Location: Corn Insects and Crop Genetics ResearchTitle: Using crop databases to explore phenotypes: from QTL to candidate genes
Submitted to: Plants
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/13/2021
Publication Date: 11/18/2021
Citation: Brown, A.V., Grant, D.M., Nelson, R. 2021. Using crop databases to explore phenotypes: from QTL to candidate genes. Plants. 10(11). Article 2494. https://doi.org/10.3390/plants10112494.
Interpretive Summary: This paper describes the steps involved in identifying a plant trait to study and outlines subsequent steps leading to the identification of potential gene(s) conditioning the observed trait. It is meant as a tutorial on how one could use a genetics and genomics database, specifically, the USDA-ARS soybean genetics and genomics database SoyBase, to identify the genes responsible for important traits like linolenic acid content in soybean seeds. Soybean oil with reduced linolenic acid is preferred by consumers because in can be stored longer and is better for frying. Identifying genes and markers associated with reduced linolenic acid will enable breeders and researchers to improve soybean seed quality using either traditional plant breeding or genome editing approaches. Importantly, due to the large number of plant genomic and genetic databases available, a similar approach can be used to identify genes of interest in a number of important crop species.
Technical Abstract: Seeds, especially those of certain grasses and legumes, provide the majority of the protein and carbohydrates for much of the world’s population. Therefore, improvements in seed quality and yield are important drivers for the development of new crop varieties to feed a growing population. Quantitative Trait Loci (QTL) have been identified for many biologically interesting and agronomically important traits, including many seed quality traits. QTL can help explain the genetic architecture of the traits and can also be used to incorporate traits into new crop cultivars during breeding. Despite the important contributions that QTL have made to basic studies and plant breeding, knowing the exact gene(s) conditioning each QTL would greatly improve our ability to study the underlying genetics, biochemistry and regulatory networks. The data sets needed for identifying these genes are increasingly available and often housed in species- or clade-specific genetics and genomics databases. In this demonstration, we present a generalized walkthrough of how such databases can be used in these studies using SoyBase, the USDA soybean Genetics and Genomics Database, as an example.