver, BC, Canada, V6T 1Z4Department of Forest Sciences, University of British Columbia, Vancouver, BC, Canada, V6T 1Z4Department of Forest Sciences, University of British Columbia, Vancouver, BC, Canada, V6T 1Z4

关键词: Random Forest;    adaptation;    association mapping;    epistasis;    phenology;    cold hardiness;    GenPred;    shared data resources;   

DOI  :  10.1534/g3.112.002733

学科分类：生物科学（综合）

来源: Genetics Society of America

【 摘 要 】

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 traitsautumn 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.

【 授权许可】

Unknown