Identifying rare alleles affecting seed coat and hilum color in soybean (glycine max) using applied genomics

Authors: KANOVSKÁ, IVANA - Palacky University; BIOVÁ, JANA - Palacky University; SLIVKOVÁ, JANA - Palacky University; Bilyeu, Kristin; ŠKRABIŠOVÁ, MARIA - Palacky University

Submitted to: Legume Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/10/2024
Publication Date: 12/23/2024
Citation: Kanovská, I., Biová, J., Slivková, J., Bilyeu, K.D., Škrabišová, M. 2024. Identifying rare alleles affecting seed coat and hilum color in soybean (glycine max) using applied genomics. Legume Science. 6(4). https://doi.org/10.1002/leg3.70019.
DOI: https://doi.org/10.1002/leg3.70019

Interpretive Summary: Identifying the different versions of genes that control traits is a key component to successful crop improvement. The increasing availability for whole genome sequence information for thousands of soybean lines presents an opportunity to craft new analysis methods for dealing with large genome data set information. The objective of this research was to develop new applied genomics methods and apply them to a common association analysis. The results demonstrated the effectiveness of the new methods by an example of the rapid and effective identification of a rare allele of a gene controlling pigmentation in soybean seeds. The impact of this work is a new tool for gene discovery that can efficiently manage large data sets to deliver meaningful results that connect traits with the genes that control them.

Technical Abstract: Breeding programs are often constrained by the genetic diversity of the parental lines, even though these lines can be a rare source of unique alleles not found elsewhere. Therefore, identifying these rare alleles is crucial for keeping them in the breeding programs while introducing new genetic resources. The growing amount of whole genome sequenced data has made Genome-wide Association Study (GWAS) dominant in investigations to find causal genes for all crops, including legumes. However, GWAS often fails to predict more than one causative mutation (CM) in multiple alleles of a single causal gene. Consequently, multiple alleles complicate breeding when not recognized by a single associated marker, which typically identifies only the most frequent CM and discriminates against the others. In this work, we focus on adopting recent applied genomics methods to identify multiple independent alleles and rare alleles in soybean as a model for other legumes. We predicted, identified, and confirmed a new and extremely rare CM for the loss of black pigmentation in the soybean seed coat and hilum color R gene, the Q25fs. The deletion of eight bases leads to a frameshift, a premature stop codon, and a truncated, nonfunctional protein. Our results also suggest a possibly new gene or an allele of the seed coat color inhibitor I gene. Using a soybean model, we demonstrate how applied genomics methods can accelerate pre-breeding, and additionally, we discuss the potential for adopting these methods for application to other legumes.