Background: The peanut (Arachis hypogaea L.) is an important oilseed crop in tropical and subtropical regions of the world. However, little about the molecular biology of the peanut is currently known. Recently, next-generation sequencing technology, termed RNA-seq, has provided a powerful approach for analysing the transcriptome, and for shedding light on the molecular biology of peanut. Results: In this study, we employed RNA-seq to analyse the transcriptomes of the immature seeds of three different peanut varieties with different oil contents. A total of 26.1-27.2 million paired-end reads with lengths of 100 bp were generated from the three varieties and 59,077 unigenes were assembled with N50 of 823 bp. Based on sequence similarity search with known proteins, a total of 40,100 genes were identified. Among these unigenes, only 8,252 unigenes were annotated with 42 gene ontology (GO) functional categories. And 18,028 unigenes mapped to 125 pathways by searching against the Kyoto Encyclopedia of Genes and Genomes pathway database (KEGG). In addition, 3,919 microsatellite markers were developed in the unigene library, and 160 PCR primers of SSR loci were used for validation of the amplification and the polymorphism. Conclusion: We completed a successful global analysis of the peanut transcriptome using RNA-seq, a large number of unigenes were assembled, and almost four thousand SSR primers were developed. These data will facilitate gene discovery and functional genomic studies of the peanut plant. In addition, this study provides insight into the complex transcriptome of the peanut and established a biotechnological platform for future research.

Improving seed oil quality in peanut (Arachis hypogaea) has long been an aim of breeding programs worldwide. The genetic resources to achieve this goal are limited. We used an advanced recombinant inbred line (RIL) population derived from JH5 × KX01-6 to explore quantitative trait loci (QTL) affecting peanut oil quality and their additive effects, epistatic effects, and QTL × environment interactions. Gas chromatography (GC) analysis suggested seven fatty acids components were obviously detected in both parents and analyzed in a follow-up QTL analysis. The major components, palmitic acid (C16:0), oleic acid (C18:1), and linoleic acid (C18:2), exhibited considerable phenotypic variation and fit the two major gene and minor gene mixed-inheritance model. Seventeen QTL explained 2.57–38.72% of the phenotypic variation in these major components, with LOD values of 4.12–37.56 in six environments, and thirty-five QTL explained 0.94–32.21% of the phenotypic variation, with LOD values of 5.99–150.38 in multiple environments. Sixteen of these QTL were detected in both individual and multiple environments. Among these, qFA_08_1 was a novel QTL with stable, valuable and major effect. Two other major-effect QTL, qFA_09_2 and qFA_19_3, share the same physical position as FAD2A and FAD2B, respectively. Eleven stable epistatic QTL involving nine loci explained 1.30–34.97% of the phenotypic variation, with epistatic effects ranging from 0.09 to 6.13. These QTL could be valuable for breeding varieties with improved oil quality.

High oil content is a crucial trait for breeding high-quality peanut varieties. Understanding the genetic mechanisms underlying peanut oil content across multiple environments and identifying valuable genetic loci that enhance oil content would provide a strong foundation for developing high-oil peanut cultivars. In this study, a recombinant inbred line (RIL) population, derived from Jihua 5 and Kaixuan01-6, was used for genetic dissection and QTL mapping of oil content across six environments. The results showed that the absolute values of skewness and kurtosis for oil content in the RIL population across the six environments were less than 1, and the broad-sense heritability was 0.799. A total of 18 QTLs were identified, with LOD scores ranging from 13.62 to 22.58, accounting for 3.18% to 14.83% of the phenotypic variation. Notably, qOC_8-1 emerged as the most stable and major QTL, with the increasing allele contributed by Jihua 6. Joint QTL analysis across multiple environments revealed 11 QTLs associated with oil content, with LOD scores ranging from 5.59 to 16.87, explaining 2.32% to 7.69% of the phenotypic variation. Among these, seven additive alleles were derived from Jihua 6, while four were from Kaixuan01-6. Additionally, nine pairs of epistatic QTLs involving 13 loci were detected, with LOD scores ranging from 8.54 to 10.90, contributing to 1.91% to 2.55% of the phenotypic variation. In conclusion, these results indicate that peanut oil content is regulated by multiple genetic loci, with interaction effects between different loci. qOC_8-1 is a particularly valuable QTL for breeding high-oil peanut cultivars. This study provides valuable insights for future precision molecular breeding efforts targeting oil content improvement.