Multi-Locus Genome Wide Association Study Uncovers Genetics of Fresh Seed Dormancy in Groundnut

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Gangurde, Khaja Mohinuddin D., and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4977357/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Dec, 2024 Read the published version in BMC Plant Biology → Version 1 posted 4 You are reading this latest preprint version Abstract Pre-harvest sprouting in groundnut leads to substantial yield losses and reduced seed quality, resulting in reduced market value of groundnuts. Breeding cultivars with 14–21 days of fresh seed dormancy (FSD) holds promise for precisely mitigating the yield and quality deterioration. In view of this, six multi-locus genome-wide association study (ML-GWAS) models alongside a single-locus GWAS (SL-GWAS) model were employed on a groundnut mini-core collection using multi season phenotyping and 58K “Axiom_ Arachis ” array genotyping data. A total of 9 significant SNP-trait associations (STAs) for FSD were detected on A01, A04, A08, A09, B02, B04, B05, B07 and B09 chromosomes using six ML-GWAS models. Additionally, the SL-GWAS model identified 38 MTAs across 14 chromosomes of groundnut. Remarkably, a single STA on chromosome B02 ( qFSD-B02-1 ) was consistently identified in both ML-GWAS and SL-GWAS models. Furthermore, candidate gene mining identified nine high confidence genes viz ., Cytochrome P450 705A, Dormancy/auxin associated family protein, WRKY family transcription factor, Protein kinase superfamily protein, serine/threonine protein phosphatase, myb transcription factor, transcriptional regulator STERILE APETALA-like, ethylene-responsive transcription factor 7-like and F-box protein interaction domain protein as prime regulators involved in Abscisic acid/Gibberellic acid signaling pathways regulating dormancy/germination. In addition, three of the allele-specific markers developed from the identified STAs were validated across a diverse panel. These markers hold potential for enhancing dormancy in groundnut through marker-assisted selection. Thus, this research offers insights into genetic and molecular mechanisms underlying groundnut seed dormancy in addition to providing markers and donors for breeding future varieties with 2–3 weeks of FSD. seed dormancy seed in-situ germination association mapping candidate genes molecular mechanism diagnostic markers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 INTRODUCTION The physiological states of seed encompassing both induction of dormancy and germination initiation play a pivotal role in regulating the success of field establishment of various crop plants after sowing (Nautiyal et al., 2023 ). Dormancy and germination are primarily governed by various physiological mechanisms and environmental factors (Koornneef et al., 2002 ). Despite their contrasting expressions, both seed dormancy and germination hold equal significance in the effective management and planning of crop cultivation. There is considerable diversity in the germination behavior among groundnut germplasm (Bomireddy et al., 2024 ). Typically, bunch types tend to be non-dormant and may undergo pre-harvest sprouting when sufficient moisture is present in the field at the time of maturity or before harvest. In contrast, spreading and semi-spreading types exhibit longer seed dormancy (Naganagoudar et al., 2016 ). Therefore, Spanish bunch groundnut varieties with a low degree of dormancy evidences advantageous in preventing in-situ germination/pre-harvest sprouting (Nautiyal et al., 2001 ). Conversely, longer dormancy could delay normal germination even under favorable conditions, leading to reduced germination percentage in the field. Previously, efforts have been undertaken to alleviate seed dormancy in dormant varieties by employing various growth regulators and chemicals (Rajan et al., 2020 ). The sustainable solution to address pre-harvest sprouting involves developing cultivars with 14–21 days of fresh seed dormancy (FSD), capable of withstanding detrimental impact of rain between maturity and harvest. However, improving pre-harvest sprouting resistance through phenotypic selection is a challenging undertaking. Where complicating factors include: (1) the presence of significant genetic and environmental interactions; (2) variation in the mechanisms governing dormancy among different plant materials (Yaw et al., 2008 ; Naganagoudar et al., 2016 ); (3) the involvement of multiple genes in controlling seed dormancy (Bomireddy et al., 2022 ; Zhang et al., 2022 ); (4) intergenic/epistatic interactions with pre-dominant role in genetic basis of seed dormancy (Khalfaoui, 1991 ; Bomireddy et al., 2022 ); and (5) environmental conditions conducive to PHS may not be always available. Controlled environments such as rooms with sprinklers or germinators offer ideal conditions for phenotyping pre-harvest sprouting, however, their efficacy in screening extensive lines within breeding programs may be limited. Genomics-assisted breeding (GAB) is a potential tool to overcome these constraints. GAB has been effectively deployed to improve leaf rust and late leaf spot resistance and high oleic acid content in groundnut (Pandey et al., 2024 ; Shasidhar et al., 2020 ; Varshney et al. 2014 ; Janila et al. 2016 ; Deshmukh et al. 2020 ; Yeri and Bhat, 2022). However, the development of highly efficient linked markers is a pre-requisite for successful deployment of GAB. Identification of genomic regions and candidate genes linked to FSD can facilitate marker development, aiding in the effective transfer of FSD trait into elite groundnut cultivars. Significant efforts have been dedicated to understand the molecular mechanisms of PHS in cereals, and several genomic regions and candidate genes have been identified in rice (Lee et al., 2017 ; Chen et al., 2023 ), wheat (Ogbonnaya et al., 2008 ; Guo et al., 2023 ), and barley (Li et al., 2004 ; Nakamura, 2017). In rice, GA20-oxidase gene was identified in the QTL region controlling PHS (Li et al., 2004 ). Similarly, mitogen-activated Protein Kinase Kinase 3 ( MKK3 ) and Alanine aminotransferase ( AlaAT ) in barley (Nakamura et al., 2016 ; Sato et al., 2016 ); mother of FT and TFL1 ( MFT ) and Phs1 in wheat (Nakamura et al., 2011 ; Torada et al., 2016 ) were identified as underlying genes regulating seed dormancy. Though PHS is a widespread constraint in groundnut, only limited efforts were made to map FSD QTLs so far, mainly using bi-parental populations (Vishwakarma et al., 2016 ; Kumar et al., 2020 ; Bomireddy et al., 2022 ; Wang et al., 2022 ; Zhang et al., 2023 ). In previous reports, only few genes, viz ., zeaxanthin epoxidase , RING-H2 finger protein (Kumar et al., 2020 ) and ethylene-responsive transcription factor (Wang et al., 2022 ) were determined as candidate genes involved in hormonal regulation of dormancy in groundnut. Bi-parental QTL mapping generally faces limitations due to limited recombination events taking place during the development of recombinant inbred line population, because of which biological processes governing dormancy remain not fully understood (Lu et al., 2018 ). Consequently, future efforts should focus on employing highly efficient and reliable QTL mapping methods to identify additional novel QTLs associated with this trait. Genome-wide association studies (GWAS) have become increasingly recognized as a potent methodology to identify QTLs and genes linked with complex traits based on the historic recombinations in a large natural population (Pandey et al. 2014 ; Zhong et al., 2021 ; Gangurde et al., 2020 ; Gangurde et al., 2024 ). GWAS can surpass the limitations of bi-parental linkage mapping, allowing for the dissection of complex traits with high mapping resolution (Bhandari et al., 2020 ). However, so far there are no reports on genome wide associations studies on FSD using diverse germplasm in groundnut. Further, methods such as mixed linear model (MLM) (Zhang et al., 2005 ), implemented in single-locus GWAS (SL-GWAS), have been extensively utilized to investigate several genetic variants linked to complex agronomic traits. However, SL-GWAS methods face limitations in identifying minimal effect significant SNP-trait associations (STAs) influenced by the stringent Bonferroni correction and multigenic background (Wang et al., 2016 ). To overcome these limitations, development of Multi-Locus GWAS models (ML-GWAS) has been introduced as a multi-faceted genome scanning approach, simultaneously estimating the effect of all the markers (Cui et al., 2018 ). In the view of above gap of the knowledge and advancements in association mapping analysis, in this study, we utilized association mapping strategies by employing six ML-GWAS and one SL-GWAS (MLM) models in the groundnut mini-core collection. We aim to identify all the possible STAs using multiple methodologies to derive candidate genes regulating FSD to facilitate marker development. MATERIAL AND METHODS Plant material The groundnut mini-core collection consisting of 184 accessions developed at ICRISAT, Patancheru was used as association mapping panel (Upadhyaya et al., 2002 ). Groundnut mini-core collection represents the genetic diversity available in the entire groundnut germplasm at ICRISAT, encompassing six botanical types viz ., hypogaea , hirsuta , fastigiata , peruviana , aequatoriana and vulgaris. Field experiment and phenotypic evaluation Phenotyping data on days to 50% flowering was recorded at two locations namely ICRISAT, Hyderabad, India and dry land farm of S.V. Agricultural College in Tirupati, Andhra Pradesh, India. A total of four seasons (Post-rainy 2018–2019, 2019–2020, 2022–2023 and Rainy season of 2022) of phenotyping data generated at ICRISAT, Patancheru located at 545 m altitude, 17° 31' 48.00" N latitude and 78° 16' 12.00" E longitude. Additionally, all 184 accessions were also grown at Tirupati, located at 182.9 m altitude, 13°54’ N latitude and 79°54’ E longitude and phenotyped during Rainy 2021. Standard agronomic procedures for groundnut cultivation were followed during all the growing seasons. To ensure that any differences in dormancy are more likely due to genetic factors rather than differences in maturity, accessions were categorized into three groups/sets (early/medium/late maturity) and harvested accordingly. Matured seeds that were freshly harvested were selected for phenotyping using an in-vitro germination assay (Upadhyaya and Nigam, 1999 ), detailed methodology of which was explained in our previous publication (Bomireddy et al., 2024 ). Over the period of 30 days, data was recorded on each accession on the number of days required to achieve 50% germination, which referred as days to 50% germination. DNA extraction and genotyping using 58K SNP array Total genomic DNA was isolated from the tender leaves of 25–30 day old plants of groundnut mini-core collection using Nucleospin Plant II kit (Macherey-Nagel, Düren, Germany). The purity and concentration of the isolated genomic DNA samples were assessed by electrophoresis on a 0.8% agarose gel and Thermo scientific’s Nanodrop 8000 spectrophotometer, respectively. Genotyping was carried out with ‘Axiom_ Arachis ’ SNP array of 58,233 SNP markers derived from DNA re-sequencing of 41 wild diploid ancestors and tetraploid accessions of groundnut (Pandey et al., 2017 ; Clevenger et al., 2017 ). The DNA samples from mini-core accessions were genotyped on the Affymetrix GeneTitan platform following previously described methods (Gangurde et al., 2020 ) and resulting data for each accession in .CEL file format was generated and stored. Subsequent SNP calling and data analysis performed using Axiom™ Analysis Suite version 1.0 (Thermo Fisher Scientific, USA) to implement quality control (QC) measures and select samples that successfully passed the QC test. Of the 58,233 SNPs retrieved from Axiom™ analysis suit, high-quality SNPs were filtered out with minor allele frequency (MAF) of ≥ 0.05 and maximum missing sites fixed to < 20% per SNP using Tassel v5.0 software. After stringent filtration a total of 10,064 high-quality SNPs were subsequently employed for association mapping studies. Genome-wide Association Analysis for days to 50% flowering Multi-season phenotyping data, along with genotyping data on 10,064 SNP of the mini-core set was used to perform genome-wide association analysis using multi-locus model. Six ML-GWAS methods, namely mrMLM, FASTmrMLM, FASTmrEMMA, pLARmEB, pKWmEB and ISIS EM-BLASSO in the mrMLM R package ( https://cran.r-project.org/web/packages/mrMLM/index.html ) were implemented. Default parameter values were utilized, and LOD score of 3 was set to identify robust STAs. All six methods utilized PCA and kinship matrices. A single-locus mixed linear model (MLM) was also performed using the Tassel software. For correcting population structure and reducing the false-positive rate, we employed the first three principal components (PCs) and a kinship matrix as covariates. The association threshold to determine significant marker-trait associations was computed with Bonferroni correction by calculating a p-value of 4.9682 × 10 − 6 , derived from the negative log transformation of α/n (n represents total number of SNPs used for GWAS analysis). Identification of candidate genes corresponding to significant STAs STAs were further used for finding of candidate genes in the genomic region of 100 kb upstream and 100 kb downstream from the identified SNP position using diploid genome assemblies on Peanutbase ( www.peanutbase.org ). Based on the previous reports available in the literature, genes that were reported to regulate ABA/GA mediated processes were then identified as candidate genes regulating seed dormancy/germination. Development of allele specific markers For designing the allele specific markers, Batch primer 3 software ( https://probes.pw.usda.gov/batchprimer3/ ) was used. A flanking sequence of 1200bp (600bp up stream and 600bp downstream) to the identified SNP positions were used to design the primers. Default parameters were used to design the primers with product size range of 400-600bp and 50% GC content. RESULTS Phenotyping for fresh seed dormancy on mini-core collection In this study, we used number of days required for an accession to achieve 50% germination as a measure of dormancy. In the GWAS panel, days to 50% germination was in the range of 1 to 30 days. Accessions of Virginia Bunch and Virginia Runner (var. hypogaea ) showed a longer duration (16–30 days) of dormancy compared to Spanish Bunch (var. vulgaris ) and Valencia (var. fastigiata ) (1–25 days). The mean performance and phenotypic distribution of these accessions (from two replications), screened for days to 50% germination across five seasons are represented in Fig. 1 & Table S1 . For more detailed information on the phenotyping data, refer to our previous publication, Bomireddy et al., 2024 . Multi-locus GWAS identified significant STAs associated with fresh seed dormancy A total of 9 STAs (LOD ≥ 3) significantly associated with FSD were identified in six ML-GWAS approaches (Fig. 2 ). Of these, seven STAs ( qFSD_A04-1 , qFSD_A08-1 , qFSD_A09-1 , qFSD_B02-1 , qFSD_B05-1 , qFSD_B07-1 , qFSD_B09-1 ) were consistently identified in at least two ML-GWAS methods (Fig. 3 ; Table 1 ). Notably, STAs on chromosomes A09 ( qFSD_A09-1 ) and B07 ( qFSD_B07-1 ) were consistently identified in all six methods, with higher LOD score ranging from 11.87–61.35 and 4.35–36.85, respectively. The STA qFSD_B05-1 on B05 was identified in four methods with LOD score ranging from 11.80-59.71. Additionally, two STAs ( qFSD_A09-1 , qFSD_B02-1 ) were identified by at least three ML-GWAS methods, with the LOD score ranging from 3.33–48.76 and 10.13–17.16, respectively. Similarly, the STA on chromosome A09 ( qFSD_A09-1 ) was consistently detected during the seasons, Post-rainy 2019–2020 and 2022–2023 through ISIS EM-BLASSO approach. Table 1 The significant STAs for fresh seed dormancy identified using multi-locus GWAS models SNP Chr Position QTL Region Season Method LOD score R 2 (%) AX_147210899 A01 36775841 qFSD_A01-1 R2021 6 3.3 2.88 AX_147221160 A04 119365410 qFSD_A04-1 PR2022_23 4,6 3.98–4.30 5.81–6.03 AX_147231175 A08 38560701 qFSD_A08-1 PR2022_23 2,4,5 3.33–48.76 70.98–74.50 AX_147233202 A09 29726644 qFSD_A09-1 PR2020_21, PR2022-23 1,2,3,4,5,6 11.87–61.35 71.75–84.54 AX_147240363 B02 3495023 qFSD_B02-1 PR2020_21 1,2,4 10.13–17.16 74.84–85.07 AX_147247942 B04 105437967 qFSD_B04-1 PR2022_23 1 7.92 70.72 AX_147250106 B05 112344292 qFSD_B05-1 PR2020_21 2,3,4,6 11.80-59.71 53.44–85.07 AX_147254360 B07 995898 qFSD_B07-1 R2022 1,2,3,4,5,6 4.35–36.85 38.58–84.56 AX_147262364 B09 143731363 qFSD_B09-1 PR2020_21 1,6 6.26–7.62 15.22–18.74 Chr: Chromosome; Methods 1:mrMLM; 2:FASTmrMLM; 3:FASTmrEMMA; 4:pLARmEB; 5:pKWmEB; 6:ISIS EM-BLASSO; LOD: Logorithm of Odds: R 2 : Coefficient of Determination/Phenotypic Variation Explained Moreover, MLM or single locus model in Tassel identified 38 significant STAs across 14 chromosomes, with R 2 values in the range of 3.1–8.9% (Figure S1 ; Table S2). Interestingly, STAs on chromosome A09 and B09 were consistently identified in all the five seasons, with R 2 value of 4.4 and 8.1%, respectively. Fascinatingly, a single STA on chromosome B02 ( qFSD_B02-1 ) identified in both ML-GWAS and SL-GWAS approaches. Identified candidate genes for fresh seed dormancy Candidate gene search in 200 kb flanking region of the 9 significant STAs identified a total of 134 genes (Table S3). Of these, previous studies have extensively reported 63 genes as potential regulators in the process of seed dormancy and/or seed germination regulating via ABA, GA, and ethylene signaling pathways (Table S4). Among 63 genes, 5 genes unveiled missense variants in the CDS coding region, 3 genes had downstream gene variants, 1 gene had 3_prime_UTR_variants, 2 genes had 5_prime_UTR_variant and 1 gene had upstream gene variant (Table S5). Remarkably, a STA ( qFSD-A09-01 ) identified in all six methods could be an important genomic region regulating FSD. Corresponding to the STA ( qFSD-A09-01 ), important genes such as dormancy/auxin associated family protein (Aradu.2Q7VA) , DUF223 domain protein (Aradu.XE42X) , hypoxia-responsive family protein (Aradu.WAM0A) were identified with functional relevance to dormancy/germination. In the genomic region of STA qFSD-A04-01 , multiple copies of protein kinase superfamily proteins (Aradu.VA4EQ) along with F-box interaction domain proteins (Aradu.XB89Z) , serine/threonine-protein phosphatase ( Aradu.X6CDT ) were identified to regulate seed dormancy as reported in various crops. The genes corresponding to the STA on chromosome A08 ( qFSD-A08-01 ) included RNA methyltransferase (Aradu.3F83L) , Pentatricopeptide repeat (PPR) superfamily protein (Aradu.H1R88) , eukaryotic aspartyl protease family protein (Aradu.HGT8J) . Similarly, ethylene-responsive transcription factors ( Araip.LL89K ; Araip.I6HJK ) from qFSD-B04-01 are recognized as promising genes involved in regulating dormancy. In the STA qFSD_B07-01 , BTB/POZ domain-containing protein (Araip.JP0WQ) , late embryogenesis abundant (LEA) protein ( Araip.S5KEZ ) and receptor-like protein kinases ( Araip.RLI4W ) from qFSD_B09-01 were prominent genes known for their involvement in regulation dormancy/germination. In the 200kb genomic region around the identified significant STAs by SL-GWAS methods, a total of 346 genes were retrieved (Table S6). Potential genes among these included auxin transport protein (Aradu.05XZ1), E3 ubiquitin-protein ligase (Aradu.7Y7XJ), WRKY family transcription factor family protein (Aradu.76148), serine/threonine protein phosphatase (Aradu.25KN6), cytochrome P450 (Aradu.FN562), abscisic acid receptor (Aradu.640E1), eukaryotic aspartyl protease family protein (Araip.2P2KT) etc ., which were identified as the prominent regulators of dormancy/germination (Table S7). To elucidate the molecular mechanisms governing dormancy and germination, we conducted a comprehensive investigation to understand the functional involvement of the identified candidate genes in the ABA and GA biosynthesis and or signaling pathways. Among these candidate genes, cytochrome P450 705A (Aradu.FN562) was notably discerned for its involvement in ABA catabolism. Dormancy/auxin associated family protein ( ARF ) ( Aradu.2Q7VA ), WRKY family transcription factor (Aradu.76148) , Protein kinase superfamily protein (Aradu.VA4EQ) , serine/threonine protein phosphatase ( PP2C ) ( Aradu.25KN6 ) and MYB transcription factor ( Aradu.GFS4B ) were observed as key players participating in ABA signaling. Transcriptional regulator of STERILE APETALA-like ( Araip.14WNT ) was known for its involvement in GA catabolism, while ethylene-responsive transcription factors ( Araip.LL89K ) and F-box protein interaction domain protein ( Aradu.XB89Z ) emerged as a noteworthy participant in GA signaling (Fig. 4 ). Based on the phenotyping data, a representative panel comprising of mini-core accessions with varied dormancy durations were selected to assess the efficacy of the identified STAs. Allele calls of the selected accessions for nine significantly associated SNP markers (identified from ML-GWAS models) were used from ‘Axiom_ Arachis ’ 58K SNP array genotyping data. There was no polymorphism between non-dormant and dormant mini-core accessions for AX_147210899 and AX_147221160 markers from A01 and A04 chromosomes (Fig. 5 ). However, AX_147233202 from A09 chromosome remarkably differentiated between dormant and non-dormant genotypes. Accessions with all favorable dormant alleles for the other 6 significant SNPs exhibited longer dormancy durations (require ≥ 30 days for 50% germination). Conversely, an increase in the number of unfavorable non-dormant alleles corresponded to a decreased dormancy duration. Therefore, these markers can be used for development of allele specific or KASP assays for their deployment in marker-assisted selection to improve popular groundnut cultivars with 14–21 days of dormancy. Development and validation of allele specific markers for fresh seed dormancy Of the nine STAs (identified from ML-GWAS models), seven were shortlisted based on clear polymorphism between non-dormant and dormant accessions. However, the remaining two markers AX-147210899 (A01) and AX-147221160 (A04) were dropped due to large proportion of heterozygous calls. Of the seven markers, AX-147233202 from A09 showed clear polymorphism between dormant and non-dormant lines, while the other six had one or two ambiguous calls between them. Based on the allelic combinations, four markers were selected to develop allele-specific primers. During validation, three out of the four primers successfully distinguished between dormant and non-dormant lines by showing clear bands. Using these three markers, entire mini-core set was genotyped along with the parents of two RIL populations, ICGV 02266 and ICGV 97045. Among these markers, GMFSD2 , GMFSD3 , and GMFSD4 successfully differentiated between dormant and non-dormant lines (Table 2 ; Fig. 6 ). As depicted in the gel images, Virginia runner and Virginia bunch lines predominantly exhibited dormancy, with a dormancy period of 23–30 days. In contrast, Valencia bunch and Spanish bunch lines are non-dormant, germinating within 1–2 days. Table 2 Primers sequences for three allele specific markers validated on diverse germplasm lines of groundnut SN Probe ID Chr Pos Marker Name Dormant Alleles F/R Primer Sequence Melting Temperature (Tm) Product Size (bp) 1. AX_147233202 A09 29726644 GMFSD2 T F AACTTGAACTTTCCTGGGAT 48 472 R TCCTGACTTCCCTGATGTTG 52 2. AX_147250106 B05 112344292 GMFSD3 G F TATTTGGTCTGCTCCGCTCT 52 286 R TCTACAAACTTCTCTCCGGTCC 55 3. AX_147262364 B09 143731363 GMFSD4 G F AACCAAGGGAAGGATCAACC 52 265 R TCAAGACTGTTCCCGAATGAC 52 Chr : Chromosome; Pos : Position DISCUSSION Groundnut or peanut is a major oilseed and grain legume mainly cultivated under rainfed regions of tropical, subtropical and temperate countries worldwide. Unlike the Virginia genotypes, the widely grown Spanish cultivars have lost the dormancy trait during domestication and selective breeding, and resulted in introduction of pre-harvest sprouting trait in cultivated groundnut. Developing commercial Spanish cultivars with 14–21 days of dormancy can prevent yield losses due to pre-harvest sprouting. Utilizing GAB offers a distinct advantage over conventional breeding by facilitating efficient tracking of alleles among segregating lines through the use of trait-linked markers (Varshney et al., 2014 ). In this context, the present investigation employed multi-model genome wide association analysis on mini-core collection using genotyping data generated from “Axiom_ Arachis ” 58K SNP array and multi-environment phenotyping data to identify the genomic regions and candidate genes regulating dormancy/germination. Conventional single-locus methods like Generalized Linear Model (GLM) and Mixed Linear Model (MLM) have been frequently deployed for identifying genetic variants in several crops (He et al., 2019 ). However, these methods have limitations as they neglect combined effects of multiple loci and face issues with multiple test corrections to determine critical values (Odesola et al., 2023 ). ML-GWAS methods, however, addresses these challenges (Liu et al., 2016 ). Comparative studies indicated that ML-GWAS has higher statistical power and lower false-positive errors as compare to SL-GWAS methods (Segura et al., 2012 ; Hu et al., 2018 ). Investigators typically integrate the strengths of various ML-GWAS algorithms to identify target loci/QTL associated with complex traits, as each algorithm possesses unique characteristics and QTL detection capabilities (Liu et al., 2020 ). A total of 9 significant STAs using ML-GWAS were identified for FSD trait using mini-core collection association panel. Previously, a QTL-Seq study reported two genomic regions on B05 and A09 chromosomes for fresh seed dormancy and also developed a potential marker on chromosome B05, GMFSD1 (Kumar et al., 2020 ). In this study we have developed three more allele specific markers namely, GMFSD2 (A09), GMFSD3 (B05) and GMFSD4 (B09). High-density genetic mapping for FSD identified two dormancy QTLs on chromosomes A04 and A05 (Wang et al., 2022 ). Similarly, a major stable QTL associated with fresh seed germination was identified on chromosome A04 (Zhang et al., 2022 ). Moreover, our previous FSD study used a 5K SNP assay based bi-parental genetic mapping and identified five major QTLs on Ah01, Ah06 Ah11, Ah16 and Ah17 chromosomes and two minor QTLs on Ah04 and Ah15 chromosomes (Bomireddy et al., 2022 ). Additionally, qFSD_A04-1 (119 Mb) on chromosome A04 was observed to be located in the close proximity of qPD_A04-2 (Wang et al., 2022 ), while the location of qFSD_B05-1 (112 Mb) on chromosome B05 was physically close to the genomic region identified by Kumar et al., 2020 . Thus, the identification of significant MTAs around previously reported genomic regions using multi-locus GWAS underscores the method's reliability. In addition to ML-GWAS, our study has also identified 38 significant STAs on 14 chromosomes of cultivated groundnut by SL-GWAS revealing all the possible genomic regions associated with FSD. Differences in the mapping results from various studies can be attributed to the factors such as seed development stage, population composition or the pedigree of the parents used in the population development and the prevailing environment during crop growth period (Cheng et al., 2014 ; Magwa et al., 2016 ). Comparable results were also documented in association mapping studies on wheat and rice seed dormancy (Lin et al., 2016 ; Lu et al., 2018 ). Because of genome similarity between the homeologous chromosomes of diploid progenitor genomes (A and B genome), homeologous associations on both sub-genomes of groundnut were identified. Nested association mapping for seed and pod weight in groundnut identified associations on homeologous chromosomes A05/B05, A06/B06 (Gangurde et al., 2020 ). In other allopolyploids such as wheat, where seed dormancy QTLs were detected on homeologous 3A/3B (Shao et al., 2018 ), 4A/4B and 5A/5B (Lin et al., 2015 ) chromosomes. As discussed earlier, identification of candidate genes underlying the QTLs/MTAs provide insights for better understanding of the trait. As ABA, GA and ethylene have been demonstrated to be associated with seed dormancy and germination regulation in many crops, identifying genes involved in the regulation of their metabolic pathways is of major interest. The involvement of ABA signaling, along its interaction with GAs/ethylene tends to modulate seed dormancy and germination initiation. Therefore, the genes retrieved in this study from both ML-GWAS and SL-GWAS models were thoroughly reviewed in previous literature for assessing their functional role in ABA and GA signaling pathways. Fascinatingly, cytochrome P450 705A ( Aradu.FN562 ) identified as an important participant in ABA catabolism. A cytochrome P450 superfamily protein ( CYP707A ) in Arabidopsis encodes ABA 8'-hydroxylases , an enzyme involved in ABA 8'-hydroxylation pre-dominant for ABA catabolism. Expression profiling indicated that cyp707a2 mutant displayed six times higher ABA levels, resulting in hyper seed dormancy compared to wild types (Kushiro et al., 2004 ). It indicated that CYP707A2 negatively regulates seed dormancy by declining ABA levels during seed imbibition. Supporting this, cytochrome P450 superfamily protein gene copies displayed high transcript abundance in ICGV 91114 (non-dormant) gene expression atlas, indicating their positive role in regulating germination (Bomireddy et al., 2022 ). WRKY family transcription factor family protein ( Aradu.76148 ) identified in this study is known to be involved in ABA signaling. Lack of WRKY transcription factor 41 ( WRKY41 ) in imbibed seeds of Arabidopsis resulted in decreased ABI3 (play crucial role in seed dormancy) expression while overexpressing transgenic WRKY41 lines had increased ABI3 expression (Ding et al., 2014 ). Examination of the double mutant wrky41 aba2 revealed that the regulation of ABI3 expression and seed dormancy is a combined effort between WRKY41 and ABA. Therefore, WRKY41 acts as a key regulator of ABI3 expression, thereby influencing seed dormancy. The identified protein kinase superfamily protein ( Aradu.VA4EQ ) in this study is known for its positive role in ABA signaling. In Arabidopsis, SNF1-RELATED PROTEIN KINASE 2.2 ( SnRK2.2 ) and SnRK2.3 , were reported to regulate ABA responses in seed dormancy and germination by mediating ABA signaling (Fujii et al., 2017). Double mutants of snrk2.2 and snrk2.3 exhibited decreased expression of several ABA-induced genes, demonstrating their positive role in ABA signaling. Similarly, in Arabidopsis, redundant ABA-activated SnRK2s were identified as prominent regulators of seed maturation and dormancy (Nakashima et al., 2009 ). Dormancy/auxin associated family protein ( Aradu.2Q7VA ) or Auxin responsive factors ( ARF ) identified in this study were reported to stimulate ABA signaling to induce seed dormancy (Liu et al., 2013 ). Defects in auxin signaling of MIR160-overexpressing plants and auxin receptor mutants significantly reduce dormancy, while increase in auxin biosynthesis prolong dormancy. Serine/threonine-protein phosphatase 7 long form homolog (Aradu.25KN6) and myb transcription factor ( Aradu.GFS4B ) identified in this study are known for their role in ABA signaling. In Arabidopsis, loss of ABSCISIC ACID-INSENSITIVE1 ( ABI1 ) which encodes 2C class of serine/threonine phosphatases ( PP2C ), leads to enhanced ABA responsiveness, indicating its negative role in ABA signaling (Gosti et al., 1999 ). Further, overexpressing HON ( Protein Phosphatase 2C family group ) lines revealed that it suppresses dormancy by impeding ABA signaling (Kim et al., 2013 ). However, some PP2Cs like HIGHLY ABA-INDUCED PP2C GENE1 ( HAI1 ) were reported to promote ABA signaling (Saez et al., 2004 ). MYB transcription factor 96 ( MYB96 ) was presumed to fine tune seed dormancy as it enhances ABA biosynthetic NCED genes and down regulate GA biosynthetic GA20ox1 , GA3ox1 genes in Arabidopsis (Lee et al., 2015 ). myb96-1 mutant seeds exhibited germination earlier than the wild MYB96-1 , while activation-tagging of myb96-1D seeds delayed the germination process. Differential gene expression analysis identified MYB60 as the promising candidate regulating dormancy with higher transcript abundance observed in the dormant Tifrunner gene expression atlas (Bomireddy et al., 2022 ). The identified Transcriptional regulator of STERILE APETALA-like (Araip.14WNT) is notably known to be involved in GA catabolism. APETALA 2 ( AP2 )-domain-containing transcription factors (ATFs), including OsAP2-39 in rice and ABI4 in Arabidopsis, were reported to play a prominent role in ABA and GA antagonistic crosstalk (Yaish et al., 2010 ; Shu et al., 2016 ). The AP2/ethylene-responsive element binding factor (AP2/ERF) family constitutes a substantial group of plant transcription factors, playing diverse roles at various plant developmental stages. In rice, transcriptome analysis of OsAP2-39 overexpression lines unveiled upregulation of OsNCED-I (ABA biosynthesis gene), increasing endogenous ABA levels, and enhanced GA-inactivating gene OsEUI , resulting in reduced GA content (Yaish et al., 2010 ). OsAP2-39 directly governs the expression of OsNCED-I and EUI , elucidating a novel mechanism regulating ABA/GA balance and ultimately influencing rice growth. Ethylene-responsive transcription factors ( Araip.LL89K ) and F-box protein interaction domain protein ( Aradu.XB89Z ) identified in this study are the significant contributors for GA signaling. Ethylene (ET) was reported to break seed dormancy by antagonizing ABA biosynthesis and signaling, thereby promoting seed germination (Corbineau et al., 2014 ). In Arabidopsis, Delay of Germination1 ( DOG1 ) interacts with ethylene responsive factor 12 ( ERF12 ) and co-regulate seed dormancy (Li et al., 2019 ). High transcript levels of ethylene-responsive transcription factors in seed and embryo of ICGV 91114 ( Arachis hypogaea sub spp. fastigiata ) gene expression atlas revealed its functional relevance in seed germination (Sinha et al., 2020 ; Bomireddy et al., 2022 ). In plants, F-box proteins regulate various physiological processes in various ways. Song et al. ( 2012 ) had isolated OsFbx352 gene (encoding for F-box domain protein) from rice to characterize its role in germination. Overexpression of OsFbx352 , (a F-box protein in rice) demonstrated lower ABA contents by reduced expression of ABA synthesis genes ( OsNced2, OsNced3 ) and increased ABA catabolism gene expression ( OsAba-ox2, OsAba-ox3 ) leading to seed germination. Whereas, knockdown of OsFbx352 led to higher ABA contents and suppressed seed germination, revealing its regulatory in modulating ABA metabolism. In groundnut, high gene expression value of F-box/RNI-like superfamily protein ( Arahy.LZ56CD ) in all the six selected tissues of seed and pod of non-dormant ICGV 91114 also indicated its positive regulatory role in germination (Bomireddy et al., 2022 ). In Arabidopsis, transgenic plants overexpressing for AtTLP9 ( Tubby-F-box like protein ) also demonstrated hypersensitiveness to ABA (Lai et al., 2004 ). As all these genes were explored for their involvement in ABA/GA/Ethylene signaling pathways, these were identified as promising candidates in the hormonal regulation of dormancy and germination dynamics. The allele calls of the representative panel of mini-core accessions for the identified significant MTAs revealed the presence of all favorable dormant alleles in an accession result in increased dormancy duration. Conversely, an increase in the number of unfavorable non-dormant alleles corresponded to a decreased dormancy duration. These MTAs show potential for developing KASP assays, and accessions with all the favorable dormant alleles can be used as donors in MABC programs aiming for dormancy of ≥ 30 days. However, if the goal is to achieve fresh seed dormancy of 14–21 days, accessions with a combination of dormant and non-dormant alleles for these markers can be utilized. Thus, these assays could play a crucial role in future molecular breeding programs, offering targeted and efficient means to select lines with 2–3 weeks of dormancy. CONCLUSION With a diverse panel of 184 groundnut mini-core accessions, this study employed a combined approach of ML-GWAS and SL-GWAS models and identified 9 and 38 MTAs for FSD, respectively. Examining the vicinity of these MTAs revealed potential candidate genes; Cytochrome P450 705A, Dormancy/auxin associated family protein, WRKY family transcription factor, Protein kinase superfamily protein, serine/threonine protein phosphatase, myb transcription factor, transcriptional regulator STERILE APETALA-like, ethylene-responsive transcription factor 7-like and F-box protein interaction domain protein to be involved in ABA/GA associated pathways. This investigation underscores dormancy as a complex trait controlled by multiple genes, highlighting the importance of understanding gene interactions across multiple biological pathways. Furthermore, examination of allelic calls in the mini-core accessions for the identified MTAs revealed the intricate regulation of dormancy and germination through the expression and suppression of multiple genes in diverse combinations, affecting dormancy duration. This suggests that successful molecular breeding strategies must incorporate multiple genes from various biological pathways. Further characterization of the candidate genes identified in this study is recommended through overexpression studies and the CRISPR/Cas9 approach, which may provide more insights on precise function of these candidate genes in FSD. Additionally, haplotype analysis for the identified candidate genes can aid in identifying superior haplotypes for FSD trait, which could be utilized in haplotype-based breeding. Declarations DATA AVAILABILITY STATEMENT The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author. AUTHOR CONTRIBUTIONS MKP conceived the idea and supervised and finalized the manuscript. KS and RS contributed to providing seed material and in-seed multiplication of the mini-core collection. DB, VS, SSG, RK phenotyped the mini-core collection. DB performed the analysis and drafting the manuscript. DB, VS, SSG contributed in improvising figures. KMD and SSG designed primers and performed validation. MR, VS, SSG, MKP contributed to reviewing and improving the manuscript. All authors have read and agreed to the published version of the manuscript. FUNDING The authors are thankful to the Indian Council of Agricultural Research (ICAR) through ICAR-ICRISAT collaborative project, Department of Biotechnology (DBT), Government of India through Indo-UK (Newton-Bhabha) project, and Bill & Melinda Gates Foundation (BMGF), USA through Tropical Legumes III project. ACKNOWLEDGMENTS The authors are thankful to Gene-Bank, ICRISAT for their support in providing seed material and assistance in phenotyping work. DB acknowledges Acharya N.G. Ranga Agricultural University for collaborating with ICRISAT and opportunity given as a PhD student to pursue this investigation at ICRISAT. 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Theoretical and Applied Genetics, 127, pp.1771-1781. Vishwakarma, M.K., Pandey, M.K., Shasidhar, Y., Manohar, S.S., Nagesh, P., Janila, P. and Varshney, R.K., 2016. Identification of two major quantitative trait locus for fresh seed dormancy using the diversity arrays technology and diversity arrays technology‐seq based genetic map in Spanish‐type peanuts. Plant Breeding, 135(3), pp.367-375. Wang, M.L., Wang, H., Zhao, C., Tonnis, B., Tallury, S., Wang, X., Clevenger, J. and Guo, B., 2022. Identification of QTLs for seed dormancy in cultivated peanut using a recombinant inbred line mapping population. Plant Molecular Biology Reporter, 40(1), pp.208-217. Wang, S.B., Feng, J.Y., Ren, W.L., Huang, B., Zhou, L., Wen, Y.J., Zhang, J., Dunwell, J.M., Xu, S. and Zhang, Y.M., 2016. Improving power and accuracy of genome-wide association studies via a multi-locus mixed linear model methodology. Scientific reports, 6(1), p.19444. Wen, Y.J., Zhang, H., Ni, Y.L., Huang, B., Zhang, J., Feng, J.Y., Wang, S.B., Dunwell, J.M., Zhang, Y.M. and Wu, R., 2018. Methodological implementation of mixed linear models in multi-locus genome-wide association studies. Briefings in bioinformatics, 19(4), pp.700-712. Yaish, M.W., El-Kereamy, A., Zhu, T., Beatty, P.H., Good, A.G., Bi, Y.M. and Rothstein, S.J., 2010. The APETALA-2-like transcription factor OsAP2-39 controls key interactions between abscisic acid and gibberellin in rice. PLoS genetics, 6(9), p.e1001098. Yaw, A.J., Richard, A., Safo-Kantanka, O., Adu-Dapaah, H.K., Ohemeng-Dapaah, S. and Agyeman, A., 2008. Inheritance of fresh seed dormancy in groundnut. African Journal of Biotechnology, 7(4). Yeri, S. B., and Bhat, R. S. (2016). Development of late leaf spot and rust resistant backcross lines in JL 24 variety of groundnut ( Arachis hypogaea L.). Electron. J. Plant Breed. 7, 37–41. doi: 10.5958/0975-928X.2016.00005.3 Zhang J, Feng JY, Ni YL, Wen YJ, Niu Y, Tamba CL, et al. PLARmEB:integration of least angle regression with empirical Bayes for multilocus genome-wide association studies. Heredity (Edinb). 2017;118(6):517–24. https://doi.org/10.1038/hdy.2017.8 Zhang, M., Zeng, Q., Liu, H., Qi, F., Sun, Z., Miao, L., Li, X., Li, C., Liu, D., Guo, J. and Zhang, M., 2022. Identification of a stable major QTL for fresh-seed germination on chromosome Arahy. 04 in cultivated peanut ( Arachis hypogaea L.). The Crop Journal, 10(6), pp.1767-1773. Zhang, X., Zhang, X., Wang, L., Liu, Q., Liang, Y., Zhang, J., Xue, Y., Tian, Y., Zhang, H., Li, N. and Sheng, C., 2023. Fine mapping of a QTL and identification of candidate genes associated with cold tolerance during germination in peanut ( Arachis hypogaea L.) on chromosome B09 using whole genome re-sequencing. Frontiers in Plant Science, 14, p.1153293. Zhang, Y.M., Mao, Y., Xie, C., Smith, H., Luo, L. and Xu, S., 2005. Mapping quantitative trait loci using naturally occurring genetic variance among commercial inbred lines of maize (Zea mays L.). Genetics, 169(4), pp.2267-2275. Zhong, H., Liu, S., Sun, T., Kong, W., Deng, X., Peng, Z. and Li, Y., 2021. Multi-locus genome-wide association studies for five yield-related traits in rice. BMC Plant Biology, 21(1), p.364. Supplementary Figure Supplementary Figure is not available with this version Additional Declarations No competing interests reported. Supplementary Files FSDSupplementaryTables.xlsx Cite Share Download PDF Status: Published Journal Publication published 27 Dec, 2024 Read the published version in BMC Plant Biology → Version 1 posted Editorial decision: Revision requested 04 Sep, 2024 Editor assigned by journal 04 Sep, 2024 Submission checks completed at journal 02 Sep, 2024 First submitted to journal 26 Aug, 2024 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4977357","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":349642333,"identity":"5b340c59-9a08-44a3-87b8-79e234898815","order_by":0,"name":"Deekshitha Bomireddy","email":"","orcid":"","institution":"International Crops Research Institute for the Semi-Arid Tropics (ICRISAT)","correspondingAuthor":false,"prefix":"","firstName":"Deekshitha","middleName":"","lastName":"Bomireddy","suffix":""},{"id":349642334,"identity":"0841de9d-ed6f-477c-beb9-e0756adccd85","order_by":1,"name":"Vinay Sharma","email":"","orcid":"","institution":"International Crops Research Institute for the Semi-Arid Tropics (ICRISAT)","correspondingAuthor":false,"prefix":"","firstName":"Vinay","middleName":"","lastName":"Sharma","suffix":""},{"id":349642335,"identity":"99002626-98f7-4126-b254-ba7863ab9d1d","order_by":2,"name":"Sunil S. Gangurde","email":"","orcid":"","institution":"International Crops Research Institute for the Semi-Arid Tropics (ICRISAT)","correspondingAuthor":false,"prefix":"","firstName":"Sunil","middleName":"S.","lastName":"Gangurde","suffix":""},{"id":349642336,"identity":"0810e44c-fe9f-4ac0-8073-8d0c82d476f7","order_by":3,"name":"Khaja Mohinuddin D.","email":"","orcid":"","institution":"International Crops Research Institute for the Semi-Arid Tropics (ICRISAT)","correspondingAuthor":false,"prefix":"","firstName":"Khaja","middleName":"Mohinuddin","lastName":"D.","suffix":""},{"id":349642337,"identity":"4b9edbe2-58b1-4b9b-997c-8b26437b118f","order_by":4,"name":"Rakesh Kumar","email":"","orcid":"","institution":"Central University of Karnataka","correspondingAuthor":false,"prefix":"","firstName":"Rakesh","middleName":"","lastName":"Kumar","suffix":""},{"id":349642338,"identity":"e9d31cd7-9564-4950-81fc-ca27cc035084","order_by":5,"name":"Ramachandran Senthil","email":"","orcid":"","institution":"International Crops Research Institute for the Semi-Arid Tropics (ICRISAT)","correspondingAuthor":false,"prefix":"","firstName":"Ramachandran","middleName":"","lastName":"Senthil","suffix":""},{"id":349642339,"identity":"c2a9f658-a086-4d46-9b7a-4c6532ca5b50","order_by":6,"name":"Kuldeep Singh","email":"","orcid":"","institution":"International Crops Research Institute for the Semi-Arid Tropics (ICRISAT)","correspondingAuthor":false,"prefix":"","firstName":"Kuldeep","middleName":"","lastName":"Singh","suffix":""},{"id":349642340,"identity":"ee9708ef-6447-4086-b8f8-b19450dd3692","order_by":7,"name":"Mangala Reddisekhar","email":"","orcid":"","institution":"S. V. Agricultural College, ANGRAU","correspondingAuthor":false,"prefix":"","firstName":"Mangala","middleName":"","lastName":"Reddisekhar","suffix":""},{"id":349642341,"identity":"75d4a156-a8a1-418f-9f62-8ef418688356","order_by":8,"name":"Sandip K. Bera","email":"","orcid":"","institution":"Directorate of Groundnut Research","correspondingAuthor":false,"prefix":"","firstName":"Sandip","middleName":"K.","lastName":"Bera","suffix":""},{"id":349642342,"identity":"d81891eb-429d-47ee-8e43-caa619f2d69d","order_by":9,"name":"Manish K. Pandey","email":"data:image/png;base64,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","orcid":"","institution":"International Crops Research Institute for the Semi-Arid Tropics (ICRISAT)","correspondingAuthor":true,"prefix":"","firstName":"Manish","middleName":"K.","lastName":"Pandey","suffix":""}],"badges":[],"createdAt":"2024-08-26 10:44:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4977357/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4977357/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12870-024-05897-6","type":"published","date":"2024-12-27T15:57:11+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":65726958,"identity":"2f04396a-552b-4b54-8006-28d9d40c7099","added_by":"auto","created_at":"2024-10-01 18:47:10","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":12653185,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhenotypic variability for fresh seed dormancy in the mini-core accessions.\u003c/strong\u003e \u0026nbsp;a) Phenotypic variability, dormant and non-dormant mini-core accessions during \u003cem\u003ein-vitro\u003c/em\u003e germination assay b) Violin plots representing days to 50% germination of the mini-core accessions during Post-rainy 2019-2020, Post-rainy 2020-2021, Rainy 2021, Rainy 2022 and Post-rainy 2022-23 seasons.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4977357/v1/83b0828bf756a78b5081c716.jpg"},{"id":65726960,"identity":"58dcb157-5922-49d8-a54a-36f5826149e8","added_by":"auto","created_at":"2024-10-01 18:47:10","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":5118433,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMulti-Locus Genome‐wide association studies of fresh seed dormancy measured as days to 50% germination using a diverse groundnut mini-core collection of 184 accessions.\u003c/strong\u003e Manhattan plots and Quantile–Quantile (QQ) plots for fresh seed dormancy during a) Post-Rainy 2019-2020 b) Post-Rainy 2020-2021; c) Rainy 2021 d) Rainy 2022 e) Post-Rainy 2022-2023. Names of the associated genomic regions/QTLs that were used for candidate gene prediction are mentioned near the pointing peak.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4977357/v1/59f19887590f55efc121b90c.jpg"},{"id":65726963,"identity":"c48904eb-0f2f-4599-a715-fe0f7f8c0527","added_by":"auto","created_at":"2024-10-01 18:47:11","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4395089,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of fresh seed dormancy QTLs measured as Days to 50% germination (D_50%) identified using ML-GWAS in groundnut mini-core collection\u003c/strong\u003e. QTLs on the right are the ones identified from ML-GWAS. QTLs on the left with different colours represents the genomic regions identified for seed dormancy from different studies (Light Purple: Kumar et al., 2020; Dark Purple: Zhang et al., 2022; Blue: Wang et al., 2022).\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4977357/v1/4b80d56a9633949dd83ef681.jpg"},{"id":65727353,"identity":"942d7b17-068c-48cb-88a6-7f1a4d702c84","added_by":"auto","created_at":"2024-10-01 18:55:10","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2749348,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAbscisic and gibberellic acid metabolism and signaling pathways in plants.\u003c/strong\u003e Picture highlights the genes identified in this study, depicting their role and how interplay of ABA/GA genes is enabling seed dormancy or germination. \u003cem\u003eAP2\u003c/em\u003e (\u003cem\u003eAraip.14WNT\u003c/em\u003e)- \u003cem\u003etranscriptional regulator STERILE APETALA-like\u003c/em\u003e; \u003cem\u003eMYB96\u003c/em\u003e (\u003cem\u003eAradu.GFS4B\u003c/em\u003e)- \u003cem\u003emyb transcription factor\u003c/em\u003e; \u003cem\u003eARF10\u003c/em\u003e (\u003cem\u003eAradu.2Q7VA\u003c/em\u003e)-\u003cem\u003eDormancy/auxin associated family protein\u003c/em\u003e; \u003cem\u003eCYP707A\u003c/em\u003e (\u003cem\u003eAradu.FN562\u003c/em\u003e)- \u003cem\u003ecytochrome P450 705A5\u003c/em\u003e; \u003cem\u003eETR\u003c/em\u003e (\u003cem\u003eAraip.LL89K\u003c/em\u003e)- \u003cem\u003eethylene-responsive transcription factor 7-like\u003c/em\u003e; \u003cem\u003eSnKR2\u003c/em\u003e (\u003cem\u003eAradu.VA4EQ\u003c/em\u003e)- \u003cem\u003eProtein kinase superfamily protein\u003c/em\u003e; \u003cem\u003eWRKY41\u003c/em\u003e (\u003cem\u003eAradu.76148\u003c/em\u003e)- \u003cem\u003eWRKY family transcription factor family protein\u003c/em\u003e; \u003cem\u003e2C\u003c/em\u003e(\u003cem\u003ePP2C\u003c/em\u003e) (\u003cem\u003eAradu.25KN6\u003c/em\u003e)- \u003cem\u003eserine/threonine-protein phosphatase 7 long form homolog\u003c/em\u003e; \u003cem\u003eF-box\u003c/em\u003e(\u003cem\u003eAradu.XB89Z\u003c/em\u003e)- \u003cem\u003eF-box protein interaction domain protein \u003c/em\u003e\u003cstrong\u003e(Source: Sohindji et al. 2020)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4977357/v1/5fbc9a367ae170796c4ce79b.jpg"},{"id":65727354,"identity":"683c9ec5-fd92-4d76-8b49-2e2360048721","added_by":"auto","created_at":"2024-10-01 18:55:10","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":8646627,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eValidation of the significantly associated markers with allele calls of the representative panel of mini-core accessions.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4977357/v1/5fd7fdabeb5db950ba1645a8.jpg"},{"id":65726962,"identity":"506357e7-7a13-45a3-bb68-674507e4171d","added_by":"auto","created_at":"2024-10-01 18:47:10","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":11012418,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eValidation of three allele specific markers in diverse mini-core collection. \u003c/strong\u003ea) GMFSD2\u003cem\u003e \u003c/em\u003e(AX_147233202) on A09; b) GMFSD3 (AX_147250106) on B05 and c) GMFSD4\u003cem\u003e \u003c/em\u003e(AX_147262364) on B09 chromosomes. These three markers differentiated non-dormant lines of Spanish Bunch (SP B); Valencia Bunch (VL B) from dormant lines of Virginia Runner (VR R); Virginia Bunch (VR B).\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4977357/v1/a426360d2d7113ae01821199.jpg"},{"id":72640533,"identity":"f1545902-a92b-4619-8023-8d206669abc4","added_by":"auto","created_at":"2024-12-30 16:06:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":45582432,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4977357/v1/97d266cd-c955-46a7-a7b5-984c0473e8f0.pdf"},{"id":65726957,"identity":"5d1caaaa-1963-492a-b9cf-9f5af867a641","added_by":"auto","created_at":"2024-10-01 18:47:10","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":86544,"visible":true,"origin":"","legend":"","description":"","filename":"FSDSupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4977357/v1/8d5cdf82e60f6167e42fa2e4.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi-Locus Genome Wide Association Study Uncovers Genetics of Fresh Seed Dormancy in Groundnut","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe physiological states of seed encompassing both induction of dormancy and germination initiation play a pivotal role in regulating the success of field establishment of various crop plants after sowing (Nautiyal et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Dormancy and germination are primarily governed by various physiological mechanisms and environmental factors (Koornneef et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Despite their contrasting expressions, both seed dormancy and germination hold equal significance in the effective management and planning of crop cultivation. There is considerable diversity in the germination behavior among groundnut germplasm (Bomireddy et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Typically, bunch types tend to be non-dormant and may undergo pre-harvest sprouting when sufficient moisture is present in the field at the time of maturity or before harvest. In contrast, spreading and semi-spreading types exhibit longer seed dormancy (Naganagoudar et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Therefore, Spanish bunch groundnut varieties with a low degree of dormancy evidences advantageous in preventing \u003cem\u003ein-situ\u003c/em\u003e germination/pre-harvest sprouting (Nautiyal et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Conversely, longer dormancy could delay normal germination even under favorable conditions, leading to reduced germination percentage in the field. Previously, efforts have been undertaken to alleviate seed dormancy in dormant varieties by employing various growth regulators and chemicals (Rajan et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe sustainable solution to address pre-harvest sprouting involves developing cultivars with 14\u0026ndash;21 days of fresh seed dormancy (FSD), capable of withstanding detrimental impact of rain between maturity and harvest. However, improving pre-harvest sprouting resistance through phenotypic selection is a challenging undertaking. Where complicating factors include: (1) the presence of significant genetic and environmental interactions; (2) variation in the mechanisms governing dormancy among different plant materials (Yaw et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Naganagoudar et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e); (3) the involvement of multiple genes in controlling seed dormancy (Bomireddy et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); (4) intergenic/epistatic interactions with pre-dominant role in genetic basis of seed dormancy (Khalfaoui, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Bomireddy et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); and (5) environmental conditions conducive to PHS may not be always available. Controlled environments such as rooms with sprinklers or germinators offer ideal conditions for phenotyping pre-harvest sprouting, however, their efficacy in screening extensive lines within breeding programs may be limited.\u003c/p\u003e \u003cp\u003eGenomics-assisted breeding (GAB) is a potential tool to overcome these constraints. GAB has been effectively deployed to improve leaf rust and late leaf spot resistance and high oleic acid content in groundnut (Pandey et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Shasidhar et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Varshney et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Janila et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Deshmukh et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Yeri and Bhat, 2022). However, the development of highly efficient linked markers is a pre-requisite for successful deployment of GAB. Identification of genomic regions and candidate genes linked to FSD can facilitate marker development, aiding in the effective transfer of FSD trait into elite groundnut cultivars. Significant efforts have been dedicated to understand the molecular mechanisms of PHS in cereals, and several genomic regions and candidate genes have been identified in rice (Lee et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), wheat (Ogbonnaya et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Guo et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and barley (Li et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Nakamura, 2017). In rice, \u003cem\u003eGA20-oxidase\u003c/em\u003e gene was identified in the QTL region controlling PHS (Li et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Similarly, \u003cem\u003emitogen-activated Protein Kinase Kinase 3\u003c/em\u003e (\u003cem\u003eMKK3\u003c/em\u003e) and \u003cem\u003eAlanine aminotransferase\u003c/em\u003e (\u003cem\u003eAlaAT\u003c/em\u003e) in barley (Nakamura et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Sato et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2016\u003c/span\u003e); \u003cem\u003emother of FT and TFL1\u003c/em\u003e (\u003cem\u003eMFT\u003c/em\u003e) and \u003cem\u003ePhs1\u003c/em\u003e in wheat (Nakamura et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Torada et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) were identified as underlying genes regulating seed dormancy. Though PHS is a widespread constraint in groundnut, only limited efforts were made to map FSD QTLs so far, mainly using bi-parental populations (Vishwakarma et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Kumar et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Bomireddy et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In previous reports, only few genes, \u003cem\u003eviz\u003c/em\u003e., \u003cem\u003ezeaxanthin epoxidase\u003c/em\u003e, \u003cem\u003eRING-H2 finger protein\u003c/em\u003e (Kumar et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and \u003cem\u003eethylene-responsive transcription factor\u003c/em\u003e (Wang et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) were determined as candidate genes involved in hormonal regulation of dormancy in groundnut. Bi-parental QTL mapping generally faces limitations due to limited recombination events taking place during the development of recombinant inbred line population, because of which biological processes governing dormancy remain not fully understood (Lu et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Consequently, future efforts should focus on employing highly efficient and reliable QTL mapping methods to identify additional novel QTLs associated with this trait.\u003c/p\u003e \u003cp\u003eGenome-wide association studies (GWAS) have become increasingly recognized as a potent methodology to identify QTLs and genes linked with complex traits based on the historic recombinations in a large natural population (Pandey et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Zhong et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Gangurde et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Gangurde et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). GWAS can surpass the limitations of bi-parental linkage mapping, allowing for the dissection of complex traits with high mapping resolution (Bhandari et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, so far there are no reports on genome wide associations studies on FSD using diverse germplasm in groundnut. Further, methods such as mixed linear model (MLM) (Zhang et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), implemented in single-locus GWAS (SL-GWAS), have been extensively utilized to investigate several genetic variants linked to complex agronomic traits. However, SL-GWAS methods face limitations in identifying minimal effect significant SNP-trait associations (STAs) influenced by the stringent Bonferroni correction and multigenic background (Wang et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). To overcome these limitations, development of Multi-Locus GWAS models (ML-GWAS) has been introduced as a multi-faceted genome scanning approach, simultaneously estimating the effect of all the markers (Cui et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the view of above gap of the knowledge and advancements in association mapping analysis, in this study, we utilized association mapping strategies by employing six ML-GWAS and one SL-GWAS (MLM) models in the groundnut mini-core collection. We aim to identify all the possible STAs using multiple methodologies to derive candidate genes regulating FSD to facilitate marker development.\u003c/p\u003e"},{"header":"MATERIAL AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003ePlant material\u003c/h2\u003e\n \u003cp\u003eThe groundnut mini-core collection consisting of 184 accessions developed at ICRISAT, Patancheru was used as association mapping panel (Upadhyaya et al., \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e). Groundnut mini-core collection represents the genetic diversity available in the entire groundnut germplasm at ICRISAT, encompassing six botanical types \u003cem\u003eviz\u003c/em\u003e., \u003cem\u003ehypogaea\u003c/em\u003e, \u003cem\u003ehirsuta\u003c/em\u003e, \u003cem\u003efastigiata\u003c/em\u003e, \u003cem\u003eperuviana\u003c/em\u003e, \u003cem\u003eaequatoriana\u003c/em\u003e and \u003cem\u003evulgaris.\u003c/em\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003eField experiment and phenotypic evaluation\u003c/h2\u003e\n \u003cp\u003ePhenotyping data on days to 50% flowering was recorded at two locations namely ICRISAT, Hyderabad, India and dry land farm of S.V. Agricultural College in Tirupati, Andhra Pradesh, India. A total of four seasons (Post-rainy 2018\u0026ndash;2019, 2019\u0026ndash;2020, 2022\u0026ndash;2023 and Rainy season of 2022) of phenotyping data generated at ICRISAT, Patancheru located at 545 m altitude, 17\u0026deg; 31\u0026apos; 48.00\u0026quot; N latitude and 78\u0026deg; 16\u0026apos; 12.00\u0026quot; E longitude. Additionally, all 184 accessions were also grown at Tirupati, located at 182.9 m altitude, 13\u0026deg;54\u0026rsquo; N latitude and 79\u0026deg;54\u0026rsquo; E longitude and phenotyped during Rainy 2021. Standard agronomic procedures for groundnut cultivation were followed during all the growing seasons. To ensure that any differences in dormancy are more likely due to genetic factors rather than differences in maturity, accessions were categorized into three groups/sets (early/medium/late maturity) and harvested accordingly. Matured seeds that were freshly harvested were selected for phenotyping using an \u003cem\u003ein-vitro\u003c/em\u003e germination assay (Upadhyaya and Nigam, \u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e), detailed methodology of which was explained in our previous publication (Bomireddy et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Over the period of 30 days, data was recorded on each accession on the number of days required to achieve 50% germination, which referred as days to 50% germination.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eDNA extraction and genotyping using 58K SNP array\u003c/h2\u003e\n \u003cp\u003eTotal genomic DNA was isolated from the tender leaves of 25\u0026ndash;30 day old plants of groundnut mini-core collection using Nucleospin Plant II kit (Macherey-Nagel, D\u0026uuml;ren, Germany). The purity and concentration of the isolated genomic DNA samples were assessed by electrophoresis on a 0.8% agarose gel and Thermo scientific\u0026rsquo;s Nanodrop 8000 spectrophotometer, respectively. Genotyping was carried out with \u0026lsquo;Axiom_\u003cem\u003eArachis\u003c/em\u003e\u0026rsquo; SNP array of 58,233 SNP markers derived from DNA re-sequencing of 41 wild diploid ancestors and tetraploid accessions of groundnut (Pandey et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Clevenger et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). The DNA samples from mini-core accessions were genotyped on the Affymetrix GeneTitan platform following previously described methods (Gangurde et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) and resulting data for each accession in .CEL file format was generated and stored. Subsequent SNP calling and data analysis performed using Axiom\u0026trade; Analysis Suite version 1.0 (Thermo Fisher Scientific, USA) to implement quality control (QC) measures and select samples that successfully passed the QC test. Of the 58,233 SNPs retrieved from Axiom\u0026trade; analysis suit, high-quality SNPs were filtered out with minor allele frequency (MAF) of \u0026ge;\u0026thinsp;0.05 and maximum missing sites fixed to \u0026lt;\u0026thinsp;20% per SNP using Tassel v5.0 software. After stringent filtration a total of 10,064 high-quality SNPs were subsequently employed for association mapping studies.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003eGenome-wide Association Analysis for days to 50% flowering\u003c/h2\u003e\n \u003cp\u003eMulti-season phenotyping data, along with genotyping data on 10,064 SNP of the mini-core set was used to perform genome-wide association analysis using multi-locus model. Six ML-GWAS methods, namely mrMLM, FASTmrMLM, FASTmrEMMA, pLARmEB, pKWmEB and ISIS EM-BLASSO in the mrMLM R package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cran.r-project.org/web/packages/mrMLM/index.html\u003c/span\u003e\u003c/span\u003e) were implemented. Default parameter values were utilized, and LOD score of 3 was set to identify robust STAs. All six methods utilized PCA and kinship matrices.\u003c/p\u003e\n \u003cp\u003eA single-locus mixed linear model (MLM) was also performed using the Tassel software. For correcting population structure and reducing the false-positive rate, we employed the first three principal components (PCs) and a kinship matrix as covariates. The association threshold to determine significant marker-trait associations was computed with Bonferroni correction by calculating a \u003cem\u003ep-value\u003c/em\u003e of 4.9682 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e, derived from the negative log transformation of \u0026alpha;/n (n represents total number of SNPs used for GWAS analysis).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eIdentification of candidate genes corresponding to significant STAs\u003c/h2\u003e\n \u003cp\u003eSTAs were further used for finding of candidate genes in the genomic region of 100 kb upstream and 100 kb downstream from the identified SNP position using diploid genome assemblies on Peanutbase (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.peanutbase.org\u003c/span\u003e\u003c/span\u003e). Based on the previous reports available in the literature, genes that were reported to regulate ABA/GA mediated processes were then identified as candidate genes regulating seed dormancy/germination.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eDevelopment of allele specific markers\u003c/h2\u003e\n \u003cp\u003eFor designing the allele specific markers, Batch primer 3 software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://probes.pw.usda.gov/batchprimer3/\u003c/span\u003e\u003c/span\u003e) was used. A flanking sequence of 1200bp (600bp up stream and 600bp downstream) to the identified SNP positions were used to design the primers. Default parameters were used to design the primers with product size range of 400-600bp and 50% GC content.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePhenotyping for fresh seed dormancy on mini-core collection\u003c/h2\u003e \u003cp\u003eIn this study, we used number of days required for an accession to achieve 50% germination as a measure of dormancy. In the GWAS panel, days to 50% germination was in the range of 1 to 30 days. Accessions of Virginia Bunch and Virginia Runner (var. \u003cem\u003ehypogaea\u003c/em\u003e) showed a longer duration (16\u0026ndash;30 days) of dormancy compared to Spanish Bunch (var. \u003cem\u003evulgaris\u003c/em\u003e) and Valencia (var. \u003cem\u003efastigiata\u003c/em\u003e) (1\u0026ndash;25 days). The mean performance and phenotypic distribution of these accessions (from two replications), screened for days to 50% germination across five seasons are represented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u0026amp; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. For more detailed information on the phenotyping data, refer to our previous publication, Bomireddy et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMulti-locus GWAS identified significant STAs associated with fresh seed dormancy\u003c/h2\u003e \u003cp\u003eA total of 9 STAs (LOD\u0026thinsp;\u0026ge;\u0026thinsp;3) significantly associated with FSD were identified in six ML-GWAS approaches (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Of these, seven STAs (\u003cem\u003eqFSD_A04-1\u003c/em\u003e, \u003cem\u003eqFSD_A08-1\u003c/em\u003e, \u003cem\u003eqFSD_A09-1\u003c/em\u003e, \u003cem\u003eqFSD_B02-1\u003c/em\u003e, \u003cem\u003eqFSD_B05-1\u003c/em\u003e, \u003cem\u003eqFSD_B07-1\u003c/em\u003e, \u003cem\u003eqFSD_B09-1\u003c/em\u003e) were consistently identified in at least two ML-GWAS methods (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Notably, STAs on chromosomes A09 (\u003cem\u003eqFSD_A09-1\u003c/em\u003e) and B07 (\u003cem\u003eqFSD_B07-1\u003c/em\u003e) were consistently identified in all six methods, with higher LOD score ranging from 11.87\u0026ndash;61.35 and 4.35\u0026ndash;36.85, respectively. The STA \u003cem\u003eqFSD_B05-1\u003c/em\u003e on B05 was identified in four methods with LOD score ranging from 11.80-59.71. Additionally, two STAs (\u003cem\u003eqFSD_A09-1\u003c/em\u003e, \u003cem\u003eqFSD_B02-1\u003c/em\u003e) were identified by at least three ML-GWAS methods, with the LOD score ranging from 3.33\u0026ndash;48.76 and 10.13\u0026ndash;17.16, respectively. Similarly, the STA on chromosome A09 (\u003cem\u003eqFSD_A09-1\u003c/em\u003e) was consistently detected during the seasons, Post-rainy 2019\u0026ndash;2020 and 2022\u0026ndash;2023 through ISIS EM-BLASSO approach.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe significant STAs for fresh seed dormancy identified using multi-locus GWAS models\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChr\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePosition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQTL Region\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSeason\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMethod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLOD score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAX_147210899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36775841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eqFSD_A01-1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eR2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAX_147221160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e119365410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eqFSD_A04-1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePR2022_23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.98\u0026ndash;4.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.81\u0026ndash;6.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAX_147231175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38560701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eqFSD_A08-1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePR2022_23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2,4,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.33\u0026ndash;48.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e70.98\u0026ndash;74.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAX_147233202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29726644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eqFSD_A09-1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePR2020_21, PR2022-23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1,2,3,4,5,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.87\u0026ndash;61.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e71.75\u0026ndash;84.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAX_147240363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3495023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eqFSD_B02-1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePR2020_21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1,2,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10.13\u0026ndash;17.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e74.84\u0026ndash;85.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAX_147247942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e105437967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eqFSD_B04-1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePR2022_23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e70.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAX_147250106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e112344292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eqFSD_B05-1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePR2020_21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2,3,4,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.80-59.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e53.44\u0026ndash;85.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAX_147254360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e995898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eqFSD_B07-1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eR2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1,2,3,4,5,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.35\u0026ndash;36.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e38.58\u0026ndash;84.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAX_147262364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e143731363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eqFSD_B09-1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePR2020_21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.26\u0026ndash;7.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e15.22\u0026ndash;18.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eChr: Chromosome; Methods 1:mrMLM; 2:FASTmrMLM; 3:FASTmrEMMA; 4:pLARmEB; 5:pKWmEB; 6:ISIS EM-BLASSO; LOD: Logorithm of Odds: R\u003csup\u003e2\u003c/sup\u003e: Coefficient of Determination/Phenotypic Variation Explained\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMoreover, MLM or single locus model in Tassel identified 38 significant STAs across 14 chromosomes, with R\u003csup\u003e2\u003c/sup\u003e values in the range of 3.1\u0026ndash;8.9% (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e; Table S2). Interestingly, STAs on chromosome A09 and B09 were consistently identified in all the five seasons, with R\u003csup\u003e2\u003c/sup\u003e value of 4.4 and 8.1%, respectively. Fascinatingly, a single STA on chromosome B02 (\u003cem\u003eqFSD_B02-1\u003c/em\u003e) identified in both ML-GWAS and SL-GWAS approaches.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eIdentified candidate genes for fresh seed dormancy\u003c/h2\u003e \u003cp\u003eCandidate gene search in 200 kb flanking region of the 9 significant STAs identified a total of 134 genes (Table S3). Of these, previous studies have extensively reported 63 genes as potential regulators in the process of seed dormancy and/or seed germination regulating \u003cem\u003evia\u003c/em\u003e ABA, GA, and ethylene signaling pathways (Table S4). Among 63 genes, 5 genes unveiled missense variants in the CDS coding region, 3 genes had downstream gene variants, 1 gene had 3_prime_UTR_variants, 2 genes had 5_prime_UTR_variant and 1 gene had upstream gene variant (Table S5). Remarkably, a STA (\u003cem\u003eqFSD-A09-01\u003c/em\u003e) identified in all six methods could be an important genomic region regulating FSD. Corresponding to the STA (\u003cem\u003eqFSD-A09-01\u003c/em\u003e), important genes such as \u003cem\u003edormancy/auxin associated family protein (Aradu.2Q7VA)\u003c/em\u003e, \u003cem\u003eDUF223 domain protein (Aradu.XE42X)\u003c/em\u003e, \u003cem\u003ehypoxia-responsive family protein (Aradu.WAM0A)\u003c/em\u003e were identified with functional relevance to dormancy/germination. In the genomic region of STA \u003cem\u003eqFSD-A04-01\u003c/em\u003e, multiple copies of \u003cem\u003eprotein kinase superfamily proteins (Aradu.VA4EQ)\u003c/em\u003e along with \u003cem\u003eF-box interaction domain proteins (Aradu.XB89Z)\u003c/em\u003e, \u003cem\u003eserine/threonine-protein phosphatase\u003c/em\u003e (\u003cem\u003eAradu.X6CDT\u003c/em\u003e) were identified to regulate seed dormancy as reported in various crops. The genes corresponding to the STA on chromosome A08 (\u003cem\u003eqFSD-A08-01\u003c/em\u003e) included \u003cem\u003eRNA methyltransferase (Aradu.3F83L)\u003c/em\u003e, \u003cem\u003ePentatricopeptide repeat (PPR) superfamily protein (Aradu.H1R88)\u003c/em\u003e, \u003cem\u003eeukaryotic aspartyl protease family protein (Aradu.HGT8J)\u003c/em\u003e. Similarly, \u003cem\u003eethylene-responsive transcription factors\u003c/em\u003e (\u003cem\u003eAraip.LL89K\u003c/em\u003e; \u003cem\u003eAraip.I6HJK\u003c/em\u003e) from \u003cem\u003eqFSD-B04-01\u003c/em\u003e are recognized as promising genes involved in regulating dormancy. In the STA \u003cem\u003eqFSD_B07-01\u003c/em\u003e, \u003cem\u003eBTB/POZ domain-containing protein (Araip.JP0WQ)\u003c/em\u003e, \u003cem\u003elate embryogenesis abundant (LEA) protein\u003c/em\u003e (\u003cem\u003eAraip.S5KEZ\u003c/em\u003e) and \u003cem\u003ereceptor-like protein kinases\u003c/em\u003e (\u003cem\u003eAraip.RLI4W\u003c/em\u003e) from \u003cem\u003eqFSD_B09-01\u003c/em\u003e were prominent genes known for their involvement in regulation dormancy/germination.\u003c/p\u003e \u003cp\u003eIn the 200kb genomic region around the identified significant STAs by SL-GWAS methods, a total of 346 genes were retrieved (Table S6). Potential genes among these included \u003cem\u003eauxin transport protein (Aradu.05XZ1), E3 ubiquitin-protein ligase (Aradu.7Y7XJ), WRKY family transcription factor family protein (Aradu.76148), serine/threonine protein phosphatase (Aradu.25KN6), cytochrome P450 (Aradu.FN562), abscisic acid receptor (Aradu.640E1), eukaryotic aspartyl protease family protein (Araip.2P2KT) etc\u003c/em\u003e., which were identified as the prominent regulators of dormancy/germination (Table S7).\u003c/p\u003e \u003cp\u003eTo elucidate the molecular mechanisms governing dormancy and germination, we conducted a comprehensive investigation to understand the functional involvement of the identified candidate genes in the ABA and GA biosynthesis and or signaling pathways. Among these candidate genes, \u003cem\u003ecytochrome P450 705A (Aradu.FN562)\u003c/em\u003e was notably discerned for its involvement in ABA catabolism. \u003cem\u003eDormancy/auxin associated family protein\u003c/em\u003e (\u003cem\u003eARF\u003c/em\u003e) (\u003cem\u003eAradu.2Q7VA\u003c/em\u003e), \u003cem\u003eWRKY family transcription factor (Aradu.76148)\u003c/em\u003e, \u003cem\u003eProtein kinase superfamily protein (Aradu.VA4EQ)\u003c/em\u003e, \u003cem\u003eserine/threonine protein phosphatase\u003c/em\u003e (\u003cem\u003ePP2C\u003c/em\u003e) (\u003cem\u003eAradu.25KN6\u003c/em\u003e) and \u003cem\u003eMYB transcription factor\u003c/em\u003e (\u003cem\u003eAradu.GFS4B\u003c/em\u003e) were observed as key players participating in ABA signaling. \u003cem\u003eTranscriptional regulator of STERILE APETALA-like\u003c/em\u003e (\u003cem\u003eAraip.14WNT\u003c/em\u003e) was known for its involvement in GA catabolism, while \u003cem\u003eethylene-responsive transcription factors\u003c/em\u003e (\u003cem\u003eAraip.LL89K\u003c/em\u003e) and \u003cem\u003eF-box protein interaction domain protein\u003c/em\u003e (\u003cem\u003eAradu.XB89Z\u003c/em\u003e) emerged as a noteworthy participant in GA signaling (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on the phenotyping data, a representative panel comprising of mini-core accessions with varied dormancy durations were selected to assess the efficacy of the identified STAs. Allele calls of the selected accessions for nine significantly associated SNP markers (identified from ML-GWAS models) were used from \u0026lsquo;Axiom_\u003cem\u003eArachis\u003c/em\u003e\u0026rsquo; 58K SNP array genotyping data. There was no polymorphism between non-dormant and dormant mini-core accessions for AX_147210899 and AX_147221160 markers from A01 and A04 chromosomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). However, AX_147233202 from A09 chromosome remarkably differentiated between dormant and non-dormant genotypes. Accessions with all favorable dormant alleles for the other 6 significant SNPs exhibited longer dormancy durations (require\u0026thinsp;\u0026ge;\u0026thinsp;30 days for 50% germination). Conversely, an increase in the number of unfavorable non-dormant alleles corresponded to a decreased dormancy duration. Therefore, these markers can be used for development of allele specific or KASP assays for their deployment in marker-assisted selection to improve popular groundnut cultivars with 14\u0026ndash;21 days of dormancy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDevelopment and validation of allele specific markers for fresh seed dormancy\u003c/h2\u003e \u003cp\u003eOf the nine STAs (identified from ML-GWAS models), seven were shortlisted based on clear polymorphism between non-dormant and dormant accessions. However, the remaining two markers AX-147210899 (A01) and AX-147221160 (A04) were dropped due to large proportion of heterozygous calls. Of the seven markers, AX-147233202 from A09 showed clear polymorphism between dormant and non-dormant lines, while the other six had one or two ambiguous calls between them. Based on the allelic combinations, four markers were selected to develop allele-specific primers. During validation, three out of the four primers successfully distinguished between dormant and non-dormant lines by showing clear bands. Using these three markers, entire mini-core set was genotyped along with the parents of two RIL populations, ICGV 02266 and ICGV 97045. Among these markers, \u003cem\u003eGMFSD2\u003c/em\u003e, \u003cem\u003eGMFSD3\u003c/em\u003e, and \u003cem\u003eGMFSD4\u003c/em\u003e successfully differentiated between dormant and non-dormant lines (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). As depicted in the gel images, Virginia runner and Virginia bunch lines predominantly exhibited dormancy, with a dormancy period of 23\u0026ndash;30 days. In contrast, Valencia bunch and Spanish bunch lines are non-dormant, germinating within 1\u0026ndash;2 days.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrimers sequences for three allele specific markers validated on diverse germplasm lines of groundnut\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProbe ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChr\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePos\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMarker Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDormant\u003c/p\u003e \u003cp\u003eAlleles\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF/R\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePrimer Sequence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMelting\u003c/p\u003e \u003cp\u003eTemperature\u003c/p\u003e \u003cp\u003e(Tm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eProduct\u003c/p\u003e \u003cp\u003eSize (bp)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAX_147233202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eA09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e29726644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eGMFSD2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAACTTGAACTTTCCTGGGAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e472\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTCCTGACTTCCCTGATGTTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAX_147250106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eB05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e112344292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eGMFSD3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTATTTGGTCTGCTCCGCTCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e286\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTCTACAAACTTCTCTCCGGTCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAX_147262364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eB09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e143731363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eGMFSD4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAACCAAGGGAAGGATCAACC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e265\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTCAAGACTGTTCCCGAATGAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003cb\u003eChr\u003c/b\u003e: Chromosome; \u003cb\u003ePos\u003c/b\u003e: Position\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eGroundnut or peanut is a major oilseed and grain legume mainly cultivated under rainfed regions of tropical, subtropical and temperate countries worldwide. Unlike the Virginia genotypes, the widely grown Spanish cultivars have lost the dormancy trait during domestication and selective breeding, and resulted in introduction of pre-harvest sprouting trait in cultivated groundnut. Developing commercial Spanish cultivars with 14\u0026ndash;21 days of dormancy can prevent yield losses due to pre-harvest sprouting. Utilizing GAB offers a distinct advantage over conventional breeding by facilitating efficient tracking of alleles among segregating lines through the use of trait-linked markers (Varshney et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In this context, the present investigation employed multi-model genome wide association analysis on mini-core collection using genotyping data generated from \u0026ldquo;Axiom_\u003cem\u003eArachis\u003c/em\u003e\u0026rdquo; 58K SNP array and multi-environment phenotyping data to identify the genomic regions and candidate genes regulating dormancy/germination.\u003c/p\u003e \u003cp\u003eConventional single-locus methods like Generalized Linear Model (GLM) and Mixed Linear Model (MLM) have been frequently deployed for identifying genetic variants in several crops (He et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, these methods have limitations as they neglect combined effects of multiple loci and face issues with multiple test corrections to determine critical values (Odesola et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). ML-GWAS methods, however, addresses these challenges (Liu et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Comparative studies indicated that ML-GWAS has higher statistical power and lower false-positive errors as compare to SL-GWAS methods (Segura et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Hu et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Investigators typically integrate the strengths of various ML-GWAS algorithms to identify target loci/QTL associated with complex traits, as each algorithm possesses unique characteristics and QTL detection capabilities (Liu et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA total of 9 significant STAs using ML-GWAS were identified for FSD trait using mini-core collection association panel. Previously, a QTL-Seq study reported two genomic regions on B05 and A09 chromosomes for fresh seed dormancy and also developed a potential marker on chromosome B05, \u003cem\u003eGMFSD1\u003c/em\u003e (Kumar et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In this study we have developed three more allele specific markers namely, GMFSD2 (A09), GMFSD3 (B05) and GMFSD4 (B09). High-density genetic mapping for FSD identified two dormancy QTLs on chromosomes A04 and A05 (Wang et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Similarly, a major stable QTL associated with fresh seed germination was identified on chromosome A04 (Zhang et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Moreover, our previous FSD study used a 5K SNP assay based bi-parental genetic mapping and identified five major QTLs on Ah01, Ah06 Ah11, Ah16 and Ah17 chromosomes and two minor QTLs on Ah04 and Ah15 chromosomes (Bomireddy et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Additionally, \u003cem\u003eqFSD_A04-1\u003c/em\u003e (119 Mb) on chromosome A04 was observed to be located in the close proximity of \u003cem\u003eqPD_A04-2\u003c/em\u003e (Wang et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), while the location of \u003cem\u003eqFSD_B05-1\u003c/em\u003e (112 Mb) on chromosome B05 was physically close to the genomic region identified by Kumar et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e. Thus, the identification of significant MTAs around previously reported genomic regions using multi-locus GWAS underscores the method's reliability. In addition to ML-GWAS, our study has also identified 38 significant STAs on 14 chromosomes of cultivated groundnut by SL-GWAS revealing all the possible genomic regions associated with FSD.\u003c/p\u003e \u003cp\u003eDifferences in the mapping results from various studies can be attributed to the factors such as seed development stage, population composition or the pedigree of the parents used in the population development and the prevailing environment during crop growth period (Cheng et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Magwa et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Comparable results were also documented in association mapping studies on wheat and rice seed dormancy (Lin et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Lu et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Because of genome similarity between the homeologous chromosomes of diploid progenitor genomes (A and B genome), homeologous associations on both sub-genomes of groundnut were identified. Nested association mapping for seed and pod weight in groundnut identified associations on homeologous chromosomes A05/B05, A06/B06 (Gangurde et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In other allopolyploids such as wheat, where seed dormancy QTLs were detected on homeologous 3A/3B (Shao et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), 4A/4B and 5A/5B (Lin et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) chromosomes.\u003c/p\u003e \u003cp\u003eAs discussed earlier, identification of candidate genes underlying the QTLs/MTAs provide insights for better understanding of the trait. As ABA, GA and ethylene have been demonstrated to be associated with seed dormancy and germination regulation in many crops, identifying genes involved in the regulation of their metabolic pathways is of major interest. The involvement of ABA signaling, along its interaction with GAs/ethylene tends to modulate seed dormancy and germination initiation. Therefore, the genes retrieved in this study from both ML-GWAS and SL-GWAS models were thoroughly reviewed in previous literature for assessing their functional role in ABA and GA signaling pathways. Fascinatingly, \u003cem\u003ecytochrome P450 705A\u003c/em\u003e (\u003cem\u003eAradu.FN562\u003c/em\u003e) identified as an important participant in ABA catabolism. A \u003cem\u003ecytochrome P450 superfamily protein\u003c/em\u003e (\u003cem\u003eCYP707A\u003c/em\u003e) in Arabidopsis encodes \u003cem\u003eABA 8'-hydroxylases\u003c/em\u003e, an enzyme involved in ABA 8'-hydroxylation pre-dominant for ABA catabolism. Expression profiling indicated that \u003cem\u003ecyp707a2\u003c/em\u003e mutant displayed six times higher ABA levels, resulting in hyper seed dormancy compared to wild types (Kushiro et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). It indicated that \u003cem\u003eCYP707A2\u003c/em\u003e negatively regulates seed dormancy by declining ABA levels during seed imbibition. Supporting this, \u003cem\u003ecytochrome P450 superfamily protein\u003c/em\u003e gene copies displayed high transcript abundance in ICGV 91114 (non-dormant) gene expression atlas, indicating their positive role in regulating germination (Bomireddy et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eWRKY family transcription factor family protein\u003c/em\u003e (\u003cem\u003eAradu.76148\u003c/em\u003e) identified in this study is known to be involved in ABA signaling. Lack of \u003cem\u003eWRKY transcription factor 41\u003c/em\u003e (\u003cem\u003eWRKY41\u003c/em\u003e) in imbibed seeds of Arabidopsis resulted in decreased \u003cem\u003eABI3\u003c/em\u003e (play crucial role in seed dormancy) expression while overexpressing transgenic \u003cem\u003eWRKY41\u003c/em\u003e lines had increased \u003cem\u003eABI3\u003c/em\u003e expression (Ding et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Examination of the double mutant \u003cem\u003ewrky41 aba2\u003c/em\u003e revealed that the regulation of \u003cem\u003eABI3\u003c/em\u003e expression and seed dormancy is a combined effort between \u003cem\u003eWRKY41\u003c/em\u003e and ABA. Therefore, \u003cem\u003eWRKY41\u003c/em\u003e acts as a key regulator of \u003cem\u003eABI3\u003c/em\u003e expression, thereby influencing seed dormancy. The identified protein kinase superfamily protein (\u003cem\u003eAradu.VA4EQ\u003c/em\u003e) in this study is known for its positive role in ABA signaling. In Arabidopsis, \u003cem\u003eSNF1-RELATED PROTEIN KINASE 2.2\u003c/em\u003e (\u003cem\u003eSnRK2.2\u003c/em\u003e) and \u003cem\u003eSnRK2.3\u003c/em\u003e, were reported to regulate ABA responses in seed dormancy and germination by mediating ABA signaling (Fujii et al., 2017). Double mutants of \u003cem\u003esnrk2.2\u003c/em\u003e and \u003cem\u003esnrk2.3\u003c/em\u003e exhibited decreased expression of several ABA-induced genes, demonstrating their positive role in ABA signaling. Similarly, in Arabidopsis, redundant ABA-activated \u003cem\u003eSnRK2s\u003c/em\u003e were identified as prominent regulators of seed maturation and dormancy (Nakashima et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). \u003cem\u003eDormancy/auxin associated family protein\u003c/em\u003e (\u003cem\u003eAradu.2Q7VA\u003c/em\u003e) or \u003cem\u003eAuxin responsive factors\u003c/em\u003e (\u003cem\u003eARF\u003c/em\u003e) identified in this study were reported to stimulate ABA signaling to induce seed dormancy (Liu et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Defects in auxin signaling of MIR160-overexpressing plants and auxin receptor mutants significantly reduce dormancy, while increase in auxin biosynthesis prolong dormancy.\u003c/p\u003e \u003cp\u003e \u003cem\u003eSerine/threonine-protein phosphatase 7 long form homolog (Aradu.25KN6)\u003c/em\u003e and \u003cem\u003emyb transcription factor\u003c/em\u003e (\u003cem\u003eAradu.GFS4B\u003c/em\u003e) identified in this study are known for their role in ABA signaling. In Arabidopsis, loss of \u003cem\u003eABSCISIC ACID-INSENSITIVE1\u003c/em\u003e (\u003cem\u003eABI1\u003c/em\u003e) which encodes 2C class of \u003cem\u003eserine/threonine phosphatases\u003c/em\u003e (\u003cem\u003ePP2C\u003c/em\u003e), leads to enhanced ABA responsiveness, indicating its negative role in ABA signaling (Gosti et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Further, overexpressing \u003cem\u003eHON\u003c/em\u003e (\u003cem\u003eProtein Phosphatase 2C family group\u003c/em\u003e) lines revealed that it suppresses dormancy by impeding ABA signaling (Kim et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). However, some \u003cem\u003ePP2Cs\u003c/em\u003e like \u003cem\u003eHIGHLY ABA-INDUCED PP2C GENE1\u003c/em\u003e (\u003cem\u003eHAI1\u003c/em\u003e) were reported to promote ABA signaling (Saez et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). \u003cem\u003eMYB transcription factor 96\u003c/em\u003e (\u003cem\u003eMYB96\u003c/em\u003e) was presumed to fine tune seed dormancy as it enhances ABA biosynthetic NCED genes and down regulate GA biosynthetic \u003cem\u003eGA20ox1\u003c/em\u003e, \u003cem\u003eGA3ox1\u003c/em\u003e genes in Arabidopsis (Lee et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). \u003cem\u003emyb96-1\u003c/em\u003e mutant seeds exhibited germination earlier than the wild \u003cem\u003eMYB96-1\u003c/em\u003e, while activation-tagging of \u003cem\u003emyb96-1D\u003c/em\u003e seeds delayed the germination process. Differential gene expression analysis identified \u003cem\u003eMYB60\u003c/em\u003e as the promising candidate regulating dormancy with higher transcript abundance observed in the dormant Tifrunner gene expression atlas (Bomireddy et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe identified \u003cem\u003eTranscriptional regulator of STERILE APETALA-like (Araip.14WNT)\u003c/em\u003e is notably known to be involved in GA catabolism. \u003cem\u003eAPETALA 2\u003c/em\u003e (\u003cem\u003eAP2\u003c/em\u003e)-domain-containing transcription factors (ATFs), including \u003cem\u003eOsAP2-39\u003c/em\u003e in rice and \u003cem\u003eABI4\u003c/em\u003e in Arabidopsis, were reported to play a prominent role in ABA and GA antagonistic crosstalk (Yaish et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Shu et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The \u003cem\u003eAP2/ethylene-responsive element binding factor (AP2/ERF)\u003c/em\u003e family constitutes a substantial group of plant transcription factors, playing diverse roles at various plant developmental stages. In rice, transcriptome analysis of \u003cem\u003eOsAP2-39\u003c/em\u003e overexpression lines unveiled upregulation of \u003cem\u003eOsNCED-I\u003c/em\u003e (ABA biosynthesis gene), increasing endogenous ABA levels, and enhanced GA-inactivating gene \u003cem\u003eOsEUI\u003c/em\u003e, resulting in reduced GA content (Yaish et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). \u003cem\u003eOsAP2-39\u003c/em\u003e directly governs the expression of \u003cem\u003eOsNCED-I\u003c/em\u003e and \u003cem\u003eEUI\u003c/em\u003e, elucidating a novel mechanism regulating ABA/GA balance and ultimately influencing rice growth.\u003c/p\u003e \u003cp\u003e \u003cem\u003eEthylene-responsive transcription factors\u003c/em\u003e (\u003cem\u003eAraip.LL89K\u003c/em\u003e) and \u003cem\u003eF-box protein interaction domain protein\u003c/em\u003e (\u003cem\u003eAradu.XB89Z\u003c/em\u003e) identified in this study are the significant contributors for GA signaling. Ethylene (ET) was reported to break seed dormancy by antagonizing ABA biosynthesis and signaling, thereby promoting seed germination (Corbineau et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In Arabidopsis, \u003cem\u003eDelay of Germination1\u003c/em\u003e (\u003cem\u003eDOG1\u003c/em\u003e) interacts with \u003cem\u003eethylene responsive factor 12\u003c/em\u003e (\u003cem\u003eERF12\u003c/em\u003e) and co-regulate seed dormancy (Li et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). High transcript levels of ethylene-responsive transcription factors in seed and embryo of ICGV 91114 (\u003cem\u003eArachis hypogaea\u003c/em\u003e sub spp. \u003cem\u003efastigiata\u003c/em\u003e) gene expression atlas revealed its functional relevance in seed germination (Sinha et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Bomireddy et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In plants, F-box proteins regulate various physiological processes in various ways. Song et al. (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) had isolated \u003cem\u003eOsFbx352\u003c/em\u003e gene (encoding for F-box domain protein) from rice to characterize its role in germination. Overexpression of \u003cem\u003eOsFbx352\u003c/em\u003e, (a F-box protein in rice) demonstrated lower ABA contents by reduced expression of ABA synthesis genes (\u003cem\u003eOsNced2, OsNced3\u003c/em\u003e) and increased ABA catabolism gene expression (\u003cem\u003eOsAba-ox2, OsAba-ox3\u003c/em\u003e) leading to seed germination. Whereas, knockdown of \u003cem\u003eOsFbx352\u003c/em\u003e led to higher ABA contents and suppressed seed germination, revealing its regulatory in modulating ABA metabolism. In groundnut, high gene expression value of \u003cem\u003eF-box/RNI-like superfamily protein\u003c/em\u003e (\u003cem\u003eArahy.LZ56CD\u003c/em\u003e) in all the six selected tissues of seed and pod of non-dormant ICGV 91114 also indicated its positive regulatory role in germination (Bomireddy et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In Arabidopsis, transgenic plants overexpressing for \u003cem\u003eAtTLP9\u003c/em\u003e (\u003cem\u003eTubby-F-box like protein\u003c/em\u003e) also demonstrated hypersensitiveness to ABA (Lai et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). As all these genes were explored for their involvement in ABA/GA/Ethylene signaling pathways, these were identified as promising candidates in the hormonal regulation of dormancy and germination dynamics.\u003c/p\u003e \u003cp\u003eThe allele calls of the representative panel of mini-core accessions for the identified significant MTAs revealed the presence of all favorable dormant alleles in an accession result in increased dormancy duration. Conversely, an increase in the number of unfavorable non-dormant alleles corresponded to a decreased dormancy duration. These MTAs show potential for developing KASP assays, and accessions with all the favorable dormant alleles can be used as donors in MABC programs aiming for dormancy of \u0026ge;\u0026thinsp;30 days. However, if the goal is to achieve fresh seed dormancy of 14\u0026ndash;21 days, accessions with a combination of dormant and non-dormant alleles for these markers can be utilized. Thus, these assays could play a crucial role in future molecular breeding programs, offering targeted and efficient means to select lines with 2\u0026ndash;3 weeks of dormancy.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eWith a diverse panel of 184 groundnut mini-core accessions, this study employed a combined approach of ML-GWAS and SL-GWAS models and identified 9 and 38 MTAs for FSD, respectively. Examining the vicinity of these MTAs revealed potential candidate genes; \u003cem\u003eCytochrome P450 705A, Dormancy/auxin associated family protein, WRKY family transcription factor, Protein kinase superfamily protein, serine/threonine protein phosphatase, myb transcription factor, transcriptional regulator STERILE APETALA-like, ethylene-responsive transcription factor 7-like and F-box protein interaction domain protein\u003c/em\u003e to be involved in ABA/GA associated pathways. This investigation underscores dormancy as a complex trait controlled by multiple genes, highlighting the importance of understanding gene interactions across multiple biological pathways. Furthermore, examination of allelic calls in the mini-core accessions for the identified MTAs revealed the intricate regulation of dormancy and germination through the expression and suppression of multiple genes in diverse combinations, affecting dormancy duration. This suggests that successful molecular breeding strategies must incorporate multiple genes from various biological pathways. Further characterization of the candidate genes identified in this study is recommended through overexpression studies and the CRISPR/Cas9 approach, which may provide more insights on precise function of these candidate genes in FSD. Additionally, haplotype analysis for the identified candidate genes can aid in identifying superior haplotypes for FSD trait, which could be utilized in haplotype-based breeding.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMKP conceived the idea and supervised and finalized the manuscript. KS and RS contributed to providing seed material and in-seed multiplication of the mini-core collection. DB, VS, SSG, RK phenotyped the mini-core collection. DB performed the analysis and drafting the manuscript. DB, VS, SSG contributed in improvising figures. KMD and SSG designed primers and performed validation. MR, VS, SSG, MKP contributed to reviewing and improving the manuscript. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are thankful to the Indian Council of Agricultural Research (ICAR) through ICAR-ICRISAT collaborative project, Department of Biotechnology (DBT), Government of India through Indo-UK (Newton-Bhabha) project, and Bill \u0026amp; Melinda Gates Foundation (BMGF), USA through Tropical Legumes III project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are thankful to Gene-Bank, ICRISAT for their support in providing seed material and assistance in phenotyping work. DB acknowledges Acharya N.G. Ranga Agricultural University for collaborating with ICRISAT and opportunity given as a PhD student to pursue this investigation at ICRISAT. VS acknowledges the Council of Scientific and Industrial Research (CSIR), Govt. of India for the award of SRF-Direct fellowship for PhD\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eETHICS APPROVAL AND CONSENT TO PARTICIPATE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe plant samples and soil samples were collected from the fields with permission from field owners.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eCOMPETING INTERESTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBhandari, A., Sandhu, N., Bartholome, J., Cao-Hamadoun, T.V., Ahmadi, N., Kumari, N. and Kumar, A., 2020. Genome-wide association study for yield and yield related traits under reproductive stage drought in a diverse indica-aus rice panel. 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BMC Plant Biology, 21(1), p.364.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Supplementary Figure","content":"\u003cp\u003eSupplementary Figure is not available with this version\u003c/p\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":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"seed dormancy, seed in-situ germination, association mapping, candidate genes, molecular mechanism, diagnostic markers","lastPublishedDoi":"10.21203/rs.3.rs-4977357/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4977357/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePre-harvest sprouting in groundnut leads to substantial yield losses and reduced seed quality, resulting in reduced market value of groundnuts. Breeding cultivars with 14\u0026ndash;21 days of fresh seed dormancy (FSD) holds promise for precisely mitigating the yield and quality deterioration. In view of this, six multi-locus genome-wide association study (ML-GWAS) models alongside a single-locus GWAS (SL-GWAS) model were employed on a groundnut mini-core collection using multi season phenotyping and 58K \u0026ldquo;Axiom_\u003cem\u003eArachis\u003c/em\u003e\u0026rdquo; array genotyping data. A total of 9 significant SNP-trait associations (STAs) for FSD were detected on A01, A04, A08, A09, B02, B04, B05, B07 and B09 chromosomes using six ML-GWAS models. Additionally, the SL-GWAS model identified 38 MTAs across 14 chromosomes of groundnut. Remarkably, a single STA on chromosome B02 (\u003cem\u003eqFSD-B02-1\u003c/em\u003e) was consistently identified in both ML-GWAS and SL-GWAS models. Furthermore, candidate gene mining identified nine high confidence genes \u003cem\u003eviz\u003c/em\u003e., \u003cem\u003eCytochrome P450 705A, Dormancy/auxin associated family protein, WRKY family transcription factor, Protein kinase superfamily protein, serine/threonine protein phosphatase, myb transcription factor, transcriptional regulator STERILE APETALA-like, ethylene-responsive transcription factor 7-like and F-box protein interaction domain protein\u003c/em\u003e as prime regulators involved in Abscisic acid/Gibberellic acid signaling pathways regulating dormancy/germination. In addition, three of the allele-specific markers developed from the identified STAs were validated across a diverse panel. These markers hold potential for enhancing dormancy in groundnut through marker-assisted selection. Thus, this research offers insights into genetic and molecular mechanisms underlying groundnut seed dormancy in addition to providing markers and donors for breeding future varieties with 2\u0026ndash;3 weeks of FSD.\u003c/p\u003e","manuscriptTitle":"Multi-Locus Genome Wide Association Study Uncovers Genetics of Fresh Seed Dormancy in Groundnut","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-01 18:47:05","doi":"10.21203/rs.3.rs-4977357/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-04T17:50:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-04T17:43:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-02T04:50:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Plant Biology","date":"2024-08-26T10:42:12+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5b83c051-6343-4ed3-996b-4ebf1cfa3d3b","owner":[],"postedDate":"October 1st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-30T15:59:49+00:00","versionOfRecord":{"articleIdentity":"rs-4977357","link":"https://doi.org/10.1186/s12870-024-05897-6","journal":{"identity":"bmc-plant-biology","isVorOnly":false,"title":"BMC Plant Biology"},"publishedOn":"2024-12-27 15:57:11","publishedOnDateReadable":"December 27th, 2024"},"versionCreatedAt":"2024-10-01 18:47:05","video":"","vorDoi":"10.1186/s12870-024-05897-6","vorDoiUrl":"https://doi.org/10.1186/s12870-024-05897-6","workflowStages":[]},"version":"v1","identity":"rs-4977357","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4977357","identity":"rs-4977357","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}