Plant Gene and Trait 2025, Vol.16, No.4, 182-193 http://genbreedpublisher.com/index.php/pgt 190 in the local language. ‘Xíitek’ is now tested in semi-arid regions. It grows well under drought and poor soil conditions, likely thanks to its wild ancestor. Though Mexico doesn’t have native yellow-skinned types (S. megalanthus), researchers imported seeds from Colombia. They grew seedlings and crossed them with local red-skinned varieties. Most hybrids failed to produce fruit or looked unattractive, but a few gave light pink-skinned, white-fleshed fruit. This is considered a breakthrough- a new intermediate type between yellow and red. These hybrids aren’t yet commercial, but they pave the way for future varieties like thornless yellow types or high-sugar white flesh. 5.4 Multi-country collaborative project on genetic diversity (Asia-Pacific and Latin America) Countries in the Asia-Pacific and Latin American regions joined a project to assess dragon fruit genetic diversity. The program is organized by the Asian Tropical Fruits Association and the Tropical Fruits Network of the Americas. Participants include China, Vietnam, Thailand, Australia, Mexico, Colombia, and Ecuador. In the first phase, each country selected 10 representative germplasm materials, including both cultivated and wild types. DNA samples were extracted and sent to a single lab for testing. The lab used 24 pairs of SSR primers provided by Prof. Tel-Zur from Israel, which have high polymorphism. Interestingly, genetic differences did not always match geography. For example, two Asian red-flesh varieties were genetically closer to some Latin American lines than to local white-flesh ones. In the second phase, the project aims to build an open genetic diversity database. This database will store fingerprint data, trait records, and images. Breeders can search it to find germplasm with traits different from their current breeding materials and reach out for collaboration. For example, a Colombian anthracnose-resistant line was sent to Thailand for testing. Also, a red-flesh self-compatible variety from Australia was sent to Mexico for hybridization. 6 Current Problems and Challenges 6.1 Insufficient resource integration and uneven research areas There is no global unified pitaya germplasm information system yet. The germplasm resources preserved by each country are relatively scattered, and there is not much communication between them. Some widely planted varieties are repeatedly preserved in resource banks in multiple countries, while some wild close relatives cannot be found in many countries. This situation of poor resource integration has forced many breeding experts to use local materials. At present, the main countries doing pitaya research in the world are China, Vietnam, and Israel. However, new countries like the Philippines, India, and Africa that have begun to grow pitaya have relatively weak scientific research levels. For example, some African countries have begun to grow pitaya on a large scale, but they have basically not done much variety improvement and scientific research. When faced with problems such as pests and diseases, there is also a lack of scientific methods to deal with them. While some countries focus on cultivation techniques or nutritional analysis, few have invested in genetic and breeding studies. Although genomic and transgenic technologies are advancing rapidly, studies on pitaya’s stress resistance mechanisms or feasibility of wide hybridization are still limited. 6.2 Barriers to the application of molecular breeding In terms of technology, the whole genome selection and gene editing technology of pitaya is just getting started. Compared with major food crops such as rice and wheat, pitaya does not have mature technology in molecular breeding, nor many successful examples. Many breeders are not familiar with these new technologies and need time to learn and master them. Some traditional breeding units are not strong enough in analyzing molecular markers and genomic data (Xu and Wang, 2024). Now if you want to use genomic selection on pitaya, there is still a big problem that there is too little data for training the model, especially the need to accumulate a large amount of phenotypic and genotypic data.
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