Forecasting Tomato Quality with Genetic Testing

Jim Crocker
28th March, 2024

Forecasting Tomato Quality with Genetic Testing

Tomato (Solanum lycopersicum)

Photo adapted from: Kalvin Chan / CC BY (Source)

Key Findings

  • Study at Sejong University improved tomato breeding by predicting traits like size and sweetness
  • Random forest model was best for predicting tomato weight, width, and skin thickness
  • Fewer genetic markers (SNPs) can predict traits more accurately, making breeding more cost-effective
Understanding the genetic makeup of crops and how it influences their characteristics is critical for agriculture. In tomatoes, traits like fruit weight, size, and sweetness are important for both farmers and consumers. Scientists have been using a method called genomic selection (GS) to predict and improve these traits. GS uses genetic markers spread across the genome to estimate the breeding values of plants, which can then inform breeding decisions. A recent study by researchers at Sejong University has made significant strides in this area[1]. The study aimed to enhance the accuracy of genomic estimated breeding values (GEBVs) for five key fruit traits in tomatoes: fruit weight, width, height, pericarp (the tomato's skin) thickness, and sugar content, measured as Brix. They used two different tomato germplasm collections, with 162 and 191 accessions each, as their genetic diversity pools for training their models. These pools are essentially large libraries of tomato genetic variations, which help scientists understand which genes are associated with which traits. Researchers used a 51K Axiom™ SNP array, a tool that can detect 31,142 single nucleotide polymorphisms (SNPs)—variations at a single point in the DNA sequence among individuals. SNPs serve as genetic markers and can be used to predict plant traits. The team then tested various models and methods to see which provided the most accurate GEBVs. They used both parametric models like RR-BLUP, Bayes A, and Bayesian LASSO, which assume a normal distribution of genetic effects, and non-parametric models like RKHS, SVM, and random forest, which do not make such assumptions. The study found that different models had varying levels of accuracy in predicting the traits, with random forest emerging as the most effective for predicting fruit weight, width, and pericarp thickness. Interestingly, the number of SNPs needed to accurately predict traits varied, and beyond a certain point, adding more SNPs did not improve the model—a phenomenon known as reaching a plateau. Moreover, the researchers discovered that using smaller sets of SNPs identified through genome-wide association studies (GWAS) actually improved prediction accuracy for fruit traits compared to using the full 31,142 SNP set[2]. This is particularly valuable as it suggests that a cost-effective approach using fewer markers could be just as, if not more, effective for genomic selection in tomatoes. This new research builds upon previous studies that have explored the genetic basis of fruit traits in tomatoes. For example, earlier GWAS have been used to identify quantitative trait loci (QTL) that influence specific traits like fruit size and sweetness[2]. These QTLs are stretches of DNA that are closely associated with the traits of interest. Another study highlighted the challenge of mapping these traits in inbred crops like tomatoes due to low genetic diversity and proposed using cherry tomatoes, which have a more mixed heritage, to improve mapping resolution[3]. Meanwhile, research into the OVATE gene has shown its significant role in determining fruit shape, with the discovery of suppressor genes that can modify its effects[4]. The Sejong University study integrates these earlier findings into a practical application, demonstrating how genomic selection can be refined and made more cost-effective for tomato breeding. By pinpointing the most predictive SNPs and choosing the most effective models, the researchers have laid the groundwork for more rapid development of tomato varieties with desirable traits. In conclusion, the study from Sejong University has taken a significant step forward in the field of plant genomics and breeding. By analyzing the prediction accuracy of GEBVs for tomato fruit traits and identifying the most efficient genetic markers and models, the researchers have provided a roadmap for developing improved tomato varieties faster and more economically. This work not only builds on previous research[2][3][4] but also paves the way for future innovations in crop improvement.

AgricultureGeneticsPlant Science

References

Main Study

1) Prediction accuracy of genomic estimated breeding values for fruit traits in cultivated tomato (Solanum lycopersicum L.).

Published 27th March, 2024

https://doi.org/10.1186/s12870-024-04934-8

Related Studies

2) Genome-wide association study identifies QTL for eight fruit traits in cultivated tomato (Solanum lycopersicum L.).

https://doi.org/10.1038/s41438-021-00638-4

3) Genome-wide association mapping in tomato (Solanum lycopersicum) is possible using genome admixture of Solanum lycopersicum var. cerasiforme.

https://doi.org/10.1534/g3.112.002667

4) Mapping of two suppressors of OVATE (sov) loci in tomato.

https://doi.org/10.1038/hdy.2013.45


Related Articles

An unhandled error has occurred. Reload 🗙