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

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dc.contributor.authorHolliday, Jason A.en
dc.contributor.authorWang, Tonglien
dc.contributor.authorAitken, Sally N.en
dc.contributor.departmentForest Resources and Environmental Conservationen
dc.date.accessioned2019-12-23T17:29:17Zen
dc.date.available2019-12-23T17:29:17Zen
dc.date.issued2012-09-01en
dc.description.abstractClimate 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.en
dc.description.notesWe thank the two reviewers for helpful comments on a previous version of this manuscript. This work was supported by Genome British Columbia, Genome Canada, and the Province of British Columbia (funding proposals by S. A., J.A.H., and T. W.), by the Natural Science and Engineering Research Council of Canada (NSERC; grant to S. A.), by an NSERC Postgraduate Scholarship to J.A.H, and by Virginia Tech 'Startup Funds' to J.A.H.en
dc.description.sponsorshipGenome British Columbia; Genome CanadaGenome Canada; Province of British Columbia; Natural Science and Engineering Research Council of Canada (NSERC)Natural Sciences and Engineering Research Council of Canada; NSERCNatural Sciences and Engineering Research Council of Canada; Virginia Tech 'Startup Funds'en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1534/g3.112.002733en
dc.identifier.eissn2160-1836en
dc.identifier.issue9en
dc.identifier.pmid22973546en
dc.identifier.urihttp://hdl.handle.net/10919/96210en
dc.identifier.volume2en
dc.language.isoenen
dc.publisherGenetics Society of Americaen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectRandom Foresten
dc.subjectadaptationen
dc.subjectassociation mappingen
dc.subjectepistasisen
dc.subjectphenologyen
dc.subjectcold hardinessen
dc.subjectGenPreden
dc.subjectshared data resourcesen
dc.titlePredicting Adaptive Phenotypes From Multilocus Genotypes in Sitka Spruce (Picea sitchensis) Using Random Foresten
dc.title.serialG3-Genes Genomes Geneticsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.dcmitypeStillImageen

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