Genetic analysis of predicted vegetative biomass and biomass‐related traits from digital phenotyping of strawberry - Cheryl Dalid; Caiwang Zheng; Luis Osorio; Sujeet Verma; Amr Abd‐Elrahman; Xu Wang; Vance M. Whitaker (The Plant Genome)
Abstract High‐throughput digital phenotyping (DP) has been widely explored in plant breeding to assess large numbers of genotypes with minimal manual labor and reduced cost and time. DP platforms using high‐resolution images captured by drones and tractor‐based platforms have recently allowed the University of Florida strawberry (Fragaria × ananassa) breeding program to assess vegetative biomass at scale. Biomass has not previously been explored in a strawberry breeding context due to the labor required and the need to destroy the plant. This study aims to understand the genetic basis of predicted vegetative biomass and biomass‐related traits and to chart a path for the combined use of DP and genomics in strawberry breeding. Aboveground dry vegetative biomass was estimated by adapting a previously published model using ground‐truth data on a subset of breeding germplasm. High‐resolution images were collected on clonally replicated trials at different time points during the fruiting season. There was moderate to high heritability (h2 = 0.26–0.56) for predicted vegetative biomass, and genetic correlations between vegetative biomass and marketable yield were mostly positive (rG = −0.13–0.47). Fruit yield traits scaled on a vegetative biomass basis also had moderate to high heritability (h2 = 0.25–0.64). This suggests that vegetative biomass can be decreased or increased through selection, and that marketable fruit yield can be improved without simultaneously increasing plant size. No consistent marker‐trait associations were discovered via genome‐wide association studies. On the other hand, predictive abilities from genomic selection ranged from 0.15 to 0.46 across traits and years, suggesting that genomic prediction will be an effective breeding tool for vegetative biomass in strawberry.