The challenges genomic selection has to overcome to revolutionise fruit, vegetable crop breeding
Despite its growing adoption in animal breeding and staple crops like rice and wheat, GS has yet to reach the same level of implementation in fruit and vegetable production. The review, compiled by researchers at the National University of Singapore and Monash University Malaysia, analysed 63 studies on 25 fruit and vegetable species conducted over the past decade.
The promise of genomic selection
GS is a modern breeding technique that uses genetic markers across the entire genome to predict desirable traits — such as disease resistance, yield, or fruit quality — in the early stages of a plant’s life cycle. This can increase selection accuracy and reduce the time required to develop new crop varieties. It is also particularly beneficial for perennial fruit crops like apples, where breeding cycles are long and early prediction can save years of work.
GS has already transformed livestock breeding and staple crop improvement. According to the review’s authors, it could similarly revolutionise fruit and vegetable breeding by increasing selection accuracy and optimising the use of genetic variation in breeding populations. Speeding up the selection process allows breeders to respond more effectively to climate change and population growth, contributing to food security and nutrition amid a global food crisis.
In fruits and vegetables, GS can enhance traits of high economic importance, such as disease resistance in spinach, and curd formation in cauliflower. Previous studies in these crops have shown encouraging prediction accuracies, which are vital for improving yield and quality. Furthermore, the reduction in genomic sequencing costs makes GS more accessible to researchers and breeders, which has led to an increase in related publications since 2013.
In fruits like apples, GS has successfully improved key traits such as yield, fruit size, colour, firmness, and post-harvest qualities like shelf-life. Similarly, GS in tomato breeding has helped improve yield, fruiting earliness, and disease resistance. These improvements hold significant economic value for farmers.
Challenges in application
Despite GS’ potential, the review uncovered several obstacles hampering widespread GS adoption in fruit and vegetable breeding programs. Firstly, the biology of fruit and vegetable crops presents unique challenges.
For instance, leafy greens, which have short life cycles, may not benefit significantly from the reduction in breeding cycle times GS offers. In contrast, longer-lived crops like fruit trees could reap more substantial gains but their extended growth periods slow down the breeding process, making GS less immediately impactful.
Another significant challenge is the lack of readily available genetic and phenotypic data, which the review termed the “phenotyping bottleneck”. Unlike livestock, which has well-established breeding pedigrees and genomic data, the availability of such resources is limited for many fruit and vegetable species. Building a database from scratch can be costly and time-consuming, slowing down the pace of GS implementation in these crops.
Additionally, the economic drivers of fruit and vegetable research are different from those of staple crops or livestock. While the global gross production value of fruits is higher than that of vegetables, certain high-value crops like grapes remain under-researched despite their economic importance.
The review noted that only four GS studies had focused on grapes, despite their $649 billion gross production value. In contrast, crops like strawberries and tomatoes, which rank lower in production value, have garnered more research due to their breeding complexities and market demand.
Moreover, phenotyping techniques differ across studies, making it hard to generalise findings and develop standardised methods for GS in fruits and vegetables. Trait categorisation is not uniform across studies, which complicates data comparison and impedes progress.
Addressing the phenotyping bottleneck
Advancements in phenotyping technologies may offer a potential solution to this bottleneck. Technologies such as 3D imaging, hyperspectral imaging, and artificial intelligence (AI) are beginning to streamline the phenotyping process.
For instance, recent research demonstrated how 3D imaging could measure strawberry uniformity as effectively as manual measurements, showing potential for AI-powered imaging integration with GS models. Additionally, a 2017 study successfully selected top strawberry parents based on genotypic information alone, demonstrating that GS could reduce reliance on phenotypic data while achieving more than 50% efficiency than traditional methods.
However, these technologies are still in their early stages of development for widespread agricultural use. Fully validating and applying AI and imaging technologies across different fruit and vegetable crops will take time.
Recommendations for future research
To overcome these barriers, the review made several recommendations for improving the GS’ efficiency and applicability in fruit and vegetable breeding. The authors stated that establishing collaborative breeding programmes to pool data across regions and institutions and provide a more comprehensive genetic database for under-researched crops.
Given the current disparity in research focus, efforts should also target crops with both high economic value and potential for improvement through GS, such as grapes and other under-studied crops. Additionally, the review recommended integrating GS methods into ongoing breeding programs to allow breeders to evaluate the genomic estimated breeding values (GEBVs) of progeny against traditional breeding methods, helping to refine and optimise GS’ predictive models.
Furthermore, breeding strategies should account for the biological diversity among fruit and vegetable crops. For example, while the focus in short-cycle crops like leafy greens should remain on traits such as disease resistance and yield, longer-cycle crops like fruit trees could benefit from GS’ ability to improve complex traits over time.
Moreover, researchers advocate for the exploration of indirect traits, which, though not directly linked to yield, can influence overall productivity. Secondary traits, like disease resistance or environmental adaptability, are critical and should not be overlooked in GS studies.
The review’s authors concluded: “We highly recommend an increased effort of research translation into tangible, appliable results, specifically towards crops that hold important positions in both food and nutritional security, yet are seldom studied.”
Source: National Library of Medicine
“Genomic selection for crop improvement in fruits and vegetables: a systematic scoping review”
https://www.doi.org/10.1007/s11032-024-01497-2
Authors: Adrian Ming Jern Lee, et al.
