Wednesday, April 23rd, 2025

Agronomy 3140, 3:00 – 4:00 pm

“Genetic analysis of pod traits in Iowa Mung bean Diversity Panel using image-based phenotyping”

Venkata Naresh Boddepalli,

Seminar Description: Mung bean (Vigna radiata (L.) R. Wilczek) is an important grain legume with a rich digestible protein source. It has significant potential in the plant-based protein industry due to its short growth period and nitrogen-fixing abilities. These traits make it a suitable choice for diverse cropping systems. Developing appropriate cultivars is essential for optimizing crop adaptation in the U.S. Midwest. In this study, we employed image-based phenotyping to measure pod-related traits—such as pod length, width, curvature, and seeds per pod—in 372 genotypes from the Iowa Mungbean Diversity Panel (IMDP) across three locations during the 2022-2023 growing season. Results from the image analysis, utilizing the CRAFT method and machine learning approaches, demonstrated strong correlations with manual measurements (r = 0.92 for pod length and r = 0.74 for seeds per pod). A Genome-Wide Association Study (GWAS) performed on the data from image analysis identified 23 quantitative trait loci (QTLs) associated with these traits. Notably, one significant SNP in a QTL region on Chromosome 1(1:19852222), labeled 5_35265704, was linked to all four traits and accounted for 12-45% of the phenotypic variance. A candidate gene, Vradi01g00001116, located in this region, was found homologous to GH3 family genes in Arabidopsis, cowpea, common bean, and soybean. These GH3 family genes were studied for their conserved pod and seed development roles. These findings provide valuable insights into the genetic basis of mung bean pod traits, paving the way for the development of genetic markers to breed superior mung bean cultivars suited to the Midwest.

Naresh Boddepalli is a 3^rd year Ph.D. graduate research assistant working under Dr. Arti Singh at Iowa State University. His research primarily focuses on genomics and phenomics-assisted mungbean crop improvement. Naresh completed his bachelor's degree in 2011 and his master's in 2013, both in horticulture in India. In 2013, he worked as a teaching associate in the Department of Horticulture. In 2014, he moved to the World Vegetable Center's South/Central Asia regional office at the ICRISAT campus in India, where he served as a scientific officer in the legume breeding program. During his time there until 2022, he worked on breeding mungbean, edamame, cowpea, and yard-long bean crops for abiotic and biotic stress resistance. Naresh has trained several international and national students, including World Food Prize interns and researchers, in legume breeding and KDDaRT database management systems. He also coordinated state-level extension projects to educate hundreds of farmers on good agricultural practices for mungbean and urd bean cultivation. He is a co-author of seven international articles published in peer-reviewed journals. 

“Development of a Single Kernel NIR Prediction Equation for the Selection of Waxy Maize Kernels”

Shelly Kinney,

Seminar Description: The waxy gene of maize is a high value breeding target, but it is time consuming to separate waxy and wild-type kernels. A common method involves staining the endosperm with iodine. Near-infrared reflectance (NIR) spectroscopy has been used in several species including maize with success. A custom-built single kernel NIR spectroscopy machine was used to scan 2880 individual kernels from 60 samples with a diversity of pedigrees, with both waxy, wild type, and heterozygous kernels represented. Chemical analysis was performed to classify the kernels with the waxy or wild type phenotypes. Linear discriminant analysis was conducted to develop a prediction equation for single kernel NIR spectroscopy. The discriminant results showed that there was an 88% accuracy in predicting waxy kernels as waxy, and a 96% accuracy in predicting wild type kernels as wild type. An ROC curve was determined to allow threshold adjustment to meet desired true positive or false negative rates. Thus, the prediction equation can be used in breeding programs to select for waxy kernels in an efficient and effective manner using a single kernel NIR machine. This approach will benefit breeders of waxy corn by providing a rapid, automated non-destructive method for identification of waxy kernels in segregating breeding populations.

Shelly Kinney grew up in East Central Minnesota, and started her academic career by graduating from Pine Technical and Community College with an associate’s degree in 2018. Afterwards, she majored in Biology for her bachelor’s degree, graduating from the University of Wisconsin-Superior in 2021. She was then accepted into Iowa State University’s Interdepartmental Genetics and Genomics program for a master’s degree, which she completed in 2024. She is now continuing at ISU with a PhD in Plant Breeding.