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Identification of Candidate Genes that Affect the Contents of 17 Amino Acids in the Rice Grain Using a Genome-Wide Haplotype Association Study

Overview of attention for article published in Rice, September 2023
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Title
Identification of Candidate Genes that Affect the Contents of 17 Amino Acids in the Rice Grain Using a Genome-Wide Haplotype Association Study
Published in
Rice, September 2023
DOI 10.1186/s12284-023-00658-9
Pubmed ID
Authors

Xiaoqian Wang, Lihong Xie, Jiachuang Fang, Yunlong Pang, Jianlong Xu, Zhikang Li

Abstract

The amino acid content (AAC) of the rice grain is one of the most important determinants of nutritional quality in rice. Understanding the genetic basis of grain AAC and mining favorable alleles of target genes for AAC are important for developing new cultivars with improved nutritional quality. Using a diverse panel of 164 accessions genotyped by 32 M SNPs derived from 3 K Rice Genome Project, we extracted 1,123,603 high quality SNPs in 44,248 genes and used them to construct haplotypes. We measured the contents of the 17 amino acids that included seven essential amino acids and 10 dispensable amino acids. Through a genome-wide haplotype association study, 261 gene-trait associations containing 174 genes for the 17 components of AAC were detected, and 34 of these genes were associated with at least two components. Furthermore, the associated SNPs in genes were also identified by a traditional genome-wide association study to identify the key natural variations in the specific genes. The genome-wide haplotype association study allowed us to detected candidate genes directly and to identify key natural genetic variation as well. In the present study, twelve genes have been cloned, and 34 genes were associated with at least two components, suggesting that the genome-wide haplotype association study approach used in the current study is an efficient way to identify candidate genes for target traits. The identified candidate genes, favorable haplotypes, and key natural variations affecting AAC provide valuable resources for further functional characterization and genetic improvement of rice nutritional quality.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 8 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 25%
Researcher 2 25%
Student > Postgraduate 1 13%
Unknown 3 38%
Readers by discipline Count As %
Agricultural and Biological Sciences 5 63%
Unknown 3 38%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 15 September 2023.
All research outputs
#16,618,724
of 24,451,685 outputs
Outputs from Rice
#193
of 408 outputs
Outputs of similar age
#94,142
of 174,789 outputs
Outputs of similar age from Rice
#3
of 4 outputs
Altmetric has tracked 24,451,685 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 408 research outputs from this source. They receive a mean Attention Score of 3.9. This one is in the 39th percentile – i.e., 39% of its peers scored the same or lower than it.
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We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one.