A breeding strategy for high-oleic acid content and high-yield peanut using Tag SNPs and genomic selection

Abstract Background Developing peanut varieties with high oleic acid content (OAC) and superior yield is critical for meeting global nutritional and economic demands. To address this, our study integrated marker-assisted breeding with genomic selection (GS), creating an efficient breeding framework. Using a diverse natural population of 169 accessions, we conducted genome-wide association studies (GWAS) and GS analyses to identify Tag single nucleotide polymorphisms (SNPs) associated with OAC and develop a robust yield prediction model. Results Phenotypic analysis indicated continuous variation in both OAC and productivity, with broad-sense heritability estimates of 0.9634 and 0.4535, respectively. Only a weak correlation was observed between these two traits. Whole-genome resequencing at approximately 10 × coverage identified 608,809 SNPs. GWAS revealed 32 significant loci associated with OAC, predominantly located on chromosomes 9 and 19, explaining 17.65–26.23% of the phenotypic variation. These loci were grouped into three distinct haplotype blocks, from which three core Tag SNPs (Arahy.9_113845844, Arahy.9_114322963, Arahy.19_154509990) were validated by regression and boxplot analyses. The GS model, developed using a genomic relationship matrix, yielded an additive genetic variance of 0.8626, a residual variance of 1.6915, a heritability estimate of 0.3377 for yield, with a prediction accuracy of 0.58. Validation in the candidate population showed optimal breeding efficiency at a 30% selection intensity using genomic estimated breeding values. Conclusions The identified Tag SNPs provides a framework for efficient early-generation selection for OAC, while GS predictions facilitate advanced-generation yield optimization. Our results suggest that this integrated strategy has the potential to improve both quality and yield traits, offering a framework for more efficient breeding of peanut varieties with enhanced OAC and productivity.

Authors

Institutions

Publication Details

Journal
BMC Plant Biology
Published
2026-04-11
DOI
https://doi.org/10.1186/s12870-026-08473-2
Primary Topic
Peanut Plant Research Studies
Type
article
Field-Weighted Citation Impact
0.00

Funders

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

A breeding strategy for high-oleic acid content and high-yield peanut using Tag SNPs and genomic selection

Taorui Zhang, Jianli Miao, Feng Luo, Li Deng et al.
BMC Plant Biology
Peanut Plant Research Studies
article

A breeding strategy for high-oleic acid content and high-yield peanut using Tag SNPs and genomic selection

Taorui Zhang, Jianli Miao, Feng Luo, Li Deng, Shaowei Li, Junping Hu, Wenhao Liu, Junhua Yin, Yang Li, Li Ren, Minjie Guo, Peiyun Wang
article en

Abstract

Abstract Background Developing peanut varieties with high oleic acid content (OAC) and superior yield is critical for meeting global nutritional and economic demands. To address this, our study integrated marker-assisted breeding with genomic selection (GS), creating an efficient breeding framework. Using a diverse natural population of 169 accessions, we conducted genome-wide association studies (GWAS) and GS analyses to identify Tag single nucleotide polymorphisms (SNPs) associated with OAC and develop a robust yield prediction model. Results Phenotypic analysis indicated continuous variation in both OAC and productivity, with broad-sense heritability estimates of 0.9634 and 0.4535, respectively. Only a weak correlation was observed between these two traits. Whole-genome resequencing at approximately 10 × coverage identified 608,809 SNPs. GWAS revealed 32 significant loci associated with OAC, predominantly located on chromosomes 9 and 19, explaining 17.65–26.23% of the phenotypic variation. These loci were grouped into three distinct haplotype blocks, from which three core Tag SNPs (Arahy.9_113845844, Arahy.9_114322963, Arahy.19_154509990) were validated by regression and boxplot analyses. The GS model, developed using a genomic relationship matrix, yielded an additive genetic variance of 0.8626, a residual variance of 1.6915, a heritability estimate of 0.3377 for yield, with a prediction accuracy of 0.58. Validation in the candidate population showed optimal breeding efficiency at a 30% selection intensity using genomic estimated breeding values. Conclusions The identified Tag SNPs provides a framework for efficient early-generation selection for OAC, while GS predictions facilitate advanced-generation yield optimization. Our results suggest that this integrated strategy has the potential to improve both quality and yield traits, offering a framework for more efficient breeding of peanut varieties with enhanced OAC and productivity.

BMC Plant BiologyVol. 26(1)
Henan Energy & Chemical Industry Group (China) (CN), Kaifeng University (CN)
Agriculture Research System of China, Science and Technology Department of Henan Province, Henan Provincial Science and Technology Research Project
Zero hunger
Openalex Percentile: Top 26%
Peanut Plant Research Studies
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.