GWAS identifies candidate genes controlling adventitious rooting in Populus trichocarpa

Abstract

Adventitious rooting (AR) is critical to the propagation, breeding, and genetic engineering of trees. The capacity for plants to undergo this process is highly heritable and of a polygenic nature; however, the basis of its genetic variation is largely uncharacterized. To identify genetic regulators of AR, we performed a genome-wide association study (GWAS) using 1148 genotypes of Populus trichocarpa. GWASs are often limited by the abilities of researchers to collect precise phenotype data on a high-throughput scale; to help overcome this limitation, we developed a computer vision system to measure an array of traits related to adventitious root development in poplar, including temporal measures of lateral and basal root length and area. GWAS was performed using multiple methods and significance thresholds to handle non-normal phenotype statistics and to gain statistical power. These analyses yielded a total of 277 unique associations, suggesting that genes that control rooting include regulators of hormone signaling, cell division and structure, reactive oxygen species signaling, and other processes with known roles in root development. Numerous genes with uncharacterized functions and/or cryptic roles were also identified. These candidates provide targets for functional analysis, including physiological and epistatic analyses, to better characterize the complex polygenic regulation of AR.

Acknowledgements

We thank the National Science Foundation Plant Genome Research Program for support (IOS #1546900, Analysis of genes affecting plant regeneration and transformation in poplar), and members of GREAT TREES Research Cooperative at OSU for its support of the Strauss laboratory. Support for the Poplar GWAS dataset is provided by the U.S. Department of Energy, Office of Science Biological and Environmental Research via the Center for Bioenergy Innovation (CBI) under Contract No. DE-PS02- 06ER64304. The Poplar GWAS Project used resources of the Oak Ridge Leadership Computing Facility and the Compute and Data Environment for Science at Oak Ridge National Laboratory, which is supported by the Office of Science of the US Department of Energy under Contract No. DE-AC05-00OR22725. We would like to thank the efforts of the personnel from the CBI in establishing the GWAS resource used for this study. This work used the COMET high-performance cluster at the San Diego Supercomputing Center (University of California, San Diego) made available through the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-1548562.

Author Contributions

Strauss, Fuxin, Jiang, and Muchero designed and directed the overall study and obtained funding for its execution; Ma and Peremyslova designed and/or executed the phenotypic analyses; Nagle, Yuan, and Kaur created, adapted, and executed the computer vision, computation, and data analysis pipelines; Zahl and Peremyslova performed manual inspection and correction of phenotype data; Nagle investigated the candidate genes; Niño de Rivera assisted with inspecting the results in IGV; Nagle wrote the manuscript with editing from Strauss; and all others contributed further edits and revisions.

Data Availability

Raw data and the code used for this project are available upon request to the authors. GitHub repositories are online for MTMC- SKAT (https://github.com/naglemi/mtmcskat) and the computer vision workflow (https://github.com/jia2lin3yuan1/GWAS-Root- analysis).

Conflicts of Interest statement

None declared.

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