Identification of photosynthetic pigment contents related genes in peanut (Arachis hypogaea L.) by GWAS and RNA-seq

Identification of photosynthetic pigment contents related genes in peanut (Arachis hypogaea L.) by GWAS and RNA-seq | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Identification of photosynthetic pigment contents related genes in peanut ( Arachis hypogaea L.) by GWAS and RNA-seq Tingting Chen, Zijun Huang, Yuwei Cui, Jing Cao, Ruier Zeng, Yiyang Liu, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6203682/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Apr, 2026 Read the published version in Theoretical and Applied Genetics → Version 1 posted 5 You are reading this latest preprint version Abstract Photosynthetic pigments are indispensable for light absorption and electron transfer in photosynthesis, which is essential for increasing crop productivity. However, genetic basis of photosynthetic pigment contents in peanuts ( Arachis hypogaea L.) at seedling and flowering stages remains poorly understood. In this study, an association panel of 241 peanut accessions was assayed for four photosynthetic pigment contents, including chlorophyll a (Chl a), chlorophyll b (Chl b), total chlorophyll (Chl a + b) and carotenoids (Car) across five environments. A total of 2,110,659 high-quality single nucleotide polymorphisms (SNPs) were obtained by whole-genome re-sequencing. A genome-wide association study was performed based on the best linear unbiased estimation values of four photosynthetic pigment contents. In total, 23 and 45 quantitative trait loci (QTLs) were associated with four photosynthetic pigment contents at the seedling and flowering stages, respectively, with eight QTLs associated with multiple traits. Thirty-two genes were identified within these QTL regions. Furthermore, through RNA-seq analysis of two peanut accessions with contrasting photosynthetic pigments contents, 3829 and 4972 differentially expressed genes were detected at seedling and flowering stages, respectively. Two candidate genes, Arahy.YWY61J and Arahy.VMJ95M , were differently expressed at flowering stages. Haplotype analysis suggested that Arahy.YWY61J was involved in Chl a and Chl b contents; and Arahy.VMJ95M gene was involved in Chl b and Car synthesis of peanut leaves. These findings will contribute to the understanding of genetic and molecular mechanisms underlying variations in photosynthetic pigments and benefits the improvement of photosynthetic efficiency using marker-assisted breeding in peanuts. Arachis hypogaea L. photosynthetic pigments genome-wide association analysis Quantitative traits loci candidate gene Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Greater crop yields must be achieved to nourish the world’s population by 2050. It is one of the important strategies to increase crop yield that improving photosynthetic efficiency through optimizing light harvesting capacity (Bailey-Serres et al. 2019 ). Chlorophyll and carotenoids (Car) play important roles for photosynthesis in plants (Sun et al. 2023 ). Chlorophyll a (Chl a) and chlorophyll b (Chl b) are types of chlorophyll in plants that absorb light energy in the chloroplast and promote photosynthesis (Wang and Grimm 2021 ). Car, as light-harvesting pigments, plays a significant role in protecting the photosynthetic apparatus against photooxidative damage by quenching excited electrons, scavenging reactive oxygen species, and dissipating excess energy in the chlorophyll (Niyogi and Truong 2013 ; Murchie and Ruban 2020 ). Therefore, considering their significant impact on crop photosynthesis and yield, the contents of photosynthetic pigments are valuable physiological characteristics in crop breeding. Chlorophyll and Car content are quantitative traits controlled by quantitative trait loci (QTLs). Studies on QTLs of chlorophyll and carotenoid content have been conducted on various crops, such as rice (Jiang et al. 2012 ; Jiang et al. 2014 ; Jang et al. 2022 ), wheat (Guo et al. 2023 ), soybean (Wang et al. 2020a ; Yu et al. 2020 ), and oilseeds (Ye et al. 2020 ). Additionally, several genes encoding proteins involved in chlorophyll metabolism were identified, such as OsChlH, OsChlD, OsChlI, YGL1 , NON-YELLOW COLORING1 ( NYC1 ), YC1-LIKE ( NOL ), SGR , and NYC4 (Jung et al. 2003 ; Zhang et al. 2006 ; Jiang et al. 2007 ; Kusaba et al. 2007 ; Wu et al. 2007 ; Sato et al. 2009 ; Yamatani et al. 2013 ). Peanut ( Arachis hypogaea L.) is one of the most important oilseed crop worldwide, consumed as a major source of edible oils and proteins for humans. The global production of peanuts reached approximately 46 million tons from approximately 37.2 million hectares in 2023 ( http://www.fao.org ). Peanut yield is always an important goal for breeder in China. Previous studies have indicated that chlorophyll in peanut leaves were crucial characteristics for photosynthesis under some abiotic stresses (Gao et al. 2024 ; Lai et al. 2024 ; Zhang et al. 2024 ). However, there are no reports on QTLs related to chlorophyll and carotenoids content in peanut. Genome-wide association studies (GWASs) could considerably elucidate the genetic architecture and the causative loci, particularly for complex quantitative traits among diverse varieties (Huang et al. 2010a ; Wang et al. 2020b ). This approach has been extensively used for the genetic analysis of complex agronomic traits in plants. With the rapid development of next-generation sequencing technology, GWASs using high-density single nucleotide polymorphism (SNP) markers and diverse resources have become possible. In the present study, a total of 241 peanut accessions, including landrace and modern varieties, were re-sequenced to determine the genetic architecture of contents of four photosynthetic pigments in peanut. Phenotyping was performed for the four traits under five different environments. We conducted GWAS to identify significant SNPs and QTLs related to photosynthetic pigments contents in peanuts based on the best linear unbiased estimation (BLUE) values under five different environments. RNA sequencing (RNA-seq) was used to identify genes exhibiting different expression patterns in peanut genotypes with contrasting photosynthetic pigments contents. Integrating GWAS and RNA-seq, two candidate genes were screened and associated with photosynthetic pigments in peanut. These results will improve the understanding of the molecular mechanism regulating photosynthetic pigments contents and contribute to improving the photosynthetic efficiency of peanut varieties via marker-assisted breeding. Materials and Methods Plant material and field experiments In the present study, we analyzed 241 peanut accessions that were previously selected worldwide, to represent peanut diversity. In total, this set includes 116 landraces and 125 modern varieties (Table S1 ). All 241 accessions were planted in five environmental locations, including Guangzhou (23°19'N, 113°49'E), Guangdong Province, from August to December 2022; Zhanjiang (21°17'N, 110°24'E), Guangdong Province, from August to December 2022; Sanya (18°09'N, 108°56'E), Hainan Province, from November 2022 to February 2023; Guangzhou (23°19'N, 113°49'E), Guangdong Province, from March to July 2023; Zhanjiang (21°17'N, 110°24'E), Guangdong Province, from March to July 2023 and the experiments were denoted as E1, E2, E3, E4, and E5, respectively. Each accession was planted in an experimental plot in 4 rows of 20 plants each, with 0.20 m and 0.25 m in-row and inter-row spacing, respectively. All accessions were arranged in a randomized complete block design with treatments replicated thrice. The cultivation management followed standard agronomic practices for commercial peanut production (Chen et al. 2023 ). Leaves of peanut varieties Zhanhei 1 and Guihuahei 2 with low- and high-photosynthetic pigments contents, respectively, were collected during the seedling and flowering stages and subsequently used for RNA-seq analysis. Phenotypic evaluation and statistical analysis We sampled the third leaf from the top of three plants for each accession to determine the content of Chl a and Chl b at the seedling and flowering stages. Chlorophyll was extracted using a method described by Lu et al. (Lu et al. 2023 ). The total chlorophyll content (Chl a + b) was obtained by combining Chl a and Chl b contents. The Car content was determined using the method described by Zheng et al. (Zheng et al. 2019 ). Phenotypic and BLUE analysis were conducted for four target traits. The BLUE value and the broad-sense heritability ( H 2 ) of photosynthetic pigments contents were calculated using the lme4 package in the R software (RStudio, Boston, MA, USA) (McClellan et al. 2010). Finally, SPSS v22.0 (IBM, Armonk, NY, USA) was used to calculate the correlation coefficients for the BLUE values for each trait, analysis of variance, and frequency distribution. Whole-genome re-sequencing, read mapping, and variant calling Young leaves at seedling stages were used to extract genomic DNA following the cetyltrimethylammonium bromide method. Genomic DNA was quantified using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA), and subjected to library construction. Paired-end (PE150) sequencing was then conducted using BGISEQ-500 sequencing platform (BGI, Shenzhen, China). The clean reads were mapped to the peanut reference genome (arahy.Tifrunner.gnm1.KYV3.genome_main.fna) (Bertioli et al. 2019 ) using Burrows-Wheeler Aligner (Li and Durbin 2009 ). The Genome Analysis Toolkit (GATK4.0, Broad Institute, St Cambridge, MA, USA) was used for variation detection (McKenna et al. 2010 ). We removed the SNPs with a call rate of < 85% and a minor allele frequency of < 0.05 to avoid spurious linkage disequilibrium (LD) and false-positive associations (Yano et al. 2016 ). We use VCFtools 4.1 software ( https://vcftools.sourceforge.net/ ) with a 500-kb sliding window to count SNP density across each chromosome, following the parameter as “vcftools --vcf genotype.vcf \--chr chrX --from-bp X --to-bp Y \--out sample --recode --recode-INFO-all” (Danecek et al. 2011 ). Population genetics and linkage disequilibrium The cross-validation error (CVE) was performed based on the number of subpopulations K = 1 to 8 to explore the convergence of peanut accessions. LD decay analysis was performed using PopLDdecay 3.41 software (Zhang et al. 2019 ). The neighbor-joining method was used to construct the phylogenetic tree in TASSEL 5.0 (Kang et al. 2008 ), and the tree was visualized using TooliTOL (v2.41) (Letunic and Bork 2019 ). The TASSEL5.0 was adopted to perform principal component analysis (PCA), and the eigenvectors were plotted using the ggplot2 package in Rstudio (Bradbury et al. 2007 ). ADMIXTURE software (Inventio, Georgetown, IN, USA) was employed to infer population structure (Alexander et al. 2009 ). Genome-wide association study A GWAS was carried out using EMMAX software (EMMAX group, Nashville, MA, USA) based on a mixed linear model, following the parameter: “emmax -t emmax_in -p emmax.trait.txt -k emmax_in.BN.kinf -o emmax.qk” (McCouch et al. 2016 ). The “CMplot” 4.3.1 R package ( https://github.com/YinLiLin/R-CMplot ) was employed to draw Manhattan and quantile-quantile plots, following the parameter as “ pmap <- read.table("result.txt", header = T) head(pmap) threshold <- 1/nrow (pmap [!is.na (pmap $ position)]) CMplot (pmap,threshold = c(1e-7,1e5), threshold.lty = c(1,2), threshold.lwd = c(1,1), threshold.col = c("black","grey"), ylim = c(0,9), amplify = T, file = "tiff", plot.type = c("m","q"))”. The significance threshold (-log 10 P ≥ 5.84) for GWAS was determined by a uniform threshold of 1/n, where n was the effective number of independent SNPs calculated using Genetic type 1 Error Calculator (v0.2) (Li et al. 2011 ; Li et al. 2022 ; Li et al. 2023 ). Based on an LD decay distance of 50 kb for the 241 peanut accessions, a region of 100 kb was defined as one QTL (Fig. S1 ). RNA-seq Peanut leaf samples were collected at the seedling and flowering stages from Guihuahei 2 and Zhanhei 1 accessions with high- and low-pigment content, respectively. Total RNA was isolated using a FastPure Plant Total RNA Isolation Kit (Vazyme, Nanjing, China), resulting in a total of 12 samples (two peanut accessions × two growth stages × three biological replicates). RNA quality was assessed on an Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA, USA) and checked using RNase free agarose gel electrophoresis. cDNA library construction was performed by Gene Biotechnology Denovo Co. (Guangzhou, China). The resulting cDNA library was sequenced using an Illumina Novaseq6000 (Gene Denovo Biotechnology Co., Guangzhou, China). The data analysis processes were done as described in our previous study (Chen et al. 2020 ). Firstly, raw data were removed reads containing poly-N, adapters, and low-quality. The clean data were then mapped to the genome sequences of the cultivated peanut Tifrunner https://www.peanutbase.org/data/v2/Arachis/hypogaea/genomes/Tifrunner.gnm1.KYV3 ) using the HISAT2 (v2.1.0) (Bertioli et al. 2019 ). Fragments per kilobase (FPKM) values were calculated to quantify its expression abundance and variations using RSEM software (Dewey and Bo 2011 ), with transcripts showing a fold change ≥ 1.5 and P value < 0.05 assigned as differential expressed. Functional annotation and enrichment analysis of differentially expressed genes were performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database ( https://www.genome.jp/kegg/genes.html ). Transcription factor prediction was performed with the plantTFDB database ( https://planttfdb.gaolab.org/index.php ) (Jin et al. 2014 ). The transcriptomic data were deposited to the National Center for Biotechnology Information Sequence Read Archive (accession no. PRJNA1084226). qRT-PCR Total RNA was isolated using a FastPure Plant Total RNA Isolation Kit (Vazyme, Nanjing, China) according to the manufacturer’s instructions. Reverse transcription was performed using HiScript RT Super Mix (Vazyme, Nanjing, China). Quantitative reverse-transcription polymerase chain reaction (qRT-PCR) was carried out using a LightCycle480 II System (Roche Applied Science, Basel, Switzerland). Normalized transcript levels were calculated using the 2 −ΔΔCT method (Livak and Schmittgen 2001 ). We performed three independent biological replicates for each treatment. The primer sequences are listed in Table S2 . Results Variation and correlation analysis of photosynthetic pigments contents in peanut leaves The contents of four photosynthetic pigments for 241 peanut accessions were determined at the seedling and flowering stages across the 5 experimental environments (Fig. 1 a). Continuous and considerable variations were observed in the five environments and their associated BLUE values. Across the five environments, Chl a, Chl b, Chl a + b, and Car content at the seedling stage ranged from 0.28–2.28 mg/g, 0.12–1.26 mg/g, 0.45–3.50 mg/g, and 0.02–0.38 mg/g, respectively, with mean values of 0.76 mg/g, 0.39 mg/g, 1.15 mg/g, and 0.14 mg/g, respectively. Similarly, the Chl a, Chl b, Chl a + b, and Car contents at the flowering stage ranged from 0.26–1.61 mg/g, 0.10–0.92 mg/g, 0.36–2.53 mg/g, and 0.30–0.43 mg/g, respectively, with mean values of 0.69 mg/g, 0.37 mg/g, 1.06 mg/g, and 0.22 mg/g, respectively. The coefficient of variation of BLUE values across the five studied environments ranged from 14.00% for Chl b to 20.00% for Car at the seedling stage. Moreover, it changed from 18.32% for Chl a to 36.40% for Car at the flowering stage. The H 2 of four traits varied from 0.82 for Chl a to 0.98 for Car at the seedling stage, and from 0.84 for Chl a + b to 0.97 for Chl b at the seedling stages, respectively, indicating that these traits were determined mainly by genetic effects. Statistical analysis for the chlorophyll and Car contents are listed in Table S3. The absolute kurtosis and skewness values of the four traits were < 1, suggesting that the four traits nearly conformed to a normal distribution (Fig. 1 b). There were highly significant correlations (P < 0.01) between four photosynthetic pigments contents, indicating a stable correlation between chlorophyll and Car content (Fig. S2 ). Moreover, the correlation coefficients between the Chl a and Chl a + b contents were the highest at the seedling and flowering stages. Genetic variation based on SNPs After mapping to the peanut reference genome Tiffrunner and SNP calling, a total of 2,110,659 high-quality SNPs were used for the subsequent population genetic structure analysis. The distribution of these SNPs on the 20 chromosomes (Chr.) of the allotetraploid peanut genome was uneven. Furthermore, 795,170 and 1,315,489 SNPs were distributed in A and B subgenomes, respectively (Fig. S3 and Table S4). The number of SNPs on each chromosome changed from 32,500 (Chr.8) to 152,216 (Chr.19), with an average of 105,533 SNPs. Chromosome 15 was the longest (160,859.59 kb) compared with other chromosomes, and its SNP density was 1.13 kb/SNP. Conversely, the shortest chromosome was Chr. 08 (51,742.57 kb) compared with other chromosomes, and its SNP density was the highest (1.59 kb/SNP). The SNP density of Chr.18 was the lowest (1.02 kb/SNP) compared with that of other chromosomes, and the average SNP density of the whole genome was 1.24 kb/SNP (Table S4). Furthermore, the value of polymorphism information content changed from 0.28 for both Chr.3 and Chr.16 to 0.37 for both Chr.8 and Chr.9, and its mean value was 0.33. LD decay for each chromosome ranged from 0.10 Mb for Chr.19 to 0.90 Mb for Chr.20, with an average of 0.41 (Table S4). Population genetic structure and linkage disequilibrium analysis A population structure of 241 peanut accessions was analyzed to elucidate the relationships among the accessions. The CVE value was calculated for K of 1–8 and reached the minimum value at K = 5, indicating that 5 subgroups could be optimal. (Fig. 2 a). Furthermore, the neighbor-joining phylogenetic tree and PCA exhibited that the 241 accessions were divided into five subgroups (Figs. 2 b, 2 c and 2 d). Subgroups 1, 2, 3, 4, and 5 comprised 75, 68, 12, 47, and 39 accessions, respectively. There were 75 landraces from around the world in subgroup 1. Subgroup 2 contained 38 landraces and 30 modern varieties mostly from China. All the peanut accessions in subgroup 3 belonged to landraces from China and South America. The peanut accessions in subgroup 4 were from China, including 10 landraces and 37 modern varieties. The accessions in subgroup 5 were mostly from Asian countries, containing 11 landraces and 28 modern varieties (Table S1 ). Additionally, the LD (indicated by r 2 ) decreased by half at 50 kb for the entire genome (Fig. S1 ). Identification of QTLs for photosynthetic pigments contents by GWAS Based on four traits related to photosynthetic pigments contents across five different environments, a GWAS was performed to investigate the genetic variants in 241 peanut accessions. A total of 102 SNPs were detected for four traits related to photosynthetic pigments contents according to the BLUE values at both the seedling and flowering stages (Figs. 3 a and 3 b and Fig. S4). Of the 37 significant SNPs associated with the photosynthetic pigments contents at the seedling stage, 2 were associated with Chl a, 31 with Chl b, and 4 with Chl a + b. 65 significant SNPs detected during the flowering stage, 31 were associated with Chl a, 6 with Chl b, 22 with Chl a + b, and 6 with Car (Table S5). Based on the LD decay, the ± 50-Kb regions of leading SNPs should be considered as one QTL interval. Therefore, a total of 68 QTLs were detected at the seedling and flowering stages. Of these, 23 QTLs were identified at the seedling stage, 1 QTL (located on Chr 19) was for Chl a, 20 QTLs (located on Chr 1, 5, 6, 7, 9, 13, 14, 16, 18 and 19) for Chl b, and 2 QTLs (located on Chr 14 and 19) for Chl a + b. Of the 45 QTLs identified at the flowering stage, 21 QTLs (located on Chr 3, 4, 5, 9, 10, 11, 13, 14, 15, 16, 18 and 20) were for Chl a, 5 QTLs (located on Chr 4, 9, 10, 19 and 20) for Chl b, 15 QTLs (located on Chr 4, 5, 9, 10, 11, 12, 13, 15,16 and 18) for Chl a + b, and 4 QTLs (located on Chr 4 and 8) for Car. Among these QTLs, eight QTLs were associated with two or three traits at flowering stage. For example, qChla/Chlb/Chla + b-9A.1 was related with Chl a, Chl b and Chl a + b. Five QTLs, including qChla/Chla + b-4A.1 , qChla/Chla + b-3B.1 , qChla/Chla + b-3B.4 , qChla/Chla + b-3B.5 , and qChla/Chla + b-5B.2 , were associated with Chl a and Chl a + b. qChla/Chlb-10B.1 was associated with Chl a and Chl b. qChlb/Car-4A.1 was associated with Chl b and Car (Table S7). Analysis of differentially expressed genes for photosynthetic pigments contents by RNA-seq In order to identify photosynthetic-pigments-responsive genes in peanut, RNA-seq was conducted using peanut accession Guihuahei 2 with high-pigment-content and Zhanhei 1 with low-pigment-content to screen the differentially expressed genes (DEGs) at the seedling and flowering stages, respectively. In total, 510,819,528 clean reads were generated by RNA-seq. After alignment with the reference genome, we obtained a total of 490,176,579 reads, with mapped read proportions ranging from 95.41–98.42% (Table S6). After transcript comparison between high- and low-pigment-content, 3829 and 4972 DEGs were identified at the seedling and flowering stages, respectively (Fig. S5a). At the seedling stage, 1782 upregulated and 2047 downregulated genes were identified. Additionally, 1770 upregulated and 3202 downregulated genes were identified at the flowering stage (Fig. S5a). The enriched pathways of the DEGs were associated with the metabolism and biosynthesis of secondary metabolites at both two stages (Fig. S5b and S5c). Transcription factor prediction of the DEGs revealed that the transcription factor families bHLH, MYB, WRKY, MYB-related, C2H2, NAC, ERF, and so on (Fig. S5d). Candidate gene screening by integration of GWAS and RNA-seq Based on the above eight QTLs associated with more than two traits detected by GWAS, we obtained 32 genes in the ± 50kb region of each QTL regions from peanut reference genome of peanut cv. Tifrunner. There were 2, 1, 3, 1, 8, 3, ,4, 10 genes in the QTLs qChla/Chlb/Chla + b-9A.1 , qChla/Chla + b-4A.1 , qChla/Chla + b-3B.1 , qChla/Chla + b-3B.4 , qChla/Chla + b-3B.5 , qChla/Chla + b-5B.2 , qChla/Chlb-10B.1 and qChlb/Car-4A.1 region, respectively (Table S7). According to this transcriptome results, two genes, Arahy.YWY61J and Arahy.VMJ95M , were differentially expressed at the flowering stage (Table 1 ). The other 30 genes were not differentially expressed between peanut accessions with high- and low- pigment content at the seedling stage. Therefore, these two genes were considered as candidate genes for the next study. Table 1 The information of candidate genes identified by GWAS and RNA-seq Name Trait Leading SNP -log (P) P value log2 (fc) Function Arahy.YWY61J Chl a 20-6416174 6.35 4.45E-07 2.78 Terpene synthase 14 Chl b 5.91 1.22E-06 Arahy.VMJ95M Chl b 04-121558770 7.56 2.79E-08 4.90 Photosystem I P700 chlorophyll A-binding protein Car 5.96 1.11E-06 Candidate gene Arahy.YWY61J for Chl a and Chl b content The Arahy.YWY61J candidate gene was in qChla/Chlb-10B.1 regionand was linked to the leading SNP 20-6416174, which was associated with Chl a and Chl b content (Fig. 4 a). It encoded the terpene synthase 14. This gene was located 99 bp upstream of the leading SNP 20-6416174 (Fig. 4 b) and contained five introns and six exons. Four non-synonymous SNP mutations were identified in exons 2, 3, and 4; however, the third SNP mutation (C/T) at nt 1,167 in exon 3 resulted in an amino acid change from Valine to a stop codon (Fig. 4 c). Two hundred and four peanut accessions carried two haplotypes of Arahy.YWY61J with distinct phenotypes: the Chl a and Chl b content of accessions with the haplotype allele CCTA was significantly higher than that of accessions with the haplotype GACG allele (P < 0.01) (Fig. 4 d). Peanut accessions 52, 55, 115, and 160 with higher photosynthetic pigments contents and accessions 32, 71, 122, and 127 with lower contents were selected to verify the expression of the candidate gene Arahy.YWY61J . The qRT-PCR results indicated that Arahy.YWY61J gene was highly expressed in the leaves of peanut accessions with high Chl a and Chl b content at the flowering stage (Fig. 4 e). Candidate gene Arahy.VMJ95M for Chl b and Car content The other candidate gene, Arahy. VMJ95M was in qChlb/Car-4A.1 region and was linked to the leading SNP 04-121558770, which was associated with Chl b and Car content (Fig. 5 a). It encoded a photosystem I P700 chlorophyll A-binding protein. Arahy.VMJ95M gene was located 182 bp downstream of the leading SNP 04-121558770 (Fig. 5 b) and contains two introns and three exons. Interestingly, a non-synonymous SNP variation (C/T) at nucleotide (nt) 1,637 in exon 2 of Arahy.VMJ95M resulted in an amino acid mutation from Argine to Valine (Fig. 5 c). One hundred and ninety-three peanut accessions carried two haplotypes of this gene with distinct phenotypes: the Chl b and Car contents of accessions with the haplotype allele TT were significantly higher than that of accessions with the haplotype CC allele (P < 0.01) (Fig. 5 d). Furthermore, the qRT-PCR results indicated that Arahy.VMJ95M was highly expressed in the leaves of peanut accessions with high Chl b and Car contents at the flowering stage (Fig. 5 e). Discussion Identification of numerous high-quality SNPs using whole-genome re-sequencing for an effective GWAS of peanuts A GWAS is a powerful approach for analyzing complex plant traits, and the findings could be directly applied to crop genetic improvement (Wang et al. 2020b ). For a typical GWAS of crops, it is critical to select a natural population with a wide geographic distribution and genetic diversity (Tam et al. 2019 ). In the present study, 241 peanut accessions were selected to analyze the genetic basis of traits related to photosynthetic pigments contents. The peanut accessions listed in Table S1 originated from different ecological peanut-planting areas worldwide. Moreover, the peanut accessions included landraces and modern varieties, indicating the diverse phylogenetic relationships among the accessions in the natural population well achieved the requirements of the GWAS. Additionally, the H 2 values of the Chl a, Chl b, Chl a + b, and Car contents at the seedling stage were 0.82, 0.89, 0.92, and 0.98, respectively, and 0.88, 0.97, 0.84, and 0.94, respectively, at the flowering stage (Table S3), suggesting that the contents of the photosynthetic pigments was rarely affected by environmental factors and was relatively stable. Therefore, the molecular markers significantly correlated with photosynthetic pigments developed by the GWAS could be used for marker-assisted selection in peanuts. The GWAS could generate a relatively high mapping resolution due to many recombination occurrences in the natural population during the domestication and evolution processes (Wang et al. 2020b ). Second-generation re-sequencing is an important method for high throughput genotyping in GWASs (Huang et al. 2010b ). In the present study, 241 peanut accessions were subjected to whole-genome re-sequencing and 2,110,659 high-quality SNP markers were identified (Table S4). The mean density of the polymorphic SNP markers was about 1.24 Kb/SNP (Table S4). The SNP density was higher than that reported by Zhang et al. (Zhang et al. 2020 ), who obtained SNP markers from a peanut 48 K SNP array. In the present study, the 241 peanut accessions were separated into 5 subgroups based on population structure, the neighbor-joining phylogenetic tree, and PCA (Fig. 2 ). The number of subgroups was more than that of the cluster number analyzed by Liu et al. and Lu et al.(Liu et al. 2022 ; Lu et al. 2024 ). Identification of QTLs related to photosynthetic pigments contents and elite-allele loci for marker-assisted breeding in peanuts Chlorophyll and Car in leaves are important photosynthetic pigments, which are crucial for plant growth and development. Chlorophyll and Car contents are quantitative traits controlled by QTLs (Guo et al. 2023 ). QTLs related photosynthetic pigments have been detected in several species based on linkage mapping and association analysis (Wang et al. 2020a ; Ye et al. 2020 ; Jang et al. 2022 ). However, no studies have reported on chlorophyll- and Car-related QTL mapping in peanuts. In the present study, 37 and 65 significant SNPs were associated with photosynthetic pigments contents at the seedling and flowering stages, corresponding to 23 and 45 QTLs, respectively (Table S5). The novel QTLs identified in the current study using GWAS should be next validated via fine mapping of bi-parent hybrid populations. Elite allele loci are useful for the development of molecular markers in crop breeding programs (Su et al. 2016 ). In the present study, we detected two candidate causal genes ( Arahy.YWY61J and Arahy.VMJ95M ) using GWAS and RNA-seq analysis. Four non-synonymous SNP mutations were detected in the exons of Arahy.YWY61J (Fig. 4 ), which had a significantly positive effect on the Chl a and Chl b contents. Moreover, one non-synonymous SNP was detected in an exon of Arahy.VMJ95M (Fig. 5 ), which had a significantly positive effect on the Chl b and Car contents. Candidate genes and their molecular function in regulating photosynthetic pigments contents Chlorophyll is essential for plant photosynthesis (Cutolo et al. 2023 ). Several genes were reported to be involved in chlorophyll metabolism (Sato et al. 2009 ; Yamatani et al. 2013 ). In the present study, 32 genes associated with the contents of photosynthetic pigments were identified using GWAS in peanut. Of these, our study focused on Arahy.YWY61J and Arahy.VMJ95M genes based on RNA-seq analysis. Moreover, peanut accessions carrying diverse haplotype alleles of these two genes exhibited significantly different photosynthetic pigments contents. Arahy.YWY61J encodes terpene synthase 14. Terpene synthases are pivotal enzymes for the initiation of terpenoid biosynthesis, which play a central role in regulating growth and development and mediating response to biotic and abiotic stresses (Gershenzon and Dudareva 2007 ; Karunanithi et al. 2020 ; Jia et al. 2022 ). In this study, we identified four non-synonymous SNPs in exons 2, 3, and 4 of Arahy.YWY61J . Interestingly, the SNP mutation (C/T) caused an amino acid mutation from Valine to a stop codon (Fig. 4 c), which may alter the protein function. There were significant differences in Chl a and Chl b contents between the associated haplotype alleles CCTA and GACG (Fig. 4 d); thus, we speculated that this may be a candidate gene involved in regulating chlorophyll content. The other candidate gene, Arahy.VMJ95M encodes a photosystem I P700 chlorophyll A-binding protein (Table 1 ). Chl-binding proteins (CBPs) play a principal role in two aspects. Firstly, they conduct light harvesting and activate energy transfer from surrounding light-harvesting complexes (LHCs) to the central complexes of photosystems I and II. Secondly, CBPs initiate charge separation in the photosynthetic reaction centers (Allen et al. 2011 ). There is a conserved Chl a/b-binding motif within the transmembrane domains of LHC proteins and LHC-like proteins. Its hydrophobic sequences of Chl a/b-binding motif contain 25–30 amino acids, of which a few conserved charged amino acid residues could bind to photosynthetic pigments (Engelken et al. 2010 ). In this study, the protein encoded by Arahy.VMJ95M gene contains three transmembrane regions (Fig. S6), which was consistent with CBPs in other plant species. However, the two candidate genes identified need to be verified in future studies. Furthermore, bi-parent genetic populations should be developed, and the candidate genes in the target regions should be fine-mapped. Moreover, virus-induced gene silencing could be used to verify the function of candidate genes quickly. Conclusion In the present study, 241 peanut accessions worldwide were genotyped by re-sequencing and clustered into five subpopulations following the screening of 2,110,659 high-quality SNPs. Thus, identified 23 and 45 QTLs were associated with photosynthetic pigments contents via a GWAS at the seedling and flowering stages, respectively. Finally, two candidate genes Arahy.YWY61J and Arahy.VMJ95M , which encodes terpene synthase 14 and photosystem I P700 chlorophyll A-binding protein, respectively, were identified using GWAS and RNA-seq analysis. Peanut accessions carrying excellent haplotypes of each of the two genes had higher photosynthetic pigments contents than that of the other accessions. The identified significant SNPs, QTLs, and candidate genes will contribute to understanding the genetic mechanisms underlying SNP variations in the contents of photosynthetic pigments, and can be used for the genetic improvement of photosynthetic efficiency through marker-assisted breeding in peanut. Declarations Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding This work was supported by the National Key R&D Program of China (2020YFD1000905), and the Guangdong Technical System of Peanut and Soybean Industry (2022KJ136-05) and China Agriculture Research System (CARS-15). Author Contributions TC, ZH, LZ and GL contributed to the study conception and design. TC and SW prepared the experimental materials. ZH performed the field trials and phenotypic evaluation. ZH, YC, YL and QZ analyzed the data. JC and RZ prepared peanut RNA samples and performed the qRT-PCR. TC and ZH drafted the manuscript. LZ and GL revised the manuscript. All authors read and approved the final manuscript. Acknowledgments The authors would like to thank all members of the Peanut physiology and ecology laboratory at the Shandong Academy of Agricultural Sciences. We are grateful to the anonymous reviewers for helpful suggestions. Data Availability The transcriptomic data are available in the NCBI PRJNA1084226. References Alexander DH, Novembre J, Lange K (2009) Fast model-based estimation of ancestry in unrelated individuals. Genome Res 19:1655–1664 Allen JF, de Paula WBM, Puthiyaveetil S, Nield J (2011) A structural phylogenetic map for chloroplast photosynthesis. Trends Plant Sci 16:645–655 Bailey-Serres J, Parker JE, Ainsworth EA, Oldroyd GED, Schroeder JI (2019) Genetic strategies for improving crop yields. 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J Agric Food Chem 67:994–1002 Supplementary Files 4Supplementaryfigures1119.docx 5SupplementaryTables1119.xlsx Cite Share Download PDF Status: Published Journal Publication published 06 Apr, 2026 Read the published version in Theoretical and Applied Genetics → Version 1 posted Editorial decision: Major revisions 26 Sep, 2025 Reviewers agreed at journal 28 Apr, 2025 Reviewers invited by journal 24 Mar, 2025 Editor assigned by journal 11 Mar, 2025 First submitted to journal 11 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6203682","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":433428397,"identity":"a885172c-a617-47d1-a16b-07a40f117e24","order_by":0,"name":"Tingting Chen","email":"","orcid":"","institution":"South China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Tingting","middleName":"","lastName":"Chen","suffix":""},{"id":433428398,"identity":"106e58cc-e376-46e4-9cd0-aec5e9050a86","order_by":1,"name":"Zijun Huang","email":"","orcid":"","institution":"South China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Zijun","middleName":"","lastName":"Huang","suffix":""},{"id":433428399,"identity":"c1e0c620-3a7c-42de-b756-c783f2af52c1","order_by":2,"name":"Yuwei Cui","email":"","orcid":"","institution":"South China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Yuwei","middleName":"","lastName":"Cui","suffix":""},{"id":433428400,"identity":"f9d68cee-58e4-4bbc-8e05-e8982fdf1fd6","order_by":3,"name":"Jing Cao","email":"","orcid":"","institution":"South China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Cao","suffix":""},{"id":433428401,"identity":"45d0c5cc-9661-4aa3-85fa-472f449c771b","order_by":4,"name":"Ruier Zeng","email":"","orcid":"","institution":"South China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Ruier","middleName":"","lastName":"Zeng","suffix":""},{"id":433428402,"identity":"8414f029-63f0-42c3-b682-3e8f022e1465","order_by":5,"name":"Yiyang Liu","email":"","orcid":"","institution":"Shandong Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yiyang","middleName":"","lastName":"Liu","suffix":""},{"id":433428403,"identity":"48f60d54-106a-463f-a834-13600a437eca","order_by":6,"name":"Qunjie Zhang","email":"","orcid":"","institution":"South China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Qunjie","middleName":"","lastName":"Zhang","suffix":""},{"id":433428404,"identity":"9f341c84-6417-439f-b462-6117376ac6a1","order_by":7,"name":"Shubo Wan","email":"","orcid":"","institution":"Shandong Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Shubo","middleName":"","lastName":"Wan","suffix":""},{"id":433428405,"identity":"b0f83abe-bccb-4778-83e7-798550ead0b8","order_by":8,"name":"Guowei Li","email":"","orcid":"","institution":"Shandong Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Guowei","middleName":"","lastName":"Li","suffix":""},{"id":433428406,"identity":"ff3e3356-d3c7-43ac-8420-3bf34324f5f7","order_by":9,"name":"Lei Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYDACCRBRwMDDD+UzNhCnxYCBR7KBVC0MBgeI1SI/u/nZwy8GNjLG5w8//MzDYCO74QDzswf4tDDOOWZuLGOQxmN2I81YmochzXjDATZzA3xamCUSzKQlDA4DtfAwALUcTtxwgIdNAp8WNon0b0At/3mM+88w/+Zh+E9YC49EjpnkB4MDPAYMOWxAWw4Q1iIhkVMmzWCQzCNxI83Mco5BsvHMw2xmeLXIz0jfJvmjws6ev//w4xtvKuxk+443P8OrBQSYeeBMUFAxE1IPBIw/iFA0CkbBKBgFIxgAAGRQPudFJcO7AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-4611-3417","institution":"South China Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Lei","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-03-11 14:02:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6203682/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6203682/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00122-026-05223-8","type":"published","date":"2026-04-06T15:58:53+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79836611,"identity":"1dfe4cf5-7e5e-4713-b2f5-bcf044e211c7","added_by":"auto","created_at":"2025-04-03 11:29:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2166202,"visible":true,"origin":"","legend":"\u003cp\u003ePhenotypic diversity of 241 peanut accessions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e Trial plots for photosynthetic pigments contents analysis of the 241 peanut accessions. \u003cstrong\u003eb\u003c/strong\u003e Frequency distributions and differences in chlorophyll a, chlorophyll b, total chlorophyll, and carotenoid contents in the peanut accessions measured in the five environments (E1–E5) and the BLUE values across the five environments. Chl a: chlorophyll a, Chl b: chlorophyll b, Chl a+b: total chlorophyll, Car: carotenoids. E1: Guangzhou, August–December 2022; E2: Zhanjiang, August–December 2022; E3: Sanya, November 2022 to February 2023; E4: Guangzhou, March–July 2023; and E5: Zhanjiang, March–July 2023. BLUE: Best Linear Unbiased Estimation, SS: seedling stage, FS: flowering stage\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6203682/v1/ec0e939bc8fa17a84f16e042.png"},{"id":79835818,"identity":"57d2f356-c16d-4fa9-b335-a5f44d7f09f5","added_by":"auto","created_at":"2025-04-03 11:21:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":975478,"visible":true,"origin":"","legend":"\u003cp\u003eResults of the population structure, principal component, and phylogenetic analysis of the 241 peanut accessions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e Cross-validation error of possible clusters (K) from 1 to 8. \u003cstrong\u003eb\u003c/strong\u003e Population structure based on STRUCTURE analysis at K = 5. \u003cstrong\u003ec\u003c/strong\u003e Neighbor-joining phylogenetic tree of 241 peanut accessions based on Nei’s genetic distances. \u003cstrong\u003ed\u003c/strong\u003e Principal component analysis of SNPs from the 241 peanut accessions. Colored dots represent the different accessions. \u0026nbsp;Subgroups 1–5 are represented by red, purple, green, yellow, and blue, respectively.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6203682/v1/25a6692764f0b996e266792a.png"},{"id":79835815,"identity":"10b0d3a2-2bb0-4164-b188-3efcffa6dd2c","added_by":"auto","created_at":"2025-04-03 11:21:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1783991,"visible":true,"origin":"","legend":"\u003cp\u003eManhattan plots for the GWAS for the BLUE values of chlorophyll a, chlorophyll b, total chlorophyll, and carotenoids at the seedling and flowering stages.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e Manhattan plots for the BLUE values of four traits at the seedling stage. \u003cstrong\u003eb\u003c/strong\u003e Manhattan plots for the BLUE values of four traits at the flowering stage. The four circles from outside to inside represent Manhattan plots of chlorophyll a, chlorophyll b, total chlorophyll, and carotenoids.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6203682/v1/a02bf75e3abdc987aa724d36.png"},{"id":79835813,"identity":"7610b7eb-678b-41d9-8302-f5a7f75c8a54","added_by":"auto","created_at":"2025-04-03 11:21:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":923395,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of the candidate gene \u003cem\u003eArahy.YWY61J\u003c/em\u003e on chromosome 20 (Chr.20) of peanut.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e Local Manhattan plot of the content of chlorophyll a (Chl a) and chlorophyll b (Chl b) on Chr.20 within the 6.36–6.46 Mb region. The solid horizontal lines in the Manhattan plots refer to the thresholds for significance at −log\u003csub\u003e10\u003c/sub\u003e(\u003cem\u003ep\u003c/em\u003e)=5.0. \u003cstrong\u003eb\u003c/strong\u003e Linkage disequilibrium block analysis of (single nucleotide polymorphisms) SNPs in the region that includes ±50 kb regions associated with SNP marker positions. The leading SNP 20-6416174 is marked by a red rectangle. The SNPs in bold are in the candidate gene region. \u003cstrong\u003ec\u003c/strong\u003e Exon-intron structure and DNA polymorphism of \u003cem\u003eArahy.YWY61J\u003c/em\u003e. Black rectangles and lines indicate exons and introns, respectively. \u003cstrong\u003ed\u003c/strong\u003e Boxplots of the Chl a and Chl b contents based on the haplotype allele CCTA and GACG. **, significant differences are expressed at P ≤ 0.01 level. \u003cstrong\u003ee\u003c/strong\u003e Expression analysis of candidate gene \u003cem\u003eArahy.YWY61J\u003c/em\u003e using quantitative reverse-transcription polymerase chain reaction. **, significant differences are expressed at P ≤ 0.01 level.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6203682/v1/8d0af1a613bdb4740d2bde51.png"},{"id":79836612,"identity":"ea4e3501-83ab-44de-8822-a0a58e2537f8","added_by":"auto","created_at":"2025-04-03 11:29:21","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":845236,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of the candidate gene \u003cem\u003eArahy.VMJ95M\u003c/em\u003e on chromosome 4 (Chr.4) of peanut.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e Local Manhattan plot of the content of Chl b and carotenoids on Chr.4 within the 121.5–121.6 Mb region. The black dot indicated by an arrow is the leading SNP 4-121558770. The solid horizontal lines in the Manhattan plots refer to the thresholds for significance at −log\u003csub\u003e10\u003c/sub\u003e(\u003cem\u003ep\u003c/em\u003e) = 5.0. \u003cstrong\u003eb\u003c/strong\u003e Linkage disequilibrium block analysis of SNPs in the region that includes ±50 kb regions associated with SNP marker positions. The leading SNP 4-121558770 is marked by a red rectangle. The SNPs in bold are within the candidate gene region. \u003cstrong\u003ec\u003c/strong\u003e Exon-intron structure and DNA polymorphism of \u003cem\u003eArahy.VMJ95M\u003c/em\u003e. Black rectangles and lines indicate exons and introns, respectively. \u003cstrong\u003ed\u003c/strong\u003e Boxplots of the content of Chl b and carotenoids based on the haplotype allele CC and TT. **, significant differential expression at the P \u0026lt; 0.01 level. \u003cstrong\u003ee\u003c/strong\u003e Expression analysis of candidate gene \u003cem\u003eArahy.VMJ95M\u003c/em\u003e using quantitative reverse-transcription polymerase chain reaction. **, significant differences are expressed at the P ≤ 0.01 level.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-6203682/v1/2e78e137e00baca6dce58d30.png"},{"id":106809699,"identity":"78ef742e-7b8c-42b9-808f-4495c8fc14e6","added_by":"auto","created_at":"2026-04-13 16:12:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7535910,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6203682/v1/d4456de3-893d-4bfa-8337-e85ce81a42b1.pdf"},{"id":79836618,"identity":"100183f1-8777-491e-90db-0e98b76cc7e8","added_by":"auto","created_at":"2025-04-03 11:29:21","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":7504496,"visible":true,"origin":"","legend":"","description":"","filename":"4Supplementaryfigures1119.docx","url":"https://assets-eu.researchsquare.com/files/rs-6203682/v1/8dde5899127e4cfc3e2ea93b.docx"},{"id":79835826,"identity":"733ae332-fafc-4eea-a391-1b842e8cc3ee","added_by":"auto","created_at":"2025-04-03 11:21:21","extension":"xlsx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":44986,"visible":true,"origin":"","legend":"","description":"","filename":"5SupplementaryTables1119.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6203682/v1/37d16429156f0a3d4ff2ff60.xlsx"}],"financialInterests":"","formattedTitle":"\u003cp\u003eIdentification of photosynthetic pigment contents related genes in peanut (\u003cem\u003eArachis hypogaea \u003c/em\u003eL.) by GWAS and RNA-seq\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGreater crop yields must be achieved to nourish the world\u0026rsquo;s population by 2050. It is one of the important strategies to increase crop yield that improving photosynthetic efficiency through optimizing light harvesting capacity (Bailey-Serres et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Chlorophyll and carotenoids (Car) play important roles for photosynthesis in plants (Sun et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Chlorophyll a (Chl a) and chlorophyll b (Chl b) are types of chlorophyll in plants that absorb light energy in the chloroplast and promote photosynthesis (Wang and Grimm \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Car, as light-harvesting pigments, plays a significant role in protecting the photosynthetic apparatus against photooxidative damage by quenching excited electrons, scavenging reactive oxygen species, and dissipating excess energy in the chlorophyll (Niyogi and Truong \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Murchie and Ruban \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, considering their significant impact on crop photosynthesis and yield, the contents of photosynthetic pigments are valuable physiological characteristics in crop breeding.\u003c/p\u003e \u003cp\u003eChlorophyll and Car content are quantitative traits controlled by quantitative trait loci (QTLs). Studies on QTLs of chlorophyll and carotenoid content have been conducted on various crops, such as rice (Jiang et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Jiang et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Jang et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), wheat (Guo et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), soybean (Wang et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e; Yu et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and oilseeds (Ye et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Additionally, several genes encoding proteins involved in chlorophyll metabolism were identified, such as \u003cem\u003eOsChlH, OsChlD, OsChlI, YGL1\u003c/em\u003e, NON-YELLOW COLORING1 (\u003cem\u003eNYC1\u003c/em\u003e), YC1-LIKE (\u003cem\u003eNOL\u003c/em\u003e), \u003cem\u003eSGR\u003c/em\u003e, and \u003cem\u003eNYC4\u003c/em\u003e (Jung et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Jiang et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Kusaba et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Wu et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Sato et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Yamatani et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePeanut (\u003cem\u003eArachis hypogaea\u003c/em\u003e L.) is one of the most important oilseed crop worldwide, consumed as a major source of edible oils and proteins for humans. The global production of peanuts reached approximately 46\u0026nbsp;million tons from approximately 37.2\u0026nbsp;million hectares in 2023 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.fao.org\u003c/span\u003e\u003cspan address=\"http://www.fao.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Peanut yield is always an important goal for breeder in China. Previous studies have indicated that chlorophyll in peanut leaves were crucial characteristics for photosynthesis under some abiotic stresses (Gao et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Lai et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, there are no reports on QTLs related to chlorophyll and carotenoids content in peanut.\u003c/p\u003e \u003cp\u003eGenome-wide association studies (GWASs) could considerably elucidate the genetic architecture and the causative loci, particularly for complex quantitative traits among diverse varieties (Huang et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2010a\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e). This approach has been extensively used for the genetic analysis of complex agronomic traits in plants. With the rapid development of next-generation sequencing technology, GWASs using high-density single nucleotide polymorphism (SNP) markers and diverse resources have become possible.\u003c/p\u003e \u003cp\u003eIn the present study, a total of 241 peanut accessions, including landrace and modern varieties, were re-sequenced to determine the genetic architecture of contents of four photosynthetic pigments in peanut. Phenotyping was performed for the four traits under five different environments. We conducted GWAS to identify significant SNPs and QTLs related to photosynthetic pigments contents in peanuts based on the best linear unbiased estimation (BLUE) values under five different environments. RNA sequencing (RNA-seq) was used to identify genes exhibiting different expression patterns in peanut genotypes with contrasting photosynthetic pigments contents. Integrating GWAS and RNA-seq, two candidate genes were screened and associated with photosynthetic pigments in peanut. These results will improve the understanding of the molecular mechanism regulating photosynthetic pigments contents and contribute to improving the photosynthetic efficiency of peanut varieties via marker-assisted breeding.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePlant material and field experiments\u003c/h2\u003e \u003cp\u003eIn the present study, we analyzed 241 peanut accessions that were previously selected worldwide, to represent peanut diversity. In total, this set includes 116 landraces and 125 modern varieties (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). All 241 accessions were planted in five environmental locations, including Guangzhou (23\u0026deg;19'N, 113\u0026deg;49'E), Guangdong Province, from August to December 2022; Zhanjiang (21\u0026deg;17'N, 110\u0026deg;24'E), Guangdong Province, from August to December 2022; Sanya (18\u0026deg;09'N, 108\u0026deg;56'E), Hainan Province, from November 2022 to February 2023; Guangzhou (23\u0026deg;19'N, 113\u0026deg;49'E), Guangdong Province, from March to July 2023; Zhanjiang (21\u0026deg;17'N, 110\u0026deg;24'E), Guangdong Province, from March to July 2023 and the experiments were denoted as E1, E2, E3, E4, and E5, respectively. Each accession was planted in an experimental plot in 4 rows of 20 plants each, with 0.20 m and 0.25 m in-row and inter-row spacing, respectively. All accessions were arranged in a randomized complete block design with treatments replicated thrice. The cultivation management followed standard agronomic practices for commercial peanut production (Chen et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Leaves of peanut varieties Zhanhei 1 and Guihuahei 2 with low- and high-photosynthetic pigments contents, respectively, were collected during the seedling and flowering stages and subsequently used for RNA-seq analysis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePhenotypic evaluation and statistical analysis\u003c/h3\u003e\n\u003cp\u003eWe sampled the third leaf from the top of three plants for each accession to determine the content of Chl a and Chl b at the seedling and flowering stages. Chlorophyll was extracted using a method described by Lu et al. (Lu et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The total chlorophyll content (Chl a\u0026thinsp;+\u0026thinsp;b) was obtained by combining Chl a and Chl b contents. The Car content was determined using the method described by Zheng et al. (Zheng et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePhenotypic and BLUE analysis were conducted for four target traits. The BLUE value and the broad-sense heritability (\u003cem\u003eH\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e) of photosynthetic pigments contents were calculated using the lme4 package in the R software (RStudio, Boston, MA, USA) (McClellan et al. 2010). Finally, SPSS v22.0 (IBM, Armonk, NY, USA) was used to calculate the correlation coefficients for the BLUE values for each trait, analysis of variance, and frequency distribution.\u003c/p\u003e\n\u003ch3\u003eWhole-genome re-sequencing, read mapping, and variant calling\u003c/h3\u003e\n\u003cp\u003eYoung leaves at seedling stages were used to extract genomic DNA following the cetyltrimethylammonium bromide method. Genomic DNA was quantified using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA), and subjected to library construction. Paired-end (PE150) sequencing was then conducted using BGISEQ-500 sequencing platform (BGI, Shenzhen, China). The clean reads were mapped to the peanut reference genome (arahy.Tifrunner.gnm1.KYV3.genome_main.fna) (Bertioli et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) using Burrows-Wheeler Aligner (Li and Durbin \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The Genome Analysis Toolkit (GATK4.0, Broad Institute, St Cambridge, MA, USA) was used for variation detection (McKenna et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). We removed the SNPs with a call rate of \u0026lt;\u0026thinsp;85% and a minor allele frequency of \u0026lt;\u0026thinsp;0.05 to avoid spurious linkage disequilibrium (LD) and false-positive associations (Yano et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). We use VCFtools 4.1 software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://vcftools.sourceforge.net/\u003c/span\u003e\u003cspan address=\"https://vcftools.sourceforge.net/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e ) with a 500-kb sliding window to count SNP density across each chromosome, following the parameter as \u0026ldquo;vcftools --vcf genotype.vcf \\--chr chrX --from-bp X --to-bp Y \\--out sample --recode --recode-INFO-all\u0026rdquo; (Danecek et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003ePopulation genetics and linkage disequilibrium\u003c/h3\u003e\n\u003cp\u003eThe cross-validation error (CVE) was performed based on the number of subpopulations K\u0026thinsp;=\u0026thinsp;1 to 8 to explore the convergence of peanut accessions. LD decay analysis was performed using PopLDdecay 3.41 software (Zhang et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The neighbor-joining method was used to construct the phylogenetic tree in TASSEL 5.0 (Kang et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), and the tree was visualized using TooliTOL (v2.41) (Letunic and Bork \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The TASSEL5.0 was adopted to perform principal component analysis (PCA), and the eigenvectors were plotted using the ggplot2 package in Rstudio (Bradbury et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). ADMIXTURE software (Inventio, Georgetown, IN, USA) was employed to infer population structure (Alexander et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eGenome-wide association study\u003c/h3\u003e\n\u003cp\u003eA GWAS was carried out using EMMAX software (EMMAX group, Nashville, MA, USA) based on a mixed linear model, following the parameter: \u0026ldquo;emmax -t emmax_in -p emmax.trait.txt -k emmax_in.BN.kinf -o emmax.qk\u0026rdquo; (McCouch et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The \u0026ldquo;CMplot\u0026rdquo; 4.3.1 R package (\u003cspan class=\"ExternalRef\"\u003e \u003cspan class=\"RefSource\"\u003ehttps://github.com/YinLiLin/R-CMplot\u003c/span\u003e \u003cspan address=\"https://github.com/YinLiLin/R-CMplot\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e \u003c/span\u003e) was employed to draw Manhattan and quantile-quantile plots, following the parameter as \u0026ldquo; pmap \u0026lt;- read.table(\"result.txt\", header\u0026thinsp;=\u0026thinsp;T) head(pmap) threshold \u0026lt;- 1/nrow (pmap [!is.na (pmap\u003cspan\u003e$\u003c/span\u003eposition)]) CMplot (pmap,threshold\u0026thinsp;=\u0026thinsp;c(1e-7,1e5), threshold.lty\u0026thinsp;=\u0026thinsp;c(1,2), threshold.lwd\u0026thinsp;=\u0026thinsp;c(1,1), threshold.col\u0026thinsp;=\u0026thinsp;c(\"black\",\"grey\"), ylim\u0026thinsp;=\u0026thinsp;c(0,9), amplify\u0026thinsp;=\u0026thinsp;T, file = \"tiff\", plot.type\u0026thinsp;=\u0026thinsp;c(\"m\",\"q\"))\u0026rdquo;. The significance threshold (-log\u003csub\u003e10\u003c/sub\u003eP\u0026thinsp;\u0026ge;\u0026thinsp;5.84) for GWAS was determined by a uniform threshold of 1/n, where n was the effective number of independent SNPs calculated using Genetic type 1 Error Calculator (v0.2) (Li et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Based on an LD decay distance of 50 kb for the 241 peanut accessions, a region of 100 kb was defined as one QTL (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eRNA-seq\u003c/h2\u003e \u003cp\u003ePeanut leaf samples were collected at the seedling and flowering stages from Guihuahei 2 and Zhanhei 1 accessions with high- and low-pigment content, respectively. Total RNA was isolated using a FastPure Plant Total RNA Isolation Kit (Vazyme, Nanjing, China), resulting in a total of 12 samples (two peanut accessions \u0026times; two growth stages \u0026times; three biological replicates). RNA quality was assessed on an Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA, USA) and checked using RNase free agarose gel electrophoresis. cDNA library construction was performed by Gene Biotechnology Denovo Co. (Guangzhou, China). The resulting cDNA library was sequenced using an Illumina Novaseq6000 (Gene Denovo Biotechnology Co., Guangzhou, China). The data analysis processes were done as described in our previous study (Chen et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Firstly, raw data were removed reads containing poly-N, adapters, and low-quality. The clean data were then mapped to the genome sequences of the cultivated peanut Tifrunner \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.peanutbase.org/data/v2/Arachis/hypogaea/genomes/Tifrunner.gnm1.KYV3\u003c/span\u003e\u003cspan address=\"https://www.peanutbase.org/data/v2/Arachis/hypogaea/genomes/Tifrunner.gnm1.KYV3\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) using the HISAT2 (v2.1.0) (Bertioli et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Fragments per kilobase (FPKM) values were calculated to quantify its expression abundance and variations using RSEM software (Dewey and Bo \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), with transcripts showing a fold change\u0026thinsp;\u0026ge;\u0026thinsp;1.5 and P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 assigned as differential expressed.\u003c/p\u003e \u003cp\u003eFunctional annotation and enrichment analysis of differentially expressed genes were performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genome.jp/kegg/genes.html\u003c/span\u003e\u003cspan address=\"https://www.genome.jp/kegg/genes.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Transcription factor prediction was performed with the plantTFDB database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://planttfdb.gaolab.org/index.php\u003c/span\u003e\u003cspan address=\"https://planttfdb.gaolab.org/index.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Jin et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The transcriptomic data were deposited to the National Center for Biotechnology Information Sequence Read Archive (accession no. PRJNA1084226).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eqRT-PCR\u003c/h3\u003e\n\u003cp\u003e Total RNA was isolated using a FastPure Plant Total RNA Isolation Kit (Vazyme, Nanjing, China) according to the manufacturer\u0026rsquo;s instructions. Reverse transcription was performed using HiScript RT Super Mix (Vazyme, Nanjing, China). Quantitative reverse-transcription polymerase chain reaction (qRT-PCR) was carried out using a LightCycle480 II System (Roche Applied Science, Basel, Switzerland). Normalized transcript levels were calculated using the 2\u003csup\u003e\u0026minus;ΔΔCT\u003c/sup\u003e method (Livak and Schmittgen \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). We performed three independent biological replicates for each treatment. The primer sequences are listed in Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eVariation and correlation analysis of photosynthetic pigments contents in peanut leaves\u003c/h2\u003e\n \u003cp\u003eThe contents of four photosynthetic pigments for 241 peanut accessions were determined at the seedling and flowering stages across the 5 experimental environments (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea). Continuous and considerable variations were observed in the five environments and their associated BLUE values. Across the five environments, Chl a, Chl b, Chl a\u0026thinsp;+\u0026thinsp;b, and Car content at the seedling stage ranged from 0.28\u0026ndash;2.28 mg/g, 0.12\u0026ndash;1.26 mg/g, 0.45\u0026ndash;3.50 mg/g, and 0.02\u0026ndash;0.38 mg/g, respectively, with mean values of 0.76 mg/g, 0.39 mg/g, 1.15 mg/g, and 0.14 mg/g, respectively. Similarly, the Chl a, Chl b, Chl a\u0026thinsp;+\u0026thinsp;b, and Car contents at the flowering stage ranged from 0.26\u0026ndash;1.61 mg/g, 0.10\u0026ndash;0.92 mg/g, 0.36\u0026ndash;2.53 mg/g, and 0.30\u0026ndash;0.43 mg/g, respectively, with mean values of 0.69 mg/g, 0.37 mg/g, 1.06 mg/g, and 0.22 mg/g, respectively.\u003c/p\u003e\n \u003cp\u003eThe coefficient of variation of BLUE values across the five studied environments ranged from 14.00% for Chl b to 20.00% for Car at the seedling stage. Moreover, it changed from 18.32% for Chl a to 36.40% for Car at the flowering stage. The \u003cem\u003eH\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e of four traits varied from 0.82 for Chl a to 0.98 for Car at the seedling stage, and from 0.84 for Chl a\u0026thinsp;+\u0026thinsp;b to 0.97 for Chl b at the seedling stages, respectively, indicating that these traits were determined mainly by genetic effects. Statistical analysis for the chlorophyll and Car contents are listed in Table S3. The absolute kurtosis and skewness values of the four traits were \u0026lt;\u0026thinsp;1, suggesting that the four traits nearly conformed to a normal distribution (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eb). There were highly significant correlations (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) between four photosynthetic pigments contents, indicating a stable correlation between chlorophyll and Car content (Fig. \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e). Moreover, the correlation coefficients between the Chl a and Chl a\u0026thinsp;+\u0026thinsp;b contents were the highest at the seedling and flowering stages.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eGenetic variation based on SNPs\u003c/h2\u003e\n \u003cp\u003eAfter mapping to the peanut reference genome Tiffrunner and SNP calling, a total of 2,110,659 high-quality SNPs were used for the subsequent population genetic structure analysis. The distribution of these SNPs on the 20 chromosomes (Chr.) of the allotetraploid peanut genome was uneven. Furthermore, 795,170 and 1,315,489 SNPs were distributed in A and B subgenomes, respectively (Fig. S3 and Table S4). The number of SNPs on each chromosome changed from 32,500 (Chr.8) to 152,216 (Chr.19), with an average of 105,533 SNPs. Chromosome 15 was the longest (160,859.59 kb) compared with other chromosomes, and its SNP density was 1.13 kb/SNP. Conversely, the shortest chromosome was Chr. 08 (51,742.57 kb) compared with other chromosomes, and its SNP density was the highest (1.59 kb/SNP). The SNP density of Chr.18 was the lowest (1.02 kb/SNP) compared with that of other chromosomes, and the average SNP density of the whole genome was 1.24 kb/SNP (Table S4). Furthermore, the value of polymorphism information content changed from 0.28 for both Chr.3 and Chr.16 to 0.37 for both Chr.8 and Chr.9, and its mean value was 0.33. LD decay for each chromosome ranged from 0.10 Mb for Chr.19 to 0.90 Mb for Chr.20, with an average of 0.41 (Table S4).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003ePopulation genetic structure and linkage disequilibrium analysis\u003c/h2\u003e\n \u003cp\u003eA population structure of 241 peanut accessions was analyzed to elucidate the relationships among the accessions. The CVE value was calculated for K of 1\u0026ndash;8 and reached the minimum value at K\u0026thinsp;=\u0026thinsp;5, indicating that 5 subgroups could be optimal. (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea). Furthermore, the neighbor-joining phylogenetic tree and PCA exhibited that the 241 accessions were divided into five subgroups (Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eb, \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ec and \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ed). Subgroups 1, 2, 3, 4, and 5 comprised 75, 68, 12, 47, and 39 accessions, respectively. There were 75 landraces from around the world in subgroup 1. Subgroup 2 contained 38 landraces and 30 modern varieties mostly from China. All the peanut accessions in subgroup 3 belonged to landraces from China and South America. The peanut accessions in subgroup 4 were from China, including 10 landraces and 37 modern varieties. The accessions in subgroup 5 were mostly from Asian countries, containing 11 landraces and 28 modern varieties (Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). Additionally, the LD (indicated by \u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e) decreased by half at 50 kb for the entire genome (Fig. \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eIdentification of QTLs for photosynthetic pigments contents by GWAS\u003c/h2\u003e\n \u003cp\u003eBased on four traits related to photosynthetic pigments contents across five different environments, a GWAS was performed to investigate the genetic variants in 241 peanut accessions. A total of 102 SNPs were detected for four traits related to photosynthetic pigments contents according to the BLUE values at both the seedling and flowering stages (Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea and \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb and Fig. S4). Of the 37 significant SNPs associated with the photosynthetic pigments contents at the seedling stage, 2 were associated with Chl a, 31 with Chl b, and 4 with Chl a\u0026thinsp;+\u0026thinsp;b. 65 significant SNPs detected during the flowering stage, 31 were associated with Chl a, 6 with Chl b, 22 with Chl a\u0026thinsp;+\u0026thinsp;b, and 6 with Car (Table S5).\u003c/p\u003e\n \u003cp\u003eBased on the LD decay, the \u0026plusmn;\u0026thinsp;50-Kb regions of leading SNPs should be considered as one QTL interval. Therefore, a total of 68 QTLs were detected at the seedling and flowering stages. Of these, 23 QTLs were identified at the seedling stage, 1 QTL (located on Chr 19) was for Chl a, 20 QTLs (located on Chr 1, 5, 6, 7, 9, 13, 14, 16, 18 and 19) for Chl b, and 2 QTLs (located on Chr 14 and 19) for Chl a\u0026thinsp;+\u0026thinsp;b. Of the 45 QTLs identified at the flowering stage, 21 QTLs (located on Chr 3, 4, 5, 9, 10, 11, 13, 14, 15, 16, 18 and 20) were for Chl a, 5 QTLs (located on Chr 4, 9, 10, 19 and 20) for Chl b, 15 QTLs (located on Chr 4, 5, 9, 10, 11, 12, 13, 15,16 and 18) for Chl a\u0026thinsp;+\u0026thinsp;b, and 4 QTLs (located on Chr 4 and 8) for Car.\u003c/p\u003e\n \u003cp\u003eAmong these QTLs, eight QTLs were associated with two or three traits at flowering stage. For example, \u003cem\u003eqChla/Chlb/Chla\u0026thinsp;+\u0026thinsp;b-9A.1\u003c/em\u003e was related with Chl a, Chl b and Chl a\u0026thinsp;+\u0026thinsp;b. Five QTLs, including \u003cem\u003eqChla/Chla\u0026thinsp;+\u0026thinsp;b-4A.1\u003c/em\u003e, \u003cem\u003eqChla/Chla\u0026thinsp;+\u0026thinsp;b-3B.1\u003c/em\u003e, \u003cem\u003eqChla/Chla\u0026thinsp;+\u0026thinsp;b-3B.4\u003c/em\u003e, \u003cem\u003eqChla/Chla\u0026thinsp;+\u0026thinsp;b-3B.5\u003c/em\u003e, and \u003cem\u003eqChla/Chla\u0026thinsp;+\u0026thinsp;b-5B.2\u003c/em\u003e, were associated with Chl a and Chl a\u0026thinsp;+\u0026thinsp;b. \u003cem\u003eqChla/Chlb-10B.1\u003c/em\u003e was associated with Chl a and Chl b. \u003cem\u003eqChlb/Car-4A.1\u003c/em\u003e was associated with Chl b and Car (Table S7).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eAnalysis of differentially expressed genes for photosynthetic pigments contents by RNA-seq\u003c/h2\u003e\n \u003cp\u003eIn order to identify photosynthetic-pigments-responsive genes in peanut, RNA-seq was conducted using peanut accession Guihuahei 2 with high-pigment-content and Zhanhei 1 with low-pigment-content to screen the differentially expressed genes (DEGs) at the seedling and flowering stages, respectively. In total, 510,819,528 clean reads were generated by RNA-seq.\u0026nbsp;After alignment with the reference genome, we obtained a total of 490,176,579 reads, with mapped read proportions ranging from 95.41\u0026ndash;98.42% (Table S6). After transcript comparison between high- and low-pigment-content, 3829 and 4972 DEGs were identified at the seedling and flowering stages, respectively (Fig. S5a). At the seedling stage, 1782 upregulated and 2047 downregulated genes were identified. Additionally, 1770 upregulated and 3202 downregulated genes were identified at the flowering stage (Fig. S5a). The enriched pathways of the DEGs were associated with the metabolism and biosynthesis of secondary metabolites at both two stages (Fig. S5b and S5c). Transcription factor prediction of the DEGs revealed that the transcription factor families bHLH, MYB, WRKY, MYB-related, C2H2, NAC, ERF, and so on (Fig. S5d).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eCandidate gene screening by integration of GWAS and RNA-seq\u003c/h2\u003e\n \u003cp\u003eBased on the above eight QTLs associated with more than two traits detected by GWAS, we obtained 32 genes in the \u0026plusmn;\u0026thinsp;50kb region of each QTL regions from peanut reference genome of peanut cv. Tifrunner. There were 2, 1, 3, 1, 8, 3, ,4, 10 genes in the QTLs \u003cem\u003eqChla/Chlb/Chla\u0026thinsp;+\u0026thinsp;b-9A.1\u003c/em\u003e, \u003cem\u003eqChla/Chla\u0026thinsp;+\u0026thinsp;b-4A.1\u003c/em\u003e, \u003cem\u003eqChla/Chla\u0026thinsp;+\u0026thinsp;b-3B.1\u003c/em\u003e, \u003cem\u003eqChla/Chla\u0026thinsp;+\u0026thinsp;b-3B.4\u003c/em\u003e, \u003cem\u003eqChla/Chla\u0026thinsp;+\u0026thinsp;b-3B.5\u003c/em\u003e, \u003cem\u003eqChla/Chla\u0026thinsp;+\u0026thinsp;b-5B.2\u003c/em\u003e, \u003cem\u003eqChla/Chlb-10B.1\u003c/em\u003e and \u003cem\u003eqChlb/Car-4A.1\u003c/em\u003e region, respectively (Table S7).\u003c/p\u003e\n \u003cp\u003eAccording to this transcriptome results, two genes, \u003cem\u003eArahy.YWY61J\u003c/em\u003e and \u003cem\u003eArahy.VMJ95M\u003c/em\u003e, were differentially expressed at the flowering stage (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The other 30 genes were not differentially expressed between peanut accessions with high- and low- pigment content at the seedling stage. Therefore, these two genes were considered as candidate genes for the next study.\u0026nbsp;\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe information of candidate genes identified by GWAS and RNA-seq\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eName\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTrait\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLeading SNP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e-log (P)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003elog2 (fc)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFunction\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eArahy.YWY61J\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChl a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"char\"\u003e\n \u003cp\u003e20-6416174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.45E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"char\"\u003e\n \u003cp\u003e2.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eTerpene synthase 14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChl b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.22E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eArahy.VMJ95M\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChl b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"char\"\u003e\n \u003cp\u003e04-121558770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.79E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"char\"\u003e\n \u003cp\u003e4.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003ePhotosystem I P700 chlorophyll A-binding protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eCandidate gene\u003c/strong\u003e \u003cstrong\u003eArahy.YWY61J\u003c/strong\u003e \u003cstrong\u003efor Chl a and Chl b content\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe \u003cem\u003eArahy.YWY61J\u003c/em\u003e candidate gene was in \u003cem\u003eqChla/Chlb-10B.1\u003c/em\u003e regionand was linked to the leading SNP 20-6416174, which was associated with Chl a and Chl b content (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea). It encoded the terpene synthase 14. This gene was located 99 bp upstream of the leading SNP 20-6416174 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb) and contained five introns and six exons. Four non-synonymous SNP mutations were identified in exons 2, 3, and 4; however, the third SNP mutation (C/T) at nt 1,167 in exon 3 resulted in an amino acid change from Valine to a stop codon (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ec). Two hundred and four peanut accessions carried two haplotypes of \u003cem\u003eArahy.YWY61J\u003c/em\u003e with distinct phenotypes: the Chl a and Chl b content of accessions with the haplotype allele CCTA was significantly higher than that of accessions with the haplotype GACG allele (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ed). Peanut accessions 52, 55, 115, and 160 with higher photosynthetic pigments contents and accessions 32, 71, 122, and 127 with lower contents were selected to verify the expression of the candidate gene \u003cem\u003eArahy.YWY61J\u003c/em\u003e. The qRT-PCR results indicated that \u003cem\u003eArahy.YWY61J\u003c/em\u003e gene was highly expressed in the leaves of peanut accessions with high Chl a and Chl b content at the flowering stage (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ee).\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCandidate gene\u003c/strong\u003e \u003cstrong\u003eArahy.VMJ95M\u003c/strong\u003e \u003cstrong\u003efor Chl b and Car content\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe other candidate gene, \u003cem\u003eArahy. VMJ95M\u003c/em\u003e was in \u003cem\u003eqChlb/Car-4A.1\u003c/em\u003e region and was linked to the leading SNP 04-121558770, which was associated with Chl b and Car content (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ea). It encoded a photosystem I P700 chlorophyll A-binding protein. \u003cem\u003eArahy.VMJ95M\u003c/em\u003e gene was located 182 bp downstream of the leading SNP 04-121558770 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eb) and contains two introns and three exons. Interestingly, a non-synonymous SNP variation (C/T) at nucleotide (nt) 1,637 in exon 2 of \u003cem\u003eArahy.VMJ95M\u003c/em\u003e resulted in an amino acid mutation from Argine to Valine (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ec). One hundred and ninety-three peanut accessions carried two haplotypes of this gene with distinct phenotypes: the Chl b and Car contents of accessions with the haplotype allele TT were significantly higher than that of accessions with the haplotype CC allele (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ed). Furthermore, the qRT-PCR results indicated that \u003cem\u003eArahy.VMJ95M\u003c/em\u003e was highly expressed in the leaves of peanut accessions with high Chl b and Car contents at the flowering stage (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ee).\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of numerous high-quality SNPs using whole-genome re-sequencing for an effective GWAS of peanuts\u003c/h2\u003e \u003cp\u003eA GWAS is a powerful approach for analyzing complex plant traits, and the findings could be directly applied to crop genetic improvement (Wang et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e). For a typical GWAS of crops, it is critical to select a natural population with a wide geographic distribution and genetic diversity (Tam et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In the present study, 241 peanut accessions were selected to analyze the genetic basis of traits related to photosynthetic pigments contents. The peanut accessions listed in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e originated from different ecological peanut-planting areas worldwide. Moreover, the peanut accessions included landraces and modern varieties, indicating the diverse phylogenetic relationships among the accessions in the natural population well achieved the requirements of the GWAS. Additionally, the \u003cem\u003eH\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e values of the Chl a, Chl b, Chl a\u0026thinsp;+\u0026thinsp;b, and Car contents at the seedling stage were 0.82, 0.89, 0.92, and 0.98, respectively, and 0.88, 0.97, 0.84, and 0.94, respectively, at the flowering stage (Table S3), suggesting that the contents of the photosynthetic pigments was rarely affected by environmental factors and was relatively stable. Therefore, the molecular markers significantly correlated with photosynthetic pigments developed by the GWAS could be used for marker-assisted selection in peanuts.\u003c/p\u003e \u003cp\u003eThe GWAS could generate a relatively high mapping resolution due to many recombination occurrences in the natural population during the domestication and evolution processes (Wang et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e). Second-generation re-sequencing is an important method for high throughput genotyping in GWASs (Huang et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2010b\u003c/span\u003e). In the present study, 241 peanut accessions were subjected to whole-genome re-sequencing and 2,110,659 high-quality SNP markers were identified (Table S4). The mean density of the polymorphic SNP markers was about 1.24 Kb/SNP (Table S4). The SNP density was higher than that reported by Zhang et al. (Zhang et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), who obtained SNP markers from a peanut 48 K SNP array. In the present study, the 241 peanut accessions were separated into 5 subgroups based on population structure, the neighbor-joining phylogenetic tree, and PCA (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The number of subgroups was more than that of the cluster number analyzed by Liu et al. and Lu et al.(Liu et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lu et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eIdentification of QTLs related to photosynthetic pigments contents and elite-allele loci for marker-assisted breeding in peanuts\u003c/b\u003e \u003c/p\u003e \u003cp\u003eChlorophyll and Car in leaves are important photosynthetic pigments, which are crucial for plant growth and development. Chlorophyll and Car contents are quantitative traits controlled by QTLs (Guo et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). QTLs related photosynthetic pigments have been detected in several species based on linkage mapping and association analysis (Wang et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e; Ye et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Jang et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, no studies have reported on chlorophyll- and Car-related QTL mapping in peanuts. In the present study, 37 and 65 significant SNPs were associated with photosynthetic pigments contents at the seedling and flowering stages, corresponding to 23 and 45 QTLs, respectively (Table S5). The novel QTLs identified in the current study using GWAS should be next validated via fine mapping of bi-parent hybrid populations.\u003c/p\u003e \u003cp\u003eElite allele loci are useful for the development of molecular markers in crop breeding programs (Su et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In the present study, we detected two candidate causal genes (\u003cem\u003eArahy.YWY61J\u003c/em\u003e and \u003cem\u003eArahy.VMJ95M\u003c/em\u003e) using GWAS and RNA-seq analysis. Four non-synonymous SNP mutations were detected in the exons of \u003cem\u003eArahy.YWY61J\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), which had a significantly positive effect on the Chl a and Chl b contents. Moreover, one non-synonymous SNP was detected in an exon of \u003cem\u003eArahy.VMJ95M\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), which had a significantly positive effect on the Chl b and Car contents.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eCandidate genes and their molecular function in regulating photosynthetic pigments contents\u003c/h2\u003e \u003cp\u003eChlorophyll is essential for plant photosynthesis (Cutolo et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Several genes were reported to be involved in chlorophyll metabolism (Sato et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Yamatani et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In the present study, 32 genes associated with the contents of photosynthetic pigments were identified using GWAS in peanut. Of these, our study focused on \u003cem\u003eArahy.YWY61J\u003c/em\u003e and \u003cem\u003eArahy.VMJ95M\u003c/em\u003e genes based on RNA-seq analysis. Moreover, peanut accessions carrying diverse haplotype alleles of these two genes exhibited significantly different photosynthetic pigments contents. \u003cem\u003eArahy.YWY61J\u003c/em\u003e encodes terpene synthase 14. Terpene synthases are pivotal enzymes for the initiation of terpenoid biosynthesis, which play a central role in regulating growth and development and mediating response to biotic and abiotic stresses (Gershenzon and Dudareva \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Karunanithi et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Jia et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In this study, we identified four non-synonymous SNPs in exons 2, 3, and 4 of \u003cem\u003eArahy.YWY61J\u003c/em\u003e. Interestingly, the SNP mutation (C/T) caused an amino acid mutation from Valine to a stop codon (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec), which may alter the protein function. There were significant differences in Chl a and Chl b contents between the associated haplotype alleles CCTA and GACG (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed); thus, we speculated that this may be a candidate gene involved in regulating chlorophyll content.\u003c/p\u003e \u003cp\u003eThe other candidate gene, \u003cem\u003eArahy.VMJ95M\u003c/em\u003e encodes a photosystem I P700 chlorophyll A-binding protein (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Chl-binding proteins (CBPs) play a principal role in two aspects. Firstly, they conduct light harvesting and activate energy transfer from surrounding light-harvesting complexes (LHCs) to the central complexes of photosystems I and II. Secondly, CBPs initiate charge separation in the photosynthetic reaction centers (Allen et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). There is a conserved Chl a/b-binding motif within the transmembrane domains of LHC proteins and LHC-like proteins. Its hydrophobic sequences of Chl a/b-binding motif contain 25\u0026ndash;30 amino acids, of which a few conserved charged amino acid residues could bind to photosynthetic pigments (Engelken et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In this study, the protein encoded by \u003cem\u003eArahy.VMJ95M\u003c/em\u003e gene contains three transmembrane regions (Fig. S6), which was consistent with CBPs in other plant species. However, the two candidate genes identified need to be verified in future studies. Furthermore, bi-parent genetic populations should be developed, and the candidate genes in the target regions should be fine-mapped. Moreover, virus-induced gene silencing could be used to verify the function of candidate genes quickly.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn the present study, 241 peanut accessions worldwide were genotyped by re-sequencing and clustered into five subpopulations following the screening of 2,110,659 high-quality SNPs. Thus, identified 23 and 45 QTLs were associated with photosynthetic pigments contents via a GWAS at the seedling and flowering stages, respectively. Finally, two candidate genes \u003cem\u003eArahy.YWY61J\u003c/em\u003e and \u003cem\u003eArahy.VMJ95M\u003c/em\u003e, which encodes terpene synthase 14 and photosystem I P700 chlorophyll A-binding protein, respectively, were identified using GWAS and RNA-seq analysis. Peanut accessions carrying excellent haplotypes of each of the two genes had higher photosynthetic pigments contents than that of the other accessions. The identified significant SNPs, QTLs, and candidate genes will contribute to understanding the genetic mechanisms underlying SNP variations in the contents of photosynthetic pigments, and can be used for the genetic improvement of photosynthetic efficiency through marker-assisted breeding in peanut.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eDeclaration of competing interest\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the National Key R\u0026amp;D Program of China (2020YFD1000905), and the Guangdong Technical System of Peanut and Soybean Industry (2022KJ136-05) and China Agriculture Research System (CARS-15).\u003c/p\u003e\u003ch2\u003eAuthor Contributions\u003c/h2\u003e \u003cp\u003eTC, ZH, LZ and GL contributed to the study conception and design. TC and SW prepared the experimental materials. ZH performed the field trials and phenotypic evaluation. ZH, YC, YL and QZ analyzed the data. JC and RZ prepared peanut RNA samples and performed the qRT-PCR. TC and ZH drafted the manuscript. LZ and GL revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThe authors would like to thank all members of the Peanut physiology and ecology laboratory at the Shandong Academy of Agricultural Sciences. We are grateful to the anonymous reviewers for helpful suggestions.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e \u003cp\u003eThe transcriptomic data are available in the NCBI PRJNA1084226.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlexander DH, Novembre J, Lange K (2009) Fast model-based estimation of ancestry in unrelated individuals. 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J Agric Food Chem 67:994\u0026ndash;1002\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taag","sideBox":"Learn more about [Theoretical and Applied Genetics](https://www.springer.com/journal/122)","snPcode":"122","submissionUrl":"https://submission.nature.com/new-submission/122/3","title":"Theoretical and Applied Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Arachis hypogaea L., photosynthetic pigments, genome-wide association analysis, Quantitative traits loci, candidate gene","lastPublishedDoi":"10.21203/rs.3.rs-6203682/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6203682/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePhotosynthetic pigments are indispensable for light absorption and electron transfer in photosynthesis, which is essential for increasing crop productivity. However, genetic basis of photosynthetic pigment contents in peanuts (\u003cem\u003eArachis hypogaea\u003c/em\u003e L.) at seedling and flowering stages remains poorly understood. In this study, an association panel of 241 peanut accessions was assayed for four photosynthetic pigment contents, including chlorophyll a (Chl a), chlorophyll b (Chl b), total chlorophyll (Chl a\u0026thinsp;+\u0026thinsp;b) and carotenoids (Car) across five environments. A total of 2,110,659 high-quality single nucleotide polymorphisms (SNPs) were obtained by whole-genome re-sequencing. A genome-wide association study was performed based on the best linear unbiased estimation values of four photosynthetic pigment contents. In total, 23 and 45 quantitative trait loci (QTLs) were associated with four photosynthetic pigment contents at the seedling and flowering stages, respectively, with eight QTLs associated with multiple traits. Thirty-two genes were identified within these QTL regions. Furthermore, through RNA-seq analysis of two peanut accessions with contrasting photosynthetic pigments contents, 3829 and 4972 differentially expressed genes were detected at seedling and flowering stages, respectively. Two candidate genes, \u003cem\u003eArahy.YWY61J\u003c/em\u003e and \u003cem\u003eArahy.VMJ95M\u003c/em\u003e, were differently expressed at flowering stages. Haplotype analysis suggested that \u003cem\u003eArahy.YWY61J\u003c/em\u003e was involved in Chl a and Chl b contents; and \u003cem\u003eArahy.VMJ95M\u003c/em\u003e gene was involved in Chl b and Car synthesis of peanut leaves. These findings will contribute to the understanding of genetic and molecular mechanisms underlying variations in photosynthetic pigments and benefits the improvement of photosynthetic efficiency using marker-assisted breeding in peanuts.\u003c/p\u003e","manuscriptTitle":"Identification of photosynthetic pigment contents related genes in peanut (Arachis hypogaea L.) by GWAS and RNA-seq","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-03 11:21:16","doi":"10.21203/rs.3.rs-6203682/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revisions","date":"2025-09-26T15:35:33+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-04-28T08:50:57+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-24T23:10:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-11T14:53:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"Theoretical and Applied Genetics","date":"2025-03-11T04:21:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taag","sideBox":"Learn more about [Theoretical and Applied Genetics](https://www.springer.com/journal/122)","snPcode":"122","submissionUrl":"https://submission.nature.com/new-submission/122/3","title":"Theoretical and Applied Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"1b393bcf-f9de-4d0c-add1-133fce096a0b","owner":[],"postedDate":"April 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-13T16:07:44+00:00","versionOfRecord":{"articleIdentity":"rs-6203682","link":"https://doi.org/10.1007/s00122-026-05223-8","journal":{"identity":"theoretical-and-applied-genetics","isVorOnly":false,"title":"Theoretical and Applied Genetics"},"publishedOn":"2026-04-06 15:58:53","publishedOnDateReadable":"April 6th, 2026"},"versionCreatedAt":"2025-04-03 11:21:16","video":"","vorDoi":"10.1007/s00122-026-05223-8","vorDoiUrl":"https://doi.org/10.1007/s00122-026-05223-8","workflowStages":[]},"version":"v1","identity":"rs-6203682","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6203682","identity":"rs-6203682","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}