Development of SSR Markers for and Fingerprinting of Walnut Genetic Resources
FORESTS(2024)
Beijing Acad Agr & Forestry Sci | Beijing Forestry Univ
Abstract
Walnut is one of four major nuts in the world. China has abundant walnut germplasm resources, but there are still shortcomings in the identification of germplasm resources. This study used different walnut varieties as materials and developed 14 high-quality SSR molecular markers from 60 pairs of primers based on genome re-sequencing results. This study analyzed the genetic diversity of Chinese walnut genetic resources using 14 selected SSR markers. A total of 64 alleles were detected in 47 walnut resources, with an average of 4.571 alleles per locus. The variation range of polymorphism information content was 0.096~0.711, with an average value of 0.422. Cluster analysis, population genetic structure, and principal coordinate analysis divided 47 walnut resources into ordinary walnuts, Juglans hopeiensis, and Liaoyi 1. In addition, core SSR markers (Jr45, Jr40, Jr29, Jr35, and Jr11) were selected from 14 SSR markers, which were sufficient to distinguish 47 walnut resources. At the same time, 47 unique molecular fingerprints of walnuts were constructed using these core SSR markers. This study provides strong scientific support for rapid and efficient identification, germplasm innovation, and a variety of property protection of walnut germplasm.
MoreTranslated text
Key words
walnut,nut phenotype,simple sequence repeats,breeding
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Related Papers
PLANTS-BASEL 2024
被引用1
Genetic Resources and Crop Evolution 2024
被引用1
SCIENTIFIC REPORTS 2024
被引用0
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper