GWAS supported by computer vision identifies large numbers of candidate regulators of in planta regeneration in Populus trichocarpa.

Abstract

Plant regeneration is an important dimension of plant propagation and a key step in the production of transgenic plants. However, regeneration capacity varies widely among genotypes and species, the molecular basis of which is largely unknown. Association mapping methods such as genome-wide association studies (GWAS) have long demonstrated abilities to help uncover the genetic basis of trait variation in plants; however, the performance of these methods depends on the accuracy and scale of phenotyping. To enable a large-scale GWAS of in planta callus and shoot regeneration in the model tree Populus, we developed a phenomics workflow involving semantic segmentation to quantify regenerating plant tissues over time. We found that the resulting statistics were of highly non-normal distributions, and thus employed transformations or permutations to avoid violating assumptions of linear models used in GWAS. We report over 200 statistically supported quantitative trait loci (QTLs), with genes encompassing or near to top QTLs including regulators of cell adhesion, stress signaling, and hormone signaling pathways, as well as other diverse functions. Our results encourage models of hormonal signaling during plant regeneration to consider keystone roles of stress-related signaling (e.g. involving jasmonates and salicylic acid), in addition to the auxin and cytokinin pathways commonly considered. The putative regulatory genes and biological processes we identified provide new insights into the biological complexity of plant regeneration, and may serve as new reagents for improving regeneration and transformation of recalcitrant genotypes and species.

Plant phenotyping relevance

再生組織を時系列で定量するセマンティックセグメンテーションを中核としたフェノミクス・ワークフローを開発し、大規模GWASに適用しているため、植物表現型取得法が中心的です。

Code and data availability

The authors publicly release their GWAS analysis code: the MTMC-SKAT R package and the inplantaGWAS repository containing phenotype data parsing, association mapping, and downstream analysis code used in this study. The SNP and image datasets are stated to be publicly available but only via a citation (Nagle et al. 202