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Genome-wide association analysis in peanut accessions uncovers the genetic basis regulating oil and fatty acid variation

BMC Plant Biology volume 25, Article number: 651 (2025) Cite this article

Abstract

Background

The cultivated peanut, Arachis hypogaea L., is a critical oil and food crop worldwide. Improving seed oil quality in peanut has long been an aim of breeders. However, our knowledge of the genetic basis of selecting for seed nutritional traits is limited. Based on AhFAD2A and AhFAD2B, scientists have now developed higher oleic acid (80–84%) in peanut. Decoding the genetic makeup behind natural variation in kernel oil and fatty acid concentrations is crucial for molecular breeding-based nutrient quantity and quality manipulation.

Results

Herein, we recognized 87 quantitative trait loci (QTLs) in 45 genomic regions for the concentrations of oil, oleic acid, and linoleic acid, as well as the oleic acid to linoleic acid (O/L) ratio via a genome-wide association study (GWAS) involving 499 peanut accessions. Eight QTLs explained more than 15% of the phenotypic variation in peanut accessions. Among the 45 potential genes significantly related to the four traits, only three genes displayed annotation to the fatty acid pathway. Furthermore, on the basis of pleiotropism or linkage data belonging to the identified singular QTLs, we generated a trait-locus axis to better elucidate the genetic background behind the observed oil and fatty acid concentration association. Expression analysis indicated that arahy.AV6GAN and arahy.NNA8KD have higher expressions in the seeds.

Conclusion

This natural population consisting of 499 peanut accessions combined with high-density SNPs will provide a better choice for identifying peanut QTLs/genes in the future. Together, our results provide strong evidence for the genetic mechanism behind oil biosynthesis in peanut, facilitating future advances in multiple fatty acid component generation via pyramiding of desirable QTLs.

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Background

Cultivated peanuts (Arachis hypogaea L.) are an allotetraploid oilseed crop that originated from an abiogenetic event in which the diploid ancestor Arachis duranensis (AA) was crossed with Arachis ipaensis (BB) in South America [1, 2]. At present, peanuts are being produced in over 115 countries, covering 26 million hectares and achieving global yields of 41 million tons respectively [3]. However, this remains inadequate in meeting the rising demand for edible peanut oil. Peanut seeds contain approximately 50% oil, 25% protein, 15% carbohydrates, 2% fiber, 2% ash, and 6% water, as well as minor bioactive compounds, namely, folates, minerals, vitamin E, resveratrol, and flavonoids, which possess robust antioxidant properties. A 1% rise in peanut oil content can potentially bring about an economic surge of 7% in oil processing [4]. Since oil is a major peanut seed component, the fatty acid configuration critically determines seed quality. Recently, monounsaturated oleic acid-based was reported to be highly beneficial to human health, for example, it lowered cholesterol contents and diminished coronary heart and inflammatory disease risks [5, 6]. Owing to its inherent antioxidant property that protects against harmful substances [7], peanut oil with augmented oleic acid levels possesses the optimal balance of fatty acid content, thereby facilitating remarkable stability, and prolonging oil storage duration. There is increasing demand to breed peanut varieties with high oil content and elevated oleic acid levels. Hence, identifying genes/loci controlling these traits is critical and optimizing selection strategies will improve peanut yield and quality.

Most plant seeds contain triacylglycerols. Till date, there has been extensive studies on the model plant Arabidopsis thaliana, which has extended our comprehension of storage-oil biosynthetic networks, as well as the genes and gene regulators [8, 9]. A number of lipid metabolism -related genes has been identified, namely, DGAT [10, 11], FAD2 [12, 13], PEPC [14], LEC1 [15], FAD3 [16], GPAT9 [17], KCS [18], MAGL3b [19]. These genes lay down the molecular foundation to enhancing fatty acid composition. Oil, oleic acid, and linoleic acid concentrations in peanut seeds are intricate traits regulated by genes with significant environmental influences, which can be improved by QTL localization analysis and the polymerization of different QTLs. Scientists have employed molecular biomarkers for peanut seed oil-, oleic acid-, and linoleic acid-related QTLs or chromosomal region discoveries. Till now, several QTL-based evaluations utilized early-generation markers namely SSRs [13, 20,21,22,23], but few polymorphic markers and relatively low frequency of target trait-related genes markedly diminish application of SSR markers. More recently, with elucidation of the peanut genome, as well as the advent of high-throughput sequencing technologies [1, 24,25,26,27,28], multiple genotyping platforms, and array-based genotyping protocols [29], it is possible to generate massive quantity of data for high-resolution trait mapping [30]. Therefore, an increasing number of QTLs with detailed locations and SNP markers robustly associated with target traits have been reported [4, 31,32,33,34,35,36,37,38]. Nevertheless, even with extensive knowledge of the plant fatty acid biosynthetic axis and associated genes, the molecular mechanism behind the natural fatty acid biosynthesis variation remains elusive in peanuts because of the limited quantity of parental lines employed in prior investigations.

Genome wide association study (GWAS) is typically used to examine the genetic makeup of complicated traits in peanuts. It is particularly beneficial due to its broad diversity and rapid linkage disequilibrium (LD) decay in a given species [39,40,41,42]. Some progress has been made in peanut quality traits recently [42,43,44,45], for instance, 1,224 SNPs were associated with at least two of four seed quality-related traits (OA, LA, ROLA and PA) which included two loci closed to FAD2A and FAD2B [45]. Zhang et al. (2021) conducted a GWAS using 13,382 single nucleotide polymorphisms (SNPs) to uncover the genetic makeup behind oil, oleic acid, linoleic acid, and the O/L ratio via a miscellaneous panel of 120 peanut accessions and detected 18, 20, 4, and 21 SNPs, respectively [42]. However, GWAS-based genetic dissection with high throughput genotyping data in peanuts quality traits is limited. Given the large genome size and limited genetic diversity of cultivated peanuts, the development of SNP array chips is essential for enabling high-throughput genotyping [46]. Overall, the first version of a peanut large-scale (58 K) SNP genotyping array ‘Axiom_Arachis-v1’ and an improved version of the peanut SNP array ‘Axiom_Arachis-v2’ were successfully used to dissect the fresh seed dormancy [47, 48], leaf chlorophyll content [49], growth habit-related traits [50], late leaf spot [51], tomato spotted wilt virus [52], seed weight and shelling percentage [29, 53,54,55]. This was done for the purposes of accelerating high-resolution mapping and molecular breeding of peanuts. In this report, the data were analyzed in an association study for the concentrations of oil, oleic acid, and linoleic acid and the O/L ratio. We used a diverse association panel, containing numerous phenotypic variations for the studied traits. Next, we developed a trait–QTL axis for evaluation of the genetic associations among the 4 traits.

Results

Phenotypic variation in oil-associated traits among AMP

Near-infrared reflectance spectroscopy (NIR) is a widely used quick and non-invasive technique for oil and fatty acid composition detection in peanuts [56,57,58,59,60]. Herein, the 4 oil-related traits were demonstrated to have plentiful diversity, with most traits appearing to be continuous and with somewhat normal distribution (Fig. 1). The variation exhibited a 1.18-fold alteration in oil composition to a 29.78-fold alteration in oleic acid composition. The oil composition was between 46.25 and 54.35, with a mean of 51.81. The linoleic acid content also varied, with an average of 31.78, ranging from 4.47 ~ 44.17. 47 cultivars (lines) were identified as high oleic acid peanuts and harbored more than 75% oleic acid contents. ANOVA suggested that the genotypic and environmental influences significantly affected individual traits (Additional file 2: Table S2), while the phenotypic variation was controlled primarily by genetic factors, with broad-sense heritability ranging between 0.64 and 0.97 (Additional file 2: Table S2). In terms of pairwise correlations, we observed marked association among oleic acid, linoleic acid, and O/L, likely due to their physiologically correlation (Fig. 1).

Fig. 1
figure 1

The phenotypic distribution and correlation among oil, linoleic, oleic, and O/L in peanut accessions. The diagonal area displayed the phenotypic variation of each trait. The lower left part showed scatter plots of every two traits. The upper right part was correlation coefficient between the composition traits, **, P < 0.01

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SNPs profiling and dispersal within the peanut genome

The SNP array (48 K) developed by Clevenger et al. (2018) [61] identified 47,837 polymorphic loci, with 19,554 (40.8%) derived from subgenome A, 21,876 (45.7%) from subgenome B, and 6,407 (13.4%) originating from scaffold regions‌. (Fig. 2a). A total of 34,588 polymorphic SNPs whose incorrect alignments and multiple alignments were removed underwent alignment to the reference genome. Approximately 10,948 polymorphisms with MAF ≥ 0.05 and missing information ≤ 20% were selected for a GWAS via a SNP array. Chromosome Arahy.01 had the largest SNPs quantity (8.07%; 884 of 10948), whereas chromosome Arahy.08, the shortest chromosome at 51.53 Mb, had the minimum (3.15%; 345 of 10948). However, the smallest and largest average distances between two adjacent SNPs occurred in Arahy.01 (127.17 kb/SNP) and Arahy.16 (388.80 kb/SNP) (Fig. 2b, Additional file 2: Table S3). The peanut genome contained a mean SNP density of 4.31 SNPs/Mb, with the highest density at Arahy.01 (7.86 SNPs/Mb) and the lowest density at Arahy.16 (2.57 SNPs/Mb).

Fig. 2
figure 2

Single nucleotide polymorphisms (SNP) dispersion among the 20 chromosomes of cultivated peanut. Horizontal axis depicting chromosomal length (Mb), Green and red shades representing SNP density (SNPs quantity per window). Vertical axis depicting the 20 chromosomes. (a) Polymorphic SNPs apart from the scaffold biomarkers; (b) Polymorphic SNPs (apart from the scaffold biomarkers) following filter

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GWAS of oil, oleic, linoleic, and O/L contents in peanut kernels

To explore the genetic makeup of oil, oleic acid, and linoleic acid concentrations, we conducted GWAS with an association panel of 499 peanut accessions and 10,948 high-quality SNPs with an MAF > 0.05. Association analysis of these polymorphisms revealed 45 loci linked to oil, oleic, and linoleic acid concentrations, and O/L traits at P < 9.13 × 10− 5 in the peanut association, with loci quantity per trait between 3 and 36 (Fig. 3a and Fig. 3c). As depicted in the quantile-quantile and Manhattan plots depicting the four traits, we observed marked direct correlations following usage of the mixed linear model that takes into account both population structure and familial relatedness (Fig. 3a and Fig. 3b). Thirteen of the four trait-associated loci resided on chromosome Arahy.13, seven on chromosome Arahy.8 and chromosome Arahy.19, five on chromosome Arahy.6, three on chromosome Arahy.9, two on chromosome Arahy.2, chromosome Arahy.3, and chromosome Arahy.12, and one on chromosome Arahy.7, chromosome Arahy.10, chromosome Arahy.15, and chromosome Arahy.18 (Fig. 3d). Twenty identified loci were independently associated with only one trait, three for the oil trait and 17 for the O/L trait. The other 25 loci were pleiotropic, including 17 loci associated with oleic, linoleic, and O/L traits simultaneously (Additional file 1: Fig. S1). The explained phenotypic variation (PVE) of individual loci was between 0.65% and 37.83%, and the total PVE of all recognized loci for individual traits was between 15.54% (oil) to 65.88% (linoleic) (Fig. 3c). Among these loci, 17.78% (8/45) exhibited substantial influences, with a PVE ≥ 15%. Majority loci affected oleic and linoleic acids, which are fatty acids that are probably regulated by a limited number of marked-influence loci given their simplicity as intermediate products in a specific metabolic network.

Fig. 3
figure 3

Overview of GWAS-identified SNPs for oil, linoleic, oleic and O/L traits. (a) Manhattan plot depicting GWAS data for 4 traits. Red dashed line representing the likelihood ratio test threshold (LRT = 4.04), subjected to log2 transformation. The circles from the inside out are oil, oleic, linoleic and O/L, respectively. (b) Quantile-quantile plot for 4 traits. (c) The number of significant loci and total phenotypic variation explaining all identified loci by GWAS analysis. (d) The distribution of significant associated SNPs on 20 peanut chromosomes

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Genetic basis of the correlations among oil, oleic acid, linoleic acid, and O/L

Pleiotropic-related traits or strongly associated genetic factors are frequently linked [46,47,48]. In case of the 4 analyzed traits, diverse associations, including strong and weak, direct and inverse, became apparent (Fig. 1). To elucidate the genetic link among various traits, we generated a trait–locus axis using data from locus–locus linkages and locus–trait associations (Fig. 4). We revealed that all traits consisted of ≥ 1 locus that served as a bridge connecting the traits. Unsurprisingly, traits belonging to the same metabolic network were closely associated within the axis, and exhibited augmented connectivity thereby suggesting the presence of multiple common loci. This is likely due to the fact that a relevant enzyme-encoding gene alteration within a specific metabolic axis can bring about changes in metabolite synthesis either downstream or upstream of the enzyme-catalyzed reaction due to a feedforward and/or feedback modulation [49]. For example, oleic acid and its associated loci on chromosome Arahy.09 were strongly linked to linoleic acid and its ratios (Fig. 4; Table 1). This finding was further corroborated by a prior QTL cluster cloning showing AhFAD2A, which encodes the enzyme fatty acid desaturase (AhFAD2A) which catalyzes the oleic acid to linoleic acid transition [12, 62]. In terms of the trait–locus axis, we also identified 20 loci specific to a singular trait and 25 loci associated with multiple traits (Fig. 4; Table 1). This trait–locus axis is highly informative in elucidating the genetic makeup of associations among various acid traits. This will unequivocally assist in enhancing multiple traits via pyramiding desirable QTLs.

Fig. 4
figure 4

Trait–QTLs axis for 4 traits and 87 single QTLs. The trait–QTL axis generated according to the QTL findings involving 4 traits and co-localization data of all 87 QTLs. Traits and loci linked by solid lines within the axis were strongly linked to the traits. Loci joined by dashed lines within the axis if two QTLs co-localized at the same loci. Table 1 summarizes the trait names in detail

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Table 1 SNPs and potential genes strongly linked to oil, linoleic, oleic Concent and O/L
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Functional annotation and prediction of candidate genes

Improving oil content and quality represents a critical objective in peanut breeding programs. Thus, GWAS was also performed to identify candidate genes for oil content and quality. A total of 3, 23, 25 and 36 candidate genes were identified for oil, oleic acid, linoleic acid, and the O/L, respectively. Based on our functional category annotation of the 45 potential genes, 3 genes were positively associated with enzymes involved in fatty acid metabolism. The 42 remaining genes were estimated to encode enzymes involved in other metabolic axes, transcription factors, transporters, and other proteins (Table 1, Additional file 1: Fig. S2). Among them, one pleiotropic SNP (AX-147234396) falled on arahy.42CZAS (named AhFAD2A) gene was associated with oleic acid, linoleic acid, and the O/L, encoding the fatty acid desaturase enzyme, played a crucial role in regulating both oleic acid and linoleic acid levels [13]. The arahy.D9C186 (named AhFUS3), a B3 domain-containing transcription factor, was also found to be identified for linoleic acid, and the O/L. Additionally, the arahy.PB1D04 (named AhKCS) gene, which encodes ketoacyl-CoA synthase, was linked to O/L. arahy.HGAZ7D gene is located 10.9 kb up-stream of AX-176,806,119 (chr13) was predicted for oleic acid, linoleic acid, and the O/L traits, and this gene encodes a member of the ERF (ethylene response factor) subfamily which have been reported to influence the accumulation of aliphatic glucosinolates [63]. In addition, four genes (arahy.AV6GAN, arahy.WHSM29, arahy.7US8C9 and arahy.NNA8KD predicted for fatty acid component) are involved in phosphoglycerate dehydrogenase, late embryogenesis abundant (LEA) protein, sucrose transporter, light-sensor protein kinase, respectively (Table 1). It is highly likely that these genes have no direct involvement in the relevant network, rather, the polymorphic markers are associated with causal polymorphisms present in nearby genes. In addition, a large number of potential genes were estimated to function outside of the fatty acid axis, even though the loci-harboring axis-related genes contributed similarly to the genetic makeup of phenotypic variation seen with linoleic and oleic traits (Additional file 1: Fig. S3). Based on these findings, the pathway gene-specific loci are critical components of the genetics of fatty acid variation. Compared with the unfavorable alleles, the lines carrying favorable alleles of the lead SNPs in the three fatty acid pathway genes presented augmented oleic acid levels or diminished linoleic acid levels at different levels (Fig. 5; Table 2). This study identified a variety of enzymes involved in metabolic processes, suggesting that the regulation of fatty acids is related to other metabolic pathways and that there may be the same metabolic intermediates between different metabolic pathways, so they can be mutually regulated. In addition, we infer that the regulation of oil-related traits was not only related to the enzymes in the metabolic pathway but also indirectly regulated by many transcription factors and proteins.

Fig. 5
figure 5

Genetic impacts of the leading SNPs at the candidate genes of fatty acid pathway on linoleic, oleic content and O/L. (a-c) Genetic effects of AhFAD2A locus on linoleic, oleic content and O/L, respectively. (d-e) Genetic effects of FUS3 locus on linoleic, oleic content, respectively. (f) Genetic effects of KCS locus on O/L. The P-values were computed according to a two-tailed Student’s t-test

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Table 2 Genetic impacts of the leading SNPs at the AhFAD2A, AhFUS3 and AhKCS loci on fatty acid content
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Expression profiling of identified candidate genes

In this study, we leveraged previously published transcriptome data from peanutbase.org (https://www.peanutbase.org/expression/expr_tissue_Hyp.html) [64] and unpublished transcriptome data from our group to delve deeper into the expression profiles of selected candidate genes potentially involved in seed development in peanuts. This dataset includes transcriptome-based expressions at 22 different developmental stages, as detailed in Clevenger et al. (2016) for the full development of peanut [64] and our group (unpublished) for the 6 different developmental stages of seed (Fig. 6b). Clearly, the expression levels of AhFAD2A and AhFUS3 were significantly elevated during grain development compared to other stages, highlighting their crucial roles in seed development. Additionally, KCS exhibited high expression during the perianth period. Among the remaining candidate genes, arahy.AV6GAN and arahy.NNA8KD demonstrated significantly higher expression levels than the others during grain development, suggesting their crucial role in pod swelling processes (Fig. 6a). Furthermore, our investigation extended to the transcriptome data of two peanut cultivars, H57 (P19-57) and L61 (P19-61), characterized by varying oleic acid contents in their mature seeds—over 75% in P19-57 and less than 45% in P19-61. Analysis of expression patterns at six seed developmental stages revealed that AhFAD2A and AhFUS3 exhibits consistently higher expression levels in P19-57 than P19-61. The expression of AhKCS was predominantly observed during the initial phase of grain development. In addition, arahy.AV6GAN and arahy.WHSM29 primarily regulating the oleic acid to linoleic acid ratio (O/L), elevated expression of L61 compared to H57. In contrast, the H57 cultivar exhibited higher expression levels of arahy.NNA8KD (Fig. 6c). Based on these observations, we hypothesize that arahy.AV6GAN and arahy.NNA8KD are the key contributor to peanut fatty acid metabolism. Given the significant roles that arahy.AV6GAN and arahy.NNA8KD appear to play, further research is essential. Furthermore, candidate gene arahy.AV6GAN showed higher expression in L61 than H57 stages in two peanut cultivars with varying oleic acid contents that may be attributed to its role in promoting linoleic acid synthesis during lipid metabolism. On the contrary, candidate gene arahy.NNA8KD had higher expression in peanut H57. Moving forward, our research will focus on functional verification of these two candidate genes, including qRT-PCR assays and gene silencing techniques to further confirm the functionality of the candidate gene.

Fig. 6
figure 6

Expression profiling of candidate genes at different development stages. (a) RNA-seq for the full development at 22 different tissue types and ontogenies, respectively [64]. (b-c) Peanut seeds at different developmental stages and RNA-seq for seed in high and low oleic acid content peanut (our group, unpublished). H57 is a high-oleic acid peanut, and L61 is a low-oleic acid peanut, both undergoing six seed developmental stages (1, 2, 3, 4, 5, 6)

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Discussion

The cultivated peanut is an allopolyploid formed by the hybridization of two wild diploid species and chromosome doubling [65]. Relative to other recurrent polyploid species, namely soybeans, which have an ancient tetraploid origin, the cultivated peanut employs a restricted genetic network with scarce amount of genetic variations among cultivars. Unfortunately, this negatively impacts polymorphic biomarker development for SNP based genetic linkage map revealed very high-density map. Predictably, only a few polymorphic biomarkers have been reported in peanut chromosomes. For example, the 2334 polymorphic peanut biomarkers reported by Hu et al. (2018) were not evenly dispersed across 20 chromosomes, and the polymorphic biomarker quantity per LG were between 13 and 369 [35]. This distribution pattern was also reported by other studies involving peanuts [66,67,68]. Our study extends the current understanding of the genetic architecture of oil and fatty acid traits in peanuts by employing a large and diverse association panel of 499 accessions. This is in contrast to previous studies that have utilized smaller panels or biparental mapping populations [21, 22, 35]. By using a high-density SNP array, we were able to identify more precise QTL locations compared to earlier investigations, which employed fewer polymorphic markers [42, 55]. The GWAS SNP density was 4.31 SNPs/Mb, which corroborated with earlier results and present as satisfactory value for GWAS assessment in peanuts [39, 40, 55]. However, this investigation faced an additional challenge: we detected several notable gaps between SNPs on some chromosomes (Fig. 1b and Additional file 1: Fig. S4). The peanut genome exhibited an overall gap between SNPs of 0.24 Mb, with the Arahy.01 and 07 (0.13 Mb) and Arahy.16 (0.39 Mb) gaps between SNPs being the lowest and highest, respectively (Additional file 1: Fig. S4 and Additional file 2: Table S4). Notably, seven gaps larger than 5 Mb were detected between SNPs on various chromosomes with the largest gap appeared on Arahy.19 (approximately 12 Mb). Wide inter-marker intervals compromise linkage disequilibrium (LD)-based signal detection, resulting in inadequate genomic resolution to capture trait-associated causal variants [69]. This sparse marker density restricts the precision of QTL mapping, expanding the physical intervals containing potential candidate genes and imposing substantial challenges for downstream functional validation through experimental approaches [70]. We concluded that undetectable genetic factors may drive fatty acid biosynthesis in genomic gaps, as current SNP markers lack these variants‌. Future studies employing more comprehensive genotyping approaches, such as whole-genome sequencing, may help uncover these missing factors and provide a more complete picture of the genetic landscape of peanut oil related traits.

Our prediction of the broad-sense heritability of the 4 seed composition traits were exceptionally elevated, indicating the large dependent of peanut seed quality on genetics. Oil and fatty acids form the primary constituents of peanut seeds, and upregulating the seed oil and fatty acid contents is absolutely crucial; of note, oleic or linoleic acid levels are generally not linked to oil concentration [42, 71]. Hence, identifying trait-specific QTLs aid in genotype selection that carry both desirable genes or QTLs for oil and fatty acid concentrations. An increase in oleic acid content does not markedly alter the oil content, even though a drastic downregulation of linoleic acid has been observed [72], such as AX-176,817,478 on Arahy.06, AX-147,234,396 on Arahy.09, and AX-147,262,366 on Arahy.19. This finding indicates that oleic acid-related QTLs can assist in biomarker-guided selection for augmented oleic acid varieties without substantially affecting the oil concentration in peanut breeding. Furthermore, constructing an interaction genetic network of oil-related traits holds great value as it allows for the exploration of the underlying genetic mechanisms of lipid metabolism from multiple perspectives. In this study, we found that 44.44% of loci were significantly associated with only one trait, and 8 and 17 loci were significantly associated with two and three traits, respectively. It is vital to acknowledge the interactions among multiple phenotypes that aim to optimize metabolic processes or biological processes [73, 74]. These interactions not only can effectively capture the developmental features of complex traits but also aids in the selection of QTLs for future breeding programs aiming to enhance multiple traits simultaneously [75].

With rapidly advancing next-generation sequencing technologies and the continued decline of related costs, GWAS is quickly emerging as a gold standard for natural variation detection explaining complex quantitative phenotypes in plants [76,77,78]. GWAS is frequently employed in multiple biological research areas with advantages of identifying several allele loci without establishing a mapping population [79]. Herein, 45 loci on 12 chromosomes were detected for oil, oleic acid, linoleic acid, and O/L traits in 499 peanut accessions. Relative to the previously identified QTLs associated with oil-associated traits via biparental linkage populations [21, 22, 35], more precise QTLs locations were revealed in this investigation. One possible reason for this difference is that we used a SNP array chip harboring more high-density SNP markers than previous studies. Furthermore, we demonstrated copious variation in oil-associated traits in the association panel of 499 peanut accessions, including augmented-oil and high-oleic acid lines. Employing GWAS, we uncovered 45 oil-, oleic acid-, and linoleic acid concentration- and O/L-related loci, as well as 3 previously cloned genes that contribute to oil biosynthesis (AhFUS3, AhFAD2A, and AhKCS). FUS3 transcription factor of B3 family is considered to be a key regulator of seed development stage. Loss-of-function mutations in the BnFUS3 transcription factor impair lipid biosynthesis by suppressing sucrose transport and glycolytic flux, thereby limiting carbon allocation and acetyl-CoA availability for fatty acid biosynthesis [80]. FAD2A, the fatty acid desaturase, were known to regulate the conversion from oleic acid to linoleic acid [13]. KCS, a β-ketoacyl-CoA synthase, regulated the biosynthesis of very long chain fatty acids in plants. CRISPR-Cas9 editing of conserved AhKCS loci enables engineered enhancement of peanut nutritional quality [18]. AhFAD2B, located on Arahy.19, was not detected, as there were no polymorphic SNP markers on this gene. Majority QTLs were first shown to be linked to oil, oleic acid, and linoleic acid contents and O/L. Overall, 45 relevant loci were detected, which included 3, 23, 25, and 36 loci in oil, oleic acid, linoleic acid, and O/L, respectively. Among them, 25 were pleiotropic for different traits. For example, 17 significant SNPs (AX-176805166, AX-176802651, AX-176817478, AX-147225829, AX-176815346, AX-176800330, AX-147234396, AX-176808914, AX-176799008, AX-176806119, AX-176801965, AX-176796664, AX-147218189, AX-176819580, AX-176815348, AX-176805249, and AX-147262366) associated with oleic acid content, linoleic acid content, and O/L were detected at the same time. The relevant QTL regions were described as the genomic region within 0.5 Mb upstream and downstream of the relevant SNPs [41, 81].

In all, 45 genes whose functions included fatty acid metabolism (AhFUS3, AhFAD2A, and AhKCS), which strictly control natural variation in fatty acid contents in peanuts. Using the arahy.Tifrunner.gnm2 reference genome, we identified the polymorphic marker ‌AX-147234396‌ within the coding region of the AhFAD2A. This marker corresponds to a ‌G-to-A substitution‌ at position ‌448‌, resulting in an aspartic acid-to-asparagine amino acid change (‌D150N‌) that significantly increases oleic acid content, consistent with prior functional studies of AhFAD2A in peanut lipid biosynthesis [82]. The markers ‌AX-176806463‌ and ‌AX-176819738‌ were localized to ‌intergenic regions‌, with AhFUS3 and AhKCS identified as candidate genes within the ‌0.5 Mb flanking regions‌ of these loci. In order to further study the functional sites of these two genes, further research is necessary, like ‌increase marker density‌ or haplotype analysis across the AhFUS3 and AhKCS genomic regions to characterize novel allelic variants influencing fatty acid profiles. In addition, our analysis revealed that a substantial number of the identified loci were associated with non-fatty acid pathway genes, many of which encode enzymes involved in other metabolic processes such as oxidation-reduction reactions, hydrolase activity, glucose metabolism, and transport complexes. The influence of these non-pathway genes on oil traits could be mediated through pleiotropic effects or indirect regulation via metabolic intermediates shared between different pathways. For example, genes involved in glucose metabolism may affect the availability of precursors for fatty acid synthesis. Based on our observations, a substantial population of the identified loci were strongly associated with non-pathway genes and exhibited comparable activities, with 6 loci elucidating more than 20% of the phenotypic variation in 1–3 fatty acid traits. Five genes, arahy.ZNL5N1, arahy.J0DG7J, arahy.X7PJ8H, arahy.MZJT69, and arahy.HR8ZWH received annotation as non-fatty acid network genes, and the favorable alleles at the leading SNPs elevated the oleic acid levels by 35.50% (AX-147233346), 26.64% (AX-176800330), 35.62% (AX-176795908), 30.26% (AX-147262366), and 47.14% (AX-176798303), in comparison with unfavorable alleles (Additional file 1: Fig. S5 and Additional file 2: Table S5). This phenomenon may stem from the oligogenic regulation of fatty acid profiles in peanut seeds, involving major-effect quantitative trait loci (QTLs) interacting with polygenic modifiers. Non-pathway genes pyramiding might be an effective way to increase oleic acid content in peanut breeding. Further research is needed to elucidate the specific biological mechanisms by which these non-pathway genes impact oil and fatty acid contents in peanuts.

Conclusion

This GWAS was conducted on the seed composition traits of an association panel involving 499 diverse inbred lines, and several relevant SNPs were discovered. Overall, 45 potential genes significantly associated with oil, oleic acid, linoleic acid, and O/L were obtained via GWAS. The QTLs and genes identified in our GWAS can potentially assist breeders to enhance the oil concentration or alter the fatty acid ratios in peanut seeds. It is imperative to continue research in this area to validate the identified potential genes. Elucidation of desirable genes and alleles with substantial impact is critical for selection of specific traits among a more diverse population. Our discovery of significant loci provides essential genetic resources and biomarkers for efficient oil improvement in peanuts and other crops.

Materials and methods

Sample collection, DNA isolation, and genotyping

In all, we employed 499 peanut accessions in this investigation which conserved at the Shandong Peanut Research Institute (Additional file 2: Table S1). Among them, 238 lines were from China, 259 lines were from the USA, one was from Japan and one was from Australia, among which 350 belonged to A. hypogaea var. hypogaea, 6 to var. hirsuta, 46 to var. vulgaris, and 26 to var. fastigiata plus 71 irregular types (Additional file 2: Table S1). The irregular categories did not belong to any previously described peanut botanical varieties; rather, they are hybrids of the four botanical varieties [83]. To ensure the genetic purity of each accession, we first cultivated all accessions in 2016 and then harvested pods of each accession from individual plants. After that the 499 lines were seeded in 1-row plots in a random block design in the experimental fields of Laixi, Weifang, and Yantai from 2017 to 2020. The oil, oleic acid, and linoleic acid contents of mature seeds were measured via near-infrared reflectance spectroscopy (NIR) [57, 59, 84]. The seed quality NIR estimation was performed via a Fourier transform NIR spectrometer (Bruker, Germany). For every individual sample, we assessed ten seeds, with two replicates. In addition, one ratio trait (O/L) was derived from the oleic and linoleic contents.

Young healthy leaves from each accession were collected, with extraction of total genomic DNA via a Plant Genomic DNA Kit (Tiangen Biotech (Beijing) Co., Ltd.). The extracted DNA quality and concentration were determined via 0.8% agarose gel electrophoresis using standard lambda DNA and a NanoDrop ND-1000 (Thermo Fisher Scientific Inc., Wilmington, DE, USA).

The 48 K SNP array incorporates 11,516 SNPs from the first version, 1,674 haplotype-based SNPs from contrasting sub-genome-specific sequences, and 28,218 newly identified SNPs from the re-sequencing of 21 tetraploid accessions including three parents of the “T” and the “S” populations [85]. All the lines were genotyped via a high-density SNP array Axiom_Arachis with 48 K SNPs, which consists of 47837 SNPs [29] mapped onto a peanut reference genome (Tifrunner.gnm2. (https://www.peanutbase.org/). Whole-genome sequencing was performed at CapitalBio Technology (Beijing) using the Affymetrix GeneTitan platform, where DNA samples from 499 accessions were genotyped following established protocols [86]. This process generated standardized.CEL files for each sample, which were securely archived. In all, 10948 SNPs with minor allelic frequencies (MAFs) ≥ 0.05 and missing data of less than 20% were used for the genotypic data analyses.

Assessment of phenotypic information

All data analyses were conducted in R version 3.1.1 (www.R-project.org). Using the R function ‘aov’, Analysis of variance (ANOVA) was conducted for individual traits to assess the genotype and environmental effects and broad-sense heritability of each trait. To minimize environmental influence, we computed the best linear unbiased predictor (BLUP) value for all lines using the linear mixed model, and both genotype and environment as random effects in the R function ‘lme4’. Phenotype description statistics was conducted for the BLUP values for all lines, followed by computation of the Pearson correlation coefficients.

GWAS

We conducted GWAS on peanut kernel oil-related traits to elucidate links between genotype and phenotype. To do this, a mixed linear model (MLM) in the TASSEL software was employed, whereby the population structure (Q) and the kinship matrix (K) were regulated to eliminate false-positive correlations [40]. SNPs with MAF ≥ 5% were entered into association analysis. Additionally, we computed the P value of all SNPs, and significance was set at 9.13 × 10− 5 (i.e., P ≤ 1/n, n = total markers used).

Linkage disequilibrium analysis

To identify genotype and phenotype association-related genes, we conducted LD analysis on SNPs from the same chromosome that showed strong significance, and threshold was set at < 0.2 for the LD statistic r2 via TASSEL version 5.2 [87]. Among the identified association signals, we validated multiple genes both in or near (within 500 kb up- and down-stream of the lead SNP) reported oil metabolism–related genes. Among those that were not in or near oil metabolism–related genes, we considered that the link may be with a more distant gene, the closest among which was regarded as the optimal candidate gene. The physical SNP location was screened on the basis of the peanut genomic sequence version arahy.Tifrunner.gnm2 (https://www.peanutbase.org/peanut_genome).

Generation of a trait–locus axis

Using the QTL mapping results, we next generated a trait-locus axis via Cytoscape 3.2.0 [88]. Traits and associated QTLs were regarded as nodes, and the links between traits and QTLs (overlapping on the basis of physical location) were taken as edges. The link thickness between the traits and QTLs represented the QTL PVE value, and the node size indicated QTLs quantity.

Expression analysis of candidate genes

Two RNA-seq datasets, one published [64] and one unpublished (provided by our group), were used for preliminary verification of the expression levels of candidate genes. Expression dynamics of candidate genes were studied in seeds of different peanut varieties and at different developmental stages to gain deeper insights into the role of identified genes.

Data availability

The data that support the findings of this study are included in the manuscript. The all sequence data was uploaded to NCBI with accession code GSE289195.

Abbreviations

BLUP:

Best linear unbiased predictor

GWAS:

Genome-wide association study

LD:

Linkage disequilibrium

MAF:

Minor allelic frequency

MLM:

Mixed linear model

NIR:

Near-infrared reflectance spectroscopy

O/L:

The oleic acid to linoleic acid ratio

PVE:

Explained phenotypic variation

QTLs:

Quantitative trait loci

SNP:

Single nucleotide polymorphism

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Acknowledgements

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Funding

This work was supported by the Natural Science Foundation of Shangdong Province (ZR2021QC172, ZR2023QC146); Major scientific and technological project in Xinjiang (2022A02008-3); Taishan Scholars Program (tstp20240523, tsqn202312292); China Agriculture Research System of MOF and MARA (CARS-13); Key R&D Program of Shandong Province (2024LZGC035); Key research and development plan of Shandong Province (2018GNC110036, 2022TZXD0031); the innovation Project of SAAS (CXGC2023F20, CXGC2024F20, CXGC2024G20); Open Project of Key Laboratory of Digital Upland Crops of Zhejiang Province (2022E10012); Open Project of Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture and Rural Affairs, P. R. China (KF2024007).

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Authors and Affiliations

  1. Shandong Peanut Research Institute, Qingdao, CN-266100, China

    Jing Xu, Xiao Jiang, Xiangzhen Yin, Xuhong Zhao, Na Chen, Lijuan Pan, Junqing Ma, Mei Yuan & Xiaoyuan Chi

  2. Weifang Academy of Agricultural Sciences, Weifang, CN-261071, China

    Chun Fu

  3. Yantai Academy of Agricultural Sciences, Yantai, CN-265500, China

    Yanlin Jiao

Authors
  1. Jing Xu
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  2. Xiao Jiang
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  3. Xiangzhen Yin
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  4. Xuhong Zhao
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  10. Mei Yuan
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  11. Xiaoyuan Chi
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Contributions

XC, MY and JX conceived the study, designed the experiments and finished the final version of the manuscript. JX drafted the manuscript. JX and XJ analyzed the data. XY, XZ, NC, LP, CF, YJ and JM participated in phenotypic data collection and prepared the genomic DNA for 48 K SNP array chip sequencing. All authors reviewed the manuscript. All the authors read and approved the final manuscript.

Corresponding authors

Correspondence to Mei Yuan or Xiaoyuan Chi.

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Xu, J., Jiang, X., Yin, X. et al. Genome-wide association analysis in peanut accessions uncovers the genetic basis regulating oil and fatty acid variation. BMC Plant Biol 25, 651 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12870-025-06690-9

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12870-025-06690-9

Keywords

BMC Plant Biology

ISSN: 1471-2229

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