The phenolic compounds in eggplant offer potential natural antioxidants for improved health. A large number of samples were examined in order to find eggplant germplasm with a high potential for health promotion. A genome-wide association study (GWAS) was conducted to identify single nucleotide polymorphisms (SNPs) associated with variations in total phenolic content (TPC) and antioxidant activity in eggplants, including ABTS ) scavenging activity and ferric reducing antioxidant power (FRAP). TPC values varied from 14.19 to 842.90 mg gallic acid equivalent (GAE)/100 g of dry weight of eggplant fruit powder. TPC showed a strong positive correlation with both FRAP and ABTS . The GWAS identified 20 SNPs that were significantly associated out of 29,183 SNPs. Out of the 20 significant SNPs, 11 showed associations with TPC, 4 with ABTS activity, and 5 with FRAP. Among the SNPs associated with TPC, one SNP was found on each of Chromosomes 3, 4, 7, and 12. In contrast, Chromosome 5 comprised two SNPs associated to TPC. Furthermore, the gene encoding IRX12 laccase-4 on Chromosome 10 was found to contain five SNPs associated with TPC. Four significantly linked SNPs on Chromosomes 1 (1 SNP), 4 (2 SNPs), and 10 (1 SNP) were found to be related to ABTS activity. The identified SNPs will be further examined as markers for selecting desirable eggplant varieties and exploring the links between candidate genes, phenolic content, and antioxidant activity. The findings of this study could assist in further study and the development of eggplants with improved health advantages through targeted breeding. Solanum melongena L.), commonly known as aubergine, brinjal, berenjena, or guinea squash, is a valuable non-tuberous nightshade crop. Eggplants have been grown for centuries in Asia, Africa, Europe, and the Middle East [Eggplant  is a potent method that utilizes natural genetic variability to decode the genetic basis of complex phenotypes . GWAS prThis study aims to leverage the genetic diversity of eggplant germplasm and identify the possible underlying genetic factors that contribute to the TPC and antioxidant activity (ABTS and FRAP) of eggplants. TPC and antioxidant activity are crucial components that provide health benefits to consumers, making them ideal candidates for selection and improvement. Through a comprehensive analysis of the genetic factors linked to these beneficial traits, the understanding of underlying mechanisms and identification of potential genetic markers will assist future breeding programs in developing eggplant varieties with enhanced nutritional value.A total of 224 eggplant accessions collected from different countries across the world, including germplasm from Korea, were among the established core collection. These germplasms belong to different species. The eggplant seedlings (eight to ten in triplicate) were planted in the National Agrobiodiversity Center (NAC) greenhouse at the Rural Development Administration (RDA), Jeonju, the Republic of Korea. The eggplants were cultivated according to the RDA-recommended eggplant cultivation method. The introduction number (IT), species name, and geographic origin of the 224 eggplant germplasms are presented in The extraction of phenolic compounds was carried out with 70% acetone as a solvent. Briefly, 1.0 g of eggplant powder was mixed with 15 mL of the solvent in a 45 mL extraction tube. The mixture was sonicated for 25 min at 25 degrees Celsius in the dark, and the supernatant was collected via centrifugation at 4000 rpm for 10 min. Total phenolic content (TPC), ABTS+ scavenging activity, and ferric reducing antioxidant power (FRAP) assays were determined using these extractions.2CO3 solution was added to the mixture and incubated for an additional 30 min in the dark. The absorbance was then measured at 750 nm , and TPC was calculated as gallic acid equivalent (mg) per gram of eggplant powder (mg GAE/g), based on triplicate measurements. The Folin–Ciocalteu method  was used+ was combined with 10 μL of sample extract and the mixture was incubated at 25 °C in the dark. After 3 min, the absorbance at 734 nm was determined . The activity of ABTS+ scavenging was evaluated in triplicate and expressed in milligrams of ascorbic acid equivalent per gram of eggplant powder weight (mg AAE/100 g). ABTS assay was conducted according to the method described by Re et al.  with minFRAP analysis was conducted according to the method described by Yen and Chen  and subsThe Genomic DNA Prep Kit  was used to extract DNA from all 224 eggplant samples, following the manufacturer’s protocol. The Illumina HiSeq X Ten sequencing platform was used for sequencing, using paired-end reads with an average length of 151 bp. The detailed GBS statistics for 224 eggplant accessions are presented in Solanum melongena L. (https://solgenomics.net/ accessed on 19 September 2022). Mapping was a preliminary step to detect raw SNPs (In/Del) between the S. melongena genome (Eggplant Genome Consortium V4.1) and sequenced samples.Raw sequences were demultiplexed into 224 samples in line with the barcode sequences. Adapter and barcode sequences were eliminated using the software Cutadapt (version 1.8.3) . DynamicSolanum melongena L), the designated SNP positions were defined as intergenic or genic regions. Clean reads were mapped to the reference genome sequence, and the obtained SAM files were used to discover raw SNPs using SAMtools (0.1.16)  and extrStructure software ,33 was up < 0.0001) or more [https://solgenomics.net (accessed on 19 September 2022)).Association analysis was conducted using 29,183 SNP datasets using a linear mixed model (LMM) . QTLmax  or more . The BasThe Microsoft Excel program was used for data summarization and descriptive statistics on TPC, ABTS, and FRAP. Principal component analysis and correlation were computed using R software (version 4.2.1). Other statistical programs and packages are mentioned in the respective sections where they were employed.S. melongena germplasm collections, K145198 (ID: 557) and K168113 (ID: 607), had the highest TPC, ABTS, and FRAP of all the tested germplasm. The number of germplasm distributions based on TPC, ABTS, and FRAP is depicted in In this study, 224 eggplant accessions from different countries and species were tested for their phenolic content and antioxidant activity using the ABTS and FRAP assays. There was a large variation in the TPC and antioxidant properties of the eggplant germplasm collected and deposited at the RDA gene bank. The TPC ranged from 14.19 to 844.57 mg gallic acid equivalent (GAE)/100 g of dried fruit powder . The ABTThe correlation among chemical traits was examined using 224 eggplant accessions. The results, presented in PCA was conducted to explore the relationships among the chemical traits  in 224 eggplant accessions a. The an. The Evanno method was used to predict the number of groups (K = 2) in the population. As depicted in The results obtained from the population structure analysis conducted on 224 eggplant accessions are presented in Figure 4Association analysis was conducted using 29,183 SNPs generated from 224 eggplant genetic resources. The distribution of SNPs within a 1 Mb window size across all 12 chromosomes can be seen in Regarding the ten SNPs associated with TPC, one SNP was found on each of Ch03, Ch04, Ch07, and Ch12, while two SNPs were discovered on Ch05. Five SNPs were identified on Ch10, located at 93641953 bp (C/G), 93641970 bp (T/A), 93641946 bp (A/G), 93642015 bp (C/T), and 93642000 bp (A/T), within the gene that encodes IRX12 laccase-4. The two SNPs (42339531 bp: T/G and 42339551 bp: G/T) associated with TPC on Ch05 were found in genes encoding PSL4 glucosidase 2 subunit beta. In Four SNPs were associated with ABTS activity in eggplant accessions. Two of these SNPs were located in the intergenic regions on Ch01 and Ch04, while the other two were found within genes encoding glyceraldehyde-3-phosphate dehydrogenase (GAPB) on Ch04 (105162639 bp) and putative disease resistance protein (RGA4) on Ch10 (99069998 bp) . Based oFurthermore, a total of five SNPs were significantly associated with FRAP. Among them, two SNPs were found on Ch01, one in the intergenic region (133053172 bp) and the other within a gene encoding a serine/threonine-protein kinase. On Chromosome 6, the remaining two SNPs were located in the intergenic region at 91226743 bp and within a gene of unknown function at 98892408 bp. The average FRAP values of genotypes with AA alleles at these specific marker positions on Ch06 were higher compared to individuals with other alleles, as shown in S. melongena, namely K145198 (ID: 557) and K168113 (ID: 607), exhibited higher levels of TPC, ABTS, and FRAP compared to other tested germplasm collections. Furthermore, the PCA analysis demonstrated that these two germplasm collections were distinct and positioned far away from other accessions  contents in the peel of eggplants ,39,40,41cessions a. These cessions .p < 0.0001) associated with phenolic compounds and antioxidant activity in eggplant, highlighting the genetic regulation of these traits in the crop. These SNPs have the potential to serve as genetic markers for breeding programs aimed at enhancing phenolic content and antioxidant activity. Similarly, a study on Tibetan wild barley revealed the identification of 20 unique QTLs (p < 0.001) associated with phenolic compounds, flavonoid content, and antioxidant activity [Molecular breeding is a powerful tool for crop improvement and has been used successfully in a variety of crop species. GWAS enables the mapping of genetic regions associated with economically relevant traits in crop species, including yield, resistance to biotic and abiotic stress, and quality ,52,53,54activity . The resactivity . Even thactivity . The lacArabidopsis  and poplar (Populus sp.) specific paralogs of LACs are the key enzymes necessary to accumulate lignin in vascular tissues, although their influence on lignin chemistry is unknown [On Chromosome 10, five SNPs significantly associated with TPC were found in a gene that encodes IRX12 laccase-4 . Several unknown . LACs ma unknown . AdditioPleiotropy is a sign that related traits might possibly have some genetic factors in common . One pleS. melongena with the codes K145198 and K168113 have higher levels of TPC, ABTS, and FRAP compared to other tested germplasm collections. These collections could be useful for future breeding and research programs aimed at developing eggplant varieties with improved nutritional and antioxidant properties. The SNPs identified in this study that were associated with TPC, ABTS, and FRAP, as well as the adjacent genes where the SNPs were identified, can help us understand the genetic associations and assist in developing effective markers for breeding programs. The present study identified SNPs associated with TPC in genes that encode IRX12 laccase-4, a protein critical to lignin biosynthesis, which includes the biosynthesis of phenolic monomers, mostly phenylpropanoids. These SNPs may shed light for further study to understand the relationship between IRX12 laccase-4 and TPC in plants. Further investigation of these SNPs is necessary to understand their potential use as markers in future studies and gain a deeper understanding of the mechanisms involved in phenolic synthesis and antioxidant activity.Molecular plant breeding tools and functional genomics approaches have the potential to accelerate the development of crops with enhanced nutritional and health-promoting properties, improving human health and well-being. The germplasm collections of


ARS-UCD1.2) and performed an exon-specific association test on the carcass traits of 16,970 commercial Hanwoo. We found 33, 2, 1, and 3 significant SNPs associated with carcass weight (CW), backfat thickness (BFT), eye muscle area (EMA), and marbling score (MS), respectively. In CW and EMA, the most significant missense SNP was identified at 19,524,263 on BTA14 and involved the PRKDC. A missense SNP in the ZFAND2B, located at 107,160,304 on BTA2 was identified as being involved in BFT. For MS, missense SNP in the ACVR2B gene, located at 11,849,704 in BTA22 was identified as the most significant marker. The contribution of the most significant missense SNPs to genetic variance was confirmed to be 8.47%, 2.08%, 1.73%, and 1.19% in CW, BFT, EMA, and MS, respectively. We generated favorable and unfavorable haplotype combinations based on the significant SNPs for CW. Significant differences in GEBV  were observed between groups with each favorable and unfavorable haplotype combination. In particular, the missense SNPs in PRKDC, MRPL9, and ANKFN1 appear to significantly affect the protein’s function and structure, making them strong candidates as causal mutations. These missense SNPs have the potential to serve as valuable markers for improving carcass traits in Hanwoo commercial farms.This study aimed to identify causal variants associated with important carcass traits such as weight and meat quality in Hanwoo cattle. We analyzed missense mutations extracted from imputed sequence data ( Bos taurus [Hanwoo cattle, endemic to the Republic of Korea, has been residing on the Korean Peninsula for over 2000 years. The refinement of Hanwoo is achieved by intensive selection focusing on economic traits, such as meat quantity and quality . Followis taurus .Genome-wide association studies (GWAS) have become an essential tool for identifying genetic markers associated with complex quantitative traits in animal genetics. These studies have successfully identified a large number of trait-associated single nucleotide polymorphisms (SNPs) ,6,7; howIn order to overcome the challenges of identifying genetic variants associated with complex traits in GWAS, many studies have used biological information to annotate classes of mutations and search for trait-associated mutations in specific regions. In human studies, protein-coding and conserved annotations have been used to identify trait-related variants ,12,13. WIn fact, SNP prioritization approaches using missense SNPs have been developed to predict disease associated bio-markers . In the longissimus thoracis muscle samples using a DNeasy Blood and Tissue Kit , and its concentration and purity were assessed using a NanoDrop 1000 . DNA samples were genotyped using the Bovine SNP50 BeadChips . Quality control was performed using PLINK1.9 software [p-value for Hardy–Weinberg equilibrium test <10−4.This study used 16,970 commercial Hanwoo with an average age of 30.2 ± 1.9 months that were born between 2006 and 2016. These animals were slaughtered at a commercial abattoir between 2013 and 2019, producing phenotypic data (carcass traits). The genomic DNA was derived from software  based on2) < 0.6 were excluded in the imputation steps, and SNPs on the autosomes were selected from the analysis. The imputed sequence-level data was annotated using the SnpEff program [UMD 3.1.1 to ARS-UCD1.2.Imputation was conducted in a two-step process using minimac3 . Initial program . GenomicWe estimated the variance explained by genome regions via restricted maximum likelihood (REML) analysis using GCTA software . The staThe genome region-specific genomic data was obtained by extracting only non-synonymous (missense) variants from the imputed sequence-level data. The phenotypic data were pre-adjusted for fixed effects, including birth year, season of birth, growing regions, and age at slaughter, utilizing a linear mixed model in R software 3.3.1 . The adjusted phenotypes and SNP information were subsequently submitted for GWAS using a mixed linear model. It can be written as:y: phenotype; a: the mean term; b: additive effect (fixed effect) of the candidate SNP; x: SNP genotype indicator variable coded as 0, 1, or 2, which are the number of minor alleles of the genotype; g: polygenic effect (random effect); e: residual.The significance test in the exon-specific association test was performed using the Bonferroni correction method (0.05/number of SNPs). The percentage of genetic contribution (%Vg) accounted for by each SNP was calculated using the formulaThe explanation power of a candidate SNP in phenotypic variance  can be calculated as follows :(4)β2Var2Var(X): variance explained by the genetic variant X; Var(Y): phenotypic variance; σ2: variance explained by other genetic variants. It can be estimated as:β: effect size of genetic variant (X); βThe haplotype phase was inferred from the significant SNPs of each chromosome for carcass weight using PHASE v2.1.1 ,32, usinThe GEBVs of all genotyped individuals were predicted with the genomic best linear unbiased prediction (GBLUP) model using the BLUPF90 programs . GEBVs wmarker j .We used PolyPhen-2 v2  to prediIn this study, we obtained 693,496 SNPs (90%) out of 777K SNPs and 25,676,502 SNPs (100%) of whole-genome sequence data. Applying the R-square criterion, imputed sequence-level data consisting of 10,443,247 SNPs were generated, and the majority of mutations were found in intron regions at 53.16%, followed by intergenic regions at 37.09%. Exons accounted for only 0.83% of the entire genome, a relatively small proportion A.In terms of heritability by genome region, the intergenic region had the highest values for carcass weight (CW), backfat thickness (BFT), and eye muscle area (EMA) at 0.22, 0.16, and 0.14, respectively, while the highest value for marbling score (MS) was observed in the intron region at 0.18. Exon regions had lower heritability values of 0.06, 0.03, 0.03, and 0.01 for CW, BFT, EMA, and MS, respectively B.Currently, the common chips universally used in Hanwoo are Illumina BovineSNP50 v2, Illumina BovineSNP50 v3, and the customized Hanwoo 50K chip. The number of SNPs incorporated in each chip is 54,609 for Illumina v2, 53,218 for Illumina v3, and 53,866 for the Hanwoo custom chip C. As a rPRKDC) gene. The analysis revealed a significant association between the EMA and the same SNP (rs449968016). The most significant marker (rs109446852) for BFT was identified at position 107,160,304 bp in chromosome BTA2, within an exon of the zinc finger AN1-type containing the 2B (ZFAND2B) gene. Moreover, the most significant marker (rs799031002) for MS was located in the activin A receptor type 2B (ACVR2B) gene at position 11,849,704 bp on chromosome BTA22. Regarding CW, significant SNPs were identified in each of chromosomes BTA6 (rs797342426), BTA14 (rs471616366), and BTA14 (rs380389290). These SNPs correspond to hypothetical genes that have not yet been identified in cattle.Based on the quantile–quantile (QQ) plot for each carcass trait, the genomic inflation factor  was observed to range from 0.82 to 0.94 . Only thTo estimate the effect of missense variants, we analyzed the extent to which each SNP contributes to the genetic variation and phenotypic variance. The results are shown in The SNP with the highest explained genetic and phenotypic variation for CW was rs449968016, with explanatory power of 8.470% and 0.026%, respectively. This result was consistent with the most significant SNP identified in the exon-specific association test. Notably, this marker was the only one present in the Hanwoo custom chip of all SNPs that appeared significant for carcass traits. For BFT, one SNP (rs109446852) explained the highest genetic variance at 2.075% and phenotypic variance at 0.006%, consistent with the most significant SNP identified in this study. In EMA, the SNP rs449968016 equivalent to carcass weight explained the highest genetic variation at 1.728% and was found to contribute 0.005% to phenotypic variance. For MS, the SNP rs799031002, the most significant in the exon-specific GWAS, was found to explain the highest genetic variation at 1.187% and accounted for 0.004% of the phenotypic variance.Haplotype estimation for each chromosome using the 33 SNPs significantly present in carcass weight identified 16, 24, and 1103 haplotypes in BTA4, BTA6, and BTA14, respectively. Favorable homozygous haplotypes and unfavorable homozygous haplotypes were selected based on the effects of significant SNPs on carcass weight. In the BTA4 region of all individuals, 78 individuals had a favorable homozygous haplotype (AACCCC-GG), and 5518 individuals had an unfavorable homozygous haplotype (GGTTTTAA). For the BTA6 region, there were no individuals with a favorable homozygous haplotype (GGTTCCCCGGTTGG), while 916 individuals had an unfavorable homozygous haplotype (AACCTTTTTTTCCAA). Similarly, in the BTA14 region, there were no individuals with a favorable homozygous haplotype (AAAAGGTTAATTTTTTCCGGTTTTTAATTC-CGGCCTTTTAAAAAA), and only 18 individuals had an unfavorable homozygous haplotype (TTGGAACCGGCCCCAATTTTCCCCGGGGGTTAAAACCCCGGGGGG). Since no individuals with favorable homozygous haplotypes in BTA6 and BTA14 were identified, to pinpoint the optimal beneficial haplotype combination, if at least five individuals appeared that were as similar as possible to the favorable homozygous haplotype, the corresponding haplotype was selected as a favorable haplotype. It was confirmed that there were 25 animals in BTA6 and 6 individuals in BTA14. In order to distinguish additive genetic differences between individuals, with respect to carcass weight, with estimated combinations of favorable and unfavorable haplotypes, the genomic breeding values of individuals with favorable and unfavorable haplotypes for chromosomes BTA4, BTA6, and BTA14 from the distribution of genetic breeding values of 16,970 Hanwoo were determined. The genetic differences between groups with favorable and unfavorable allele combinations were analyzed for statistical significance using independent t-tests, and the results are presented in In BTA4, the average GEBV and standard deviation for individuals with favorable and unfavorable homozygous haplotypes were 24.62 ± 23.68 and −0.77 ± 26.67, respectively, and a significant difference between the two groups was confirmed. For the BTA6, individuals with favorable and unfavorable haplotype combinations showed an average GEBV and standard deviation of 47.07 ± 33.39 and −2.98 ± 25.26, respectively, with a significant difference observed between the two groups. Similarly, in BTA14, the average GEBV and standard deviation for those with favorable and unfavorable haplotype combinations were 41.46 ± 27.33 and 0.63 ± 20.29, respectively, presenting a significant difference between the groups.PolyPhen-2 predicts the effects of amino acid substitutions on proteins by considering their sequence, phylogenetic, and structural information. We predicted the impact on proteins when mutations targeted the missense SNPs most significantly associated with the carcass traits and a substantially influenced genetic and phenotypic variance. The results are presented in PRKDC) missense variant  exhibited a damaging score of 0.988, indicating that it is deleterious and has a significant impact on the protein’s function and structure. Additionally, the missense variant  of mitochondrial ribosomal protein L9 (MRPL9) and missense variant  of the ankyrin repeat and fibronectin type III domain containing 1 (ANKFN1) showed damaging scores of 0.988 and 0.961, respectively, confirming that they have a detrimental impact on the protein. In contrast to previous results, the cordon-bleu WH2 repeat protein (COBL) missense variant  and the zinc finger AN1-type containing the 2B (ZFAND2B) missense variant  are tolerated, indicating they do not affect protein function or structure. The impact on protein function due to the amino acid substitution of a ligand-dependent nuclear receptor corepressor like (LCORL) missense variant  has not been estimated as there is no reported research data on it.The protein kinase, DNA-activated, catalytic subunit  member C5 β (DNAJC5B) and corticotropin-releasing hormone (CRH) genes located on BTA14 [PRKDC was detected in EMA, consistent with our study.Upon examining previous research on QTL regions for Hanwoo carcass traits, the major QTLs for CW were identified on chromosomes 4, 6, and 14; EMA on chromosome 14; BFT on chromosome 2; and MS content on chromosomes 3, 19, and 22. These observations align with the findings of our study ,36,37. Ion BTA14 . FurtherCOBL), most significantly manifested on chromosome 4, and is a member of the G/F-actin binding protein family acting as an actin nucleator with WH2 domains [COBL plays a pivotal role in axis patterning during developmental processes. Utilizing repeated WH2 domains, it interacts with actin, undertaking G-actin sequestration, actin filament nucleation, filament severing, and barbed-end dynamics regulation. All of these are essential for modulating cellular morphogenesis by controlling cytoskeletal dynamics [For CW, the cordon-bleu WH2 repeat protein  gene associated with carcass traits. Additionally, it has been demonstrated that the LCORL region is related to growth traits in European and African Bos taurus [LCORL has been confirmed to be associated with feed intake in bovine muscle tissue [LCORL  appeared to influence approximately 4.5% of the genetic variance, representing the most substantial influence on BTA6. Unfortunately, we could not determine the impact of this specific missense mutation on the protein’s structural alteration due to the amino acid substitution.In agreement with previous studies—where specific f cattle , and bodf cattle —we also s taurus , and thee tissue . SimilarPRKDC gene is essential for embryonic development, interferon tau expression, and trophoblast development rate and has been reported to be involved in early bovine embryo development [PRKDC missense variant (p.Ala895Thr) undergoes a side-chain transition from hydrophobic to hydrophilic (uncharged) amino acids, changing the amino acid’s chemical properties. This shift, due to a single amino acid variation (SAV), results in a protein structure distinct from the norm  most significantly associated with marbling exhibited a change in protein structure due to its transition from hydrophilic (uncharged) to hydrophobic amino acids. This alteration implies that it might have different roles in interactions with other proteins and various biochemical reactions. The Mitochondrial Ribosomal Protein L9 (MRPL9) gene is a critical component of its gene product in cell proliferation and protein biosynthesis [In BTA3, the ynthesis .ANKFN1 missense variant  related to MS involves an amino acid change, but both residues fall under hydrophobic amino acids; even though their chemical properties are similar, they were found to have distinct protein structures. The ANKFN1 gene has been implicated in determining human height [For BTA19, the most significant n height ,53. HoweACVR2B) is a gene linked to myostatin and plays a role in its signaling and control. In cattle, genetic mutations in myostatin lead to prominent effects on skeletal muscle, manifesting as a dual muscle characteristic in certain breeds [Activin-type II receptor B (n breeds .PRDKC, MRPL9, and ANKFN1 account for a significant portion of the genetic and phenotypic variance in CW and MS, with the change in the single amino acid sequence having a critical impact on the protein’s function and structure, suggesting a high probability of these mutations being causal variants. These findings can provide insights into understanding the genetic factors influencing the carcass traits of Hanwoo cattle, and further functional research is necessary to fully comprehend the genetic mechanisms behind these findings.This study used a functional GWAS approach focused on the missense variants causing amino acid sequence changes to identify markers associated with carcass traits in Hanwoo cattle. Our results suggest that missense variants may impact carcass trait performance by altering amino acids, even though most candidate markers had little contribution to genetic variance. The haplotype combination of significant SNPs identified in this study can be used as a valuable marker for genome prediction in Korean cattle breeding programs. Significantly, the missense mutations in


Micronutrient deficiencies, particularly of iron (Fe) and zinc (Zn), in the diet contribute to health issues and hidden hunger. Enhancing the Fe and Zn content in globally staple food crops like rice is necessary to address food malnutrition. A Genome-Wide Association Study (GWAS) was conducted using 85 diverse rice accessions from the Democratic Republic of Congo (DRC) to identify genomic regions associated with grain Fe and Zn content. The Fe content ranged from 0.95 to 8.68 mg/100 g on a dry weight basis (dwb) while Zn content ranged from 0.87 to 3.8 mg/100 g (dwb). Using MLM and FarmCPU models, we found 10 significant SNPs out of which one SNP on chromosome 11 was associated with the variation in Fe content and one SNP on chromosome 4 was associated with the Zn content, and both were commonly detected by the two models. Candidate genes belonging to transcription regulator activities, including the bZIP family genes and MYB family genes, as well as transporter activities involved in Fe and Zn homeostasis were identified in the vicinity of the SNP markers and selected. The identified SNP markers hold promise for marker-assisted selection in rice breeding programs aimed at enhancing Fe and Zn content in rice. This study provides valuable insights into the genetic factors controlling Fe and Zn uptake and their transport and accumulation in rice, offering opportunities for developing biofortified rice varieties to combat malnutrition among rice consumers. The global population is projected to reach approximately 9.7 billion by 2050 , and a sOryza sativa L.) is the most important food crop, feeding more than half of the world’s population [Rice  to chelate Fe(III) and form Fe(III)-PS complexes, which are then transported into the plant through specific plasma membrane transporters . Rice alArabidopsis, which identified the founding members of metal transporters, specifically focusing on Fe and Zn. These include the zinc-regulated transporter/iron-regulated transporter (ZRT/IRT)-related protein (ZIP) family [1B-type heavy metal ATPase (HMA) family, the vacuolar Fe transporter (VIT) family, and the cation exchange (CAX) family [The genetic control for metal transport has been studied in rice and ) family , the nat) family .Aegilops tauschii, the wild progenitor of the bread wheat [The identification of the molecular factors influencing mineral uptake and transport as well as understanding the genes that regulate mineral homeostasis and localization in rice, would greatly contribute to the development of focused breeding approaches for biofortification purposes. A Genome-Wide Association Study (GWAS) is a highly effective tool for elucidating the genetic basis of traits in plants . It has ad wheat  and in bad wheat .This study aimed to identify genomic regions associated with the Fe and Zn content in rice grains, using SNP markers generated from a set of 85 diverse rice accessions, including local landraces. The results of this study present significant assets that can be utilized for breeding and improvement programs aimed at developing rice lines with high grain Fe and Zn content by integrating the identified markers into a Marker Assisted Selection (MAS) approach. This could enhance the nutritional value of rice and contribute to improved health outcomes in the DRC.−1 were utilized for fertilization purposes. The Fe and Zn concentration in the soils were 178.26 mg/kg and 17.62 mg/kg; and 118.2 mg/kg and 8 mg/kg, at both the Kalemie and Ruzizi plain, respectively. Samples from the two sites were pooled for micronutrient analysis.A set of 85 rice accessions  maintain3. The extracts were analyzed using the Perkin Elmer Avio 550 Max ICP-OES instrument. For the quantification of Fe and Zn in the extract, ICP-OES mix standard CatNo.43843  was used. The serial dilution of the standard was performed using 2% HNO3 to obtain the calibration standards of 80, 320, 800, and 1600 µg/L, and the external standard calibration method was then applied. The calibration was performed using Perkin Elmer syngistixTM software version 5.1. The obtained data were used to calculate the content for each element in mg/100 g using Microsoft® Excel® (2018) by applying the formulae below:3; W is the weight of the sample used; 1000 is the conversion factor from µg/l to mg/l. The results were corrected for moisture content and reported on the dry weight basis (dwb).Fifteen grams (g) of paddy rice were sampled from each accession, dehusked, and milled for Fe and Zn concentration analysis at the Mycotoxin and nutrition platform at the International Livestock Research Institute (ILRI), Nairobi, Kenya. The AOAC official method 985.01 for metals and other elements in plants and pet foods, as described by Hou et al. , was useFor Quality Control (QC) purposes after every batch of 30 samples , a QC saPstI and MseI), ligating barcoded adapters, and amplifying the adapter-ligated fragments using polymerase chain reaction (PCR) to generate a library of DNA fragments for sequencing [Genotypic data were acquired using DArTseq technology, as previously described by Kimwemwe et al. . Brieflyquencing .The libraries were then sequenced with HiSeq2500  and the resulting sequences were scored for DArTseq markers using an in-house marker scoring pipeline, DArTsoft14. The sequenced reads were then aligned to the rice reference genome version 7.0, from the Rice Genome Annotation Project (RGAP) database  to detec2). The averages of all the r2 across each of the 12 rice chromosomes were plotted against the physical distance of the SNPs using R software [The genome-wide linkage disequilibrium pattern for our diverse panel, based on the retained SNP markers after filtering, were estimated using Trait Analysis by aSSociation Evolution, and Linkage (TASSEL) software version 5.2.88 . Pairwissoftware  to estimThe distribution of the evaluated rice accessions based on Fe and Zn content was plotted using rcompanion package of R software . The des−4.Genome-Wide Association Studies (GWAS) for each micronutrient was performed with rMVP package of R software  using twOryza sativa reference genome version 7.0, available in the MSU-Rice Genome Annotation Project (RGAP) database [After the GWAS, candidate genes for Fe and Zn content in rice grains were identified and annotated from the database . All thedatabase  were minThe expression levels of the candidate genes were investigated by performing an in silico analysis of gene expression in different rice seed-related tissues, including aleurone tissues, anthers, panicles, pistils, and seeds. The expression levels in seed tissue were measured in units of Fragments per Kilobase Million (FPKM) and categorized based on the scale by Davidson et al. : FPKM vaThe Fe content in rice grains showed a continuous variation but did not follow the normal distribution while the Zn content showed a normal distribution . The Fe After filtering, 8379 polymorphic SNPs were retained and used for the GWAS and LD decay analysis. The number of markers ranged from 386 SNPs on chromosome 10 to 1018 SNPs on chromosome 1, with an average of 1289 SNPs across chromosomes. The average marker density ranged from 38.61 SNPs/Mb in chromosome 4 to 60 SNPs/Mb in chromosome 10 . 2) values between adjacent markers against the corresponding physical distance in base pairs (bp)  . A notabThe GWAS for Fe and Zn content in rice grains was conducted using two multi-locus models, MLM and FarmCPU. Two SNPs significantly associated with Fe content were detected on chromosomes 1 (S1_34232231) and 11 (S11_2567279). Among the two, S11_2567279 was identified by both models and exhibited the strongest association with Fe content . The SNPp-value = 2.41 × 10−4, while the FarmCPU model detected eight SNPs on chromosomes 3, 4, 5, 7, 11, and 12  with a , and 12 . Among tFor the Fe content GWAS, two SNPs on chromosomes 1 and 11 harbored 127 and 120 genes, respectively. For the zinc content, 166, 118, 127, 258, 107, and 121 candidate genes were detected on chromosomes 3, 4, 5, 7, 11, and 12, respectively . Among tWe further selected all the candidate genes that are involved in Fe and Zn homeostasis based on functional annotations. A total of 36 genes related to metal homeostasis were identified, with a focus on genes associated with transporter activity and transcription regulatory activity. LOC_Os01g58760 and LOC_Os01g59760), one in the MYB family transcription factor (LOC_Os01g59660), and one encodes for transcription factor- TGA5 (LOC_Os01g59350) (LOC_Os11g05640 and LOC_Os11g06170) and two are in the transporter family (LOC_Os11g05390 and LOC_Os11g05700). It is interesting to report that two genes  encode for transcription factors.For the Fe content GWAS, two candidate genes tagged by significant SNP S1_34232231 encode for bZIP transcription factor (1g59350) . At the LOC_Os03g25470, LOC_Os03g60130, LOC_Os03g60850, LOC_Os03g61100, LOC_Os04g56330, LOC_Os04g56470, LOC_Os05g37470, LOC_Os07g01070, LOC_Os07g01560, LOC_Os03g60820, LOC_Os11g38160, and LOC_Os12g05830) encode for proteins with transporter activity, two encode for proteins with bZIP domains (LOC_Os07g03220 and LOC_Os12g06520) and five encode for MYB (myeloblastosis) transcription factors  (LOC_Os03g25430), transcription elongation factor (LOC_Os03g60130 and LOC_Os12g06850), transcription termination factor nusG (LOC_Os03g61030), AP2-like ethylene-responsive transcription factor AINTEGUMENTA (LOC_Os04g55970), AP2-like ethylene-responsive transcription factor PLETHORA 2 (LOC_Os07g03250), BEL1-like homeodomain transcription factor (LOC_Os12g06340), and E2F family transcription factor protein (LOC_Os12g06200) as shown in For the zinc content GWAS, 12 candidate genes (7g02800) . The othLOC_Os11g05640 had an intermediate expression level (19.561 FPKM) in the aleurone, LOC_Os12g06520 had an intermediate expression in the anther and panicle , and the LOC_Os11g06170 gene was highly expressed in the pistil (29.41 FPKM)  . Among tGenome-wide association mapping is an effective approach that aids in the discovery of genes responsible for controlling specific traits of interest. The efficacy of this method depends on the genetic diversity of the association panels used . In our Aegilops Tauschii (30.33–69.44 ppm for Fe and 17.54–49.78 ppm for Zn) [In the present study, we analyzed a set of 85 rice germplasm accessions maintained at the rice breeding program of the INERA, for two important mineral micronutrients, namely, Fe and Zn, in milled rice. The observed range of variability for Fe in this study (0.95–8.68 mg/100 g dwb) was higher than in the previous studies, 0.118 to 0.787 mg/100 g  and 0.65 for Zn) , rice exThe GWAS panel demonstrated a significant variability for the traits studied. Based on our previous work , the obsA total of 8379 high-quality SNPs were obtained from the 85 rice accessions in this diversity panel. This number was relatively higher than what was reported in previous studies by Mogga et al.  and Isla2 < 1 within a distance of 401 kilobases (kb). Although the LD decay was relatively slower, these LD decay estimates were lower than the findings of Mather et al. [Association mapping is a population-based study conducted to identify the relation-ships between traits and markers using linkage disequilibrium (LD). In this study, the genome-wide LD decays to rr et al. , showingr et al. , 109.37 r et al. . The sloGWAS is designed to evaluate the associations between genotypes and phenotypes , and, thAmong the SNPs identified, the ones located on chromosome 11 were found to have the highest impact on Fe content, while those on chromosome 4 had the greatest influence on the Zn content in rice. These SNP markers explained a significant portion of the phenotypic variation observed in the respective traits.This study identified genes belonging to the ZIP and MYB transcription factor families. According to Meng et al. , the ZIPIn this study, we conducted a GWAS of Fe and Zn content in rice grains of 85 rice accessions and 8379 DArTseq-derived SNPs. Ten SNPs were significantly associated with Fe and Zn content. Among them, SNP S11_2567279 on chromosome 11 and SNP S4_33308504 on chromosome four were identified for Fe and Zn traits, respectively, using both the MLM and FarmCPU models. Genes belonging to bZIP family genes, MYB family genes, and genes involved in transporter activities were identified within the LD decay window. The identified markers and candidate genes represent valuable resources that can be utilized in rice breeding programs and implemented in Marker Assisted Selection (MAS). Furthermore, these resources can be used to develop improved rice lines with enhanced grain Fe and Zn content, thereby enriching their nutritional value and contributing to enhanced global health outcomes. Additional functional validation of the identified candidate genes will provide deeper insights into their roles in Fe and Zn uptake, transport, and accumulation in rice grains.


Through the comparison of polyploid GWAS on the whole set of the 204 F1, 93 high-AN-containing F1, and 111 low-AN-containing F1 populations, a total of two (consists of six SNPs), two (14 SNPs), four (eight SNPs), and nine (214 SNPs) significantly associated signals were identified for the variations of SC, DM, SRFW, and the relative AN content, respectively. Of them, a novel signal associated with SC, which was most consistent in 2019 and 2020 in both the 204 F1 and 111 low-AN-containing F1 populations, was identified in homologous group 15. The five SNP markers associated with homologous group 15 could affect SC improvement with a degree of positive effect (~4.33) and screen high-starch-containing lines with higher efficiency (~68%). In a database search of 62 genes involved in starch metabolism, five genes including enzyme genes granule-bound starch synthase I (IbGBSSI), α-amylase 1D, α-amylase 1E, and α-amylase 3, and one transporter gene ATP/ADP-transporter were located on homologous group 15. In an extensive qRT-PCR of these genes using the storage roots harvested at 2, 3, and 4 months after field transplantation in 2022, IbGBSSI, which encodes the starch synthase isozyme that catalyzes the biosynthesis of amylose molecule, was most consistently elevated during starch accumulation in sweet potato. These results would enhance our understanding of the underlying genetic basis of a complex set of breeding traits in the starchy roots of sweet potato, and the molecular information, particularly for SC, would be a potential platform for molecular marker development for this trait.Given the importance of prioritizing genome-based breeding of sweet potato to enable the promotion of food and nutritional security for future human societies, here, we aimed to dissect the genetic basis of storage root starch content (SC) when associated with a complex set of breeding traits including dry matter (DM) rate, storage root fresh weight (SRFW), and anthocyanin (AN) content in a mapping population containing purple-fleshed sweet potato. A polyploid genome-wide association study (GWAS) was extensively exploited using 90,222 single-nucleotide polymorphisms (SNPs) obtained from a bi-parental 204 F Ipomoea batatas (L.) Lam.] is a key crop in Japan  breeding and was found to be effective for mining new genes and molecular markers for a wide number of breeding traits .1 mapping population between the cultivars ‘Konaishin’ (which has a high yield and starch content but no anthocyanin) and ‘Akemurasaki’ (which has a high anthocyanin content but low-to-moderate yield and starch content)  that is homologous to the genes associated with the starch metabolism pathway. The objective of this study is to i) understand and dissect the underlying genetic basis of SC in the storage roots of sweet potato when associated with other complex breeding traits and ii) provide a useful resource base for the development of molecular markers for SC.In order to properly elucidate the genetic mechanism underlying anthocyanin accumulation in the storage roots of sweet potato and thereby contribute to the molecular system for anthocyanin differentiation in a sweet potato breeding program, in our laboratory, polyploid GWAS performed with 59,675 SNPs obtained from the 94 Fcontent) . This encontent) , we clarge roots . In the ckground . We meas1 progeny were used from a bi-parental cross between two I. batatas cultivars, ‘Konaishin’ (KNS) and ‘Akemurasaki’ (AKM). As described previously  according to Plants were harvested at 4 months after transplantation. After the number and weight (kilograms) of the storage roots were recorded, two to four medium-sized storage roots were selected, and SC and DM were measured as percentages as described previously . The relG − MSGE)/re, genotype × environment interaction variance GE − MSϵ)/r, error variance e, MSG = genotype mean square, MSGE = genotype × environment interaction mean square, MSe = error mean square, and r and e are the numbers of replicates and environments.Pearson’s coefficients of correlation among different traits were calculated using SPSS 19.0 statistical software . A two-factor ANOVA was performed using RStudio (v1.4.1717-3). Broad-sense heritability was estimated as I. trifida pseudomolecule . From these SNPs, two subsets of SNPs containing 89,982, and 88,976 sites were derived for the 93 high-AN-containing F1 population and 111 low-AN-containing F1 populations, respectively.The total DNA of each individual and their parents was isolated from folded young leaf tissues. ddRADSeq analysis was performed according to the method of molecule  and filtqqman  was established at −log10p > 4.9, based on the previous report  , starch synthases (the granule-bound starch synthases and the soluble starch synthases), starch-branching enzymes, starch-debranching enzymes (isoamylases and pullulanase), and starch degradation enzymes (α- and β-amylases). Various types and isoforms exist for each type of enzyme in sweet potato . To idencaffold)  using a 1 lines were grown from June to September 2022 at KARC/NARO under the same experimental field used for GWAS. The planting method, cultivation conditions, and other operations were the same as those of GWAS. Experiments were carried out using a randomized complete block design with three biological replications for each of the three time-point of harvesting  after field transplantation. A total of 36 samples (12 genotypes with three replications) were collected from each harvest. Thus, in total, 108 root samplings were carried out 2, 3, and 4 months after field transplantation.Storage roots of KNS, AKM, and their five high- and five low-starch-containing F®Premix Ex Taq™ II (Tli RNaseH Plus) kit  in accordance with the manufacturers’ instructions. The PCR conditions were as follows: 95°C for 30 s, 40 cycles of 95°C for 5 s, 60°C for 30 s, and 72°C for 34 s, and a final melt curve profile (65°C–95°C). The transcript levels of target genes were normalized to Actin as an internal control, and the changes in gene expression were calculated relative to AKM plants using the 2−ΔΔCt method.Frozen root slices (~2 g) were ground using 10-ml grinding jars  containing 10-mm stainless steel balls  and TissueLyser II . Total RNA from finely crushed powder (100~200 mg) was extracted by using an ISOSPIN Plant RNA Kit . cDNA was synthesized from 1 μg of total RNA in a 20-μl reaction volume using a PrimeScript™ RT reagent kit . Primer pairs of the respective genes were designed from the conserved regions of KNS (DRR267133) and AKM (DRR267132) . The nuc1 population was normal  of clones showing a low-to-undetectable level (A530< 0.5) (1 population was 3.31–15.43 A530 (with a population mean of 8.10 A530) and 3.31–15.52 A530 (with a population mean of 7.62 A530) in 2019 and 2020, respectively. The average relative AN content for KNS and AKM was 0.26 A530 and 12.39 A530 in 2019 and 0.28 A530 and 11.73 A530 in 2020, respectively. Transgressive segregation was also observed for the relative AN content , SC (H2 = 94.23%), and the relative AN content (H2 = 92.00%) compared to SRFW (H2 = 75.65), indicating that DM, SC, and the relative AN content are more stably inherited than SRFW , SC , and the relative AN content  showed a strong positive correlation between 2019 and 2020 compared to SRFW   and low-AN-containing population (n = 111) fits an expected segregation ratio of 1:1  . Taken together, the whole set of the 204 F1 (designated as WSF1) population, high-AN-containing 93 F1 (HAF1) population, and low-AN-containing 111 F1 (LAF1) population was used to search for GWAS. Significant loci and associated SNPs were declared when they were significant in at least one respective year.Since the segregation ratio of the high-AN-containing population  were associated with the WSF1 population and four SNPs (p = 8.48 × 10−06–5.07 × 10−07), including two of the above three SNPs, in the LAF1 population. Only one SNP (p = 7.65 × 10−06) was identified on HG 11. GWAS for the variation of DM, greatly followed by SC, identified one major signal on HG 15 in the WSF1 population (1 population (p = 1.18 × 10−05 and p = 3.24 × 10−06) in the WSF1 population and four SNPs (p = 1.18 × 10−05–4.27 × 10−06) in the LAF1 population, including one of the two identified in the WSF1 population. With respect to the signal on HG 15, two SNPs (ITR_CHR15_2753578 and ITR_CHR15_4116419) were commonly identified between these two traits. A second significant signal for the variation of DM was identified on HG 5 of the WSF1 population only in 2020. A total of nine SNPs were identified on HG 5 for the variation of DM. All nine and one of them were commonly identified in the same region for the variations of the relative AN content and SRFW, respectively.When investigated GWAS in all three sets of mapping populations in 2019 and 2020, a signal on HG 15 associated with SC was initially identified in the WSF1 population , 6, 7, 10, 13, and 15 for the variation of the relative AN content  on HG 15. To evaluate the effects of the peak observed on HG 15 for the variation of SC, the F1 plants were grouped based on the genotypes (homozygous or heterozygous) of the identified SNPs (p< 0.001) . As shown in the bottom panels of A–E, markers ITR_CHR15_985591, ITR_CHR15_2753575, ITR_CHR15_2753578, ITR_CHR15_4116419, and ITR_CHR15_3884083 showed 55%, 68%, 64%, 53%, and 63% putative efficiencies to screen high-starch-containing F1 lines, respectively.Overall, the comparative polyploid GWAS on WSFied SNPs Table 3.granule-bound starch synthase I (IbGBSSI), α-amylase 1D (ItAMY1D) , α-amylase 1E (ItAMY1E) , and α-amylase 3 (ItAMY3)  and one transporter gene homologous to ATP/ADP-transporter (StAATP)  were located on HG 15 of the I. trifida pseudomolecule  from HG 15 , which were prominent from 3 months after field transplantation , which corresponds to Chr 5 of ITR_r2.2.The positive-effect SNPs of SRFW on HGs 10 and 13 of the WSFpulation Table 3  of SRFW . Interesulations . In thes1 population  (1 population (0.10–2.52 kg/plant) (1 population (3.31–15.52 A530) (1 population (0.11–15.52 A530)  and α-(1→6) glycosidic linkages. As a biomacromolecule, starch consists of two major polymers; linear and slightly branched amylose  and highly branched amylopectin , with approximately 5% α-(1→6) branch points . The meton HG 15 Table 4.GBSSI has been well characterized in other plant species as well as in sweet potato  and the qRT-PCR year (2022). The year-to-year correlation coefficient in sweet potatoes for agronomic traits including SC was reported to be greatly affected by the genetic variation in the population and the environmental factor . AMY (EC 3.2.1.1) is an endoamylase hydrolyzing α-(1→4) linkages to release soluble glucans, either linear or branched  can be found below: DNA Data Bank of Japan (DDBJ) accession number: DRA015713.1 mapping population. EH performed the experiments. EH, HT, and MT obtained the phenotypic data. KSh and SI performed ddRADSeq analyses. MT and EH performed polyploid GWAS. EH and KSu performed qRT-PCR. EH performed data analysis and wrote the manuscript. All authors contributed to the article and approved the submitted version.MT conceived and supervised the research. MT and EH developed the F


Exserohilum turcicum, causing leaf blight. A total of 101 sorghum accessions collected from Niger and Senegal, SC748-5 and BTx623, were evaluated in three different environments  in Senegal for their reactions against the leaf blight pathogen. The results showed that 11 out of the 101 accessions evaluated exhibited 100% incidence, and the overall mean incidence was 88.4%. Accession N15 had the lowest incidence of 50%. The overall mean severity was 31.6%, while accessions N15, N43, N38, N46, N30, N28, and N23 from Niger recorded the lowest severity levels, ranging from 15.5% to 25.5%. Accession N15 exhibited both low leaf blight incidence and severity, indicating that it may possess genes for resistance to E. turcicum. Also, the accessions evaluated in this study were sequenced. A GWAS identified six novel single-nucleotide polymorphisms (SNPs) associated with an average leaf blight incidence rate. The candidate genes were found in chromosomes 2, 3, 5, 8, and 9. Except for SNP locus S05_48064154, all five SNPs associated with the leaf blight incidence rate were associated with the plant defense and stress responses. In conclusion, the candidate genes identified could offer additional options for enhancing plant resistance against E. turcicum through plant breeding or gene editing.In Senegal, sorghum ranks third after millet and maize among dryland cereal production and plays a critical role in the daily lives of millions of inhabitants. Yet, the crop’s productivity and profitability are hampered by biotic stresses, including  Sorghum bicolor (L.) Moench) is one of the most indispensable and versatile crops in terms of its uses and adaptability, especially in the arid and semi-arid regions of the world, where it plays a critical role in subsistence farming and the daily calorie needs of hundreds of millions of people [Exserohilum turcicum  K. J. Leonard & E. G. Suggs [syn. Helminthosporium turcicum (Pass.)]  . The pat(Pass.)] . Studies sorghum ,9,10. Thin maize . The patin maize ,11. Sorgin maize ,12,13,14in maize  surveyedin maize  evaluateHowever, the recent advances made using genomic resources  have made it possible to identify single-nucleotide polymorphisms (SNPs) to locate genes associated with economically important traits such as disease resistance . For insThe leaf blight incidence rate across the three locations in the Nigerien and Senegalese accessions was high, with the average rate = 88.4 ± 0.76 . The lowThe mean leaf blight incidence rate and severity were calculated based on three different locations: Kaymor, Kolda, and Ndiaganiao. The mean incidence rate in Kolda (mean ± S.E.= 75 ± 1.5) was statistically lower than the other two locations (Kaymor = 96.2 ± 12.8 and Ndiaganiao = 96 ± 13.6 for mean ± S.E.) . NdiaganPearson’s correlation indicates a clear positive correlation between the leaf blight incidence rate and severity . As descA PCA plot  and a deE. turcicum is an important pathogen of sorghum, causing sorghum leaf blight [E. turcicum. One sorghum line, “Hageen Durra”, was found to be resistant to leaf blight when evaluated in Central Sudan [E. turcicum in sorghum [E. turcicum [E. turcicum and identified a total of six quantitative trait loci (QTLs) across the two populations. In this study, we examined E. turcicum resistance to over one hundred accessions originally collected from Niger and Senegal and evaluated in three locations in Senegal. The accessions tested in Ndiaganiao showed high incidence and severity levels, indicating that Ndiaganiao provided a favorable environment for E. turcicum infection. Among the accessions, the lowest incidence rates were observed in N15, N48, and S17, while several accessions, including N15, N43, N38, N46, N30, N28, and N23, exhibited the lowest severity levels. As expected, the correlation test proved a clear positive correlation between the two traits.f blight ,17. Figuf blight . Among tal Sudan . In Indial Sudan . In this sorghum . In a maturcicum . Lipps eturcicum  used two2 > 0.3 with the significant SNP marker S09_38670567 span a 2.21 Mb region, while SNPs in pairwise r2 > 0.5 span 194 bp), it is unsurprising that the nearest annotated gene is 22,641 bp away from the SNP. Zing-finger-related SNPs were identified as top candidates in sorghum against fungal pathogens such as the Senegalese and sorghum mini-core collection [A GWAS identified six novel SNPs associated with an average incidence rate. Sobic.009G099450, a gene annotated as a zinc finger, was 22,641 bp away from the top SNP locus S09_38670567. As the SNP locus is in the LD with other SNPs in 2.2 Mb  identified 22 SNPs significantly associated with NCLB in three tropical maize germplasm mapping panels [E. turcicum, and when these genes were compared to those found in NCLB in maize, they concluded that they play a role in resistance to both crops. Using a GWAS on NCLB in maize, 81 genes were revealed, and 12 of these genes were shown to play a role in plant defense [E. turcicum will continue to enhance our knowledge of the resistance mechanisms in these two economically important crops, thereby increasing our food security globally.Among the top candidate genes, Sobic.008G070700 and Sobic.009G057200 were available for the predicted protein network nodes . Sobic.0ogenesis . Peroxisogenesis . Anotherogenesis . Likewisogenesis ,25,37,38g panels  detected defense . RecentlStudy area: The research fields were established in Senegal, West Africa, during the 2022 growing season. Senegal is located between latitudes 12°30′ and 16°30′ N and longitudes 11°30′ and 17°30′ W, with the drier northern part lying in the Sahelian zone and the southern part with more rain in the Sudanian zone [ian zone . The thrian zone . These sian zone ,22. A toian zone ,15:IncidDisease severity scale: The severity scale was previously described by Prom et al.  and spanStatistical Analysis: Student’s t-test for all possible comparisons was performed across the population with JMP Pro 15 . In addition, the incidence rate and severity were compared across the three experimental locations using Student’s t-test in JMP Pro 15. A Pearson correlation coefficient was computed to test the correlation between the incidence rate and severity of the disease as well. ® QuickDrop™ Micro Volume spectrophotometer (Molecular Devices), and the samples were run on a 1% agarose gel stained with ethidium bromide for quality control. The Genomics and Bioinformatics Service performed the sequencing at Texas A&M  using an Illumina NovaSeq 6000 (2.6X average coverage). All the samples were repurified before library preparation. The sequencing raw data were processed using the GATK  best practices for variant calling, implemented in the DRAGEN platform . The sequences were aligned against Sorghum bicolor v3.1.1, available at Phytozome , as the reference genome for SNP calling. The calls were filtered using the following parameters: minimum coverage depth = 3 and minimum genotype quality = 9 on a Phred scale. The resulting variants were then filtered to only accept SNPs with a minimum minor allele frequency of 0.05 and a maximum rate of missing data of 0.5. Variant filtration was performed using bcf tools. The SNP data were finally inputted using Beagle (Beagle 8.7e1.jar). The output was split by chromosome in a VCF format and contained over 5 × 103 SNPs. PLINK v1.9 [https://string-db.org/, accessed on 17 October 2023).DNA extraction of fresh sorghum leaves sampled from 120 genotypes from Niger and Senegal (60 from each country) was conducted using either a NucleoSpin Plant II kit  or a modified 2% Cethyl Trimethyl Ammonium Bromide (CTAB) method . The DNAINK v1.9  was usedINK v1.9 , using tINK v1.9  and FactINK v1.9  and visuINK v1.9 . The linINK v1.9 . For theINK v1.9 . AssociaINK v1.9 . Top canE. turcicum, a widespread and devastating disease in West Africa, which is essential for breeding strategies aimed at predicting germplasm with disease resistance and high-yielding germplasm to ensure future food security in the region.In Senegal, sorghum ranks third after millet and maize among dryland cereal production, while in Niger, sorghum ranks second to pearl millet in cereal production ,5,50. In


Sorghum is considered the fifth most important crop in the world. Despite the potential value of Senegalese germplasm for various traits, such as resistance to fungal diseases, there is limited information on the study of sorghum seed morphology. In this study, 162 Senegalese germplasms were evaluated for seed area size, length, width, length-to-width ratio, perimeter, circularity, the distance between the intersection of length & width (IS) and center of gravity (CG), and seed darkness and brightness by scanning and analyzing morphology-related traits with SmartGrain software at the USDA-ARS Plant Science Research Unit. Correlations between seed morphology-related traits and traits associated with anthracnose and head smut resistance were analyzed. Lastly, genome-wide association studies were performed on phenotypic data collected from over 16,000 seeds and 193,727 publicly available single nucleotide polymorphisms (SNPs). Several significant SNPs were found and mapped to the reference sorghum genome to uncover multiple candidate genes potentially associated with seed morphology. The results indicate clear correlations among seed morphology-related traits and potential associations between seed morphology and the defense response of sorghum. GWAS analysis listed candidate genes associated with seed morphologies that can be used for sorghum breeding in the future. Sorghum bicolor (L.) Moench] is a gluten-free cereal widely consumed throughout Africa [Sorghum [t Africa ,2. It ist Africa . Plant gt Africa . Sorghumt Africa . To datet Africa ,6,7, butMorphological variations in seed characters, such as differences in seed size and shape, are important traits in plant identification and classification of taxa, and are useful parameters for analyzing plant biodiversity ,9. Seed Sakamoto et al. evaluated 329 sorghum germplasms from various origins and identified SNPs potentially associated with seed morphology, including SNP loci S01_50413644, S04_59021202, and S05_9112888 based on multi-traits GWAS . A GWAS To add to our understanding of sorghum seed morphology, in this study, 162 Senegalese sorghum germplasms along with controls  were evaluated for various seed morphologies, including seed area size, length, width, length-to-width ratio (LWR), perimeter, circularity, the distance between the intersection of length and width (IS) and center of gravity (CG), and seed darkness and brightness. In addition to identifying variation for each trait, possible correlations among the characteristics were evaluated across the subset of the Senegalese collection. Furthermore, Ahn et al. previously studied identical germplasms to evaluate resistance to anthracnose at the seedling and 8-leaf stages and for head smut based on distinctive spot appearance rate and average time for spot appearance on the first leaf under water ,16,17. Bp < 0.0001 for all evaluated traits (raw data available through p = 0.086), but other traits showed abnormal distributions with p < 0.0001  and circularity . Other seed morphology-related traits did not show correlation to the diseases’ associated traits.Based on multivariate correlation studies between seed morphology-related traits and sorghum responses to anthracnose and head smut, we identified two major correlations supported with significant cularity a. On thecularity b. AlthouThe population structure analysis revealed that there are two major groups across the accessions tested in this study . The dent-test. The number of SNPs for each trait varied . After filtering with a t-test, multiple SNPs were excluded from the list, leaving the top SNPs that can be used as novel sources for sorghum seed morphology-related traits in breeding programs.Seed weight and size are critical yield components, and selecting for large seeds has long been a goal in crop domestication ,21. MeasSporisorium reilianum (Kühn) Langdon & Fullerton (syns. Sphacelotheca reiliana (Kühn) G.P. Clinton and Sorosporium reilianum (Kühn) McAlpine) causing sorghum head smut [S. reilianum at the seedling stage. The top candidate genes (F-box and leucine-rich repeat protein (Sobic.004G202700), ankyrin repeats (Sobic.002G174700) and xyloglucan endotransglucosylase (Sobic.004G273200)) were located near growth and development-related genes such as rhodanese-like domain-containing protein-like (Sobic.004G202600) [In the multivariate correlation studies examining the relationship between seed morphology-related traits and sorghum responses to anthracnose and head smut, we observed two significant correlations. Firstly, we found a moderate negative correlation between the head smut spot appearance rate (%) and circularity. Secondly, the head smut spot appearance rate (%) showed a moderate positive correlation with the distance between IS and CG. Craig and Frederiksen conducted a seedling inoculation for sorghum using peat pellets against ead smut ,22. The ead smut ,22. Althead smut . A studyead smut . Specifiead smut . A positead smut . Ahn et ead smut  reportedG202600) , cellulaG202600) , legume G202600) , transcrG202600) , ubiquitG202600) , and MYBAcacia crassicarpa [Zinc finger proteins play essential roles in plant growth, development, and responses to abiotic stresses such as drought, salt, temperature, reactive oxygen species, and harmful metals . As listssicarpa . DUF3537ssicarpa . Examplessicarpa , polygalssicarpa ,39,40, assicarpa .The role of ribonucleotide reductase (perimeter and width: SNP loci S02_36533975 and S02_36673863) in the de novo synthesis of deoxynucleoside triphosphates (dNTPs) in DNA replication and cell cycle progression is critical . HomeodoThe SNP locus that S06_58756099 (PCs) tagged to farnesyl-pyrophosphate synthetase is known to be associated with plant development . The N-tThe brightness-related SNP locus S01_2657706 is found near amyloid beta precursor protein-binding protein 1 (APPBP1). Numerous amyloids are involved in pathogenesis; however, plant amyloids are poorly studied but are known to play roles in the autonomous flowering pathway and post-translational modification . Ribonuct-test and filtered out any SNP that failed to pass p < 0.05. Even with these strict methods to minimize false positives, there are many novel SNPs potentially conferring changes in various seed morphologies. Hence, it is expected that most identified SNPs are genuinely associated with seed morphology-related traits that can be used for sorghum breeding in the future. Further studies should investigate the relationship between seed morphology-related characteristics and molecular markers to better understand seed morphology-related genes in the subset of the Senegalese sorghum collection and other collections within the National Plant Germplasm System of the US.Most of the genes identified are involved in plant development and physiological processes with potential aspects of seed morphology. Bonferroni correction is often considered highly conservative , and in 2), length (mm), width (mm), length-to-width ratio (LWR), perimeter (mm), circularity , the distance between the intersection of length & width (IS) and center of gravity (CG), and seed darkness & brightness . CG is the point where the seed’s mass is concentrated and IS are points where the width and length hit the boundary for the seed parameter [A total of 162 cultivars from the Senegalese germplasm collection . With the SmartGrain (version 1.3) high-throughput phenotyping software, the scanned seed images were measured for seed area size, length, width, LWR, perimeter, circularity, and distance between IS and CG . Any errTukey’s HSD test for all possible cultivar comparisons was performed with JMP Pro 15  in each trait for statistical analysis. One-way ANOVA was performed for each trait separately. Pearson’s correlation was calculated for all possible pairs of seed morphology-related traits with JMP Pro 15. For anthracnose and head smut resistance traits ,7,16,17,https://phytozome.jgi.doe.gov, (accessed on 25 May 2023)) [p < 0.05) using JMP Pro 15 . SNPs that did not pass the t-test were excluded from the list of candidate SNPs.For GWAS, a total of 193,727 SNP data were extracted from an integrated sorghum SNPs dataset based on sorghum reference genome version 3.1.1 and initially genotyped using GBS ,66,67,68y 2023)) . The meaPLINK v1.9  was usedIn this study, we analyzed seed morphology-related traits in a subset of Senegalese germplasm. Even though a low genetic diversity was found, the accessions showed a wide range of morphological traits. Intriguingly, there were potential associations between seed morphology-related traits and head smut spot appearance rate, explained by possible genetic linkages. Nearly all the candidate genes from GWAS analysis had known roles in plant growth and development. The identified genes’ functions can be validated by using modern and cutting-edge techniques such as real-time quantitative reverse transcription PCR , RNA sequencing analysis (RNA-Seq), and CRISPR-Cas9-associated gene editing. Although applying gene-editing techniques in monocot crops is challenging, rapid developments in gene editing technology will offer fast and precise functional validations of the candidate genes. On the other hand, it is essential to survey seed morphology-related traits in other sorghum populations to identify additional candidate genes and genes that overlap in multiple populations.


These genes’ coding SNPs (cSNPs) were reported to be significantly associated with the PUFA contents in common carp. However, the promoter SNPs (pSNPs) effectiveness for the PUFA contents has not been evaluated yet. This study identified the genetic variants in the promoter regions of fads2a/2b and elovl5a/5b. Moreover, the joint effects of multiple cSNPs and pSNPs in fads2b and elovl5b, two major genes related to the PUFA biosynthesis, were evidenced by the higher explained percentages of phenotype. Furthermore, the gene expression level of fads2a and fads2b showed significant positive correlation with the contents of multiple PUFAs. These SNPs would be potential markers for future selection to improve the PUFA contents in common carp.Fish is an important PUFA source for human diet. Much research has focused on increasing the PUFA products of fish. Improving the biosynthesis of PUFAs would also be a way to contribute to nutrition supplement for human health. Aquaculture selective breeding by genetic markers is essential for increasing the content of PUFAs. Fatty acid desaturase 2 (fads2 genes (fads2a and fads2b) and two duplicated elovl5 genes (elovl5a and elovl5b). The coding SNPs (cSNPs) of these genes were reported to be significantly associated with the PUFA contents. Whether the promoter SNPs (pSNPs) were associated with the PUFA contents has not been reported yet. In this study, after sequencing the promoters of these four genes, we identified six pSNPs associated with the contents of PUFAs in common carp, including one elovl5a pSNP, one elovl5b pSNP, and four fads2b pSNPs. The pSNPs were predicted in the locations of transcriptional factor binding sites. Together with previously identified cSNPs in fads2b and elovl5b, the pSNPs and cSNPs of these two genes had the joint effects on the PUFA contents with higher explained percentage of phenotypic variation of the PUFA contents than single gene. The expression levels of both fads2a and fads2b were significantly positively correlated with the contents of six PUFAs. The fads2b pSNPs corresponding to higher fads2b expression levels were associated with higher PUFA contents. The pSNPs and cSNPs will be useful for the future selection breeding of common carp with higher PUFA contents.The allo-tetraploid common carp encodes two duplicated  The poly-unsaturated fatty acids (PUFAs) are involved in numerous critical biological processes ,2,3. Fisfads2 was responsible for introducing double bonds at specific positions of the fatty acyl chain [elovl2, elovl4, and elovl5, which have specific substrates in the elongation of PUFAs [fads2 and elovl5 have been proven to be associated with the PUFAs content in human [fads2 and elovl5 would benefit for the selective breeding of fish with high PUFA contents.Some freshwater fish have the biosynthesis ability of PUFAs by converting C18 PUFAs to longer-chain PUFAs . Fatty ayl chain . The eloof PUFAs . The SNPin human , beef cain human , bovine in human ,17, pigsin human . TherefoCyprinus carpio) is cultured worldwide [fads2 genes (fads2a and fads2b) and two duplicated elovl5 genes (elovl5a and elovl5a) [Common carp  . We prevelovl5a) ,25. Howefads2a, fads2b, elovl5a, and elovl5b and detected the pSNPs of these genes, and then expected to identify the pSNPs significantly associated with the PUFAs content. Further, we also studied the joint effects the cSNPs and pSNPs of these genes on the PUFA contents. The pSNPs and cSNPs would be used as biomarkers for the selective breeding of common carp with high PUFAs content.To better understand these questions, we sequenced the promoter regions of common carp Previously we measured the PUFA contents of 269 one-year-old common carp from three strains, including 124 individuals of the Furui strain (FRC), 98 individuals of the Jian strain (JC), and 47 individuals of the Huanghe strain (HHC) [TM, Waltham, MA, USA) and 1% agarose gel electrophoresis. The full-length transcripts of common carp fads2a, fads2b, elovl5a, and elovl5b were aligned to the recent chromosome level of common carp . The promoter region of each gene was deemed as the 500 bp upstream region of the first exon [The genomic DNA of each sample was extracted from the muscle with TIANamp Genomic DNA Kit . The concentration and quality of DNA were determined with NanoDrop 2000 spectrophotometer , and the expected heterozygosity (He) were measured with the Genepop software 4.7 . The polhttps://tfbind.hgc.jp/, accessed on 10 December 2022) with the default parameters [We predicted the potential transcriptional factors (TFs) and their binding sites (TFBSs) in each promoter using TFBIND  and general linear model (GLM) analysis with 100,000 permutations in Tassel 5 , respectp value was corrected using the false discovery rate (FDR) method for multiple hypothesis testing. The GLM model was performed with the genetic distance matrix and 100,000 permutations. One pSNP was significantly associated with the content of one PUFA if it had a p value < 0.05 in the GLM method and an FDR corrected p value < 0.1 in the ANOVA. The explained percentage of phenotypic variation (PV) of each pSNP was measured using Tassel 5.Each ANOVA To study the strain effect on the association study, we performed the GLM analysis incorporated with the population structures from the PCA analysis (GLM-PCA). We also used the analysis of covariance (ANCOVA) with the strain information to estimate the putative strain effect.elovl5b and fads2b on the contents of 12 PUFAs. The fads2b and elovl5b were two major effect genes on the contents of multiple PUFAs since they had more associated cSNPs with the contents of at least two PUFAs [fads2b (F2b.cpSNPs); (ii) cSNPs and pSNPs in elovl5b (E5b-cpSNPs); (iii) cSNPs and pSNPs in fads2b and elovl5b (F2b-E5b-cpSNPs). In each SNP set, we generated different genotype combination. If one genotype combination was observed in at least three individuals, this combination was used in the comparison. For each PUFA, we performed the pairwise comparisons of the PUFA contents among different retained combinations using ANOVA. The explained percentages of PV of the genotype combination to each PUFA content was estimated with the function of ‘lm’ in R [Previously we demonstrated the joint effects of 25 cSNPs from wo PUFAs ,25. We fwo PUFAs . The LD lm’ in R .p value between its expression level and each PUFA content. Finally, we used the FDR method to adjust the p values for multiple testing.The livers of 44 randomly selected individuals were used to extract the total RNA with TRIzol . The liver is the major organ where the PUFAs are biosynthesized . The conFor each pSNP, the samples were classified into different groups based on their genotypes. The normalized expression levels of the corresponding gene in these individuals were distributed into different groups.fads2a, fads2b, elovl5a, and elovl5b were successfully sequenced in 228 individuals . With the minor allele frequency (MAF) over 0.03 and the allowed missing genotypes per individual less than 25%, nineteen pSNPs were identified in fads2a, fads2b, elovl5a, and elovl5b genes in 208 individuals, including one in fads2a, eleven in fads2b, five in elovl5a, and two in elovl5b (The pSNPs positions were numbered according to their positions relative to the first base of the full-length mRNA sequences of  elovl5b .The observed heterozygosities (Ho) of 19 loci ranged from 0.049 to 0.458 where only five pSNPs had the Ho values smaller than 0.1. The expected heterozygosities (Ho) of these loci were from 0.072 to 0.5 and few pSNPs had the He values smaller than 0.1. Similarly, the PIC value of only one pSNP was smaller than 0.1. The number of pSNPs having the diversity rates  higher than 0.1 was more than the ones of cSNPs in these four genes ,25, suggfads2a associated with the PUFA contents. For elovl5a and elovl5b, only one pSNP in each gene was associated with the content of one PUFA  and the fads2b expression levels. We observed 18 combinations where the individual numbers were over two  [fads2 of carnivorous marine fish. The TF of CEBP (CCAAT/enhancer-binding protein) could enhance the fads2 expression [fads2 promoter activity involving in the process of PUFAs biosynthesis in the large yellow croaker (Larimichthys crocea), and rainbow trout (Oncorhynchus mykiss) [The associations of the pSNPs with the PUFA contents could be explained by their locations in the TFBSs. The TF of Sp1 could enhance the PUFA biosynthesis by increasing culatus) . Lack ofpression . It coul mykiss) . These pfads2a and fads2b were significantly positively correlated with the contents of multiple PUFAs (FDR p values listed in p value of each coefficient. Ching et al. suggested a sample size of 25 to achieve a power of 0.8 or greater [fads2 expression influenced the PUFAs in breast milk [fads2b corresponded to higher fads2b expression, possibly generating more fads2b enzyme. The higher PUFA contents in the H1 samples might be due to more fads2b enzyme.The expressions of  greater . Bottoml greater . In our ast milk , giltheaast milk , and pigast milk . The inffads2a, fads2b, elovl5a, and elovl5b. Together with our previous study, our data of explained PVs suggested that fads2b might be the major effect gene on the contents of multiple PUFAs and should be target for selective breeding to improve the PUFA contents. The increasing explained PVs with the pSNPs and cSNPs demonstrated that both changing the expressions of these genes in liver and altering their enzyme activities would influence the PUFA contents. We focused on the polymorphisms of these four genes. In the future, the genome wide association study would be used to identify more SNPs and indels related to the PUFA contents at the entire genome.Herein, we investigated the polymorphisms in the promoters of fads2a, fads2b, elovl5a, and elovl5b. The association study identified six pSNPs in the latter three genes significantly related to the PUFA contents. The joint effects of pSNPs and cSNPs in either fads2b or elovl5b improved the explained percentages of PVs of the PUFA contents. Further, the joint effects of both genes had higher explained PVs than single gene. The pSNPs were in the predicted TFBSs. The expression levels of fads2a and fads2b were positively correlated with the contents of multiple PUFAs. The samples with one genotype combination corresponding to higher fads2b expression had higher PUFA contents. These pSNPs and cSNPs would be used to selectively breed common carp of higher PUFA contents.We identified the polymorphisms in the promoters of common carp


Weighted gene co-expression network analysis (WGCNA) is frequently used to identify modules of genes that are co-expressed across many RNA-seq samples. However, the current R implementation is slow, is not designed to compare modules between multiple WGCNA networks, and its results can be hard to interpret as well as to visualize. We introduce the PyWGCNA Python package, which is designed to identify co-expression modules from large RNA-seq datasets. PyWGCNA has a faster implementation than the R version of WGCNA and several additional downstream analysis modules for functional enrichment analysis using GO, KEGG, and REACTOME, inter-module analysis of protein–protein interactions, as well as comparison of multiple co-expression modules to each other and/or external lists of genes such as marker genes from single cell.We apply PyWGCNA to two distinct datasets of brain bulk RNA-seq from MODEL-AD to identify modules associated with the genotypes. We compare the resulting modules to each other to find shared co-expression signatures in the form of modules with significant overlap across the datasets.The PyWGCNA library for Python 3 is available on PyPi at pypi.org/project/PyWGCNA and on GitHub at github.com/mortazavilab/PyWGCNA. The data underlying this article are available in GitHub at github.com/mortazavilab/PyWGCNA/tutorials/5xFAD_paper. Weighted gene co-expression network analysis (WGCNA) is a widely used method for describing the correlation patterns of genes across a large set of samples . WGCNA cwww.model-ad.org) using 5xFAD and 3xTg-AD mouse models of Alzheimer’s disease (AD) and matching WT mice.We introduce PyWGCNA, which is designed to perform WGCNA and downstream analytical tasks natively in Python . PyWGCNAThe PyWGCNA object stores user-specified network parameters such as the network type and major outputs such as the adjacency matrix. PyWGCNA can be initialized from data in csv, tsv or AnnData . We produced 15 subset datasets with a reduced number of genes or transcripts to calculate how runtime changes as the number of features increases. For each subset, we ran PyWGCNA and R WGCNA three times each on the same number of CPUs (32 cores) and memory allocation (300 GB). We found that while both packages had similar performance up to 16 000 genes, PyWGCNA was twice as fast on larger datasets. Furthermore, we were able to identify modules for 96 000 transcripts with PyWGCNA but were not able to compute a co-expression network in R with the same dataset due to memory constraints  and transcript-level (from bulk long-read RNA-seq) expression datasets of 100 samples from the ENCODE portal  as illustrated by the module eigengene expression  in both sexes  (To assess the similarity between modules found in the 5xFAD and 3xTg-AD experiments, we used PyWGCNA to perform module overlap tests. We find several modules with significant overlap that are also enriched for similar functions . As expeWe have developed a Python package based on the original R implementation of WGCNA. PyWGCNA is capable of handling larger datasets and provides an additional set of well-documented functions. There are several additional downstream functions for the analysis and visualization tools for results such as functional enrichment analysis and identifying protein–protein interactions, as well as multi ways comparison between multiple PyWGCNA networks or with any other gene list. For example, the PyWGCNA Jaccard similarity-based gene list overlap test allows for associating specific cell types to individual modules for further interpretation of the possible functions of these modules. We expect such comparative analyses to grow as the number of datasets grows exponentially. We hope that this package will fill a gap in the Python bioinformatics community.


GS1 and GS2 genes encode, respectively, the main cytosolic and the plastidic isoforms of glutamine synthetase (GS). In the present study, the wheat GS1 and GS2 homoeogenes located in the A, B and D genome chromosomes have been sequenced in a group of 15 bread wheat varieties including landraces, old commercial varieties and modern cultivars. Phenotypic characterization by multi-environment field trials detected significant effects of specific GS homoeogenes on three of the seven agronomic and grain quality traits analyzed. Based on the gene sequence polymorphisms found, biallelic molecular markers that could facilitate marker-assisted breeding were developed for genes GS1A, GS2A and GS2D. The remaining genes encoding main wheat GS were excluded because of being monomorphic (GS1D) or too polymorphic (GS1B and GS2B) in the sequencing panel varieties. A collection of 187 Spanish bread wheat landraces was genotyped for these gene-based molecular markers. Data analyses conducted with phenotypic records reported for this germplasm collection in López-Fernández et al. (Plants-Basel 10: 620, GS1A, a cytosolic GS isoform coding gene, and GS2A or GS2D, plastidic GS enzyme coding genes, were found to affect TKW and KS. The finding that some alleles at one locus may mask the effect of positive alleles at hypostatic GS loci should be kept in mind if gene pyramiding strategies are attempted for the improvement of N-use efficiency-related traits.The online version contains supplementary material available at 10.1007/s11032-022-01354-0. Improvement of N-use efficiency (NUE) is a current critical issue for plant breeding in order to maximize crop production with less harmful environmental consequences. Recent review papers have dealt with the genetic basis of this complex trait  located on the homoeologous group-6 chromosomes, while the plastidic enzyme is controlled by three genes  located on the homoeologous group-2 chromosomes  catalyses the synthesis of the amino acid glutamine, the first step by which plants incorporate inorganic N into an organic molecule, thus having a key role in the assimilation and mobilization of N to the target plant organs  and grain protein content (GPC) , GS1 activity having a greater influence on traits which depend on N remobilization during grain development  in a RIL population. However, no significant effect of that allele was found in a wide collection of Chinese winter wheat cultivars where, by contrast, the allele previously associated to lower TKW was present in the entries with the highest TKW values  associated with higher TKW and GPC, whose transgenic overexpression increased yield and several yield components . Renaming of some wheat GS genes and alleles in later reports and the use of almost identical nomenclature for genes controlling grain size  widely cultivated in Spain, either in the past or the present , cloned by Bernard et al. , cloned by Li et al. (https://plants.ensembl.org/Triticum_aestivum/Tools/Blast). The obtained alignments were used to manually annotate the GS genes, determine the position of the UTRs and obtain the complete gene sequences including a 500 pb upstream and downstream window. From these sequences, primers were designed to cover the complete genomic sequence of each of the genes with three overlapping amplicons. The details of primers combinations and melting temperatures are given as supplementary material . Sequences were analyzed with Sequencher® version 5.0 sequence analysis software  and aligned against the Chinese Spring sequence (IWGSC RefSeq v1.0) . Nucleotide sequences for each identified allele were translated with ExPASy translation tool (http://web.expasy.org/translate/).The GS2A and GS2D), and CAPS marker (Cleaved amplified polymorphic sequence) when the underling polymorphism was a SNP located within a restriction site (GS1A). For the CAPS marker, GS1A 2nd amplicon was amplified as described in Online Resource GS2A INDEL region was amplified with the pair of primers TTGATTGACTTCCATGAGAGCACA-Fw and TTAGCATAAAGCACGTCCAGATGA-Rev. For GS2D INDEL region, the primers employed to amplify the 2nd amplicon of this gene were used . The trial design at La Canaleja  consisted of three replicate plots . In Limagrain’ station at Elorz , the plots (1.5 × 8 m) were sown by duplicate. In both locations, completely randomized block designs were used. Finally, small plots (1 × 1 m) without replicate were established in the experimental fields of Universidad Politécnica de Madrid , the completely randomized design including two repeated control varieties. Fertiliser treatment in LC and UPM trials was 68 N units ha−1 at 12% humidity) was estimated from grain weight at harvest in trials LC15, LC17, LG16 and LG18, while no GY estimation was attempted in trials with small plot sizes (LC16 and UPM17). After harvest, test weight  and thousand-kernel weight  were measured for all accessions. Spike number  was determined by extrapolating to 1 square meter the counting of the spikes in all plants contained in one-meter-long row. The number of kernels per spike (KS) was estimated as the mean number of kernels counted on 10 main spikes. Grain protein content  and gluten quality were evaluated on wholemeal flour samples. GPC was measured by near-infrared reflectance analysis using a PerCon Inframatic 8600 . Gluten strength was estimated on 1 g of flour samples by the sodium dodecyl sulfate sedimentation volume  test following the procedure of Dick and Quick  were used to determine any significant effect of the replications in LC and LG trials. Once no effect was found, mean values of a given cultivar in each trial were used for further data analyses. The set of data for the agronomic and quality variables analysed in the diversity panel was taken from López-Fernández et al.  procedure, the significance threshold being set at P < 0.01. The GS genes, the environment (E) and GS gene × E interactions were used as sources of variation. In the sequencing panel, only haplotypes present in at least three cultivars were considered for analysis. In the diversity panel, interaction effects between GS genes were also estimated. Comparison of least squares means between alleles of a given GS gene, and between non-allelic variants combinations, was performed using Fisher’s protected least significant difference (LSD). Contingency chi-tests were used to check two-by-two independence between allelic variants at different GS loci in the diversity panel. All statistical analyses were completed with the InfoStat statistical package  with respect to Chinese Spring  was present in 9 cultivars including all the landraces. The other two haplotypes differed only in one SNP located in an intronic region, each of them being found in 3 varieties. At the gene GS1B we identified 13 SNPs and 3 INDELs, all except one (UTR) being located at intronic regions (see Online Resource GS1B-hap1 to 4). GS1B-hap4, only present in the local landrace Rojo Caravaca, was the most distant from the reference presenting a total of 15 polymorphisms. The other haplotypes differed only in one SNPs between them and were present in six (GS1B-hap1), six (GS1B-hap2) and two (GS1B-hap3) cultivars. No polymorphism was detected for gene GS1D, for which all 15 varieties presented the Chinese Spring reference sequence.For the GS2 homoeoloci  presenting 8 SNPs and 5 INDELs with respect to Chinese Spring was identified. One of the polymorphisms was located in an UTR, another in an exon (synonymous) and the rest of them at intronic regions. GS2A-hap2 was present in 12 cultivar including all the landraces, whereas the Chinese Spring allele (GS2A-hap1) was present in the remaining 3 cultivars. For GS2B we only obtained high quality sequence from 1200 pb downstream from the start codon to the end of the gene. In the sequenced region, which fully covered the 2nd and 3rd amplicons, we were able to detect a total of 9 SNPs and 1 INDEL. Two of the SNPs were located at exonic regions and one of them, located 1390 bp from the starting codon, produced a change in the coded protein (H163Q). This non-synonymous SNP was only present in cultivar Marius . In total, we were able to identify 8 different alleles, the most frequent being the allelic variant present in Chinese Spring  and GS2B-hap2 (4 cultivars). Finally, when we sequenced GS2D more than 50 polymorphisms were detected compared to the Chinese Spring reference. Thus, we decided to include in the alignment the GS2D sequence of cultivar Xiaoyan-54, reported by Li et al. (GS2D-hap1) or the Xiaoyan-54 allele (here named GS2D-hap2), the only exceptions being two new SNPs. One of these changes was identified in cultivar Califa Sur (designated GS2D-hap1bis), that presented A instead of G at 72 bp from the start codon while no other polymorphism compared to Chinese Spring was detected for the rest of the gene. The other new SNP was detected in cultivar Nogal (designated GS2D-hap2bis), whose GS2D sequence was identical to GS2D-hap2 except by a change A2190C .The haplotypes of the six GS genes under study in the varieties forming the sequencing panel are shown in Table GS1 and GS2 homoeogenes are summarized in Online Resource The data obtained for the seven agronomic and quality traits evaluated by field experiments on the sequencing panel of varieties are provided as supplementary information , and for GS2D-hap1bis and 2bis with regard to the Chinese Spring and Xiaoyan-54 haplotypes, GS2D-hap1 and GS2D-hap2, respectively  while the amplicon will be excised in varieties with the GS1A-hap2 . For GS2A, the pair of primers designed is able to detect by PCR the presence/absence of the 239 bp deletion. Thus, a band of 935 bp is amplified in varieties with the GS2A-hap1 sequence  whereas a band of 1174 bp is obtained for GS2A-hap2 . Finally, the 1st amplicon of the GS2D gene, containing several INDELs (not shown) was used to discriminate varieties carrying the GS2D-hap1 against those with the GS2D-hap2, a smaller amplicon being obtained in the former  than in the latter . Figure For GS1A, GS2A and GS2D allowed allelic profiling of 99.5, 96.8 and 93.6%, respectively, of the Spanish bread wheat landraces forming the diversity panel  while GS2D-M1 was present in 71% of the genotyped varieties. It can be noted that for GS1A and GS2D the predominant alleles were the noted above in all the four populations but, contrary to the overall trend, the GS2A-Del allele was the most frequent in Pop 4 .The gene sequence-based markers developed for el Table . GS1A-SMGS1A, GS2A and GS2D, but also their two-by-two interactions. The environment had always a great effect on the evaluated traits while none of the GS gene × E interaction was significant.The results of the statistical analyses conducted to determine the influence of environmental and GS genotype factors, as well as GS × E, on the traits analysed in the diversity panel by López-Fernández et al.  are summGS2 homoeogenes, GS2A-Del and GS2D-M2 having a positive effect on this yield component trait , the GS1A-SM allele produced higher TKW than its alternative allele GS1A-LM , GS2A-NoDel (with negative effect on TKW) and GS2D-M1 (with negative effect on TKW as well as on KS in GS1A-LM accessions).Grain protein content was highly affected by Allelic variation at none of the loci examined showed to influence SVol, a wheat quality parameter tightly associated to gluten strength. No significant interaction among loci was either detected  can affect the function of a gene by influencing its RNA processing and/or stability. Actually, a recent study has described differential expression of a glutamine synthetase GSr gene, related to differences in TKW, in two wheat varieties which have identical coding regions  is present in a quite similar proportion in the Chinese and Spanish sets of landraces . Regarding GS2D, the Chinese Spring allele is the most frequent in both collections but much more abundant in the Chinese local germplasm (95% versus 66% in the present study), its allelic alternative  being clearly minority in the two cases.For the three molecular markers that were genotyped in the diversity panel, we have found that one of the alleles was predominant Table . It makeKhalid et al.  determinGS1A, GS2A and GS2D genes on the traits that were evaluated in both germplasm sets: TKW, KS, GPC and SVol. Such discrepancies refer mostly to the finding of a significant effect of a specific GS gene in the diversity panel, but not in the sequencing panel. This holds for GS1A on KS and GPC, for GS2A on TKW, and for GS2D on TKW and GPC  was the associated to lower TKW when some effect of this locus was detected in our study. The discovery of genetic factors which simultaneously increase TKW and GPC is a matter of great breeding interest in order to select genotypes where the usual inverse relation between both traits can be broken  as well as a major QTL for KS on 6DL, where GS1D is located. However, none of these GS1 homoeogenes could be included in our marker-trait study for having been found to be too polymorphic (GS1B) or monomorphic (GS1D) among the accessions in the sequencing panel.It can be added that Habash et al.  colocaliGS2A and GS2D on TKW and GPC in the diversity panel of landraces . It is then likely that they are functionally similar, which closely agrees with the finding of higher GPC in landraces presenting the GS2A-NoDel marker allele at GS2A locus. For TKW our study indicates some increasing effect of the Chinese Spring allele . This is not coincident with the reported in Li et al.  was evidenced. These authors neither found colocalization between any QTL for GS activity and some large-effect QTLs for TKW and GPC that could be associated to GS2B. All that reinforces the doubtful reliability of using GS activity to predict improved NUE phenotypes as has been suggested in some instances . These authors found that the Chinese Spring allele was associated with higher TKW, and also higher N uptake before flowering. On the contrary, our results have indicated that genotypes bearing the Xiaoyan-54 allele have higher TKW, but mean comparison analysis of non-allelic combinations at GS1A and GS2D further reveals that the improving effect of GS2D-M2 is not significant in landraces bearing the GS1A-SM allele  reported by  is associated to lower TKW . Its allopolyploid nature makes the handling of epistatic effects a much more troublesome challenge in wheat than in other major crops. Because of their practical implications, validation of the marker-trait relationships detected here on other wheat germplasm collections would be of great interest.Supplementary file1 (PDF 83 KB)Supplementary file2 (PDF 85 KB)Supplementary file3 (XLSX 14.4 KB)Supplementary file4 (XLSX 12 KB)Supplementary file5 (XLSX 11.6 KB)Supplementary file6 (XLSX 13.9 KB)Supplementary file7 (XLSX 11.4 KB)Supplementary file8 (PDF 162 KB)Supplementary file9 (PDF 66.9 KB)Supplementary file10 (PDF 178 KB)Supplementary file11 (PDF 63.8 KB)Below is the link to the electronic supplementary material.