Advances in quantitative genetics and high-throughput pipelines have allowed for rapid identification of genomic markers associated with changes in phenotype. However, linking those markers to causal genes is still difficult, as many genes may be linked to one marker. We aimed to improve candidate gene selection by creating a new method that identifies conserved genes underlying GWAS loci in multiple species. So far, we have tested this method in two different experiments: 1) using GWAS from Arabidopsis, soybean, rice, maize, and sorghum measuring 19 elemental uptake (ionomic) traits and 2) GWAS from Arabidopsis, soybean, rice, and maize measuring seed weight traits. We identified 14,336 candidates in the ionomics GWAS comparison. The most likely candidates belonged to ortholog groups linked to GWAS loci in all five species for their given trait according to calculations using random permutations of the data. For the seed weight GWAS comparison, we identified 192 candidates, and again, the most likely candidates belonged to ortholog groups linked to GWAS loci in all species in the comparison. Focusing on these most likely candidate genes from Arabidopsis, we obtained T-DNA lines with mutant alleles for each candidate gene and performed a high-throughput phenotyping screen utilizing ICP-MS for ionomics and the image analysis software PlantCV for seed weight. Preliminary results show 59 ionomic candidates and 9 seed weight candidates have one line with confirmed phenotypes. We plan to further verify these preliminary confirmations by obtaining and screening additional T-DNA lines with different alleles for each candidate gene.

Advances in automated image analysis using open-source computer vision tools, such as PlantCV, have greatly increased the throughput of aboveground phenotyping in a variety of crop species. However, PlantCV was largely optimized to analyze images collected under controlled laboratory conditions, and has seldom been used to analyze images collected under field conditions. Further, there are no known applications of PlantCV for analyzing images collected belowground, such as those obtained from minirhizotron imaging devices. In this study, we demonstrated applications of PlantCV for extracting plant trait information from aboveground and belowground images collected in two perennial crop mapping populations. The first population was composed of nearly 1,200 individuals of a potential perennial oilseed crop (Silphium Integrifolium x Perfoliatum ), and the second population was composed of nearly 1,700 individuals of a perennial cover crop (Trifolium ambiguum , Kura Clover). We designed and used a field-based imaging cart to collect overhead and profile images of individuals from both populations in August and October, which improved the efficiency of field-based image capture. Around the time of aboveground image collection, belowground images of root networks were collected using minirhizotron imaging devices. We then assessed the application of PlantCV for measuring aboveground traits (crop canopy area, height, leaf color and growth rates) and belowground traits (root length and growth rates), and we explored future directions of PlantCV for field-based image analysis of aboveground and belowground crop tissues.

Nitrogen inputs can be an important cost consideration for farmers in terms of economic profit as well as environmental impact. Elucidating genetic regions that are associated with plant phenotype response to nitrogen stress can help in facilitating breeding approaches that can mitigate these costs. A diverse population of 272 maize lines was planted at a field site in Champaign, IL in two consecutive years in reduced nitrogen conditions. 302 phenotypes were recorded including: seed ionomic content, root structural traits derived from 2-dimensional images as well as a 3-dimensional representation generated from x-ray computed tomography (XRT) scans, root traits extrapolated from mini-rhizotron systems, drone images across the growing season and end of season agronomic traits such as biomass and yield. BLUP models were fit to obtain estimates of single year genotypic values as well as across years values both of which took into account year specific spatial variation. Individual years and combined years BLUP values were used as response variables in genome wide association studies (GWAS) to identify loci significantly associated with each set of values. Significant associations were identified for all phenotype categories.

Seashore paspalum (Paspalum vaginatum Swartz) is a halophytic turfgrass and emerging genomic model system for the study of salt tolerance in cereals and other grasses. Despite recent interest and an increase in available tools, little is known about the diversity present in wild populations of P. vaginatum and its close relative P. distichum. Variation in ploidy, clonal propagation, hybridization, and subgenome composition appear to occur in the wild and may interact to influence geographic patterns of adaptation, particularly in response to environmental salinity levels. Using 218 accessions representing >170 wild collections from throughout the coastal southern United States plus existing USDA germplasm, we employed genotyping-by-sequencing, cpDNA sequencing and flow cytometry to identify genetic differentiation and ploidy variation. Within P. vaginatum, there are two morphologically distinct ecotypes: the fine-textured ecotype is diploid and appears to reproduce in the wild both sexually and by clonal propagation; in contrast, the coarse-textured ecotype consists largely of clonally-propagating triploid and diploid genotypes. The coarse-textured ecotype appears to be derived from hybridization between fine-textured P. vaginatum and an unidentified Paspalum species. These clonally propagating hybrid genotypes are more broadly distributed than clonal fine-textured genotypes and may represent a transition to a more generalist adaptive strategy. The triploid genotypes vary in whether they carry one or two copies of the P. vaginatum subgenome, indicating multiple evolutionary origins. This variation in subgenome composition shows associations with local ocean salinity levels across the sampled populations and may play a role in local adaptation.