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      A Genome-Wide Association Study for Resistance to the Insect Pest Leptocybe invasa in Eucalyptus grandis Reveals Genomic Regions and Positional Candidate Defense Genes

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          Abstract

          The galling insect, Leptocybe invasa, causes significant losses in plantations of various Eucalyptus species and hybrids, threatening its economic viability. We applied a genome-wide association study (GWAS) to identify single-nucleotide polymorphism (SNP) markers associated with resistance to L. invasa. A total of 563 insect-challenged Eucalyptus grandis trees, from 61 half-sib families, were genotyped using the EUChip60K SNP chip, and we identified 15,445 informative SNP markers in the test population. Multi-locus mixed-model (MLMM) analysis identified 35 SNP markers putatively associated with resistance to L. invasa based on four discreet classes of insect damage scores: (0) not infested, (1) infested showing evidence of oviposition but no gall development, (2) infested with galls on leaves, midribs or petioles and (3) stunting and lethal gall formation. MLMM analysis identified three associated genomic regions on chromosomes 3, 7 and 8 jointly explaining 17.6% of the total phenotypic variation. SNP analysis of a validation population of 494 E. grandis trees confirmed seven SNP markers that were also detected in the initial association analysis. Based on transcriptome profiles of resistant and susceptible genotypes from an independent experiment, we identified several putative candidate genes in associated genomic loci including Nucleotide-binding ARC- domain (NB-ARC) and toll-interleukin-1-receptor-Nucleotide binding signal- Leucine rich repeat (TIR-NBS-LRR) genes. Our results suggest that Leptocybe resistance in E. grandis may be influenced by a few large-effect loci in combination with minor effect loci segregating in our test and validation populations.

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          An efficient multi-locus mixed model approach for genome-wide association studies in structured populations

          Population structure causes genome-wide linkage disequilibrium between unlinked loci, leading to statistical confounding in genome-wide association studies. Mixed models have been shown to handle the confounding effects of a diffuse background of large numbers of loci of small effect well, but do not always account for loci of larger effect. Here we propose a multi-locus mixed model as a general method for mapping complex traits in structured populations. Simulations suggest that our method outperforms existing methods, in terms of power as well as false discovery rate. We apply our method to human and Arabidopsis thaliana data, identifying novel associations in known candidates as well as evidence for allelic heterogeneity. We also demonstrate how a priori knowledge from an A. thaliana linkage mapping study can be integrated into our method using a Bayesian approach. Our implementation is computationally efficient, making the analysis of large datasets (n > 10000) practicable.
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            The genome of Eucalyptus grandis.

            Eucalypts are the world's most widely planted hardwood trees. Their outstanding diversity, adaptability and growth have made them a global renewable resource of fibre and energy. We sequenced and assembled >94% of the 640-megabase genome of Eucalyptus grandis. Of 36,376 predicted protein-coding genes, 34% occur in tandem duplications, the largest proportion thus far in plant genomes. Eucalyptus also shows the highest diversity of genes for specialized metabolites such as terpenes that act as chemical defence and provide unique pharmaceutical oils. Genome sequencing of the E. grandis sister species E. globulus and a set of inbred E. grandis tree genomes reveals dynamic genome evolution and hotspots of inbreeding depression. The E. grandis genome is the first reference for the eudicot order Myrtales and is placed here sister to the eurosids. This resource expands our understanding of the unique biology of large woody perennials and provides a powerful tool to accelerate comparative biology, breeding and biotechnology.
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              The advantages and limitations of trait analysis with GWAS: a review

              Over the last 10 years, high-density SNP arrays and DNA re-sequencing have illuminated the majority of the genotypic space for a number of organisms, including humans, maize, rice and Arabidopsis. For any researcher willing to define and score a phenotype across many individuals, Genome Wide Association Studies (GWAS) present a powerful tool to reconnect this trait back to its underlying genetics. In this review we discuss the biological and statistical considerations that underpin a successful analysis or otherwise. The relevance of biological factors including effect size, sample size, genetic heterogeneity, genomic confounding, linkage disequilibrium and spurious association, and statistical tools to account for these are presented. GWAS can offer a valuable first insight into trait architecture or candidate loci for subsequent validation.
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Plant and Cell Physiology
                Oxford University Press (OUP)
                0032-0781
                1471-9053
                July 2020
                July 01 2020
                May 07 2020
                July 2020
                July 01 2020
                May 07 2020
                : 61
                : 7
                : 1285-1296
                Affiliations
                [1 ]Department of Biochemistry, Genetics and Microbiology, Forestry and Agricultural Biotechnology Institute (FABI), University of Pretoria, Private Bag X20, Pretoria 0028, South Africa
                [2 ]Mondi South Africa, Forests Operations, Research and Development Department, Trahar Technology Centre—TTC, PO Box 12, Hilton 3245, South Africa
                [3 ]Department of Forestry and Environmental Resources, College of Natural Resources, North Carolina State University, 2820 Faucette Drive, Raleigh, NC, USA
                Article
                10.1093/pcp/pcaa057
                32379870
                d548152c-7856-402d-bdf6-f60d0d8ad1ff
                © 2020

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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