Abstract Detail

Molecular Ecology

Cobo, Irene [1], Herndon, Nic [2], Staton, Margaret [3], Grau, Emily [4], Buehler, Sean [5], Richter, Peter [6], Risharde , Ramnath [5], Demurjian, Charles [5], Almsaeed, Abdullah [3], Wegrzyn, Jill [7].

Integrative imputation-driven meta-analysis of genome-wide and genome-environment association analyses provides insights into genetic control of fitness related traits in trees: Populus trichocarpa as case study.

Climate change is imposing an adaptive challenge for many species worldwide. Forest trees are particularly concerning, given their ecological and economical importance, as well as their theoretically slower adaptive rate, as long-lived sessile organisms. Thus, the rate of environmental change may exceed their adaptive potential. Hence, the identification of genes controlling traits that provide improved tolerance constitutes one of the most important questions to secure future forest health. However, these analyses are challenging since they require the integration of disparate data sources: genotypic, phenotypic and environmental.  CartograTree is the first web-based application which integrates genotypic, phenotypic and environmental data, and associated meta-data, from georeferenced plants, connected by analytic workflows (http://cartogratree.org/). This robust and integrative resource is relevant and timely since consistent data collection for plant population studies is lacking.  Here we present an imputation-based meta-analysis workflow for genome-wide (GWAS) and genome-environment association (GEA) analyses, developed to be implemented in CartograTree. This workflow will improve the ability to analyze multiple datasets from independent studies in this integrative platform. On the one hand, meta-analyses will allow the integration of genomic, phenotypic and environmental data from different studies. On the other hand, imputation methods are useful when working with low-density SNPs, since they predict unobserved genotypes, enhancing true association signals and facilitating meta-analysis. Furthermore, to date, integrative meta-analysis, combining GEA and GWAS studies, has not been performed in trees.  Populus trichocarpa is used as model species given its compact genome size, economic and ecological importance, rapid growth and the high number of genomic and phenotypic resources available, since it was the first sequenced tree genome. Our analysis is based on three different GWAS studies containing SNPs from georeferenced P. trichocarpa individuals. Regarding phenotypic traits, bud set, bud flush and height were selected, since they are related to fitness and show high heritability. Regarding environmental data, temperature, precipitation, and photoperiod, were selected. This integrative workflow will contribute to our understanding of the genomic basis of traits providing tolerance to the changing environmental conditions in trees. This knowledge is key to develop suitable conservation and management strategies, such as marker-assisted selection of resilient individuals in repopulations, as well as to secure future forest health in the face of a changing climate.

Related Links:

1 - University of Connecticut, Department of Ecology and Evolutionary Biology, Engineering and Sciences Building, 67 N. Eagleville Road, Storrs, CT, 06269, USA
2 - East Carolina University, Department of Computer Science, NC, USA
3 - University of Tennessee, Department of Entomology and Plant Pathology, Knoxville, TN, USA
4 - University of Connecticut, Department of Ecology and Evolutionary Biology, Storrs, USA
5 - University of Connecticut, Department of Ecology and Evolutionary Biology, Storrs, CT, USA
6 - University of Connecticut, Department of Ecology and Evolutionary Biology, CT, USA
7 - University Of Connecticut, EEB, 67 N. Eagleville Road, Unit 3124, Storrs, CT, 06269, United States

Workflow development
genome-wide association study
Environmental association analyses
Populus trichocarpa.

Presentation Type: Poster
Session: P, Molecular Ecology Posters
Location: Virtual/Virtual
Date: Tuesday, July 28th, 2020
Time: 5:00 PM Time and date to be determined
Number: PME001
Abstract ID:447
Candidate for Awards:None

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