Crop biofortification is a successful strategy to ameliorate Vitamin A deficiency. Sorghum is a good candidate for vitamin A biofortification, as it is a staple food in regions with high prevalence of vitamin A deficiency. β-carotene—the main provitamin A carotenoid—is below the target concentration in sorghum grain, therefore biofortification breeding is required. Previous studies found evidence that sorghum carotenoid variation is oligogenic, suggesting that marker-assisted selection can be an appropriate biofortification method. However, we hypothesize that sorghum carotenoids have both oligogenic and polygenic components of variation. Genomics-assisted breeding could accelerate breeding efforts, but there exists knowledge gaps in the genetics underlying carotenoid variation, as well as appropriate germplasm to serve as donors.zeaxanthin epoxidase is a major gene underlying variation for not only zeaxanthin, but also lutein and β-carotene. High carotenoid lines were found to have limited genetic diversity, and originated predominantly from only one country. Potential novel genetic diversity for carotenoid content was identified through genomic predictions in 2,495 accessions of unexplored germplasm. Oligogenic variation of carotenoids was confirmed, as well as evidence for polygenic variation, suggesting both marker-assisted selection and genomic selection can facilitate breeding efforts.In this study, we characterized carotenoids in 446 accessions from the sorghum association panel and carotenoid panel using high-performance liquid chromatography, finding high carotenoid accessions not previously identified. Genome-wide association studies conducted with 345 accessions, confirmed that zeaxanthin epoxidase gene was identified as a good candidate for use in marker-assisted selection. Due to the oligogenic and polygenic variation of sorghum grain carotenoids, both marker-assisted selection and genomic selection can be employed to accelerate breeding efforts.Sorghum vitamin A biofortification could be beneficial for millions of people who rely on it as a dietary staple. Carotenoid content in sorghum is low, but high heritability suggests that increasing concentrations through breeding is possible. Low genetic diversity among high carotenoid lines might be the main limitation for breeding efforts, therefore further germplasm characterization is needed to assess the feasibility of biofortification breeding. Based on germplasm here evaluated, most countries’ germplasm lacks high carotenoid alleles, thus pre-breeding will be needed. A SNP marker within the The online version contains supplementary material available at 10.1186/s12870-023-04176-0. Carotenoids are the main source of vitamin A in many developing countries where diets are primarily plant based. Cereals provide the majority of calories in developing countries, and most cereal grains accumulate carotenoids —particularly lutein, zeaxanthin, β-carotene, and β-cryptoxanthin . HoweverGlobally, vitamin A deficiency affects an estimated 190 million preschool age children and 19 million pregnant women, contributing to poor growth, intellectual impairment, vision loss, perinatal complications, and increased mortality , 8. CereSorghum bicolor (L.) Moench] is a good candidate for biofortification as it is a staple food in regions with high prevalence of vitamin A deficiency, such as in South East Asia and sub-Saharan Africa [Sorghum [n Africa . Studiesn Africa , 13–20. n Africa , 22, butn Africa , 24. Hown Africa , 16, 20.phytoene synthase (PSY), phytoene desaturase (PDS), and geranylgeranyl diphosphate synthase (GGPS) genes [zeaxanthin epoxidase (ZEP), a gene that has also been identified in several other crops as underlying carotenoid variation [To use genomics-assisted breeding to develop a carotenoid biofortified sorghum variety, genomic regions associated with variation of provitamin A carotenoids must be identified, as well as efficient selection methods and parental donors. The carotenoid biosynthetic pathway is well understood and conserved in plants , facilitS) genes , 16. Forariation –29. Prevariation , 16, 20,ariation  and wheaariation . The oliIdentifying germplasm that harbor alleles for high β-carotene concentrations in sorghum grains is also essential for biofortification breeding. This aspect is perhaps the most challenging, because even though carotenoids are naturally present in sorghum grains, studies have shown that the majority of sorghum varieties have low β-carotene concentrations , 18, 32.ZEP gene and found evidence of selection for a high carotenoid allele found in only a few countries.Given the potential impact of a high vitamin A sorghum developed through biofortification breeding, and the current gaps in knowledge for sorghum biofortification, in this study we further explored the potential of genomics-enabled breeding tools for increasing carotenoids in sorghum. We hypothesize that both oligogenic and polygenic components of variation exist for sorghum carotenoids Fig. , such thr = 0.74; p < 10–16), β-carotene and lutein , and lutein and zeaxanthin . Four accessions, two of which had not previously been phenotyped, had higher concentrations of β-carotene than any accessions previously phenotyped in the SAP/CAP collection.To characterize phenotypic variation of carotenoids in sorghum grain, and to confirm previously published phenotype data on one year of samples, we quantified lutein, zeaxanthin, β-carotene, and α-carotene for 446 accessions in the sorghum association and carotenoid panel (SAP/CAP) global collection using high-performance liquid chromatography HPLC Table S. Lutein To account for unbalanced data and accurately predict the genetic merit for carotenoids of the SAP/CAP accessions, best linear unbiased predictors (BLUPs) and heritabilities were calculated for each of the carotenoid traits Table . Due to Next, we sought to characterize the genetic architecture of sorghum carotenoids. A previous study  suggesteZEP gene.For lutein, only 1 SNP, on chromosome 4 S04_275231), was above the Bonferroni threshold of significance  that was also associated with lutein. There was also an association on chromosome 2 (S2_61694864), which is in proximity to Sobic.002G225400 (42 kb away), an a priori candidate gene annotated as an abscisic acid 8'-hydroxylase 3 (CYP707A).β-carotene had ten significant marker-trait associations for a total of six regions of association across chromosomes 2, 6 and 10  to evaluate the genetic diversity of the high carotenoid accessions identified in the SAP/CAP collection. The high carotenoid accessions were defined as those within the top 5% for β-carotene BLUP estimates, which consisted of 19 accessions ranging from 0.40 to 0.80 μg/g β-carotene. The majority of the high carotenoid accessions originated in the United States (8 accessions), followed by Nigeria (3 accessions) and Lebanon (3 accessions) . We analyzed a window of 1 Mb upstream and downstream of S06_47123508, which encompassed 1,665 SNPs. Nucleotide diversity was decreased in the high carotenoid accessions, but not in the low carotenoid accessions (defined as the lowest 5% for β-carotene BLUP estimates) or in the complete set of SAP/CAP collection accessions. The most prominent region of low nucleotide diversity was surrounding SNP S06_47123508, a region which includes the a priori candidate gene encoding 7123508. Next, we sought to explore if there exists unidentified high carotenoid germplasm in additional germplasm collections. Publicly available genotype data was obtained for germplasm collections from six countries: Ethiopia, Haiti, Niger, Nigeria, Senegal, and Sudan. Together with the SAP/CAP collection, the dataset consisted of 60,129 common SNPs with less than 20% of data missing for 2,488 accessions. There were 361 accessions from Ethiopia, 296 from Haiti, 516 from Niger, 180 from Nigeria, 420 from Senegal, 319 from Sudan, and 396 from the SAP/CAP collection. Most of this germplasm is photoperiod sensitive making it difficult to phenotype in temperate regions such as the United States. For example, among the 396 accessions from the SAP/CAP collection for which we had genotype information, we were only able to phenotype the 345 accessions that were photoperiod sensitive. The inclusion of germplasm from Haiti’s breeding program served to generate hypotheses on the best biofortification approach for the program, providing guidance to the breeder by indicating if there is currently promising germplasm in the program or if donor lines must be introduced to introgress high carotenoid alleles.Genomic prediction has the potential to guide resource allocations by identifying the most promising germplasm to test in future work. We first explored the feasibility of the SAP/CAP collection as a training population for the unexplored germplasm collections. For this, the genetic relationship among the unexplored germplasm collections and the SAP/CAP collection was tested with a PCA, highlighting the country of origin for each accession Fig. . Based or = 0.89; p < 10–16), β-carotene and lutein , and lutein and zeaxanthin .Next, we estimated genomic estimated breeding values (GEBV) from the BLUPs of β-carotene, lutein, and zeaxanthin in the unexplored germplasm collections and the SAP/CAP collection Table S. Lutein To further explore geographic patterns of sorghum carotenoid distribution beyond the SAP/CAP collection, we aggregated GEBV by country using the unexplored germplasm collections Fig. B-D. NigeNext, we looked at the genetic relationships among the predicted top 5% for each carotenoid using a PCA for the GEBV  > 0.05. Due to low marker density, the majority of these SNPs were absent in the unexplored germplasm collections. However, SNP S06_46717975 was present in the SAP/CAP collection and the SNP data set for Haiti, Niger, and Nigeria germplasm  [zeta-carotene desaturase  [ZEP (Sobic.006G097500) [ZEP for lutein, zeaxanthin, and β-carotene  , the secG177400)  and ZEP G097500) . InteresZEP gene in sorghum could be a candidate to initiate such efforts. Based on this and our previous study [ZEP is a core gene controlling variation in the sorghum β-branch carotenoids, i.e. zeaxanthin and β-carotene. ZEP is also a good candidate for breeding efforts though MAS as it seems to have allelic variants with strong correlation with carotenoid concentrations. Marker S06_46717975 is a biallelic allele (A/G) in the ZEP coding sequence and is in proximity to S06_47123508, here associated with β-carotene and zeaxanthin. In the SAP/CAP collection, the allele was minor with only 10% of the accessions having the ‘A’ allele. The geographic distribution of the ‘A’ allele also correlates with Nigeria, United States, and Lebanon, the countries that had the highest observed or predicted β-carotene concentrations  to be more cost-effective than simple phenotypic selection . This geLastly, incorporation of a GS scheme for a young breeding program can be very challenging. GS has the biggest potential for genetic gain per unit of time when breeding cycles are closed rapidly and effectively –57. HoweZEP gene into breeding programs followed by GS for continuous improvement.In this study we evaluated carotenoid concentrations in SAP/CAP collection, identifying the accessions with the highest β-carotene concentrations. Also, it was established that current concentrations of β-carotene are low and current known high β-carotene germplasm has a narrow genetic diversity. We used the SAP/CAP collection as a training population to predict the genetic merit or GEBV via genomic prediction for unexplored germplasm. Based on GEBV, we present 107 accessions with the highest predicted concentrations for β-carotene that potentially represent novel genetic variation for the trait. Finally, we proposed that MAS should be initially used to introduce high carotenoid alleles like S06_46717975 inside the Grain carotenoid concentration was evaluated for a total of 446 sorghum accessions, which included 316 from the sorghum association panel (SAP)  and 130 2 stream at room temperature. Finally, the residue was reconstituted in 100 μl of Methanol:Ethyl Acetate (1:1) and centrifuge for 5 min at 5000 rcf. An aliquot of 40 μl of the clear supernatant was utilized for the HPLC analysis. Resolution of lutein, zeaxanthin, β-carotene, α-carotene and β-cryptoxanthin was conducted using an Agilent 1290 Infinity UHPLC with Eclipse Plus C18 column .Carotenoid extractions were performed following a modified solid phase method . All ste2 represents the broad sense heritability on a entry-mean basis, Concentrations for lutein, zeaxanthin and β-carotene were analyzed with ASReml-R package , which aP = 0.05 was used to identify significant SNPs. Significant associations were compared with candidate genes that are annotated as enzymes involved in the carotenoid pathway in Phytozome or that have been identified in other carotenoid association studies (Table SThe genetic architecture and the genomic regions underlying carotenoid variation in sorghum grain were investigated through a genome-wide association study (GWAS) implemented in GAPIT , versions Table S.Genetic diversity among the high carotenoid lines identified was examined. We prioritized assessing the genetic diversity among the accessions with the highest β-carotene BLUP estimates, because β-carotene is the most abundant provitamin A carotenoid in sorghum. Accessions were ranked as top 5% and bottom 5% based on their BLUP estimate for β-carotene. A marker-derived additive relationship matrix or kinship, was estimated with the ‘A.mat’ function in the rrBLUP R package . The eigPublicly available genotype data from unexplored germplasm collections were gathered. Accessions and their corresponding genotype information from Ethiopia , Haiti , Niger , NigeriaPredictions of the genomic estimated breeding values (GEBV) and genomic heritability were conducted using the genomic data from unexplored germplasm, representing country collections of Ethiopia, Haiti, Niger, Nigeria, Senegal, Sudan, and the SAP/CAP collection. The accessions in the SAP/CAP collection for which we had genotype information and BLUPs estimates for lutein, zeaxanthin, and β-carotene were used as the training population (n = 345). GEBV were estimated for lutein, zeaxanthin and β-carotene using the G-BLUP model with the additive relationship matrix or kinship as implemented in the rrBLUP package in R . A fivefZEP allele for the SAP/CAP collection and the unexplored germplasm collections was examined using country of origins and the allelic classes for the SNP S06_46717975. Countries that had less than 3 accessions were discarded from the analysis. We also aggregated the allelic variants present in the high β-carotene accessions from the SAP/CAP collection and the unexplored germplasm collections, as defined by the top 5% of BLUP or GEBV for β-carotene.Distribution of the Additional file 1: Table S1. Raw carotenoid concentrations in SAP/CAP. Table S2. BLUPs for carotenoids in the SAP/CAP. Table S3. Carotenoids marker-trait associations in the SAP/CAP. Table S4. Carotenoid GEBV in SAP/CAP and unexplored germplasm. Table S5. Top 5% β-carotene GEBV in unexplored germplasm. Table S6. Allelic distribution of ZEP SNP S06_46717975. Table S7. A priori carotenoid candidate genes. Table S8. Common name, endosperm color, kernel color, race, and working group of evaluated accessions.Additional file 2: Fig. S1. Comparison of GEBV for lutein, zeaxanthin, and β-carotene for the SAP/CAP global collection and the unexplored germplasm collections. Boxplot of GEBV for A) lutein; B) zeaxanthin; and C) β-carotene.Additional file 3: Fig. S2. PCA of genetic relationship between SAP/CAP and unexplored germplasm.Additional file 4: Fig. S3. LD for 1 Mb region upstream and downstream of marker S06_47123508.


Schizophrenia has been proposed to involve altered left-hemispheric dominance for language in the brain, but research in limited sample sizes has not clarified whether structural asymmetry differs in this condition. In MRI data from 5,080 affected individuals and 6,015 controls, we found altered asymmetry of two brain regions driven by thinner left-hemisphere cortex in schizophrenia: the rostral anterior cingulate and middle temporal gyrus. The latter is a core region of the left-hemisphere language network. Effects were very small in terms of macroanatomical asymmetry, but might be compatible with altered lateralized function. Across all brain regions considered together, 7% of variance in asymmetry was linked to case–control status, indicating a more diffuse pattern of subtly altered anatomical asymmetry. Left–right asymmetry is an important organizing feature of the healthy brain that may be altered in schizophrenia, but most studies have used relatively small samples and heterogeneous approaches, resulting in equivocal findings. We carried out the largest case–control study of structural brain asymmetries in schizophrenia, with MRI data from 5,080 affected individuals and 6,015 controls across 46 datasets, using a single image analysis protocol. Asymmetry indexes were calculated for global and regional cortical thickness, surface area, and subcortical volume measures. Differences of asymmetry were calculated between affected individuals and controls per dataset, and effect sizes were meta-analyzed across datasets. Small average case–control differences were observed for thickness asymmetries of the rostral anterior cingulate and the middle temporal gyrus, both driven by thinner left-hemispheric cortices in schizophrenia. Analyses of these asymmetries with respect to the use of antipsychotic medication and other clinical variables did not show any significant associations. Assessment of age- and sex-specific effects revealed a stronger average leftward asymmetry of pallidum volume between older cases and controls. Case–control differences in a multivariate context were assessed in a subset of the data , which revealed that 7% of the variance across all structural asymmetries was explained by case–control status. Subtle case–control differences of brain macrostructural asymmetry may reflect differences at the molecular, cytoarchitectonic, or circuit levels that have functional relevance for the disorder. Reduced left middle temporal cortical thickness is consistent with altered left-hemisphere language network organization in schizophrenia. Schizophrenia is a serious mental illness characterized by various combinations of symptoms that may include delusions, hallucinations, disorganized speech, affective flattening, avolition, and executive function deficits . Left–ri2611172126However, alterations in structural asymmetry of the cerebral cortex in schizophrenia have so far only been reported in studies with relatively small samples 17, 30–36; 303740http://enigma.ini.usc.edu) consortium aims to perform large-scale analyses by combining imaging data from research groups across the world, processed with standardized protocols , based on an automated approach quantifying local concave versus convex surface curvature , we had a particular interest in whether typical structural asymmetries of the core cerebral cortical language network might be reduced in schizophrenia—this includes asymmetries of lateral temporal cortex and inferior frontal cortex . HoweverWe also performed various secondary/exploratory analyses of the data. We explored possible associations of structural brain asymmetries with medication use and other disorder-specific measures: age at onset; duration of illness; as well as total, positive, and negative symptom scores. In addition, we tested age- and sex-specific asymmetry differences. Finally, for 14 datasets for which individual-level data were available, we tested for a multivariate association of case–control status simultaneously with regional AIs across the brain.Together, these analyses aimed to provide insights into the extent and mapping of structural brain asymmetry alterations in schizophrenia, and how they relate to key clinical variables.SI Appendix, Table S1A). The datasets came from various countries around the world and were collected over the last roughly 25 y . For each of the datasets, all relevant local ethical regulations were complied with, and appropriate informed consent was obtained for all individuals. The present study was carried out under approval from the Ethics Committee of the Faculty of Social Sciences of Radboud University Nijmegen. Sample size-weighted mean age across datasets was 33.3 (range 16.2 to 44.0) years for individuals with schizophrenia and 33.0 (11.8 to 43.6) years for controls. Affected individuals and controls were 67% and 52% males, respectively. Diagnostic interviews were conducted by registered clinical research staff using different diagnostic criteria  . No controls had present or past indications of schizophrenia.Structural MRI data were derived from 46 separate datasets (45 case–control and one case-only) totaling 11,095 individuals, via researcher participation in the ENIGMA schizophrenia working group. Of these, 5,080 were affected with schizophrenia and 6,015 were unaffected controls  (http://enigma.ini.usc.edu/protocols/imaging-protocols/). Briefly, this included outlier detection in the derived cortical and subcortical measures and visual inspection of segmentations projected onto the T1-weighted image of each individual. Predefined guidelines for visual inspection were followed. Measurements from regions with poor segmentation were excluded, as well as individuals whose data failed overall quality checks. Data-sharing limitations did not allow the central analysis group to have access to individual-level data for the majority of participating study sites. For further processing and analyses of the data, a script running in R software   . For eacersions) . Volumesersions)  were derect.org)  was prepL) and right (R) hemispheric measurements to calculate R > L) and a positive value a larger left hemispheric measurement (L > R). Left or right measurements equal to 0 were set to missing, as these most likely reflected data entry errors. Furthermore, when a left or right measurement was missing, the corresponding measurement in the opposite hemisphere was also set to missing. Calculated AIs were used for additional quality control of image orientation in each dataset .For each bilaterally paired brain regional measure, we used the left , we added n-1 binary dummy covariates (where n is the number of scanners in a given dataset), to statistically control for scanner effects. Collinearity between predictor variables was assessed using the R-package usdm (v1-1.18)  as the main binary predictor, and sex and age as covariates (model 1 in Supporting Information 2). For ten datasets where more than one scanner had been used (v1-1.18) , and hig1.96×sed . In thesSI Appendix, Figs. S2–S4), effect sizes for diagnosis from each case–control dataset were meta-analyzed in a random-effects model fitted with a restricted maximum likelihood estimator, using the function “rma” in the R package metafor (v3.0-2) (www.brainder.org/research/brain-for-blender/), using Matlab R2020a . We calculated false discovery rate (FDR)-corrected P values using the Benjamini–Hochberg method to account for multiple tests , to help describe the asymmetry differences.For each brain regional AI ((v3.0-2) . The metle tests   Individual datasets with “outlier” effects. 2) Heterogeneity of technical, diagnostic, or geographic differences between datasets. 3) Handedness, intracranial volume, or nonlinear age effects. Secondary analyses assessed medication group differences and correlations of asymmetries with clinical variables . In addition, for all AIs in all case–control datasets, we applied models which were the same as the primary analysis but also included either diagnosis-by-age or diagnosis-by-sex interaction terms.SI Appendix, Table S1C). We separately adjusted the left and right measurements using ComBat harmonization  to remove dataset effects . AIs for cortical and subcortical measures were then calculated using the same formula as above, and collinearity between AIs was assessed by calculating a correlation matrix. AIs did not show higher pair-wise correlations than 0.5 . A multivariate analysis of covariance (MANCOVA) using the “manova” function in R was applied, testing all 76 regional structural brain AIs simultaneously against case–control status, with age and sex as covariates. We ran one million label-swapping permutations of case–control labels and calculated a permutation p-value by assessing the number of times the F-statistic of an analysis with permuted data was equal to or larger than the F-statistic of the analysis with real data, divided by the total number of permutations. When permuting case–control labels, we conserved case–control numbers within each dataset (and within scanner for multiscanner datasets). To help interpret the MANCOVA results, we also derived univariate case–control association statistics for each separate structural AI from the multivariate association analysis output, using univariate analysis of covariance (ANCOVA) (“summary.aov” function in R).To examine case–control group differences across all brain regional AIs simultaneously in one model, we conducted a multivariate analysis based on 14 datasets for which individual-level data were available to the central analysis team. For this analysis, we only retained individuals with complete data for all bilateral measures of cortical and subcortical structures, which were 935 individuals affected with schizophrenia and 1,095 unaffected controls  and surface area asymmetry  did not significantly differ between affected individuals and controls. At a regional level , there was a small but significant case–control difference in cortical thickness asymmetry of the rostral anterior cingulate cortex , and also in cortical thickness asymmetry of the middle temporal gyrus  . Post hoc analysis of unilateral effects showed that both of these regional asymmetry differences were driven primarily by thinner left than right cortex in individuals with schizophrenia compared to controls . The middle temporal cortex is a core language network region .In our primary analysis (model 1), total hemispheric average cortical thickness asymmetry  . For middle temporal gyrus thickness asymmetry, the exclusion of two outlier datasets also yielded a similar result compared to the primary analysis , again with the same directionality .For rostral anterior cingulate thickness asymmetry, there were three datasets in the primary meta-analysis which had outlier case–control effect sizes when compared to the meta-analyzed effect. After excluding these datasets and repeating the meta-analysis for this AI, the case–control difference remained, with the same directionality  , i.e., Cohen’s d effect sizes reflecting asymmetry differences between individuals with schizophrenia and unaffected controls were not significantly influenced by scanner strength, scanner manufacturer, use of a single scanner versus multiple scanners, image slice orientation, FreeSurfer version, diagnostic tool, or the geographic origin of datasets.Meta-regression analysis did not identify any significant moderators , the case–control differences for both of these regional AIs remained nominally significant, with similar directions and magnitudes of effect compared to the case–control differences found in the primary analysis , despite differences in sample sizes resulting from limited availability of some of these variables.In models that included either handedness, ICV, both handedness and ICV, or ageSI Appendix, Table S9). For the middle temporal gyrus, there was a nominally significant increase in average rightward asymmetry in affected individuals taking first-generation versus second-generation antipsychotics at the time of scanning , i.e., this was not significant after multiple testing correction .Rostral anterior cingulate thickness asymmetry did not differ between affected individuals across medication groups (model 6)   and between middle temporal gyrus thickness asymmetry and duration of illness  , but these correlations did not remain significant when correcting for multiple testing. No correlations with chlorpromazine-equivalent medication dose, age at onset, Positive and Negative Syndrome Scale (PANSS) scores , or Scale for the Assessment of Positive Symptoms (SAPS) scores were found for either the rostral anterior cingulate thickness asymmetry or middle temporal gyrus thickness asymmetry .We found nominally significant correlations between rostral anterior cingulate thickness asymmetry and negative symptom severity measured with the Scale for the Assessment of Negative Symptoms (SANS)  . This association was driven by a significantly decreased average leftward asymmetry with increasing age in controls  that was not present in affected individuals . In terms of the corresponding unilateral effects, left and right pallidum volume decreased with increasing age in individuals with schizophrenia  and unaffected controls , but the two groups differed with respect to the side showing the stronger effect . No significant diagnosis-by-sex interactions were found (model 9) .In secondary analyses across all AIs using models with interaction terms, we found a significant diagnosis-by-age interaction (model 8) for pallidum volume asymmetry  = 1.87, P = 1.25 × 10−5). Only three of the F-statistics resulting from one million label-swapping permutations were larger than the F-statistic from the true analysis, resulting in a permutation P = 3.0 × 10−6. We also derived univariate (ANCOVA) association statistics from the multivariate model to understand which AIs contributed most to the significant multivariate association. The structural AIs that showed nominally significant, univariate case–control differences in the 14 datasets available for this analysis were those for pallidum volume, nucleus accumbens volume, and eight regional surface area or thickness measures distributed widely over the cerebral cortex , there was a significant multivariate structural brain asymmetry difference between cases and controls that accounted for roughly 7% of the variance considered across all 76 AIs . The pallidum is prominently involved in reward and motivation , reducing the sample size and thus statistical power in these secondary analyses. More detailed clinical data would be useful to gather in future large-scale studies of structural asymmetries. For example, a future study could investigate middle temporal gyrus thickness asymmetry in relation to the presence and severity of auditory verbal hallucinations .This was the largest study of structural brain asymmetries in schizophrenia to date, and made use of a single image processing and analysis pipeline to support analysis across multiple datasets. The fact that we used data from a range of imaging equipment, diagnostic tools, and regions of the world ensures generalizability of our findings, as they pertain to the diverse manner in which schizophrenia is diagnosed and studied internationally. Therefore, a major strength of our approach is in showing consensus effects across intersite variations in techniques and samples. Unlike in a highly selected, single-site or single-equipment study, the broad and generalizable total dataset made it unlikely that any single factor confounded our findings. We used a meta-analytic approach after testing for effects separately within each dataset, where cases and controls were matched for technical and demographic factors within each dataset. This allowed us to assume and control for variations between datasets in our main analysis. In addition, meta-regression analyses indicated that between-dataset variability in technical, diagnostic, or geographic aspects had no significant impact on the associations between schizophrenia and regional brain asymmetries identified in this study. It is also worth noting that several findings from the ENIGMA-Schizophrenia working group (not related to asymmetry) have been replicated by The Cognitive Genetics Collaborative Research Organization in a sample collected in Japan , supportWe used cross-sectional datasets, limiting the possible interpretation with respect to cause–effect relations, longitudinal changes in asymmetry, or medication effects on asymmetry. Many of the individuals with schizophrenia were likely to be past or current users of medication, although data on medication were only available for a subset of datasets and were also limited to medication use at the time of scanning. We found no evidence that the asymmetries of rostral anterior cingulate thickness or middle temporal gyrus thickness were different in affected individuals using medication versus those not using medication, which may indicate that the case–control differences of asymmetry that we detected had a developmental origin, rather than reflecting medication use. Indeed, medication effects on cortical thickness may be predominantly bilateral, without necessarily affecting asymmetry. We are not aware of any comparably sized prospective/randomized study in which medication effects could be disentangled from case–control effects.We found a tentative difference of middle temporal gyrus thickness asymmetry between individuals who were taking first-generation versus second-generation antipsychotics. In principle, this finding might reflect a change of asymmetry in response to first-generation medication in particular, or else clinical differences of disorder presentation linked to asymmetry which then affect treatment choices. We saw nominally significant evidence that this same regional asymmetry relates to illness duration. However, the medication subgroup analyses were limited by relatively small sample sizes compared to the primary case–control analysis, and this particular association did not survive multiple testing correction. Also, medication status did not include information on previously used antipsychotics. This association therefore remains uncertain until replicated.We used macroanatomical brain atlases for both the cortical and subcortical structures, which is the most feasible approach for large-scale analysis across multiple datasets, but limits spatial resolution. With higher resolution mapping, regions that showed negative results in our study may harbor more focal case–control asymmetry differences, which could be revealed for example through vertex-wise cortical mapping , 94, 98,This study focused on group average differences, but individual-level deviations in affected individuals may be highly heterogeneous and not well captured by group-average approaches . Future In summary, we performed the largest study of asymmetry differences between individuals with schizophrenia and unaffected controls to date. Effect sizes were small, but several regional case–control asymmetry differences in cortical thickness and subcortical volume were suggested, and multivariate analysis indicated that 7% of variation across all regional asymmetries could be explained by the case–control group difference. Our findings therefore support a long-standing theory that the brain’s asymmetry can be different in schizophrenia , 11, eveAppendix 01 (PDF)Click here for additional data file.


Distichiasis is a condition characterized by aberrant hairs along the eyelid margins. The symptoms are usually mild but can lead to ulcerations and lesions of the cornea in severe cases. It is the most frequently noted ocular disorder in Norwegian Staffordshire bull terriers (SBT), with a prevalence above 18% in the adult population. A complex inheritance is assumed, but there is sparse knowledge about the genetic background of distichiasis in dogs. We have performed a genome-wide association study of distichiasis in SBT and used genomic data in an attempt to predict genomic values for the disorder.We identified four genetic regions on CFA1, CFA18, CFA32 and CFA34 using a mixed linear model association analysis and a Bayesian mixed model analysis. Genomic values were predicted using GBLUP and a Bayesian approach, BayesR. The genomic prediction showed that the 1/4 of dogs with predicted values most likely to acquire distichiasis had a 3.9 -4.0 times higher risk of developing distichiasis compared to the quarter (1/4) of dogs least likely to acquire the disease. There was no significant difference between the two methods used.Four genomic regions associated with distichiasis were discovered in the association analysis, suggesting that distichiasis in SBT is a complex trait involving numerous loci. The four associated regions need to be confirmed in an independent sample. We also used all 95 K SNPs for genomic prediction and showed that genomic prediction can be a helpful tool in selective breeding schemes at breed level aiming at reducing the prevalence of distichiasis in SBTs in the future, even if the predictive value of single dogs may be low.The online version contains supplementary material available at 10.1186/s40575-023-00132-1. Distichiasis is a condition where abnormal hairs grow along the margin of the eyelids. It's common in Staffordshire bull terriers and can cause eye problems of variable severity. The abnormal eye hairs can be found during an eye inspection performed by a veterinarian.We performed a genome-wide association analysis and identified four genomic areas associated with the condition. But more genes may be involved in causing the disease.We have used genomic data to predict genomic values. Genomic values can be used to predict the total load of disease-associated alleles. Genomic prediction would therefore be helpful at the breed level, similar to pedigree-based breeding values, to reduce the prevalence of dogs with distichiasis, even if the low accuracy to predict phenotypes in individual dogs may be a challenge. More research is needed to confirm these findings and see if genomic prediction could be a helpful tool within dog breeding in the future.The online version contains supplementary material available at 10.1186/s40575-023-00132-1. Distichiasis is a condition with abnormal growth of eye hairs along the margins of the eyelid. The aberrant hairs arise from ectopic hair follicles near the meibomian glands and emerge through the excretory duct opening of the sebaceous glands , 2. In mDistichiasis is common in dogs , 4 and tPolled locus on the bovine chromosome 1 [FOXC2 gene, both alone and as a part of a syndrome with lymphedema [TWIST2 [CTNND1 and CDH1 encoding proteins in the cadherin–catenin complex [Distichiasis is less common in other species than the dog but has been described in cats , ferretsmosome 1 . In humamphedema –18. Othe [TWIST2  and Blep complex . So far,We were interested in using distichiasis as a model for canine genomic prediction, by estimating the joint effect of all genomic markers to predict a phenotype. Genomic predictions have had great success in livestock breeding . Until nIn the present study, we aim to investigate the genetic background of distichiasis in SBTs through a genome-wide association analysis (GWAS). We have compared two approaches for genomic prediction: GBLUP and BayesR and have used the results to investigate the potential value of using genomic data to predict genomic values for the disorder in SBTs.p < 2.0 × 10–16). On average, affected dogs carry 4.3 risk alleles and unaffected 3.3. risk alleles.The association study was based on 731 SBTs (407 controls and 324 cases), and 94,697 autosomal markers. Four genomic regions were identified using a mixed linear model-based association analysis (MLMA) in Genome-wide Complex Trait Analysis (GCTA) , locatedGAS1 ~ 260 kb downstream and TUT7 ~ 210 kb upstream for the SNP  and DNJAC (583 kb), and FXR1 (582 kb) (both downstream).The SNP on CFA34, BICF2S232639, reached the second highest signal and is situated in an intron in the Visual inspection of the principal components analysis (PCA) plots showed no general stratification. PCA plots including country of origin, genotyping arrays and cases and controls, are included in Supplementary Figs. P = 0.984) in the AUC between the two methods. The AUC was 0.655 (CI 0.612–0.699) in GBLUP , with the 25% of dogs with a GV least likely to develop the disease, the odds ratio was 4.02 (95% CI 2.48–6.63) in GBLUP and 3.86 (95% CI 2.31- 6.55) in BayesR. No covariates had any effect on the performance of the models  from a receiver operating characteristic curve (ROC curve). There was no significant difference P = 0.84 in the5 CI 0.61–0.699 inCTNND1, seen in connection with "Blepharocheilodontic syndrome" with distichiasis in humans [We have identified four potential candidate regions on CFA1, 18, 32 and 34 associated with distichiasis. The four risk alleles represent four novel genomic regions associated with distichiasis. n humans , is situGAS1, located around 260 kb downstream of the top SNP on CFA1, is involved in growth suppression, apoptosis and embryonal development [SOX2 is located 65 kb away from the top SNP on CFA34. SOX2 is a transcription factor involved in regulating embryonic development [API5 is the closest gene to the top SNP. API5 is an apoptosis inhibitor [There are several genes of interest within the associated genomic regions. elopment . SOX2 iselopment . On CFA1nhibitor .A GWAS intends to detect markers in LD with the causal variant. The LD within a single dog breed can be extensive and span regions of several megabases , 32. Thi–05, according to the number of independent SNPs after pruning the data, and after considering the LD structure and haploblock sizes. This is the same levels as suggested by Karlsson et al. based on the average size of 1 MB of independent haploblocks in a 2.4 GB dog genome [We have set the significance level at 1.24 × 10g genome , and theg genome . This siThe dataset contained genotyped dogs from two Illumina arrays, only the SNPs shared between the two arrays were used. Potential batch effects were assessed during the quality control. Including batch effect as a covariate did not have any effect on the genomic prediction. There is a high level of relationship between the dogs in the dataset. The GRM included in the mixed linear model account for part of this relationship. However, keeping siblings from the same litter may introduce some bias due to shared environment in early life and maternal effects. To account for such effects, we ran one analysis after removing siblings of equal affection status; however, the same four top SNPs remained.In our study, the four genomic regions disclosed contribute only with a moderate effect on the phenotype. The top SNP on CAF32 obtained the lowest significance among the top SNPs in the MLMA and received low signals in BayesR. At the top SNP on CFA1, the minor allele (A) is protective, and carrying the major risk allele, G, gave 2.66 increased odds of developing distichiasis. In the other three SNPs, the minor allele is the risk allele and carrying one of the risk alleles gives a twofold increased risk of developing distichiasis. The chance of developing distichiasis increased with the number of risk alleles.distichia may be difficult to observe [In a previous study conducted on the same SBT population, we found that most SBTs was only mildly affected by distichiasis . Additio observe , 35. As  observe . These eAccording to the registration in The Norwegian Kennel Club (NKK), the prevalence of distichiasis in the SBTs has persisted over the last twenty years . Marker-all SNP markers across the whole genome, can be used to predict phenotypes [In complex traits where the effect size of most risk alleles is small and therefore not captured by the association analysis, genomic prediction, including the combined effect of enotypes . Since benotypes . The twoThere was a significant difference between the GV predicted from all 95 K SNPs between the cases and controls. Comparing the 25% of dogs most likely to acquire distichiasis with the 25% of dogs with the "best" GV (least likely to develop the disease) gave four times increased risk of developing distichiasis in the first group. The predictive accuracy for the individual dog was low and can, therefore, not be used to predict the phenotype in individual dogs. However, using the GVs at the breed level in the same manner as traditional pedigree-based breeding values, it should be possible to reduce the prevalence of distichiasis in the SBT population. Even if, on average, there would be an improvement in the population, the number of dogs with a "false" prediction may represent a challenge for the communication with the breeders.Prediction of complex traits using genomic data typically requires large training datasets and testing in independent data sets –40, whicThe material includes only dogs with a phenotype and represents only a subset of the overall SBT population. However, because most dogs used for breeding undergoes an eye examination, we believe the material is representative of the breeding population.The use of imported SBTs in breeding is extensive in Norway, and more than half of the litters are from combinations where at least one parent is registered in another country. This can increase the genetic variation within the population and reduce the accuracy of genomic prediction.Thorsrud et al. reported a higher AUC (0.94 with GBLUP) in their genomic prediction of distichiasis compared to our AUC of 0.66 with GPLUP. The divergent results between the SBT and guide dog populations emphasize that the results from genomic predictions of one disease trait may not be easily transferable between different breeds or populations and depend on the heritability and genetic complexity of the disease, number of disease cases and controls, and population structure and effective population size.Our study indicates that distichiasis in SBT is a complex trait with multiple genetic loci involved. We have identified four potential genomic regions on CFA1, 18, 32 and 34. Further studies must be conducted to validate the findings.The genomic prediction, estimated from the joint effect of all 94,697 SNP, has the potential to aid in selective breeding, to reduce the prevalence of distichiasis in the SBTs but has a low predictive value for phenotypes in individual dogs. The genomic prediction of distichiasis must be validated in each other target population.www.dogweb.no). The dogs were classified as affected or unaffected according to the diagnosis on the eye examination records. In our study, dogs with a positive distichiasis diagnosis were regarded as affected (case) regardless of examination age and a later negative examination. Dogs were considered unaffected (controls) when diagnosed as negative for distichiasis after one year of age. This is consistent with the findings from a previous study where we found that a negative distichiasis status in puppies did not give a reliable picture of the distichiasis status in the adult dog [A subset of SBTs with an official eye examination record registered by NKK between 2005 and April 2022 were included. Eye examinations were performed by veterinarians certified by the European College of Veterinary Ophthalmologists (ECVO). The eye examination records are available in "dogweb", an open database established by NKK (dult dog .Samples were collected from a biobank established in collaboration between the Norwegian University of Life Sciences (NMBU) and the NKK. DNA from EDTA blood was extracted using E.Z.N.A. Blood DNA Mini Kit from Omega, following the manufacturer's description. The DNA quality was measured with Epoch from BioTek. Seven hundred and thirty-four samples were genotyped on the Illumina 220 K CanineHD Bead chip , and 118 samples from a previous study were genotyped on the Illumina 170 K CanineHD bead chip. Only the 170 K markers shared between the two datasets were kept in the joint analysis.–6 in controls and -1.0 × 10–10 in cases, using the Fisher exact test incorporated in Plink. Dogs with a missing phenotype were removed. To assess potential batch effects of the two arrays 170 K and 220 K, a PCA plot was constructed. In addition, the SNP markers were regressed on the two batch (170 K and 220 K) to assess differences in the allele frequency in the two batches. After quality control, the material consisted of 97,185 markers and 731 dogs, where 407 were controls and 324 cases, 442 female (206 cases) and 289 males (118 cases); Seventy-six dogs (16 cases) were genotyped on the 170 k array, and 655 dogs (308 cases) were genotyped on the 220 k array. Mean age of last eye examination in the controls was 2.7 years and 1.9 years in the cases.Quality control was performed in Plink 1.9 , 43 and There is no data on the current population size of SBT in Norway. According to NKK there is around 1000 new registrations of SBTs every year. Between 2005 and April 2022, NKK had registered 1481 imported dogs from 30 different countries, the majority imported from Sweden , followed by the United Kingdom (10.05%). Among 2339 litters registered during the same time period, 1493 (61.52%) litters were of mattings with at least one parent from another country. Population structure attributable to country of origin (according to pedigree number), was assessed using principal components analysis conducted in Plink. Plots were constructed in R using the R package ggplot2 . In addiThe dataset contained 72 families with offspring, and both parents genotyped with an equal number of affected and unaffected offspring. The total number of genotyped full siblings was 220, distributed on 96 different litters. A GWAS excluding 79 dogs with an equal affection status as another litter mate was conducted.–blocks function. Plink uses the haploblock definition suggested by Gabriel et al. [indep-pairwise with the options; window size: 50, step size: 5 and r2: 0.2. The mean heterozygosity rate was estimated in Plink using the –het function.Linkage disequilibrium (LD) and haploblock size were estimated using Plink 1.9 , 43 and l et al. . To ideny = a vector of the phenotypes, μ is the general mean term, X is a matrix of the genotypes, and a a vector of SNP effects, and e is a vector of residual errors [N . BayesR l errors . BayesR β the fixed additive genetic effect of the SNP considered in the analysis, X = genotype of the SNP coded as 0, 1 and 2 for homozygous, heterozygous and opposite homozygous, respectively, g is the random effect of background genes assumed distributed as g ~ N, and e is the residual error. We have used two significance levels, one using Bonferroni correction according to number of all SNPs in the dataset (0.05/94697 = 5.28 × 10–07). Bonferroni correction based on all SNP markers is often considered over-conservative because SNPs are in LD and not independent. Therefore, we calculated a second significance level in accordance with the number of independent SNPs after pruning the data (0.05/4032 = 1.24 × 10–05). Manhattan plots were made in R with the qqman package [In addition, a traditional mixed linear model-based association analysis (MLMA) was run in Genome-wide Complex Trait Analysis (GCTA) version 1.93.2 , 48. A r package .The genomic position on the arrays was given in CanFam3.1 . To convA t-test in R was used to assess if there was a significant difference in the mean risk allele load between cases and controls . Haplobly is a vector of the phenotypes, α the mean term, g is the genetic value and ε the residual error. The values of g and e were estimated from the formulas: ĝ = VgAV−1y and ê = VeV−1y, where A is the GRM, Vg the genetic variance and Ve residual variance and V = A*Vg + I*Ve is the variance matrix of the records y [Two approaches were used to predict the dogs' genetic value (GV) based on whole genome SNP markers. Genomic best linear unbiased prediction (GBLUP) calculated in GCTA  and a Baecords y , 48. Theecords y  to compuWe compared the odds of developing distichiasis in the 25% of dogs with GV predicted to be most likely to develop the disease, with the odds of developing distichiasis of the 25% of dogs with GV least likely to acquire distichiasis.Additional file 1.


Triticum aestivum (N = 149 measured in two years in two different fields). We replicated one known locus associated with the concentration of Zn (IAAV1375). We identified four novel loci—BS00022069_51 (associated with concentrations of Ca and K), RFL_Contig6053_3082 (associated with concentrations of Fe and Mn), Kukri_rep_c70864_329 , and IAAV8416 (associated with concentrations of Fe and Mn)—three of them were located near the genes TraesCS6A02G375400, TraesCS7A02G094800, and TraesCS5B02G325400. Our result adds novel information on the loci involved in wheat grain element contents and may be further used in genomic selection.Wheat is a cereal grain that plays an important role in the world’s food industry. The identification of the loci that change the concentration of elements in wheat seeds is an important challenge nowadays especially for genomic selection and breeding of novel varieties. In this study, we performed a multivariate genome-wide association study (GWAS) of the seven traits—concentrations of Zn, Mg, Mn, Ca, Cu, Fe, and K in grain—of the Russian collection of common wheat  Wheat is a cereal grain that plays an important role in the world’s food industry. Wheat lines have been studied using many genomic approaches, such as genome sequencing and resequencing ,2,3), trOne of the important components of wheat seeds are micronutrients, such as calcium, zinc, and magnesium. Their concentrations are of special interest for genomic selection studies due to their role in grain food value. Micronutrient deficiency, also known as hidden hunger, is a result of food intake with low concentrations of micronutrients and vitamins . It may The identification of the loci-changing element concentrations as well as genomic selection for concentrations is an important goal in agronomy nowadays. In this respect, special attention is to be paid to local varieties, which are adapted to particular climatic and geographical conditions of the place where they are grown. Russia ranks first in wheat exports in the world (FAOSTAT); therefore, studying Russian wheat lines and searching for the loci important for wheat quality leads to improving the food situation in the world. Until now, there has been only one study on spring Russian varieties  where spMultivariate analysis allows to increase the power of analysis and to inspect different relations between traits, especially for polygenic traits when one locus may have an opposite effect on different traits of interest. For instance, a certain locus may positively affect the concentration of one element and negatively affect the concentration of another. This approach is widely used in animal breeding and human genetics but not widely used in plant genomics ,22,23,24Triticum aestivum. Seven traits that are the concentration levels of Zn, Mg, Mn, Ca, Cu, Fe, and K in grain were studied. As multivariate analysis allows for the investigation of combinations of traits, it becomes useful in breeding because it enables researchers to avoid the cases when one concentration increases and another decreases.In this study, we performed a multivariate genome-wide association study on the dataset of the common Russian wheat lines Triticum dicoccum, Triticum timopheevii, and Triticum kiharae). Introgression lines were developed on the base of hybridization of spring bread wheat varieties with wild relatives  and synthetic hexaploid wheat T. kiharae. More information on the origin of accessions including a list of plant material is available in [A panel of 157 common spring wheat genotypes was used in this study. The panel consisted of 105 bread wheat varieties, 48 introgression lines (ILs), and 3 wheat relatives (lable in .www.traitgenetics.com (accessed on 1 August 2023)). Genomic DNA was isolated from 5–7 day-old seedlings as described in . DNA purThe dataset of 157 wheat lines, which included 13,007 SNP markers, was mapped on the wheat genome . The genThe chromosomes 1A, 1B, 1D…7A, 7B, and 7D were labeled as 1, 2, 3, …, 19, 20, 21, respectively. The “Unknown” chromosome was labeled as 22. The distribution of SNPs along chromosomes  shows thPCA analysis was performed using plink , and PCWheat genotypes were planted on the field of the Federal Research Center Institute of Cytology and Genetics of Siberian Branch of the Russian Academy of Sciences  in 2018 and 2019. The experiment was conducted using randomized block design in two replicates on plots of 1 m width, 80 grains per row, and between-row spacing of 25 cm. Samples were sown in the second half of May and harvested in September in the phase of full grain ripeness. The soil of the experimental field consisted of leached chernozem; the fertile soil layer varied within 40–60 cm, and the humus content was 4.2%. The contents of nitrogen, phosphorus, and potassium were 0.34%, 0.30%, and 0.13%, respectively.The growing season of 2018 was characterized by low temperatures in May  and high waterlogging in May–June. The weather conditions in 2019 were unstable due to uneven precipitation and temperature fluctuations in the second half of the growing season. Rainy weather was observed in May and July of 2019, and a slight drought was observed in June and August. Randomly selected 20 spikes were harvested and bulk-threshed manually, and 10 g of grain from each genotype was used for evaluation of seven macro- and microelements . The element contents were measured by atomic absorption spectrometry with flame atomization on a ContrAA 800 D instrument . Statistical processing of the results was performed using the Statistica v.10.0 software package.2) was calculated using REML analysis implemented in GCTA (v1.94) [We used data on the concentration of seven different elements in grain—Zn, Mg, Mn, Ca, Cu, Fe, and K—measured consequently in 2018 and 2019 for each wheat line. There were two replicas for each line and year for a total of 157 × 2 × 2 phenotypic measurements. For each element measurement in each year, we removed values outside the range of 3 IQR. Visualization and descriptive statistics were performed in R. Heritability (h (v1.94) . ProportAs for each wheat line the phenotypic information about two replicates for each year was available, we represented each line as a “twin pair”, where genotypes were the same while phenotypes were different related to each of the samples. Thus, we performed GWASs using 149 × 2 lines for each year. This approach gives an opportunity to increase the sample size and consequently the power of the study. GWAS analysis for each year and concentration level of the element was performed using mixed model approach implemented in GCTA (v1.94) ) fastGWAWe meta-analyzed the results for each concentration for two years to obtain a total sample size of 149 × 2 × 2 lines. Z-based sample-size-weighted meta-analysis was performed using the METAL (version 2011-03-25) . As the next step, we combined different concentration levels into four multivariate traits and performed a multivariate GWAS. Multivariate traits clusters were formed based on the correlation matrix between traits : (1) Ca p-value threshold for meta-analysis and multivariate analysis was set at 4.01 × 10−7. This value was obtained by the formula ), where 11,334 is the number of SNPs used in the analysis, 7 is the number of univariate GWASs, and 4 is number of multivariate GWASs. Lambda values for each GWAS analysis were calculated in R. Variance of the elements explained by each significant SNP was estimated using p-value and number of genotypes involved in meta-analysis for traits. We used function qchisq  in R to obtain a chi-square value with df = 1 and then divided it by the number of genotypes. The result was multiplied by 100 to receive variance of the elements explained by SNP in percent. The For QQ plots, R library qqman (v.0.1.8) was used, and for Manhattan plots and phenotypic correlation matrix , we used R libraries RcolorBrewer (v.1.1.3), corrplot (v.0.92), qqman (v.0.1.8), and dplyr (v.1.1.1). The gene nearest to a significant loci was determined with the EnsemblPlants genome browser  using trp-values for these loci obtained from the meta- and multivariate analyses were significant; i.e., p-value was below 0.05/35, where 35 is the number of significant loci from the papers.We compared results from meta-analysis for each element and from clusters of multivariate analysis with already published results from ,13. FirsWe checked the distributions of the element concentration levels for each year . Some of2) were moderate almost for all traits  with a good resemblance of estimates between years  with the level of K. We performed a GWAS for each element and each year separately with the consequent meta-analysis of the GWAS for two years. In total, seven univariate GWAS results were obtained. Genomic control lambdas varied from 0.98 to 1.12 , with thAs the next step, we performed the analysis of four multivariate traits. The joint results for the univariate and multivariate GWASs are presented in Four SNPs were significantly associated with at least one multivariate trait, with one of the SNPs (BS00022069_51) significantly associated with K levels. For more details, see We used the EnsemblPlants genome browser  to invesp-value in meta-analysis 0.001065, in multivariate analysis in cluster Mg and Zn—0.0029 and in cluster with all elements—0.0020) discovered in [We replicated one known published locus associated with the Zn concentration  and discovered four novel loci that reached the stringent Bonferroni-corrected genome-wide significance. One of these loci was detected both in the meta- and multivariate analyses, while three others were detected in the multivariate analysis only. The know replicated locus IAAV1375 is located on the chromosome 5A, position 502220915 inside the intron of TraesCS5A02G291300 protein-coding gene. According to , this ge2 = 0.09, D’ = 0.51). We can speculate that this chromosomal region  may contain a cluster of genes related to the regulation of element concentrations.Interestingly, according to the novel wheat genome assembly (RefSeq v1.0), locus IAAV1375 is located on the same chromosome as locus BS00022069_51, which was found in our discovery analysis. These loci are relatively distant from each other (79.3 Mb). However, they exhibit a small but significant nonzero linkage disequilibrium , we specified positive alleles associated with concentrations of elements. For the BS00022069_51—allele, C was positively associated with the K concentration; for the RFL_Contig6053_3082—allele, T was positively associated with concentrations of Fe and Mn; for the Kukri_rep_c70864_329—allele, T was positively associated with the concentration of Fe and allele C was positively associated with the concentration of Ca; for the IAAV8416 allele, T was positively associated with the concentration of Mn; and for the IAAV1375 allele, C was positively associated with the concentration of Zn. Multivariate analysis indeed increases the power of analysis. This approach is widely used in animal and human genetics but not in plants ,22,23,24To conclude, we performed a multivariate genome-wide association study of seven concentrations of elements and discovered four new loci and replicated one known.