Bridging the genotype-phenotype gap for a Mediterranean pine by semi-automatic crown identification and multispectral imagery

Progress in high-throughput phenotyping and genomics provides the potential to understand the genetic basis of plant functional differentiation. We developed a semi-automatic methodology based on unmanned aerial vehicle (UAV) imagery for deriving tree-level phenotypes followed by genome-wide association study (GWAS). An RGB-based point cloud was used for tree crown identification in a common garden of Pinus halepensis in Spain. Crowns were combined with multispectral and thermal orthomosaics to retrieve growth traits, vegetation indices and canopy temperature. Thereafter, GWAS was performed to analyse the association between phenotypes and genomic variation at 235 single nucleotide polymorphisms (SNPs). Growth traits were associated with 12 SNPs involved in cellulose and carbohydrate metabolism. Indices related to transpiration and leaf water content were associated with six SNPs involved in stomata dynamics. Indices related to leaf pigments and leaf area were associated with 11 SNPs involved in signalling and peroxisome metabolism. About 16-20% of trait variance was explained by combinations of several SNPs, indicating polygenic control of morpho-physiological traits. Despite a limited availability of markers and individuals, this study is provides a successful proof-of-concept for the combination of high-throughput UAV-based phenotyping with cost-effective genotyping to disentangle the genetic architecture of phenotypic variation in a widespread conifer.

Keywords: Pinus halepensis; SNPs; genome-wide association study (GWAS); genomics; remote sensing; unmanned aerial vehicle (UAV).