Background: Genome wide association studies (GWAS) are a powerful tool for identifying quantitative trait loci (QTL) and causal single nucleotide polymorphisms (SNPs)/genes associated with various important traits in crop species. Typically, GWAS in crops are performed using a panel of inbred lines, where multiple replicates of the same inbred are measured and the average phenotype is taken as the response variable. Here we describe and evaluate single plant GWAS (sp-GWAS) for performing a GWAS on individual plants, which does not require an association panel of inbreds. Instead sp-GWAS relies on the phenotypes and genotypes from individual plants sampled from a randomly mating population. Importantly, we demonstrate how sp-GWAS can be efficiently combined with a bulk segregant analysis (BSA) experiment to rapidly corroborate evidence for significant SNPs.Results: In this study we used the Shoepeg maize landrace, collected as an open pollinating variety from a farm in Southern Missouri in the 1960’s, to evaluate whether sp-GWAS coupled with BSA can efficiently and powerfully used to detect significant association of SNPs for plant height (PH). Plant were grown in 8 locations across two years and in total 768 individuals were genotyped and phenotyped for sp-GWAS. A total of 306 k polymorphic markers in 768 individuals evaluated via association analysis detected 25 significant SNPs (P ≤ 0.00001) for PH. The results from our single-plant GWAS were further validated by bulk segregant analysis (BSA) for PH. BSA sequencing was performed on the same population by selecting tall and short plants as separate bulks. This approach identified 37 genomic regions for plant height. Of the 25 significant SNPs from GWAS, the three most significant SNPs co-localize with regions identified by BSA.Conclusion: Overall, this study demonstrates that sp-GWAS coupled with BSA can be a useful tool for detecting significant SNPs and identifying candidate genes. This result is particularly useful for species/populations where association panels are not readily available.

MGWAS of Gln-related traits reveals an unexpected seed-specific interaction between glutamine and glucosinolates and in shaping the metabolic homeostasis in Ara

Maize (Zea mays) seeds are a good source of protein, despite being deficient in several essential amino acids. However, eliminating the highly abundant but poorly balanced seed storage proteins has revealed that the regulation of seed amino acids is complex and does not rely on only a handful of proteins. In this study, we used two complementary omics-based approaches to shed light on the genes and biological processes that underlie the regulation of seed amino acid composition. We first conducted a genome-wide association study to identify candidate genes involved in the natural variation of seed protein-bound amino acids. We then used weighted gene correlation network analysis to associate protein expression with seed amino acid composition dynamics during kernel development and maturation. We found that almost half of the proteome was significantly reduced during kernel development and maturation, including several translational machinery components such as ribosomal proteins, which strongly suggests translational reprogramming. The reduction was significantly associated with a decrease in several amino acids, including lysine and methionine, pointing to their role in shaping the seed amino acid composition. When we compared the candidate gene lists generated from both approaches, we found a nonrandom overlap of 80 genes. A functional analysis of these genes showed a tight interconnected cluster dominated by translational machinery genes, especially ribosomal proteins, further supporting the role of translation dynamics in shaping seed amino acid composition. These findings strongly suggest that seed biofortification strategies that target the translation machinery dynamics should be considered and explored further.

Advanced publicly available sequencing data from large populations have enabled informative genome-wide association studies (GWAS) that associate SNPs with phenotypic traits of interest. Many publicly available tools able to perform GWAS have been developed in response to increased demand. However, these tools lack a comprehensive pipeline that includes both pre-GWAS analysis such as outlier removal, data transformation, and calculation of Best Linear Unbiased Predictions (BLUPs) or Best Linear Unbiased Estimates (BLUEs). In addition, post-GWAS analysis such as haploblock analysis and candidate gene identification are lacking. Here, we present HAPPI GWAS, an open-source GWAS tool able to perform pre-GWAS, GWAS, and post-GWAS analysis in an automated pipeline using the command-line interface. HAPPI GWAS is written in R for any Unix-like operating systems and is available on GitHub (https://github.com/Angelovici-Lab/HAPPI.GWAS.git). Supplementary data are available at Bioinformatics online.

Unmanned aerial vehicles (UAVs) provide an intermediate scale of spatial and spectral data collection that yields increased accuracy and consistency in data collection for morphological and physiological traits than satellites and expanded flexibility and high-throughput compared to ground-based data collection. In this study, we used UAV-based remote sensing for automated phenotyping of field-grown switchgrass (Panicum virgatum), a leading bioenergy feedstock. Using vegetation indices calculated from a UAV-based multispectral camera, statistical models were developed for rust disease caused by Puccinia novopanici, leaf chlorophyll, nitrogen, and lignin contents. For the first time, UAV remote sensing technology was used to explore the potentials for multiple traits associated with sustainable production of switchgrass, and one statistical model was developed for each individual trait based on the statistical correlation between vegetation indices and the corresponding trait. Also, for the first time, lignin content was estimated in switchgrass shoots via UAV-based multispectral image analysis and statistical analysis. The UAV-based models were verified by ground-truthing via correlation analysis between the traits measured manually on the ground-based with UAV-based data. The normalized difference red edge (NDRE) vegetation index outperformed the normalized difference vegetation index (NDVI) for rust disease and nitrogen content, while NDVI performed better than NDRE for chlorophyll and lignin content. Overall, linear models were sufficient for rust disease and chlorophyll analysis, but for nitrogen and lignin contents, nonlinear models achieved better results. As the first comprehensive study to model switchgrass sustainability traits from UAV-based remote sensing, these results suggest that this methodology can be utilized for switchgrass high-throughput phenotyping in the field.