ST and XP-EHH, we identified 35 candidate regions that overlapped between at least two methods. Nineteen regions harboured QTLs for parasite resistance, immune capacity and disease susceptibility and, ten regions harboured QTLs for production (growth) and meat and carcass (fatness and anatomy) traits. The analysis also revealed candidate regions spanning genes enhancing innate immune defence , intestinal wound healing/repair  and GIN expulsion . Our results suggest that traditionally managed indigenous sheep have evolved multiple strategies that evoke and enhance GIN resistance and developmental stability. They confirm the importance of obtaining information from indigenous sheep to investigate genomic regions of functional significance in understanding the architecture of GIN resistance.Gastrointestinal nematode (GIN) infections have negative impacts on animal health, welfare and production. Information from molecular studies can highlight the underlying genetic mechanisms that enhance host resistance to GIN. However, such information often lacks for traditionally managed indigenous livestock. Here, we analysed 600 K single nucleotide polymorphism genotypes of GIN infected and non-infected traditionally managed autochthonous Tunisian sheep grazing communal natural pastures. Population structure analysis did not find genetic differentiation that is consistent with infection status. However, by contrasting the infected versus non-infected cohorts using ROH, LR-GWAS, F The control and treatment of GIN infections is estimated to cost tens of billions of dollars worldwide3 and has traditionally relied on chemotherapeutics but, their extended use has resulted in economic losses and raised concerns of environmental health, food safety, and the development of resistance in parasites for all the major groups of anthelminthic drugs5. The evolution of anthelmintic resistance and changes in climate, land-use and farming practices are likely to alter the geographic distribution and infection patterns of parasites and their impacts, calling for the development of sustainable control strategies6.Small ruminants (sheep and goats) make immense socio-economic and cultural contributions across the globe. However, gastrointestinal nematode (GIN) infections pose the main constraint to grazing small ruminants7. Indicator traits, such as faecal egg count (FEC), antibody assays, packed cell volume (PCV) and FAMACHA scores have been used to identify resistant and resilient animals9. However, the mechanisms underlying genetic differences in resistance to GIN infections remain poorly understood, with resistance being a physiologically complex trait that develops over time, and indicator traits measured at specific time points may fail to represent all the pathways involved. Most often, quantifying resistance has been through artificial challenge using variable doses of larvae with which the rate, time and specificity of infection are controlled. However, traditional communal grazing management practised in smallholder mixed farming, nomadic and pastoral systems predominate in the (sub)tropics. In these systems, multiple nematode species account for natural infections which occur gradually, and findings from single infection studies may not mirror the patterns of infection encountered in these traditional systems10. There are reports showing between- as well as within-breed variation in resistance to GIN in small ruminants13. These observations and the genetic fragmentation observed in most breeds, implies that information derived from one breed cannot be extrapolated to another but needs to be validated for individual breeds5.Evidence for host genetic variability for GIN resistance has been observed in ruminant livestock suggesting selective breeding is a feasible control option14. The investigation and discovery of putative candidate genes, genomic and regulatory variants underpinning GIN resistance may provide a better understanding of the underlying molecular mechanisms and accelerate genetic gains in breeding programs. Genome-wide scans using single nucleotide polymorphisms (SNPs) have identified candidate regions, genes and QTLs putatively associated with GIN resistance on almost all ovine chromosomes19. Most of the regions lie within and/or in proximity to MHC II and the interferon family of genes including the multitude of genes activated following interferon exposure21, that are components of the immune response. Other QTLs for GIN resistance implicate other mechanisms, including innate and acquired immune response, gastric mucosal protection and haemostasis pathways18. Here, we utilise genome-wide autosomal genotype data generated with the Illumina Ovine 600 K SNP BeadChip and bioinformatic analysis, to conduct genome-wide screens in the hope of identifying regions and loci, linked to individual animal variability to GIN resistance in traditionally managed autochthonous Tunisian sheep.Advances in genomic technologies offer the opportunity to investigate the nature of genetic variation underlying complex traits22. The blood samples were collected using EDTA vacutainers by puncture of the jugular vein. DNA was extracted from each blood sample using WIZARD Genomic DNA Purification Kit  and then stored at − 80 °C. Out of the 309 samples, 101 were selected and assigned to two extreme groups based on GIN infection rates i.e., Non-infected (n = 58) and Infected (n = 43), based on faecal egg counts (FEC). FEC were estimated from 5 grams of stool by concentration floatation followed by the McMaster technique and the eggs per gram calculated to estimate the degree of infection23. The FEC readings ranged from zero (0) to 3800 eggs per gram. All individuals reporting an FEC = 0 were classified as non-infected and those with FEC > 100 were classified as infected.This study required no ethical approvals as all the samples were procured from slaughterhouses and in the presence of a veterinarian. In total, 309 blood samples were collected from indigenous sheep brought for slaughter at eight commercial slaughterhouses (Tunis (Tunis abattoir), Ariana (Ariana abattoir), Bizerte , Béja (Béja abattoir) and Jendouba (Jendouba and Tabarka abattoirs)) in northern Tunisia where GIN infections present one of the main constraints to small ruminant production23 suggesting infections from multiple GIN species. The DNA samples were genotyped with the Illumina 600 K SNP BeadChip  at GeneSeek Neogen Genomics, Lincoln NE, USA. The BeadChip comprises 606,006 probes that target genome-wide SNPs, among which 577,401 are autosomal, 27,314 are on the X chromosome and 1291 are unassigned.Information on the grazing pattern and history of anthelmintic use was obtained from animal owners before slaughter. From faecal examinations, eggs of gastro-intestinal helminths (including nematodes and cestodes), Eimeria spp., Trichuris spp. and Nematodirus spp., were identified in the study individuals24. The following criteria was used: (1) one individual was randomly selected from one pair of highly related animals with an identity-by-state score (IBS) of greater than 0.99, (2) SNPs with minor allele frequencies (MAF) of no less than 0.01 were retained, (3) individuals and SNPs with call rates lower than 90% and 95%, respectively were discarded and (4) all unmapped SNPs and those on the sex chromosomes were excluded. This generated a dataset of 540,528 autosomal SNPs and 92 samples . This dataset was subjected to LD pruning using the parameters 50 5 0.5 representing window size, step size and r2 threshold, respectively resulting in 335,070 SNPs that were used for population structure and phylogenetic analysis.The 606,006 raw SNP genotypes were processed for quality control with PLINK1.9E) and observed (HO) heterozygosity, effective population size (NE), and patterns of LD decay were investigated for the two cohorts (infected and non-infected). HE and HO were calculated using PLINK v1.9. Pair-wise LD was evaluated using the correlation coefficient (r2) between alleles at two separate SNP loci using PLINK v1.9 with default settings. Following Sved25, NE over generation time was estimated with the equation NEt = (1/4c) (1/r2 − 1), where NEt is the effective population size t generations ago (t = 1/2c); r2 is the LD between pairwise SNPs; and c is the genetic distance in Morgan between pairs of SNPs.Expected (HF) calculated with PLINK v1.9 and (2) runs of homozygosity (ROH) based inbreeding coefficient (FROH). For the latter, ROH streatches were first computed using the “detectRUNS” package in R26. The FROH, was computed as the ratio of the total length of ROH to the length of autosomes (2.45 Gb)27.For each cohort, two measures of inbreeding were calculated; (1) SNP based inbreeding coefficient  the minimum number of SNPs in a sliding window was 50; (2) the minimum ROH length was set to 1 Mb to eliminate the impact of strong LD; (3) each ROH spanned a minimum of 80 consecutive SNPs; (4) one heterozygous and five missing calls per window were allowed to avoid false negatives that may arise due to occasional genotyping errors or missing genotypes; (5) the minimum SNP density was set at one SNP every 100 kb, and the maximum gap between consecutive SNPs was set to 1 Mb.Three estimates of ROH were calculated taking into account three genomic distance categories, ROH < 5 Mb, 5 Mb < ROH < 10 Mb, and ROH > 10 Mb, indicative of ancestral (more than ten generations), middle (5–10 generations), and recent (within five generations) inbreeding, respectively29. This mode does not assume any background information on the number and frequency of alleles in ancestral populations. The ADMIXTURE tool was run with values of K increasing gradually from 1 to 6, to derive cross-validation (CV) errors. The lowest value of the CV error indicates the most likely number of ancestral populations. Five runs were performed for each K. To test for consistency in the ADMIXTURE results, we repeated the ADMIXTURE runs using three datasets of 6,000 SNPs, each drawn at random without replacement from the 335,070 SNPs. The pairwise allele sharing distance (ASD)30 was computed using the program “asd”. A distance matrix of all pairwise ASD dissimilarities, calculated as 1-ASD, was generated and subjected to hierarchical clustering with the Neighbour-Joining algorithm to yield an individual clustering dendrogram. ASD calculation does not require estimates of allele/genotype frequencies making it valid when sample sizes are small. It is also suitable for detecting outliers and is robust to high LD among SNPs.The 335,070 SNPs were used to infer genetic structure and divergence using three methods. Principal component analysis (PCA) was performed using PLINK v1.9 and the first two PC’s were plotted to visualize individual relationships. The proportion of shared ancestry between individuals was inferred with the unsupervised mode of ADMIXTURE tool v1.30To investigate the molecular genetic basis underlying natural variation in GIN infection, we investigated genome-wide distributions of ROH in each individual in the infected and non-infected cohorts using PLINK v1.9. For this analysis, we used the 540,528 SNPs that passed the quality thresholds. Using the “detectRUNS”, we counted the number of times a given SNP occurred in the identified ROH in each cohort and presented a Manhattan plot of all the tested SNPs against their autosomal positions. The most frequently observed SNPs in ROHs occurring at the top 25% of the empirical distribution were taken as the most significant loci underlying an ROH under selection. To identify regions of ROH that are likely associated with variations in GIN infection, we compared the ROH regions between the infected and non-infected cohorts and identified the ones that were specific to the non-infected cohort.ST and XP-EHH analyses to investigate further the genome regions associated with variation in GIN infection. LR-GWAS was performed with PLINK v1.9 using the non-infected cohort as the test sample and the infected cohort as the control. To obtain the 99% confidence intervals for the estimated parameters, the “-ci 0.99” and “-covar” options were invoked and Fisher’s exact test was used to generate the p-values considering location, breed, sex, and age as covariates. The raw p-values were subjected to Bonferroni correction to control the likelihood of any false positive results among the markers identified to be under selection. The corrected p-values were standardized and the − Log 10  of 4.25 (the top 0.001) was set as the cut-off threshold to identify candidate regions. The estimations were summarized in 200 Kb window sizes and the genes falling within the candidate regions were identified. The package ’qqman’ in R version 3.5.1 was used to generate the Manhattan plot.We performed the logistic regression (LR) GWAS, FFST, was used to investigate regions of the genome that have diverged between the two cohorts. The unbiased pairwise FST31 was computed using the HIERFSTAT Package of R32 using a window size of 200 Kb and a window-step size of 100 Kb. Windows with less than five SNPs were excluded from the analysis. The FST values were then standardized by Z transformation following Ahbara et al.33. Windows falling within the top 0.001% of the FST values in each chromosome were considered the putative candidates under divergent selection.The population differentiation statistic, 34 was used to compare expected haplotype homozygosity (EHH) and integrated haplotype score (iHS) between the two cohorts to detect selection and its direction. XP-EHH scans SNPs that are homozygous in one population but polymorphic in the other through pairwise comparison of EHH scores. Positive XP-EHH scores indicate selection in the test sample, while negative scores indicate selection in the control. The XP-EHH scores were estimated as:IA is the integrated EHH value of the test population and IB is the integrated EHH value of the reference population. Haplotype phasing was inferred for each cohort simultaneously on all SNPs using BEAGLE v3.3.135. The XP-EHH test was performed with the “rehh” package of R36 and the raw XP-EHH scores were standardized to a distribution with zero mean and unit variance. Selection candidates were considered as the regions contained in any of the 200 Kb windows with a significance threshold of p < 0.001; this equates to an XP-EHH value of 4 at the default settings of “rehh” estimation.The cross-population extended haplotype homozygosity (XP-EHH)ST and XP-EHH candidate regions and the ones that overlapped between at least two approaches were identified and merged using Bedtools v.2.28.037. Genes that were spanned by the ROH (non-infected cohort) and overlapping candidate regions were retrieved using the Biomart/Ensembl (http://www.ensembl.org/biomart) tool based on the Ovine v3.1 reference genome assembly. The set of genes identified in the candidate regions were assessed for biological enrichment gene ontology (GO) and KEGG Pathway (www.kegg.jp/kegg/kegg1.html) terms compared to the full list of Ovis aries autosomal protein-coding genes with the functional annotation tool in DAVID v6.838 using O. aries as the background species. We also mapped the ROH (non-infected) and overlapping candidate regions with those reported in the sheep quantitative trait loci (QTL) database Release 42  to identify overlapping QTLs, which may suggest associations with response to parasite infections. To provide further biological interpretation, gene functions were determined from the NCBI database (http://www.ncbi.nlm.nih.gov/gene/) and review of literature.The candidate regions identified by ROH were analysed and the ones that were specific to the non-infected cohort identified. We also analysed the ROH regions of the non-infected cohort, LR-GWAS, FHO, HE, F, FROH and ROH size did not differ significantly (p = 0.05) between the two cohorts   The selection pressure driving the differences in GIN infection is/has not been stringent or long enough to result in differentiation at the genome level; (2) the level of parasite infection may be too low to result in meaningful genomic variation; (3) it may point to a lack of farmer-driven selection that is biased towards the use of uninfected animals for breeding; and/or (4) there could be a high natural selection pressure whereby all animals effectively have the resistance genotype and therefore rather than a distribution, spanning susceptible to resistant, there is a skewed distribution that spans “moderately resistant” to “highly resistant” individuals. One or all of these reasons may explain the lack of genetic differentiation between the two cohorts. The level of genetic diversity in the two cohorts is higher than that observed in commercial breeds, but it falls within the range of values reported in other sheep found in Africa and China46.Although we expected the two cohorts to show differences in genetic diversity and structure, this was not the case; they showed similar levels of genetic diversity and inbreeding and the three clustering algorithms provided corroborating evidence of lack of stratification that was consistent with infection status. The lack of genetic differentiation and structure was also reported between prolific and non-prolific cohorts of Bonga sheep from EthiopiaE over generation time for each cohort. The two cohorts had a high frequency of ROH in the shorter (0–5 Mb) length category reflecting older evolutionary events such as past or ancestral inbreeding. The two cohorts showed similar patterns and trends in LD decay and NE. However, the non-infected cohort had higher values of NE across all generations. This difference is difficult to explain but we presume it may be the result of higher mortalities in the infected cohort. Both cohorts show a gradual increase in NE from 1000 generations ago. This is followed by a drastic decline from around 330 generations ago to the present time suggesting a bottleneck event. A similar trend was observed in Ethiopian and Sudanese local sheep47. Assuming a generation time of three years for traditionally managed local sheep, it can be inferred that the increase in NE started around 3000 years ago. The start of the decline in NE 330 generations back translates to around 990 years ago. Between 240 and 1140 years ago, three favourable climatic periods interspersed with short extreme dry spells were experienced in the continent48. We suggest that the sheep populations most likely thrived when conditions were optimal but shrunk during subsequent droughts. The footprints of these demographic perturbations appear to have been retained in the genomes of the indigenous sheep.We assessed the average length and number of ROH per individual at three genomic distance categories and the trends in N49. The convergence of our results points to the reliability of our findings and suggest that they are unlikely to be the outcome of chance events or analytical artefacts.Our analysis revealed 35 candidate regions, spanning 110 genes, that overlapped between at least two out of the four methods we used to detect genomic regions associated with differences in endo-parasite infections. The identification of overlapping candidate regions that are under selection by different approaches suggests plausible evidence for selective influences at the genomeST, XP-EHH) approaches identified genomic regions that could be driving GIN resistance in autochthonous Tunisian sheep. At least two or more methods identified the same candidate regions that colocalized with the FECGEN, TFEC_1, HFEC, NFEC, LATRICH_2, IGA, SAOS, WORMCT, PEPSL and CEOSIN QTLs that have been associated with health traits and in particular parasite resistance, immune capacity and disease susceptibility in sheep showing resistance to GIN52. This result indicates that some common QTLs underly parasite resistance traits in sheep and support a role for convergent evolution in driving host GIN resistance. For instance, the FECGEN QTL which results in reduced FEC in unmanaged, naturally-parasitized domestic sheep53 and in the primitive Soay sheep20, has been associated with a microsatellite allele (o(IFN)-γ126) found in the first intron of the interferon gamma (IFN)-γ gene and with increased titre of Teladorsagia circumcincta-specific IgA. It has been shown that the effects of the o(IFN)-γ126 allele and IgA on FEC are not independent, and that IgA may mediate the (IFN)-γ effect on FEC20. Furthermore, the IGA QTL could be related to early response to incoming larvae, whereas the FECGEN QTL may be associated with the ability to avoid the development of adult parasites54. The fact that the candidate regions spanned QTLs associated with different QTL traits was expected because the animals analysed in this study are grazed in communal pastures where challenge from multiple GIN parasite species is common and could present different aspects of host-parasite interaction during infection. Indeed, eight nematode species, T. circumcincta, T. trifurcate, Haemonchus contortus, Marshallagia marshalli, Trichostrongylus vitrines, T. axei, Ostertagia lyrate, O. ostertagi and O. occidentalis, were found to colonize the abomasum of the study individuals23.Both the within (ROH) and between-population  and SLC22A5 (OCTN2) which are carriers of hydroxyurea, in an overlapping candidate region on OAR 5  in an overlapping candidate region on OAR 2 Table . Ligands65. In sheep, Jacobs et al.66 observed that impaired IL-4 signalling promoted the establishment of H. contortus and increased larval burden. An increase in IL-13 in intestinal lymph cells was observed in sheep selected for increased resistance to nematodes67. Two genes RUFY4 (RUN and FYVE Domain Containing Protein 4) and VIL1 (Villin 1) and two IL-8 receptors (CXCR1 and CXCR2) occurred in a strong candidate region on OAR 2 that was also identified by ROH to be specific to the non-infected cohort. RUFY4 is a positive regulator of autophagy and is expressed in a cell-specific manner or under specific immunological conditions associated with IL-4 expression68. VIL1, has been shown to protect gut epithelial cells by decreasing epithelial damage69. It was among the top 30 proteins that were differentially regulated between resistant and susceptible sheep and may play an important role in maintaining the epithelial integrity of abomasal mucosa in response to haemonchosis70. It can therefore be hypothesized that the interaction between RUFY4 and VIL1 with IL-4 may enhance long-term endoparasite resistance in sheep. The two IL-8 receptors were found to be expressed and distributed in normal and morphologically damaged large intestines, suggesting that IL‐8 may play an important role in mediating immune response in gastrointestinal tract, beyond that of potentiating neutrophil recruitment and inflammation71 following intestinal epithelial damage by endoparasites.The IL-4 (Interleukin-4) and IL-13 (Interleukin-13) occurred in a candidate region on OAR 5 that was specific to the non-infected cohort. IL-4 plays a crucial role in the differentiation of naive T helper (Th) cells into Th2 effector cells which promote humoral immunity and provide protection against intestinal helminths74. Nematode expulsion is known to rely on smooth muscle contraction and increased mucus production75. The latter was found to be mediated by intelectin 1 (ITLN), a calcium dependent lectin, that is expressed by Paneth cells in the small intestine of mouse76 and mucus neck cells in the abomasum of sheep77. The expression of ITLN in sheep goblet cells was found to be upregulated by IL-477. High expression of ITLN was also found to be induced during nematode expulsion in Trichinella spiralis and Nocardia brasiliensis in rodents79 and T. circumcincta infections in sheep80. Th2 response has also been described as a mediator for acute wound healing during helminth infection81. We therefore suggest that the upregulation of ITLN by IL-4 may play a role in enhancing GIN resistance in autochthonous Tunisian sheep through parasite expulsion and enhancing wound healing.Protective immunity against GIN and subsequent parasite expulsion is known to be mediated by Th2 immune response that is orchestrated by the secretion of cytokines such as IL-4, IL-5, IL-10 and IL-13, which promote the recruitment of eosinophils, basophils, mast cells, goblet cell hyperplasia and concurrent mucus and antibody production, FAM78B, SPATA5, SPPL2B and FAM96B. FGF2 also known as basic fibroblast growth factor (bFGF and/or FGF-β), is a growth factor and signaling protein that can synergize with IL-17 in the gut to activate the ERK pathway and induce genes for repairing damaged intestinal epithelium82. The gut epithelium is essentially the first line of defence against microbiota and pathogens and therefore, it plays a critical role in enhancing mucosal immunity. FAM78B (Family with Sequence Similarity 78-Member B) was in an overlapping region on OAR 1. In a genome-wide association study of endo-parasite phenotypes, FAM78B was found in a region on bovine chromosome 3 that spanned SNPs that were most strongly associated with antibody response to O. ostertagi40. SPATA5 (Spermatogenesis Associated 5) was among five genes that were included in the most significant functional term cluster linked with immunity-related and cell-proliferation processes in an investigation of genomic regions and genes for gastrointestinal parasite resistance in Djallonke sheep83. SPPL2B , a member of the signal peptide peptidase-like protease (SPPL) family, localizes to endosomes, lysosomes and plasma membrane and plays a role in enhancing innate and adaptive immunity by cleaving TNFα in activated dendritic cells that triggers IL-12 production84. In humans and chicken, there is strong evidence linking polymorphisms in FAM96B (family with sequence similarity 96 member B) with gastrointestinal and metabolic diseases, and with the development of the digestive system and disorder networks86. These findings suggest that several mechanisms may be employed to trigger and sustain GIN resistance in sheep under traditional grazing and exposure to multiple GIN species.Other possible candidate genes that have been associated with GIN resistance and occur in our overlapping candidate regions include FGF287 reported a reduction of 5% and 31% in average daily feed intake and average daily weight gain, respectively in parasitized pigs. Endoparasites tend to limit host nutrient availability by reducing host food intake, digestion, absorption and nutrient assimilation, resulting in nutritional deprivation and destabilization of host growth and development89. To counter against these negative effects, we suggest that natural selection may be acting on the regions spanning growth QTLs to ensure growth and developmental stability of the study populations under GIN challenge. The parallel selection signatures overlapping the growth QTLs may therefore be adaptive strategy that ensures the survival of the study populations.Our analysis revealed several candidate genomic regions spanning a number of production (growth), and meat and carcass (fatness and anatomy) QTLs. This result was unexpected given our analytical strategy. Kipper et al.In conclusion, we assessed the diversity and population structure of two cohorts (infected and non-infected) of autochthonous sheep from Tunisia that were classified based on FEC levels. The two cohorts were characterised by similar levels of genetic diversity and the same genome background suggesting common history and genetic admixture. Four methods of detecting selection signatures identified regions of the genome that were most likely associated with GIN resistance suggesting that the animals have established a certain level of immunity under natural challenge. The functions of the putative genes and the overlapped QTLs suggest multiple strategies, including host immune response, intestinal epithelium damage repair, mucus production and parasite expulsion, play a role in GIN resistance in sheep. This may be due in part to the fact that GIN resistance is the net outcome of many physiological and immunological pathways, and thus resistance in animals could be owing to variation at a large number of loci. Furthermore, natural selection is also acting concurrently on regions spanning growth related QTLs to ensure developmental stability under GIN challenge. Although the data used in our study is relatively small, we believe this does not compromise the integrity of our findings as it is compensated for by the high density of the marker loci used. We have also limited our discussion to candidate regions that were specific to the non-infected cohort and those that overlap between at least two analytical approaches. This research confirms the importance of obtaining data from local sheep populations managed in their production environments to gather information on genomic regions of functional significance in GIN resistance.Supplementary Information.


Brassica juncea L.), GSL content and composition are seed-quality-determining traits affecting its economic value. Depending on the end use, i.e., condiment or oil, different GSL levels constitute breeding targets. The genetic control of GSL accumulation in Indian mustard, however, is poorly understood, and current knowledge of GSL biosynthesis and regulation is largely based on Arabidopsis thaliana. A genome-wide association study was carried out to dissect the genetic architecture of total GSL content and the content of two major GSLs, sinigrin and gluconapin, in a diverse panel of 158 Indian mustard lines, which broadly grouped into a South Asia cluster and outside-South-Asia cluster. Using 14,125 single-nucleotide polymorphisms (SNPs) as genotyping input, seven distinct significant associations were discovered for total GSL content, eight associations for sinigrin content and 19 for gluconapin. Close homologues of known GSL structural and regulatory genes were identified as candidate genes in proximity to peak SNPs. Our results provide a comprehensive map of the genetic control of GLS biosynthesis in Indian mustard, including priority targets for further investigation and molecular marker development.Glucosinolates (GSLs) are sulphur- and nitrogen-containing secondary metabolites implicated in the fitness of Brassicaceae and appreciated for their pungency and health-conferring properties. In Indian mustard ( B. napus (canola and rapeseed), B. juncea (Indian mustard), B. oleracea (cabbage) and B. rapa  [Arabidopsis thaliana [Arabidopsis that were identified and characterised over the last two decades [Arabidopsis, the respective regulatory and metabolic networks in the allotetraploid Brassica crops (B. napus and B. juncea) are suggested to be much more complex due to their intricate evolutionary history [Glucosinolates (GSLs) are a class of well-studied sulphur (S)- and nitrogen (N)- containing secondary metabolites almost exclusively found in Brassicaceae, which include the economically and nutritionally important crops  turnip) ,2,3. Mosthaliana ,4,5. GSL history .Brassica, cultivated for two distinct markets. In India, Bangladesh, China and the Ukraine, and more recently in Canada and Australia, it is grown as an oilseed crop [B. juncea grown as a canola-type oilseed crop, GSLs have largely been selected against, which enables seed meal to be used for animal feed after oil extraction. Breeding for low-GSL B. juncea was spearheaded by Canadian breeders through the introgression of low-GSL “Bronowski” alleles from canola B. napus into an Indian high-GSL B. juncea line [B. juncea has become a viable alternative oil crop [Indian mustard is an economically important eed crop . On the eed crop . In B. jcea line . The rescea line . As suchoil crop ,12,13. Foil crop . Sinigrioil crop ,16. Notaoil crop ,17,18.B. juncea variety improvement [B. juncea [Brassica crops, GWAS has been successfully employed for dissecting the genetic architecture of seed quality traits such as GSL accumulation, fatty acid composition and shattering resistance [B. juncea, high-density single-nucleotide polymorphisms (SNPs) were identified through different strategies, including double-digest restriction-associated DNA (dd-RAD) [B. juncea, in conjunction with deploying two new genome sequences representing vegetable and oilseed varieties. Among the candidate genes proposed in these studies, only a MYB28, a major regulatory gene for aliphatic GSL biosynthesis, could be linked to the current inventory [Arabidopsis. This suggests that a large number of possible genetic mechanisms may yet be uncovered through GWAS. Thus, the aim of this study was to perform GWAS on a set of oilseed Indian mustard to further elucidate the genetic basis and add to the current understanding of seed GSL accumulation in Indian mustard.Enhancing the health-beneficial GSL levels in varieties aimed for vegetable or condiment use and reducing the overall GSL and erucic acid levels, while increasing desirable fatty acids in oilseed cultivars, remain among the key seed quality traits for rovement . A betterovement ,20. Earl. juncea ,22,23. Rsistance ,26,27,28(dd-RAD) , RNA seq(dd-RAD) , specifi(dd-RAD) , genotyp(dd-RAD)  and rese(dd-RAD) . With th(dd-RAD) ,32. Akhanventory  of GSL gA total of 69,594 SNP sites, with 61,931 (89%) anchored onto chromosomes, was obtained from the variant calling. An initial filtering for SNPs anchored onto chromosomes for 60% call rate, non-maf  filtered and 10% maximum marker heterozygosity resulted in 15,263 SNPs , and missing states were imputed. Following imputation, a final set of 14,125 SNPs resulted from filtering for 5% minor allele frequency and 20% maximum heterozygosity and was used for GWAS. The diversity panel consisted of 158 accessions from 28 countries, representing South Asia , Asia , Europe (11%), North America (6%), Australia (6%), Africa (6%) and unknown origin (8%) . Three a−1) compared to gluconapin (0.01–174.57 µmol/g−1). However, the gluconapin concentration was more variable, with a coefficient of variation (CV) of 108% compared to sinigrin concentrations with a CV of 79.61%. Residual distribution showed an approximately normal distribution with a mean of zero for total GSLs, sinigrin and gluconapin . Sinigrip-values in the lower range and returned a limited number of deviations at high log10 p-values. SUPER returned highly inflated −log10 p-values even in the lower ranges, suggestive that many detected loci were from spurious associations, which might also explain the exceptionally high number of significant SNPs detected under this model  = 6.09–10.39). The association at B02_725738 from FarmCPU was the strongest association (−log10 (p) = 9.38) for total GSLs considering only the BLINK and FarmCPU models. For sinigrin concentration, five SNPs were associated in BLINK, one SNP each on chromosomes A01, B01 and B08 and two on B04 (p) = 7.51–10.34) and at SNP B08_24075810 (−log10 (p) = 6.02–7.68), were commonly detected by both models. The strongest association (−log10 (p) = 10.34) was at SNP B01_43311767 in BLINK. Compared to total GSLs and sinigrin, more SNPs were found to be significantly associated with gluconapin concentration. BLINK detected a total of 14 associated SNPs, comprising one SNP each on chromosomes A03, A06, A08, A10, B02, B03, B04 and B05, two on B01 and four on A02. FarmCPU returned six associated SNPs on chromosomes A02, A06, A08, B02, B03 and B05. These associations were distinct, although the association at SNP B02_48309648 in BLINK and SNP B02_48309753 in FarmCPU were only 105 bp apart. SNP B02_48309648 in BLINK represented the strongest association (−log10 (p) = 17.47). FarmCPU appeared the most suitable model for total GSLs and sinigrin with respect to the control of spurious associations as most observed p-values correlated with expected p-values, with only a few p-values deviating sharply at the tail end  at distances above 500 kb (B. juncea var. tumida V1.5 annotation) proximal to the trait-associated SNPs was limited to 250 kb upstream and downstream of the SNP position. Based on their homology with Arabidopsis genes and respective annotation, candidate genes were classified as known or potential GSL genes  at 213.38 kb upstream and SAL1 at 246.34 kb were found. SNP B08_66155255, detected only in FarmCPU, albeit at 37% PVE, was a genic SNP within a potential GSL gene, a putative CYP18-3. Moreover, at 17.56 kb, another potential GSL gene, a putative 2-oxoglutarate-dependent dioxygenase gene was found.For total GSLs, homologues of two known GSL genes were identified near SNP A02_3567961, a significant SNP detected in BLINK and explaining around 7% of the observed trait variation (phenotypic variation explained—PVE). These were GSTF11 ,34,35 at [SCPL17  at 68.54ynthesis . SNP B02MYB28 [MYB34 [MAM1 [GS-OX5FMO [phosphoserine aminotransferase 1 (PSAT1) at 12.75 kb. For sinigrin, SNP A03_27702263 with 4% PVE, detected by FarmCPU and SUPER, had homologues of several known GSL regulatory genes in proximity. These included a putative MYB28 ,39 at 118 [MYB34 ,41 and a34 [MAM1 ,43 homolMOGS-OX5 ,45 at 1.LSU2 homologue, a potential GSL gene, at 5.75 kb downstream. SNP A02_34995417 with 1% PVE, detected in BLINK, was found to be located near additional homologues of MYB28 and MYB34 at 81.62 kb and 96.36 kb downstream, respectively. SNP A10_999168, solely detected by BLINK, was flanked by potential GSL genes monothiol glutaredoxin S11 (GRXS11) at 105.45 kb upstream and a UDP-glycosyltransferase 71C3 (UGT71C3) at 115.13 kb downstream. Variation within these two potential GSL genes may have contributed to the 11% PVE of this SNP. With 7% PVE, SNP B01_44925254, detected in BLINK and SUPER, was located near potential GSL genes RETICULATA-RELATED 3 (RER3) at 105.45 kb upstream and a Cysteine Synthase D1 (CYSD1) at 213.34 kb upstream. The strongest association from both BLINK and FarmCPU was only 105 bp apart and was considered the same association, SNP B02_48309648-753 with 3% PVE. This association was 180 kb upstream of a HY5 homologue, encoding a known regulator of GSL biosynthesis [SULPHUR DEFICIENCY-INDUCED 2 (SDI2), at 23.76 kb. On the other hand, SNP B03_7408562, detected in BLINK and MLMM, was found to be near a potential GSL gene, aldehyde dehydrogenase family 2 member B7 (ALDH2B7), located at 135.05 kb downstream.For gluconapin, SNP A02_34185026, detected by BLINK at 11% PVE, was found to be flanking an ynthesis . SNP B03The population structure of a diversity panel can confound GWA analysis through spurious associations . PopulatArabidopsis GSL biosynthetic and regulatory genes, as well as potential GSL genes, in the vicinity of most of the significantly associated SNPs  [Arabidopsis [With the development of newer models with improved statistical power, GWAS recently incorporated multiple model approaches to maximise the power of QTL detection ,54,55,56ted SNPs . The majer 2021) , a curatBjuA041358 and BjuA041338 were homologues of two GSL structural genes, GSTF11 and SCPL17, respectively, and were linked to SNP A02_3567961. GSTF11 encodes glutathione S-transferase F11, responsible for converting the intermediate derivative aci-nitro compounds to reduced glutathione (GSH) conjugates during aliphatic GSL core structure synthesis [BjuA041358 the stronger candidate. SCPL17, on the other hand, is involved in the production of benzoyloxy GSLs in Arabidopsis [BjuA041338 a less likely candidate. SNP A02_11235033 was a high-confidence association, considering that it was detected in all four models. The only candidate gene in this region was BjuA045411, a homologue of OBP2 encoding a DNA-binding-with-one-finger (DOF) transcription factor [Arabidopsis [B. juncea are aliphatic, however, this OBP2 homologue would need to have a divergent role to account for the total GSL variation. The association at SNP B02_7295738, the SNP with the second highest PVE (11%) for total GSLs, was linked to two potential GSL genes: BjuB047551, a homologue of AAP4 encoding an amino acid permease 4, and BjuB047557, a homologue of SAL1 encoding an inositol polyphosphate 1-phosphatase [AAP4 homologue was likely the better candidate gene compared to the SAL1 homologue at 65% similarity. Despite an exceptionally high PVE of 37.03% for total GSLs, no homologues to known GSL genes were found in the vicinity of SNP B08_66155255. However, SNP B08_66155255 was located within the gene model of BjuB019211, a homologue of CYP18-3, a putative peptidyl-prolyl cis-trans isomerase potentially involved in GSL metabolism, as suggested by co-expression with known GSL genes [AOP2 and AOP3 similarly encode 2-oxoglutarate-dependent dioxygenases, which catalyse the side-chain oxygenation in the aliphatic GSL core synthesis [Of the seven listed candidate genes for total GSL concentration, three were found on chromosome A02, two on B02 and two on B08 . BjuA041ynthesis ,35, makibidopsis , making n factor , demonstbidopsis . Since nsphatase . Given tSL genes . Furtherynthesis . The higBjuA042263, BjuA042229 and BjuA042223 were homologues of MYB28, MYB34 and MAM1, respectively. MYB28, also known as HAG1 (HIGH ALIPHATIC GLUCOSINOLATE 1), positively regulates aliphatic GSLs [MYB28 was further identified and validated through combined multi-omics approaches, including GWAS, as the major gene controlling leaf and seed GSL content in B. napus [B. juncea, targeted silencing of a MYB28 orthologue led to the down-regulation of GSL biosynthesis [BjuA042263 a very strong candidate for this QTL region and a high priority for our further validation efforts. On the other hand, MYB34 mainly exerts its role in the roots to regulate indolic GSL synthesis [MAM1 is a methylthioalkylmalate synthase involved in the GSL side-chain elongation of short-chained aliphatic GSLs [B. juncea homologues to be less likely causal for the effects associated with SNP A03_27702263. SNP B04_9016612, with 7% PVE, was a genic SNP within BjuB028146, a homologue of GS-OX5FMO encoding a flavin-containing monooxygenase. GS-OX5FMO functions in aliphatic GSL side-chain modification by S-oxygenation of the basic aliphatic GSL derivatives [BjuB028146 a high-priority candidate gene. BjuB028703, homologous to the potential GSL gene PSAT1, was located near SNP B04_17138489, with 12% PVE. PSAT encodes a putative phosphoserine aminotransferase in the serine biosynthetic pathway [PSAT1 has not been directly associated with aliphatic GSL metabolism. However, serine is a substrate for tryptophan biosynthesis, which in turn is a precursor for the production of indolic GSLs [Arabidopsis, it is regulated by MYB34 and MYB51, two activators of indolic GSL biosynthesis [Of the five candidate genes associated with sinigrin, homologues of three known GSL regulatory genes were found in the vicinity of SNP A03_27702263. tic GSLs ,39, withtic GSLs . MYB28 wB. napus , suggestynthesis ,41 and Mtic GSLs , suggestivatives ,45, makilic GSLs . FurtherBjuA033112 a homologue of LSU2 (RESPONSE TO LOW SULPHUR 2), was found less than 6 kb from SNP A02_34185026. While LSU proteins are of unknown function, they were demonstrated to be important stress-related hubs [BjuA033112 a good candidate to account for the considerable 11% PVE of this locus. Interestingly, MYB28 and MYB34 homologues, additional copies of which were already implicated in the variation in sinigrin concentration on chromosome A03, were found in the vicinity of SNP A02_34995417, although this SNP contributed little to the observed gluconapin variation. BjuA002140 was a homologue of MYB28, while BjuA001524 was a homologue of MYB34. Copy number variation (CNV) of MYB28 homologues on different chromosomes might have led to the divergence that specifically accounts for sinigrin and gluconapin accumulation in different genetic backgrounds. Recently, CNV was uncovered on MYB28 loci through pairwise sequencing of a vegetable variety, T84-66, and an Australian oilseed variety, AU213 [BjuA037341 was a homologue of UGT71C3 encoding an UDP-glycosyltransferase, and BjuA037371 a homologue of GRXS11 encoding monothiol glutaredoxin, implicated in nitrogen signalling [UGT74B1 [UGT74C1 in aliphatic GSL core synthesis [BjuA037341 is the higher-confidence candidate for this association. Having been detected in BLINK and SUPER, SNP B01_44925254 was a reliable and strong association (−log10 (p) = 16.86) for gluconapin. However, homologues of only two potential GSL genes were found in proximity. These were BjuB006588, homologous to RER3 encoding RETICULATA-RELATED 3, and BjuB006607, homologous to CYSD1, a cysteine synthase and a member of the O-acetylserine(thiol)lyase (OASTL) gene family. OASTLs include OASA1, an S assimilation pathway gene that catalyses the biosynthesis of cysteine and a precursor for GSL formation [Ten candidate genes, three known and seven potential GSL genes, can be speculated to contribute to gluconapin variation. Among these, ted hubs  and consted hubs , making gnalling . The dir[UGT74B1  and of Uynthesis  suggestsormation .LONG HYPOCOTYL 5 (HY5) homologue, BjuB009816, was located near the high-confidence gluconapin associations SNP B02_48309748-53 at a PVE of 3%. HY5, a transcription regulator, was shown to partly control the light regulation of GSL biosynthetic genes, as well as many genes in the sulphate assimilation pathway [hy5 Arabidopsis mutants showed altered expression of GSL biosynthetic genes and MYB TFs associated with aliphatic GSL regulation [BjuB005751, a homologue of another potential GSL gene, SDI2 encoding SULPHUR DEFICIENCY-INDUCED 2, was located near SNP B03_474869. Under sulphur-limiting conditions in Arabidopsis, SDI2 acts as a repressor of aliphatic GSL biosynthesis at transcript and metabolite levels [B. juncea SDI2 homologue could affect GSL composition. Lastly, BjuB003011 a homologue of a potential GSL gene ALDH2B7 encoding an aldehyde dehydrogenase family 2 protein, was located near SNP B03_7408562. While two models detected this association for gluconapin, no literature support was found for the involvement of ALDH2B7 in GSL biosynthesis, aside from it being listed as a potential GSL gene in SuCCombase [A  pathway . Additiogulation . BjuB005e levels . DespiteCCombase .p) ≥ 3 threshold and proposed proximate candidate genes encoding for shikimate kinases (chromosome A04), chorismate mutase (chromosomes A06 and B04), jasmonate O-methyltransferase (chromosome B03), branched-chain-amino-acid transaminase (chromosome B06), cytochrome P450 enzyme CYP81G1 (chromosome B06) and MYB44 transcription factor (chromosome B06). Of these candidates, only the CYP81G1 was listed as a potential GSL potential gene in SuCCombase and no genes had homologues of known and validated function in GSL biosynthesis or regulation. In contrast, our GWAS study used a controlled-environment growing condition, coupled with HPLC-MS-based analysis for the accurate quantification of individual GSLs, and applied a stringent Bonferroni threshold for the detection of associations. Yang et al., 2021, identified only two major control loci in a panel of 183 mixed vegetable and oilseed accessions phenotyped for individual GSLs using HPLC and genotyped at a density of 689,411 SNPs. MYB28 (chromosome A02 and A09) was highlighted as a priority candidate gene, supporting the role of MYB28 as a key regulator of GSL accumulation in B. juncea. Thus, our findings add value to previous studies and provide an exceptional resource of novel candidate gene homologues to known structural and regulatory genes of GSL metabolism. Further validation through allele mining and gene expression profiling is warranted, especially for associations explaining high levels of phenotypic variation and detected in multiple models.We found no overlap in proposed candidate genes with the GWAS study by Akhatar et al., 2020, probably owing to different aims, translating to differences in panel composition, different methods of GSL quantification and differences in cultivation. Furthermore, they limited the candidate gene search to a narrow window of 25 kb upstream and downstream of peak SNPs. The Akhatar et al., 2020, study was conducted under field conditions, with the aim to study the effects of various nitrogen levels. They used only 92 accessions, which were phenotyped for GSL content using Near-Infrared Reflectance Spectroscopy (NIRS) on intact seeds to predict total GSLs. In contrast, we phenotyped a larger, more diverse panel grown under controlled conditions, using quantitative approaches for several specific GSLs. Their study detected associations using a relaxed −log10  at Southern Cross University Lismore, New South Wales , from March to mid-May 2020. Several seeds per accession were sown at 5 mm depth in a 10-cm-diameter free-draining plastic pot filled with commercial potting soil and thinned to one plant per pot two weeks after emergence. Each accession was grown in triplicate in a complete randomised block design. Three-week-old seedlings were supplied with 25 mL of diluted to half strength liquid fertiliser Canna A + B  , were gr−1 glucotropaeolin (a GSL not found in Brassicas) as internal standard. The tubes were mixed by inverting several times. Eight individual GSLs were quantified, including sinigrin (SIN), gluconapin (GNP), progroitrin (PGT), epi-pogroitrin (EPI), glucoiberin (GIB), glucoraphanin (GRF), glucobrassicin (GBS) and gluconarturtiin (GNT), using an Agilent 1260 Infinity II High Performance LC-MS instrument . HPLC-MS parameters used are detailed in In total, three biological replicates per accession  were used for quantifying GSL concentrations, following the method by Borpatragohain et al., 2019 . In brieB. juncea var. tumida T84-66 V1.5 genome reference  [Illumina’s FastQ sequence outputs were demultiplexed using Axe . Both rery 2022) . SNP calry 2022) , using dry 2022) . Missingry 2022)  with defResidual distribution and quantile–quantile plots were visualised using Genstat 64-bit Release 18.1  to assess the normality and homoscedasticity of the phenotype data. Data were log10 (x + 0.01) transformed for subsequent estimation of the variance components and heritability values using REML Restricted Maximum Likelihood (REML) implemented in Genstat 64, as well as input for GWA. Best Linear Unbiased Predictions (BLUPs), calculated using genotype and replicate effects in REML, were used as phenotype input in GWAS. Correlations among GSL traits using raw mean values were computed using the ‘ggpubr’ package , implemeMarker–trait association was performed using the Genome Association and Prediction Integrated Tool (GAPIT Version 3) ,79. To s2 threshold of 0.4 [https://itol.embl.de/, accessed on 3 September 2021). A maximum likelihood estimate for population structure was carried out in ADMIXTURE [A separate set of 1174 higher-confidence SNPs imputed and filtered for >80% call rate, 5% minor allele frequency (maf) and 10% maximum heterozygosity, covering pseudochromosomes, and linkage-disequilibrium (LD) pruned, was used for cluster, population and principal components analyses. LD pruning was done using Plink  , and associating the annotation of the Arabidopsis genes in the top-scoring hits. All these annotations and genome information were integrated into the SCPS Galaxy. Next, we matched the Arabidopsis locus identifiers from our BLAST+ list and that of “known” and “potential GSL genes” curated in SuCCombase  for listing our candidate genes. Top hits identified as either “known” or “potential GSL genes” based on SuCCombase were prioritised as candidate genes. In a few cases, we chose the “known” or “potential GSL gene” even if they ranked second to third in BLASTP, provided that the percent identity was more than 60% across more than 50% of the total length alignment.Predicted candidate genes within 250 kb upstream and downstream of each significantly associated SNP were identified using the I BLASTP ,89, inte


Post GWAS integration of multiple data resources prioritized 208 putative candidate genes delimited into 64 QNRs, which can be critical in understanding the genetic architecture of stem WSCs accumulation in wheat under optimum and water-stressed environments. At least 19 stable QTNs were found associated with 24 prioritized candidate genes. Clusters of fructans metabolic genes reported in the QNRs qWSC-4A.2 and qWSC-7A.2. These genes can be utilized to bring an optimum combination of various fructans metabolic genes to improve the accumulation and remobilization of stem WSCs and water stress tolerance. These results will further strengthen wheat breeding programs targeting sustainable wheat production under limited water conditions.Water-soluble carbohydrates (WSCs) play a vital role in water stress avoidance and buffering wheat grain yield. However, the genetic architecture of stem WSCs’ accumulation is partially understood, and few candidate genes are known. This study utilizes the compressed mixed linear model-based genome wide association study (GWAS) and heuristic post GWAS analyses to identify causative quantitative trait nucleotides (QTNs) and candidate genes for stem WSCs’ content at 15 days after anthesis under different water regimes . Glucose, fructose, sucrose, fructans, total non-structural carbohydrates , total WSCs (anthrone based) quantified in the peduncle of 301 bread wheat genotypes under multiple environments (E01-E08) pertaining different water regimes, and 14,571 SNPs from “35K Axiom Wheat Breeders” Array were used for analysis. As a result, 570 significant nucleotide trait associations were identified on all chromosomes except for 4D, of which 163 were considered stable. A total of 112 quantitative trait nucleotide regions (QNRs) were identified of which 47 were presumable novel. QNRs  Current photosynthates and the photosynthates reserved in various vegetative tissues are two primary carbon sources for developing grain in wheat. Current photosynthates are directly transferred to developing grains during optimal conditions when photosynthesis is undergoing. When photosynthesis is absent during the dark period or depressed due to senescence and/or hostile growth conditions, developing grains become more dependent on the redistributed reserved photosynthates for their carbon requirement . So, it FEH  final content or concentration of total WSC depends on the initial content/concentration of each component, (2) fructans, which make nearly eighty per cent of the total stem WSC, are derived from the polymerization of sucrose, (3) the amount of glucose, fructose, and sucrose triggers the hydrolysis of fructans to hexose and monosaccharides, which later becomes the source of carbon to developing grains. Presumably, this is the first report on GWAS that aimed to unveil the genetic architecture of accumulation of WSC and its major components 15 days after anthesis (15DAA) under three water regimes .GWAS and QTL mapping studies on WSC in wheat have contributed to decipher the various genetic aspects of this trait. However, the number of these studies is few. Moreover, none covered the individual components of stem WSCs, Triticum aestivum L.) genotypes selected based on the pedigree information and Shanon index from a hefty germplasm pool of seven thousand lines , rainfed (RF), and complete drought (DT)] at two locations: Chaudhary Charan Singh Haryana Agricultural University (CCS HAU) Hisar (29°09′N 75°42′E) and ICAR-Indian Institute of Wheat and Barley (ICAR-IIWBR) Karnal (29.686°N 76.989°E). The Hisar location represents the hot typic arid subeco region 2.3 of India, and Karnal represents semiarid eco-subregion 4.1 (An IIWBR Wheat Association Mapping Panel (IWAMP) was developed with a total of 301 bread wheat .Glucose, fructose, sucrose, fructans, and total WSC were assayed in each genotype’s peduncle (the first internode to spike base) under all the environments using Fourier-transformed near spectroscopy (FT-NIR). Briefly, from ten randomly chosen plants from each plot, peduncles of the main culm were sampled at 15DAA by cutting with a sharp knife just above the first internode and near the base of the spike, followed by immediate removal of leaf blades. Samples were dried for 60 min in a pre-heated (100°C) dry-air oven, followed by drying at 60°C for 48 h. Furthermore, each sample was chopped into small pieces of 1-2 mm. Finally, the whole sample was used for FT-NIR spectra acquisition. The in-house partial least square regression models (PLS-R), which are in line with lmerTest”  fitted with restricted maximum likelihood (REML) methods were used to create a type III ANOVA table with Satterthwaite’s method for the eight individual environments and environments pooled across three water regimes in R-studio using the package “merTest” , and thein the above models “(1| X)” indicates a random intercept of the source of variation with a fixed mean.h2) was calculated from the obtained variances using the following equations and was expressed in percentage:Broad-sense heritability  and the polymorphism information content (PIC) for extracted SNP markers were calculated with PowerMarker v3.2.5  iterations. Optimal sub-population was identified using ad hoc statistics with ΔK  cluster analysis as performed with R-based GAPIT v2.0 .Genetic diversity  to 10%. A nucleotide-trait association (NTA) with threshold -10 p >log 3.0 was declared significant. Quantile-Quantile and Manhattan plots were recreated with the CMplot-R package in R-studio.A compressed mixed linear model (CMLM) was implicated in GAPIT-R v2.0  to estimqWSC-1A.1).Each significant SNP was called a quantitative trait nucleotide (QTN), and the region within the genome-wide attenuation distance was called a quantitative nucleotide region (QNR). Each QNR is called “q” followed by the abbreviation of the trait , a hyphen, a chromosome, a period, and a sequence in Arabic numbers  knowledge-based gene prioritization (KGP), (2) differential expression-based gene prioritization (DEGP), and (3) co-regulatory network-based gene prioritization (CNGP). The detailed method of gene prioritization can be found in Physical positions of significant SNPs in IWGSC erealsDB . Candida GSE9767  and GSE8 GSE9767  were retThe IWAMP carried continuous variation for all the studied traits under individual environments (E01-E08) and in the pooled data across three water regimes . Summaryp < 0.0001 for all the six traits. Pooled ANOVA further demonstrated a significant effect of genotype (G) x environment (E) interaction on all the studied traits at p < 0.0001. Each of the studied traits showed high environmental wise h2  to 0.32 (1D), 0.26 (5D) to 0.39 (1D) and 0.18 (5D) to 0.23 (5A), respectively. A summary of statistics generated from 14,751 polymorphic SNP markers is tabulated in A 35K SNP array-based genotyping of IWAMP produced 14,571 polymorphic SNPs after pre-processing of data. These markers covered a genome-wide genetic distance of 4,527 centimorgans (cM) with an average density of 0.31 cM . About 5r2 = 0.1), LD decayed at genetic distances 2.8 cM, 6.5 cM, and 12.8 cM for sub-genomes A, B, and D, respectively, and at 2 cM for the whole genome , which received three different water regimes, le plots  indicatedatasets  on all cdatasets . The maxThere was no significant QTN for WSC under E01 environment and fructans with pooled BLUEs corresponding to irrigated conditions. The effect of significant QTNs and explained phenotypic variance (PVE%) ranged from −0.61 to 0.72 and 3.47 to 6.69%, −1.10 to 1.47 and 3.55 to 7.76%, −0.87 to 1.03 and 3.47 to 7.01%, −23.19 to 15.09 and 3.16 to 10.31%, −24.51 to 16.34 and 3.1 to 9.7%, and −15.63 to 13.89 and 4.55 to 7.15% for individual traits glucose, fructose, sucrose, fructans TNSC, and WSC, respectively.viz. glucose (21), fructose (34), sucrose (24), fructans (20), TNSC (30), and WSC (34), which were located on all chromosomes except on Chromosomes 1D, 4A, 4D, and 5D.NTA common across two or more enviroments and/or with BLUEs was considered stable. Thus, we identified a total of 121 QTNs producing 163 stable NTAs , correspAegilops speltoides. The numbers of QNRs per trait were 37 (glucose), 39 (fructose), 36 (sucrose), 39 (fructans), 42 (TNSC), and 32 (WSC). We identified 25 QNRs that were consistent for two or more traits in more than one environment. A QNR, qWSC-3B.2, at physical position 3B:83.69-85.27cM, is likely to be a hotspot for WSCs metabolism since it carried 32 QTNs and was comprehensively associated with all the studied traits in different environments and water regimes. Another QNR, qWSC-7A.2 at 7A:29.86-30.66 cM harboring 22 NTAs for fructose, sucrose, fructans, and TNSC, was likely to be a hotspot for WSC metabolism under water-stressed environments; however, within this genomic region, there were two QTNs for WSC content under E05 (irrigated conditions).We used genome-wide LD attenuation distance (2 cM) to delimit detected QTNs into 112 genomic regions . These rTraesCS7B02G446300 (7B: 709513978). Furthermore, nearly 1K genes were found in the 1-Mbp window of stable QTNs  and 14151.17 . In the latter experiment  to 161406.53 tpm  in controlled environment and from 0.64 tpm  to 153250.26 tpm  in water stress environment. Likewise, the high-yielding drought-tolerant genotype C-306 carried a range between 1.30 tpm  and 33444.93 tpm  in controlled environment and 0.69 tpm  to 32144.72 tpm  in water stress environment. Additionally, a Genevestigator platform , but, due to low heritability (h2 < 30%), glucose, fructose, and sucrose will require multiple selection cycles. Additionally, the high GxE interaction suggests that varietal development programs must be environment specific for these traits. These findings agreed with previous studies , which is an improved version of Axiom® HD 820K wheat array and employed in previous studies . Likewise, in sub-population “D,” we can find a cluster of genotypes that are either collected from the Gujarat, Rajasthan, or Madhya Pradesh (MP) states of India or has been selected from the idigenous collection originated from these states. The substantial admixture further suggests that this population results from the extensive modern breeding program. On the other hand, most indigenous collections and some varieties  were admixture less. These results indicate the broad genetic base of our study material and suitability for a successful GWAS.The genetic population used in this study stratifies into seven distinct sub-populations (A to G). The genotypes clustered according to their pedigree or geographical origin. For instance, ten of the twenty members of the sub-population “B” had Pastor in their pedigree, and few others have “Bobwhite.” Interestingly, Pastor is the result of PFAU/SERI-82//(SIB)BOBWHITE , namely AX-94583145 and AX-95137931 were just 0.4 cM and 0.54 cM away from the SNPs previously reported for stem WSCs   from qWSC-4A.2 was associated with glucose and sucrose content under water-stressed condition and had 6-SFT (4A:739.06 Mbp), a gene responsible for β-2,6-linked fructans synthesis from sucrose  controlling glucose, sucrose, fructans, TNSC, and WSC in water-stressed plants.Seven of the previously cloned genes of WSC importance were found within the vicinity of QTNs identified in this GWAS. A sugar transporter gene, .56 Mbp) , was ideaMYB13-1   found in the neighborhood (0.69 Mbp) of QTN AX-94619716 (4A:739763001 bp). Earlier, QGfc.aww-7A.1 (7A:2.2-8.2 cM), for grain fructans concentration in Berkut × Krichauff double-haploid lines, from which they further cloned a cluster of four fructans biosynthesis genes  and TraesCS7A02G009800 (7A:4430034-4430058 bp), which they designated as Ta1-FFT and Ta6-SFT, respectively. These two genes were within the 1-Mbp window of QTN AX-95231996 (7A:3982822:4627547 bp) associated with fructose content under rainfed conditions. The QTN region, namely qWSC-7A.2 (7A:29.86-30.66 cM), to which these two genes assigned in this study, seems to be the same region. In the findings of Furthermore, gene is genes . We BLASOs01g0227100 orthologous chlorophyll-b reductase gene (TraesCS3A02G151900). The chlorophyll-b reductase initiates the process of reduction of chlorophyll-b to 7-hydroxymethyl chlorophyll-a. The downregulation or absence of chlorophyll-b reductase genes in Arabidopsis and Oryza sativa has been associated with stay-green traits (TraesCS3A02G151900 in the drought-tolerant C-306 genotype in contrast to the drought-sensitive WL-711 genotype under water-stress conditions (TraesCS2B02G395700) overlapped the QTN AX-94491160 associated with fructose content under severe drought-stress environment E04. The PsbQ protein is a part of the photosystem-II oxygen-evolving complex and is required for the calcium ion retention that is crucial for the stability of photosystem-II  overlapped the QTN AX-94619716 associated with glucose and sucrose content under a rainfed environment (E06). As revealed by Knetminer, the TraesCS4B02G371700 has a direct physical relationship with the sucrose transporter gene (SUT4) that cooccurs with carbohydrate and sucrose content in bread wheat. Likewise, there were a total of 72 overlapping genes that had a direct or indirect connection with either or all the studied traits in Knetminers. About 26 stable QTNs were likely to be associated with 24 genes prioritized for stem WSCs metabolism in the present study (TraesCS2A02G566000 and TraesCS2A02G566100). Two stable QTNs (AX-94995102 and AX-94495814) for fructans and TNSC content under severe water stress were found causative from a MYB-domain protein TraesCS1B02G316200. Earlier, TraesCS4B02G371700) was tagged under optimum water conditions for total stem WSC content with a stable QTN AX-94546364 (4B:657172059 bp). The stable and novel QTN from novel QNR qWSC-3A.7, AX-94501648 (3A:667793607 bp), was significant for TNSC in severe drought, tagged Os01g0919400-like sucrose phosphate synthase gene TraesCS3A02G425500. Furthermore, the co-regulatory network (CRNs) study based on the publicly available Affymetrix data (GSE9767 and GSE87325) indicated that a complex network of many genes works behind the veil for regulating the accumulation of stem WSCs. We reported that the number and type of participatory genes significantly change with the genetic background and growth conditions at an individual level. The twelve CRNs allowed us to better narrow down to a few hub genes (301). No hub gene was reported common across the twelve CRNs. However, there were hub genes common between different genetic backgrounds. The functional enrichment analysis followed by functional group-based networking  with the help of PlantCARE  and QTN x environment (QnxE) interactions. Stability of QNRs over the QTNs across the environments indicated that the phenotypic expression of the studied traits is limited to the fewer genomic regions with a substantial number of genes bound within a single and complex regulatory network with a fewer number of hub genes that further modulate with the genetic background of an individual and external stimuli. Within 1.5Kbp upstream from the starting coordinates (bp) of the possible candidate and overlapping genes, we identified 130 different lantCARE . These ClantCARE . ProbablqWSC-4A.2 and qWSC-7A.2, with the clusters of fructans metabolic genes, two of which were the center of gene networks; however, their expression was subjected to genotype and water conditions. The genevestigator based in silico expression analysis of these genes showed differential expression, depending upon the pertubation (TraesCS4A02G485000 and TraesCS7A02G009100) but structurally different proteins. The multiple sequence alignment showed many conserved amino acids across the fructans metabolic genes  can be found in the article/SS, AG, and YJ designed the study. SS, RT, and AG managed the trials at ICAR-IIWBR, Karnal. AG, YJ, VS, and DeK managed the trials at CCS HAU, Hisar. AG carried out phenotyping at Karnal and Hisar. SN guided in wet lab analysis. JS provided the FTNIR spectroscope facility. AG, SS, SJ, MI, and UA conducted GWAS. AG and SS conducted post GWAS analysis. AG and SS drafted the manuscript. YJ, SS, VS, GS, DK, GPS, and AR provided overall guidance and finalized the edited manuscript. All authors read and approved the final version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Genotyping platforms, as critical supports for genomics, genetics, and molecular breeding, have been well implemented at national institutions/universities in developed countries and multinational seed companies that possess high-throughput, automatic, large-scale, and shared facilities. In this study, we integrated an improved genotyping by target sequencing (GBTS) system with capture-in-solution (liquid chip) technology to develop a multiple single-nucleotide polymorphism (mSNP) approach in which mSNPs can be captured from a single amplicon. From one 40K maize mSNP panel, we developed three types of markers , and generated multiple panels with various marker densities (1K–40K mSNPs) by sequencing at different depths. Comparative genetic diversity analysis was performed with genic versus intergenic markers and di-allelic SNPs versus non-typical SNPs. Compared with the one-amplicon-one-SNP system, mSNPs and within-mSNP haplotypes are more powerful for genetic diversity detection, linkage disequilibrium decay analysis, and genome-wide association studies. The technologies, protocols, and application scenarios developed for maize in this study will serve as a model for the development of mSNP arrays and highly efficient GBTS systems in animals, plants, and microorganisms. This study reports the development of high-resolution multiple-SNP (mSNP) arrays, which can capture multiple SNPs from single amplicons, through integrating an improved genotyping by target sequencing approach with liquid chip technology. From a 40K maize mSNP panel, multiple panels with 1K–40K mSNPs (including up to 251K SNPs and 690 haplotypes) were generated by sequencing at different depths, then tested in various genetic analyses using a large number of maize inbred lines. The technical protocol can be widely used in other organisms. Assisted by DNA markers and other genomics tools, genetic research and breeding have been accelerated during the past two decades, particularly in government-funded breeding programs and multinational breeding companies, where established breeding facilities and platforms can be shared across countries and crop plants . HoweverAs a major molecular tool, marker-assisted selection (MAS) has been widely applied through the genotyping of breeding materials to obtain desired targets with specific marker alleles and their combinations. Various types of DNA markers and genotyping platforms, from first-generation agarose gel-based genotyping of RFLP markers to sequencing- and chip-based genotyping of SNP markers, have been developed and used in MAS . In addiTo develop high-throughput molecular markers that cover whole genomes, chip- and sequencing-based technologies have been developed. In maize, several chip-based genotyping platforms have been established using Illumina and Affymetrix systems; these platforms contain from 1536  to 50K–5Three sequencing-based strategies have been adopted to date. Full genome sequencing, as a complete solution for identifying all sequence variability, is still too expensive for genotyping many individuals, as required in genomics, genetics, and breeding. Partial or selective sequencing using restriction enzyme–digested DNA, generally called genotyping by sequencing (GBS) , can be Although significant progress has been made recently in target sequencing and in-solution capture , the curA total of 83 916 target regions were selected as candidate mSNP loci and went through the alpha panel test using 96 temperate maize inbred lines, and 46 377 mSNP regions were retained. In the beta test, 647 regular maize inbreds were employed. The regions retained in the alpha test were ranked based on their average missing rates and average sequencing depths, and 6377 loci were removed. As a result, 40 000 mSNP markers were finally selected to cover the whole genome . The selEach mSNP locus had an average of 6.62 SNPs with a range of 1–29, and 12 757 (34.7%) of the mSNP loci had 6 or 7 SNPs . From muIn the following data analyses, the tested markers were classified into five categories: (1) 40K high-PIC SNPs (one SNP with the highest polymorphic information content (PIC) value from each mSNP), (2) 40K random SNPs , (3) 251K SNPs , (4) 690K haplotypes , and (5) 159K haplotypes (with MAF > 5%).We developed multiple mSNP panels from the same mother panel (40K mSNPs). Based on PIC values, the six developed mSNP panels each contained a subset of mSNPs . The 20KTo understand how much sequencing depth is needed to cover the target mSNPs at a given missing rate, sequencing data were analyzed for each target mSNP number . In geneUPGMA (unweighted pair-group method with arithmetic means) trees were constructed with the 867 maize inbred lines using both 40K high-PIC SNPs and 251K SNPs. Almost identical phylogenetic relationships were identified with both marker panels, revealing nine groups, including the eight heterotic groups  reported previously using 20K GBTS markers  and SNP K) was 3, PCA revealed a clear separation of the three major germplasm groups: temperate, tropical, and sweet. Some sweet maize lines were clustered with the tropical group, whereas others were separate from both the temperate and tropical groups. When K = 6, sweet maize became a separate group. Several temperate heterotic groups, including SPT and PB, appeared, and three heterotic groups  remained together at this level. When K = 9, six of the seven temperate heterotic groups could be distinguished, whereas the sweet inbreds were dispersed into two independent subgroups, one of which stayed with LRC.Principal component analysis (PCA) showed a good agreement with cluster analyses . When thTo reveal genetic differences among different maize groups, we examined the differences in allele frequencies among three pairwise comparisons: temperate versus tropical lines, temperate versus sweet lines, and tropical versus sweet lines . Most ofComparison of genetic diversity in the 867 maize inbred lines revealed significant differences among groups . HaplotyTo develop SNP markers to cover the whole genome, no intentional selection was made for specific nucleotides or genomic regions, and thus the markers included in the mother mSNP panel can be used to evaluate the specific genetic variation. The most frequent mSNP type was [C/T] , followed by [A/G] (32.09%), [G/T] (9.85%), [A/C] (9.25%), and [A/T] (8.52%). Most mSNPs (74.3%) were intergenic, 15.3% were intronic, and only 6.2% were from other regions  . IntergeTo evaluate the power of markers in the detection of DNA variation, PIC and gene diversity (GD) were evaluated for markers from different genomic regions and for different marker types . CompareSelection of high-quality target regions in a marker development procedure provides an opportunity to identify indels at each SNP locus; this has been largely neglected in most studies owing to mapping error from non-specific capture sequence or indels of a single base. In this study, indels were evaluated for their frequencies and distribution across the genome and in tested germplasm.Across the 251K SNP loci, 2167 (0.86%) were insertions and 3633 (1.44%) deletions . Indels Selection of high-PIC SNPs from each mSNP locus resulted in more SNPs with higher MAF, PIC, and GD in the 40K high-PIC SNPs than in the 251K SNPs, whereas the distribution of observed heterozygosity was almost the same between the two datasets . For exar2 = 0.1, the distances in intergenic versus genic regions estimated from the 40K high-PIC SNPs and 251K SNPs were 73K versus 12K and 149K versus 26K, respectively  analysis was performed by marker type and with markers from different genomic regions for various germplasm groups . As indiectively A. The saectively . Among tectively B and 5C. smaller .Figure 5Our results largely confirm previous reports on maize LD decay  and also10P value of 54.5 was obtained with the 251K SNPs and was much higher than the threshold and than that reported in a previous association study (pericarp color 1 (P1), which spans chromosome 1 from 48118782 to 48147883 and regulates red pigmentation in the cob, pericarp, and tassel glumes , 20, 72, 129, and 72 SNPs on chromosome 1 were found to be significantly associated with cob color using four marker types: 40K random SNPs, 40K high-PIC SNPs, 251K SNPs, and 690K haplotypes, respectively . Using ton study . The phel glumes , was detCompared with genotyping technologies that detect only one SNP locus per amplification, mSNP markers, by which SNPs can be captured in solution (by liquid-phase chips), have contributed to the improvement of the genotyping platform in four ways. First, capturing multiple SNP markers at each target region increased the number of detectable SNPs 6.6-fold in this study. Second, 690K haplotypes, as an additional marker type, could be inferred from the multiple SNPs. Third, a core or high-PIC SNP marker set was developed from the SNPs with the largest PIC in each mSNP locus (amplicon). Fourth, detected DNA variation could be compared by within- and between-mSNPs and between SNPs and haplotypes. However, it should be noted that the multiple SNPs captured within an amplicon are more related to each other, although they are less likely to arise from alignment errors. First, to develop the mSNP array, target regions were selected from low-repeat genomic regions with high variability and more frequent SNPs. Second, regions with alignment errors were removed during the locus selection and optimization step. When a locus appears to be heterozygous in >50% inbred lines, an alignment error must have occurred. Therefore, the probability of mSNPs arising from alignment errors in this study should be very low, although it cannot be excluded.mSNPs were developed to capture not only multiple SNPs  but alsomSNPs are functionally suitable for multiple purposes. First, mSNPs can be developed for almost any organism, including plants, animals, and microorganisms. mSNPs are functionally suitable for all species, including those with polyploid chromosomes or very low genetic diversity. The numbers of SNPs identified in each amplicon range from 5.5 in wheat to 4.5 in rice and 6.6 SNPs in this study. Using the protocol developed here, high-density mSNP arrays have been developed in 13 plant and animal species to date . As an example of a polyploid species, wheat has been targeted for the development of mSNP markers. Because probes designed for target regions will capture all homologous fragments across the A, B, and D subgenomes, only homologous regions that are suitable for mSNPs were selected for probe design, bypassing regions that are less variable across the subgenomes. Thanks to the well-sequenced wheat subgenomes, 202 970 SNPs (5.1 SNPs/mSNP) were identified across 40 017 target regions (mSNPs) . Second, mSNP arrays are developed using variation information, and highly repetitive sequences are avoided; a perfect reference genome is therefore not required. Third, the marker number that can be included in an mSNP marker panel is very flexible, and it can be adapted to many potential application scenarios. The power of the 251K SNPs generated with the 40K mSNP array was not equivalent to that of the same number of SNPs obtained from separate amplicons, and thus the 40K mSNP array may not be powerful enough for performing high-resolution GWAS in highly diverse species such as maize. Application scenarios for the mSNP approach include DNA variation identification, germplasm fingerprinting, gene mapping and cloning, MAS, genomic prediction, and detection of transgenic events and genomic edits. In this study, a diverse maize germplasm collection of 867 inbred lines was evaluated, and detailed phylogenetic trees and high-resolution heterotic groups were constructed . LD decaTraditional marker platforms such as TaqMan, KASP, or DNA arrays (chips) allow only one SNP per amplicon; otherwise, interference will occur. Thus, the potential of mSNP markers cannot be fully explored with such platforms. Integrated with the GBTS platform, the mSNP approach has several advantages for genotyping. First, the mSNP approach makes marker panel design more flexible and upgradable. Marker panels can be designed based on the requirements of very specific projects for any marker number and any sample size. Designed marker panels can then be upgraded by adding more markers to the existing marker panel when necessary. This kind of flexibility is not possible for chip-based markers, as, once designed, the marker number and sample size per chip are fixed.The second advantage of the mSNP approach is associated with SNP calling and data management. Because SNP calls can be confirmed with each other using multiple SNPs within an mSNP locus, over 99.9% repeatability has been achieved regardless of the production batch, shelf-life, or laboratory associated with library construction and sequencing. Such levels of reliability and repeatability will greatly facilitate information management, including data collection, cross-comparison, accumulation, integration, and mining. Traditional GBS systems randomly sequence reduced genomes. The same set of samples, if genotyped at different times or across labs, will be sequenced at different genomic regions, making it difficult to compare and integrate data because of many missing calls. In some cases, missing alleles can be filled by imputation, if supported by high-quality reference genomes, fine-tuned haplotype maps, and a very large amount of accumulated genotypic data. Such resources exist in maize  but are The mSNP system, when integrated with GBTS, has fewer demands in terms of genotyping platform, information management, and decision support. Compared with solid DNA chips that require specific genotyping facilities, mSNP markers can be genotyped by almost any currently available sequencing platform. A regular biology lab can establish a genotyping system for mSNPs without any additional technical support or professional bioinformatics assistance. mSNP information can be managed through a current lab information management system without any special improvement or upgrades because of the high-quality marker data and the same set of genotyped markers. The simplicity of data management in mSNP genotyping provides an opportunity to develop a smart model for SNP calling, data management, and reporting. In conclusion, the mSNP approach with improved GBTS developed in this study combines the advantages of both solid-chip and traditional GBS platforms; the former is more stable and reliable, whereas the latter is more flexible and cost-effective. The most significant limiting factor, however, is that the improved GBTS may not have a genotype-cost advantage at the level of super-high marker density.The third advantage of the mSNP approach is that genotyping can be performed with two different systems, GenoPlexs based on multiplex PCR and GenoBaits based on capture-in-solution, covering a very flexible range of marker numbers from several markers to 40K mSNP loci or over 200K SNPs. For situations such as selection for single major genes, where only one or several markers will be genotyped, KASP would be the best choice at the current stage . An inteThe fourth advantage of the mSNP approach is its cost-effectiveness. Cost-effective genotyping platforms are a basic prerequisite for molecular breeding in small- and medium-size companies and for breeding programs in developing countries. GBTS has reduced the cost by at least half compared with a chip-based genotyping platform . The imphttp://gobiiproject.org) can be established among breeding companies and institutions to share genetic materials, breeding resources, benefits, and risks, as well as precision genotyping and phenotyping of all relevant breeding materials. The benefits produced in the breeding initiatives can be allocated among partners based on the genetic contributions of the parental lines involved. With the accumulation of very large genotypic, phenotypic, and envirotypic datasets and the optimization of genetic models and prediction methods, the genetic gain that can be achieved in breeding will be improved  content of 30%–70% were selected as candidate regions. Probes with a GC content of <30% are difficult to capture, reducing their capture ability, and genomic regions with a GC content above 70% have an adverse effect on PCR during sequencing.3.The hybridization reagents and wash buffer were optimized, including the concentration of the saline-sodium citrate buffer. As a result, capture efficiency and uniformity were improved.4.An improved library construction procedure free of quantification was developed, reducing the relevant cost by up to 50%.In traditional SNP genotyping, a pair of specific amplification primers (as in KASP and TaqMan) or a probe (for a chip) is designed based on each SNP marker, and only one SNP marker is generated per amplicon or hybridization. Therefore, the single identified SNPs form a uniform distribution on the genome. To maximize the use of the DNA sequences obtained from each amplicon, we developed a method for the identification of multiple SNPs in each single amplicon. The multiple SNPs developed from a single amplicon are called mSNPs. To develop the mSNP approach, we improved the currently available GBTS system  as folloThe above optimization procedures greatly improved both uniformity and capture efficiency, two of the most important cost contributors; uniformity was as high as 95% and capture efficiency as high as 70% in the 40K mSNP array.To select SNP markers for the development of an mSNP panel, HapMap3 data  were useGenoBaits Probe Designer  was used for probe design, and each locus was covered by two 110-nt probes. The probe set was synthesized by a semiconductor-based in-situ synthesis process. A 96-genotype panel  was usedUsing the 647 regular maize inbreds tested in this study as a genotype test panel, 46 377 SNP loci were ranked by their average missing rates and average sequencing depths, and 6377 loci were removed. Finally, 40 000 loci were selected to form a 40K mSNP marker panel. From this marker panel, we developed 20K, 10K, 5K, 2K, and 1K mSNP marker panels through the following steps. The genotype test panel was used to select 20K, 10K, 5K, 2K, and 1K mSNPs according to sequencing depth and even distribution on maize chromosomes. The higher the sequencing depth that can be captured at a marker locus, the less overall sequencing depth will be required in future genotyping; a lower minimum genotyping cost can therefore be achieved while keeping the marker missing score below the threshold. This means that the same set of 40K mSNPs can be used to generate 20K, 10K, 5K, 2K, and 1K mSNPs by sequencing at different depths.sh2 or sh2su1. Among the tested lines, 12  were genotyped twice as biological replicates.To evaluate the marker panels developed in this study, we used 867 diverse maize germplasm accessions: 288 tropical/subtropical, 246 sweet maize, and 333 temperate inbred lines from China, the US, and the International Maize and Wheat Improvement Center (CIMMYT). One hundred forty-five of them were used previously in the HapMap3 project . SeventyThe genomic DNA was extracted from 15 pooled leaf samples using the high-throughput CTAB method . DNA quality and concentration were measured with a NanoDrop 2000 instrument. DNA libraries were constructed through DNA fragmentation, end-repair, adaptor ligation, and PCR. Subsequently, library hybridization capture was performed using the 40K mSNP panel developed above. All the other experimental steps followed those of Raw sequencing reads were filtered to obtain clean reads. This was performed using fastp   to trim 2n, by which the number of theoretical haplotypes was determined.Haplotypes were constructed when two or more SNPs were scored from a single amplicon. Because the individuals sampled came from inbred lines, the vast majority of SNP genotypes were homozygous, making haplotypes unambiguous. Theoretically, for mSNPs within a single amplicon, the number of haplotypes within the amplicon is Marker analysis was performed at three levels. At the level of marker types, four marker types were derived from the 40K mSNP mother panel: 40K high-PIC SNPs , 40K random SNPs , 251K SNPs , and 159K haplotypes (MAF > 5%). At the level of genomic regions, SNP markers were classified into five categories: UTR5, intergenic, CDS, intronic, and UTR3. At the level of marker alleles, data analysis was performed for di-allelic SNPs and indels.Pi and Pj are the population frequencies of the ith and the jth alleles. GD is relevant to the sum of squares of allele frequencies and estimated as:Pi is the frequency of the ith allele. The genetic distance between genotypes was evaluated using the average nucleotide difference of the genotype in TASSEL 5.0  between each pair of inbred lines. PCA was performed with TASSEL 5.0 to reduce false positives , the Central Public-interest Scientific Institution Basal Research Fund (Y2020PT20), the 10.13039/501100012421Agricultural Science and Technology Innovation Program (ASTIP) of the 10.13039/501100005196Chinese Academy of Agricultural Sciences (CAAS) (CAAS-XTCX2016009), the Key Research Area and Development Program of Guangdong Province (2018B020202008), the Shijiazhuang Science and Technology Incubation Program (191540089A), and the Hebei Innovation Capability Enhancement Project (19962911D). Research activities at CIMMYT were supported by the 10.13039/100000865Bill and Melinda Gates Foundation and the 10.13039/501100015815CGIAR Research Program MAIZE.This research is supported by the Yunbi X. and J.Z. conceived the project and designed the experiments. Yunbi X., J.Z., Y.W., M.S.O., and B.M.P. raised funding. Z.G., Q.Y., F.H., Yanfen X., C.Z., J.T., Q.Y., H.Z., J.Z., and Z.S. performed the experiments. Yunbi X., J.Z., Z.G., Q.Y., F.H., Yanfen X., C.Z., Y.W., K.W., and J.T. analyzed and interpreted the data. Yunbi X., J.Z., Z.G., F.H., and K.W. prepared and wrote the manuscript. All authors contributed to discussion of the manuscript.