Azospirillum brasilense and an intriguing issue is which genes of the plant are involved in the recognition and growth promotion by these Plant Growth-Promoting Bacteria (PGPB). We conducted Genome-Wide Association Studies (GWAS) using additive and heterozygous (dis)advantage models to find candidate genes for root and shoot traits under nitrogen (N) stress and N stress plus A. brasilense. A total of 52,215 Single Nucleotide Polymorphism (SNP) markers were used for GWAS analyses. For the six root traits with significant inoculation effect, the GWAS analyses revealed 25 significant SNPs for the N stress plus A. brasilense treatment, in which only two were overlapped with the 22 found for N stress only. Most were found by the heterozygous (dis)advantage model and were more related to exclusive gene ontology terms. Interestingly, the candidate genes around the significant SNPs found for the maize–A. brasilense association were involved in different functions previously described for PGPB in plants . Our findings are a benchmark in the understanding of the genetic variation among maize hybrids for the association with A. brasilense and reveal the potential for further enhancement of maize through this association.Maize genotypes can show different responsiveness to inoculation with  Currently, major agro-systems are highly dependent on chemical fertilizers and pesticide inputs. One of the main strategies to develop sustainable agriculture in the face of natural resource scarcity and environmental impacts caused by the application of these products is the use of Plant Growth-Promoting Bacteria (PGPB) inoculants. These bacteria in association with plants may generate several benefits to the host, such as phytohormone biosynthesis, biological nitrogen fixation (BNF), and induction of resistance mechanisms. Consequently, there are positive effects on the enhancement of root traits, tolerance to abiotic stress, and defense against pathogens ,2.Azospirillum brasilense is a well-known PGPB marketed by several companies in South American countries . It is used as a inoculant in some cereal crops such as maize and wheat [A. brasilense [Genome-wide Association Studies (GWAS) is a powerful approach for the identification of genomic regions associated significantly with phenotypic trait variations and has been widely applied in the study of the genetic basis of plant–microbe interactions, including pathogens [Rhizobium tropici in a panel of 259 common beans [Arabidopsis thaliana inoculated with Pseudomonas simiae WCS417r [nd wheat . Some stasilense –6. In thathogens ,8 arbuscathogens ,10, and athogens . As far on beans . The sec WCS417r . HoweverMoreover, the growing of plants on unsterilized soil should be considered in studies concerning the relationship of plants with PGPB. The soil characteristics may influence this association, particularly due to the interaction of the inoculated strain with the soil microbiome. For instance, they might compete for resources and site, or show antagonist effects . The undAnother challenge is the heterosis (or hybrid vigor) of several maize traits –20. TherA. brasilense interaction is absent. However, this information can contribute to the understanding of its genetic base and how to apply it in plant breeding programs aimed at improving the germplasm for this association. Hence, we aimed with this study (i) to understand the genetic variation of maize in response to A. brasilense inoculation under low-N soil conditions, and (ii) to perform GWAS analyses using additive and heterozygous (dis)advantage models to identify Single Nucleotide Polymorphisms (SNPs) significantly associated with traits related to this association, the underlying candidate genes, and the importance of non-additive effects on the maize–A. brasilense association.Knowledge about the genetic variation available and the genetic architecture of the traits involved in maize‒A. brasilense Ab-V5 was selected from maize roots in Brazil and is registered by the Brazilian Ministry of Agriculture, Livestock and Food Supply (MAPA) for the inoculant production for maize, rice and wheat [A. brasilense Ab-V5 was prepared in the Laboratory of Genetics of Microorganisms “Prof. João Lúcio de Azevedo” at ESALQ/USP, Piracicaba-SP, Brazil, and taken immediately to the experimental area. Bacterial inoculum was prepared by growing Ab-V5 in Dextrose Yeast Glucose Sucrose (DYGS) liquid medium [8 UFC mL–1 and transferred with a pipette into plastic bags containing the maize seeds of each genotype individually. Sowing was done about 30 min after inoculation.The bacterial strain nd wheat ,35. In ad medium  at 28°C A. brasilense inoculation. The decision to non-input N fertilizer was due to its reported negative effects on N fixation by diazotrophic bacteria [The association panel was comprised of 118 single-cross hybrids from a diallel mating design between 19 tropical maize inbred lines with genetic diversity to nitrogen-use efficiency –39. The bacteria ,41. In ebacteria . After gTM . This software provided the measurements of root average diameter , root volume , and the total length of a series of root diameter classes. The length of fragments with a diameter class less than or equal to 0.5 mm were considered as the lateral root length , while that with diameter classes greater than 0.5 mm were considered as axial root length  [–1) and specific root surface area  dividing the total root length and the surface area by the RDM, respectively. Furthermore, the root/shoot ratio  was obtained by dividing the RDM by the SDM. In total, 10 traits were evaluated and approximately 1416 root systems analyzed.Approximately 35 days after the emergence, when most of the hybrids had reached the V7 stage (seven expanded leaves), plant height  was measured from the soil level to the insertion of the least expanded leaf. The shoot was harvested and dried in a forced-draft oven at 60°C for 72 h to determine the shoot dry mass . The soil particles in each root system were carefully removed with water and the individual storage was performed inside plastic pots in 25% ethanol solution for preservation. The root images acquired by an Epson LA2400 scanner (2400 dpi resolution) were analyzed by WinRHIZOARL, cm) . We detey is the vector of phenotypic observations of the traits evaluated on maize hybrids; βE is the vector of fixed effects of year; βB is the vector of fixed effects of block within year; βC is the vector of fixed effects of countertop within block and year; βI is the vector of fixed effects of inoculation; βEI is the vector of fixed effects of inoculation × year interaction; uG is the vector of random effects of genotype, where uGE is the vector of random effects of genotype × year interaction, where uGI is the vector of random effects of genotype × inoculation interaction, where uGEI is the vector of random effects of genotype × year × inoculation interaction, where ε is the vector of errors, where XE, XB, XC, XI, XEI, ZG, ZGE, ZGI, and ZGEI are the respective incidence matrices related to each vector. The significance of fixed effects was tested using the Wald test implemented in the ASReml R package, while the significance of random effects was assessed by Likelihood Ratio Test (LTR) from the asremlPlus R package [j and r are the number of years and replications in each experiment, respectively.The analyses were conducted using Restricted Maximum Likelihood/Best Linear Unbiased Predictor (REML/BLUP) mixed models, by ASReml R package , conside package . The var® Axiom® Maize Genotyping Array [in silico from the genotypes of the corresponding parental inbred lines. After that, one more filter was applied to the matrix, eliminating SNPs with Minor Allele Frequency (MAF) ≤ 0.05. A final SNP set of 59,215 was obtained and used for the subsequent analyses.The Affymetrixng Array  of 616,2ng Array . The hyby is the vector of phenotypic observations of the traits evaluated on maize hybrids; βE is the vector of fixed effects of year; βB is the vector of fixed effects of block within year; βC is the vector of fixed effects of countertop within block and year; βG is the vector of fixed effects of the genotype; βGE is the vector of fixed effects of genotype × year interaction; and ε is the vector of errors, where XE, XB, XC, XG, and XGE are the respective incidence matrices for each vector. Density and box plots were used to compare the means between both treatments. In addition, the changes due to A. brasilense inoculation on the hybrid traits were calculated by Δ = M1−M2, where M1 is the adjusted mean under N stress plus A. brasilense and M2 is the adjusted mean under N stress.Marker-trait association analyses were performed for the traits with significant inoculation effect. For these traits, the adjusted means for each hybrid were calculated by treatment (inoculated and non-inoculated), separately, considering the following model:Population structure was estimated by principal component analysis (PCA) using the genomic matrix through the SNPRelate R package . The GWA1 and A2), the SNP matrix was coded by 0 (A1A1), 1(A1A2), and 2 (A2 A2), considering the A2 as the minor allele. In this context, the additive GWAS model assumes there is a linear change in the phenotype regarding the minor allele number of copies. On the other hand, in the heterozygous (dis)advantage GWAS model, the homozygous genotypes (A1A1 or A2A2) were assumed to have the same effect, while the heterozygous genotypes a different one, implying an increase or decrease of the effect on the trait. Therefore, the SNP matrix was coded by 0 (A1A1), 1 (A1A2), and 0 (A2A2) [The additive and heterozygous (dis)advantage models were applied in GWAS analyses by using specific encodings for the SNP matrix. Concerning the additive SNP effect with two alleles (A0 (A2A2) ,34. Box r2 between all pairs of SNPs across the chromosomes using PLINK v.1.9 software [r2 values against the physical distance of the SNPs. Moreover, the heterozygosity by hybrid and by SNP marker was estimated dividing the number of heterozygous loci by the total of SNP markers and maize genotypes, respectively.The average linkage disequilibrium (LD) in the hybrid panel was investigated using the square allele frequency correlation coefficient software . The exthttps://www.maizegdb.org/). Complementary information was collected from the U.S. National Center for Biotechnology Information (http://www.ncbi.nlm.nih.gov/) and the Universal Protein Resource (http://www.uniprot.org/). Venn diagrams were constructed to summarize the number of candidate genes identified using the VennDiagram R package [The candidate genes associated with the significant SNPs were obtained from the B73 genome reference (version 4) in the MaizeGDB genome browser . The average heterozygosity of hybrids was 0.32, ranging from 0.03 to 0.38 with most of the individuals presenting around 0.35  was used as a covariate in all GWAS analyses.Thus, the population structure information was not included due to the increase in the deviation from the expected p-values showed by Q–Q plots (not presented). Furthermore, based on the LD decay for this hybrid panel, the gene annotation was performed within a 50 kb sliding window around each significant SNP.The additive and heterozygous (dis)advantage GWAS models were used to dissect the genetic basis of the traits RDM, RV, RAD, SRL, and SRSA under N stress and N stress plus A. brasilense advantage GWAS model. Seventeen significant SNPs were found associated with traits under N stress plus 7, and 8  Figs. Se7, and 8  Figs. Fo7, and 8 . No candA. brasilense and 22 to N stress. Regarding the models, 13 significant associations were identified by using the additive GWAS model and 34 the heterozygous (dis)advantage model. There was no candidate gene shared between them , including some of the maize hybrids showing negative effects on the traits due to the inoculation. This result shows that adding only one PGPB to the microbiome is enough to expand the range of maize plant responses under low-N stress. This may be because microbes alter the plant functioning and confer different characteristics to the host plant, reinforcing the emerging idea of the holobiont being a unit of selection that possess a larger variability to be explored in plant breeding [Our results did not show pronounced differences among the distributions of adjusted means of the hybrids under N stress and N stress plus breeding –65.A. brasilense, are not common in the literature [A. brasilense. For example, triggering plant defense responses incurs an energetic cost [Studies reporting a decrease in the phenotypic traits of host plants due to the inoculation with PGPBs, such as terature . One postic cost , which mtic cost ,69; thenA. brasilense on the plant can vary according to the concentration of the inoculant [A. brasilense in the root environment may increase the release of plant hormones that consequently inhibit root growth [Another explanation for the negative responsiveness is because the effect of noculant ,70. In gnoculant . The higt growth . Thus, cA. brasilense inoculation some plant genotypes could reduce the investment in root growth in order to allocate it to shoot development. However, further studies are needed to better understand the influence of inoculation with this PGPB on the distribution of dry matter between roots and shoots.On the other hand, the reduction in root traits due to inoculation would not necessarily be a negative factor for the plant. Under abiotic stress conditions, such as low N supply and drought, high root/shoot ratios are common ,73. In tA. brasilense suggest the influence of several genes of small effect and a strong environmental influence. In summary, these results reinforce the complex PGPB × plant × environment interactions. Furthermore, they show the possibility of improving plants to be more efficient by the association with PGPB.The continuous phenotypic variation and the moderate estimates of heritability for the traits related to the maize responsiveness to A. brasilense. Several candidate genes related to the maize responsiveness to A. brasilense were detected. Considering the panel size used in our study, possibly the power of our GWAS analyses has been low and only theose SNPs with more greater effect have been identified [To the best of our knowledge, this is the first report employing GWAS to assess the genetic architecture of the association of maize with entified . Korte &entified  suggest entified . This isFusarium graminearum [A. brasilense inoculation in medicinal plants [It is known that the colonization of host plants by beneficial microbes depends on their ability to manipulate defense-related pathways . In thisminearum . Anotherminearum , inducesminearum . Gene Zml plants , but theA. brasilense are aspects also frequently reported [The modulation of plant hormones and related signaling pathways by reported ,61. For reported . Moreovereported  and wheareported . Among oreported .A. brasilense negatively affects the attraction of the pest insect Diabrotica speciose to maize by inducing higher emissions of the volatile (E)-β-caryophyllene. Therefore, the validation of this candidate gene in further studies could help to understand better the role of plant defense against pests induced by A. brasilense.Some studies show modulation of the induction and emission of plant volatiles by plant-associated microorganisms, including PGPBs and Rhizobia ,86. In tRegarding the candidate genes related to abiotic stress mitigation, we found Zm00001d020747 (additive model), encoding Aquaporin TIP4-1. Under N deficiency, this plant transporter is up-regulated in Arabidopsis  and it iWe found a candidate gene directly involved in plant root growth encoding hydroxyproline-rich glycoprotein family protein , a protein family from plant cell walls classified as arabinogalactan-proteins (AGPs), extensins (EXTs), and proline-rich proteins (PRPs). It plays a key role in several processes of plant development such as root elongation and root biomass, especially under stress conditions . AdditioBurkholderia sp. in Arabidopsis thaliana leads to modification in the expression of this protein [The major part of N in the leaf is allocated to the chloroplast proteins, and deficiency in this nutrient leads to a reduction in photosynthetic efficiency . The Zm0 protein . Moreove protein .A. brasilense and N stress in maize. Therefore, besides these results indicating that the stress applied in our experiment was effective, they also could be helpful in further studies to understand better the genetic control of root traits under N stress in the early stages of plant development for improving tolerance in maize.Although the candidate genes found for the N-stress treatment were not the main focus of this study, many of them have been previously described due to their direct or indirect relation to plant responses to abiotic stress conditions. The LOC109941493 gene (heterozygous model) encodes the plasma membrane ATPase 2-like, this being an ion pump in the plant cell membrane important for root growth and architecture under different nitrogen regimes . In addiSome of the candidate genes found by the heterozygous (dis)advantage GWAS model were identified for more than one trait; this was not observed using the additive model. For these, the effect on phenotypes was always in the same direction; for example, the candidate gene Zm00001d029115 (protein strictosidine synthase-like) had a negative effect on both RDM and RV. Possibly, this occurred because these pleiotropic candidate genes were only found between RDM, RV, and RAD traits which showed a positive correlation among them.A. brasilense and N stress. This was also evident through the results of GO terms, where an increase in exclusive biological functions was verified. This indicates that the PGPB provide the plant with a broader spectrum of internal activities, which may be an advantage for growth in stressful environments, such as N deficiency, with possible consequences in plant evolutionary potential.GWAS analyses using non-additive models are common in human and animal studies –104. HowFurthermore, our results showed that heterozygous genotypes can have advantages or disadvantages on the root traits (both treatments) depending on the allelic combinations that are formed by the parental crossing. Thus, the strategy of use of SNP‒trait associations found by heterozygous loci in breeding programs depends on the effect of the heterozygous genotype. This is a challenge to plant breeders because during hybrid development the allele combination should be predicted by parental selection in order to benefit its association with PGPB. In this sense, further studies underlying these candidate genes are required to understand better the biological mechanisms of heterotic performance, in comparison to homozygous, in the presence of these PGPB. For those providing an advantage, the alleles should be improved separately in different heterotic groups for their subsequent combination in the mating process. On the other hand, when the heterozygous genotypes are a disadvantage, one or other allele should be improved simultaneously in both heterotic groups in order to obtain homozygous genotypes in hybrids.A. brasilense, with key roles particularly in plant defense, hormonal biosynthesis, signaling pathways, and root growth providing insights into their complex genetic architecture. In this context, non-additive effects contribute substantially to the maize phenotypic variation in response to the inoculation and are related to a wider spectrum of biological functions. Together, these findings allow to be started the marker-assisted selection and genome editing in breeding programs for the development of maize hybrids that can take advantage of this association more efficiently. Finally, our results also represent a benchmark in the identification of homologous genes in important related species, such as rice and wheat, besides advancing the understanding of the genetic basis of plant–PGPB interactions.Our study modeling additive and heterozygous (dis)advantage effects in GWAS analyses revealed 25 candidate genes for the responsiveness of maize to S1 TableAzospirillum brasilense treatments.Wald Test for fixed effects and Likelihood Ratio Test for random effects from the joint diallel analysis of 118 maize hybrids evaluated under N stress and N stress plus (DOCX)Click here for additional data file.S1 FigA. brasilense. (B) Estimates of variance components ((A) Pearson correlation between maize hybrid adjusted means under N stress and N stress plus (TIF)Click here for additional data file.S2 Fig(A) root dry mass, (B) root volume, (C) root average diameter, (D) specific root length, (E) specific root surface area, and (F) root/shoot ratio.(TIF)Click here for additional data file.S3 Fig(A) root dry mass, (B) root volume, (C) root average diameter, (D) specific root length, (E) specific root surface area, and (F) root/shoot ratio.(TIF)Click here for additional data file.S4 Fig(A) root dry mass, (B) root volume, (C) root average diameter, (D) specific root length, (E) specific root surface area, and (F) root/shoot ratio.(TIF)Click here for additional data file.S5 Fig(A) root dry mass, (B) root volume, (C) root average diameter, (D) specific root length, (E) specific root surface area, and (F) root/shoot ratio.(TIF)Click here for additional data file.S6 FigAzospirillum brasiliense and (B) N stress.(A) N stress plus (TIF)Click here for additional data file.S7 Fig(TIF)Click here for additional data file.S8 Fig(TIF)Click here for additional data file.


Two strong and distinct association signals associated with berry development periods were found on chromosome 16. Most candidate genes of the interval were highlighted as receptor-like protein kinase. For berry weight, significant association loci were identified on chromosome 18, as previously known, and on chromosome 19 and chromosome 17, as newly mapped. Berry flesh texture was newly located on chromosome 16; candidate genes in the interval were related to calcium. Berry flavor was determined on chromosome 5. Genomic regions were further investigated to reveal candidate genes. In this work, we identified interesting genetic determinants of grape berry-related traits. The identification of the markers closely associated with these berry traits may be useful for grape molecular breeding.Deciphering the genetic control of grape berry traits is crucial for optimizing yield, fruit quality, and consumer acceptability. In this study, an association panel of 179 grape genotypes comprising a mixture of ancient cultivars, landraces, and modern varieties collected worldwide were genotyped with genotyping-by-sequencing using a genome-wide association approach based on 32,311 single-nucleotide polymorphism (SNP) markers. Genome-wide efficient mixed-model association was selected as the optimal statistical model based on the results of known control loci of grape berry color traits. Many of the associated SNPs identified in this study were in accordance with the previous QTL analyses using biparental mapping. The grape skin color locus was found to be associated with a  Genetic analysis of 179 types of vine has revealed regions of the genome and associated genes and proteins that control many of the key traits in the grapes. These traits are crucial for determining fruit quality and yield, in addition to conferring flavors that are highly valued by consumers and wine makers. Researchers in China, led by Da-Long Guo at Henan University of Science and Technology, used DNA sequencing to analyze genetic markers in a mixture of ancient, traditionally cultivated and modern grape varieties. The analysis identified specific regions of the genome linked to grape fruit development, weight, texture, skin color and flavor. The molecular basis of the effects of the identified genetic regions were also explored. The findings will help generate new varieties of grapes for the food and wine industries. Vitis vinifera L.] is an economically important fruit-tree crop in many temperate and subtropical countries. Its economic importance and potential health benefits make it a common choice for fruit-tree cultivation. As a result of long-term natural selection and artificial domestication and breeding, many fruit variations have accumulated in grape, with wide diversities of berry color, size, weight, texture, aroma, and shape. These variations could serve as important resources for grape breeding and genetic improvement. A better understanding of their genetic control could facilitate the selection of desirable traits. Most fruit-related agronomic traits are complex quantitative traits. The efficiency of selecting desirable traits could be enhanced by gaining a better understanding of their genetic control.Grape [6. However, the results of quantitative trait locus (QTL) mapping usually vary greatly among genotypes7. A biparental population makes use only of the recent recombination information that arose during the crossing, which is often population specific and less applicable against wider genetic backgrounds8. In contrast, studies of diverse populations  can exploit all historical recombination events accumulated in the sampled individuals9. The wide diversity in grape genotypes remains largely underexplored.The identification of genotype-to-phenotype associations is an important focus in plant breeding. The genetic control of major berry-related traits in grape, such as phenology, berry weight, berry firmness, and seedlessness, has been explored via simple sequence repeat (SSR) markers in biparental populations11. Genome-wide association mapping is a widely used method to dissect the genetic basis of complex traits9. In a large plant natural population, GWAS was a powerful method for identifying QTLs with multiple alleles at the whole-genome level based on linkage disequilibrium (LD)12. In a recent study, a GWAS was performed with 129 peach accessions to identify QTLs controlling 12 key agronomic traits13. A GWAS was conducted to explore the genetic structure and domestication history of the grape collection of the United States Department of Agriculture (USDA)14. The genetic basis of leaf shape was investigated through the GWAS evaluation of 961 grape genotypes using Vitis9kSNP array15. Several other GWAS analyses have been conducted to investigate grape genetic diversity17, aroma18, and a few candidate genes19. However, the genetic mechanisms controlling the phenotypic diversity of grape berry traits based on GWASs have yet to be fully explored.The advent of next-generation sequencing (NGS) has made it possible to obtain genome-wide coverage markers affordably using the “reduced representation approach” in any species. The flexibility and low cost of genotyping-by-sequencing (GBS) make it an excellent tool in genome-wide association studies (GWASs)20. Efficient Mixed-Model Association eXpedited (EMMAX) assesses variance components (or their ratios) and then fixes them to test genetic markers21. Genome-wide Efficient Mixed-Model Association (GEMMA) was developed to assess population parameters for individual markers22. However, there is no general software package applicable to all association panels.Population structure and kinship from the genetic background are two common indirect, non-causal associations that lead to false positives in GWASs. Various software packages have been developed to eliminate false positives in different situations. TASSEL  employs general linear model and mixed linear model approaches to simultaneously account for population structure and unequal relatedness among individuals23 of the National Grape Germplasm Repository at Zhengzhou Fruit Research Institute of the Chinese Academy of Agricultural Sciences was conducted based on the GBS method. The primary objective of this study was to use a GWAS to identify single-nucleotide polymorphisms (SNPs) associated with important berry agronomic traits. The identified SNPs may be used to improve the fruit quality of grape.To improve our understanding of the genetic bases of grape berry agronomic traits, an association analysis of 179 grape elite genotypes from the primary core collection2); cluster density ; berry weight ; berry flesh texture ; berry color ; berry shape ; and berry flavor . Ten ripe clusters were harvested from each plant and subjected to measurements of the above traits. Field experiments in which berry traits were conducted in 2014 and 2015. In general, three plants per variety were maintained. For quantitative traits , the average phenotypic value of each trait and year was considered for the association tests8. Qualitative traits  were verified in the second season, and no differences between years were observed. The equality of variance and means were analyzed by analysis of variance (ANOVA). Pearson correlation coefficients of traits between 2014 and 2015 were calculated. All analyses were conducted using R 4.3.2.For genome-wide genotyping, a total of 179 grape genotypes . The raw sequencing data and SNP calling were analyzed using SOAP family software (http://soap.genomics.org.cn/). The short reads were aligned to the reference grape genome (http://genomes.cribi.unipd.it/DATA/GENOME_12×/) using SOAPaligner/soap2 (http://soap.genomics.org.cn/), and SOAPsnp v1.05 was used for SNP calling.Young and fresh leaves were harvested from each individual grape genotype. Samples were immediately stored in liquid nitrogen and moved to a −80 °C freezer. DNA was extracted according to the method of Zyprian et al2, between SNPs were based on SNPs within 1 Mb windows using PLINK v1.07 software25. The program ADMIXTURE was run 1000 times for K values 2–10 to generate admixture proportions. Then, the best value of K was determined by cross-validation (CV) and log-likelihood estimates. The log-likelihood difference between the minimum and maximum of each K was calculated. To construct the neighbor-joining tree, PHYLIP 3.696 software was used.Calculations of pairwise LD, r25 Given that none of the traits were binary and that the category phenotypes could be treated as special quantitative traits, the “–assoc” option was applied to implement the regression statistics and write the results to a.qassoc file. (2) TASSEL 5.0:20 The K+Q module  was used in the TASSEL analysis. K was a kinship matrix built by TASSEL, and the best admixture results representing population membership served as covariates in the model, denoted Q. (3) EMMAX:21 On the command lines the options emmax-kin -v -h -s -d 10; emmax -v -d 10 were entered for the creation of the identity-by-state (IBS) relationship matrix and for the association test, respectively. (4) GEMMA:22 Association mapping was performed in R using the GEMMA implementation of the standard linear mixed model, y = Wα + Xβ + u + ε see Zhou et al22.To ensure the accuracy of the results, SNPs with a missing genotype frequency greater than 0.05 or a minor allele frequency (MAF) less than 0.05 were excluded from analysis. Imputation was not performed. Four methods were used to implement the GWAS: (1) Plink:26, which applied Bonferroni correction according to the effective number of independent SNPs. The Q–Q plots  > 5 were considered significant. Manhattan plots of −log10 (P) values for each SNP versus chromosomal position were generated as the GEMMA results.The final genome-wide significance thresholds were calculated by GEChttp://genomes.cribi.unipd.it/) to identify the target genes for the corresponding associated regions.The regions upstream and downstream of the significant SNPs along the genome were investigated to identify the annotated genes by scanning the genome in ~1 Mb windows. In the case where two moderately distant SNPs showed association, the entire genome region between them was explored. The annotated gene sequences of the 12 × V2 grape genome assembly were retrieved from CRIBI , respectively. The ANOVA results indicated that these three traits were significantly influenced by genotype; however, year and genotype by year had no significant effects (Table P < 0.01)  content were 92.08% and 45.14%, respectively. High-quality reads were aligned to the grape PN40024 genomic sequence. A total of 358,070 SNPs were initially obtained for these genotypes from the SOAPsnp utility calling. After removing those nucleotide polymorphisms with more than two alleles, a set of 306,015 SNPs was generated. After excluding those SNPs with an MAF of < 0.05 and a SNP detection rate of < 95%, 32,311 high-quality SNPs remained for further analysis. These high-quality SNPs covered all 19 chromosomes and were approximately evenly distributed across the whole genome  was estimated by ADMIXTURE’s cross-validation for the number of sub-populations (k), which varied from 2 to 20. Structure simulation demonstrated that the cross-validation error was minimized at k = 8  clustering of the samples was performed. Consistent with the ADMIXTURE results, the hierarchical clustering dendrogram showed distinct differentiation among the cultivar complexes Fig. . Eight m2 = 0.03 across all chromosomes  plots of the observed and expected P-values were used to evaluate the fit of each model  was generally observed for the four models . The ~1 Mb regions of interest surrounding the significantly associated SNPs were scanned. In addition, the full regions between two associated SNPs were explored when GWAS detected specific patterns of association. The transcripts within or neighboring the associated loci were screened according to the grape genome of CRIBI (http://genomes.cribi.unipd.it/grape/).Application of the GEMMA model identified some SNPs significantly associated with six berry traits. An arbitrary number of top candidates were selected based on the probability value . The transcripts in this region included VIT_218s0001g01360 (toprim domain-containing protein) and VIT_218s0001g01390 (gibberellin 20-oxidase), whereas on chromosome 19, the two associated loci were annotated as VIT_219s0015g00730 (cellulose synthase-like protein e6-like). The genes that were found in this enlarged interval comprised VIT_219s0015g00710 (cellulose synthase-like protein e1), VIT_219s0015g00740 (protein plastid movement impaired 2-like), VIT_219s0015g00750 (sucrase-like protein), and VIT_219s0015g00750 (sec14 cytosolic factor-like). The transcripts of VIT_217s0119g00330 on chromosome 17 were listed as uncharacterized protein.Only one locus, VIT_216s0098g01280 (senescence-associated protein din1), was found to be significantly associated with BFT. VIT_216s0098g01140 , VIT_216s0098g01180 , VIT_216s0098g01230 (cytochrome b5), VIT_216s0098g01560 (glucan endo- -beta-glucosidase), and VIT_216s0098g01590  were within or neighboring the locus  decayed below 0.2 within 10 kb31. In other grape accession panels, LD reached 0.2 at physical distances of ca. 250 bp30 and < 10 kb14. In the present study, pairwise LD analysis revealed that LD reached the threshold (r2 = 0.03) at an average physical distance of 16.6 kb and a range of distances from 0.4 to 299 kb 7. Because the genes responsible for berry color are well known32, we selected the trait of berry color as a reference trait to evaluate the efficiency of the association models. An anthocyanin-related clade of a group of nine R2R3–MYB genes were identified within a 150 kb (14.16-14.31 M) cluster on chromosome 233. Based on the population in this study, we successfully identified significant loci associated with grape berry color, which were in the region of chr2:14316046 (around 14.31 M)  that can correct for population structure and family relatedness. There are many offline and online software programs that run GWAS analysis based on general linear models (GLMs) and MLMs28, such as PLINK25 and TASSEL;20 these programs are commonly used for association analyses in plants. However, the definitions of unrelated individuals suggested by Yu et al35. are somewhat arbitrary; and for the MLM approach, the computational demand is heavy for datasets with large samples20. Improvement in EMMAX21 was accomplished through complex approximate estimation methods for variance components that reduced computational processing34. However, it is difficult to predict the accuracy of variance components without running an exact calculation for EMMAX. GEMMA presents the advantage that it directly and accurately estimates variance components, including fixed effects in MLM, which reduces the calculation burden22.The selection of optimal statistical models is beneficial for accurately evaluating the associations between markers and phenotypes. As genotypic data become increasingly available, more comprehensive statistical models are needed to distinguish true biological associations from false positives that result from population structure and LD25, TASSEL20, EMMAX21, and GEMMA22–were used to predict loci significantly associated with grape berry quantitative traits.In this study, four statistical models of GWAS analysis–PLINKP-values from expected P-values. The Q–Q plots of eight berry traits from four statistical models are shown in Supplementary Fig. P-values deviated from the expected values, indicating that these two models were not appropriate for this study. The Q–Q plot results from GEMMA and EMMAX showed high associations in eight berry traits. The GEMMA model showed better fit to the expected values than did the EMMAX model and therefore offered better control of type I false positives. Regarding the berry color trait, the GEMMA method presented substantially higher power for significant loci detection than did the EMMAX method, regardless of what P-value criteria were used; and the loci detected were consistent with previous reports. Consequently, only the association results obtained from GEMMA are shown and discussed for the other berry traits.A Q–Q plot is a graphic representation of the deviation of observed We evaluated the GWAS results in light of previous reports and functional information. The most intriguing result of the current study was that many of the QTLs identified based on the GWAS were in accordance with the results of previous QTL analyses.MYB-type transcription factor genes on chromosome 233, which has been verified by other reports7. The position of the significantly associated SNPs detected in this study was annotated as mybA3 and around 14.31 Mb on chromosome 2, which is not the exact position of the known causal mutation sites of the grape color locus (VvMybA1 and VvMybA2). A similar result was found in the association mapping analysis of Migicovsky et al17., who found that the SNPs significantly associated with skin color were 3.6 Mb from the known causal mutation site on chromosome 2. Similarly, the strongest association signals detected by GWAS in rice36 and Arabidopsis27 were not at the exact known loci of the target traits. Previous studies have proved that mutation of two functional MYB-related genes (mybA1 and mybA2) result in white-skinned cultivars33, whereas mybA3 is regarded as non-functional32. Azuma et al37. reported that a functional VvmybA1 in ‘Benitaka’ (V. vinifera L.) was restored from homologous recombination between VvmybA1a and VvmybA3. Many MYB-like genes distributed in the region of VvmybA1 to VvmybA3 might be associated with the different colors of grape berry skins and are not necessarily functional37. VvmybA3 has been detected in cultivars of V. vinifera and its hybrids38. The above observations support the annotation of the identified SNP loci associated with BC as mybA3 in this study.The grape color locus has been identified as being a cluster of 3. A QTL affecting veraison on chromosome 16 was identified by Costantini et al4. In addition, six independent quantitative trait loci were detected for veraison on chromosomes 16 and 18, and a QTL associated with veraison on chromosome 16 around marker VVMD37 was identified39. However, the position of this trait differs between previous studies and Duchêne et al39., although all are located on chromosome 16. CMa3, one of the QTLs related to grape maturation period, was detected on chromosome 16 near the marker UDV052 based on a hybrid (F1) population in which an early-maturing female parent “87-1” was crossed with a late-maturing male parent ‘9-22’40. Zyprian et al24. identified a prominent QTL on chromosome 16 associated with the timing of the onset of veraison near the marker of UD0V52. In the present study, two SNPs considered to have the strongest associations with BDP were both on chromosome 16. The genes around the significantly associated SNP locus (VIT_216s0098g00420) were annotated as probable receptor-like protein kinase at1g67000-like. Zhang et al41. and Jia et al42. revealed the important role of receptor kinase during fruit ripening in strawberry and apple, respectively. These results suggest that the association signals revealed by this study are reliable. Moreover, control of the BDP trait in grape may be complicated, and the candidate genes detected in this study are worthy of further investigation.BDP is a trait that is dependent on environmental conditions; however, different varieties have different development periods. Thus, there might be a specific genetic control for this trait regardless of environmental conditions43. Since berry weight and berry size (diameter) are highly correlated, overall berry size can be analyzed with either trait3. Seed development inhibitor (SDI) has been detected on chromosome 18 by different authors in different years and for different progenies7. The GWAS results of this study confirm the existence of significantly associated SNPs for berry weight on chromosome 18. However, the position of the loci in this study differed from that determined from biparental QTL mapping. This discrepancy may be due to the previous studies’ employment of segregating populations derived from crosses involving seedless cultivars, in which the SNPs colocalized with QTLs of seed number, indirectly affecting berry size44. Chialva et al44. identified the transcript of VIT_218s0001g08610, annotated as VviANT1 (AINTEGUMENTA), as the transcript of a candidate gene of berry size (berry weight). The gene for berry weight identified from the GWAS analysis in the present study was VIT_218s0001g01370, near VviANT1. Further investigation of this associated region showed that the annotated genes of wall-associated receptor kinase 2-like and gibberellin 20-oxidase were located there. Costantini et al.4 also localized a marker of berry weight corresponding to gibberellin 20-oxidase. Thus, we verified the relationship of the gibberellin 20-oxidase gene with grape berry weight. Another region associated with berry weight detected in this study was located on chromosome 19 and chromosome 17. Previous reports have not described the corresponding QTLs in these regions. Berry weight is affected by numerous factors  and is expected to be controlled by polygenes, with different causal polymorphisms segregating in different populations43. The functional roles of these regions are unclear. More genetic and molecular evidence will be needed to confirm this association.Previously published QTL studies in seedless grape demonstrated that berry weight is controlled by a major QTL related to seed traits on chromosome 18 near the SSR marker VMC7F26. However, its genetic determination is poorly understood. A complex genetic control of grape firmness was revealed by QTL analyses. Carreño et al.45 detected QTLs for berry firmness at the genome-wide level in seven genomic regions on chromosomes 1, 4, 5, 9, 10, 13, and 18.Berry flesh texture, i.e., berry firmness, is an important quality trait in table grape production5 Correa et al.6 showed that the determinants for this trait are distributed in chromosome 8 and chromosome 18. Previous QTL studies revealed polygenic control of this trait. In the present study, we mapped the SNPs for this trait on chromosome 16 by GWAS analysis. One explanation for this lack of agreement among studies is that this trait might be controlled by many genes involved in complex metabolic pathways45. Among the tens of genes found in the two QTLs of Correa et al6., a cation/calcium exchanger gene was highlighted. Among the genes found for this SNP of chromosome 16 in the present study, two genes were related to calcium: VIT_216s0098g01140  and VIT_216s0098g01590 . Calcium is an essential nutrient with structural roles in the cell wall, and it has an important impact on fruit firmness by reducing the action of cell wall-degrading enzymes46. Balic et al.47 demonstrated that varieties with lower calcium content at harvest were less firmer than those with higher calcium concentration in the cell wall. The functions of other genes need to be analyzed. One of the factors explaining the lack of stability of these QTLs is the high sensitivity of this trait to environmental changes or planting practices.Two QTLs for firmness were found to be located on chromosome 3 and chromosome 10 by Ban et al.48. In this study, berry flavor was associated with chr5:5515972. These marker–trait associations corroborate previous QTL mapping and association analysis studies in grape that identified similar genic regions. Muscat flavor has been determined by evaluating monoterpenoid levels through QTL analysis in grape. Doligez et al.49 described the QTL for muscat flavor on chromosome 5 based on tasting data. Battilana et al.50 identified 1-deoxy-d-xylulose-5-phosphate synthase (VvDXS) on chromosome 5 as a candidate gene for geraniol, nerol, and linalool content. Emanuelli et al19. demonstrated co-localization of 1-deoxy-D-xylulose 5-phosphate synthase (VvDXS) with the major QTL positioned on chromosome 5. Yang et al.18 found that DXS was nearest to UDV060 (4.4 Mb) on chromosome 5 based on GWAS analysis, a finding also obtained by Doligez et al49. Thus, both the association mapping approach and QTL analyses have mapped the QTL for berry flavor at the identified region and indicate that a major QTL is located on chromosome 5.Berry flavor depends on the aromas of grape, which arise from volatile compounds, such as terpenes, norisoprenoids, and thiols. Linalool, geraniol, nerol, citronellol, and α-terpineol are generally regarded as the main monoterpenes in Muscat cultivarsUltimately, this study advances our understanding of the genetic control of grape berry traits and provides insight into the genetic control of grape berry traits. Our work provides new evidence that may lead to new research areas in grape molecular breeding.Supplementary Figure S1: QQ-plot for eight berry traits of grape resulted from the different statistical modelsSupplementary Fig.S2: Decay of linkage disequilibrium with genome distance in grapeSupplementary Fig.S3: Two-dimensional principal component analysis (PCA) plot of the 179 genotypes. PCA analysis was based on the SNP data from the GBSSupplementary Table S1: Phenotypic description of 179 grapegenotypes used in this study


Solanum insanum (INS2). These accessions were genotyped based on the 7335 polymorphic single-nucleotide polymorphisms (SNP) markers. After that, genotypes were crossed in half diallel fashion to produce 45 hybrids. The INS2 displayed the highest values for the total phenolics and chlorogenic acid content (CGA). For all of the biochemical traits studied, significant values of general and specific combining ability (GCA and SCA) effects were determined. The baker ratio estimates were high (>0.75) for all of the traits. Highly significant and positive heterosis (%) was determined for the dry matter, total phenolics, CGA, and area (%) of CGA content. The phenolics content of the fruit  was not significantly correlated with flesh colour and browning related traits. However, when the path coefficient analysis was performed considering the CGA as a dependent variable, it was determined that the flesh colour related traits most considerably affected the CGA. The genetic distance showed a diminutive correlation with the hybrid means, heterosis, and SCA values. Overall, this study provides important information regarding the underlying genetics of important biochemical traits of eggplant fruit.Eggplant varieties rich in bioactive chlorogenic acid along with less browning are preferred by consumers. Therefore, genetics of fruit phenolics, fruit flesh colour, and browning related traits were studied in the genotypes of eggplant, comprising of nine cultivated varieties and one accession of eggplant‘s primary genepool wild relative  Solanum melongena L.) is the third most consumed fruit of the family Solanaceae 0.5 defined in detail by Prohens et al. [Fruit flesh browning was measured using a CR-300 chromameter  at the midpoint position (the centre of the fruit) in each of the five fruits that constituted one sample. The values for CIELAB colour parameters L*, a*, b* were measured immediately after the fruit was cut  of the eggplant flesh as defined in detail in . The totn = 10) and hybrid (n = 45) groups. The mean values of parents and their hybrid combinations were compared with t-tests to detect differences among the two groups. The significance of differences among the group means was evaluated at p < 0.05 using the Statgraphics Centurion XVI software . Path coefficient analysis was performed by considering chlorogenic acid content as the dependent variable, using the software package Lavaan in R environment [For each trait measured, the mean and range were calculated for the parental  and Model 1 (fixed effects) . These cGenotypic data was obtained for the ten accessions used in the study following the RAD sequencing approach used in previous studies ,44. In t


Triticum aestivum L.) is an important goal for breeding. Here we study the genetic factors underlying GFeC trait by genome-wide association studies (GWAS) and the prediction abilities using genomic prediction (GP) in a panel of 369 European elite wheat varieties which was genotyped with 15,523 mapped single-nucleotide polymorphism markers (SNP) and a subpanel of 183 genotypes with 44,233 SNP markers. The resulting means of GFeC from three field experiments ranged from 24.42 to 52.42 μg·g−1 with a broad-sense heritability (H2) equaling 0.59 over the years. GWAS revealed 41 and 137 significant SNPs in the whole and subpanel, respectively, including significant marker-trait associations (MTAs) for best linear unbiased estimates (BLUEs) of GFeC on chromosomes 2A, 3B and 5A. Putative candidate genes such as NAC transcription factors and transmembrane proteins were present on chromosome 2A . The GP for a GFeC trait ranged from low to moderate values. The current study reported GWAS of GFeC for the first time in hexaploid wheat varieties. These findings confirm the utility of GWAS and GP to explore the genetic architecture of GFeC for breeding programs aiming at the improvement of wheat grain quality.Malnutrition of iron (Fe) affects two billion people worldwide. Therefore, enhancing grain Fe concentration (GFeC) in wheat ( Wheat grains contain mainly carbohydrates with a small proportion of proteins and essential micronutrients such as iron (Fe) and zinc (Zn) [Wheat is the second most produced and consumed food crop worldwide and its products form a fundamental diet in the daily life for people in the whole world  . Micronuinc (Zn) . More thinc (Zn) ,6. Thereinc (Zn) .Triticum spelta × T. aestivum RIL population, of which three mapped to chromosome 1A while two QTLs mapped to chromosomes 2A and 3B [Understanding the genetic basis of Fe concentration in wheat grains is imperative for enhancing Fe values in newly developed varieties. Therefore, we performed a genome-wide association study (GWAS) approach which is one of the main approaches for dissecting complex traits including nutritional quality traits that are controlled by many genes and influenced by the environment ,9. SeverA and 3B . To our A and 3B  studied A and 3B  performeA and 3B  and seedA and 3B ; howeverRecently, genomic prediction (GP) or genomic selection (GS) approaches were developed based on genome-wide marker information to predict the breeding value of complex traits for which only genotyping data are provided (test population) . These pThe goals of this study were (i) to study the natural phenotypic variation of wheat GFeC in a panel of 369 elite wheat varieties grown for three years in the field, (ii) to investigate the genetic architecture of this trait and to identify QTLs by applying a GWAS approach, (iii) to define the gene content in the respective genomic region of the wheat reference sequence as well as to identify potential candidate genes, and (iv) to examine the prediction ability in the present wheat panel by using different statistical models.p < 0.001) (−1 dry weight (DW) in the whole and subpanel of genotypes  for Fe concentration in grains showed a significant effect of both genotype and years (< 0.001) . Wide ge< 0.001) . The gen< 0.001) . The aveenotypes  with a renotypes . This trenotypes . In factr = 0.26 to 0.39 (p < 0.001) was found for grain Fe in all three years (p < 0.001) was present between Fe and thousand kernel weight (TKW) in all three years (p < 0.001) over years  ≥ 3)  ≥ 3) A which w 59.8 cM . Due to  59.8 cM . In the  59.8 cM A with R2o 13.09% . The higp-values were close to the distribution of expected associations  . Based oGP was evaluated for GFeC trait with three statistical models including GBLUP, ridge regression best linear unbiased prediction (RR-BLUP) and Bayes-Cπ in the whole panel. Prediction ability values were 0.29 to 0.38, 0.27 to 0.35, and 0.20 to 0.35 based on using these methods: GBLUP, RR-BLUP and Bayes-Cπ respectively . The hig−1 [−1 among years and identifying 23 lines equal or above the required target (≥52 μg·g−1). A similar range of GFeCs was reported for a wheat RIL population (17.8–69.0 μg·g−1) which resulted from crossing Triticum boeoticum with Triticum monococcum [−1 for 41 winter w·μg heat cultivars except for one spring wheat cultivar, that had GFeC of 56 μg·g−1. Also, the range of Fe concentrations in Indian and Pakistan hexaploid wheat grains was found to be in the range of (9.2 to 49.7 μg·g−1) [In human nutrition, the estimated average requirement (EAR) of Fe is 1460 μg/day/person, while the target level for sufficient Fe concentration in wheat grains was established as 52 μg·g−1 . In our nococcum . Morgounnococcum  found GF μg·g−1) . TherefoH2 = 0.59, suggesting a quantitative nature of inheritance and a considerable environmental influence on the expression of the trait. Gorafi et al. [The heritability of Fe concentration among the years was moderate equaling i et al.  reportedi et al.  reportedr = 0.25–0.38, p < 0.001), indicating a relatively stable measurement of the phenotypic data. The resulting correlation values were moderate; that may be attributed to the influence of genetics and environment on the GFe accumulation which can also explain the moderate heritability value (0.59) of GFe. Tiwari et al. [GFeCs showed a significant positive correlation among years . There were no obvious candidate genes detected within the aforementioned region. In the subpanel, three physical regions contained significant SNPs, on chromosomes 2A , 3B  and 5A , but only the 2A region conferred candidate genes involved in iron uptake or homeostasis.NAM-B1) accelerated senescence and nutrient remobilization from leaves to grains. The reduction in RNA levels of the multiple NAM homologs by RNA interference delayed the senescence process and reduced wheat grain protein, Zn, and Fe content by more than 30%. In the same context, Ricachenevsky et al. [NAM-B1 which is one of the NAC TFs has a major role in regulating key genes responsible for the senescence process which leads to higher Fe and Zn concentrations in wheat grains.Chromosome 2A conferred six putative genes related to the NAC ) domain family proteins , which ay et al.  showed tAnother four genes encoded transmembrane proteins on chromosome 2A. It has been reported that transmembrane proteins are responsible for nutrient uptake in plants and play an important role in enhancing the micronutrient content of grains ,33. TherGP or GS has been proposed as a method to improve the breeding efficiency of quantitative and complex traits. Therefore, we extended our analyses and included GP as a suggested tool for improving a polygenic trait such as GFeC in wheat. Our predictability results showed low to moderate values according to three different years and BLUE values, which agrees with another report that obtained low to moderate predictability values for the macro- and micro-nutrients including Fe in wheat landraces . In spriA population comprised of 369 elite European wheat varieties including 355 genotypes of winter wheat and 14 genotypes of spring wheat, mainly from Germany and France was used in this study. Field experiments were carried out at IPK, Gatersleben, Germany over three consecutive years  using plot with a size of 2 × 2 m for each genotype with six rows spaced 0.20 m apart and more details were described in a previous study by Alomari et al. [Three hundred sixty-nine wheat genotypes harvested from three different field experimental trials were prepared for milling process by collecting 50 kernels for each genotype to measure thousand-grain weights (TGW) using a digital seed analyzer/counter Marvin . Wheat grains were milled by using a Retsch mill , afterward, the whole panel of the milled wheat grains was dried by incubating overnight at 40 °C in the oven.3 69%, Bernd Kraft GmbH, Germany). The digestion process was completed using a high-performance microwave reactor . All digested samples were filled up to 15 mL final volume with de-ionized distilled (Milli-Q) water . Element standards were prepared from Bernd Kraft multi-element standard solution (Germany). Fe as an external standard and Yttrium (Y)  were used as internal standards for matrix correction. Fe concentrations were measured by Inductively Coupled Plasma Optical Emission Spectrometry  combined with a CETAC ASXPRESS™ PLUS rapid sample introduction system and a CETAC autosampler .Fifty mg of dried and milled wheat grain flour was taken to be digested by (2 mL) nitric acid (HNOr) which were calculated for the grain Fe data resulted from the three years. The broad-sense heritability was calculated using the equation:σ2G is the genotype variance, σ2e represents the variance of the residual and nE is the environments number.We used Sigma Plot package 13 to perform the ANOVA and Pearson’s correlation coefficient  algorithm were applied to calculate the Best linear unbiased estimates (BLUEs) of Fe concentration in wheat grains for each genotype across the years  by consi® Wheat Breeder’s Genotyping Array, http://www.cerealsdb.uk.net/) [www.traitgenetics.com). Moreover, a novel 135K Affymetrix array was used to genotype a subpanel of 183 genotypes from the whole genotypes panel [2-populations and then physically anchored on the chromosome-based sequence of hexaploid wheat [The whole wheat germplasm (369 varieties) was genotyped using two marker arrays: a 90K iSELECT Infinium array  and a 35es panel  and thises panel ,39 was uid wheat .To identify the MTA and QTL  for Fe concentration in wheat grains, association analyses were conducted between SNP markers and Fe data for each genotype in each year and BLUEs value. For SNP markers, quality control was applied by considering a minor allele frequency (MAF) ≤3%  with rejecting SNPs having missing values or heterozygosity ≥3%, resulting in 15,523 polymorphic SNP markers from both of the 90K iSELECT array and the 35K Affymetrix array and 28,710 polymorphic SNP markers from the 135K Affymetrix array, which were used for association analysis.10  ≥ 3 where considered as a significant MTA.GWAS was carried out for Fe concentration data from both panels (whole and subpanel) over individual year plus BLUE values by applying the implemented mixed linear model (MLM) and principal component analysis (PCA) as a correction factor for population structure. Whole wheat genotypes panel was analyzed by using the combination of two SNP chips (90K and the 35K chips) based on their genetic reference map whereas, the subpanel was analyzed by the combination of 90K, 35K and 135K chips which were anchored based on physical locations. The purpose of combining the SNP chips was to increase the density of the used markers, achieving good mapping resolution and to further enhance the power of GWAS output within the germplasm panel. All the detected marker-trait associations (MTAs) above the threshold of −logQ–Q plots that were obtained by plotting “expected−log10 ” on the x-axis and “observed−log10 ” on the y-axis. The population structure of the investigated genotypes panel was described in a previous study by Kollers et al. [GWAS analysis was computed based on a MLM and PCA which was used for population correction and stratification by using Genome Association and Prediction Integrated Tool (GAPIT) in R . The apps et al. .The significant SNP markers which defined the significant associations underlying GFeC trait were listed to obtain their annotation based on the newly released reference genome sequence of Chinese Spring by blasting their sequance against IWGSC RefSeq annotation v1.0 to detect potential candidate genes ,44.We used GBLUP to impute GP for GFeC trait data by using Tassel version 5.2.10 . In thisWe evaluate the prediction ability with the two GS models that are ridge regression best linear unbiased prediction (RR-BLUP) and Bayes-Cπ ,46. For This study characterized many lines of a diverse wheat panel for GFeCs to understand the natural diversity that exists for Fe grain trait and to identify potential genes that contribute to this phenotypic variation and to examine the prediction accuracy. Broad-sense heritability calculation revealed moderate variation that could be attributed to both genetic and environmental effects. Overall, the resources generated in this study can be used to identify suitable candidate genes for further validation analysis. Results of applying GP models to GFeC showed that the correlation between observed and predicted values was relatively moderate; therefore, it would be useful to study the effects of GxE interactions that may improve the predictability value.


Genomic selection (GS) is increasingly applied in breeding programs of major aquaculture species, enabling improved prediction accuracy and genetic gain compared to pedigree-based approaches. Koi Herpesvirus disease (KHVD) is notifiable by the World Organization for Animal Health and the European Union, causing major economic losses to carp production. GS has potential to breed carp with improved resistance to KHVD, thereby contributing to disease control. In the current study, Restriction-site Associated DNA sequencing (RAD-seq) was applied on a population of 1,425 common carp juveniles which had been challenged with Koi herpes virus, followed by sampling of survivors and mortalities. GS was tested on a wide range of scenarios by varying both SNP densities and the genetic relationships between training and validation sets. The accuracy of correctly identifying KHVD resistant animals using GS was between 8 and 18% higher than pedigree best linear unbiased predictor (pBLUP) depending on the tested scenario. Furthermore, minor decreases in prediction accuracy were observed with decreased SNP density. However, the genetic relationship between the training and validation sets was a key factor in the efficacy of genomic prediction of KHVD resistance in carp, with substantially lower prediction accuracy when the relationships between the training and validation sets did not contain close relatives. Genomic selection (GS) has become a cornerstone of genetic improvement in both plant and livestock breeding, enabling improved prediction accuracy, control of inbreeding, and (in some cases) reduction in generation interval compared to traditional pedigree-based approaches . The lanCyprinus carpio) is one of the world’s most important freshwater aquaculture species, particularly in Asia and Europe. However, koi herpesvirus disease (KHVD), also known as Cyprinid herpesvirus-3 (CyHV-3) disease is a major threat to carp farming and is listed as a notifiable disease by the European Union , they tend to be relatively expensive and can suffer from ascertainment bias . GenotypFamily-based breeding programs are at a formative stage in common carp, including a program focused on the Amur mirror carp breed in Europe ,b, wheren = 100) was confirmed by PCR according to guidelines by the Centre for Environment, Fisheries and Aquaculture Science, United Kingdom (Cefas)  . The entGCA_000951615.2 . Only single SNPs from each individual RAD locus where considered for downstream analysis to minimize the possibility of genotypic errors. SNPs with minor allele frequency (MAF) below 0.05, greater than 25% missing data were discarded. The R package hsphase  was used as the phenotype to assess the potential of GS for improved resistance to KHVD in common carp. Several commonly used GS models were tested on the data using the R package BGLR for binary traits : specifil is the vector of latent variables, b is the vector of the fixed effects , X is the incidence matrix relating phenotypes with the fixed effects, Z the incidence matrix relating the underlying liability with the genotypes, α the vector of SNP effects using the corresponding prior distribution for each of the aforementioned Bayesian models and e the vector of residuals. The parameters of each model were estimated by Markov chain Monte Carlo (MCMC) using Gibbs sampling . Convergence of the resulting posterior distributions was assessed both visually (inspecting the resulting MCMC plots) and analytically using the R package coda v0.19-1 software (where software .The prediction performance of the utilized models was tested using the following metrics:•Accuracy•Receiver operator characteristic (ROC) curvesThe prediction accuracy was approximated as:y is the vector of recorded phenotypes, (G) EBV is the (genomic) estimated breeding values and h is the square root of the heritability  metric  was usedGenomic prediction models were applied using datasets of varying SNP density using either MAF or linkage disequilibrium (LD) values as thresholds for filtering. In particular, to obtain the reduced density SNP panels for genomic prediction, a strategy of retaining SNPs surpassing a sequentially increased MAF threshold was applied, as described in In addition, reduced density SNP datasets were obtained by applying filtering based on LD values. LD amongst SNP pairs was calculated using SNPrune . Thereafn = 1008) and validation sets (n = 251). The number of resistant and susceptible animals in each validation set was proportional to the overall survival of the challenged population. In the validation sets, the phenotypes of the animals were masked, and their (genomic) estimated breeding values – (G)EBV – were estimated based on the prediction model derived from the training set. This cross-validation procedure was repeated five times to minimize potential bias.Five-fold cross-validation was performed for all the density varying SNP datasets in order to test the efficiency of correctly classifying animals in the validation set as resistant or susceptible. The dataset was randomly split into sequential training , the formation of training and validation sets required the existence of full-siblings in both sets for each family. For scenario 2 (S2) the formation of validation and training sets allowed the existence of only half siblings between the two sets (and no full siblings). Both in S1 and S2 the cross validation procedure was repeated five times in order to reduce potential bias, while the size of the validation set was 290 animals on each replicate. In scenario (S3) the genomic prediction models were tested by sequentially assigning each of the four factorial crosses  as a validation set, using the remaining three as a training set. This approach resulted in relatively unrelated training and validation sets, since it avoided the inclusion of full/half sibs in both the training and the validation sets. The genomic prediction models were tested on the dataset comprised of the full SNP data. Since pedigree information was not available for prior generations, pBLUP could not be used for obtaining meaningful predictions across the factorial cross groups. Finally, a scenario 4 (S4) was performed as control where no restrictions were applied in the formation of training and validation sets . Cross validation in S4 was performed five times with the size of the validation sets being set to 290 animals. The S4 scenario was in fact similar with the approaches tested in the previous section regarding varying SNP densities with the only difference being the size of the validation set. The full SNP dataset was used for all the tested scenarios.The mean weight of the genotyped carp juveniles was 16.3 g (SD 4.6) and the mean standard length (SL) was 77 mm (SD 7.1). Mortalities began at 12 dpi reaching a maximum daily rate between 21 and 24 dpi  decreasing thereafter (Supplementary File GCA_000951615.2) representing approximately 82% of reads passing initial quality filters . Approximately 5 % of those reads had a mapping quality below 40 and were discarded. In total 397,047 putative RAD loci were identified with a mean coverage of 21X . 15,615 SNPs found in more than 75% of the genotyped animals and with a MAF above 0.05 were retained for downstream analysis , 2,895 (LD2) and 5,118 (LD3) respectively. The genomic prediction accuracy obtained for LD1 was very slightly higher than pBLUP using the BayesB and BayesC models (<1% increase), while the AUC was the same. In the case of rrBLUP and BayesA for the same SNP dataset the estimates were 2 and 1% lower compared to pBLUP for accuracy and AUC respectively. Using datasets of higher SNP density resulted in the increase of both the accuracy and the AUC metrics as observed previously for the reduced density datasets filtered by MAF. In particular, accuracy for LD2 and LD3 ranged between 0.52 and 0.54 and AUC between 0.72 and 0.74 , which wFor the scenario S1, where all animals in the validation set had full sibs in the training set the genomic prediction accuracy was approximately 0.56, which was marginally higher (∼ 4% increase) than the random allocation of animals into training and validation sets described above. In S2 where the design of the validation set allowed the inclusion of only corresponding half sibs in the training and validation set, the genomic prediction accuracy fell to ∼ 0.53. In S3 where the training and validation sets were set up to correspond to separate factorial crosses, the mean accuracy for the genomic models was markedly lower, and ranged between 0.16 and 0.20. Finally, in the scenario where training and validation sets were set up without posing any restrictions estimated, such that close relatives are likely to be included in both sets, accuracy ranged between 0.52 and 0.54 for the genomic prediction models and 0.49 for pBLUP .The obtained AUC values from the ROC curves were 0.74 BayesB;  and 0.72In the current study, genotyping by sequencing was applied to study genomic prediction of resistance to KHVD in carp, including testing the impact of SNP marker density and genetic relationship between training and validation sets. While genomic data in the form of genetic markers can be a valuable addition to selective breeding for disease resistance, the methods of applying the data depend on the underlying genetic architecture of the trait. In the case of major QTL such as resistance to Infectious Pancreatic Necrosis in salmon , it may Since genotyping cost is generally related to SNP marker density, determining the lowest SNP density that retains maximum genomic prediction accuracy is a logical goal. In the current study, reducing SNP density from 15,615 to 2,895 resulted in minor decreases in prediction accuracy, with 1,000–1,600 SNPs giving approximately the same accuracy as pBLUP. Furthermore, the LD-pruned dataset of approximately 5,000 SNPs resulted in the same prediction accuracy performance as the full dataset . A more drastic impact of genetic relationship between training and validation sets on prediction accuracy was observed. The highest prediction efficiency was observed in scenario S1 where animals in the validation set had full siblings in the training set. Prediction efficiency decreased 6–8% in the scenario allowing for only the inclusion of half-siblings (and no full siblings) in the training and validation sets but was still comparable to the results when the sets were established at random. Interestingly, the impact of the lower genetic relationships on pBLUP accuracy was greater, and it dropped by approximately 16% between S1 and S2. This may indicate that genomic prediction models have the potential to utilize distant relationships compared to pBLUP, especially in the current set up where there was only a two generation pedigree. Furthermore, when the training set comprised three of the factorial cross groups and the validation set comprised the fourth, thus resulting in no shared full/half sibs between the two sets, the accuracy dropped massively to 0.16–0.17 . The decrease in prediction accuracy with more distant relationships is to be expected, thus close relationships between training and validation sets is a necessary prerequisite for successfully implementing GS , and it Testing genomic prediction on binary traits such as survival, presents a challenge to define a suitable test metric for selecting the best performing model, especially when survival deviates significantly from 50%. Solely relying on correlation derived accuracy for model assessment in this case could result in suboptimal selection decisions. Suitable metrics for evaluating prediction efficiency in binary traits and thus selecting the best performing models for estimating breeding values include the AUC from ROC curves.The AUC values provide a commonly used metric for assessing the prediction efficacy of binary classifiers, taking into consideration both the rate of false positives and false negatives with values of one suggesting 100% successful classification. This approach has been routinely used to test the efficacy of prediction models in disease resistance studies both in humans , livestoh2 = 0.5 – 0.79), but genetic markers were required to assign the pedigree in the factorial crosses. Future studies testing the efficiency of single-step BLUP approaches  upon its approval by Institutional Animal Care and Use Committee (IACUC).TV, MK, MP, VP, and RH conceived the study and contributed to designing the experimental structure. TV, DP, and LP carried out the challenge experiment. CP carried out DNA extractions, RD library preparation, and sequence data processing. CP and RH carried out parentage assignment and the quantitative genetic analyses. All authors contributed to drafting the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer PM declared a past co-authorship with several of the authors CP and RH to the handling Editor.


Genetic correlations ranged from − 0.10 to 0.23 . There were some signs of an inverse concordance between greater thalamus volume and risk for AN . A genetic variant in the vicinity of ZW10, a gene involved in cell division, and neurotransmitter and immune system relevant genes, in particular DRD2, was significantly associated with AN only after conditioning on its association with caudate volume (pFDR = 0.025). Another genetic variant linked to LRRC4C, important in axonal and synaptic development, reached significance after conditioning on hippocampal volume (pFDR = 0.021). In this comprehensive set of analyses and based on the largest available sample sizes to date, there was weak evidence for associations between risk for AN and risk for abnormal subcortical brain volumes at a global level , but suggestive evidence for effects of single genetic markers. Highly powered multimodal brain- and disorder-related genome-wide studies are needed to further dissect the shared genetic influences on brain structure and risk for AN.In MRI scans of patients with anorexia nervosa (AN), reductions in brain volume are often apparent. However, it is unknown whether such brain abnormalities are influenced by genetic determinants that partially overlap with those underlying AN. Here, we used a battery of methods  to investigate the genetic covariation between subcortical brain volumes and risk for AN based on summary measures retrieved from genome-wide association studies of regional brain volumes  contains supplementary material, which is available to authorized users. Anorexia nervosa (AN) is an often life-threatening, adolescent-onset eating disorder characterized by severe emaciation, and typically by severe food restriction. AN mortality rates are the highest in psychiatry .–80% for AN [Despite high twin-based heritability estimates of around 50% for AN , we do n% for AN –6.In acutely ill patients with AN, reduction of brain tissue is often readily visible in individual patients’ brain scans. A number of structural MRI studies  have documented decreases in both gray and white matter volumes . MorCOMT and 5-HTTLPR genotype may modulate functional connectivity in AN patients.In light of these structural abnormalities, one question is whether certain structural brain differences share a similar genetic architecture with AN and may possibly even reflect a predisposition for AN. Magnetic resonance imaging (MRI) measures of brain structure are moderately heritable  and seveWe are now able to leverage results from large-scale, genome-wide association studies (GWAS) based on tens of thousands of individuals. Data from GWAS on brain structure and on genetic risk for AN allows us to investigate the genetic covariation between brain structure and disease risk. Investigating this genetic correlation should inform us about shared genetic influences between brain structure and AN. A large degree of genetic overlap could indicate potential pleiotropic effects, where the same genetic variants influence both traits. Results could then help to derive or adapt hypotheses about how brain structure is involved in AN etiology. Several groups have investigated genetic overlap between structural brain measures and risk for schizophrenia as well as other psychiatric disorders –22.In the current study, we followed a roadmap for the analysis of genetic covariation between brain volumes and AN using a battery of complementary methods including those suggested by Franke et al. . To dateIn this article, we used independent data from separate GWAS on brain structure and on genetic risk for AN to study the genetic covariation between these measures.http://enigma.usc.edu/research/download-enigma-gwas-results/. These analyses were based on brain MRI scans and genome-wide genotype data for 13,170 subjects from 28 cohorts ENIGMA MRI summary measures from genetic association analyses of ICV and seven subcortical volumes  were avahttps://www.med.unc.edu/pgc/results-and-downloads) [Anorexia nervosa (AN) cases met DSM-IV criteria for either lifetime AN (restricting or binge-purge subtype) or lifetime eating disorders ‘not otherwise specified’ AN-subtype  . Summarywnloads) . These awnloads) , 6 (Tablwnloads)  for phaswnloads) . Tests ownloads)  with thewnloads)  with invwnloads) .overlap = 1,161,164), as these tend to be well-imputed across studies and alleles will match those listed in the data used to estimate the LD score. No SNPs had out-of-bound p values or were strand-ambiguous. Because ENIGMA subcortical brain volume and PGC-ED AN measures were based on European samples, we used pre-computed LD scores for European populations, as provided on the LDSC website (https://github.com/bulik/ldsc). Standard errors were estimated using a block jackknife procedure and used to calculate p values. p values were adjusted for seven tests  regression  was used et al.,  and Resup value thresholds  and adjusted for 28 tests  to account for multiple testing.We used the grs.summary function developed by Johnson  and implp value thresholds . Using the binom.test function from the stats package in R, we tested the significance of the number of SNPs with opposite direction effects between datasets at these four thresholds over the total number of SNPs. p values were adjusted for 28 tests  to account for multiple testing.We employed a sign test as an additional method to investigate a potential overlap of positive or inverse direction effects of SNPs between both datasets at AN relates to a larger BETAsubcortical). However, since we were interested in the opposite relationship between these variables , we derived and used the inverse of the ORAN in the concordance analysis. For all SECA analyses, overlapping SNPs between both datasets  were pruned for LD using a p value informed method, a 1 Mb window and r2 > 0.1 . This resulted in N = 26,558 SNPs, which were entered in the analysis. p values were adjusted for seven tests  tests for pleiotropy, concordance, and ‘pleiotropy-informed’ conditional false discovery rate (FDR) results between two sets of GWAS summary results . SECA es et al.,  and Resuwww.mrbase.org) platform [To investigate potentially causal relationships between subcortical brain volumes and AN, we applied a two-sample Mendelian Randomization (MR) approach, which only requires GWAS summary level data. MR is a method of investigating causal relationships by using genetic variants as instrumental variables . The maiplatform  was usedThe following analyses were based on summary statistics for (a) eight brain volume measures of 13,170 participants from the ENIGMA consortium and (b) AN case-control data from 3495 AN patients and 10,982 healthy individuals. We focused on ICV and all subcortical regions  that were investigated in , and henLinkage disequilibrium score (LDSC) regression examines the relationship between two sets of GWAS test statistics using linkage disequilibrium. Restricting our analyses to Hapmap3 SNPs  left 1,161,164 SNPs in the ENIGMA datasets and 1,217,311 in the AN dataset with a total of 1,094,348 overlapping SNPs. SNP-based heritability estimates for the traits were 26% , 15% , 9% , 31% , 14% , 18% , and 23% . For four brain regions , nominal effects were observed, but the amount of explained variance was negligible (less than 0.034%). See Table p value thresholds , we could not identify any significant effects after adjusting for multiple testing. There was a nominally significant effect (pthreshold < 0.05 = 0.019) for the thalamus, where we found a higher proportion of SNP alleles with a negative effect on brain volume (BETA < 0) and an increased risk for AN (OR > 1) than would be expected by chance. This effect was not found when restricting the analyses to SNPs passing thresholds based on the AN GWAS results instead. See Table Testing for an accumulation of positive or negative direction effects of SNPs in the PGC-ED AN and ENIGMA datasets at four different ENIGMA p value thresholds (resulting in 144 subsets) for an excess of SNPs associated in both datasets (pleiotropy), for concordance of effect directions, and for conditional effects. After LD-pruning, 26,558 SNPs were left in the analysis. There was an overall effect for an inverse concordance between the thalamus and AN , which remained significant only at a trend level after correcting for seven tests  and TMPRSS5 (highly expressed in brain tissue) and also in the vicinity of neurotransmitter and immune system relevant genes such as DRD2, HTR3B, HTR3A, and NCAM . Follow-up analyses on functional effects of rs3863294 on DNA methylation or gene expression indicated an influence on DNA methylation linked to ZW10 in blood, but not in brain tissue and an effect on gene expression of TMPRSS5 in brain tissue and of DRD2 in peripheral tissue  extracts subsets of independent SNPs (present in both datasets) to test at 12 different CAM Fig. —was signLRRC4C which is important in axonal and synaptic development—gained significance after conditioning on hippocampal volume (pFDR-noCond = 0.272 to pFDR-hipp-Cond = 0.0208). Two SNPs that were conditional on volumes of the nucleus accumbens and the pallidum  were located in intergenic regions. For further details on these conditional analyses, see Similarly, rs7945461—linked to To assess potential causal effects of brain volume on AN, genetic variants linked to each of the four brain volumes with genome-wide level markers were used as instruments. Wald ratios did not indicate causal effects of any brain volume on risk for AN Table .We evaluated the relationship between common genetic variants implicated in subcortical brain volumes and those associated with a clinical diagnosis of AN. The sample sizes were the largest yet applied to these questions. With a comprehensive set of analyses, we found weak evidence for strong genetic correlations at a global level  and suggestive evidence for effects of single genetic markers.AN). For individual genetic variants, we identified a variant in the vicinity of neurotransmitter, development and immune system relevant genes such as ZW10, TMPRSS5 and DRD2, HTR3B, HTR3A, and NCAM to be significantly associated with AN only after conditioning on its association with caudate volume. Another variant linked to LRRC4C, important in axonal and synaptic development, gained significance after conditioning on hippocampal volume.There were signs of an inverse concordance between thalamus volume and risk for AN  or select individuals with more severe and enduring psychopathologies or those with particularly high genetic risk for AN. Third, the data analyzed in this study are largely based on adult samples. Heritability estimates vary with age and the degree of genetic overlap between subcortical volume and AN may be larger in younger populations. Fourth, we evaluated only common genetic variation. It is possible that rare variants play a role in the shared genetic architecture between brain volume and AN. Last but not least, it is indeed possible that there is no link between the genetics of subcortical volumes and AN. However, the results of the conditional analysis draw such a conclusion into question.Our results are similar to other studies that reported a lack of a shared genetic basis between brain volumes and schizophrenia as well as major depression , 22. Howg = 0.53; ref. [Despite our negative findings, several studies have reported genetic correlations between AN and different clinical traits. A significant twin-based genetic correlation between AN and OCD has been observed using data from the population-based Swedish Twin Registry  and has53; ref. ). A rece53; ref.  has founWe detected an inverse concordance between genetic determinants of thalamus volume and AN. Gray matter atrophy in the thalamus in AN has been reported in previous studies , 14 and ZW10 (involved in cell division) and TMPRSS5 (highly expressed in brain tissue) and also in the vicinity of neurotransmitter and immune system relevant genes such as DRD2, HTR3B, HTR3A, and NCAM, was significantly associated with AN after conditioning on its genetic association with caudate volume. A direct link between rs3863294 and these genes deserves further investigation. However, it is interesting to note that prior candidate gene studies suggest that many genes found in this region could be associated with AN. For instance, genetic variants associated with the serotonergic system (linked to the genes HTR3A and HTR3B) may be associated with the restrictive subtype of AN [DRD2) might play a role in the susceptibility for AN in some populations [We also found that genetic variant rs3863294, located close to the genes pe of AN  and geneulations , 51. Intulations  and implulations , 54. A nulations , althougulations , 57. AN-ulations . Howeverulations –64.LRRC4C, important in axonal and synaptic development [LRRC4C mRNA is abundant in hippocampal pyramidal neurons and dentate granule cells [Last, the genetic variant rs7945461—linked to elopment , 66—gainle cells  and sevele cells , but thele cells  and withle cells .In this comprehensive set of analyses, we found weak evidence for a relationship between common genetic variants implicated in AN and those associated with subcortical brain volumes at a high level , but some suggestive evidence for effects of single genetic markers. Despite the sample sizes being the largest yet applied to these questions, more detailed multimodal brain- and genome-wide studies are needed to dissect the potential impact of genetic risk for AN on brain structure or function.ESM 1(DOCX 773 kb)