Genome-wide association studies (GWAS) use statistical models to correlate single nucleotide polymorphisms (SNPs) to a phenotype of interest. This scan of the entire genome identifies regions of association with a phenotype, but due to linkage disequilibrium (LD), GWAS on their own cannot identify single genes responsible for phenotypic variation. Rather, fine-mapping of GWAS regions is required, necessitating the use of additional tools and software. With the introduction of more pangenomic resources in a number of crops (Guo et al. 2025; Hufford et al. 2021), the fidelity of these fine-mapping efforts is growing, presenting the opportunity to leverage new information about allelic variation towards gene discovery (Shi et al. 2023; Della Coletta et al. 2021). Panvar is a tool developed to integrate existing software and resources to perform GWAS and fine-mapping in one seamless step. For each identified GWAS peak, panvaR outputs information about LD and SNP effect prediction for each SNP and by layering locations of nearby genes, creates a refined list of possible candidate genes. We have implemented Panvar as an R package, “panvaR”, which runs the analysis functions, creates interactive and static visualizations, and outputs results tables. This tool seeks to bridge the gap between GWAS and gene speeding up an important step of quantitative genetic studies.
Collin Luebbert, Rijan R. Dhakal, Phillip Ozersky et al.· bioRxiv· 0 citations
Synthetic nitrogen fertilizers have greatly increased crop yields, yet much of the applied nitrogen is lost from agroecosystems and contributes to environmental pollution and higher economic costs. Improving nitrogen uptake efficiency (NUpE) benefits from understanding how root system architecture (RSA) governs soil nitrogen capture. Although root traits have seldom been explicit breeding targets, selection for variation in above-ground nitrogen accumulation has also likely shaped differences in RSA. The Illinois Protein Strain Recombinant Inbred population, derived from more than a century of divergent selection for seed protein concentration, offers a powerful resource for dissecting RSA variation. Using multi-year field phenotyping of excavated root crowns and genome-wide association analysis, we identified a quantitative trait locus on chromosome 10 containing E1OGDH1, which encodes the E1 subunit of the 2-oxoglutarate dehydrogenase (OGDH) complex. OGDH performs a key step in the tricarboxylic acid cycle that also modulates 2-oxoglutarate, an important entry point into nitrogen metabolism and a co-factor for enzymes involved in hormone and secondary product synthesis. Long-read sequencing of inbreds derived from the divergent IHP and ILP parental populations revealed promoter polymorphisms defining E1OGDH1 alleles and differed in E1OGDH1 expression in root tissue. Field experiments in IPSRI lines carrying IHP- or ILP-associated E1OGDH1 alleles showed differences in root architectural traits over two years. CRISPR-Cas9 knockout mutants confirmed a functional role for E1OGDH1 in whole-plant performance and nitrogen-responsive root development. Mutants were shorter, had reduced biomass, and exhibited altered architectural responses to soil nitrogen levels. Transcriptome analysis further showed that loss of E1OGDH1 altered basal and nitrogen-responsive expression of genes associated with root development and nitrogen uptake and metabolism. Together, these findings identify E1OGDH1 as a strong candidate quantitative regulator of maize RSA and nitrogen plasticity, suggesting that central carbon–nitrogen metabolic genes can contribute to root developmental responses relevant to NUpE.
Michelle S. Cho, Zhengbin Liu, Collin Luebbert et al.· bioRxiv· 0 citations