Integration of Genomic Data Enhances Prediction Accuracy of Apple Fruit Traits
Researchers Discover Combining Genomic Data from Various Genotyping Systems Improves Predictions at Seedling Stage
Researchers in Japan have demonstrated that integrating genomic data from different genotyping systems can enhance the accuracy of predicting apple fruit traits at the seedling stage. This finding, published in Horticulture Research on July 8, 2024, shows promise for more efficient fruit tree breeding practices.
Genotyping techniques, which help select fruit trees with desirable traits early on, have traditionally relied on various genotyping systems, each producing unique datasets. The recent study highlights that merging data from these different systems, along with historical data, significantly improves genomic prediction accuracy.
Over recent decades, genomic analysis tools have advanced significantly, proving valuable in fields beyond biology and medicine, including agriculture. With numerous DNA markers from next-generation sequencing technologies, breeders can make genomic predictions and select individuals based on predicted traits.
In agricultural genetic analysis, techniques like genetic selection (GS) and genetic prediction (GP) are used to evaluate an individual’s genetic profile using statistical models based on previously collected genomes and their associated traits. This allows breeders to predict future fruit traits at the seedling stage. Meanwhile, genome-wide association studies (GWAS) focus on identifying specific genetic variants responsible for particular traits.
Historically, GP and GWAS have used DNA markers from a single system. When a system became outdated, populations had to be re-analyzed with a newer system, a challenging task since DNA from discarded individuals could not be re-obtained. The research team, led by Associate Professor Mai F. Minamikawa from Chiba University‘s Institute for Advanced Academic Research, investigated whether combining apple data from different systems could yield more accurate GP and GWAS results. The team included Dr. Miyuki Kunihisa from the Institute of Fruit Tree and Tea Science at Japan’s National Agriculture and Food Research Organization and Professor Hiroyoshi Iwata from the University of Tokyo’s Graduate School of Agricultural and Life Sciences.
The researchers combined apple datasets from two genotyping systems: Infinium and genotyping by random amplicon sequencing direct (GRAS-Di). They performed GP and GWAS on 24 different fruit traits, including acidity, sweetness, harvest time, and solid soluble content, comparing the performance of models trained on individual datasets and the combined dataset.
The results were promising, showing a significant increase in the accuracy of genomic predictions and the detection power of the GWAS system when using the combined datasets. This indicates that integrating data from different systems and leveraging historical data offers substantial benefits.
Furthermore, the researchers incorporated inbreeding effects into the GP model, finding improved performance for certain traits like Brix and Degree of mealiness. However, these findings were less conclusive, as Dr. Minamikawa noted the need for further studies to understand the relationship between fruit traits and inbreeding.
Overall, this study suggests that combining existing datasets can significantly enhance GS and GWAS accuracy, with positive implications for agriculture. Dr. Minamikawa emphasized the potential impact: “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 high-accuracy GS at the seedling stage and detecting genetic variants for a target trait using precise GWAS.”
Advancements in this field could make fruit breeding more efficient and reliable, benefiting our diets and agricultural practices.
About Associate Professor Mai F. Minamikawa Dr. Minamikawa, who holds a PhD from Chiba University’s Graduate School of Horticulture (2013), is a Tenure-track Associate Professor specializing in genomic selection, data science, and statistical and molecular genetics, with a focus on fruit breeding. She is a member of The Japanese Society of Breeding and The Japanese Society for Horticultural Science.
Funding This research was supported by grants from the Ministry of Agriculture, Forestry and Fisheries of Japan, the Cabinet Office’s Cross-ministerial Strategic Innovation Promotion Program (SIP), and the Bio-oriented Technology Research Advancement Institution, NARO, among others.
Reference:
Title of original paper: Genomic prediction and genome-wide association study using combined genotypic data from different genotyping systems: Application to apple fruit quality traits
Authors: Mai F. Minamikawa1,2*, Miyuki Kunihisa3, Shigeki Moriya3, Tokurou Shimizu3,
Minoru Inamori2, and Hiroyoshi Iwata2
Affiliations:
1Institute for Advanced Academic Research (IAAR), Chiba University, Japan
2Laboratory of Biometry and Bioinformatics, Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Japan
3Institute of Fruit Tree and Tea Science, National Agriculture and Food Research Organization (NARO), Japan
Journal: Horticulture Research
