High genetic variation in two European maize landraces can be harnessed to improve Gibberella ear rot resistance by integrated genomic tools.Fusarium graminearum (Fg) causes Gibberella ear rot (GER) in maize leading to yield reduction and contamination of grains with several mycotoxins. This study aimed to elucidate the molecular basis of GER resistance among 500 doubled haploid lines derived from two European maize landraces, “Kemater Landmais Gelb” (KE) and “Petkuser Ferdinand Rot” (PE). The two landraces were analyzed individually using genome-wide association studies and genomic selection (GS). The lines were genotyped with a 600-k maize array and phenotyped for GER severity, days to silking, plant height, and seed-set in four environments using artificial infection with a highly aggressive Fg isolate. High genotypic variances and broad-sense heritabilities were found for all traits. Genotype-environment interaction was important throughout. The phenotypic (r) and genotypic  contains supplementary material, which is available to authorized users. Fusarium graminearum, F. verticillioides, Aspergillus flavus, and/or Stenocarpella maydis are global threats to maize production. In Germany, a recent survey on the prevalence of Fusarium species showed that F. graminearum (Fg) and F. verticillioides (Fv) were dominant, their relative occurrence depending on temperature and humidity  causes Gibberella ear rot (GER) which reduces the quantity and quality of maize kernels and more importantly, contaminates the grains with mycotoxins such as deoxynivalenol (DON) and zearalenone (ZON) (Trail Ear rot infections caused by N) Trail . These mN) Trail . EmpiricN) Trail . BecauseN) Trail . An inteIn maize, GER resistance is inherited quantitatively with mostly small-effect quantitative trait loci (QTLs)  offers an attractive alternative to conventional or marker-assisted selection  and genomic selection (GS). Specifically, we aimed to (1) estimate variances and covariances for GER severity and the agronomic traits, days to silking, plant height, and seed-set, (2) map QTLs using a multi-SNP GWAS model based on the markers from a 600 k SNP array, and (3) compare the prediction accuracies of MAS and two GS approaches for GER severity. Therefore, 250 DH lines from each landrace were artificially infected by F. graminearum (Fg) isolate, FG 163 , plant height , seed-set , and GER severity (%) were recorded in all 4 environments (= location × year combinations). Briefly, days to silking were recorded as the number of days taken to achieve ≥ 50% female flowering per plot. PHT was measured plotwise from ground level to the first tassel branch using a meter rule in cm. At physiological maturity (about 18–20% grain moisture), we manually dehusked each ear and assessed visually seed-set as the proportion of kernels per cob (%), where 0% = no kernels on the cob and 100% = cob fully covered with kernels. GER severity was visually assessed on the same ears on a quantitative scale from 0 to 100%, where 0% = no Fg mold visible and 100% = entire ear covered with Fg mold.Plant materials consisted of a panel of 500 DH lines produced from two European flint landrace populations, KE and PE by KWS SAAT SE & Co. KGaA, Einbeck, Germany. We phenotyped 250 DH lines per population plus 10 checks  in 2018 and 2019 at Hohenheim (HOH) near Stuttgart, Germany, and at Gondelsheim (GON) near Karlsruhe, Germany. The DH lines represent a random sample of the DH lines described by Hölker et al.  were estimated by standard procedures described by Hallauer et al. (r) between BLUEs of traits were estimated using the function “cor.test” in R programming language  were previously genotyped using a high-density Affymetrix® Axiom® Maize Genotyping Array optimized for temperate maize  was carried out by the default method in the R package Genome Association and Integrated Prediction Tool (GAPIT) 3.0 : Step 2. Random Effect Model (REM): The BLUEs and the high density filtered SNPs were used to perform GWAS for GER severity (%), DS (days), PHT (cm), and SS (%), employing the multi-locus-based method, FarmCPU  ≤ 4) and Bonferroni-corrected threshold of  = 6.89). The total proportion of genotypic variance  based on the positions of two most important SNPs explaining > 5% of genotypic variance for GER resistance in KE  2 and ZmSYNBREED_53695_527 on chr. 6). Descriptions and ontology terms of genes located within ≤ 1 cM (approx. ≤ 250 kb) around the SNPs .We searched for possible genes for GER resistance using the publicly available B73 reference genome version 4  and weighted ridge regression-BLUP (wRR-BLUP) using the R package “rrBLUP” Endelman . In wRR-The quality of prediction of these models was evaluated by cross-validation using 80% of the data as training set (TS) and the remaining 20% as validation set (VS)  (Table P ≤ 0.0001) genotypic and genotype–environment interaction variances and high H2 estimates for all traits (Table 2 was higher for KE than PE for most traits. Phenotypic and genotypic correlations between GER severity and DS and SS were significant (P ≤ 0.01) in most cases but very low and similar for KE and PE . The first, second, and third PCs explained 16.75%, 3.36%, and 3.25% of the molecular variation, respectively. Within KE, the percentage of variation explained by the first three PCs were 7.27%, 4.41%, and 4.16%, respectively. Similarly, among PE lines, the first three PCs explained 8.56%, 5.22%, and 3.47% of the molecular variation, respectively. The genomic relationship plot also showed two major groups corresponding to KE and PE landraces, with smaller sub-clusters within each landrace .N = 236), at P = 0.0001, 8 QTLs collectively explaining 34% of Among KE DH lines N = 26, at P =N = 226), no QTL were detected for GER severity at P = 0.0001 . Ten QTLs were detected for DS and PHT while one QTL was detected for SS in PE .For PE , MAS and RR-BLUP yielded similar VS = 181, NVS = 45), the mean We evaluated the prospects of MAS and GS for GER resistance. For KE, we used the two SNPs explaining > 5% Over the past decade, only few phenotypic and molecular studies have been conducted on GER resistance in elite maize germplasm compared to wheat . Genotypes flowering late had reduced seed-set, but this might have been caused by unusually low rainfall and higher temperatures towards the end of the silking period in 2018 and partially also in 2019 rather than by inbreeding depression. PHT is also affected by inbreeding depression, but the association between PHT and SS was not significant  and a high-density marker array . As the complexity of a trait highly affects the outcome of the molecular analyses . In GWAS, strong linkage disequilibrium (LD) between a marker and a QTL allele is required to detect minor-effect QTLs , and ZmSYNBREED_53695_527 (chr. 6) detected for GER resistance in KE, were associated with candidate genes which code for proteins belonging to families like cytochrome P450, mitogen-activated protein kinase kinase kinase (MAP3Ks), serine/threonine kinase, tetratricopeptide repeat (TPR)-like superfamily protein, leucine-rich repeat (LRR) family protein and armadillo (ARM) repeat superfamily protein. They are associated with functional groups such as binding activities, kinase activity, response to stress/stimulation, signal transduction, catalytic activity, metabolic and biosynthetic processes (Supplementary Table 4). Similar functional categories were reported for differentially expressed genes for Arabidopsis  performing GWAS for QTL detection and (2) establishing the GS model with 80% of the population in the training set, and application of the model to the remaining 20% of the population. The unweighted GS approach is a possibility when most of the low-effect QTLs underlying a trait cannot be detected by a GWAS model like in PE.In practice, independent TS and VS are used for GS. However, in this study, we simulated the prospect of MAS and GS in the same experimental material using a fivefold cross-validation procedure  into elite flint lines to increase GER resistance in this heterotic group. The BC lines should be subjected to testcrossing for selecting maturity, further adaptation traits, and finally grain yield. A subsequent selection for GER resistance on testcross basis could be beneficial, because the correlation between line and testcross performance for this resistance trait has been shown to be only moderate Supplementary file2 (XLSX 15 kb)Below is the link to the electronic supplementary material.


Theobroma cacao L.), the source of chocolate, is one of the most important commodity products worldwide that helps improve the economic livelihood of farmers. Diseases like frosty pod rot caused by Moniliophthora roreri and witches’ broom caused by Moniliophthora perniciosa limit the cacao productivity, this can be solved by using resistant varieties. In the current study, we sequenced 229 cacao accessions using genotyping-by-sequencing to examine the genetic diversity and population structure employing 9,003 and 8,131 single nucleotide polymorphisms recovered by mapping against two cacao genomes (Criollo B97-61/B2 v2 and Matina 1-6 v1.1). In the phenotypic evaluation, three promising accessions for productivity and 10 with good tolerance to the frosty pod rot and witches’ broom diseases were found. A genome-wide association study was performed on 102 accessions, discovering two genes associated with productivity and seven to disease resistance. The results enriched the knowledge of the genetic regions associated with important cacao traits that can have significant implications for conservation and breeding strategies like marker-assisted selection.Cacao ( Theobroma cacao L (cacao) is an economically important perennial crop. It was originally domesticated from wild ancestors in Central America  commonly known as monilia that invades growing pods (Moniliophthora perniciosa that causes witches’ broom disease (WBD); this disease produces hypertrophy and hyperplasia of distal tissue forming abnormal stems, such as a flower cushion broom and deformed branches  ing pods , and 2) Moniliophthora spp.The development of resistant or high-yielding varieties could be accelerated by using marker-assisted selection (MAS), which refers to the selection of individuals based on molecular markers associated with quantitative trait loci (QTL) that control defense mechanism resistance . During However, these studies have two disadvantages. First, the use of bi-parental populations produce a low mapping resolution because only a limited number of recombination events can be evaluated . The secT. cacao. The aims of this work were: 1) to assess the genetic diversity and population structure; 2) to characterize Agrosavia’s diverse collection of cacao germplasm with regards to productivity and disease resistance to FPRD and WBD; 3) to identify marker-trait associations, and finally 4) to identify genomic regions that have undergone selection.Our study is the first to explore GWAS using GBS data to identify new SNP variants distributed across the genome and associate them with important agronomic traits in Cordia gerascanthus L. and C. alliodora L. were distributed in lanes every 12 m to provide shade. The accessions selected were those that represented the genetic diversity of the four clusters reported by A total of 229 cacao accessions from the national germplasm bank located at the research center C.I. La Suiza  of Corporación Colombiana de Investigación Agropecuaria (Agrosavia) (previously known as Corpoica) were used (Table S1). Agrosavia s collection is a representation of the diversity of the species and conserves not only native materials but also improved materials (BsaXI ((N)9AC(N)5CTCC(N)10) (New England Biolabs) and CspCI ((N)10-11CAA(N)5GTGG(N)12-13) (New England Biolabs), with 2.0 units each according to the library preparation protocol proposed by Genomic DNA of the 229 accessions was extracted from young leaf tissue using the DNeasy Plant Mini Kit . The DNA concentration and quality were estimated using a Qubit Fluorometer v2.0  and by electrophoresis on 1% agarose. Genomic DNA was digested using the enzymes BsaXI and CspCI enzymes in the two reference genomes  over an intermarker genetic distance with a threshold value of 0.1.The observed (Ho) and expected heterozygosity (He) were obtained with Cervus v3.0.7 .yi refers to the counting of the disease in the nth observation, ti is the time in the nth observation, and n is the total number of observations.With the resulting data, the area under the disease progress curve (AUDPC) for all harvest periods was calculated using the formula proposed by Healthy pods (productivity).AUDPC of pods infected by FPRD.AUDPC of flower cushion broom caused by WBD.AUDPC of deformed branches caused by WBD.The results were compared among the accessions through an analysis of variance (ANOVA). Correlations among traits were calculated using Pearson’s correlation coefficient (r) at P ≤ 0.05. All statistical analyses were performed in the R software.The following four variables were evaluated:Furthermore, we conducted a principal component analysis (PCA) with the prcomp function of the R software using the AUDPC values for each genotype. Then, a cluster analysis using the Ward method and Euclidean distance was conducted in the R package factoextra using the two first components of the PCA.y is the vector for phenotypes, a is the vector of marker fixed effects, b is a vector of fixed effects, u is the vector of random effects (the kinship matrix), and e is the vector of residuals. Moreover, X denotes the genotypes at the marker, Q is the Q-matrix, and Z is an identity matrix.A GWAS was performed for healthy pods (productivity) and AUDPC of the two diseases with the Genome Association and Prediction Integrated Tool (GAPIT) software package (10(p) values for each trait.The Q-matrix was determined previously by the admixture v1.3 software, and the K-matrix was calculated using GAPIT with the Loiselle method. The quantile-quantile plots (Q-Q plots) were constructed to validate the appropriateness of the MLM. Further, Manhattan plots were generated using the −logThe site frequency spectrum and the LD pattern between polymorphic sites were calculated with the SweeD , respectin silico analysis of the restriction enzymes. Figure S2 presents the number of sequenced reads of each accession. Figure S3 shows the correlation among traits of the entire population. Figure S4 contains the QQ-plots for each evaluated trait. The R script for the detection of common outliers in selective sweeps is available at http://pop-gen.eu/wordpress/wp-content/uploads/2013/12/combined_analysis.zip. The raw sequencing data of the reference samples were downloaded from the BioProject PRJNA486011 . Genotyping the 229 accessions without missing data yielded a total of 9,003 SNPs using the Criollo genome as reference, whereas 8,131 SNPs were found with the Matina genome. The distance between SNPs markers ranged from 31.1 kb (chromosome 10) to 43.3 kb (chromosome 8), with an average of 39.8 kb.in silico digestion analysis showed a substantially higher number of fragments with optimal sizes for sequencing in an Illumina system (200 to 700 bp) in the Criollo genome  compared to the Matina genome . The number of base pairs was slightly lower in Criollo (8.5 million bp) compared to the value found in Matina (9.1 million bp) . Finally, the number of sequenced fragments produced per accession was lower in Matina compared to those predicted in silico for Criollo .The Values for He and Ho of the 229 cacao accessions were slightly higher with the dataset generated using the Criollo genome as a reference (Ho = 0.350 and He = 0.317) compared with the dataset generated using the Matina genome (Ho = 0.316 and He = 0.314). Medium to high levels of genetic diversity and an excess of heterozygosity were found in this collection.2r = 0.1)  . The aveThe analyses were carried out with 3,712 SNPs that were successfully intersected between the reference panel of 69 fully sequenced genomes and the dataset generated in this study. PCA analysis showed that the first two components provided the most information and accounted 52% of the total variation, where each component explained 35%, and 17% of the overall variation, respectively. The most differentiated group was the Criollo genetic group and the rest were not fundamentally genetically distinct from each other, as the accessions were distributed evenly among the first two PCs .e.g., IMC-67 (Iquitos), C58 (Amelonado), SCA-6 (Contamana)), and correctly assign the ancestry of previously uncharacterized accessions  and infected pods  was found. The percentage of pods that reached harvest was only 20.77%, whereas 79.23% of the pods were affected by FPRD. No pods infected by WBD were found during the harvest periods.Correlations between phenotypic variables ranged from 0.12 to 0.54 and are shown in Figure S3. The WBD variables (flower cushion broom and deformed branches) were also positively correlated (0.54). In contrast, symptoms of FPRD and WBD diseases are weakly correlated. Finally, as expected, disease variables did not show a strong correlation with the productivity trait considered .P ≤ 0.001), suggesting that the accessions presented in the collection are highly diverse.Mean, standard deviation, and coefficient of variation of the phenotypic data are shown in According to the analysis of the four harvest periods, the highest number of healthy pods was produced by the accessions GS-29 (279 pods), FCM-39 (208), and EET-8 (165) (Table S2). The genotypes CRICF-13, EBC-06, EBC-09, and SUI-72, were less affected by FPRD, as shown by the AUDPC values (Table S2). The floral cushions of genotypes EET-377, SCC-85, SCC-86, and UF-273 were less affected by WBD. The genotypes EET-377 and SCC-85 also showed the lowest number of branches affected by WBD, as well as the genotypes SUI-99 and FCM-19 (Table S2).Based on the conglomerate analysis, the cacao accessions were divided into three main groups with different levels of productivity and susceptibility/resistance responses to WBD and FPRD . The firP ≤ 0.005) and suggested that the population structure was adequately controlled in the GWAS model .The associated SNPs were distributed in five of the 10 cacao chromosomes . The Q-Q10(p)-values for the SNP markers according to their positions in each chromosome are shown in Manhattan plots showing the logi.e., S1_27025499, identified with the Matina genome. A second SNP located in the gene Thecc1EG003830 was identified using the Matina genome  reduced variability regions, and ii) local patterns of high linkage disequilibrium. The highest number of genes under positive selection was identified in chromosome 3 (39), and the lowest number was found in chromosome 1 (5) (Table S4). The genes under selection in chromosome 1 allowed identifying a selective sweep on a region comprised between 11.1 and 22.6 million base pairs (Mbp), in which the previous associated gene Tc00cons_t021470.1 was located . The regThe main objective of the cacao breeding program is to accumulate favorable alleles for productivity and disease resistance . For thiPstI and ApekI  and to confirm that this was suitable to study the cacao genome, we performed an in silico digestion using the two reference genomes and recovered 3 million bp more than the study of PstI and MseI. The results of the current study indicated the suitability of using a double-digestion for GBS libraries for cacao employing CutSmart enzymes (BsaXI and CspCI).Originally, GBS used single-enzyme digestion of frequent cuts like The genetic analysis showed that the cacao population conserved in the germplasm bank of Agrosavia has a medium to high level of genetic diversity (Ho = 0.350 and He= 0.317), that is equivalent to other studies assessing cacao diversity (2r = 0.1) indicated that the LD blocks were small, which was expected considering the out-crossing nature of the species. In contrast, 2r = 0.1), possible due to the use of SSRs. The LD 2r average of our study was 0.255, higher than values reported by 2r values of 0.147 and 0.188, respectively), but lower compared than the population Malvinas . Distinct methodologies, number of markers, population sizes, genetic origins, and standard errors among the studies may account for the different results.High-density SNP mapping facilitates understanding the genetic determinants of complex traits in GWAS. In the current study, the genome-wide LD pattern was explored using 9,003 SNPs, which provided a valuable resource for association mapping. In our association panel, the LD decay at ∼320 bp using a sliding window of 0.5 kb  was found between flower cushion brooms and deformed branches infected both by WBD. It has been observed that One important goal of this study was to identify accessions in the germplasm collection with excellent productivity or disease resistance response to FPRD and WBD. The evaluation during the four harvest periods allowed identifying three accessions with the highest productivity, GS-29 (279 pods), FCM-39 (208), and EET-8 (165) (Table S2). The accession GS-29 from Grenade was previously identified as a high productivity clone . The EETMoniliopthora roreri (M. roreri (Table S2). Accession SUI-72 has additional qualities reported by As expected, the incidence of FPRD was elevated because the germplasm collection is conserved at the research center La Suiza, located in the Magdalena Valley region of Colombia, considered the diversity center for During the four harvests carried out in this research, differences were observed in the total number of healthy and infected pods. Besides, the incidence of WBD was significantly lower compared to FPRD. These results are probably because in Colombia, during 2016 and 2017, the “El Niño” phenomenon caused adverse conditions for the production of pods and the development of these diseases that require highly humid conditions and rain all year round for its fast spread and survival . The evaThe accessions that presented the highest values for the evaluated variables were highlighted in the conglomerate analysis , in whicBesides, the comparison of the admixture and the conglomerate analyses  allowed P ≤ 0.05) before the FDR correction; however, only the associations for flower cushions with WBD were maintained significant after the correction. The non-significant p-values using the FDR correction is possibly due to the reduced sample size used in this study. QTL and association studies are often limited by the relatively small population sizes mapped, resulting in low statistical power and thus, rendering small- or even medium-effect QTL that are statistically non-significant and difficult to detect. Such statistically underpowered populations may also suffer from severe inflation of effect size estimates  (The associations found were significant ( effect) . Hence,  effect) . Althoug1 and F2 populations reported QTL for disease resistance to FPRD on chromosomes 1, 2, 5, 7, 8, 9 and 10 ; however, the SNPs were located in different genomic regions  gene family. In plants, RLKs show important roles in pathogen resistance induced after the activation of the recognition receptors of microbe-associated molecular patterns  related to ion channels (MLO) protein. The MLO is a gene family specific to plants and plays significant roles in the resistance to powdery mildew and response to a variety of abiotic stresses, plant growth, and development , but further studies are necessary to determine their role in plant defense.For WBD resistance response traits, three statistically significant genes were found. The first SNP was related to a G-type lectin S-receptor-like serine/threonine protein (GsSRK) that is associated with the receptor-like protein kinases  which describes the frequency of a beneficial mutation that shifts toward a high or low frequency derived alleles . The secThe chromosomes 1 and 3 showed the largest regions under selection comprising 5 and 39 genes, correspondingly. These genes were involved in different biological process; in particular genes Tc01v2_g016260, Tc03v2_g026440, and Tc03v2_g006130 could have an importance for pod productivity because they have a role in the ovule development (Our findings expand previous genetic mapping efforts and allow increasing the mapping resolution of the regions responsible for productivity and disease resistance traits. Leveraging this information could contribute to select promising accessions in juvenile stages in greenhouse conditions, and consequently, reduce breeding cycles.This research reported the first GWAS study of disease resistance trait for WBD and FPRD in cacao, based on SNPs produced by the GBS method. In total, two candidate genes related to productivity and seven related to disease resistance response to FPRD and WBD were detected. We also identified a total of 10 promising accessions that presented a degree of resistance response, and three accessions with promising productivity values. The current work provides new knowledge on genomic regions involved in the productivity and disease resistance response, which, after functional validation, could be useful in MAS for cacao breeding programs.


IVW = 9.25 × 10–5). The causal relationship was robust when sensitivity analyses were applied , and repeated analysis yielded consistent result (PIVW = 0.0087). Similarly, also dihomo-linoleate (20:2n6) and p-acetamidophenylglucuronide showed robust association with intelligence. Our study provides novel insight by integrating genomics and metabolomics to estimate causal effects of genetically determined metabolites on human intelligence, which help to understanding of the biological mechanisms related to human intelligence.Intelligence predicts important life and health outcomes, but the biological mechanisms underlying differences in intelligence are not yet understood. The use of genetically determined metabotypes (GDMs) to understand the role of genetic and environmental factors, and their interactions, in human complex traits has been recently proposed. However, this strategy has not been applied to human intelligence. Here we implemented a two-sample Mendelian randomization (MR) analysis using GDMs to assess the causal relationships between genetically determined metabolites and human intelligence. The standard inverse-variance weighted (IVW) method was used for the primary MR analysis and three additional MR methods  were used for sensitivity analyses. Using 25 genetic variants as instrumental variables (IVs), our study found that 5-oxoproline was associated with better performance in human intelligence tests (P Intelligence affects all aspects of human life . During Understanding the role of genetic characteristics and their interaction with environmental factors is the key to reveal the biological mechanisms underlying differences in human intelligence . CurrentMendelian randomization (MR) is a novel genetic epidemiology study design using genetic variants as instrumental variables (IVs) to investigate whether a modifiable exposure is causally related to a medically relevant disease risk . The fun. reported the most comprehensive exploration of genetic influences on human metabolism so far, by performing a GWAS of non-targeted metabolomics on 7824 healthy adults. [http://metabolomics.helmholtz-muenchen.de/gwas/).Shin et al adults. . Metabol−5, so as to account for a proportion as large as possible of the variance explained for the corresponding metabolite. We next performed a clumping procedure (linkage disequilibrium threshold of r2 < 0.1 within a 500-kb window) to select the independent SNPs using the PLINK software (v1.9). To avoid the negative impact of weak IVs, we further used the proportion of variation explained by each IV (R2) and the F statistics to select SNPs strong enough to be valid IVs. Typically, an F statistic > 10 is considered sufficient for MR analysis [The foundational principle of MR relies on the existence of valid IVs. A genetic variant is a valid IV if it is (i) significantly associated with the exposure, (ii) independent of confounders, and (iii) associated with the outcome only through the exposure . To idenanalysis .. [g). Although differences in assessment methods might reduce the power to detect associations in meta-analyses, this approach can at the same time reduce type I errors by removing measurement errors, and therefore identify SNPs with robust associations to the common latent factor underlying intelligence across different methods. Stringent quality control procedures were applied to the summary statistics for each cohort. Association analysis was conducted controlling for covariates of age, sex, genotyping array, socioeconomic status for specific cohort, and twenty European-based ancestry principal components. Finally, a total of 9,295,118 SNPs were included in the meta-analysis.GWAS summary statistics for intelligence were obtained from the study by Savage et al. . Briefly–4 (= 0.05/486). Associations with P < 0.05, but not reaching the Bonferroni-corrected threshold, were reported as suggestive of association.Primary two-sample MR analyses were performed using the standard inverse-variance weighted (IVW) method. The IVW method provides a consistent estimate of causal effects by combining the ratio estimates of each variant in a fixed-effect meta-analysis model . The P-vI2 and the Cochran Q test. Typically, I2 > 25% or Cochran Q-derived P < 0.05 were used as indicators of possible horizontal pleiotropy. Analyses were carried out using the packages MendelianRandomization and MR-PRESSO in R (version 3.6.1).The IVW method provides an unbiased estimate under the assumption that all genetic variants are valid IVs. However, this assumption is easily violated, leading to inaccurate estimates, when horizontal pleiotropy occurs (some variants act on the outcome via a different intermediary) . To avoi. reported another GWAS for intelligence with a larger sample size, but the summary data for full dataset is not available due to data permissions [We next used GWAS datasets of four other related outcomes to replicate the findings of our MR estimates. The first dataset was obtained from another GWAS of intelligence with 248,482 samples from the UK Biobank . Summarymissions .https://www.metaboanalyst.ca/) [IVW < 0.05). Two libraries of metabolic pathways or metabolite sets were selected for enrichment analysis, namely the Small Molecule Pathway Database  [https://www.kegg.jp/) database [Metabolic pathway analysis was carried out using the web-based tool suite MetaboAnalyst 4.0 (yst.ca/) . For thimpdb.ca)  and the database . P-valueIVW = 9.25 × 10–5). Using 25 SNPs as proxy, we observed a 0.24 increase in the score of the Spearman’s g test for an increase of one standard deviation (SD) in the level of 5-oxoproline . We also found 15 other metabolites to be suggestive for association, including indolelactate , mannitol , and 2-oleoylglycerophosphocholine .We selected 3–675 independent genetic variants as IVs for each of the 486 metabolites , and no horizontal pleiotropy was observed . Two other metabolites also showed robust associations with intelligence, namely dihomo-linoleate (20:2n6)  and p-acetamidophenylglucuronide , and there were no evidence of horizontal pleiotropy  and PIntercept =0.06, PGlobal test = 0.06, I2 = 17%, PHeterogeneity = 0.13 for p-acetamidophenylglucuronide; Table IVW = − 0.14; 95% CI − 0.25 to − 0.04), while the association between p-acetamidophenylglucuronide and intelligence was positive . The causal association between 5-oxoproline and human intelligence is shown on Fig. Table IVW = 0.0087) to the result of initial MR estimates, and the causal associations were robust when different methods were performed . The results also showed that 5-oxoproline was significantly associated with cognitive performance . However, no evidences for association were found between 5-oxoproline and educational attainment , as well as income . Besides, the effects of dihomo-linoleate (20:2n6) and p-acetamidophenylglucuronide on intelligence were also significant in the replication stage . Notably, it also showed a strong association signal with intelligence . Moreover, this SNP had the largest effect sizes on both 5-oxoproline and intelligence, suggesting that the related genetic locus might provide valuable information on the biological mechanisms of intelligence, and that 5-oxoproline might be an important functional intermediate to understand the biological process through which genetics affects intelligence. The IVs for dihomo-linoleate (20:2n6) and p-acetamidophenylglucuronide are shown in Additional file We further investigated the genetic variants that affected both metabolite levels and intelligence. Table Alpha linolenic acid and linoleic acid metabolism (P = 0.0062). Two metabolites identified by IVW, docosapentaenoate  and linolenate (18:3n3 or 6), are involved in Alpha linolenic acid and linoleic acid metabolism according to the SMPDB database. Importantly, many of the metabolites found by our analysis have not been assigned to any metabolic pathway currently recorded in the SMPDB or KEGG databases. Extensive further research will be needed to explore whether these metabolites are involved in biological processes relevant to differences in human intelligence.Table We implemented a two-sample MR analysis to assess the causal relationships between genetically determined metabolites and human intelligence. Using genetic variants as IVs, we found that the genetically determined levels of 5-oxoproline were associated with better performance in human intelligence tests. This causal association was not affected by confounders such as educational attainment and household income, and was well replicated using samples from other data source. Our study also identified other metabolites and metabolic pathways involved in biological processes related to human intelligence, such as dihomo-linoleate (20:2n6) and p-acetamidophenylglucuronide. To the best of our knowledge, this is the first study combining information from genomics and metabolomics to assess the causal effects of metabolome traits on human intelligence.l-glutamic acid that participates substantially in the glutamate and glutathione metabolism [5-Oxoproline, also known as pyroglutamic acid, is a cyclized derivative of tabolism . Disturbtabolism . Moreovetabolism , 37. Howtabolism . Our stuEXOSC4 gene) was the most significantly associated to both 5-oxoproline levels and human intelligence. Although rarely discussed in the past literature, EXOSC4 is known to be related to the protein kinase R (PKR)-like endoplasmic reticulum kinase , which regulates gene expression [EXOSC4 might be a causal risk gene participating in physiological processes important for human intelligence.Genetic factors played a central role in our study of the causal relationship between metabolic traits and intelligence. The SNP rs11986602  contributed substantially to the variation of metabolite levels and differences in human intelligence. Moreover, our findings suggest that alpha linolenic acid and linoleic acid metabolism might be involved in the biological processes underlying intelligence. Though further evidence from experimental data is needed, our study provides novel clues that would improve our understanding of the biological mechanisms related to human intelligence.In summary, our study identified multiple metabolites that might have causal effects on human intelligence, among which 5-oxoproline presented significant association signals after Bonferroni correction. The association was shown to be robust by sensitivity analyses. Our study also highlighted that genetic factors  on other intelligence-related outcomes from other data sources.Additional file 3: Fig. S3. Mendelian randomizationassociations of p-acetamidophenylglucuronide on other intelligence-relatedoutcomes from other data sources.Additional file 4:Table S1. Information of the instrumental variables used for Mendelian randomization estimates. Table S2. Unknown metabolites identified by the Mendelian randomization estimates. Table S3. Genetic predictors for dihomo-linoleate (20:2n6) and their association with Intelligence. Table S4. Genetic predictors for p-acetamidophenylglucuronide and their association with Intelligence.


Breeding for new macadamia cultivars with high nut yield is expensive in terms of time, labour and cost. Most trees set nuts after four to five years, and candidate varieties for breeding are evaluated for at least eight years for various traits. Genome-wide association studies (GWAS) are promising methods to reduce evaluation and selection cycles by identifying genetic markers linked with key traits, potentially enabling early selection through marker-assisted selection. This study used 295 progeny from 32 full-sib families and 29 parents (18 phenotyped) which were planted across four sites, with each tree genotyped for 4113 SNPs. ASReml-R was used to perform association analyses with linear mixed models including a genomic relationship matrix to account for population structure. Traits investigated were: nut weight (NW), kernel weight (KW), kernel recovery (KR), percentage of whole kernels (WK), tree trunk circumference (TC), percentage of racemes that survived from flowering through to nut set, and number of nuts per raceme.Seven SNPs were significantly associated with NW , and four with WK. Multiple regression, as well as mapping of markers to genome assembly scaffolds suggested that some SNPs were detecting the same QTL. There were 44 significant SNPs identified for TC although multiple regression suggested detection of 16 separate QTLs.These findings have important implications for macadamia breeding, and highlight the difficulties of heterozygous populations with rapid LD decay. By coupling validated marker-trait associations detected through GWAS with MAS, genetic gain could be increased by reducing the selection time for economically important nut characteristics. Genomic selection may be a more appropriate method to predict complex traits like tree size and yield. Macadamia integrifolia Maiden & Betche, M. tetraphylla L.A.S. Johnson and their hybrids have high-quality edible kernels, and are the first indigenous Australian food species to be commercialised internationally. The industry is largely based on cultivars developed in Hawaii in the late nineteenth century [2 ≈ 0.12), largely influenced by environment, and, as such, difficult to select [Macadamia is a large nut tree native to the coastal rainforests of southern Queensland and northern New South Wales, Australia.  century . Current century . A majoro select . To dateo select .The use of genomics in plant breeding is expanding –6, incluSeveral fruit and nut crops have employed GWAS to identify markers associated with key traits –18. FurtTarget traits for GWAS and potential MAS in macadamia include commercially important traits, such as nut and flowering characteristics, as well as tree size. Nuts consist of an inner edible kernel, with two cotyledons, which is enclosed by a hard shell (testa) and outer husk (pericarp) , 20. NutMacadamia trees can produce about 2500 pendant racemes 6–30 cm long, each with an inflorescence of 100–300 florets , 25. It O’Connor  investig10(x)) observations for NW, KW and NPR, as well as square root transformed observations for RSN appeared more normally distributed than raw observations , ln) nor square root transformations led to more normally distributed data, even for individual sites. This indicates that GWAS is not appropriate for yield, and association analysis was not performed for this trait.Raw (untransformed) phenotypes for KR, WK and TC were normally distributed Fig. . Log-trap < 0.01) were observed between young and mature phenotypes for NW, KW and KR  and another marker remained significant, but the unmapped SNP s2607 was redundant. The number of SNPs significantly associated with TC decreased to 16 after multiple regression analysis.Fifty-two of the 57 (91%) significant SNPs across the traits were mapped to scaffolds of the v2 macadamia genome assembly Table . Some man = 309, SD = 1.29), 8.20 g , and 9.54 g , respectively , 72.9% , and 62.3%  respectively  and AA/GG (p < 0.001) but not for AG/GG, and for WK at s0201 a significant difference existed between genotypes AA/GG and AG/GG (p < 0.001), but not AA/AG.The phenotypic  distributions across the three genotypic states were examined with boxplots for the most significant marker for NW and WK Fig. . The ave2 = 0.74) means that the aim to select for higher KR is achievable with truncation selection. This form of selection is where trees with phenotypes or estimated breeding values below a certain threshold are excluded from parent populations, and the mean values of progeny should increase for this trait over generations [Large phenotypic diversity was observed for many of the traits in this study. Average phenotypic values observed here for NW, KW and KR were all slightly higher compared with the same traits in the preliminary study when the trees were young . The moderations . Resultserations  which anThis study demonstrates that linear mixed models are useful for analysing phenotypic and genetic data in macadamia to identify QTLs for target traits, which is beneficial, as developing new macadamia varieties is time-consuming, laborious and expensive. Additionally, the large tree size and numbers involved in macadamia breeding means that multiple environments are typically needed during evaluation trials. The mixed models employed in this study account for the average effect of the environment, as well as G x E interactions for some traits. Thus, the best model was fitted to the data on a trait-by-trait basis.in preparation), and the location of scaffolds in the genome will be informative for determining locations of genes detected by SNPs in this study.The current study used 4113 SNP markers imputed with high accuracy, though analysis of LD using the same markers and population found that LD declined rapidly over short distances . The nump-values at lower significance levels. QQ plots showing this pattern demonstrate that population structure has been effectively accounted for by the GRM [p-values. The previous study [Population structure affects LD, and this needs to be accounted for in GWAS to avoid spurious associations and over-prediction of allelic effects. For most traits investigated here, the QQ plots showed that only the highly significant markers deviated from the null expectation (y = x line), and did not show inflation of the observed versus expected  the GRM . One exp the GRM . This geus study  did not MAS, using the findings of GWAS, is effective for traits controlled by few genes, and, as such, has little value for complex traits like yield –39. Howep-value in each case should be investigated in further studies. Neither NPR nor RSN had any significant associations, which may be partly due to the very low heritability of both traits. Additionally, while there was no G x E detected in RSN, there may be a large environmental influence on the capacity of a tree to retain racemes from flowering through to nut set [This study identified SNP markers significantly associated with NW, WK and TC. Although no significantly associated markers were detected for KW or KR, the marker with the lowest  nut set , 28.in preparation) may further aid the understanding of whether markers are in LD or are separate QTLs.For TC, 16 of the 44 significant markers were non-redundant, suggesting that there may be 16 QTLs controlling this trait. Multiple regression suggested that all of the the markers significantly associated with NW may have detected the same or linked QTLs, with the most significant SNP (s2204) being the only non-redundant marker. The location of scaffolds in linkage groups , and so candidate genes could be identified in future studies.When a more complete reference genome is assembled, the location of these markers can be determined, and LD between markers more accurately estimated with population structure and cryptic relatedness taken into account. Due to the rapid decay of LD over short distances in macadamia , using aAlthough there was a lack of significant associations in some traits in the current study, these should still be investigated in future work. The polygenic nature of TC, as well as the complexity of yield, means that these traits may be more suited for genomic selection, where many markers may have a small effect on the trait, and all markers are modelled simultaneously , rather The findings of this study have important implications for macadamia breeding, but also highlight the difficulties of employing GWAS in heterozygous populations with rapid LD decay. Significant associations were detected for NW, WK and TC, but no markers exceeded the significance threshold for KW, KR, RSN or NPR. The traits with significant SNPs identified are likely to be controlled by fewer genes than the other traits. Multiple regression determined that several significant markers were detecting the same QTL, and, as such, were redundant. By coupling validated marker-trait associations detected through GWAS with MAS, genetic gain could be increased by reducing the selection time for economically important nut characteristics and other yield component traits. Genomic selection may be a more appropriate method to predict complex traits like yield. This study provides a foundation for genomics-assisted breeding in macadamia and nut crops more broadly, and advances our understanding of the genetic control of yield component traits.Methods for association analysis are similar to those in a preliminary study by O’Connor et al. , and areThis study involved 295 seedling progeny from 32 full-sib families, as well 18 of their 29 parents (that were phenotyped), from the Australian macadamia breeding population. Trees were planted between 2001 and 2003 across four sites in Queensland, with East Gympie (EG) and Amamoor (AM) in the Gympie region, and Alloway (AL) and Hinkler Park (HP) in the Bundaberg region. Clones of five of the parents were measured at all four sites. Yield and yield component traits were measured on each tree between 2016 and 2018; hence, trees were mature-aged (aged 14–17 years). Details of genotyping methods for this population were reported in O’Connor et al. , which uPhenotypic data used in this study were collected across two seasons from August 2016 to July 2018, with all traits except RSN and yield measured only in one season. A sample of nuts was taken from each tree and dried to 1% moisture content in an oven at 35 °C for 2 days, 45 °C for 2 days and 55 °C for a final 2 days, based on protocol by Prichavudhi and Yamamoto . Twenty Tree trunk circumference (TC) was measured at a height of 50 cm above the ground, or below any low branches. Flowering racemes present in a 30 cm length of branch, 20 cm from the branch apex were flagged and counted on two branches per tree. Where necessary, trees with terminal racemes were also flagged and counted, to make a total of at least ten racemes per tree. At nut maturity , the number of flagged racemes that had set at least one nut was counted, and the percentage of racemes that survived from flowering through to nut set (RSN) was calculated. The number of nuts per raceme (NPR) was counted from ten racemes per tree. Component trait means were calculated for each tree for analysis where at least six observed units per tree were evaluated. For example, trees with five or fewer nuts measured were considered to have missing data for this trait. Mean RSN was calculated for each tree over the 2 years.Yield data were collected from March through to July over two successive seasons in multiple harvests. Yield was measured on each tree by manually harvesting nuts from the ground and collecting any nuts still in the tree at the end of the season. Nuts were dehusked after each harvest, weighed, and a 1 kg sample was dried to 1% moisture content. The dry nut-in-shell (DNIS) weight was estimated for each harvest using calculations of moisture content in the 1 kg sample. The DNIS weight for each harvest was summed across the whole season to give total DNIS yield. One site (AL) was not harvested in 2017 due to an extreme weather event, and in 2018 another site (EG) was not harvested due to management issues.Histograms were used to check the distribution of phenotypes to conform with assumptions of normality for GWAS . Data try is a vector of phenotypes, 1 is a vector of ones, μ is a fixed intercept, X is a design matrix allocating fixed effects  to observations, b is a vector of these unknown fixed effects, Zg is a design matrix allocating records to the unknown average breeding value of each individual across sites; g is a vector of averaged breeding values of the individuals across sites, assumed random ~ N  among the individuals, modelled from SNP effects ; Zgsgs describes the genotype by environment (G x E) interaction, where Zgs is a design matrix allocating a specific effect of an individual at a site not accounted for by the mean of the individual across sites, and gs is a vector of the breeding values at a specific site, assumed random ~ N  was constructed following methods of VanRaden . Prelimi2) was calculated from variance components  using the Wald statistic. After removing insignificant fixed effects , log likelihoods of models both including and excluding G x E as a random term were compared via a chi-square test to determine if the models were statistically different. The most parsimonious models were those with the least number of parameters that fit the data as well as more complex models: the G x E term was excluded for a trait if the models were not statistically different, as well as any insignificant fixed effects. Narrow-sense heritability , and m is the effect of the marker currently being fitted in the model, as a fixed effect. All other effects are the same as per Eq. Association analysis was performed for each trait using the most parsimonious model, as per O’Connor et al.  using ASmentclass2pt{minim10 significance values of each SNP and ensuring that inflation had not occurred at the lower levels of significance [QQ (quantile-quantile) plots were constructed for each trait to evaluate whether population structure had been accurately accounted for in the model, by comparing the observed and expected –logificance . To deteificance  using thLocations of significant SNPs (FDR < 0.05) on the most recent macadamia genome scaffolds  repository, Analysis: ERZ792049, Assembly accession: ERS2953073 SAMEA5145324)), were estimated as per O’Connor et al. . Locatio4), were


Puccinia graminis f. sp. secalis can be found in all European rye growing regions. When the summers are warm and dry, the disease can cause severe yield losses over large areas. To date only little research was done in Europe to trigger resistance breeding. To our knowledge, all varieties currently registered in Germany are susceptible. In this study, three biparental populations of inbred lines and one testcross population developed for mapping resistance were investigated. Over 2 years, 68–70 genotypes per population were tested, each in three locations. Combining the phenotypic data with genotyping results of a custom 10k Infinium iSelect single nucleotide polymorphism (SNP) array, we identified both quantitatively inherited adult plant resistance and monogenic all-stage resistance. A single resistance gene, tentatively named Pgs1, located at the distal end of chromosome 7R, could be identified in two independently developed populations. With high probability, it is closely linked to a nucleotide-binding leucine-rich repeat (NB-LRR) resistance gene homolog. A marker for a competitive allele-specific polymerase chain reaction (KASP) genotyping assay was designed that could explain 73 and 97% of the genetic variance in each of both populations, respectively. Additional investigation of naturally occurring rye leaf rust (caused by Puccinia recondita ROEBERGE) revealed a gene complex on chromosome 7R. The gene Pgs1 and further identified quantitative trait loci (QTL) have high potential to be used for breeding stem rust resistant rye.Rye stem rust caused by  Puccinia graminis f. sp. secalis ERIKSS. & HENNING can be a severe threat for rye  in epidemic years. After the fungus has entered the plants in early summer, it produces high amounts of urediniospores that subsequently infect new plants and spread the disease over large areas. Infected plants show spore-filled cracks all over the stems, and if the infection level is high, even on leaves and heads. As a result, water and nutrient transport breaks down and grain yield loss can reach levels of 60%  resistance  protein was successfully transformed into a susceptible wheat line  and wheat (tritici) can hybridize and, like wheat and rye, probably also had a common ancestor , a F1 plant was backcrossed with L403. The resulting BC1F1 plant was self-pollinated like P1 and P2. Because the resistance donor of P4 was one single self-incompatible plant, it could not be further maintained and consequently was not part of the experiments. Additionally, all inbred lines from P1 were crossed with a SR-susceptible single cross tester (developed by and being proprietary to KWS LOCHOW GmbH) and the resulting three-way hybrids were also tested in the field (P1TC).Three mapping populations  with 68 to 70 progenies each were analyzed in this study. P1 and P2 were developed by crossing a self-fertile and SR-susceptible breeding line with a SR resistance donor. The resistance donors for P1 and P2 were self-fertile lines previously developed from genetic resources listed in Resistance donors  were “VITwo-year field trials with scorings in 2017 and 2018 were conducted in Poland in Koscielna Wies (KOS) and in Germany in Stuttgart-Hohenheim (HOH), Berlin-Dahlem (DAH), Petkus near Baruth/Mark (PET) and Wohlde near Bergen (WOH), in a total of ten environments (5 sites × 2 years). Additionally, 70 breeding lines  and a further bi-parental population with 72 genotypes (P3) were grown. Twenty-five breeding lines (B1) were overlapping between all locations . Both, t2) using a spinning disk sprayer . Starting with the first visually distinguishable symptoms between the plots , the percentage of stem surface between the second leaf (F-1) and the node above covered with urediniospores was visually assessed (0–100%). For a single score, stems from all plants in the plot were considered, so that an average score for a plot was given. Rating was repeated three times at intervals of about 1 week.Details about spore production and inoculation procedure can be found in In all years, the additional traits heading date (HD), in days from first of January, and plant height (PH), measured as the distance from ground to the top of the ear, were evaluated. In all locations in the second year (2018), also a rating for naturally occurring leaf rust (LR) was conducted for all populations. The percentage of leaf area (first leaves below the flag leaf) covered with spores was assessed plotwise and was available for all bi-parental populations.–1) and silver nitrate (1.5 mg kg–1). After inoculation with urediniospores by a cotton swab, the petri dishes were placed for 24 h in dark chambers at 20°C with 100% humidity followed by 14 days of permanent light. The infection type was scored following a modified scale from The LST was used to distinguish between APR and ASR. The parents and selected genotypes from the progenies were tested with six SR isolates differing in their reaction to the differential lines and including the three isolates used for field inoculation . Eight tFor DNA extraction, a sample of six seeds per inbred line was taken. Genotyping of all lines was done with a custom 10k Infinium iSelect SNP chip that is proprietary to KWS SAAT SE & Co. KGaA, Einbeck, Germany. The SNPs of this assay are partially overlapping with the 5k-SNP assay of Markers were filtered by population requiring them to have less than five missing values  and three allele states with two calls per state at minimum . Some markers showed the same segregation across all genotypes from a single population in terms of their allelic state (not necessarily same SNP bases). They were kept for the analysis nonetheless, because segregation can be different between them in other populations. About 700 (P4) to 3000 (P1) markers remained after filtering per population .Marker genotypes were converted to 0, 1, 2 coding, where 0 stands for the marker genotype of the susceptible parent, 1 for heterozygotes, and 2 for the marker genotype of the resistant parent. As we expected a single dominant allele from phenotypically segregating plants in heterogenous  plots , set s and interaction terms for genotype-year (gy), genotype-location (gl) and genotype-environment (gyl). Indicated by the indices, the factors block (b), replicate (r), set (s), location (l) and year (y) were nested within each other. For the terms rjklm and bjklmn and the error eijklmno, heterogeneous variance at the level of the environment-wise sets sjkl was allowed for by using the at function within the asreml-R. To do a quality check of the model fit .Compared to this model (1), the one-stage model was extended by the additional factors year odel fit , residuaTo compare variances, genotype was taken as a random factor. Further, different genetic variances for the different sets were expected, so that a set-specific genetic variance was allowed for using the at function for the genotype effect.The experiments were laid out in a way that the set was a main plot in the experimental design. This main plot consisted either of the additional lines  or of a mapping population . The set was considered as a fixed effect to calculate population-wise genetic variance. Beside the logistical issues, the aim of this experimental set-up was to minimize the errors of a difference within the biparental populations. The least-significant difference (LSD) was calculated by multiplying the average standard error of a difference (average SED) with the 97.5% quantile of the t-distribution with the residual degrees of freedom of the mixed model, that can be extracted from asreml-calculation. The average SED was calculated for each population separately. Before the calculation of the BLUEs, outliers were removed based on the Bonferroni-Holm method with studentized residuals . Heritabzth marker (m) and a random marker-environment interaction (myl). As all populations were developed from separate crosses and different markers were expected to be significant in the different populations, dummy variables were used to account for the stratification. The dummy variable was equal to one when the observation could be referred to a respective population and zero for all other observations. For each population a separate dummy variable D was necessary. The model tested with all markers was:QTL mapping was performed by a single-point analysis over all markers. To test the significance, the one-stage phenotypic model (2) was extended by a fixed main effect for the djklmn and all interactions regarding the genotype in (gyl)ijk.For display reasons the factors of the experimental design of model (2) are summed up in p-values could be derived from the Wald-statistics of the fixed effects. The effect sizes for the markers could be estimated from model coefficients. Regression coefficients were interpreted as marker effects. The standard error of marker effects was calculated by taking the square root from the variance of the coefficient estimates.The As P2 and P4 showed similar significant markers, a dummy variable was created that grouped both together so that in an extra run significance and marker effects could be calculated for both populations combined.G) of a fitted SNPs fitted by assessing the associated drop in genetic variance. If the genotype was considered as random factor in model (2), the total genetic variance could be estimated. Every SNP fitted in model (3) should capture some of the genetics so that the amount of genetic variance explained by a SNP could be estimated by comparing genetic variances of model (2) and (3).We could estimate the explained genetic variance  required adjustments described in the following. Markers being significant in a single-marker model were combined in the order of increasing We used the method proposed by ∗ by adding a penalty term to C. It was calculated as follows:If we consider C as the critical threshold based on a given level of α (=5%) for the Chi-square test statistics of the Wald test, than we can calculate CV is the sum of absolute differences between square roots of the likelihood test statistics T of successive marker tests (0 to n) along the chromosome:where 0 to θn (sign change of first derivative). An additional turning point with value of zero was added at the start and end of each chromosome.As our model was based on single point test, we interpolated the profile of Wald test statistic with the R function smooth.spline  and extrz = 7) was than calculated by:The global significance level γ of all chromosomes (k in formula (4) and (5) depends on the population being studied  were dropped and the model extended to a bivariate form. To make use of the covariance between the traits it was aimed for unstructured variance for all reasonable fitted factors, but to still allow for the variance heterogeneity of location-set interactions it was possible to reach convergence with unstructured variance for the genotype and residual term (plot error) only. Diagonal variance was fitted for all other factors.CovG) was assessed likewise and additionally to pG.To use a multivariate model for QTL mapping, the model was extended by trait-specific fixed marker effects and random marker-location interaction, the latter again with diagonal variance. All bivariate models were used with marker genotype coding of 0, 1, 2  for all populations. As covariance was estimated for the factor genotype (CovG), the amount of explained genetic covariance  and PH (PHRep = 0.50–0.87).Of the three repeated ratings conducted in each environment most often the final rating had the highest repeatability (0.45–0.99) and was, therefore, used in the one-stage model. The raw data for stem rust in the final model ranged between 0 and 98 % infection. All observations from KOS in 2017 were discarded because of low repeatability for SR  a single resistant progeny as proven in the field test were used. All tested genotypes from P1 were susceptible at seedling stage, whereas the resistance donor from P2 and a resistant genotype from P4 were resistant for all tested isolates . ConsideFor HD differences of three to 5 days between the earliest and latest genotypes could be observed in each population . HoweverHigh genetic variance for LR could only be observed in population P2 . In the Given the small population sizes and few recombination events  a high amount of redundant markers was present . CombineIn P1, we could identify three QTL for stem rust resistance on chromosomes 1R, 2R, and 6R, all having effect sizes between 6.5 and 6.9% points of stem rust reduction . ExplainWhen mapping the testcross population, only a single QTL on chromosome 6R remained significant. For the top marker of chromosome 6R, a single allele change in P1TC had almost doubled effect size (−10.5) compared to a single allele change in P1 (−6.5) so that we concluded dominant gene action according to our marker coding . The QTLSelected markers from the SNP array were transferred into KASP assays. Unfortunately, the markers were chosen based on simple fits in QTL mapping software and first year data, so that one of the converted SNPs was located about 15 cM away from C31257_184 and the conversion of a second SNP into a KASP marker resulted in monomorphic response even though the respective SNP segregated. KASP2 and KASP3 were based on markers at the same position as C2420_561 and produced similar results as the respective SNP .p-value differed. When both populations were combined, isotig12934, was the most significant marker for both populations. The marker isotig12934 was converted to a KASP assay and yielded even higher significance than the related SNP. The marker effects (codominance) were estimated to cause a reduction of 12.0 and 22.3% points stem rust severity in P2 and P4, respectively  was located 9 cM away from isotig12934, but the LD was estimated to be 0.93 and correlation of the two markers was significant (p < 0.001). Whereas in P2 the QTL LR1a mainly explained genetic variance for LR (24%), a high amount of genetic covariance (50%) was explained by Pgs1  which is 11,179 basepairs long . When thn = 68–70). Most probably, we found a single resistance gene segregating that caused most of the genetic variance in P2 and P4 and was also active in seedling stage. In P1, however, a clear quantitative inheritance acting only in APR with at least three QTL was found of which one was inherited dominantly as revealed by the TC progenies.For the first time, we mapped three self-fertile rye populations  for the occurrence of stem-rust resistance genes in the adult-plant stage in the field. Additionally, P1 lines were analyzed as testcross progenies. Because we expected monogenic inheritance, population sizes were rather small  for the identified resistance gene. We are aware that gene denominations require Mendelian segregation ratios and thus consider the name as temporary. However, we could clearly separate resistant and susceptible plants in the field  an unbalanced experimental design with only single overlapping environments between the mapping populations combined with heterogeneous error structure and (2) the combination of DNA analysis from bulked seeds and averaged plot scores for heterozygous genotypes. Further, the extension of the mixed model to a bivariate form allowed us not only to estimate genetic correlation between testcross and line performance and between SR and LR infection, but also to use estimated covariance between SR and LR to map the latter by an improved model. The model improvement from a single to a multivariate model could be shown by comparisons of AIC and BIC. If a bivariate model (LR and SR) for phenotypic data was fitted with zero covariance (diag-option) the AIC was larger than with estimated covariance (us-option) for the factors genotype and residual. The more conservative measure BIC behaved the opposite way; it should be kept in mind, however, that the penalty term in BIC is not well defined and the definition used in mixed model package tends to favor too simple models . If biva® CoreTM i5-6600K processor) instead of 5 min for a simple regression approach with BLUEs. Consequently, well-known, but computational intensive procedures like significance threshold calculation by permutation  that is commonly combined with the heritability measure to assess the explained genetic variance (pG) is not simple to extract from mixed model calculations. Even though procedures for calculating R2 for mixed models have been described , 25 (P2) and 12.5% of heterozygous genotypes were expected and observed on the marker level  donors of resistance in P1, P2, and P4 were genetic resources from Russia, i.e., self-incompatible populations. They were crossed with a self-fertile elite line, thus allowing self-pollination in the following generations because our self-fertility is dominantly inherited. Inbreeding by strict self-pollination provided a challenge due to strong inbreeding depression reducing the number of progenies from one self-pollination step to the next. Doubled-haploid techniques are not feasible in rye. For shortening the procedure, we used unselected Fer level . If bother level  and thiser level . A ratioThe great advantage of this scheme compared to single-plant investigations was that it was possible to replicate genotypes within and at different locations and to get reliable estimates of stem rust infection in the adult plant stage. Nevertheless, the assessments of heterogeneous plots were a challenge and they tend to have larger errors and artificially influence the genotype-environment interaction if by chance the ratios do not fully meet the expectations. Whereas in P2 and P4 the segregation was evident in the field, it was less clear in P1, so that marker coding remained unchanged. By use of testcrosses, dominant gene action of QTL-SR3 could be shown. Besides the deduced dominance by P1TC, the LOD curve  also shoPgs1 to be identical (pleiotropic) or at least closely linked to a leaf rust resistance gene (gene complex). As mentioned previously for SR, this was also not confirmed by Mendelian segregation ratios. Still, the markers at this locus were significant for both traits and explained a high amount of genetic covariance pCovG  resistance gene (Pr2) in rye at the distal end chromosome 7R. When the sequence of the reverse primer of cMWG682 linked to Pr2 was searched for in the IPK rye contig database3  from different resistance donors at similar positions on chromosome 1R. Assuming that the susceptible line of P1 and P2 is carrier of a LR resistance (QTL-LR1a), that was segregating in P2 and was fixed for resistance in P1, there must have been an additional gene segregating in P1 (QTL-LR1b) coming from the stem rust resistance donor and thus having an opposite effect. It was not possible to properly separate those two QTL because we observed marker discrepancies in this region of the linkage maps (Assuming that in P2 and P4 the same gene (complex) was causing resistance for SR and LR resistance, differences in effect size and explained genetic variance  could alage maps . InteresPgs1, we tried to find candidate genes. Already Sr50 in wheat . Even though it could not be infected by any of the collected 196 isolates, we cannot make any reliable statements about the race-specificity or even durability of that gene. In the recent past, there was no selection pressure by SR-resistant cultivars in Germany that would have boosted the propagation of more virulent races. Important for the emergence of new races is the sexual recombination on the barberry plant (Berberis vulgaris) where several virulences can be combined resulting in new and more virulent races. Systematic eradication of the barberry in the United States , although from different resources . SolodukPgs1 and QTL-SR3 are valuable sources for breeding hybrid rye. Firstly, they act dominantly so that the introgression in only a single gene pool is necessary. Secondly, potential differences in resistance type (APR vs. ASR) indicate different resistance mechanisms what can be promising for successful gene pyramiding and thirdly, if a gene complex of Pgs1 with Pr2 (or a different LR resistance) can be proven in further studies, it can be appealing as it allows to breed for SR and LR resistance in a single step.Summing all up, the stem rust resistance genes Research on resistance mechanisms conferred by different genes will help to explain durability, at least partially. Until then, every resistance gene is a valuable resource and good markers will help breeders to include them in their breeding material, preferably stacked.The datasets for this manuscript are not publicly available because the breeding material investigated  and marker sequences from the SNP chip are property of KWS LOCHOW GmbH, Ferdinand-von-Lochow-Str. 5, 29296 Bergen, Germany and can only be shared by personal request directed to Dr. Andres Gordillo. Breeding material developed at the University of Hohenheim (population P4) can be requested from the corresponding author.TM and BS designed the experiments. A-KS and KF produced inoculum for field experiments and conducted the leaf-segment tests. BS and SK developed the breeding material for QTL mapping. BS, F-JF, and KM provided the additional breeding lines. All authors conducted the field trials except MS and VK who were responsible for the DNA extraction, analysis with the SNP chip and KASP design and analysis. PG analyzed the data and wrote the manuscript. H-PP supervised the statistical issues. TM edited the manuscript. All authors read and approved the manuscript.BS, JE, AG, and MS were employed by the company KWS LOCHOW GmbH. VK was employed by the company KWS SAAT SE & Co. KGaA. FF and DS were employed by the company HYBRO Saatzucht GmbH & Co. KG. KM, RK, and MN were employed by the company Danko Hodowla Roślin Sp. z o.o.The remaining 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.


In this context, this study aimed to establish a broad picture of the genetic attributes of the Japanese population, by examining a representative sample of 18,889 individuals participating in the Tohoku Medical Megabank Project cohort. We applied linear mixed model methods to 17 traits related to obesity and associated diseases to estimate the heritabilities explained by common genetic variants and the genetic correlations between each pair of traits. These analyses allowed us to quantify the SNP heritability of health indicators such as BMI (0.248 ± 0.032) and HDL cholesterol (0.324 ± 0.031), and to provide one of the few estimates of the SNP heritability of cystatin C in unrelated individuals (0.260 ± 0.025). We discuss potential differences between the Japanese and people of European ancestry with respect to the genetic correlations between urinary biomarkers and adiposity traits, for which large estimates were obtained. For instance, the genetic correlations between urine potassium level and the values for weight, BMI, waist circumference, and waist-to-height ratio ranged from 0.290 to 0.559, much higher than the corresponding estimates in the UK Biobank.Over the past few decades, obesity has become a public health issue of global concern. Even though disparities exist between human populations,  Historically, obesity first emerged as a public health concern in Western high-income countries . NowadayDespite this worrying global trend, there is a gap between the amount of research conducted on the genetics of obesity in populations of European descent and non-European populations , and becIt is essential to investigate how these differences relate to genetics. Examining population-level characteristics such as heritability, which measures the proportion of the total phenotypic variation that is due to genetic variation, provides insights into this diversity between populations and key information about the genetic basis of complex traits. Given that phenotypic variation is strongly dependent on both environmental and genetic factors, changes in the environment lead to changes in heritability. Studying heritability is therefore critical not only because of the genetic diversity that exists between genetic groups, but also because of the environmental differences that can be found across the world’s populations, as well as within a given population over time.In this study, we focused on the genetic characteristics of the Japanese population, which is largely understudied compared with populations of European descent despite obvious genetic and environmental differences. By examining 17 traits related to obesity and associated conditions in a dataset of 18,889 individuals from the Miyagi and Iwate Prefectures, in Northeast Japan, we aimed to clarify the genetic correlations between each pair of traits and the SNP heritability of these traits in the Japanese population. We discuss the similarities and differences with other populations, such as that of the UK Biobank cohort.The 23K dataset of the larger 150K Tohoku Medical Megabank Project (TMM) Community-Based and TMM Birth and Three-Generation cohorts was used for this study , diastolic blood pressure (DBP), triglycerides (TG), HDL cholesterol (HDL-C), height, weight, BMI, WC, waist-to-height ratio (WHtR), urine creatinine (uCre), urine chloride (uCl), urine potassium (uK), serum cystatin C (CysC), serum creatinine (SCre), serum uric acid (SUA), blood urea nitrogen (BUN), and hemoglobin (Hb). For each trait, the number of individuals with data available is listed in To remove extreme phenotypes, participants with a BMI lower than 16 ) were excluded, and in each sex group, individuals whose phenotypic value lay more than six standard deviations from the mean were also excluded.The phenotypes were standardized by regressing the values for each trait on age in each sex group and converting the resulting residuals to z-scores  , Human Omni 2.5 , and OmniExpressExome (Illumina) genotyping arrays (P ≥ 10−6). All individuals with a heterozygosity rate more than three standard deviations from the mean were removed.Only SNPs that were available on all platforms and had an info score over 0.5 in each platform were included in our analyses. The following criteria were applied in PLINK v1.90 , which was calculated after performing linkage disequilibrium pruning (-indep-pairwise 50 5 0.2); for each pair of individuals, the individual with the highest genotyping rate was kept. Pruning was performed again before calculation of the first ten principal components (-indep-pairwise 50 5 0.2) and to produce the final dataset (-indep-pairwise 50 5 0.7).After the quality control and filtering steps, 18,889 individuals and 866,089 autosomal SNPs remained for the downstream analysis.The following univariate linear mixed model was used to estimate the genome-wide SNP heritabilities.i.e., the sex- and age-adjusted z-scores), where e.g., n × 1 vectors of observations on the i-th trait , p × 1 vectors of fixed effects on the i-th trait (p being the number of levels for fixed effects), n × p design matrices relating the n × 1 vectors of random effects on the i-th trait, and i-th trait. These methods are described in more detail elsewhere  by using all SNPs that passed quality control. The following equation was used to calculate the genetic relationship between two individuals i.e., computation of GRMs, PCA, and univariate and bivariate Genomic Restricted Maximum Likelihood (GREML) analyses. The Average Information Restricted Maximum Likelihood (AI-REML) procedure was used to estimate variance components (GCTA v1.91.4 (ibid.) was used to perform most of the computations and statistical analyses: mponents .et al. 2013) and was reviewed and approved by the ethics committee of Tohoku University. All participants gave their written consent prior to study enrollment.This project was conducted in accordance with the Japanese National Ethical Guidelines for Human Genome/Gene Analysis . To protect the privacy of the cohort participants, requests for the use of ToMMo biobank data for research projects should be made by applying directly to ToMMo, and are subject to review and approval by the Sample and Data Access Committee. Supplemental material available at figshare: https://doi.org/10.25387/g3.11295944The analyses presented in this study were based on data accessed through the Tohoku Medical Megabank Organization (ToMMo)  in women and from 16 to 42.3 kg/m2  in men.The main population characteristics are presented in 2), ‘normal’ (18.5 ≤ BMI < 25 kg/m2), ‘overweight’ (25 ≤ BMI < 30 kg/m2), and ‘obese’ (BMI ≥ 30 kg/m2). Detailed information on the number of individuals in each BMI category is provided in Supplementary Table 1. To better visualize the profile of the target population, we also calculated the mean and standard deviation of each of the 17 traits in individuals grouped by BMI and WC category (Supplementary Tables 2 and 3). A large WC was classed as ≥85 cm in males and ≥90 cm in females, and a small WC was classed as <85 cm in males and <90 cm in females, following the clinical criteria applied for the diagnosis of metabolic syndrome in Japan .The participants were divided into four BMI categories: ‘thin’ (BMI < 18.5 kg/mP < 0.05), indicating that a substantial share of the phenotypic variation in these complex human traits is due to common genetic variants in the Japanese population. The highest heritability estimate (± SE) was that of height (0.536 ± 0.031), with that of the other traits ranging from 0.033 (± 0.023) to 0.324 (± 0.031).For each of the traits examined, we computed the SNP heritability by performing univariate model analyses . Given tWe also computed the heritabilities by using bivariate models to compare them with the estimates of the univariate analyses. Although bivariate models are theoretically more accurate given that they take into account the information on both traits to simultaneously estimate the random effects, in practice the difference in accuracy between the two models was negligible. For instance, the SNP heritability estimates (± SE) of height calculated by using bivariate models ranged from 0.535 (± 0.031) to 0.537 (± 0.031), which was similar to 0.536 ± 0.031 in the univariate model; and those of BMI ranged from 0.245 (± 0.032) to 0.255 (± 0.032), which was comparable to 0.248 (± 0.032) in the univariate model. Given the closeness of the results, the heritability estimates calculated with the bivariate model analyses are not presented in further detail.vs. WC, 0.890 for BMI vs. WHtR, 0.543 for SCre vs. CysC, and -0.397 for TG vs. HDL-C.Phenotypic and genetic correlations , Table 3e.g., uK vs. uCl (0.575 ± 0.245). Although this is consistent with the pattern observed above, it is an interesting result given the low heritability of these traits. This finding highlights the fact that a low contribution of genetic components to phenotypic variation for a given set of traits does not necessarily imply a low genetic correlation between these traits. On the other hand, the low heritability of these traits was reflected in the higher standard error of the corresponding genetic correlation coefficients.High genetic correlations were also observed among urinary traits, The phenotypic correlations were stronger between CysC and each of the adiposity traits  and HDL-C than between SCre and these traits; this result supports previous research that showed that CysC is more closely associated with obesity than SCre . When examining women and men of normal weight only, the subgroups with a large WC also displayed higher DBP and higher TG and Hb levels (P < 0.01). More surprisingly, however, overweight men with a small WC displayed lower CysC levels than men with a normal BMI and a large WC (P < 0.01). These findings strongly suggest that increases in WC within each BMI category are associated with a worsening of many health risk indicators in both women and men; they are also in line with previous research indicating that abdominal fat, as measured by WC, may represent a substantial health risk factor independently of BMI, and that WC may be an effective tool for the detection of at-risk individuals, including normal weight individuals (By dividing the target population by BMI and WC group (Supplementary Tables 2 and 3), we observed that regardless of gender, in normal weight and overweight individuals, within each BMI category the subgroups with a large WC displayed significantly higher weight, WHtR, higher CysC and SUA levels, and a lower HDL-C level than those with a small WC  individuals, the heritability estimates (± SE) were 0.685 (± 0.004) for height, 0.274 (± 0.004) for BMI, 0.277 (± 0.004) for weight, 0.155 (± 0.004) for WC, 0.156 (± 0.004) for SBP, and 0.184 (± 0.004) for DBP .Several observations deserve to be emphasized with respect to the genetic correlations . First, e.g., 0.104 between uK and BMI, vs. 0.380 in our study) . Surprisingly, however, despite phenotypic correlations of over 0.2, we could not reproduce genetic correlations between BMI and SBP, DBP, TG, or SUA , renal function biomarkers , and cardiovascular risk factors . This highlights the complex, intertwined relationship between these traits, as do the large negative genetic correlations between SCre and uK and between SCre and uCl (vs. genetic correlation estimates for the 17 traits was high (0.767). These results therefore seem to support the view that phenotypic correlations may prove valuable in the context of linear mixed model methodology, for instance in the calculation of the preliminary estimates of the parameters used for the initialization of maximum likelihood procedures.Even though the correspondence between phenotypic and genetic correlation estimates is not a systematic result , it has i.e., the traits for which environmental factors accounted for almost all of the variation in phenotype, displayed a difference of over 0.3 between the phenotypic and genetic correlations with at least one of the other traits. At the same time, all three traits with low heritability, e.g., 0.436 between BMI and uCl; 0.380 between BMI and uK), as well as between SBP and uK (-0.555), provide a starting point for further research into how the genetic architecture of these biomarkers fits into the broader context of disease onset and etiology.A substantial contribution of this study is the estimation of the genetic correlations between traits associated with obesity and indicators used as health markers for obesity-related diseases. The link between obesity and obesity-related diseases is often complex, and furthering our understanding of this relationship requires undertaking a variety of analyses on a wide range of traits and in diverse populations. This complexity is well illustrated by the case of CKD: despite the known strong association between metabolic syndrome and CKD, it has been reported that traditional CKD risk factors (such as diabetes mellitus and hypertension) may be independent of kidney dysfunction in obesity, and that kidney dysfunction may appear long before these factors develop in individuals affected with metabolic syndrome (More broadly, these observations underscore the fact that the relationship between obesity and obesity-related diseases is not a simple cause-and-effect relationship, and compel us to revisit and better quantify the connection between the many associated traits and biomarkers, even if they typically display a somewhat low heritability and do not necessarily constitute the deciding factor in terms of disease diagnosis.Given that our main objective was to shed light on the genetic characteristics of the Japanese population, our findings may not be generalizable to other human populations or age groups. This is partly due to the diversity found across genetic groups, but it is also a consequence of the physiological and social differences that exist between human populations, as illustrated for instance by the differences in the WC threshold values used for the diagnosis of metabolic syndrome in Japanese and European populations. Factors such as smoking, alcohol consumption, and physical activity, as well as potential subgroup effects within the cohort, are also aspects that have not been addressed in this paper and warrant further research. When comparing our findings with the results of other studies, it is also important to keep in mind that the TMM project targets the regions affected by the Great East Japan Earthquake of March 11, 2011.On the other hand, this study is one of only a few analyses of large-scale cohorts that focus on identifying the general genetic characteristics of the Japanese population; it includes rare and valuable information, such as one of the few estimates of the SNP heritability of cystatin C in unrelated individuals, as well as estimates of the genetic correlations between urinary biomarkers and obesity-related traits. Also, given that this study was based on high-quality clinical data, and that participants were recruited as part of the general population regardless of their medical background, it constitutes a useful point of comparison for similar studies conducted in the Japanese and other populations.By quantifying the genomic parameters of a large cohort of Japanese adults, our study provides evidence that the contribution of common SNPs to phenotype variation is significant in the Japanese for traits related to obesity and obesity-related diseases, and therefore supports the assumption that common genetic variants account for a considerable share of phenotypic variation. Our study also shows that the heritability estimates for these traits are similar in magnitude to those of populations of European descent, and suggests potential differences with respect to the genetic correlations; however, further research is needed to compare the genetic architecture of each trait in these populations. The heritabilities presented in this paper are useful in that they provide information about the proportion of phenotypic variance that could be explained by common-variant GWAS of the traits studied, and the high genetic correlations estimated for several pairs of traits with low heritabilities underscore that we must be careful not to neglect research on the genetics of biomarkers with low heritabilities.