Using Crop Databases to Explore Phenotypes: From QTL to Candidate Genes.

Anne V Brown,David Grant,Rex T Nelson

doi:10.3390/plants10112494

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.

Highlights

Since the introduction of bi-parental Quantitative Trait Loci (QTL) analysis in plants [1] in the early 1980s, QTL regions have been described in both plant and animal species [2]
We demonstrated how a genetics/genomics database can be used as a tool to help identify the gene(s) conditioning a QTL
We used SoyBase in this exercise, other species- or clade-specific databases may contain equivalent data and tools that can be used in concert to accomplish a similar investigation

Summary

Introduction

Since the introduction of bi-parental QTL analysis in plants [1] in the early 1980s, QTL regions have been described in both plant and animal species [2]. As more genomic data become accessible by quick and easy data sharing [7], some clade and species genome databases are actively curating both bi-parental QTL and GWAS QTL information. This information can be used to identify candidate regions, these regions typically contain many candidate genes. The list of candidate genes can often be reduced by considering molecular function annotations and tissue expression patterns To illustrate this process, we will use, as an example, the information curated in the species database SoyBase [8]. This process has often been referred to as phenotype to genotype (P2G) or field to genes (F2G)

Example Walkthrough

Conclusions