Enhancing genomic association studies in slash pine through close-range UAV-based morphological phenotyping.

Abstract

In forestry genetics and industry, tree morphological traits such as height, crown size, and shape are critical for understanding growth dynamics and productivity. Traditional methods for measuring these traits are limited in efficiency, scalability, and accuracy, posing challenges for large-scale forest assessments. This study focuses on integrating unmanned aerial vehicle (UAV) technology with GWAS to improve genomic association studies in slash pine (Pinus elliottii). Seven key morphological traits have been identified (canopy area (CA), crown base height (CBH), crown length (CL), canopy volume (CV), crown width (CW), crown width height (CWH), and tree height (H)) through advanced UAV-based phenotyping. These associations account for a remarkable range of heritability in slash pine, with traits such as CBH, CL, CV, and H showing relatively high heritability across both Single nucleotide polymorphisms (SNP) and pedigree methods, indicating strong genetic influence, while traits such as CWH show lower heritability, suggesting greater environmental influence or non-additive genetic variance. The GWAS identified 28 associations, including 22 different SNPs localized to 16 candidate genes, that were significantly associated with the morphological traits of Slash Pine. Notably, two of these candidate genes, annotated as putative DEAD-like helicase and ethylene-responsive element binding factor (ERF), were present at different mutation sites and were significantly associated with CW and CA traits, respectively. These results demonstrate that the UAV imaging enables a comprehensive analysis of the Morphological growth response of slash pine and can facilitate the discovery of informative alleles to elucidate the genetic structure underlying complex phenotypic variation in conifers.