Genomic predictions should increase fruit tree breeding efficiency by selecting seedlings with desired traits using genotyping techniques. The inveterate trouble is that the more available genotyping systems are, the more kinds of data they generate. Now, researchers from Japan have shown that genomic data from these systems can be integrated and combined with historical data to improve genomic predictions.
Genomic analytical tools have undergone major developments over the past few decades and have benefited not just biology and medicine but agriculture as well. Breeders can make genomic predictions using many DNA markers issuing from next-generation sequencing technologies to select promising individuals based on predicted traits.
There are several systems of genetic analysis oriented towards improvement of fruit quality. Among them are genetic selection and genetic prediction. Modern breeding makes use of statistical models which pronounce the evaluation of the genetic material of an individual against a previously collected database of a genome associated with traits, thus leading to the prediction of fruit traits at a seedling stage. GWAS, on the other hand, emphasizes exactly which genetic variations lead to certain fruit traits.
This traditionally made GP and GWAS dependent upon DNA markers from a single system. Once the system became obsolete, then re-analysis would be required with newer systems. Re-analysis of populations in fruit tree breeding was out of question as it was impossible to re-obtain DNA from discarded individuals. In an article published in Horticulture Research on 8 July, 2024, the team of researchers headed by Associate Professor Mai F. Minamikawa of Chiba University, Japan, collected apple data from different systems to test whether doing so would improve GP and GWAS accuracy. The group included Dr. Miyuki Kunihisa of the National Agriculture and Food Research Organization and Professor Hiroyoshi Iwata of the University of Tokyo.
By combining the apple datasets obtained from two different genotyping systems— Infinium and genotyping by random amplicon sequencing direct (GRAS-Di)—the scientists carried out GP and genome-wide association studies for a total of 24 different fruit traits, such as acidity, sweetness, harvest time, and solid soluble content using these combined genotype markers. From models trained with one of the data or both of them trained together, the performance of the prediction was compared.
These results are promising in terms of improving genomic prediction accuracy and increasing GWAS detection power for several fruit traits where Infinium and GRAS-Di datasets were combined. This result shows that integrated data coming from different systems and reusing already existing data is beneficial.
The researchers regarded effects of inbreeding by further training the GP model with interesting results. According to them, the combined approach returned better performance for some traits, like Brix and Degree of mealiness, although the conclusions from these findings remained obscure. Dr. Minamikawa commented, Although knowledge on inbreeding improves the accuracy of GS for fruit traits in apples, further studies are required to uncover how fruit traits are related to inbreeding.
This research effort, therefore, is of the view that it is possible to improve GS accuracy and GWAS using exiting datasets—adding a positive effect to agriculture. Dr. Minamikawa emphasized that “the issues such as large plant size and long juvenile periods in fruit trees can be overcome by selecting superior genotypes from among many individuals at the seedling stage with high accuracy GS and the detection of genetic variants for a target trait using precise GWAS.
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