Genome‐wide SNP discovery and genotyping delineates potential QTLs underlying major yield‐attributing traits in buckwheat

Buckwheat (Fagopyrum spp.) is an important nutritional and nutraceutical‐rich pseudo‐cereal crop. Despite its obvious potential as a functional food, buckwheat has not been fully harnessed due to its low yield, self‐incompatibility, increased seed cracking, limited seed set, lodging, and frost susceptibility. The inadequate availability of genomics resources in buckwheat is one of the major reasons for this. In the present study, genome‐wide association mapping (GWAS) was conducted to identify loci associated with various morphological and yield‐related traits in buckwheat. High throughput genotyping by sequencing led to the identification of 34,978 single nucleotide polymorphisms that were distributed across eight chromosomes. Population structure analysis grouped the genotypes into three sub‐populations. The genotypes were also characterized for various qualitative and quantitative traits at two diverse locations, the analysis of which revealed a significant difference in the mean values. The association analysis revealed a total of 71 significant marker–trait associations across eight chromosomes. The candidate genes were identified near 100 Kb of quantitative trait loci (QTLs), providing insights into several metabolic and biosynthetic pathways. The integration of phenology and GWAS in the present study is useful to uncover the consistent genomic regions, related markers associated with various yield‐related traits, and potential candidate genes having implications for being utilized in molecular breeding for the improvement of economically important traits in buckwheat. Moreover, the identified QTLs will assist in tracking the desirable alleles of target genes within the buckwheat breeding populations/germplasm.


INTRODUCTION
Buckwheat (Fagopyrum spp.Moench; 2n = 2x = 16) is an annual, diploid, cross-pollinated, pseudocereal crop with a genome size of 516.32 Mb and having more nutritional value than many cereals (Singh et al., 2021).Buckwheat plays a pivotal role in sustaining the livelihoods of the poor and marginal farmers, especially those living in the food deficit and remote areas, particularly in the Himalayan regions.
The crop has a short harvesting cycle and can be grown under adverse environmental conditions not suitable for the cultivation of other traditional crops (Gahukar, 2014).Buckwheat is a nutrient-dense, gluten-free plant source with a higher presence of various bioactive components such as flavonoids, polyphenols, polysaccharides, saponins, proteins, fatty acids, and trace elements (Goncalves et al., 2016).Due to the presence of bioactive compounds, buckwheat has engrossed the attention of researchers owing to its healing and functional food properties (Zargar, Hami, et al., 2023).It has significantly proven to be anti-oxidative, cardio-protective, anti-cancer, hepato-protective, anti-hypertension, anti-tumor, anti-inflammatory, anti-diabetic, neuro-protection, and also lowers the blood cholesterol level (Ge & Wang, 2020;He et al., 2019;Kwon et al., 2018).Despite having high economic, agricultural, and nutritional status, the crop is considered a minor/underutilized crop and its production is on the decline due to low productivity, self-incompatibility, seed shattering, and adhering seed coat (Farooq et al., 2016).Due to the lack of adequate intraspecific polymorphisms in buckwheat, no robust genes/QTLs (quantitative trait loci) linked with various yield-attributing traits have been identified that can be further utilized for marker-assisted genetic improvement in buckwheat.Hence, for dissecting such complex quantitative traits in buckwheat, diverse buckwheat germplasm must be utilized for mapping desirable QTLs and then further used for map-based QTL cloning.The approach would essentially take on a wider perspective with the discovery of large-scale genome-wide markers for multiple traits adopting high-throughput genotyping platforms for generating genotypic data of diverse germplasm.
A plethora of genetic molecular marker systems have been developed for this purpose (Hara et al., 2011;Konishi & Ohnishi, 2006;Yabe et al., 2014;Yasui et al., 2004); however, single nucleotide polymorphism (SNP) markers are considered most efficient because of their abundance nature, heritability, and high resolution.Using next-generation sequencing technologies and high throughput genotyping platforms, SNP markers have been developed and used to study genetic diversity (Diapari et al., 2015;Siol et al., 2017), genetic mapping (Zhang et al., 2021), and linkage disequilibrium (LD) in buckwheat (Cui et al., 2017;Holdsworth et al., 2017).The potential of using a novel array-based genotyping system for the construction of high-density linkage maps and QTL mapping has also been implemented for the analysis of buckwheat genome (Yabe et al., 2014).
Genome-wide association study (GWAS) is an efficient approach to dissect the genetic basis of complex traits using naturally occurring recombination (Korte & Farlow, 2013).GWAS provides higher mapping resolution than classical biparental populations to detect associations between molecular markers and traits of interest and has been used for identification of markers associated with desirable traits in a wide range of crops (Xu et al., 2017;Zargar, Manzoor, et al., 2023).It requires an assessment of the population structure of the diversity panel to determine the genetic relatedness of individuals, thus minimizing the detection of false associations, and is dependent on the use of an adequately large number of markers (Korte & Farlow, 2013;Sul et al., 2016).
However, in the case of buckwheat, the genotyping by sequencing (GBS) approach is still to be used for largescale mining and genome-wide SNPs validation in diverse natural population that could facilitate the construction of high-density genetic linkage maps, molecular mapping of genes/QTLs, high-resolution genome-wide trait association analysis, and will also categorize various molecular tags controlling complex quantitative traits (such as yield-related traits).We have adopted GBS approach for the detection, validation, and genotyping of SNPs in 132 diverse genotypes of buckwheat (Fagopyrum esculentum and Fagopyrum tataricum) at a genome-wide scale.The genotyping information obtained from validated SNPs was further utilized to understand their functional implication and can be used to determine the LD patterns among different buckwheat accessions.

Plant phenotyping
A collection of 162 accessions (Table S1) comprising of F. esculentum (68) and F. tataricum (94) were sourced from the traditional buckwheat growing areas of Jammu and Kashmir and Ladakh (Kishtwar, Machil, Gurez, Kargil, Leh) and some accessions were procured from National Bureau of Plant Genetic Resources (NBPGR), Delhi, India.).The germplasm was planted using randomized complete block experimental design following standard agronomical practices.The morphological observations were recorded for both qualitative (flower color, leaf color, leaf margin color, stem color, petiole color, leaf shape, plant growth type, mature seed

Core Ideas
• Qualitative traits in buckwheat were greatly influenced by the altitudinal variations.• A total of 34,978 evenly distributed single nucleotide polymorphisms across eight chromosomes in buckwheat were identified.• A total of 71 significant quantitative trait loci revealed the genomic regions associated with major yield-attributing traits.• The identified marker-trait associations have implications on improvement of economically important traits.
color, immature seed color, seed shape, and seed surface) and quantitative traits (plant height, number of primary branches, number of secondary branches, leaf number, leaf blade, leaf blade length, leaf blade width, petiole length, inflorescence length, days to 50% flowering, days to maturity, yield per plant (g), and number of seeds per plant) at physiological maturity as described in Naik et al. (2022).The standardization of phenotypic data, estimation of genetic distance based on Euclidean coefficient (EUCLID), PCA, Shannon-Weaver diversity index, and cluster analysis/tree construction among different genotypes were executed using Windostat 8.0, NTSYS-pc V2.1 and POPGENE V1.

GBS library preparation, sequencing, and post-sequencing analysis
For genome-wide identification and genotyping of SNPs, 132 morphologically diverse buckwheat (F.esculentum and F. tataricum) accessions were selected from the core germplasm set.The genomic DNA was extracted from 30 days old leaves by using the modified CTAB (cetyltrimethylammonium bromide) method.Purified DNA was digested using ApeKI and ligated with unique adapters using T4 ligase.The libraries were pooled and the sequencing (150 bp paired end) was performed with Illumina HiSeqTM X10 platform (Illumina Inc.) using V4 sequencing by following the procedure of Elshire et al. (2011) and Spindel et al. (2013).The FASTQ sequence reads obtained from the sequencing platform were analyzed for different quality parameters (base quality score distribution, sequence quality score distribution, and GC distribution in the reads) and the reads having low quality (Phred score < 30) were discarded.The high-quality reads were de-multiplexed based upon their unique barcodes to extract the individual sequencing reads of 132 samples.The sequenced reads were then trimmed for the universal adapter sequence (AGATCGGAAGAGC) using trim galore (version 0.6.2,https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/).The trimmed reads were then separately aligned and mapped to the reference genome of F. tataricum (GCA_002928575.1)using Bowtie v2 (http://bio-bwa.sourceforge.net/)with default parameters (Langmead & Salzberg, 2012).The variant calling was done using the UGBS-GATK pipeline (v3.6 https://gatk.broadinstitute.org/hc/en-us).Variants were filtered and indels were removed using vcf tools (v0.1.17,http:// vcftools.sourceforge.net/).All SNPs were filtered at minor allele frequency (MAF) of 5% MAF and 20% missing rate.

Molecular diversity and population genetic analysis
The SNP genotypic data (MAF ≥ 5% with missing data <20%, Zhao et al., 2011) were analyzed with PowerMarker v3.51 (Liu & Muse, 2005) and MEGA v5.0 (Tamura et al., 2011) for estimation of various diversity attributes and construction of phylogenetic tree.The filtered SNPs were converted to structure format using the PGD Spider v2.1.1.5.Population structure was estimated using a Bayesian Markov Chain Monte Carlo (MCMC) model implemented in STRUCTURE v2.3.4.Three runs were performed for each number of populations (k) set from 2 to 7. Burn-in time and Bayesian MCMC model replication number were both set to 100,000 and 300,000, respectively, for each run.The most probable K-value was determined by the Structure Harvester, using the log probability of the data [LnP(D)] and ΔK based on the rate of change in [LnP(D)] between successive K-values.The dendogram was constructed using Tassel software by neighbor-joining method, and the tree was visualized using iTOL (https://itol.embl.de/upload.cgi).

Genome-wide association analysis
Genomic Association and Prediction Integrated Tool (GAPIT; Lipka et al., 2012) was used for genome wide marker trait association study.GWAS was performed with 132 core diverse genotypes using the compressed mixed linear model (CMLM) for the identification of SNPs associated with the traits.Van Raden method (K) was used to calculate the kinship matrix (Kang et al., 2008;Loiselle et al., 1995).The negative log (1/n) was used to establish a significance threshold (Wang et al., 2012;Yang et al., 2013).
Loci with minimum allele frequency <0.05 were filtered out.Only dimorphic variation sites were retained.These SNPs were screened based on the secondary allele frequency being >0.05 and the integrity >0.8.The high-quality and locally unique SNPs uniformly distributed on eight chromosomes in buckwheat were used for subsequent analysis.
The correlation matrix was estimated as a covariable to record the minimum p value of the complete genome, and the Manhattan and QQ-plot was generated by the CM plot R package in R Studio (Yin, 2018).

Physical mapping of QTLs in buckwheat
MapInspect (https://mapinspect.software.informer.com/)was used to physically map the QTLs.The total length of the chromosomes is measured in megabases (Mb).The chromosome location and position of QTLs in buckwheat were determined by identifying the SNPs that correlate with an observed trait and are given in Table 1.

Prediction of candidate genes
According to the SNPs obtained from GWAS, the 200-kb SNP flanking regions (100 kb upstream and 100 kb downstream) were considered for candidate regions identification.Gene function was analyzed and predicted by BLAST in the gene ontology (GO) databases.Candidate genes were selected based on the gene annotation and the function of their homologs in other plants and the SNP variations.

Phenotypic variation of qualitative and quantitative traits
A total of 162 buckwheat genotypes were evaluated for quantitative (morphological and seed quality traits) and qualitative traits at two different locations (SKUAST-K, Shuhama and MAR&ES, SKUAST-K, Izmarg, Gurez) during 2020 and 2021, respectively.Twelve different morphological and seed quality traits were evaluated at physiological maturity as detailed in Naik et al. (2022) (Figure 1).In general, the date of flowering and date of maturity were found to be the most significantly differing among genotypes at two locations (Table S2).Eleven different qualitative traits (flower color, leaf color, leaf margin color, stem color, petiole color, leaf shape, plant growth type, immature seed color, mature seed color, seed surface/seed coat, and seed shape) were also recorded.Among all the qualitative traits, only leaf color, leaf margin color, stem color, and petiole color show significant variation between two locations.The genotypes at Gurez showed more frequency of red and pink color as compared to those at Shuhama (Figure 2; Table S3).

T A B L E 1
List of common/stable quantitative trait loci (QTLs) for different traits at two locations.

S.
no.

Sequencing analysis and SNP identification
The sequence-based bar-coding followed GBS of 132 genotypes generated a total of about 208 million paired-end reads with an average of 1.58 million reads per genotypes and a read length of 150 base pairs.The mean read quality (Phred score) of the samples was 35.02 and about 95% of the reads had a Q score > 30, indicating that most of the raw data were of good quality.The processed reads were assembled into contigs followed by their alignment to the reference genome of F. tataricum (GCA_002928575.1)using default parameters.The mapping percentages for individual reads ranged from 81.25% to 99.14% with an average of 90.78.The alignments of contig sequences to the reference genome were used for the identification of SNPs.After filtering the homoeologs, a total of 3,728,028 SNPs were found.However, while considering 5% minor allele frequency and 30% missing rate, a total of 34,978 SNPs were finalized that were distributed across eight chromosomes.Chromosome 1 harbors a total of 6750 evenly distributed SNPs, while the lowest were observed in case of chromosome 7 (3190) (Figure S1).

Diversity and population analysis
A total of 34,978 significant SNP markers widely distributed across the buckwheat genome were used for characterizing the core set to develop diversity profile of 132 genotypes.The dendrogram constructed using marker allelic data grouped the genotypes into three main clusters.The first cluster is further subdivided into two subclusters, second into three subclusters, and third into five subclusters.The third cluster is immensely divided into many sub-sub clusters depicting a diverse range of genotypes that are closely related to each other with respect to studied locus.The selected markers were able to differentiate the genotypes from two species, that is, F. tataricum and F. esculentum into different clusters.The population structure of the genotypes was estimated under the Hardy-Weinberg equilibrium and scored for K values ranging from one to 12. Based on the maximum likelihood and ΔK values, the number of optimal population groups was identified as three (Figure 3).

Association mapping analysis
The association analysis to identify markers associated with morphological, floral, maturity, and yield traits (plant height, number of primary branches, number of secondary branches, leaf number, leaf blade length, leaf blade width, petiole length, inflorescence length, days to 50% flowering, days to maturity, yield per plant, and number of seeds per plant) was carried out using a set of SNPs uniformly distributed across all the chromosomes of buckwheat.In order to determine the true marker-trait associations, we used both p values and marker r 2 value for association and only those significant associations are considered where the p values were <10 −4 .The core set of 132 genotypes was used for GWAS analysis using the CMLM model by combining the phenotypic data of all traits in two seasons and genotypic data.The overall falsepositive rate of GWAS was controlled properly as revealed by Manhattan and quantile-quantile plots.A total of 71 SNPs were found to be significantly associated with different morphological, floral, maturity, and yield traits at both locations (Shuhama and Gurez).In Shuhama, 25 SNPs were found to be significantly associated with these traits in which one SNP was significantly associated with plant height on chromosome 7, two SNPs were associated with number of primary branches on chromosomes 4 and 8, with number of secondary branches, five SNPs were associated on chromosomes 1, 3, 6, and 8, five SNPs for leaf number on chromosomes 1, 3, and 8, with leaf blade length, four SNPs were associated on chromosomes 2, 5, and 6, two SNPs were associated with trait leaf blade width on chromosomes 1 and 3, one SNP was associated with inflorescence length on chromosome 2, no SNP was found significantly associated with days to 50% flowering, one significant SNP for days to maturity on chromosome 4; three significant SNPs for yield per plant on chromosomes 3, 6, and 8; one significant SNP for number of seeds per plant on chromosome 5 (Table S4; Figure S2).However, in Gurez, a total of 45 SNPs were found to be associated with differ-ent morphological and yield-attributing traits (Figure S3).For primary branches, two SNPs were found significantly associated that are located on chromosome 4 and 8; two SNPs were found associated with the number of secondary branches that are located on chromosomes 1 and 8; and five SNPs were found associated with leaf number that are positioned on chromosomes 1, 3 and 8. Further, three SNPs were found associated with leaf blade length that are positioned on chromosomes 4, 5 and 7.The highest number of significant SNPs (14) was found associated with leaf blade width that are positioned on chromosomes 1, 3, 5 and 6.Further, six SNPs were found associated with petiole length that are located on chromosomes 1, 3, 7, and 8.One SNP was found associated with inflorescence length, and two SNPs located at chromosomes 2 and 7 were found significantly associated with days to 50% flowering.One SNP associated with days to maturity was found on chromosome 3, and four SNPs associated with yield per plant were located on chromosomes 1, 2, 3 and 5. Furthermore, five SNPs associated with number of seeds per plant were found located on chromosomes 3, 4, 5, 6, and 7 (Table S5).Out of total QTLs identified in two different locations, there are 13 loci that are common and are referred to as stable QTLs.These QTLs are observed for number of primary branches, number of secondary branches, leaf number, leaf blade length, leaf blade width, days to 50% flowering, days to maturity, yield per plant, and number of seeds per plant and are found at chromosomes 1, 2, 3, 5, 6, and 8, respectively (Table 1).

F I G U R E 2
The significant differences in the qualitative traits (flower color, leaf pigmentation, stem color, leaf margin color, and petiole color) in buckwheat plants due to difference in the temperature, wind velocity, UV exposure, and altitude.The pink and red color indicates that the leaf margin color, petiole color, leaf color, and stem color was more intense due to high expression of flavonoids when germplasm was grown at MAR&ES Gurez than at SKUAST-K, Shuhama.
The physical mapping of the QTLs also revealed that the QTLs are randomly distributed among the eight chromosomes (Figure 4).

Candidate gene analysis and protein prediction
The flanking sequences (100 kb upstream and 100 kb downstream) of SNPs were compared against the Tartary database using BLASTX (cutoff E-value of 1E-10) to identify the corresponding sequences in the database.The candidate genes that are identified found to have various cellular components, and biological and molecular functions such as lipid metabolic process, carbohydrate metabolic process, Zinc ion binding, and so on in the GO terms annotation analysis (Table 2; Figure S4).However, the identified candidate genes needed to be further validated through expression studies to confirm sold proof for their involvement in the respective biological pathways of a particular trait.

DISCUSSION
Buckwheat is a potential crop of future in light of the growing needs for diversification as well as growing demand for nutraceutical crops with functional food properties.However, the crop needs extensive research in terms of analyzing nature and extent of genetic variability present in available germplasm, trait-specific germplasm (QTL/SNP) identification, as well as filling essential gaps in crop management to develop genotypes and management technologies for ensuring sustainable yield, so that it is able to compete with mainstream crops being grown especially in North Western Himalayan region, which is major adaptation niche for this crop.The present study was the first comprehensive effort in this direction in Jammu and Kashmir, India, where the crop is grown sporadically in small pockets owing to lack of suitable cultivars and management modules that could make this an attractive option for diversification.Genetic diversity is an important parameter for studying variability in any crop and identifying superior alleles controlling qualitative and quan-

T A B L E 2
List of trait specific candidate genes along with their probable functions.titative traits through association mapping (Susmitha et al., 2023).Moreover, to facilitate the integration of genomics assisted breeding into a standard crop breeding program, mining of tightly linked markers for essential agronomic traits is must.These markers assist in effectively tracking the desirable alleles of target genes within the breeding population (Pandey et al., 2017).The variation/difference in frequency of colors of qualitative traits might be due to altitude difference and the amount of UV-rays received at two locations.The oxidative stress occurred due to high UV rays is also the reason for high production of flavonoid content/accumulation of anthocyanins which in turn result in more pink/red color of flowers/stem.Differences in the morphology and coloration of plant parts, such as the color of flowers, stems, leaves and seeds, and the shape of seeds, can serve as valuable diagnostic traits for the selection of genotypes with higher levels of phenolic components (Fang et al., 2019;Kapoor et al., 2018;Klykov et al., 2016).Research on various buckwheat species have shown that color visual assessment of the vegetative parts is a marker for selection of buckwheat genotypes with high anthocyanin and rutin content (Sytar et al., 2014).However, variations in the color of plant vegetative organs and content of flavonoids often depend upon variety, area of collection, and ecological conditions (Fabjan et al., 2003;Podolska, 2016;Rauf et al., 2020).

Trait
SNPs have an important role in studying genetic diversity in most crops including buckwheat (Nachimuthu et al., 2015;Zargar et al., 2016).Insights into the genomic diversity and population structure of buckwheat germplasm can expedite the genetic gains in buckwheat-breeding programs (Zhang et al., 2021;Zargar et al., 2023).In order to have knowledge about the subpopulations in a particular crop, structure analysis was performed.Out of total 162 genotypes, a core collection of 132 accessions were selected based upon the morphological diversity studies A total of 132 buckwheat genotypes were classified into three major groups.A similar pattern of population structure K = 3 was also found in tartary buckwheat germplasm from China (Zhang et al., 2021).The deviations in the results can be attributed to the different germplasm, the different marker system, and the different geographical locations.
For the identification of genes associated with different morphological and seed quality traits in a large population, GWAS offers much higher mapping resolutions (Mamo et al., 2014).The present study reports the association of SNPs related to floral, maturity, and yield traits (days to 50% flowering, days to maturity, yield per plant, and number of seeds per plant) from the Himalayan region.Occurrence of common genomic region among more than one environment shows common adaptation and tolerance mechanism of genotypes under these different environments.These results provided a higher correlation for environmental conditions than previ-ous association and linkage mapping studies on wheat (Bálint et al., 2007) model legume Medicago truncatula (Sankaran et al., 2009), pea (Gali et al., 2019), and chickpea (Srungarapu et al., 2022).The moderately significant effect of environmental conditions on the morphological, floral, maturity, and yield traits has also been reported by other researchers in pea (Ma et al., 2017), common bean (Blair et al., 2016), and rice (Garcia-Oliveira et al., 2009).The associations common among two different environments and associations significant for different conditions are of vital importance and can be used in marker-assisted selection to improve buckwheat.Our GWAS analysis led to the identification of promising QTLs and candidate genes for morphological, floral, maturity, and yield traits at two different locations (Figure 5).
GWAS is a valuable tool frequently employed in comparative genome analysis, aiding in the dissection and comprehension of complex quantitative features.It helps pinpoint significant SNPs associated with traits that may not be directly linked to the region of interest, sometimes even residing in noncoding or nonregulatory chromosome regions (Pandey et al., 2020).Therefore, it becomes imperative to identify candidate genes proximate to these significant SNPs.In our study, we scrutinized the regions of significant SNPs for the potential identification of protein-coding genes (Figure S3).Validating these genes will further enhance our understanding of the various mechanisms involved in a plant's response to different stressors, particularly in the case of buckwheat.

CONCLUSION
This is the first study on mapping of QTLs in buckwheat targeting the yield related traits.Hence, it offers an understanding of the importance of variation and the impacts of environmental conditions.We have recorded observations from two locations which vary in altitude vis-à-vis abiotic stress condition.In high altitude location (Izmarg, Gurez), germplasm is very much exposed to high level of UV rays that is the reason of high flavonoid content.Further, the results of this research reveal the marker-trait associations and environmental interactions for various morphological and yield traits.This study supports the use of GBS technology which allows the identification of a large number of SNP markers.The genetic loci data, especially the pivotal SNPs linked to these traits, hold promise for identifying candidate genes, unraveling molecular mechanisms, and developing molecular markers for breeding applications.The precision and reliability of QTLs have clearly demonstrated that employing structured association mapping with genome-wide molecular markers is a promising approach to reveal major-effect QTLs for diverse traits across varying environmental conditions.Further exploration

C O N F L I C T O F I N T E R E S T S T A T E M E N T
The authors declare no conflicts of interest.

D A T A AVA I L A B I L I T Y S T A T E M E N T
Single nucleotide polymorphisms identified in this study have been submitted to the NCBI database under the GEO Accession ID: GSE118778.

F
Box plots analysis showing the variation of different morphological and yield-attributing traits in buckwheat genotypes.

F
Single nucleotide polymorphism (SNP) markers-based population relationship among genotypes; (a) unweighted pair group method with arithmetic mean (UPGMA) dendrogram showing genetic relationship among 132 diverse buckwheat genotypes, (b) STRUCTURE analysis of buckwheat genotypes with SNP markers forming three clusters (K-value [K]).The colored blocks correspond to three clusters/sub-populations.F I G U R E 4 Chromosomal localization and distribution of quantitative trait loci (QTLs) in buckwheat found in two different locations.QTLs in red color are common for both the locations.Position of each QTL is indicated by line, whereas scale bar represents the length of chromosomes in megabases (Mb).

F
Manhattan plots of different marker-traits association in buckwheat grown at (a) Shuhama and (b) Gurez.Different colors represent different chromosomes. of common regions among different environments through marker assisted selection can help in understanding complex mechanisms and fine mapping of these regions can lead to new gene discoveries.AU T H O R C O N T R I B U T I O N S Samiullah Naik: Data curation; formal analysis; methodology; validation; writing-original draft.Jebi Sudan: Formal analysis; methodology; writing-original draft.Uneeb Urwat: Software.Mohammad Maqbool Pakhtoon: Data curation; methodology.Basharat Bhat: Formal analysis; software.Varun Sharma: Formal analysis; software.Parvaze A. Sofi: Writing-review and editing.Asif B. Shikari: Writing-review and editing.Bilal A. Bhat: Resources.Najeebul Rehman Sofi: Formal analysis; writing-review and editing.P. V. Vara Prasad: Formal analysis; writingreview and editing.Sajad Majeed Zargar: Conceptualization; formal analysis; funding acquisition; project administration; supervision; validation; visualization; writing-original draft.A C K N O W L E D G M E N T S Sajad Majeed Zargar acknowledges the funds received as research grant from NMHS GBPNIHESD, Almora, Uttarakhand, India (vide project sanction order No. GBPNI/NMHS17-18/SG24/622), and DBT, New Delhi, for sanctioning of project on Buckwheat Genetics (No. BT/PR45195/NER/95/1930/2022).The authors also acknowledge Hon'ble Vice Chancellor, SKUAST-Kashmir (Prof.Nazir A. Ganai) for his support and encouragement for the international collaboration.