Predicting Apple Quality Using Combined Genetic Data from Various Systems

Greg Howard
12th July, 2024

Predicting Apple Quality Using Combined Genetic Data from Various Systems

Image Source: Natural Science News, 2024

Key Findings

  • Researchers at Chiba University found that combining data from two genotyping platforms improves the accuracy of genomic prediction for apple quality traits
  • The study showed that this combination also enhances the detection power of genome-wide association studies (GWAS) for identifying important genetic regions
  • A new genomic prediction model that includes inbreeding effects further increased accuracy for seven fruit traits, suggesting historical selection for quality traits
The breeding of fruit trees, such as apples, is notoriously challenging due to their long generation times, large plant sizes, and extended juvenile phases. Recent advancements in genomic analysis technologies have opened up new avenues to tackle these issues. A recent study conducted by Chiba University investigates the potential benefits of combining data from different genotyping platforms for genomic selection (GS) and genome-wide association studies (GWAS) in apple breeding[1]. Traditional plant breeding methods are often slow and cumbersome, especially for fruit trees. However, genomics-assisted breeding, which leverages advanced genomic tools, has shown promise in accelerating the process[2]. Genomic selection (GS) and genome-wide association studies (GWAS) are two such tools that have revolutionized plant breeding. GS uses all marker data as predictors of performance, allowing for more accurate predictions and potentially faster breeding cycles[3]. GWAS, on the other hand, helps identify specific genetic regions associated with desirable traits. In the study by Chiba University, researchers explored the effectiveness of combining data from two different genotyping platforms: the Illumina Infinium single-nucleotide polymorphism (SNP) array and the genotyping by random amplicon sequencing-direct (GRAS-Di) system. The objective was to determine whether this combination could improve the accuracy of genomic prediction (GP) and the detection power of GWAS for various fruit quality traits in apples. The results were promising. The accuracy of genomic prediction and the detection power of GWAS increased for most fruit quality traits when data from both genotyping platforms were combined. This suggests that integrating data from different sources can provide a more comprehensive genetic picture, thereby enhancing the effectiveness of GS and GWAS. The study also introduced a GP model that considered inbreeding effects, which further improved the accuracy for seven fruit traits. Inbreeding, which refers to the mating of closely related individuals, can sometimes lead to decreased vigor or inbreeding depression. However, in this study, runs of homozygosity (ROH) islands—regions of the genome where homozygosity is unusually high—overlapped with regions significantly associated with desirable fruit traits. This indicates that breeders have historically selected these regions to enhance apple quality, thereby increasing homozygosity. These findings align with earlier research that highlights the potential of genomics-assisted breeding in overcoming traditional barriers in fruit tree breeding[2][4]. The integration of high-throughput sequencing technologies has already contributed to a significant accumulation of publicly available genomic resources, offering unprecedented opportunities for genetic studies in fruit crops[4]. The Chiba University study builds on this foundation by demonstrating that combining genotypic data from different platforms can further enhance the accuracy and effectiveness of these genomic tools. However, the study also acknowledges that further analysis is required to fully understand the relationship between fruit traits and inbreeding depression. While the current findings are promising, a deeper understanding of how inbreeding affects various traits could provide even more precise tools for breeders. In summary, the study conducted by Chiba University provides compelling evidence that combining data from different genotyping platforms can significantly improve the accuracy of genomic prediction and the detection power of GWAS for fruit quality traits in apples. This advancement could potentially accelerate the breeding of high-quality apple varieties, offering a valuable tool for breeders to overcome the traditional challenges associated with fruit tree breeding.

FruitsBiotechGenetics

References

Main Study

1) Genomic prediction and genome-wide association study using combined genotypic data from different genotyping systems: application to apple fruit quality traits.

Published 9th July, 2024

https://doi.org/10.1093/hr/uhae131

Related Studies

2) Genomics-assisted breeding in fruit trees.

https://doi.org/10.1270/jsbbs.66.100

3) Genomic selection in plant breeding: from theory to practice.

https://doi.org/10.1093/bfgp/elq001

4) Genomic insights into domestication and genetic improvement of fruit crops.

https://doi.org/10.1093/plphys/kiad273


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