Integration of genomic data improves prediction accuracy of apple traits

  • Food
  • July 10, 2024

Over the past few decades, the world has seen tremendous advances in the tools used for genomic analysis. Although these tools are more commonly associated with the fields of biology and medicine, they have also proven to be extremely valuable in agriculture. Using numerous DNA markers obtained from next-generation sequencing technologies, breeders can make genomic predictions and select promising individuals based on their predicted trait values.

Several systems and methodologies aimed at improving fruit quality use genetic analysis. One of them is genetic selection (GS) and genetic prediction (GP). This modern breeding approach uses statistical models to assess the complete genetic profile of a given individual based on previously collected genomes and their associated traits. This allows breeders to make predictions about the fruit traits that will be produced in the future at the seedling stage. In contrast, genome-wide association studies (GWAS) are instead aimed at finding the exact genetic variants responsible for a given fruit trait.

Until now, GP and GWAS have mainly used DNA markers from one system, and when the system used became outdated, it had to be reanalyzed with a more modern system. However, it has proven difficult to reanalyze populations for selection in fruit tree breeding that were analyzed in previous systems, because it is not possible to re-obtain DNA from individuals that were discarded during selection. Therefore, a recent study published in Horticultural research On July 8, 2024, a research team led by Associate Professor Mai F. Minamikawa from the Institute for Advanced Academic Research, Chiba University, Japan, set out to investigate whether combining apple data from different systems could lead to more accurate results when conducting GP and GWAS. Other members of the team included Dr. Miyuki Kunihisa from the Institute of Fruit Tree and Tea Science, National Agriculture and Food Research Organization, Japan, and Professor Hiroyoshi Iwata from the Graduate School of Agricultural and Life Sciences, University of Tokyo, Japan.

First, the researchers combined apple datasets obtained from two different genotyping systems, namely Infinium and genotyping by random amplicon sequencing direct (GRAS-Di). They then used these combined genotyping markers to perform GP and GWAS for a total of 24 different fruit traits, including acidity, sweetness, harvest time, and solid soluble content. The team compared the performance of predictions made using models trained on either dataset alone or both in combination.

The results were very encouraging; the accuracy of genomic predictions and the detection power of the GWAS system increased significantly when using the combined Infinium and GRAS-Di datasets for multiple fruit traits. This suggests that there are advantages to combining data from different systems and leveraging historical data.

To push the boundaries even further, the researchers also trained the GP model to consider inbreeding effects. Interestingly, these results also indicated that the combined approach performed better for certain traits, including Brix and Mealyness. However, these findings were less conclusive, as Dr. Minamikawa notes: “Although the accuracy of GS for fruit traits in apples can be improved by inbreeding data, further studies are needed to understand the relationship between fruit traits and inbreeding..

Overall, the findings of this study point to a useful way to improve the accuracy of GS and GWAS by leveraging existing datasets. This could have many positive implications for agriculture, as Dr. Minamikawa emphasizes, “The challenges such as large plant size and long juvenile periods in fruit trees can be addressed by identifying superior genotypes from numerous individuals using highly accurate GS as seedling stage and by detecting genetic variants for a target trait using accurate GWAS.

Let us hope that further advances in this area will make fruit breeding more efficient and reliable, so that we can continue to enjoy it in the future.

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