From aerial drone to QTL: Leveraging next-generation phenotyping to reveal the genetics of color and height in field-grownLactuca sativa

Abstract

In recent years, the automation of genotyping has significantly enhanced the efficiency of genome-wide association studies. Due to this development, phenotyping is now usually the rate-limiting step, especially in the field. Efforts are now focused on further automating in-field phenotyping. Here we present a GWAS study on 194 field-grown accessions of lettuce (Lactuca sativa). These accessions were non-destructively phenotyped at two time points 15 days apart using an unmanned aerial vehicle. Our high throughput phenotyping approach integrates an RGB camera, a multispectral camera to measure the reflectance at 5 wavelengths (blue, green, red, red edge, near-infrared), and precise height estimation. We used the mean and other descriptives such as median, quantiles, minimum and maximum to quantify different aspects of color and height variation in lettuce from the drone images. Using this approach, we confirm several previously described QTLs, now in populations grown under field conditions and identify several new QTLs for plant-height and color.