Genomics and Phenomics Enabled Prebreeding Improved Early-Season Chilling Tolerance in Sorghum

Abstract

SUMMARY In temperate climates, earlier planting of tropical-origin crops can provide longer growing seasons, reduce water loss, suppress weeds, and escape post-flowering drought stress. However, chilling sensitivity of sorghum, a tropical-origin cereal crop, limits early planting and over 50 years of conventional breeding has been stymied by coinheritance of chilling tolerance (CT) loci with undesirable tannin and dwarfing alleles. In this study, phenomics and genomics-enabled approaches were used for prebreeding of sorghum early-season CT. Uncrewed aircraft systems (UAS) high-throughput phenotyping platform tested for improving scalability showed moderate correlation between manual and UAS phenotyping. UAS normalized difference vegetation index (NDVI) values from the chilling nested association mapping population detected CT QTL that colocialized with manual phenotyping CT QTL. Two of the four first-generation KASP molecular markers, generated using the peak QTL SNPs, failed to function in an independent breeding program as the CT allele was common in diverse breeding lines. Population genomic F ST analysis identified SNP CT alleles that were globally rare but common to the CT donors. Second-generation markers, generated using population genomics, were successful in tracking the donor CT allele in diverse breeding lines from two independent sorghum breeding programs. Marker-assisted breeding, effective in introgressing CT allele from Chinese sorghums into chilling-sensitive US elite sorghums, improved early-planted seedling performance ratings in lines with CT alleles by up to 13–24% compared to the negative control under natural chilling stress. These findings directly demonstrate the effectiveness of high-throughput phenotyping and population genomics in molecular breeding of complex adaptive traits.

Plant phenotyping relevance

UASを用いた高スループット表現型計測のスケーラビリティ検証、手動計測との比較、NDVIによる耐寒性QTL検出が研究の主要な技術的要素であるため。

Code and data availability

The supplied blocks describe UAS NDVI phenotyping, joint linkage mapping, and FST analyses, but contain no public deposit of this paper's phenotype datasets, UAS imagery, or author analysis code. The only URL mentioned (uavmissionplanner.netlify.app) is a generic third-party flight-planning tool, not a paper-specific,`

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