AX-95025823 was identified for TKW in both E4 and E5 environments along with pooled data, which is located at 68.9 Mb on 6A chromosome. In silico analysis revealed that the SNPs were located on important putative candidate genes such as Multi antimicrobial extrusion protein, F-box domain, Late embryogenesis abundant protein, LEA-18, Leucine-rich repeat domain superfamily, and C3H4 type zinc finger protein, involved in iron translocation, iron and zinc homeostasis, and grain size modifications. The identified novel MTAs will be validated to estimate their effects in different genetic backgrounds for subsequent use in marker-assisted selection. The identified SNPs will be valuable in the rapid development of biofortified wheat varieties to ameliorate the malnutrition problems.Genetic biofortification is recognized as a cost-effective and sustainable strategy to reduce micronutrient malnutrition. Genomic regions governing grain iron concentration (GFeC), grain zinc concentration (GZnC), and thousand kernel weight (TKW) were investigated in a set of 280 diverse bread wheat genotypes. The genome-wide association (GWAS) panel was genotyped using 35 K Axiom Array and phenotyped in five environments. The GWAS analysis showed a total of 17 Bonferroni-corrected marker-trait associations (MTAs) in nine chromosomes representing all the three wheat subgenomes. The TKW showed the highest MTAs (7), followed by GZnC (5) and GFeC (5). Furthermore, 14 MTAs were identified with more than 10% phenotypic variation. One stable MTA i.e.  Iron, zinc, and vitamin A are the three nutrients that are recognized as limiting factors in the diet by the world health organization2. Approximately, one-fourth of the population around the globe is suffering from anemia due to iron deficiency3. The women of reproductive age are more vulnerable as one in every three women is anemic, which lead to 0.12 million deaths and a loss of 48.2 million disability-adjusted life years (DALY) in 20104. Anemia due to acute iron deficiency, particularly in children, pregnant, and lactating women lead to life-threatening health complexes such as chronic heart disease, kidney failure, and inflammatory bowel disease5. Zinc is another important micronutrient essential for various immunological and biochemical functions and severe deficiency may lead to impaired growth and development, altered immunity, pregnancy issues, and neuro-behavioral difficulties6. Around 17% of the world’s population is suffering from diseases related to zinc deficiency7, which leads to 97,330 deaths and a loss of 9.1 million DALY’s in 20104. Micronutrient deficiency is the major risk factor for health loss in developing countries and the most vulnerable groups are pregnant women and children8.Over three billion global population suffers from diseases associated with micronutrient deficiencies including iron and zinc and the problem is more severe in the countries where the food habits are dominated by cereal-based diets9. Therefore, enhancement of the nutritional value of crops through conventional and molecular approaches, termed as “biofortification”, has been recognized as an economical and sustainable strategy to reduce the problems associated with micronutrient and protein malnutrition.Wheat is one of the most widely cultivated cereals and plays a key role in global food and nutritional security. Although wheat is nutritionally rich as compared to the other two major cereals (rice and maize), still most wheat-based diets fail to meet the required quantity of essential nutrients including iron and zinc. The problem of micronutrient malnutrition can be overcome by food fortification, supplementation, and diet diversification, but were unsustainable in a long run. The affordability and accessibility, particularly for the rural poor in remote areas are the other shortcomings associated with the above-mentioned approaches13. In the past decade, extensive efforts have been made to identify QTLs associated with GFeC and GZnC28, and TKW33 in wheat through bi-parental populations based QTL mapping. However, QTLs identified in such approaches had a low resolution due to the restricted number of crossovers. In contrast, the mapping resolution could be greatly increased by using linkage disequilibrium (LD)-based association mapping approach where the mapping population represents a more diverse gene pool and considers historical recombination events34. This approach allows the detection of non-random associations of genome-wide markers with the phenotype35 and has been used widely to detect the markers associated with the genomic regions governing complex traits in crop plants36. The QTL resolution in association mapping has been significantly improved by using unrelated diverse genotypes that have accumulated many historical crossover events since their last common progenitors diverged37.Genetic dissection of complex quantitative traits through trait mapping approaches is essential for developing better marker-assisted breeding and genomic selection strategies. The identification of linked molecular markers governing complex traits is highly useful and economical for trait improvement, especially in the post-genomics era where the genotyping costs become much cheaper. The quantitative inheritance of wheat quality traits and significant effects of environment and genotype-environment interaction (GEI) on the expression of GFeC GZnC and TKW were documented in several studies38, and LD decay has not been well characterized. Alomari et al.39 identified 40 MTAs for GZnC covering all the three wheat subgenomes in a panel of 369 genotypes using a high-density SNP array. Similarly, Bhatta et al.40 used a diversity panel of synthetic hexaploid wheat (SHW), being a great reservoir of grain micronutrients, to identify 92 MTAs for 10 micronutrients including GFeC and GZnC. Velu et al.41 reported 39 MTAs for GZnC in a set of 330 bread wheat genotypes phenotyped in a wide range of environments. Liu et al.42 identified 14 significant MTAs for GFeC and GZnC, and manganese in a panel of 161 wild emmer-derived advanced lines. Genetic dissection of micronutrients including GFeC and GZnC has been performed in a diverse HarvestPlus association mapping panel consisting of 330 genotypes from CIMMYT’s biofortification breeding program43. A total of 16 loci were identified which are associated with the GZnC on 11 different chromosomes covering all three wheat subgenomes in a set of 246 wheat varieties44. Similarly, Calderini et al.45 used a set of 167 Ae. tauschii accessions to map nine MTAs governing GFeC and GZnC46. A total of 29 unique loci associated with grain GZnC was identified in a diversity panel of 207 bread wheat genotypes47.Although many GWAS studies have been performed for various agro-morphological traits, only a limited number of studies were conducted for nutritional quality traits in wheat. Furthermore, hexaploid wheat has a genome size of ~ 17 Gb91 using different compositions of GWAS panels. Therefore, more GWAS studies would be helpful to identify the genomic regions governing nutritional traits in wheat and also to identify the candidate genes to develop biofortified cultivars. The present study aimed to identify the genomic region(s) associated with GFeC, GZnC, and TKW in diverse bread wheat genotypes in a range of environments through the GWAS approach and the putative candidate genes associated with the SNPs.The TKW has no nutritional value per se in wheat, however, it has a dilution effect on protein and micronutrients. Therefore, TKW is one of the important breeding objectives due to its twin effects on yield and protein. The MTAs have been identified for TKWo 54′ N, 75o 48′ E, 247 m AMSL). The crop was sown in the first fortnight of november during the 2020–2021 Rabi (winter) season under irrigated condition. The genotypes were planted in an augmented block design with only the checks  repeated in a 2 row of 2 m length with a row spacing of 20 cm.A set of 280 genetically diverse bread wheat genotypes (Supplementary Table 52. Coefficient of variation (CV), broad-sense heritability (h2BS), genotypic variance (σ2G), and environmental variance (σ2E) were calculated using the following formula:w is the degrees of freedom within groups from the ANOVA table. treatments – MSresiduals)/ nBlock; residual; nBlock = Number of blocksRandomly selected 20–25 spikes were harvested and bulk-threshed manually in a clean cloth bag without touching any metal to avoid contamination. Around 20 g of grain sample from each genotype were used for phenotyping GFeC and GZnC through high-throughput Energy Dispersive X-ray Fluorescence (ED-XRF) machine  calibrated with glass beads-based values. To record TKW, the Numigral grain counter was used to count the grain number, the reading was set at 1000 grains and the weight of the grains was recorded in grams with an electronic balance. Phenotypic data were analysed using the R package ‘augmentedRCBD’2BS is the proportion of phenotypic variation that is attributable to an overall genetic variation for the genotypes. LSD is the value at a particular level of statistical probability, when exceeded by the difference between two genotypes means, then the two genotypes are said to be distinct for at that or lesser levels of probability. The σ2G is the genetic or inherent variation that remains unaltered by environmental changes, this kind of variation responds to the selection during breeding process. In contrast, σ2E does not respond to selection as it is non-heritable, which is entirely due to environmental effects.The CV indicates the degree of precision with which the treatments are compared and is a good index of the experimental reliability. It expresses the experimental error as percentage of the mean and if the value is high then the precision of the experiment is low and vice versa. The h53. The panel was genotyped using Axiom Wheat Breeder’s Genotyping Array  having 35,143 genome-wide SNPs. The monomorphic, markers with minor allele frequency (MAF) of < 5%, missing data of > 20%, and heterozygote frequency > 25% were removed from the analysis. The remaining set of 14,790 high-quality SNPs was used in GWAS analysis (Supplementary Table Genomic DNA of the GWAS panel was extracted from the leaves of 21 days-old seedlings by Cetyl Trimethyl Ammonium Bromide (CTAB) methodr2) between the SNPs located in each chromosome were calculated with Trait Analysis by aSSociation Evolution and Linkage (TASSEL) version 5.054. The LD block size in three different subgenomes as well as in the whole genome was calculated by keeping r2 threshold at half LD decay  replications after burn-in. The STRUCTURE HARVESTER56 was used to detect the optimal K-value based on ad-hoc method described by Pritchard et al. 201090 as well as Evanno’s method57. The suitability of the model to account for population structure was assessed using quantile–quantile (Q–Q) plots.The pair-wise LD values  version 3.080 in R software package. Determining the correct P-value threshold for statistical significance is critical to differentiate true positives from false positives. To determine the statistical significance threshold in GWAS, Bonferroni correction has been employed. To estimate Bonferroni correction, α was set to 0.05 and which is divided by total number of SNPs. The Bonferroni-corrected SNPs were considered for significant association and R2 was used to describe the percentage variation explained (PVE) by significant MTAs.The phenotypic values of GFeC, GZnC, and TKW of 280 diverse genotypes along with corresponding genotyping data were used in GWAS analysis. Significant MTAs were identified using BLINK (Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway) modelhttp://plants.ensembl.org/index.html) of the bread wheat genome (IWGSC (RefSeq v1.0)). The genes found in the overlapping region and within the region of 10 Kb intervals flanking either side of the associated marker were considered as putative candidate genes and their molecular functions were determined. In addition, their expression patterns were investigated using the Wheat Expression database (http://www.wheat-expression.com/) and potential links to phenotypes was determined using Knetminer tool integrated with Wheat Expression database. The role of the identified putative candidate genes in the regulation of GZnC and GFeC, and TKW was also determined with the previous reports.The sequence information of the significant SNPs was used to search for putative candidate genes with Basic Local Alignment Search Tool (BLAST) using default parameters in the Ensemble Plants database (2G) and environmental variance (σ2E) are presented in Table The environment-wise heritability and variance components of the GWAS panel for GFeC, GZnC, and TKW are presented in Table r2) of GFeC, and GZnC by keeping TKW as a controlling factor was determined. Highly significant and positive correlation was observed between GFeC and GZnC in E1 (0.296**), E2 (0.276**), E3 (0.202**), E4 (0.520**), and E5 (0.35**) and also in pooled data (0.358**).The trait and environment-wise mean values are illustrated graphically through boxplots and presented in Fig. The quality processing of 35,143 SNPs from 35 K array resulted in a set of 14,790 cured genome-wide SNPs. These high-quality set of SNPs were further used for GWAS analysis. The chromosome and genome-wise marker distribution are presented in Table 2) for all the SNPs and plotted against the genetic distance (bp). The LD decay for the whole genome was 4.9 cM and it was found that the decay was rapid in the A subgenome (3.6 cM) followed by the B subgenome (5.7 cM) and the D subgenome (5.2 cM)  on 7B and 5A chromosomes located at 706.0 Mb and 558.3 Mb explained the highest phenotypic variation of 24.1% and 23.1%, respectively in E4 environment. One SNP each on chromosome 6A (AX-94423274), 3B (AX-94490975), and 1A (AX-95195514) were mapped at 609.1 Mb, 795.8 Mb, and 354.9 Mb, respectively with the phenotypic variation of 15.6%, 12.7%, and 13.0% in E2 environment.A total of five significant MTAs were identified for GFeC in E2 and E4 environments on chromosomes 6A, 3B, 1A, 7B, and 5A explaining the phenotypic variation ranged from 12.7% to 24.1%. Two major SNPs (AX-95118780 and AX-95140213) on 7B chromosome located at 91.6 Mb and 94.2 Mb explained the highest phenotypic variation of 10.9% and 10.0%, respectively in E1 and E3 environments. Another major SNP (AX-95113687) on the A subgenome (6A chromosome) mapped at 595.5 Mb, explained 10.1% phenotypic variation in E1. The remaining two SNPs (AX-94390652 and AX-94524014) on 2B and 5B chromosomes mapped at 201.4 Mb and 440 Mb explained 5.7% and 8.8% phenotypic variation, respectively in E1.A total of 5 MTAs were identified for GZnC on chromosome 7B, 6A, 2B, 5B, and 7B explaining the phenotypic variation ranged from 5.7% to 10.9%. The B subgenome contributed more MTAs (4) followed by A subgenome (1), whereas, D subgenome didn’t contribute for GZnC in the present study. Two major SNPs  were identified in E4 on chromosome 5A, 6A, 7B, 5B, 2D, and 1A at 444.8 Mb, 68.9 Mb, 131.7 Mb, 689.9 Mb, 461.3 Mb, and 499.8 Mb, respectively with a corresponding phenotypic variation of 16.1%, 16.1%, 14.9%, 13.7%, 17.4%, and 16.7%. A total of 2 MTAs (AX-95117294 and AX-95025823) were mapped in E5 located at 290.3 Mb and 68.9 Mb, which explained 10.7% and 11.7% phenotypic variation on 5D and 6A, respectively.A total of seven MTAs were identified covering all the subgenomes. Major phenotypic variation was observed from those MTAs which were ranging from 10.7% to 17.4%. The three subgenomes mapped more or less the same number of MTAs . Six MTAs (AX-95025823) was mapped on 6A and located at 68.9 Mb, which explained 16.9% phenotypic variation. One stable MTA i.e. AX-95025823 was identified in both E4 and E5 environments along with pooled data, which is located at 68.9 Mb on 6A chromosome.For pooled TKW data, one MTA . Similarly, another SNP i.e. AX-94699865 associated with GFeC encodes an important F-box domain (TraesCS7B02G312400). Two important SNPs i.e. AX-94524014 (TraesCS5B02G257700) and AX-95203413 (TraesCS7B02G083600) associated with GZnC were found to encode Late embryogenesis abundant protein, LEA-18 and RNA recognition motif domain. Similarly, AX-95235178 encoding Leucine-rich repeat domain superfamily (TraesCS1A02G309000) and AX-95117294 encoding C3H4 TYPE ZINC FINGER PROTEIN (TraesCS5D02G188300) identified for TKW.The significant SNPs associated with GFeC, GZnC, and TKW were used to identify the putative candidate genes using the annotated wheat reference sequence (RefSeq V1.0) and are presented in Table Understanding the genetic basis of complex traits such as GFeC, GZnC, and TKW through GWAS with a diverse panel of genotypes can significantly improve QTL mapping resolution compared to bi-parental populations-based QTL mapping. Using the genome-wide SNPs and multi-environment data, several significant SNPs were identified in this study.11, and TKW13. The magnitude of environmental interaction decides the identification of environment-specific QTL(s) as well as QTL(s) that can express stably across environments.The expression of GFeC, GZnC, and TKW is significantly affected by the environment and genotype-environment interactions (GEI). Among all traits, GZnC was the most environment-sensitive trait, whereas, TKW was relatively the most stable with minimum environmental influence. The greater magnitude of the environment and GEI have also been reported in previous studies for the expression of GFeC and GZnC72. The associations were highly significant positive in all the environments betweenGFeC and GZnC. Significant and positive correlations found in this study have also been reported in earlier studies26. The significant positive correlations between GZnC and GFeC indicated the possibility to map the genomic regions controlling multiple traits. Such co-mapped SNPs will be much useful in marker-assisted selection for simultaneous improvement of correlated traits.The highest heritability was recorded for TKW followed by GFeC, and GZnC, whereas, the trend for the coefficient of variation (CV) was exactly opposite with the lowest CV recorded for TKW and the highest CV for GZnC. The highest and lowest heritability for TKW and GZnC respectively is also concurred with earlier studies74. The LD blocks are usually larger in self-pollinated crops such as wheat and hence decay slowly75, whereas, in outcrossing crop species like maize76, the LD decays rapidly. The presence of high LD across the genome would reduce the QTL mapping resolution and vice versa77. In such cases, a better QTL resolution will be achieved by using genome-wide SNPs. The decay of LD was found comparable in the B and D subgenomes (~ 5 cM) compared to the A subgenome, which had a shorter decay distance of around ~ 3 cM. A similar pattern of LD decay was also observed in other GWAS studies in wheat91.The STRUCTURE model explained 8 sub-groups in the populations. The genotypes in GWAS panel consists of advanced breeding lines suitable for various agro-climatic and production conditions. The first subgroup consists of genotypes mostly selected from international breeding material and suited for North West and North East Plains Zone in India. Similarly, the second group consists of international selections for restricted irrigated or rainfed production conditions. The third subgroup consists of genotypes dominated by 1B.1R translocation with genes for wider adaptation. Subpopulation 4 is mainly dominated by GW322, PASTOR, and OPATA parentage, whereas, 5th subpopulation largely consists of Indian wheat varieties/germplasm in their parentage. High frequency of SOKOLL, KIRITATI, PBW65, and MILAN was present in the 6th subpopulation parentage. Genotypes in 7th subpopulation are dominated by old salinity/alkalinity tolerant varieties. Whereas, 8th subpopulation contains mainly indigenous germplasm, old landraces, and breeding lines. The PC1, PC2 and PC3 of PCA analysis were used as covariates in the GWAS analysis to identify the MTAs. The LD may vary in different populations due to population size, genetic drift, admixtures, selection, mutation, non-random mating, pollination behavior, and recombination frequency41 and yield-contributing traits50.A total of 17 Bonferroni-corrected MTAs were identified for GFeC (5), GZnC (5), and TKW (7). The identified genome-wise MTAs are much higher for B subgenome (8) and A subgenome (7) compared to the D subgenome (2). A similar trend on MTAs identified in the D subgenome for GFeC and GZnC44, 1A, 3B, and 5A26, 1A40 and 6A, 3B91 were identified at different positions. A total of five novel MTAs was identified for GZnC on chromosome 7B, 6A, 2B, 5B, and 7B. MTAs in the same chromosomes were also identified in different GWAS panels in previous experiments on 6A47, 2B47, 5B47, and 7B91. Zhou et al.47 identified an MTA on 5B chromosome located in a interval of 407.0 Mb – 412.1 Mb, which was similar to that of AX-94524014 located on 5B chromosome and mapped at 440.1 Mb explained 8.8% phenotypic variation.The identified MTAs (5) for GFeC on chromosomes 6A, 3B, 1A, 7B, and 5A in this study were novel, as the earlier reported MTAs on the same chromosomes namely 3B, 7B, and 5A50, 5B49, 5A26, 1D29, 1A48 and 2D, 5D, 7A and 7B91.A total of seven MTAs in different environments were identified covering the three subgenomes and all were major MTAs as they explained more than 10.0% phenotypic variation. The TKW was relatively the most stable trait compared to the rest of the other two traits, as TKW recorded the highest heritability and lowest coefficient of variation which reflected in detecting the highest number of MTAs as well. All the identified MTAs were mapped on 5A, 6A, 7B, 5B, 2D, 7A, and 5D located at 444.8 Mb, 68.9 Mb, 131.7 Mb, 689.9 Mb, and 461.3 Mb, 499.8 Mb, and 290.3 Mb respectively. Previous reports were also identified MTAs on 6A and 7BMulti antimicrobial extrusion protein (TraesCS3B02G562500) has a role in the translocation of iron during iron deficiency stress in bread wheat59 and multi antimicrobial extrusion protein (MATE) family proteins were observed under iron excess in rice. Few protein members of MATE family were known to be involved in efficient iron translocation from roots to shoots in rice60. Also, MATE transporter mediates iron homoeostasis under osmotic stress in Arabidopsis61. Subfamily III of the MATE gene members plays an important role in plant aluminum tolerance and iron translocation in Arabidopsis65. FRD3 MATE transporter locus reveals cross-talk between Fe homeostasis and Zn tolerance in Arabidopsis by loading Zn into xylem62. MATE is also a candidate for the mechanism of Fe influx into aerial parts of the plant and the distribution of intracellular Fe64.The various putative candidate genes underlying MTAs with high phenotypic variation for GZnC, GFeC, and TKW were identified through BLAST search Table . The MTAOs01g0684900, Os10g0345100, and Os06g0495500 of citrate transporters family (MATE family protein) were observed under excess iron conditions and involved in iron transportation in rice83. Similarly, a MATE gene (OsFRDL1), the closest homolog of barley HvAACT1  is involved in the efficient translocation of Fe under limited Fe conditions60. FRD3 is a member of the multidrug and toxin efflux (MATE) family, which is involved in the efficient translocation of iron in Arabidopsis84. FRD3 is mainly expressed in root vascular tissues and is necessary to solubilize Fe and Zn in the extracellular space. Similarly, overexpression of MtMATE69 affected Fe and Zn accumulation in Medicago truncatula hairy roots, further suggesting a function for MtMATE69 in Fe nutrition85. Also, two MATE proteins namely, GmFRD3a and GmFRD3b play a significant role in iron efficiency in soybean86. Cloning and characterization of an Arabidopsis gene i.e. FRD3, a member of the multidrug and toxin efflux family is involved in iron homeostasis81. The FRD3, which is an efflux transporter of the efficient Fe chelator citrate is involved in Fe homeostasis maintenance throughout plant growth and development. Additionally to its well-known root expression, FRD3 is also strongly expressed in seeds and flowers82.Three up-regulated genes i.e. AX-94699865 associated with GFeC encodes an important F-box domain (TraesCS7B02G312400) regulates STOP1 in Arabidopsis. STOP1-ALMT1 pathway promotes iron accumulation into the apoplast of root tip regions under Pi-deficient conditions67. Another SNP i.e. AX-94524014 (TraesCS5B02G257700) associated with GZnC was found to encode LEA protein, where LEA-18 was involved in the transportation of iron in the phloem of castor68. The binding of LEA proteins to different molecules like Zn ion, DNA and ATP binding, were the major activities for the action of upland LEA proteins69.One SNP i.e. i.e. AX-95235178 encoding Leucine-rich repeat domain superfamily (TraesCS1A02G309000) was associated with TKW. A total of 32 barley orthologs were identified as potential candidate genes that determine barley grain size or weight. The barley ortholog of the rice OsBDG1 gene is mapped on 3H chromosome at 666.35 Mb (HORVU3Hr1G104350), which encodes the leucine-rich repeat receptor-like protein kinase family70. The rice OsBDG1 gene encoding a small protein with short leucine-rich-repeats possessing cell elongation activity, has previously been proven to positively regulate grain size in rice71. Therefore, HORVU3Hr1G104350could be a reliable candidate gene affecting grain size as the function of the OsBDG1 gene.The SNP FASCIATED EAR2 (FEA2) encodes the maize ortholog of CLAVATA2 (CLV2), encoding a leucine-rich repeat receptor-like protein that regulates meristem size by transmitting signals from CLAVATA3 (CLV3) peptide ligand to the WUSCHEL (WUS) homeodomain transcription factor. The FEA2 has a role in total kernel number and kernel size in maize87. Similarly, IKU pathway represents one of the well-studied genetic networks involves four major genes including HAIKU2 (IKU2), which encodes a leucine-rich repeat kinase, mutational analyses of these genes in Arabidopsis revealed their physiological significance in controlling endosperm development and thereby seed size through regulating endosperm proliferation and cellularization88, and loss of function mutations in IKU pathway genes cause a decrease in seed size. Another SNP (AX-95117294) encoding C3H4 type zinc finger protein (TraesCS5D02G188300) was associated with the expression of TKW. Functional prediction of maize C2H2—zinc finger gene revealed its involvement mainly in the formation of important agronomic traits in maize yield89.Another grain weight controlling gene i.e. The study with 280 diverse set of bread wheat GWAS panel has shown that GFeC, GZnC, and TKW were quantitatively inherited traits. The strong positive correlation between the GFeC and GZnC suggested the possibility of improving both the traits simultaneously. A total of 17 MTAs including 5 for GFeC, 5 for GZnC, and 7 for TKW were identified from the GWAS approach. The environment-specific and pooled-data MTAs identified in the present investigation represented novel genomic regions associated with trait expression. Several putative candidate genes encoding important molecular functions such as iron translocation, iron and zinc homeostasis, and grain size modifications were associated with the identified MTAs. Further validation and functional characterization of the candidate genes to elucidate the role of these genes in wheat is envisaged. The identified SNPs could be useful in marker-assisted selection programs to develop biofortified varieties to reduce micronutrient malnutrition.The set of 280 genotypes used in the present experiment were selected from All India Coordinated Research Project on Wheat and Barley and the imported genotypes have been obtained through the nodal agency for germplasm exchange i.e. National Bureau of Plant Genetic Resources, New Delhi following the prescribed guidelines. Also, the authors have all the required permissions and rights to collect and use the genotypes for research purpose. The experimental research and field experiments in the present study are duly approved by the institute research council of ICAR-Indian Institute of Wheat and Barley Research, Karnal.Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.


Oncorhynchus mykiss) and to assess the accuracy of GP based on imputed WGS and different preselected single nucleotide polymorphism (SNP) arrays. A total of 192 and 764 fish challenged to a heat stress experiment for 62 days were genotyped using a customized 1 K and 26 K SNP panels, respectively, and then, genotype imputation was performed from a low‐density chip to WGS using 102 parents  as the reference population. Imputed WGS data were used to perform GWAS and test GP accuracy under different preselected SNP scenarios. Heritability was estimated for body weight (BW), body length (BL) and average daily gain (ADG). Estimates using imputed WGS data ranged from 0.33 ± 0.05 to 0.55 ± 0.05 for growth traits under chronic heat stress. GWAS revealed that the top five cumulatively SNPs explained a maximum of 0.94%, 0.86% and 0.51% of genetic variance for BW, BL and ADG, respectively. Some important functional candidate genes associated with growth‐related traits were found among the most important SNPs, including signal transducer and activator of transcription 5B and 3  and cytokine‐inducible SH2‐containing protein (CISH). WGS data resulted in a slight increase in prediction accuracy compared with pedigree‐based method, whereas preselected SNPs based on the top GWAS hits improved prediction accuracies, with values ranging from 1.2 to 13.3%. Our results support the evidence of the polygenic nature of growth traits when measured under heat stress. The accuracies of GP can be improved using preselected variants from GWAS, and the use of WGS marginally increases prediction accuracy.Through imputation of genotypes, genome‐wide association study (GWAS) and genomic prediction (GP) using whole‐genome sequencing (WGS) data are cost‐efficient and feasible in aquaculture breeding schemes. The objective was to dissect the genetic architecture of growth traits under chronic heat stress in rainbow trout ( The incRainbow trout have a narrow optimal temperature that ranges from 12°C to 18°C, with 25°C the upper limit for suitable trout rearing Raleigh, . AdaptabGenome‐wide association studies (GWASs) are commonly used to dissect the genetic architecture of disease resistance  through genomic prediction (GP) schemes, including traits controlled by a high number of quantitative trait loci (QTLs) with small effects, such as growth‐related traits. GP strategies have revolutionized most breeding schemes globally, including aquaculture species, particularly for traits that are expensive or impossible to measure in selection candidates  perform a genome‐wide association study to dissect the genetic architecture, and identify molecular markers and candidate genes associated with growth traits in rainbow trout; and (ii) investigate the accuracy of genomic predictions based on imputed WGS, using different scenarios of preselected variants from GWAS for growth traits under chronic heat stress in rainbow trout.22.13, with a similar representation of each family per tank. The temperature was increased gradually (1°C/day) until the 9th day and then kept between 18 and 22°C for 62 days. Water flow was maintained at a rate of 1 to 1.5 L per hour and the oxygen saturation in the water was kept above 80% during the experiment. The fish received two weekly treatments of seawater at 10 ppt to avoid opportunistic bacterial infections and the challenge test spanned 62 days, with mortality recorded daily. More details about the breeding programme and the upper‐thermal challenge were described by Yoshida, Bangera et al.  and body weight  after the challenge as growth traits for further genetic evaluation, and the initial body length  and initial body weight  at the beginning of the challenge as covariates, respectively. Additionally, we also used the average daily gain  = (BW − IBW)/ as the growth trait measured during the challenge test.2.2Oncorhynchus mykiss (GenBank Assembly Accession GCA_013265735.3 USDA_OmykA_1.1). The samples were genotyped using the targeted genotyping by sequencing SeqSNP technology , minor allele frequency (MAF) <0.01, and genotyping call rate for SNPs and samples of <0.70. A total of 0.37 K and ~10 K SNPs from 613 and 192 animals were retained, respectively.Genomic DNA was extracted and purified from fin clip samples of 956 fish using the DNeasy Blood & Tissue Kit (Qiagen) according to the manufacturer's protocol. A total of 764 and 192 samples were genotyped with a customized 1 K and 26 K SNP panels (SeqSNP), respectively. The SNPs were selected from a denser SNP panel  consisting of 2.18 GB of total sequence comprising 1228 contigs with a contig N50 of 15.5 Mb. The Burrows–Wheeler Aligner (BWA) analysis tools  was used. The final VCF file consisted of a set of 22.6 million nonredundant variants from 102 rainbow trout. Quality control was performed with Plink v1.90 , MAF <0.01 and call rate for SNPs <0.80, retaining a total of ~3.2 million SNPs from 102 samples.Genomic DNA was extracted from fin clips of 102 parents  from the challenged population using the DNeasy Blood & Tissue Kit (Qiagen) according to the manufacturer's instructions and submitted to the Beijing Genomics Institute  for whole‐genome sequencing using DNBseq technology. Raw sequencing data were aligned to the r2 = squared correlation between true and imputed genotypes) and remove variants with low imputation accuracy, we randomly divided the resequenced animals into five cross‐validation sets: five exclusive reference sets with 80% of animals genotyped with ~3.2 million SNPs and validation sets with 20% of animals genotyped with 10 K SNPs masking the remaining SNPs from WGS genotypes. After imputation, a total of 1,821,336 SNPs with r2 value greater than 0.80 were retained as the reference data set for the final imputation.To assess the overall imputation accuracy  resulted in a total of 1,390,748 imputed SNPs available for 850 individuals denoted as imputed WGS genotypes.Stepwise genotype imputation was used to perform the final imputation from 613 animals genotyped with 0.37 K SNPs to 10 K SNPs using 192 animals as the reference data set (step 1), and 805 individuals  with 10 K SNPs were imputed to ~1.8 million SNPs using 102 animals as the reference (step 2). FImpute v. 3.0 .2.3y is a vector of phenotypes ; β is a vector of tank as fixed effect for all traits and body length and body weight at initial challenge test for BL and BW as covariate, respectively; a is a vector of additive genetic effects that follows a normal distribution ∼N, where σa2 is the additive variance and H is the kinship matrix , where I is the identity matrix and σe2 is the residual variance. In the wssGBLUP, the pedigree relationship matrix −1A is replaced by matrix −1H  method .The top five SNPs that explain the largest proportion of genetic variance for each trait were selected as the lead SNP and used to search for candidate genes based on a window within 100 kb upstream and downstream of each lead SNPs to be considered putative candidate genes associated with the trait. The gene search was performed using BLAST  against the 2.4σa2) and residual variance (σe2) were estimated using the kinship matrices A and H for pedigree‐based BLUP (PBLUP) and ssGBLUP using imputed WGS, respectively. The following equation was used to compute the heritability for each growth‐related trait:The total additive genetic variance , all available markers from imputed WGS genotypes were used. The 50 K pruned scenario (50K_pruned) was selected to produce a subset of markers proportionally distributed across the genome according to chromosome size, as evenly spaced as possible and in similar levels of linkage disequilibrium (LD) with each other SNP  or ∼N, for, respectively, pedigree relationship matrix A in PBLUP and genomic relationship matrix G in GBLUP, as described by VanRaden  were randomly divided into five exclusive data sets, determined one at a time, where 80% of the animals were used as the training data set to estimate the SNP effects, and the phenotypes of the remaining 20% of the animals were masked and used as the validation set.The predictive abilities of pedigree‐ and genomic‐based models were assessed using the same subgrouping of the fivefold cross‐validation used to select the most important SNPs in scenarios 50K_wssGBLUP and 1K_wsGBLUP. All genotyped and phenotyped animals  and the phenotype, while h is the square root of heritability.To evaluate the performance of each scenario and model, the accuracies were measured in the validation sets using the following equation , BL was 29 cm (SD = 2.4 cm) and BW was 388 g (SD = 96 g) , and for 26 K and 1 K SNP panels ~3.3 K and 94 SNPs were discarded, respectively (Table A uniform MAF distribution and a right‐skewed distribution were observed for imputed WGS genotypes (MAF average = 0.23) and the 50K_pruned scenario (MAF average = 0.21), respectively Figure . In contThe additive genetic variance and heritability were slightly higher when using the pedigree‐based method compared with the imputed WGS. For instance, heritability values using PBLUP and ssGBLUP, respectively, were 0.40 and 0.36 for ADG, 0.39 and 0.33 for BL, and 0.59 and 0.55 for BW, with a reduction in the standard error of heritability for ssGBLUP compared with PBLUP  for ADG: signal transducer and activator of transcription 5B (STAT5B) for BL, and RNA polymerase I subunit G (POLR1G), interferon regulatory factor 2‐binding protein 2 (IRF2BP2) and solute carrier family 17 member 9 (SLC17A9) for BW. A top SNP for ADG and BW, and two top SNPs for BL on Omy01 intercept the gene DNA nucleotidylexotransferase (DNTT or TDT) on intronic regions. Also, some biologically relevant genes located within 100 kb upstream and downstream of each top five SNP are available in Table RHOA) and cytokine‐inducible SH2‐containing protein (CISH) both located on chromosome Omy07, and associated with ADG. For BL, we identified signal transducer and activator of transcription 3 (STAT3) on Omy12, glycogen phosphorylase brain form (PYGB) and lysophosphatidylserine lipase ABHD12 (ABHD12) both on Omy01, and retinol‐binding protein 4 (RBP4) and myoferlin (MYOF) on Omy20. Both the fatty acid‐binding protein (FABP1) and histone‐lysine N‐methyltransferase Smyd1 (SMYD1) on Omy29 were associated with BW.Some genes were found to be intercepted by the top five SNPs for each growth traits, located in exonic or intronic regions Table , as cAMP3.3The summary statistics and sum of the proportion of genetic variance captured by each marker were estimated separately for each trait in different scenarios based on wssGBLUP Table . The 50KBased on the five repetitions of fivefold cross‐validation, the prediction accuracy for GEBV from genomic methods outperformed the accuracy for EBV from PBLUP, independent of the genotype scenario Figure . The acc44.1Salmonids have adapted to cold water and, in the case of rainbow trout, are optimally reared at temperatures up to 20°C Mäkinen, . A lowert test; p < 0.05) was found in favor of fish reared at increased temperatures  activity, a potent regulator of cell growth and differentiation in many cell types   signalling , prolactin and the insulin signalling pathway, which are involved in growth and reproduction traits , MYOF, along with other genes, may play an important role in muscle hardening  that may contribute to revascularization of ischaemic cardiac and skeletal muscles  in mice J recombination , probably due to the genetic architecture of traits, limited ability to correctly estimate QTL or used more independent populations for preselected SNPs and genomic predictions. Furthermore, in some populations with small effective population size and long‐range LD, it might be difficult to increase the accuracy of genomic prediction, as in Holstein cattle (MacLeod et al., We also created a 50K_pruned scenario, in which SNPs are in approximated LD with each other, and two other scenarios which incorporated only the most important SNPs selected a priori from WGS imputed data. SNPs were chosen based on the level of genetic variance explained for each of the three traits, better exploiting the potential direct or indirect relationship with causal mutations. Our results demonstrate that 50K_pruned is actually a selection of WGS imputed data with 10% to 14% of genetic variance explained, and much lower average and minimum value of percentage of genetic variance compared with 50K_wssGBLUP. These results suggest that the pruned selection of a 50 K SNP panel leads to the inclusion of SNPs with importance equal or near zero Table , reinforVariation in accuracy between ADG, BL and BW was expected, especially due to the different heritability values of the traits. For ADG and BW, within‐trait differences observed for the 50K_wssGBLUP and 1K_wssGBLUP scenarios were almost nonexistent. However, within‐trait differences were significantly higher for BL in 50K_wssGBLUP versus 1K_wssGBLUP. Including more genetic variants is expected to increase the fraction of genetic variation explained Table , resultiSome studies have suggested an inverse relationship between the SNP effect and allele frequencies (Abdollahi‐Arpanahi et al., GWAS of rainbow trout using imputed WGS data confirmed the polygenic architecture of growth traits under increased thermal stress. Interestingly, we identified candidate genes related to ADG, BL or BW under increased thermal stress in rainbow trout, providing a better understanding of the molecular basis of growth under heat stress. Accuracy was not greatly improved when using imputed WGS data to perform genomic prediction compared with 50K_pruned. In contrast, a considerable increase in the accuracy of genomic predictions was observed when 50 K and 1 K SNPs were preselected based on GWAS and compared to PBLUP, WGS data and 50K_pruned. Our results may be associated with the same population used to select SNPs and for genomic predictions. Even using a cross‐validation strategy, fish from the training and validation set are probably related. Therefore, further studies using different population sets would be useful to further validate the use of preselected SNPs from GWAS for genomic predictions.Both authors declare no conflict of interest.GMY designed the study, performed the analyses and wrote the manuscript. JMY designed the study and contributed to the analysis. Both authors have reviewed and approved the manuscript.Fig S1Click here for additional data file.Fig S2Click here for additional data file.Fig S3Click here for additional data file.Fig S4Click here for additional data file.Fig S5Click here for additional data file.


SLC4A11and eleven borate transporters fromA. thalianaandO. sativato complement aBOR1deletion inS. cerevisiae. We show thatAtBOR4,AtBOR5,AtBOR7,OsBOR2, andOsBOR3can each complementScBOR1, while the rest of the transporters tested do not rescue growth. Additionally, quantification of intracellular borate content demonstrates that SLC4A11 does not export borate in yeast, supporting studies that its transported substrate is not borate.Borate is an essential micronutrient in plants regulated by borate transporters, which also protect both yeast and plants from toxically high levels of borate and share homology with the human SLC4 transporters. SLC4A11 is linked to congenital hereditary endothelial dystrophy and was initially reported to transport borate before subsequent studies rebutted this conclusion. To better understand the transport activities of purported borate transporters, we tested the ability of S. cerevisiae, the borate transport protein Bor1p mediates resistance to toxically high levels of borate . In plants, the role of borate transport is more complicated given the organism’s need to prevent borate toxicity while also maintaining high enough levels of borate as a micronutrient that supports growth . As a result of these complex needs, the borate transporter family has expanded in plants, with seven borate transporters inArabidopsis thaliana(AtBOR1throughAtBOR7) and four borate transporters inOryza sativa(OsBOR1throughOsBOR4). The human transporter SLC4A11 was initially characterized as a borate transporter , but subsequent studies have rebutted this finding . Understanding SLC4A11 function and substrate transport activities is important because mutations in SLC4A11 have been shown to cause corneal diseases like congenital hereditary endothelial dystrophy .InS. cerevisiaestrain where we have knocked outBOR1. We tested complementation with all seven borate transporters inA.thaliana(AtBOR1throughAtBOR7) and all four inO.sativa(OsBOR1throughOsBOR4). We also tested the full-length B isoform  of human SLC4A11, which has been reported to be the most common of three isoforms found in the corneal endothelium . To test whether the SLC4A11 membrane domain alone can express and function in yeast, we also created an N-terminally truncated construct that removed the cytosolic domain and left intact its predicted membrane domain . We challenged the yeast with either 0, 10, or 20mM boric acid and imaged plates 4 days after plating. Shown in Figure 1A are representative plates from at least three distinct biological replicates of this experiment for all 15 samples. For a negative control we chose a plasmid bearingAQY1, a yeast aquaporin gene that does not support borate transport .AtBOR4,AtBOR5,AtBOR7,OsBOR2, andOsBOR3can each complement yeastBOR1, with theAtBOR7andOsBOR2phenotypes most evident in the 10mM but not the 20mM boric acid condition. Interestingly, all five plant transporters that complement are found in one of two clades of borate transporters . Further studies will be necessary to determine whether this distinction illuminates important functional differences between the two clades.We examined borate transport using genetic complementation assays in aScBOR1. The inability of both SLC4A11 constructs to rescue growth in our experimental conditions does not on its own establish an inability to transport borate, as a lack of expression in yeast could explain these data. To rule out this possibility, we performed Western immunoblot analysis with the full-length and truncated SLC4A11-B constructs, along with ScBor1p as a positive control. Total protein staining demonstrates the same amount of protein was loaded per sample, while a primary antibody recognizing His-tag residues reveals that full-length SLC4A11-B does not express, but that the truncated membrane domain construct expresses comparably well compared with ScBor1p . Surprisingly, our results are the inverse of a previous study in HEK293 cells that shows the full-length protein to express at the cell surface while the membrane domain in isolation expresses and functions poorly . The weaker, higher molecular weight band is likely to be dimer, which has been previously observed to persist for borate transporters on SDS-PAGE gels . Both ScBor1p and the truncated SLC4A11-B are found localized to pelleted membranes but not to the supernatant corresponding to cytosolic proteins. Our results show that the inability of the truncated SLC4A11-B construct to complementScBOR1cannot be attributed to a lack of expression.Neither the full-length nor truncated form of human SLC4A11-B complementsScBOR1and support yeast growth on plates containing boric acid. To test this possibility, we quantified the amount of intracellular borate in yeast cells expressingScBOR1,AtBOR1, and our twoSLC4A11constructs using a curcumin based colorimetric assay . Cells were grown up in liquid culture, treated with 1mM boric acid for 90 minutes, and then their lysates were harvested and analyzed for borate content. Eight independent biological replicates were performed for each sample, along with standards used to generate standard curves to calculate borate quantities for each experiment. Data are presented normalized to cell pellet weights and with error bars representing 95% confidence intervals. As expected, our data show that cells expressing ScBor1p have lower borate amounts relative to the Aqy1p negative control. Interestingly, despite its inability to complementScBOR1,AtBOR1shows borate export comparable toScBOR1. Our data highlight a curiosity in the literature regarding the ability ofAtBOR1to support transport borate in yeast: multiple studies have reported thatAtBOR1cannot complement aBOR1deletion in yeast , and multiple other studies have reported that it can . Here we give detailed experimental conditions in whichAtBOR1does not complement aBOR1deletion, but also show using the same media reagents thatAtBOR1demonstrates robust borate transport activity by an alternative assay. Genetic complementation and borate quantification assays have both been used to study borate transporter phenotype and function in numerous studies, and our data here suggest that caution is warranted in relying too heavily on any one experiment to draw conclusions about borate transporter function.It is possible that a transporter could export borate but at levels insufficient to complement+(OH-) transporter, and their data and ours here provide strong evidence that SLC4A11 does not transport borate. In sum, our data identify multipleA. thalianaandO. sativaborate transporters that can be further characterized in yeast and demonstrate that borate transport experiments inS. cerevisiaecannot be used to study the function of SLC4A11.The borate quantification data show both SLC4A11-B constructs display borate levels comparable to that of the Aqy1p negative control . These results suggest that expression of SLC4A11 in yeast does not result in borate transport. Previous studies indicating a lack of borate transport by SLC4A11 all relied on experimental methods that did not directly measure borate quantities, but instead relied on measuring outcomes predicted by borate transport, including cell acidification upon borate addition  and cell swelling upon borate addition . These studies report SLC4A11 to be an electrogenic HGenetic complementation assayBOR1(MATα his3::GAL1-GAL4 pep4 prb1-1122 bor1::KanMX) was transformed with plasmids that differ only by the transporter they encode. In all experiments reported here, the plasmid backbone is pRS423 and the genes are under inducible control of theGAL1promoter and possess a C-terminal thrombin site and His10tag. Overnight cultures were grown in media consisting of yeast nitrogen base (YNB+nitrogen), complete supplement mixture lacking histidine and supplemented with adenine (CSM-His w/Ade40), with 2% raffinose for a sugar source. Cells were diluted to an OD600 of 0.5 and then serial fivefold dilutions were prepared and added to plates containing CSM-His w/Ade40, 2% raffinose, 0.05% galactose to induce expression, and either 0mM, 10mM, or 20mM boric acid to challenge yeast growth. 10 μL of each dilution was plated, and plates were stored at 30°C for 4 days until imaged. The phylogenetic tree was generated using phylogeny.fr .A yeast strain (BTSY2) with a knocked outWestern immunoblot analysisMATα his3::GAL1-GAL4 pep4 prb1-1122), and after 16 h induction by 2% galactose the cells were lysed by passing once through an Emulsiflex-B15 homogenizer at 22,500 psi. Samples were spun for 10min at 10,000 x g, and then supernatants were collected and spun a second time for 1 h at 164,000 x g. The supernatants and pelleted membranes from this second, high-speed spin were used to run the gel for the Western immunoblot with 8.75μg total protein loaded per lane. Proteins were transferred to a PVDF membrane in a wet tank. The primary antibody was incubated at 4°C overnight, and the secondary antibody was incubated at room temperature for one hour before exposure using chemiluminescent horseradish peroxidase substrate. Total protein loading controls were imaged using Bio-Rad stain-free gels.Protein overexpression protocols were adapted from previously reported methods , but with the following distinctions: 50mL yeast cultures were grown using strain BTSY1 . Briefly, yeast colonies were used to inoculate overnight cultures which were then seeded to an OD600 of 0.25 the following morning and allowed to grow for 7 h until the OD600 was around 0.9. Protein expression was induced for 16 h by the addition of 2% galactose, after which cultures had 1mM boric acid added for 90min. Cells were pelleted and washed with water before being pelleted a second time, resuspended in 350μL water, and lysed by incubating at 98°C for 30min. After a 5min spin at 16,100 x g, 300μL of supernatant was prepared for curcumin addition per the previously reported protocol, with the data collected using a quartz cuvette that was washed twice with 91% isopropyl alcohol in between measurements. Standard curves were generated from standards containing 0, 0.625, 1.25, 2.5, and 5.0 mg/L of borate. All solutions in this experiment never touched glass to avoid the risk of borate entering the experiment via borosilicate glassware.Table 1: Yeast strains used in this studyTable 2: Antibodies used in this studyTable 3: Chemical reagents used in this study


L*, lightness; a*, redness; b*, yellowness) in 1518 three-way crossbred pigs. All individuals were genotyped using the GeneSeek Porcine 50K BeadChip. In sum, 30 SNPs and 20 genes are found to be associated with eight meat quality traits. Notably, we detect one significant quantitative trait locus (QTL) on SSC15 with a 143 kb interval for meat pH (pH_12h), together with the most promising candidate TNS1. Interestingly, two newly identified SNPs located in the TTLL4 gene demonstrate the highest phenotypic variance of pH_12h in this QTL, at 2.67%. The identified SNPs are useful for the genetic improvement of meat quality traits in pigs by assigning higher weights to associated SNPs in genomic selection.Meat quality is of importance in consumer acceptance and purchasing tendency of pork. However, the genetic architecture of pork meat quality traits remains elusive. Herein, we conducted genome-wide association studies to detect single nucleotide polymorphisms (SNPs) and genes affecting meat pH and meat color ( L*, lightness; a*, redness; b*, yellowness) of pork are low-heritability traits and varies among different populations [PRKAG3 gene plays an important role in affecting pork meat pH and color [PHKG1 gene causes high glycogen content and low meat quality in pigs. Although numerous QTLs and genes affecting meat pH and meat color traits have been identified [Pork contributes to a large share of the world’s meat provision, especially in the Chinese market. People pay more attention to meat quality with the improvement in living standards. Pork quality can increase both customer purchasing tendency and pork consumption, and then enable the pig industry to breed pigs with better meat quality. Studies demonstrate that meat pH and meat color are closely connected with the quality of meat, affecting freshness, shearing force, and drip loss of pork ,2,3. Theulations ,8, implyulations . Due to ulations . To dateulations . Previound color ,13,14. Mnd color  demonstrentified ,17,18, tThe aim of this study was to detect QTLs and genes associated with meat quality traits in pigs. To this end, we conducted GWAS for eight meat pH and meat color traits in a three-way crossbred commercial Duroc × (Landrace × Yorkshire) (DLY) pig population, numbering 1518 individuals. We detect numerous significant SNPs and promising candidate genes affecting meat quality and further reveal the complexity of the genetic architecture of meat quality. In terms of improving the genetic process of meat quality, the identified significant SNPs can be used for the genetic improvement of meat pH and color in pigs by assigning higher weights to associated SNPs in genomic selection. The expansion of the basis of meat quality can potentially provide novel insights into future molecular breeding of pork and achieve better meat quality.All animals used in this study met the guidelines for the care and use of experimental animals established by the Ministry of Agriculture of China. Tissue samples from pigs were collected with the approval of the ethics committee of South China Agricultural University  under 2018F098.L*, a*, and b*) were measured on the exposed cut surface after blooming for 30 min of the LT at 45 min and 12 h post-mortem, respectively, using a CM-2600d/2500d Minolta Chromameter  with an 8 mm measuring port, D65 illuminant, and one permanent observer to avoid human factor. Meat samples of LT were kept at 4 °C inside the refrigerator until the pH, L*, a*, and b* were measured at 12 h post-mortem.The experiment animals were from a three-way crossbred commercial pig population. Briefly, 84 Duroc boars were mated to 397 Landrace × Yorkshire sows to produce a large-scale offspring. All pigs sustained uniform feeding conditions and were raised on four farms of Wen’s Foodstuffs Group Co., Ltd. . After fattening, 1518 individuals born from 2018 to 2019 were slaughtered for phenotype recording at an average body weight of 115 kg in 13 batches. For each animal, longissimus thoracis (LT) muscle was removed from the left side of each carcass and was used to determine meat quality traits. Meat pH and meat color traits measurements were performed on LT muscle as described previously [Sscrofa) 10.2 to build Sscrofa 11.1. Quality control (QC) procedures were conducted using PLINK v1.07 software [p > 10−6 for the Hardy–Weinberg equilibrium test. Only SNPs located on the autosome chromosomes were retained in this study. After QC, we removed 203 SNPs because of missing genotype data, 18,004 SNPs due to Hardy–Weinberg exact test, and 45 SNPs due to minor allele threshold. Moreover, 4245 SNPs not located on autosome chromosomes were discarded. Finally, 1518 pigs and 28,206 SNPs remained for subsequent analyses.Genomic DNA of each pig was extracted from ear tissue via a standard phenol/chloroform method and was diluted to 50 ng/μL  for genosoftware  with ther2). The window size of LD block was set as 1000 using PLINK v1.07 software.The qualified SNPs were used to conduct principal component analysis (PCA) and linkage disequilibrium (LD) analysis to investigate the population structure of the DLY pigs. PCA was conducted with GCTA software . The SNPL*_45min, a*_45min, b*_45min, pH_12h, L*_12h, a*_12h, and b*_12h. The mixed linear model used in this study was as follows:We conducted GWAS with GEMMA software  for eighp < 0.05/N and chromosome-wide significance at p < 1/N, where N refers to the total number of qualified SNPs. The quantile–quantile (Q–Q) plots of the eight meat quality traits were constructed for the assessment of population stratification effects on GWAS results.In the current study, we used Bonferroni correction to acquire the genome-wide significant and chromosome-wide significant thresholds. A SNP was considered to have genome-wide significance at We used Haploview v4.2 software  to perfoIn this study, we conducted SNP-based heritability estimation of each meat quality trait and calculated trait-associated SNP’s contribution to phenotypic variance via GCTA software . The SNPSscrofa 11.1 genome version . Genes nearest the significant SNPs are list in the Tables. Furthermore, we manually queried PubMed and the literature for information about the association between all candidate genes nearest peak SNPs and the analyzed meat quality traits.The functional genes were searched for based on L*, a*, and b* are explored (see below). The heritability of meat pH ranges from 0.12 to 0.17, while the heritability of meat color traits ranges from 0.03 to 0.12. As previous studies report [L*, a*, b*) of pork are low-heritability traits. Miar et al. [The descriptive statistics of eight meat quality traits in DLY pigs are listed in s report , meat pHr et al.  report tr et al. .r2 of 0.2 is about 200 kb apart  and contribute to 2.36% and 1.27% of the phenotypic variance, respectively. The SNP (rs81274518) on SSC6 is located within the GRIK5 gene and the SNP (rs81324442) on SSC16 is located within the MTMR12 gene. GRIK5 is a protein-coding gene that belongs to the glutamate-gated ionic channel family. This gene encodes glutamate ionotropic receptor kainite type subunit 5 in diverse ophthalmologic and vascular disorders when its expression levels are reduced [MTMR12 is a protein-coding gene and the protein encoded by this gene functions as an adaptor subunit in a complex with an active PtdIns(3)P 3-phosphatase. There is no study that highlights the MTMR12 gene as a candidate gene related to meat quality traits in pigs. Significant SNPs detected by GWAS for meat pH_12h of the DLY pigs are shown in p < 1.78 × 10−6) and 10 surpass the chromosome-wide threshold (p < 3.56 × 10−5). Notably, we detect one consistent QTL on SSC15 for meat quality, and the top SNP demonstrate the highest phenotypic variance of pH_12h in this QTL, at 2.67% (see below). We also identify three new SNPs associated with pH_12h, which are located on SSC2 and SSC9, respectively. The rs81303631 SNP and rs81295472 SNP on SSC2, which explain 0.72% and 0.31% of the phenotypic variance, respectively, are close to the protein-coding genes FAM170A and PRR16. The FAM170A gene acts as a nuclear transcription factor that positively regulates the expression of heat shock genes. The PRR16 is a protein-coding gene involving in the positive regulation of cell size and positive regulation of translation [PRR16 is highly expressed in human placental endothelial cells. Although it is shown to regulate cell size, PRR16’s role in meat pH is unclear. The rs81316230 SNP on SSC9, which explains 0.31% of the phenotypic variance, is located inside the PHOX2A gene, which encodes a protein that contains a paired-like homeodomain most similar to that of the Drosophila aristaless gene product. It is shown that the PHOX2A gene is associated with respiratory rhythm and autonomic nervous system development [The GWAS for pH_45min identifies two SNPs that are located on SSC6 and 16 . Figure nslation . PRR16 iIn this study, we measured meat color parameters at two different time points (45 min and 12 h) after slaughter, which represent the meat color of fresh meat and chilled meat with different economic values.p < 3.56 × 10−5) and are found to be associated with three meat color traits . For L*_45min, the SNP rs80971313 on SSC4  and are found to be associated with L*_12h and b*_12h. Three SNPs are associated with L*_12h  and the lambda is 1.02 , including ive SNPs  surpass h L*_12h A and theh L*_12h B. The rsutosomes C and theutosomes D. Howeveant SNPs E that ex is 1.02 F. The rstivation . The EPYproteins . The potIn this study, there are no overlapped GWAS signals among meat color traits measured in 45 min and 12 h. Our results demonstrate that the genetic factors affecting meat color in the two stages may be different. Meat color is an important economic trait and the genes causing the different meat color are shown in different pig breeds , implyinSscofa 11.1). TNS1. The GWAS peak is approximately 733 kb away from the PRKAG3 gene , a gene affecting glycogen in Hampshire and meat quality traits in pigs [PRKAG3 gene gives a small p-value. Moreover, low LD (r2) is observed between the SNPs in the TNS1 gene and PRKAG3 region  genotype. However, the LD between the two SNPs is 0.48, implying an independent effect on meat pH by the TTLL4 genes. Therefore, the TTLL4 gene is proposed as another candidate gene responsible for meat pH in this QTL. TTLL4 is a protein-coding gene and Gene Ontology annotations related to this gene include ligase activity and tubulin binding. It is involved in KLF4 glutamylation, which impedes its ubiquitination, thereby leading to somatic cell reprogramming, pluripotency maintenance, and embryogenesis [TTLL4 gene in meat pH needs further investigation.In this study, 13 SNPs associated with pH_12h are located in a QTL region on SSC15 between 117.25–122.99 Mb  are likely to be responsible for the meat pH difference among individuals according to their functions. From this, we detect several genetic markers affecting meat pH and color. Results from this study are useful for the genetic improvement of pork meat quality in swine by assigning higher weights to associated SNPs in genomic selection.This study conducted a GWAS for eight meat quality traits in a 1518 DLY pig population, and provided valuable insights into elucidating the genetic architecture of meat pH and meat color. We identify one significant QTL on SSC15 with a 143 kb interval affecting pork pH, and two related genes (


SAA2) gene and explore their relationships with milk production traits in Chinese Holstein cows.This study was conducted to detect potential polymorphisms of the serum amyloid A2 (sequencing technology conducted in 532 Chinese Holstein cows.Three single nucleotide polymorphisms (SNPs) were identified within intron 1, named g.14061A>G, g.14072G>C and g.14819C>T. Eight estimated haplotypes were identified, of which three major haplotypes had a frequency of Hap3 (‐ACC‐), Hap5 (‐GCC‐) and Hap2 (‐AGT‐), with 17.9%, 12.30% and 8.10%, respectively.p < 0.05).The association analysis of single markers (g.14061A>G and g.14819C>T) and combined genotypes (Hap1/5) revealed prominent effects on milk production traits in Chinese Holstein cows (SAA2 gene is associated with economic traits in Chinese Holstein cows and may be used as candidate gene for marker‐assisted selection and management in breeding programs.Our results suggest that the    LTD . All of the cows were fed with the same regular total mixed ration composed of concentrated feed and coarse fodder and were routinely milked three times a day . The milk yields were recorded with a computerized herd management system. The dairy herd improvement (DHI) in milk samples were provided from Xi'an Caotan Animal Husbandry Co. LTD.2.2Genomic DNA from blood samples were collected by TIANamp Blood DNA kit . The quality of DNA was measured using a NanoDrop ND‐2000c Spectrophotometer (Thermo Scientific).2.3SAA2 gene (GenBank accession no. AC_000186.1), four pairs of PCR primers were designed with Primer Premier Software (Version 4.0). Detailed information of primers is depicted in Table 2O 11.8 μl, DNA 2.0 μl. PCR conditions were as follows: initial denaturation at 95°C for 5 min, followed by 35 cycles at 94°C for 30 s; annealing temperature for 30s; 72°C for 40 s; and a final extension at 72°C for 10 min, and then sequenced using an ABI 3730 sequencer .According to the sequence of the bovine Gene frequencies and Hardy–Weinberg equilibrium (HWE) were computed using POPGENE software (Version 3.0). The extent of linkage disequilibrium (LD) between the identified single nucleotide polymorphisms (SNPs) was estimated using Haploview 3.32 software  , jG is the fixed effect of the jth genotype , kL is the fixed effect of the kth calving age , lS is the random effect of the lth sire , and ijklmE is the random error.The associations of the SNPs and milk production traits were analyzed by analysis of variance (ANOVA) the SPSS 16.0 software . The equation was as follows: The additive, dominance, and allele substitution effects of single SNPs were evaluated by SPSS 16.0 software . The equation is as follows:33.1The bovine SAA2 gene is located on chromosome 29 of the bovine genome. The total length of SAA2 is 3447 bp, comprising the genomic coordinates starting from 26458422 to 26461868 . This gene comprises 04 exons, the ORF that started from the start codon to the stop codon is 393 bp, and the putative protein contains 131 amino acids  this genotype at g.14819C>T locus may never exist in the population and (2) the experimental population size was insufficient to capture full genetic variation . GG of g.14072G>C was more frequent (54.70%) compared to the others genotype. CC was the most prevalent genotype (73.87%) at g.14819C>T locus. The χ2 test indicated that the g.14061A>G and g.14072G>C were in HWE (p < 0.05), while the g.14819C>T was severely out of HWE (P > 0.05). It was speculated that the g.14819C>T loci carried out significance selection pressure  within the 5′‐regulatory region of the SAA2 gene were found to be statistically significance for milk yield, fat yield and protein yield in Chinese Holstein cows as a result of alteration in SAA2 gene transcriptional activity . Compared to animals with genotype TT, the role of CT genotype at g.3208C>T locus caused the highest average for lactose percentage (p < 0.05). Different from previous studies, the location of mutations was found on the introns in the current study. The mutation differences may be explained by the diversity of the sample population.So far, litter polymorphism on the p < 0.05).Haplotypes composed of SNPs provided greater power than single‐marker analysis for associations of inheritable characters because of the ancestral structure captured in the distribution of haplotypes  influenced growth traits  gene was significantly correlated with withers height and chest depth in Nanyang cattle  indicated that a numbers of candidate genes  are involved in complex mechanisms, thereby altering milk performance traits  and combined genotypes (Hap1/5) revealed prominent effects on comparison of milk traits in Chinese Holstein cows. Further research should be conducted in a large population before they could be applied as genetic markers for selection purposes.In summary, three SNPs  and eight haplotypes were discovered within the The authors declare no conflict of interest.Conceptualization, data curation, formal analysis, investigation, software, validation, visualization, writing—original draft and writing—review and editing: Sayed Haidar Abbas Raza. Conceptualization, data curation, formal analysis, investigation: Chengcheng Liang. Formal analysis, validation and writing—review and editing: Ahmed Mohajja Alshammari. Conceptualization, formal analysis, methodology and writing—review and editing: Bandar H. Aloufi. Formal analysis and Writing—review and editing: Linsheng Gui. Software, validation and writing —review and editing: Rajwali Khan. Funding acquisition, investigation, methodology, project administration, resources, supervision and writing—review and editing: Linsen Zan.The experimental animals were dealt as per standard operating procedures (SOPs) formulated by Chinese Council of Animals Care, and further ap‐proved by ‘Experimental Animal Management Committee Approval Number is (EAMC 2020‐1298)’ of the Northwest Agricultural and Forestry University. The sampling was performed after human killing of the experimental animals at College of Animal Science, Shaanxi, Yangling, China.https://publons.com/publon/10.1002/vms3.796.The peer review history for this article is available at


After a Bonferroni correction (p < 0.005), there were 12 SNPs (single-nucleotide polymorphism) associated with Zn and 6 for iron. SNP annotations revealed proteins involved in membrane transport, Zn and Fe binding, linked to nutrient remobilization in grains. These results can be used to develop biofortified barley via marker-assisted selection (MAS), which could alleviate mineral malnutrition.Micronutrient deficiency affects half of the world’s population, mostly in developing countries. Severe health issues such as anemia and inadequate growth in children below five years of age and pregnant women have been linked to mineral deficiencies (mostly zinc and iron). Improving the mineral content in staple crops, also known as mineral biofortification, remains the best approach to address mineral malnutrition. Barley is a staple crop in some parts of the world and is a healthy choice since it contains β-glucan, a high dietary protein. Barley mineral biofortification, especially with zinc and iron, can be beneficial since barley easily adapts to marginalized areas and requires less input than other frequently consumed cereals. In this study, we analyzed zinc and iron content in 496 barley samples. The samples were genotyped with an Illumina 50 K SNP chip. Genome-wide association studies (GWAS) identified 62 SNPs and 68 SNPs ( Agriculture production for staple crops has increased since the green revolution, which prevented hunger for millions of people worldwide. However, these staple crops lack essential micronutrients such as iron, vitamin A, vitamin D, zinc, and iodine. As a result, a lack of meal diversity is the leading cause of malnutrition, termed “hidden hunger”. Malnutrition remains a worldwide crisis, but it is especially severe in developing countries, with the most damaging nutritional deficiencies being iron (Fe) and zinc (Zn). A third of the population worldwide suffers from zinc deficiency, while more than half lack iron . DeficieWays to alleviate malnutrition include the biofortification of crops, increased meal diversity, fortification of products, and the use of supplements. Biofortification of staple crops is the most sustainable and relatively low-cost way to alleviate malnutrition since poor farmers in rural areas can benefit from eating their crops. However, meal diversity, supplements, and fortification are expensive, hard to deliver, and unsustainable. With this growing importance for biofortification, Harvestplus, a Consultative Group on International Agricultural Research (CGIAR) Research Program, works on increasing micronutrient content such as zinc, iron, and provitamin A in crops that are important for developing countries .Hordeum vulgare) is a staple food in some parts of the world. Its consumption ranges between 2 and 36 kg/person in North Africa, Ethiopia, and central and East Asia [Barley  . Subpopu2 equal to 0.2, the average Linkage disequilibrium (LD) decay was about 200 Kbp (For an r 200 Kbp . This dip < 0.001) associated with iron and zinc, respectively. At (p < 0.005) after Bonferroni correction, the number of SNPs were 12 and 6 for Fe and Zn, respectively. 2), p-value, position, the adjusted p-value after the Bonferroni correction, and other associated information such as the candidate gene. The quantile-quantile (Q–Q) plot and Manhattan plot for both Zn and Fe are displayed in This study identified 62 and 68 significant SNPs (p < 0.005) using BARLEX and BarleyVarDB are displayed in Arabidopsis thaliana. Nascent polypeptide-associated is also expressed under Fe deficiency [Annotations of significant SNPs after Bonferroni correction  are displayed in p-value. They are involved in several functions related to metal transport, ion binding, and remobilization of Fe and Zn into grains. For example, candidate genes such as the cytochrome P450 superfamily participate in element transport [All significant SNPs . They correspond to genes such as the cytochrome P450 superfamily protein, which is involved in element transport, iron and zinc binding, and is found in genotypes with a higher Fe and Zn content [The SNP annotation revealed the identification of candidate genes involved in Fe and Zn transport and their accumulation in grains. Proteins such as nascent polypeptide-associated complex subunit alpha-like protein 3 are expressed in low Fe barley . On the  content . This st content . Acyl-AC content . Recepto content ; the NAC content  (see Tabp < 0.001) associated with Zn on all seven barley chromosomes. After the Bonferroni correction to reduce false SNPs, there were 12 remaining SNPs. Lonergan et al. [Arabidopsis thaliana ZIP1gene and rice OsZIP3 gene. Both genes are members of the family of zinc-regulated transporters and iron-regulated transporter protein (ZIP), which are known to contribute to Zn uptake [We identified 68 SNPs . After the Bonferroni correction (p < 0.005), 6 SNPs remained associated with Zn. On chromosome 3H, we found 8 SNPs at 595 Mbp (83 cM), which are in the region of the QTL Fe-3H-83.63 identified earlier by [Our study identified 62 SNPs associated with Fe on all barley chromosomes except 6H  . HoweverThe bioavailability of Zn and Fe can be limited by anti-nutrients such as phytic acid or phytate. Breeding for their low quantity in crops and breeding for phytase enzymes that degrade phytic acid will improve the bioavailability of Fe and Zn . Mature Barley resists Fe deficiency more than other cereals; the genes involved can be adapted to other cereals. For instance, nicotianamine aminotransferase genes (NAAT genes) intervene in the liberation of phytosiderophores , which are used by the plant to uptake Fe. They are expressed in high quantities in barley when the iron content is low. Takahashi et al.  observedHordeum vulgare ssp. Vulgare) genotypes that included 95 cultivars, 28 improved lines, and 373 landraces. Of those, 419 lines are 6-row, and others are 2-row. Among the 496 lines, 105 are naked, while 6 are black seeds. Seeds originating from different countries were obtained from the USDA, ARS, and Plant Genetic Resources Unit (PGRU) (https://plantbreeding.r-forge.r-project.org/ accessed on 1 May 2022).This study used a collection of 496 spring barley (t (PGRU) . The fie3 and H2O2 methods [The iron (Fe) and zinc (Zn) content analyses were carried out at the Cereal and Quality Laboratory at ICARDA, Rabat, Morocco. Barley seeds from the 2017–2018 crop season were analyzed with inductively coupled plasma-optical emission spectrometry  following the modified HNO methods ,37.3) was added and incubated overnight. The tube was placed in a digestion bloc  for 1 h at 90 °C, then shaken for 15 min and 45 min. Then, we added 3 mL of hydrogen peroxide (H2O2) to each tube at 90 °C. At the end of digestion, as characterized by the solution’s discoloration and the discontinuation of brown smoke, the tube was removed from the digestion block and cooled at room temperature. The sample was filtered through Whatman papers, diluted at 1:10 with 6M of HCl, and then loaded into the ICP-OES machine to estimate Fe and Zn. Due to available funding, we could not analyze both crop seasons with ICP. For barley seeds of the 2018–2019 crop season, iron and zinc concentrations in barley grain were determined using X-ray fluorescence spectroscopy . The X-ray fluorescence spectroscopy was calibrated using the inductively coupled plasma-optical emission spectroscopy (ICP-OES);  following a modified HNO3-H2O2 method [Clean and dried barley seeds obtained from each plot were ground in a clean cyclone sample mill . Flour samples (500 mg) were placed in a clean digestion tube, and 8 mL of nitric acid (70% HNO2 method .DNA was extracted from young barley leaves following the CTAB (cetyltrimethylammonium bromide) protocol  with a shttps://www.cog-genomics.org/plink/1.9/accessed on 1 May 2022) with the parameters “--indep-pairwise 50 50 0.5”. Then, a random set was selected to estimate population structure using the fast structure and multidimensional scaling (MDS) in plink 1.9. Next, kinship was calculated in TASSEL 5.0  using centered IBS [Population structure (k1-k15) was estimated in fast structure  using a ered IBS . Using TThe extent of linkage disequilibrium (LD) for the population was determined as described in . For thip < 0.005). Significant SNPs were annotated with the BARLEX database at https://apex.ipk-gatersleben.de/apex/f?p=284:10 (accessed on 1 May 2022) for candidate genes [http://146.118.64.11/BarleyVar/ (accessed on 1 May 2022 and Barley map [http://floresta.eead.csic.es/barleymap/ (accessed on 1 May 2022 were used to find their function.GWAS was performed in Tassel 5.0 software  using dite genes . The Barte genes  at http:rley map  at http:This study used a GWAS approach to map genetic loci associated with Zn and Fe content in barley grains. A collection of 496 diverse barley lines was screened for zinc and iron content in grains in 2 locations for 2 crop seasons. The Fe and Zn content in grains showed a wide range of variations. We identified genotypes that showed high Zn and Fe content in grains; they constitute the best lines to make optimal crosses in developing biofortified barley. GWAS revealed information on the SNP markers associated with Fe and Zn content in barley grains. Their validation in different materials could demonstrate their effectiveness and be applied to the breeding of biofortified barley.


Arachis hypogaea subspecies hypogaea, differ in several agricultural and industrial characteristics. One such trait is time to maturation (TTM), contributing to the specific environmental adaptability of each subspecies. However, little is known regarding TTM's genetic and molecular control in peanut in general, and particularly in the Runner/Virginia background. Here, a recombinant inbred line population, originating from a cross between an early-maturing Virginia and a late-maturing Runner type, was used to detect quantitative trait loci (QTL) for maturity. An Arachis SNP-array was used for genotyping, and a genetic map with 1425 SNP loci spanning 24 linkage groups was constructed. Six significant QTLs were identified for the maturity index (MI) trait on chromosomes A04, A08, B02 and B04. Two sets of stable QTLs in the same loci were identified, namely qMIA04a,b and qMIA08_2a,b with 11.5%, 8.1% and 7.3%, 8.2% of phenotypic variation explained respectively in two environments. Interestingly, one consistent QTL, qMIA04a,b, overlapped with the previously reported QTL in a Virginia × Virginia population having the same early-maturing parent ('Harari') in common. The information and materials generated here can promote informed targeting of peanut idiotypes by indirect marker-assisted selection.'Runner' and 'Virginia', the two main market types of  Arachis hypogaea L.) is an economically important grain legume widely cultivated in tropical, subtropical, and warm temperate regions. It is a global nutritional source of food and edible oil and serves as a good fodder source. Domesticated peanut is a self-pollinated allotetraploid  that evolved from a natural hybridization event between two diploid progenitors, A. duranensis  and A. ipaensis 2. The commercial A. hypogaea cultivars are categorized into four market types, in which the 'Spanish' and 'Valencia' types belong to the subspecies fastigiata, while the 'Virginia' and 'Runner' market-types belong to the subspecies hypogaea. The two subspecies differ by plant architecture, flowering pattern and seed size3. A. hypogaea ssp. hypogaea types exhibit an alternative flowering pattern and indeterminate spreading or bunch habit and relatively big seeds, whereas A. hypogaea ssp. fastigiata are characterized by a sequential flowering pattern, erect growth habit and small seeds4.Peanut ], whereas ssp. fastigiata are characterized by early fruit maturation (90–120 DPP)4. Variation in TTM also exists between Virginia and Runner, the two A. hypogaea ssp. hypogaea market types. The Runner type has become the dominant peanut type grown in the United States due to the introduction of varieties  in the early 1970s with a substantial increase in peanut yields. The name "Runner" originated from these cultivars' spreading growth habit. Runners have gained wide acceptance because of a desirable range in kernel size, particularly for the peanut butter industry8. This market type is grown mainly in US states such as Georgia, Alabama, Florida, Arkansas, Mississippi, Oklahoma and Texas. It accounts for over 80% of total USA production. The Virginia-type peanuts have a larger kernel size than the Runner types. They account for most of the "in-shell" peanut market. When shelled, the large kernels are sold as salted peanuts. Virginia peanuts are grown mainly in southeastern Virginia and northeastern North Carolina, northern South Carolina and West Texas, and they are accountable for ~ 15% of total US production. Virginia-type also gained popularity in other world regions, such as the Near East (mainly Egypt and Israel), grown for in-shell production and export to Europe. The diverse environmental conditions across peanut growing regions resulted in the selection of peanut cultivars with varied TTM. In areas with limited water supply or end-of-season cool temperatures and early frost9, the peanut maturation process is retarded which often results in incomplete pod filling, low yield, grade and quality (including low oleic to linoleic acid ratios). In these regions, early maturing peanut varieties are preferred11. Therefore, most of the Virginia peanuts were historically developed as early-maturing (130–140 DPP) or medium-maturing (140–150 DPP), while the Runners are mostly late-maturing (150–170 DPP) (https://issuu.com/onegrower/docs/peanut_grower_2019_variety_guide)12.The growing period, or time to maturation (TTM), is another essential trait for crop adaptability and yield in peanut. While the environmental conditions and agricultural practices influence TTM14 and influenced by many genes and environmental factors16. Domesticated peanut has a narrow genetic base, resulting from the bottleneck of a single hybridization event that gave rise to this species and the crossing barriers between tetraploid cultivated peanut and diploid wild ancestors due to ploidy differences17. Apart from the low polymorphism among cultivars, another hurdle is phenotyping TTM due to the unique underground formation of peanut pods. The hull-scrape method18 is commonly used to determine TTM in peanut, but this method is laborious and may be subjective.TTM is an important breeding objective, particularly for introducing early maturation to both Virginia- and Runner-type idiotypes, with better adaptation to specific geographical regions, yield characteristics and agronomic performance. However, introducing early maturation to late-maturing cultivars is a challenging task due to the insufficient knowledge regarding the genetic and molecular mechanisms that control TTM. Peanut maturity level was reported as a quantitative trait with low heritabilityfastigiata × ssp. hypogaea crosses21. Most of these were with low-density genetic maps and resulted in identification of relatively small effect QTLs for early maturation. Through the advancement in SNP array technology23, a dramatic increase in number of genetic markers alleviated the existing limitation in constructing high-density genetic maps. A recent study involving Virginia-late × Virginia-early background used a SNP-based mapping approach to identify significant QTLs for TTM, some of which were associated with harvest index, pod yield, and branching habit trait related QTLs7.Currently, only a few attempts to define genomic loci that control TTM were made using breeding materials from ssp. 7:9 RIL population derived from Runner (late) × Virginia (early) market-type background. A QTL analysis was performed for TTM with a two-year field experiment phenotyping data set. New QTLs associated with TTM are reported, and the translation of these QTLs into user-friendly marker platforms for facilitating marker-assisted selection is suggested.In this study, a SNP-based linkage map was constructed for an FP ≤ 0.0001), with 53.9 ± 1.97 and 70.7 ± 1.97 for IGC119 and Harari, respectively  and Harari (early-maturing) cultivars Fig. . A highlThe MI trait was distributed normally or close to normal in the RIL population Fig. ; Table 1Arachis_SNP array consisting of 47 K SNP markers (Thermofisher Scientific). After initial filtering, a set of 3116 polymorphic SNP markers were obtained between the two parental lines. After filtering for 65–35% call rates in the RIL population and removing the duplicates, 2744 SNPs were retained. Subsequently, a genetic map was constructed with 243 RILs and 2744 SNPs as input  resulted in a total physical distance of 1884.7 Mbp and an average physical interval between markers of 3.1 Mbp  to 107.3 cM (B03). The average number of markers per LG ranged from 59, reaching 246 loci in LG A07. The average distance between neighboring loci was 1.9 cM, ranging from 0.1 cM in LGs A05_1 and B10 to 7.1 cM in A03_2  was observed on LG A04 between AX-176819644_A04–AX-147221341_A04, spanning 7.9 Mbp, with PVE values of 11.5 and 8.1% for 2019 and 2020, respectively  region was observed on LG A08_2 within marker interval of AX-177639781_A08–AX-176821672_A08, spanning 1 Mbp, explaining 7.3 and 8.2% PVE in 2019 and 2020, respectively. The other two QTLs were identified on LG B02 (qMIB02) and LG B04 (qMIB04), which were significant only in 2019  . The first QTL region (defined as the region I) on chromosome A04, with flanking markers AX-176819644_A04–AX-147221341_A04, spanned a genomic distance of 7.9 Mb  and enclosed 537 gene models , suggesting no association with MI trait in this population. In particular, pod size, a strong candidate component trait that can affect MI in peanut , was even the opposite in the two parental lines. Harari, which has a bigger pod size, is earlier matured than IGC99, which has a smaller pod size . This also contradicts earlier reports in other legume crops51, showing a strong correlation between HI and maturity due to the a higher sink/source level in early maturing genotypes.Phenotypic distributions of the MI trait among the RIL population extended beyond parental values suggesting the presence of transgressive segregation. The broad-sense heritability for MI was found to be moderate (~ 0.44), similar to our recent publicationqMIA04a,b and qMIA08_2a,b were consistently detected in both years. Most of the QTLs reported in the study are novel and different from those reported for TTM by others10. However, one consistent QTL from the current study, qMIA04a,b , overlapped with the previously reported consistent QTLs qMIA04a , and qMIA04b  in our Virginia × Virginia background7. This overlapping QTL has the early maturing Harari as the common parent in both RIL populations, signifying the importance of this stable QTL region for Harari-based MAS breeding in different late-maturing backgrounds. Yet, it is important to note that this QTL region is still specific to a single genetic background. Harari has some fastigiata related heritage in its genetic background7, indicating that this QTL can be anticipated to be consistent across varying genetic backgrounds. However, further work is needed to inspect this contribution in other early-maturing Virginia-type germplasms.The identified QTLs have small to moderate effects on MI Table , and two52. The QTLs for MI that were detected in only one year and showed no association with other traits may be largely affected by environmental factors, and further validation will be required from multi-location experiments and development of additional populations using distantly related crossing parents. The information and materials generated here, particularly on the stable cross-population QTL, can be integrated into informed targeting of peanut idiotypes by indirect marker-assisted selection.The inadequate marker density and the low polymorphism existing among both parents used in the study limited our ability to predict target candidate genes for TTM. Also, a general enriched GO annotation study did not show any significant biological processes that are over-represented in these loci (data is not shown). Therefore, more efforts should be made to find more SNPs across this region to identify significant candidate genes for TTM. Since the cost of sequencing dropped dramatically, it may be feasible to significantly enrich marker density by sequencing the whole genome7, and multiplied as bulks for another two generations (F7:9). IGC119 is part of the Israeli Groundnut Collection, and it is the local name for the runner cv. GK-7 HIGH OLEIC (PI 599592). It is a late-maturing cultivar with small, smooth, hard pods and high-oleic kernels. Harari is an early-maturing cultivar grown in northern Israel. Harari has big pods with reticulated and soft shells targeted for the local shelled peanut industry. Its growing season is limited by the regional conditions of late sowing time (due to the double-cropping system) and early harvest . IGC119 has a spreading growth habit, while Harari is a bunch type. Common traits between IGC119 and Harari include flowering pattern, lateral branch length and flowering rate.The RIL population used in the study was developed from a cross between a Runner-type cv. IGC119 and the Virginia-type cv. Harari, differing in TTM by 10–15 days. RILs were developed by a single seed descent (SSD) method, up to F7:9 RILs in two successive years. The first year was planted in April 2019 in the Magen, Western Negev, Israel (33°11′17.7″N 35°34′25.6″E), and the second in April 2020 in Urim, Western Negev, Israel (31°20′27.4″N 34°29′46.1″E). Both locations are characterized by fine sandy-loam soil. A randomized complete block design with three replications was applied in the field experiments in both years. Plot size of 2 m × 1 m consisted of two rows on a bed, rows spaced 90 cm apart and seeding rates of 10 seeds/m2  was implemented. Parental lines were grown as control plots in six replications. Field experiments were maintained under full irrigation, and all recommended agronomic practices were carried out as previously described53. All rights for the plant material, including the parental and the RILs, belong to the Hovav laboratory and are part of the ARO breeding program.Field experiments were performed with 243 F7, by sampling both parental lines from 130 DPP every few days until Harari reached ~ 60% maturity on average. This approach was preferred to capture the widest variation in TTM among the RILs. The hull-scrape method21 was applied to measure the maturity level as described7. Briefly, 2–3 random plants per plot were sampled, and the exocarp was removed from the pods using a PICO water pressure machine  . The precise evaluation date was determined as described7 to define the effects of RIL, Year, Year X RIL and Block. To calculate the broad-sense heritability, all effects in the linear model were defined as random, and heritability (H2) was estimated with the equation H2 = σg2/(σg2 + σge2 + σe2), by the ANOVA analysis with QTL IciMapping v4.2 (http://www.isbreeding.net/software/?type=detail&id=29) as described40. The σg2, σe2 and σge2 denoted the variances of genotypes (G), environment (E) and interaction of genotypes and environments (G × E). Distribution, histograms, and boxplots were performed with JMP® Pro 15 .The normality of distribution of MI among the RILs was tested by Anderson–Darling test. The Student's t-test was used to determine statistical differences between the parental lines. ANOVA model was used in the same manner as describedArachis2 SNP array comprising 47,837 SNPs, separated into their AA and BB subgenomic origin54. Genotyped data was analyzed by the Axiom analysis suite Software 3.125. Call-rate frequencies of 65–35% were applied in retaining polymorphic homozygous (AA and BB) and polymorphic heterozygous (AA or BB and AB) SNPs among the RILs resulting in retention of 2744 SNPs for further analyses.Genomic DNA was extracted from young leaflets from each RIL and the two parents using GenElute™ Plant Genomic DNA Miniprep Kit . DNA was quantified with Qubit . DNA samples were diluted according to protocol guidelines to 40 ng/μL and genotyped through Affymetrix Axiom_55 with regression algorithm to calculate locus genotype frequency, retaining only SNPs with a chi-square P-value ≤ 0.05 (1 degree of freedom). Groupings were established according to an independent LOD that started at 2.0 and ended at 10. Map distances were estimated using Kosambi mapping function. The graphical representation of the LG was obtained through MapChart v2.356. Confirmation of loci positions was made as previously described39 with few modifications  and mismatch of less than 2). LGs developed were assigned to the pseudo-molecules of the tetraploid A. hypogaea cv. Tifrunner36 (https://peanutbase.org) (Table The genetic map was constructed with 243 RILs and 2744 SNPs as input. The linkage map (LG) was constructed using Joinmap v4.157 (Supplementary Dataset: Table A. hypogaea genome were obtained from markers flanking the QTL.MI trait mapping was performed with the mean phenotypic data collected each year on 243 RILs using MapQTL v6All the experiments on plants, including the collection of peanut seed materials, were performed in accordance with relevant guidelines and regulations.Supplementary Figures.Supplementary Tables.