High-Throughput Phenotyping Enabled Genetic Dissection of Crop Lodging in Wheat

Abstract

Novel high-throughput phenotyping (HTP) approaches are needed to advance the understanding of genotype-to-phenotype and accelerate plant breeding. The first generation of HTP has examined simple spectral reflectance traits from images and sensors but is limited in advancing our understanding of crop development and architecture. Lodging is a complex trait that significantly impacts yield and quality in many crops including wheat. Conventional visual assessment methods for lodging are time-consuming, relatively low-throughput, and subjective, limiting phenotyping accuracy and population sizes in breeding and genetics studies. Here, we demonstrate the considerable power of unmanned aerial systems (UAS) or drone-based phenotyping as a high-throughput alternative to visual assessments for the complex phenological trait of lodging, which significantly impacts yield and quality in many crops including wheat. We tested and validated quantitative assessment of lodging on 2,640 wheat breeding plots over the course of 2 years using differential digital elevation models from UAS. High correlations of digital measures of lodging to visual estimates and equivalent broad-sense heritability demonstrate this approach is amenable for reproducible assessment of lodging in large breeding nurseries. Using these high-throughput measures to assess the underlying genetic architecture of lodging in wheat, we applied genome-wide association analysis and identified a key genomic region on chromosome 2A, consistent across digital and visual scores of lodging. However, these associations accounted for a very minor portion of the total phenotypic variance. We therefore investigated whole genome prediction models and found high prediction accuracies across populations and environments. This adequately accounted for the highly polygenic genetic architecture of numerous small effect loci, consistent with the previously described complex genetic architecture of lodging in wheat. Our study provides a proof-of-concept application of UAS-based phenomics that is scalable to tens-of-thousands of plots in breeding and genetic studies as will be needed to uncover the genetic factors and increase the rate of gain for complex traits in crop breeding.

Keywords: GWAS; Triticum aestivum; UAV/UAS; genomic selection; high-throughput phenotyping; lodging; unmanned aerial systems; wheat breeding.

FIGURE 1

Workflow of digital and visual phenotypic analysis approaches used to assess crop lodging in wheat.

FIGURE 2

Processing of pre- and post-lodging digital elevation models (DEM) to obtain differential DEM of lodging. Post-lodging DEM is subtracted from pre-lodging DEM to generate a differential DEM of lodging. Panels are (A) pre-lodging, (B) post-lodging, and (C) differential DEM. Elevation differences are color coded with red corresponding to low elevation in (A,B) or high differences in (C), blue is areas of high elevation (A,B) or low differences (C).

FIGURE 3

Relationship of visual and digital lodging scores. Pairwise correlation matrix of visual and digital measures of lodging in (A) year 2016, (B) year 2017. Diagonal panels show the trait distributions and broad-sense entry mean heritability; upper triangle is the Pearson’s correlation coefficient values with significance levels as superscript (^∗∗∗P < 0.001); lower triangle is the scatter plot. LOI, lodging incidence; LOS, lodging severity; LI, lodging index; DLmean, digital lodging mean; DLmix, digital lodging mixture.

FIGURE 4

Manhattan plot of genome-wide associations. Manhattan plots of visual and digital lodging scores from combined analysis of genotypes from 2016 and 2017 (no. of genotypes = 1,035). The dashed lines on y-axis correspond to the genome-wide false discovery rate (FDR = 0.05) threshold. LOI, lodging incidence; LOS, lodging severity; LI, lodging index; DLmean, digital lodging mean; DLmix, digital lodging mixture.

FIGURE 5

Effect of 2NS translocation on lodging. The notched boxplot of phenotypic values of lodging measures for 2NS positive (2NS+) and negative (2NS–) genotypes. The asterisks show the significant p-value for each trait (t-test; n = 1010; ^∗P < 0.05, ^∗∗P < 0.01). LOI, lodging incidence; LOS, lodging severity; LI, lodging index; DLmean, digital lodging mean; DLmix, digital lodging mixture.