BackgroundSorghum is a tropical C4 cereal that recently adapted to temperate latitudes and mechanized grain harvest through selection for dwarfism and photoperiod-insensitivity. Quantitative trait loci for these traits have been introgressed from a dwarf temperate donor into hundreds of diverse sorghum landraces to yield the Sorghum Conversion lines. Here, we report the first comprehensive genomic analysis of the molecular changes underlying this adaptation.ResultsWe apply genotyping-by-sequencing to 1,160 Sorghum Conversion lines and their exotic progenitors, and map donor introgressions in each Sorghum Conversion line. Many Sorghum Conversion lines carry unexpected haplotypes not found in either presumed parent. Genome-wide mapping of introgression frequencies reveals three genomic regions necessary for temperate adaptation across all Sorghum Conversion lines, containing the Dw1, Dw2, and Dw3 loci on chromosomes 9, 6, and 7 respectively. Association mapping of plant height and flowering time in Sorghum Conversion lines detects significant associations in the Dw1 but not the Dw2 or Dw3 regions. Subpopulation-specific introgression mapping suggests that chromosome 6 contains at least four loci required for temperate adaptation in different sorghum genetic backgrounds. The Dw1 region fractionates into separate quantitative trait loci for plant height and flowering time.ConclusionsGenerating Sorghum Conversion lines has been accompanied by substantial unintended gene flow. Sorghum adaptation to temperate-zone grain production involves a small number of genomic regions, each containing multiple linked loci for plant height and flowering time. Further characterization of these loci will accelerate the adaptation of sorghum and related grasses to new production systems for food and fuel.

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We explored the capability of fusing high dimensional phenotypic trait (phenomic) data with a machine learning (ML) approach to provide plant breeders the tools to do both in-season seed yield (SY) prediction and prescriptive cultivar development for targeted agro-management practices (e.g., row spacing and seeding density). We phenotyped 32 SoyNAM parent genotypes in two independent studies each with contrasting agro-management treatments (two row spacing, three seeding densities). Phenotypic trait data (canopy temperature, chlorophyll content, hyperspectral reflectance, leaf area index, and light interception) were generated using an array of sensors at three growth stages during the growing season and seed yield (SY) determined by machine harvest. Random forest (RF) was used to train models for SY prediction using phenotypic traits (predictor variables) to identify the optimal temporal combination of variables to maximize accuracy and resource allocation. RF models were trained using data from both experiments and individually for each agro-management treatment. We report the most important traits agnostic of agro-management practices. Several predictor variables showed conditional importance dependent on the agro-management system. We assembled predictive models to enable in-season SY prediction, enabling the development of a framework to integrate phenomics information with powerful ML for prediction enabled prescriptive plant breeding.

Sorghum varieties suitable for grain production at temperate latitudes show dwarfism and photoperiod insensitivity, both of which are controlled by a small number of loci with large effects. We studied the genetic control of plant height and flowering time in five sorghum families (A–E), each derived from a cross between a tropical line and a partially isogenic line carrying introgressions derived from a common, temperate-adapted donor. A total of 724 F2:3 lines were phenotyped in temperate and tropical environments for plant height and flowering time and scored at 9139 SNPs using genotyping-by-sequencing. Biparental mapping was compared with multiparental mapping in different subsets of families (AB, ABC, ABCD, and ABCDE) using both a GWAS approach, which fit each QTL as a single effect across all families, and using a joint linkage approach, which fit QTL effects as nested within families. GWAS using all families (ABCDE) performed best at the cloned Dw3 locus, whereas joint linkage using all families performed best at the cloned Ma1 locus. Both multiparental approaches yielded apparently synthetic associations due to genetic heterogeneity and were highly dependent on the subset of families used. Comparison of all mapping approaches suggests that a GA2-oxidase underlies Dw1, and that a mir172a gene underlies a Dw1-linked flowering time QTL.

The Kansas Dipel-resistant and susceptible European corn borer, Ostrinia nubilalis (Hübner), were evaluated in the greenhouse on different Bt transgenic events expressed in corn hybrids. There were important differences in the resistance offered by the different Bt event corn hybrids. Hybrid comparison tests indicate that these Dipel-resistant first-instar European corn borer were not able to survive to adulthood on whorl-stage MON810, Bt11, or 176 Bt event corn plants. Third instars did not survive to adulthood on whorl-stage MON810 or Bt11 event corn plants but a small number of fifth instars were found on whorl-stage DBT418 plants infested with Dipel-resistant larvae. First and third instars of these Dipel-resistant European corn borers caused more leaf-feeding damage and more tunneling on whorl-stage Bt-corn plants than did the Dipel-susceptible European corn borers. However, in the single Bt corn hybrid test, there was no survival of the Dipel-resistant European corn borers on DK580BtX or MAX454 Bt plants 35 to 42 d after they had been infested with first instars. These results demonstrate that the current Kansas selection of Dipel-resistant European corn borer strain cannot establish reproducing populations in the tested Bt corn lines and hybrids.

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