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

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          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.

          Most cited references32

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          Epistasis--the essential role of gene interactions in the structure and evolution of genetic systems.

          Epistasis, or interactions between genes, has long been recognized as fundamentally important to understanding the structure and function of genetic pathways and the evolutionary dynamics of complex genetic systems. With the advent of high-throughput functional genomics and the emergence of systems approaches to biology, as well as a new-found ability to pursue the genetic basis of evolution down to specific molecular changes, there is a renewed appreciation both for the importance of studying gene interactions and for addressing these questions in a unified, quantitative manner.
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            Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions.

            Polymorphisms in human genes are being described in remarkable numbers. Determining which polymorphisms and which environmental factors are associated with common, complex diseases has become a daunting task. This is partly because the effect of any single genetic variation will likely be dependent on other genetic variations (gene-gene interaction or epistasis) and environmental factors (gene-environment interaction). Detecting and characterizing interactions among multiple factors is both a statistical and a computational challenge. To address this problem, we have developed a multifactor dimensionality reduction (MDR) method for collapsing high-dimensional genetic data into a single dimension thus permitting interactions to be detected in relatively small sample sizes. In this paper, we describe the MDR approach and an MDR software package. We developed a program that integrates MDR with a cross-validation strategy for estimating the classification and prediction error of multifactor models. The software can be used to analyze interactions among 2-15 genetic and/or environmental factors. The dataset may contain up to 500 total variables and a maximum of 4000 study subjects. Information on obtaining the executable code, example data, example analysis, and documentation is available upon request. All supplementary information can be found at http://phg.mc.vanderbilt.edu/Software/MDR.
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              Association genetics of complex traits in conifers.

              Association studies are becoming the experimental approach of choice to dissect complex traits in many organisms from humans to model plant systems. The candidate gene based-association approach has several important advantages for complex trait dissection in many coniferous forest tree species, including random mating and unstructured populations, adequate levels of nucleotide diversity, rapid decay of linkage disequilibrium, and precise evaluation of phenotype from clonal or progeny testing. Allele discovery using association approaches should lead to more-efficient methods of marker-assisted breeding and a deeper understanding of genetic adaptation in forest trees.
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                Author and article information

                Journal
                G3 (Bethesda)
                Genetics
                ggg
                ggg
                ggg
                G3: Genes|Genomes|Genetics
                Genetics Society of America
                2160-1836
                1 September 2012
                September 2012
                : 2
                : 9
                : 1085-1093
                Affiliations
                [* ]Department of Forest Resources and Environmental Conservation, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, and
                []Department of Forest Sciences, University of British Columbia, Vancouver, BC, Canada, V6T 1Z4
                Author notes

                Supporting information is available online at http://www.g3journal.org/lookup/suppl/doi:10.1534/g3.112.002733/-/DC1

                [1]

                These authors contributed equally to this work.

                [2 ]Corresponding author: Department of Forest Resources and Environmental Conservation, Virginia Polytechnic Institute and State University, 304 Cheatham Hall, Blacksburg, VA 24061-0001. E-mail: jah1@ 123456vt.edu
                Article
                GGG_002733
                10.1534/g3.112.002733
                3429923
                22973546
                9386a5c9-fb63-44e5-8728-55fe4bcd70e7
                Copyright © 2012 Holliday et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution Unported License ( http://creativecommons.org/licenses/by/3.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 08 April 2012
                : 03 July 2012
                Categories
                Investigations
                Custom metadata
                v1

                Genetics
                phenology,random forest,epistasis,association mapping,cold hardiness,adaptation,genpred,shared data resources

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