Identification of Quantitative Trait Nucleotides and Development of Diagnostic Markers for Nine Fatty Acids in the Peanut
Plants（2023）
Shandong Peanut Res Inst
Abstract
The cultivated peanut (Arachis hypogaea L.) is an important oilseed crop worldwide, and fatty acid composition is a major determinant of peanut oil quality. In the present study, we conducted a genome-wide association study (GWAS) for nine fatty acid traits using the whole genome sequences of 160 representative Chinese peanut landraces and identified 6-1195 significant SNPs for different fatty acid contents. Particularly for oleic acid and linoleic acid, two peak SNP clusters on Arahy.09 and Arahy.19 were found to contain the majority of the significant SNPs associated with these two fatty acids. Additionally, a significant proportion of the candidate genes identified on Arahy.09 overlap with those identified in early studies, among which three candidate genes are of special interest. One possesses a significant missense SNP and encodes a known candidate gene FAD2A. The second gene is the gene closest to the most significant SNP for linoleic acid. It codes for an MYB protein that has been demonstrated to impact fatty acid biosynthesis in Arabidopsis. The third gene harbors a missense SNP and encodes a JmjC domain-containing protein. The significant phenotypic difference in the oleic acid/linoleic acid between the genotypes at the first and third candidate genes was further confirmed with PARMS analysis. In addition, we have also identified different candidate genes (i.e., Arahy.ZV39IJ, Arahy.F9E3EA, Arahy.X9ZZC1, and Arahy.Z0ELT9) for the remaining fatty acids. Our findings can help us gain a better understanding of the genetic foundation of peanut fatty acid contents and may hold great potential for enhancing peanut quality in the future.
MoreTranslated text
Key words
peanut oil,fatty acids,GC-MS,GWAS,PARMS
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
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
Summary is being generated by the instructions you defined