Identification of a Major QTL for Seed Protein Content in Cultivated Peanut (Arachis hypogaea L.) Using QTL-Seq

Peanut (<i>Arachis hypogaea</i> L.) is a great plant protein source for human diet since it has high protein content in the kernel. Therefore, seed protein content (SPC) is considered a major agronomic and quality trait in peanut breeding. However, few genetic loci underlying SPC have been identified in peanuts, and the underlying regulatory mechanisms remain unknown, limiting the effectiveness of breeding for high-SPC peanut varieties. In this study, a major QTL (<i>qSPCB10.1</i>) controlling peanut SPC was identified within a 2.3 Mb interval in chromosome B10 by QTL-seq using a recombinant inbred line population derived from parental lines with high and low SPCs, respectively. Sequence comparison, transcriptomic analysis, and annotation analysis of the <i>qSPCB10.1</i> locus were performed. Six differentially expressed genes with sequence variations between two parents were identified as candidate genes underlying <i>qSPCB10.1</i>. Further locus interaction analysis revealed that <i>qSPCB10.1</i> could not affect the seed oil accumulation unless <i>qOCA08.1<sub>XH13</sub></i> was present, a high seed oil content (SOC) allele for a major QTL underlying SOC. In summary, our study provides a basis for future investigation of the genetic basis of seed protein accumulation and facilitates marker-assisted selection for developing high-SPC peanut genotypes.

Authors

Institutions

Publication Details

Journal
Plants
Published
2024-08-25
DOI
https://doi.org/10.3390/plants13172368
Citations
6
Primary Topic
Peanut Plant Research Studies
Type
article
Field-Weighted Citation Impact
3.60

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Identification of a Major QTL for Seed Protein Content in Cultivated Peanut (Arachis hypogaea L.) Using QTL-Seq

Shengyao Guo, Li Huang, Huifang Jiang, Xiangyu Chen et al.
6 citations
Plants
Peanut Plant Research Studies
3.60
article

Identification of a Major QTL for Seed Protein Content in Cultivated Peanut (Arachis hypogaea L.) Using QTL-Seq

Shengyao Guo, Li Huang, Huifang Jiang, Xiangyu Chen, Hao Chen, Nian Liu, Jianhong Chen, Rirong Xu, Dongxin Huai
article en
6 citations

Abstract

Peanut (<i>Arachis hypogaea</i> L.) is a great plant protein source for human diet since it has high protein content in the kernel. Therefore, seed protein content (SPC) is considered a major agronomic and quality trait in peanut breeding. However, few genetic loci underlying SPC have been identified in peanuts, and the underlying regulatory mechanisms remain unknown, limiting the effectiveness of breeding for high-SPC peanut varieties. In this study, a major QTL (<i>qSPCB10.1</i>) controlling peanut SPC was identified within a 2.3 Mb interval in chromosome B10 by QTL-seq using a recombinant inbred line population derived from parental lines with high and low SPCs, respectively. Sequence comparison, transcriptomic analysis, and annotation analysis of the <i>qSPCB10.1</i> locus were performed. Six differentially expressed genes with sequence variations between two parents were identified as candidate genes underlying <i>qSPCB10.1</i>. Further locus interaction analysis revealed that <i>qSPCB10.1</i> could not affect the seed oil accumulation unless <i>qOCA08.1<sub>XH13</sub></i> was present, a high seed oil content (SOC) allele for a major QTL underlying SOC. In summary, our study provides a basis for future investigation of the genetic basis of seed protein accumulation and facilitates marker-assisted selection for developing high-SPC peanut genotypes.

PlantsVol. 13(17)
Fujian Academy of Agricultural Sciences (CN), Chinese Academy of Agricultural Sciences (CN), Oil Crops Research Institute (CN), Ministry of Agriculture and Rural Affairs (CN), Institute of Crop Sciences (CN)
National Natural Science Foundation of China, Natural Science Foundation of Fujian Province, Fujian Academy of Agricultural Sciences
Zero hunger
Openalex Percentile: Top 8%
Peanut Plant Research Studies
3.60
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.