Glycine max [L.] Merr.) is one of the most important oil and protein crops. Ever-increasing soybean consumption necessitates the improvement of varieties for more efficient production. However, both correlations among different traits and genetic interactions among genes that affect a single trait pose a challenge to soybean breeding.Soybean  contains supplementary material, which is available to authorized users. Glycine max [L.] Merr.) is a major crop of agronomic importance as a predominant source of protein and oil [Soybean  and positional cloning identified a set of loci that are responsible for flowering and maturity, biotic and abiotic stresses, and growth habits . Howevebidopsis , rice [9bidopsis , maize [bidopsis , 13, andbidopsis . In soybbidopsis , 16, sudbidopsis , cyst nebidopsis , 19, andbidopsis  were conNevertheless, the dissection of a specific trait is insufficient for molecular breeding because many complex traits exhibit correlation and tend to be tightly integrated, resulting in heritable covariation , 22, whiIn this study, we collected 809 diverse soybean accessions, cultivated them at three locations for two years, and phenotyped them for 84 agronomic traits. Whole-genome sequencing (WGS) at an 8.3 × depth produced more than 11 million genetic markers. The endeavor from comprehensive GWAS analyses enabled the identification of the underlying genetic loci, loci interaction, and genetic networks across traits.On the basis of our previous investigated 130 landraces and 110 cultivars , we collThe neighbor-joining tree suggested that the 809 accessions could be classified into four main clades Fig. , which wWe grew all of the 809 accessions in Beijing for two years (in 2013 and 2014). We assayed 45 morphology traits each year, including those related to yield, color, architecture, organ shape, and growth period  exhibited P values equal to those expected under the null hypothesis, suggesting that the MLM controlled population structure and cryptic relationships well. To control both false positives and false negatives, we also conducted permutation tests by randomly shuffling the phenotypes to break their relationship with genotypes to derive a genome-wide threshold  genotyped from the 809 accessions through a MLM implemented in Efficient Mixed-Model Association eXpedited (EMMAX) software. The population structure was represented by the first three principal components, which was fitted as fixed effects. Kinship was used to define the variance structure of the random variables for the total genetic effects of the 809 accessions. No inflated Dt1 exerts an epistatic effect on Dt2 in the regulation of plant height in soybean [Dt1 locus [E2, a locus that is responsible for bloom date [Dt2 locus was not detected. If an epistatic gene exhibits a significantly strong effect, it can hinder the identification of other interactive genes that exert minor effects [Dt1 and dt1 subgroups), based on the genotypes of the highest association site of the Dt1 locus. A GWAS of the plant height in each of these two subgroups revealed two additional SAL in the Dt1 subgroup, which included the Dt2 locus , plant stearoyl-acyl-carrier protein desaturase (SAD), and fatty acid desaturase 3 (FAD3), have been reported to be responsible for fatty acid accumulation in soybean –45. In tWe observed that the TFA content increased with the accumulation of high-fatty-acid alleles of these genes in the soybean germplasm Fig. . FurtherWe found that the 84 traits related to growth period, architecture, color, seed development, oil content, or protein content tended to be correlated within these trait classifications  play important roles in identifying correlations among phenotypes . To dissE2 may exhibit pleiotropy across the traits related to yield and seed quality. Plant height (PH) and beginning bloom date (BBD) exhibited a significantly positive correlation . The results showed that the values of PH, BBD, and R3:2 in the E2 lines were significantly higher values than those in the e2 lines  during the summer seasons in 2013 and 2014. The 275 accessions collected from northern areas were planted in Mudanjiang (44°58′N and 129°60′E), Heilongjiang Province during the summer seasons in 2014 and 2015. The remaining 534 accessions collected from Huang Huai Hai and southern areas were planted in Zhoukou (33°62′N and 114°65′E), Henan Province during the summer seasons in 2014 and 2015. Normal seeds were selected and sowed in deeply ploughed fields with proper moisture content (15–20%). The seed was planted in three-row plots in a randomized complete block design with three replications for each environment. Only one accession was planted in each plot and the plots were 5 m in length with a row spacing of 0.4 m. The space between two plots was 0.4 m. After three weeks, the seedlings were manually thinned to achieve an equal density of 120,000 individuals per hectare.We used the same phenotyping procedure and scoring standards in all six environments. In total, we characterized 84 sets of phenotypes related to yield, coloration, architecture, growth period, and seed composition with a miss rate < 10%. The identification of growth periods, including BBD, full bloom date, pod maturity date, and reproduction stage length, was based on a previous description of reproductive stages . Traits g and dried by flow nitrogen. The dried samples were re-dissolved in 500 μL of hexane for further GC-MS analysis.After drying at 80 °C for 2 h, approximately 5 g of mature and well-rounded seeds were milled to a fine powder with an electric grinder. Solid fractions were filtered out using a 0.25-mm sieve. The powders were divided into two sub-samples and measured at the same time. Six micrograms of soybean power were used to determine the lipid content, according to a previously reported protocol with minor modifications . Fatty ag, 50 μL of supernatant was transferred to a new 1.5 mL Eppendorf tube and dried at 100 °C. The dried samples were derivatized according to Fiehn’s protocol [For total amino acid analysis, 6 mg of soybean power was completely hydrolyzed by adding 300 μL of 6 M HCl spiked in 0.5 mg/mL L-norleucine  for 24 h at 100 °C . After cprotocol .One microliter of the prepared sample  was injected into the Trace DSQII GC-MS system (Thermo Fisher Scientific), which was equipped with a DB-23 column  at a split ratio of 1:20 for fatty acid analysis and a DB-5MS column  at a split ratio of 1:50 for amino acid analysis. For fatty acid measurement, the oven was programmed as follows: 150 °C for 1 min, ramp to 200 °C at 4 °C/min, ramp to 220 °C at 2 °C/min, and finally ramp to 250 °C at 25 °C/min, holding 5 min with 1.1 mL/min helium as carrier gas , 58. TheThe overall performances of the 809 soybean accessions were calculated as the best linear unbiased prediction (BLUP), the same method used to calculate the overall performances of 5000 maize inbred lines to eliminate environment effects . The calhttp://www.berrygenomics.com/. Beijing, China). Detailed information of the 809 accessions, including geographical distribution and sequencing depth, is provided in Additional file Among the 809 soybean accessions, 240 were obtained from our previous study  (Additiohttp://www.phytozome.net/soybean) with BWA [http://broadinstitute.github.io/picard/, version: 1.87) was applied to filter the duplicated reads.The re-sequencing reads of the 809 accessions were mapped to the soybean reference genome  (Williamwith BWA  (versionwith BWA  (versionhttp://202.127.18.228/fimg/intr.php) was then used for missing data imputation, after which the miss rate decreased from 2.1% to 0.057% and the heterozygous rate decreased from 3.4% to 0.17%. To evaluate the SNPs calling and imputation accuracy, we randomly selected ten fragments , 5’UTRs, 3’UTRs, intragenic regions, upstream and downstream regions (within a 1-kb region upstream/downstream from the transcription start/end site), and intergenic regions. The SNPs in coding regions were further categorized into non-synonymous SNPs (cause amino acid changes), synonymous SNPs (do not cause amino acid changes), stopgain SNPs (create a stop codon), and stoploss SNPs (eliminate a stop codon). The INDELs in coding regions were further categorized into non-frameshift (do not cause frameshift changes), frameshift (cause frameshift changes), stopgain, and stoploss INDELs.A neighbor-joining tree was constructed using the PHYLIP software  Quantitative traits with normal distributionQuantitative traits with skewed distributionBinary-like quantitative traits: examples include four-seed pod number and ratio with extremely skewed frequency distributionsIdeally, each trait should have its own threshold. To derive robust thresholds, we grouped the 84 traits into four types based on their phenotypic distribution. We found the thresholds were very similar within each of the types we defined as follows:10P values = 6.5–6.7). We used the most stringent threshold (6.7) as the criterion for these three types of traits. Although this criterion may have caused false negatives, it guaranteed that the type I error was below 5% for every trait. The last type of traits had much more stringent criteria. For example, the four-seed per pod ratio had threshold of 8.3 . We used this threshold for all binary-like quantitative traits.We tested multiple traits in each category and randomly selected one trait out of each category to illustrate the empirical thresholds  as the criterion for the traits in other two categories. The significant associated loci and not having been identified before grouping were considered as new identified association signals.To identify minor-effect loci by eliminating the effect of epistasis or interactions between genes, additional GWAS were performed. We first divided the 809 accessions into two subgroups according to the genotype of the SNP with the lowest h2 = 0.25, 0.5, and 0.75) were evaluated for examination of statistical power in all settings of causal loci. For each combination of heritability and number of causal loci, a total of 1000 replicates were conducted for the simulation of phenotypes and association tests. In each of GWAS, the threshold was set as 2 × 10–7, the cutoff from permutation tests on real traits with normal distribution.Using the genotype data of 809 soybean accessions, a set of SNPs  were randomly selected as causal loci for the simulated traits using the method described previously . Three lStatistical power and false positive rate (FDR) were evaluated on the intervals around the loci above the threshold. An interval was defined as the consecutive region with SNPs in LD (above 0.6) around the associated locus. Statistical power was calculated as the proportion of intervals containing causal loci over the total number of causal loci weighted by variance they explained. FDR was calculated as the proportion of the intervals without causal locus over the total number of intervals with a SNP above the threshold. The averages and standard error of statistical power and FDR over the 1000 replicates were reported.r2 ≥ 0.4) as edges. The effective score for each SAL was represented by the lowest P value. The link between each two SAL was represented by their average LD (Inter-LD). Inter-LD was calculated as follows:The association networks were constructed using the software Cytoscape   equals the mean of pairwise LD value (r2) between all the SNPs from SAL1 to all the SNPs from SAL2; PmaxLD (SAL1) equals the largest possible LD value within the SAL1 region, obtained by calculating the mean r2 of each SNP to all SNPs from the SAL1 region, and then choosing the maximum mean of the LD value to represent this region’s PmaxLD; and PmaxLD (SAL2) equals the largest possible LD value within the SAL2 region, obtained by calculating the mean r2 of each SNP to all SNPs from SAL2 region, and then choosing the maximum mean of the LD value to represent this region’s PmaxLD. Pairwise r2 values were calculated between all significant SNPs using PLINK [where LD Additional file 2: Table S2.Whole-genome SNP and INDEL distribution. (PDF 133 kb)Additional file 3: Table S3.Primers used for SNP validation. (XLSX 11 kb)Additional file 4: Table S4.The SNPs getting from before imputation, after imputation, and Sanger sequencing validation. (XLSX 55 kb)Additional file 5: Figures S1-S98.with legends. GWAS results of individual traits and the correlation of different traits.  (PDF 21766 kb)Additional file 6: Table S5.Information of 84 phenotype traits. (XLSX 15 kb)Additional file 7: Table S6.Permuted significant thresholds of representative traits. (XLSX 10 kb)Additional file 8: Table S7.Genome-wide association signals of 57 agronomic traits. (XLSX 30 kb)Additional file 9: Table S8.Genome-wide association signals of additional minor-effect loci. (XLSX 23 kb)Additional file 10: Table S9.Candidate causal genes in the fatty acid biosynthesis pathway. (XLSX 10 kb)Additional file 11: Table S10.Candidate causal genes in the lipid biosynthesis pathway. (XLSX 11 kb)Additional file 12: Table S11.Link loci across 51 traits. (XLSX 21 kb)


Profiling elemental contents in wheat grains and clarifying the underlying genetic systems are important for the breeding of biofortified crops. Our objective was to evaluate the genetic potential of 269 Afghan wheat landraces for increasing elemental contents in wheat cultivars. The contents of three major  and three minor  elements in wheat grains were measured by energy dispersive X-ray fluorescence spectrometry. Large variations in elemental contents were observed among landraces. Marker-based heritability estimates were low to moderate, suggesting that the elemental contents are complex quantitative traits. Genetic correlations between two locations (Japan and Afghanistan) and among the six elements were estimated using a multi-response Bayesian linear mixed model. Low-to-moderate genetic correlations were observed among major elements and among minor elements respectively, but not between major and minor elements. A single-response genome-wide association study detected only one significant marker, which was associated with Zn, suggesting it will be difficult to increase the elemental contents of wheat by conventional marker-assisted selection. Genomic predictions for major elemental contents were moderately or highly accurate, whereas those for minor elements were mostly low or moderate. Our results indicate genomic selection may be useful for the genetic improvement of elemental contents in wheat. Elements, along with nucleic acids, proteins, and metabolites, are essential building blocks of cells, and are involved in almost every process in living organisms . Ionome www.wheat.org). To the best of our knowledge, biofortification efforts involving wheat have been limited, with only one major program that focused on Zn (www.harvestplus.org). Dietary supplements and agronomic practices involving the use of Fe- and Zn-containing fertilizers can help address the nutrient deficiency problem. However, ensuring sufficient nutrient uptake through food is a more sustainable solution. Identifying germplasm with varying elemental contents, and using this variation to breed new cultivars represents a viable solution that takes advantage of the genetic diversity of wheat.Of the three major staple crops, wheat is the primary source of protein in developing countries, with 2.5 billion consumers in 89 countries worldwide . In wheat, a few studies have reported the occurrence of genetic variations influencing Fe and Zn contents [Ionomics has been extensively used to study model plant species, such as contents –25. Rececontents . Moreovecontents , furtherIn the present study, we assessed the variation in elemental contents in wheat seeds using 269 Afghan landraces. We also characterized the genetic nature of elemental compositions by estimating the heritability of elemental contents and the genetic correlations between environments and elements. Additionally, we completed genomic predictions based on 8,465 genome-wide markers and multiple model-building methods, and evaluated the accuracy of the predictions through cross-validations to clarify the potential of GS. The model-building methods were linear and nonlinear to enable the prediction of the genetic mechanisms regulating elemental contents as suggested by Onogi et al. (2015) . We alsoWe used the whole set of Kihara Afghan wheat landraces (KAWLR), which were registered in the National Bio-Resources Project in Japan, along with 10 Afghan improved wheat varieties as checks to evaluate the phenotypic variations in elemental contents and the underlying genetic basis. Plant materials were grown in an experimental field at the Kihara Institute for Biological Research in Totsuka, Yokohama, Japan using normal field management practices . Healthy seeds were sown in trays and transplanted to the field before winter in November 2011. For elemental analyses, we chose 267 landraces and seven check varieties that yielded normal grains under the environmental conditions in Japan (Online Resource 1). Border plants were excluded, and grains were harvested from the remaining plants and pooled together for each plot. Twelve grains were selected randomly from the pooled grains for elemental analysis. A subset of landraces was also grown in Afghanistan (2013–2014) using normal field management practices . Wheat grains were collected and pooled as described above. In Afghanistan, 207 landraces and the same seven check varieties were analyzed . Out of An EDX-720/800 HS energy dispersive X-ray fluorescence (EDXRF) spectrometer  was used for elemental analysis. Although it enables the measurement of elements in a non-destructive way, we used a destructive method involving a hand-operated pressing tool (Shimadzu) to avoid measuring elements present only in the seed coat and/or aleurone layer. Wheat grains were dried by incubating at 65°C overnight. Four grains were wrapped in weighing paper and pre-crushed with a hammer. The crushed grains were placed between two TC-604 tungsten beads (6.0-mm diameter)  in a tube  and homogenized for 4 min using a Shaker Master Auto shaker . The powder collected from each tube was placed in a plastic ring  without any chemical treatments and pressed at 20–32 MPa to produce a tablet . Three tablets per sample were prepared for elemental content measurements. Three major nutrients  and three minor nutrients  were analyzed. Details on the methodology used are given in Kondou et al . A portiTotal genomic DNA was extracted from 5-week-old leaves of the 269 accessions and seven check varieties using the modified extraction protocol of the DNeasy Plant Mini Kit (Qiagen). Genotyping was completed by Diversity Arrays Technology Pty. Ltd, Yarralumla, Australia. Details regarding genotyping and chromosomal mapping were as described by Manickavelu et al. (2014) –31 were We estimated heritability and genetic correlations between environments and among elements based on genetic relationships calculated from genome-wide marker polymorphisms.yN×1 is the vector of the mean phenotype  for each genotype of the length N (N = number of genotypes), 1N×1 is the vector of ones of length N, μ is the overall mean, gN×1 is the vector of breeding value for each genotype, and εN×1 is the vector of residual errors. The breeding values gN×1 and residuals εN×1 were assumed to exhibit multivariate normal distributions:AN×N is the realized additive relationship matrix derived from genome-wide markers, IN×N refers to the identity matrix, and AN×N was calculated using the “A.mat” function in the R package “rrBLUP” [yi was scaled to have a mean of 0 and variance of 1. The number of MCMC cycles was set to 13,000, with a thinning interval of 10 and a burn-in period of 3,000.We estimated the marker-based heritability using the R package “MCMCglmm”  as descr“rrBLUP” . An estiyN×12 is the vector of the mean phenotype  for each genotype of the length 2N in the pairs of elements (or locations). The first N elements of the vector correspond to the first element (or location), while the second N elements correspond to the second element (or location). In the equation, μ2×1 is the vector of the overall means of two elements (or locations), xN×22 is the corresponding design matrix of 2N × 2, gN×12 is the vector of the breeding value for each genotype in the pairs of elements (or locations), and εN×12 is the vector of residual errors. The breeding values gN×12 and residuals εN×12 were assumed to exhibit multivariate normal distributions:GN×2N2 and RN×2N2 are the expected covariances of the random effects and residuals, respectively. GN×2N2 and RN×2N2 have variance structures of the form:VG 2×2 is the additive genetic covariance matrix in the pairs of elements (or locations), and VR 2×2 is the residual covariance matrix in the pairs of elements (or locations). AN×N and IN×N are the same as above. We estimated genetic variances and covariances in pairs of elements (or locations) as follows:To calculate the genetic correlations between locations and among elements, a multi-response Bayesian method  was usedr) as follows:i.We then estimated the genetic correlation  of 0.5 and degree of belief parameter (nu) of 2 for h2 and r were obtained from the mean of 1,000 MCMC samples, and the 95% highest posterior density (HPD) interval of h2 and r was also based on 1,000 MCMC samples. The R function “HPDinterval” in the “lme4” package was used to obtain the 95% HPD interval.The point estimates of Based on the genetic correlation matrix estimated for the six elements, we completed the cluster analysis based on the Unweighted Pair Group Method with Arithmetic Mean and checked the relationship between each element.To detect significant associations between marker genotypes and phenotypes, we completed a GWAS using the “GWAS” function in the R package “rrBLUP” . For thehttps://mathgen.stats.ox.ac.uk/genetics_software/snptest/snptest.html). In this program, the Bayes factor (BF) was used as a criterion for detecting significant associations. The BF threshold was set to 3.0 as suggested by Kass and Raftery (1995) [We also completed a multi-response GWAS to detect marker–phenotype associations in elemental contents for use in a combination analysis of the phenotypic values obtained at the two locations. To complete the multi-response GWAS, we used SNPTEST ver. 2.5.2 (y (1995) . We set To evaluate the potential of selecting micronutrient-rich genotypes, we completed the following six GS methods: genomic best linear unbiased predictor (G-BLUP), reproducing kernel Hilbert space regression (RKHS), random forest (RF), ridge regression (RR), elastic net (EN), and LASSO. For G-BLUP and RKHS, we used the “kinship.BLUP” function in the R package “rrBLUP”  to buildTo assess the accuracy of the genomic prediction of elemental contents, we completed a 10-fold cross-validation using five replicates, and calculated the average accuracy statistic value. Regarding the accuracy statistic, we calculated the correlation coefficients between predicted and observed values  for all accessions. To assess the degree of shrinkage in predicted values, we also completed regression analyses using predicted and observed values, and estimated the slope of the regression line.2 ppm) was the most available, followed by P (34–91 × 102 ppm) and Mg (8–39 × 102 ppm). Of the main biofortification targets, plants accumulated more Fe than Zn. However, for Fe, the average value (86.47 ppm) of the check varieties was higher than the average value (84.71 ppm) of the landraces. Outliers were detected for all elements, which increased the likelihood that germplasm with high elemental contents would be identified. Location effects were observed in all cases. Some elements  accumulated more in plants grown in Japan, while other elements  accumulated more in plants grown in Afghanistan.The range of variation for major and minor elements in 269 KAWLRs along with seven check varieties at two different locations is presented in The narrow-sense heritabilities of elemental contents at both locations were estimated using the Bayesian linear mixed model . Irresper ≤ 0.3). Only K had high genetic correlations (0.5 < r), while the other two major elements  exhibited low genetic correlations. Phenotypic correlations between locations were slightly smaller than the genetic correlations for most elements.The genetic and phenotypic correlations between locations varied among the elements , Fig 2. r ≤ 0.5) between P and K and P and Mg, and low (0 < r ≤ 0.3) between K and Mg. Genetic correlations among the minor elements were low (0 < r ≤ 0.3) for all possible combinations. There were no correlations between the major and minor elements except between P and Zn (r = 0.21). Although the genetic correlations among elements were lower in Afghanistan than in Japan, the patterns were similar. The genetic correlations among major elements were higher than those of the minor elements. There were also no genetic correlations between the major and minor elements.We also estimated the genetic and phenotypic correlations among elements . GeneralBased on the estimated genetic correlations, we completed cluster analyses to visualize the relationships among the elements . The denp) and FDR values were 4.87 and 11.3%, respectively. In the multi-response GWAS treating phenotypes in two locations as multiple responses, no significant marker was detected at a weak BF threshold (3.0). Some markers were marginally significant. Marker 1045015|F|0 on chromosome 1A had a BF value of 2.95 for P, while marker 1017361|F|0 on chromosome 2B had a BF value of 2.77 for Mn.With the single-response model, the GWAS detected only one marker (1208679|F|0—64:T>C) at the FDR level of 20%. The marker was significantly associated with Zn in Afghanistan, and was located on chromosome 6D (26.98 cM). The −log10(r > 0.5). The predictions were moderately accurate (0.3 < r ≤ 0.5) for the other two major elements  as well for two minor elements . The genomic predictions for Mn exhibited a low degree of accuracy (0 < r ≤ 0.3). Among the six methods used to build prediction models, G-BLUP, RKHS, and RF produced the most accurate predictions. Of these three methods, G-BLUP was the most accurate for P, Mg, Fe, and Mn, while RF was the most accurate for K and Zn. The RKHS predictions were almost as accurate as those of G-BLUP for all elements. The EN and LASSO methods produced the least accurate genomic predictions for all elements, suggesting that the genetic mechanism assumed by these two methods  is inappropriate for the evaluated elements. The slope of the regression line for observed and predicted values was much smaller than 1.0  or even moderately (0.3 < r ≤ 0.5) accurate. The accuracy of the predictions for the three major elements  as well as for two minor elements  was low (0 < r ≤ 0.3). There was no correlation between the observed and predicted elemental contents for Fe. Similar to the results in Japan, of the six methods used, G-BLUP, RKHS, and RF produced the most accurate predictions in Afghanistan. Additionally, RF was the most accurate for all elements. The G-BLUP method exhibited the same degree of accuracy as RF for Mg, and this method was the most accurate for Mg in Japan. Similar to what was observed in Japan, the accuracy of the RKHS predictions in Afghanistan was very similar to that of the G-BLUP predictions. The EN and LASSO predictions were the least accurate for all elements, which was consistent with the results in Japan. For all elements, EN and LASSO indicated the observed and predicted values for elemental contents were negatively correlated. The slope of the regression line for observed and predicted values was less than 0.1 for all elements  is an important crop improvement objective. Most previous studies on the elemental contents of wheat grains focused on Fe and Zn , 25, 39.Accessions producing grains with high micronutrient concentrations are often identified in landraces, wild wheat, and their relatives –44. The vice versa for the other elements. Although the experiments were conducted with uniform nutrient management practices, the soil type and nutrient content are important factors to consider. The soils of Afghanistan are alkaline, with 50% of the soils having a pH between 8 and 8.5. They are also generally rich in alkaline earth carbonates [http://www.fao.org/soils-portal/en/). These soil conditions may have been responsible for the observed differences in the elemental contents of plants grown in Japan and Afghanistan.In the present study, we performed field experiments in Japan and Afghanistan using the same set of Afghan wheat landraces. We observed similar trends between the two locations, suggesting the potential of the Afghan landraces to be grown outside of Afghanistan. The genetic nature of the elemental contents was also confirmed at both locations. However, there were clear location effects, with plants accumulating more P, K, and Zn in Japan than in Afghanistan, and rbonates . CalcareThe genetic nature of elemental contents was assessed by estimating marker-based heritabilities and genetic correlations among elements and between locations. For this approach, genetic variance and covariance were estimated according to the realized relationship matrix derived from marker genotypes , and werIn the present study, the only significant marker detected in the single-response GWAS was located on chromosome 6D and was associated with Zn. Although we used a relatively high FDR threshold (0.2), no significant association was detected except for the marker associated with Zn. This result indicates the potential difficulty in identifying major quantitative trait loci for elemental contents in wheat grains through a GWAS. In this study, the detection power of the multi-response GWAS was similar to that of the single-response GWAS. That is, significant markers were not detected even with a weak BF threshold of 3.0 , suggestOnly one study that assessed the accuracy of genomic predictions of elemental contents in crop plants has been published . BecauseWe used six methods to determine the accuracy of the genomic predictions. The RF was one of the most accurate methods, especially for the minor elements or when the heritability of elemental contents was low . These findings are consistent with those of other studies –52. In tEven for the elements in which the accuracy of genomic predictions was low, there are still the following advantages of GS over phenotypic selection: (1) The time and cost required for phenotypic selection are greater than those for GS. Replications  are necessary for phenotyping, but not for genotyping. Additionally, plants must be cultivated in a field for phenotyping, but not for genotyping, which eliminates the associated costs. (2) Selection cycles can be accelerated during GS breeding. Phenotyping can only be completed once per growing season because elemental contents can be affected by cultivation conditions. Therefore, phenotypic selection should take place under consistent environmental conditions. In contrast, time and location do not influence genotyping. Thus, rapid generation advancement technologies can be used for GS breeding. (3) Segregation of target traits in the next generation and the improvement of target traits in future generations can be simulated during GS breeding –54. The The potential of Afghan wheat landraces for increasing grain elemental contents was analyzed in this study. An EDXRF-based high-throughput method was used to analyze 269 landraces for six elements. The measured elements showed varied genetic nature, with major elements exhibiting higher heritability than minor elements. The lack of a significant correlation between major and minor elements suggests that genetic systems are independent between major and minor elements. A GWAS revealed only one significant marker-trait association. The low power of the GWAS could probably be improved by a larger sample size and a larger number of markers. Although the GWAS detected no significant association for most elements, genomic predictions for elemental contents were moderately to highly accurate. Based on our results, the possibility of establishing a “remote breeding” approach involving genomic selection is under discussion.S1 Tablea Variety names are shown for seven check varieties b National Bio-Resource Project, Japan.(PDF)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.


Its performance relative to genotype imputation and practical utility has not yet been fully investigated. To this end, we compared the two approaches on real (genotyped and imputed) data from 120K samples from the UK Biobank and show that, genotype imputation boasts a 3- to 5-fold lower root-mean-square error, and better distinguishes true associations from null ones: We observed the largest differences in power for variants with low minor allele frequency and low imputation quality. For fixed false positive rates of 0.001, 0.01, 0.05, using summary statistics imputation yielded a decrease in statistical power by 9, 43 and 35%, respectively. To test its capacity to discover novel associations, we applied summary statistics imputation to the GIANT height meta-analysis summary statistics covering HapMap variants, and identified 34 novel loci, 19 of which replicated using data in the UK Biobank. Additionally, we successfully replicated 55 out of the 111 variants published in an exome chip study. Our study demonstrates that summary statistics imputation is a very efficient and cost-effective way to identify and fine-map trait-associated loci. Moreover, the ability to impute summary statistics is important for follow-up analyses, such as Mendelian randomisation or LD-score regression.As most of the heritability of complex traits is attributed to common and low frequency genetic variants, imputing them by combining genotyping chips and large sequenced reference panels is the most cost-effective approach to discover the genetic basis of these traits. Association summary statistics from genome-wide meta-analyses are available for hundreds of traits. Updating these to ever-increasing reference panels is very cumbersome as it requires reimputation of the genetic data, rerunning the association scan, and meta-analysing the results. A much more efficient method is to directly impute the summary statistics, termed as  association summary statistics and are typically publicly shared through publication. Typically, GWASs are carried out by genotyping ∼ 500′000 SNVs for each individual which are then combined with sequenced reference panels to infer untyped SNVs in each’ individuals genome. This process of genotype imputation is resource intensive and can therefore be a limitation when combining many GWASs. An alternative approach is to bypass the use of individual data and directly impute summary statistics. In our work we compare the performance of summary statistics imputation to genotype imputation. We observe that genotype imputation shows a 3- to 5-fold lower RMSE compared to summary statistics imputation, as well as a better capability to distinguish true associations from null results. Furthermore, we demonstrate the potential of summary statistics imputation by presenting 34 novel height-associated loci, 19 of which were confirmed in UK Biobank. Our study demonstrates that given current reference panels, summary statistics imputation is a very efficient and cost-effective way to identify common or low-frequency trait-associated loci.Genome-wide association studies (GWASs) quantify the effect of genetic variants and traits, such as height. Such estimates are called  The genotyping arrays used in these studies only interrogate a small proportion of the genome and are therefore typically unable to pinpoint the causal variant. Such arrays have been designed to be cost-effective and include only a set of tag single nucleotide variants (SNVs) that allow the inference of many other unmeasured markers. To date, thousands of individuals have been sequenced , 2 to pr minimac , 4, whicSummary statistics imputation [summary statistics imputation uses summarised data as input, it is not bounded to privacy restrictions related to the use of individual data. Another advantage is its substantially lower computation time compared to genotype imputation. For example, for imputation of the UK Biobank data, it is about 500 times faster .summary statistics imputation directly to genotype imputation and focuses on its practical advantages using real data. In particular, we evaluated two experiments: 1) we ran a GWAS on both simulated traits and human height using data from 120′086 individuals from the UK Biobank and compared the performances of summary statistics imputation and genotype imputation, using direct genotyping/sequencing as gold standard; 2) we imputed association summary statistics from a HapMap-based GWAS study [et al. [n = 336′474). We extended summary statistics imputation [summary statistics imputation.This study compares  [et al.  and the putation , 14 whicBy combining summary statistics for a set of variants and the fine-scale LD structure in the same region, we can estimate summary statistics of new, untyped variants at the same locus.ai are available for SNVs i = 1, …, I from a linear regression between a continuous phenotype y and the corresponding genotype gi measured in N individuals. Without loss of generality we assume that both vectors are normalised to have zero mean and unit variance. Thus Σ represents the pairwise covariance matrix of effect sizes of all i = 1, …, I SNVs.We assume a set of univariate effect size estimates αu of an untyped SNV u in the same sample, one can use the conditional expectation of a multivariate normal distribution. The conditional mean of the effect of SNV u can be expressed using the effect size estimates of the tag SNVs [tag SNVs, u and all To estimate the univariate effect size tag SNVs , 15:a^un individuals and denote them γ and Γ, with c = N ⋅ s and C = N ⋅ S being the estimates for the correlation matrices. Further, by assuming that SNV u and the trait are independent conditioned on the We assume that estimates for the two covariances are available from an external reference panel with et al. [One can also choose to impute the Z-statistic instead, as derived by Pasaniuc et al. :z^u|M=cet al. [u. To speed up the computation when imputing SNVs genome-wide, we apply a windowing strategy, where SNVs within a 1 Mb window are imputed simultaneously using the same set of Similar to Pasaniuc et al. , we chosC (and c) we use an external reference panel of n individuals. Since the size of C often exceeds the number of individuals (q ≫ n), shrinkage of matrix C is needed to guarantee that it is invertible.To estimate C are shrunk towards zero and the extent of which is characterised by a shrinkage parameter λ. As a consequence, it also lowers the RMSE in summary statistics imputation [C close to zero, may represent pure noise (and zero LD), which can be inflated when inverting the matrix.Off-diagonal values of putation , as valuBy applying shrinking, the modified matrix becomesc is not inverted, we still shrink it to curb random fluctuations in the LD estimation in case of no LD.Even though cλ and Cλ, Inserting C to the identity matrix, while λ = 0 leaves C unchanged. Schäfer & Strimmer [C. Wen & Stephens [C in a way that they represent recombination hotspots correctly. A similar idea is to set small absolute correlation values to 0. Here, we mainly focus on two commonly used λ values: λ fixed at 0.1 [n: Note that λ can vary between 0 and 1, with λ = 1 turning Strimmer  find an Stephens  propose d at 0.1 , and λ cn: λ=2/n .r2, is defined as the squared correlation between the imputed and true genotypes. An r2 value of 1 means perfect imputation, whereas r2 of 0 indicates poor imputation [summary statistics imputation this quantity is the total variance explained by a linear model where the imputed genotype is regressed onto all measured markers. It was proposed by Pasanuic et al. [q) and sample size (n) [qeff [Imputation quality, putation . In summc et al.  to be essize (n) . Due to n) [qeff :r^pred,Negative values in summary statistics imputation methods assume that all effect estimates are based on the same set of N individuals. This assumption does not hold most of the time since meta-analysis studies use different genotyping chips or different imputation reference panels. As a result, the covariance between effect estimates will change. In the extreme case when effect estimates are computed in two non-overlapping samples, the correlation will be zero even if there is very high LD between the two SNVs.All previously published zu, and any observed partial Z-statistic, To perform imputation, we require the correlation between any target complete Z-statistic, Nk as the sample size of SNV k, N as a vector recording the sample size of each tag SNV, Nmax as the maximum in N, and assume that every tag SNV k the sample of individuals is a subset of a complete sample of Nmax individuals.We define D, where each element is calculated as follows:By defining δkl. Typically, we do not know the details of the exact sample overlap for every pair of SNVs, Nk∩l, and instead simply know Nmax and the vector N. Therefore, we must derive the sample overlap Nk∩l based on assumptions about the dependence structure of missingness.We present two estimators of Nk∩l = min. To enable the dependent approach, we construct a D matrix by replacing Nk∩l with min,The most conservative assumption is maximum possible overlap, resulting in maximum dependence, as this minimises the imputed Z-statistic. If each SNV has a corresponding binary missingness vector, the correlation between these missingness vectors will be maximised when the sample overlap is at its maximum, independent of each other, the expected overlap can be estimated asIf the missingness vectors are d from D from either, Finally, we impute In order to convert d′D−1d is the corresponding imputation quality.Note that δ can be found in Details to the estimation of n = 120′086). For SNVs, the number of individuals range between n = 3′431 and n = 120′082. Additionally to custom SNP array data, UK Biobank contains imputed genotypes [M variants were imputed by UK Biobank, using SHAPEIT2 and IMPUTE2 [The UK Biobank  compriseenotypes . A subse IMPUTE2 .summary statistics imputation within 1 Mb-wide regions, by blinding one at the time and therefore allowing the remaining SNVs to be used for tagging. As tag SNVs we used all SNVs except the focal SNV within a 1.5 Mb window.We imputed GWAS Z-statistics (ran on directly genotyped data) using P ≥ 10−5) SNV. More specifically, within each height-associated region we only imputed SNVs that have LDmax> 0.2. LDmax was defined as the largest squared correlation between a SNV and all height-associated SNVs on the same chromosome. In the 171 null regions we chose only those variants with LDmax≤ 0.05 with any associated marker on the same chromosome. These selection criteria lead to 44′992 variants being imputed. We did not analyse palindromic SNVs (A/T and C/G) (3′306 variants), SNVs with missing genotypes for more than 36′024 (30%) individuals (2′317 variants), SNVs with MAF < 1% (3′010 variants). These restrictions left us with 37′467 of the 44′992 imputed SNVs.We selected 706 regions in total, consisting of 535 loci containing height-associated SNVs , 13 and summary statistics imputation and genotype imputation followed by association we compared each method to the directly genotyped data association as gold standard. To compare the performance between k = 1 … K SNVs is:summary statistics imputation for SNV k and Zk the Z-statistic resulting from genotype data for SNV k (our gold standard). Likewise, we replaced genotype imputation.More precisely, the RMSE and the Bias for a set of Zu as follows ZGTimp, ZSSimp), derived from the full sample.Note that for height-associated SNPs with missing genetic data we rescaled the association Z-statistic genotype imputation and summary statistics imputation. For summary statistics imputation, we first ran a GWAS within ± 0.75 Mb of the focal SNV, and subsequently used the estimated summary statistics to perform summary statistics imputation. For SNVs with a real association we calculated the power as the fraction qA of SNVs with a P < α , whereas for SNVs with no association we calculated FPR as the fraction qN of SNVs with P < α . We varied α between 0 and 1 and visualised FPR versus power for each method. The standard deviation was calculated based on the assumption of a binomial distribution for fA and fN: fi ∼ B. The respective variance estimation for qi is then: Var(qi) = qi(1 − qi)/mi.Additionally, we calculated power and false positive rate (FPR) for each method. For this, we randomly selected 3′390 SNVs and used each once as null and once as associated SNV. For the null scenario, we simulated a random, standard normal phenotype. For the alternative scenario, we simulated a phenotype such that the SNV explained 0.01% of the simulated phenotype variance . For both scenarios we calculated the summary statistics via associated SNVs, while SNVs not associated with height stem from 171 regions and are termed null SNVs. Throughout the manuscript, LD is estimated as the squared correlation [The obtained (summary statistics) imputation results were grouped based on the imputed SNVs (i) being correlated (LD > 0.3) to any height-associated SNV on the same chromosome or being a null SNV (LD < 0.05); (ii) low-frequency (1% < MAF ≤ 5%) or common SNV (MAF > 5%); (iii) being badly-, except for the 111 exome variants reported in Marouli et al. [We imputed all non-HapMap variants that were available in UK10K, using the summary statistics in Wood et al.  as tag Si et al. , which wsummary statistics imputation we screened for SNVs with P-value ≤ 10−8 and applied conditional analysis, aiming to limit the results to SNVs acting independently from known HapMap findings. The significance threshold of 10−8 was chosen based on the effective number of SNVs evaluated (< 9′276′018). For each imputed 1 Mb window, we started the conditional analysis by defining two sets of SNVs. The first set contained all imputed SNVs that had an imputed P-value ≤ 10−8, ranging from position bp(1) to bp(2). The second SNV set contained all reported HapMap SNVs  within a range of bp(1) − 1 Mb and bp(2) + 1 Mb. Having two SNV sets—the first set with newly detected variants, the second set with reported HapMap variants—we could then condition each SNV in the first set on all SNVs in the second set, using approximate conditional analysis [P-value ≤ 10−8. Additionally, for each (35) lead variant in the candidate regions we performed conditional analysis using each HapMap SNV (in turn) within 1 Mb vicinity. Finally, we performed a conditional analysis for nearby candidate loci (neighbouring windows), to avoid double counting. In each candidate locus we report the imputed variant with the smallest conditional P-value as the top variant.After applying analysis  and UK10α = 0.05/k, with k as the number of candidate loci.We replicate our findings using our UK Biobank height GWAS results and for SNVs present on the exome chip we also use the recent height GWAS . For botn = 336′474 individuals, provided by the Neale lab [n = 120′086)).For replication using UK Biobank we used summary statistics based on the latest release of genetic data with eale lab . For SNVα = 10−6. We only extract the information for variants that were defined as as novel discoveries.We use two databases to annotate newly discovered SNVs. First, we use GTEx , an eQTLCEU, GBR, FIN, TSI and IBR of the 1000 Genomes reference panel [C and the other half to simulate the association study with an in silico phenotype. The simulation procedure is described in more detail in θmiss, with zero value reflecting missingness at random and one corresponding to the maximum possible sample overlap between SNVs.We simulated genetic data on 25’000 individuals was used. In brief, we used data from the five European subpopulations ce panel  to 5′000C and c  publications: Wood et al. [et al. [To assess the performance of  [et al. , an exomu, α and various missingness design. We compare the MSE of the conventional estimation to the MSE of two other estimators, Ddep)( and Dind),  is unbiaDdep) as it is the most conservative assumption. An alternative is to assume randomly distributed missingness (Dind). Most pairs of SNVs in GIANT attain close to the maximum possible missingness-overlap ( performs better (as expected). With lower overlap (first column) Dind) and (3) associations imputed from summary statistics calculated using genotype data  associations calculated from original genotype data (ground truth); (2) associations calculated from imputed genotype data (ype data . For ourvs. null SNVs) with the lead SNV of each of the 535 height-associated regions ; (ii) frequency ; and (iii) imputation quality based on summary statistics imputation  their association status . For this we used four measures: the root mean squared error (RMSE), bias, the linear regression slope, and the correlation. Second, we calculated power and false positive rate for genotype imputation and summary statistics imputation directly.We focused on two aspects of the imputation results. First, we compared how summary statistics imputation performs on average well with a correlation and a slope close to 1 (cor = 0.998 and slope = 0.98), but it drops to cor = 0.928 and a slope = 0.83) for low imputation quality, low-frequency variants. On the other hand, for genotype imputation  slope and correlation.For the 31′567 null SNVs we present the same metrics as for associated SNVs. We analysed 13′556 low-frequency and 18′011 common variants. First, the green dots in summary statistics imputation and genotype imputation in terms of RMSE among associated variants (for the same six SNV categories), shown in the upper part of genotype imputation had a smaller RMSE than summary statistics imputation. The difference between the two methods in terms of RMSE increases as imputation quality decreases. For the largest SNV subgroup—well-imputed and common SNVs—summary statistics imputation had a RMSE of 0.33 versus 0.093 for genotype imputation. In case of summary statistics imputation, the RMSE is more influenced by a decrease in imputation quality than by a reduction of MAF. For example, the RMSE for common variants with medium-quality imputation is 1.02 (a 3.1-fold increase), while the RMSE for low-frequency variants with high-quality imputation is 0.48 (a 1.4-fold increase). However, for genotype imputation a decrease in MAF or imputation quality seems to have a similar effect. For example, the RMSE for well-imputed, low-frequency variants is 0.14 for genotype imputation (a 1.5-increase), and the RMSE for medium-imputed, common variants is 0.19 for genotype imputation (a 2.1-increase)  . For nulα from 0 to 1) for simulated phenotypes. As before, we stratified the results by MAF and imputation quality categories. We observe that for common SNVs with genotype imputation and summary statistics imputation are almost identical in terms of FPR and power. For low-frequency and well-imputed variants, genotype imputation offers some power advantage compared to summary statistics imputation, in particular for intermediate FPRs. As we approach lower imputation quality and MAF, genotype imputation advantage becomes more and more apparent for all range of FPR values. Averaged over all SNV categories, for false positive rates of 0.001, 0.01, 0.05, summary statistics imputation yielded a decrease in statistical power by 9, 43 and 35%, respectively.Analogous to a ROC curve et al. [While previous studies have examined the role of (common) HapMap variants for height , 36, theet al. , since tsummary statistics imputation and used all HapMap SNVs as tag SNVs. We imputed variants that were available in UK10K with a MAFUK10K ≥ 0.1%, as well as all reported exome variants in Marouli et al. [By imputing > 6M additional SNVs summary statistics using HapMap variants  as tag Si et al. . In totaP-value ≤ 10−8). We identified 35 such candidate loci. Within each locus we defined the imputed variant with the lowest conditional P-value as the top variant. All 35 variants are listed in We subjected all 9′276′018 variants with an imputation quality ≥ 0.3 to a scan for novel candidate loci. A region was defined as a candidate locus if at least one imputed variant was independent from any reported HapMap variant nearby .Next, we used the UK Biobank to replicate the associations with height of these 35 candidate variants and subsequently grouped them into replicating (20 variants) and not replicating (15 variants)  includes SNVs replicating already published exome chip associations (one locus), group (ii) includes SNVs that contain no reported HapMap variant nearby (three loci), and group (iii) includes SNVs that contain one or more reported independent HapMap variants nearby (31 loci). Replication success with UK Biobank is 1/1 in group (i), 2/3 in group (ii), 17/31 in group (iii). We only term categories (ii) and (iii) as P-value between 10−6 and 10−8 in [Although group (ii) only contains loci that had no reported HapMap variants nearby, three candidate loci .rs112635299, MAF = 2.32%). Conversely, among the 15 candidate variants that did not replicate in the UK Biobank, 10 are rare, three are low-frequency variants, and only two are common.We observed that variants with higher MAF have higher chance to replicate. Among the 20 candidate variants that did replicate in UK Biobank, 19 were common and one a low-frequency variant , is a proxy of rs28929474 (LD = 0.88), has been associated with alpha-1 globulin [rs28929474 was identified in the GIANT exome chip study to be height-associated (P = 1.39 × 10−45) [P-value calculated with summary statistics imputation was P = 1.06 × 10−13. rs28929474 is a low-frequency variant (MAF = 2.3%) and replicates in the UK Biobank with P = 1.66 × 10−25.globulin  and is aglobulin . rs28929× 10−45) . The P-vLocus #2: rs76306191 is a common variant on chromosome 1, located in gene DCST1. There was no reported HapMap variant nearby to condition on. However, the absolute correlation to the HapMap variant with the lowest P-value (> 10−8) in the same region was 0.8. One of the 122 variants reported by the exome chip study, rs141845046, was in this region, but had an imputed P-value > 10−3. rs76306191 replicated in the UK Biobank with P = 1.09 × 10−7. rs76306191 is an eQTL in artery  for gene ZBTB7B and in thyroid gland for gene DCST2.Locus #5: rs12795957 is a variant on chromosome 11 and an eQTL for gene RAD9A in artery .Locus #6: rs503035 is a variant on chromosome 5. It is an eQTL for gene PITX1 in testis tissue. rs62623707, one of the 122 reported exome variants, was in this region, but had an imputed P-value > 10−3.Locus #15: rs78566116 is a variant on chromosome 6. rs78566116 has been associated with HPV8 seropositivity in cancer [n cancer , rheumatn cancer  and ulcen cancer .Locus #20: rs58402222 is an intronic variant on chromosome 1, located in gene NASP. It is an eQTL for genes CCDC163P, MAST2 and TMEM69 in cells (transformed fibroblasts); and for GPBP1L1 in thyroid tissue.et al. [P-value threshold of α = 0.05/111 we could classify variants into the ones that replicated and those that failed replication. This is summarised in Next, we focussed on 122 novel variants of Marouli et al. . For thigenotype and summary statistics imputation. In contrast to previous work by others [summary statistics imputation through real data applications for different SNV subgroups characterised by allele frequency, imputation quality and association status (null/associated).In this article, we focussed on the comparison between y others , 14, hersummary statistics imputation method [summary statistics imputation has a lower MSE in all scenarios. We then evaluated the performance of our improved summary statistics imputation method in terms of different measures and showed that summary statistics imputation is a very efficient and fast method to separate null from associated SNVs. However, genotype imputation outperforms summary statistics imputation by a clear margin in terms of accuracy of effect size estimation. By imputing GIANT HapMap-based summary statistics we have demonstrated that summary statistics imputation is a rapid and cost-effective way to discover novel trait associated loci. We also highlight that the principal limitations of summary statistics imputation are rooted in the LD estimation and in imputing very rare variants with sufficient confidence. Finally, we implemented summary statistics imputation that accounts for varying sample size as a command-line tool [First, we adapted the published n method , by alloine tool .Dind) and independent (Dind) approaches. Overall, replacing C and c with D and d yields a lower RMSE. Furthermore, we note that the dependent approach has lower RMSE when the sample size variance is low and the missingness correlation approaches one. Ddep) and varying correlation in missingness between variants ). The reason for this is that SNPs are either entirely missing from a study or being available for all study participants depending on its genotyping chip or imputation panel, which induces positive missingness correlation between markers.Ideally, for any pair of SNVs that are in LD with each other, we would know the exact number of individuals that are in the overlap, i.e. the number of individuals for which both SNVs were genotyped. Using the individual study missingness and sample sizes from the Genetic Investigation of ANthropometric Traits (GIANT) consortium, we demonstrate in Fig. summary statistics imputation and genotype imputation by using individual-level data from the UK Biobank.We compared summary statistics imputation leads to a larger RMSE than genotype imputation in all twelve SNV subgroups investigated . For low-frequency null variants the ratio is up to 0.6 lower . The inflation for well-imputed SNVs can be explained by the genomic lambda, while for badly-imputed SNVs it is aggravated by the underestimated standard error.summary statistics imputation for rare variants will require not only larger reference panels to allow estimation of LD of rare variants, but also methods which would allow non-linear tagging of variants. It should be kept in mind that, just like for genotype imputation, even with very large reference panels, one will not be able to impute variants with extremely rare allele counts. To investigate these SNVs full genome sequencing is indispensable [Because the number of associated SNVs with MAF < 1% was too low (13 variants) to draw any meaningful conclusions, we refrained from analysing this MAF group. One other reason to exclude rare variants from this analysis is, that the reference panel used (UK10K) contains 3′871 individuals and therefore estimations for LD of rare variants are unreliable and rare variants can (in theory) only be covered down to MAF = 1/(2 ⋅ 3′871). We believe improving pensable .c and C estimated from a reference panel  n = 3′871 is 0.016), which becomes problematic in cases of low correlation.The discrepancy in imputation quality metric between putation  can be esummary statistics imputation we imputed Wood et al. [et al. and from each HapMap SNP, we then replicated these using (independent) UK Biobank data.As a showcase of the utility of d et al.  to highed et al. , then seet al. [et al. [While Wood et al.  is the l [et al.  exceeds rs28929474) and 19 replicated in UK Biobank (at α = 0.05/35 level). Two candidate loci  and werThe 15 non-replicating candidate loci were on average on a lower allele frequency spectrum . Allele frequency was higher among the 20 replicating candidate variants (19 were common and one a low-frequency variant).P-value per locus, is provided as an additional column α = 0.05/529) we find evidence that 18 of the 35 variants are not only independent from all [We also ran an additional approximate conditional analysis, where we conditioned each of the 35 variants onto their neighbouring HapMap SNP (one-by-one). The resulting maximum conditional from all  reportedsummary statistics imputation of the the 111 reported exome chip variants [summary statistics imputation decreases with lower MAF and imputation quality.We then focussed on the variants . KnowingFor replication of summary statistics from European individuals we use the UK Biobank, which represents only a subset of all European ancestries and is genotype-imputed (instead of sequenced), but on the other hand provides a reliable resource due to its sample size.genotype imputation done for genotyped variants can only partially be compared to genotype imputation for untyped variants, as genotyped variants were used for phasing  and matching the population diversity of the conducted GWAS. At the first glance, 1000 Genomes reference panel could have been used to appropriately match GIANT allele frequencies, however, the 8-fold higher sample size of UK10K panel offers a larger benefit, ultimately reducing the RMSE .summary statistics imputation to our method taking into account variable missingness, we used an upsampling technique called HAPGEN2 [For the simulation study comparing standard  HAPGEN2 , which lsummary statistics imputation method itself has several limitations too.The Due to the size of publicly available sequenced reference panels we can not explore the performance of rare variants (MAF < 1%).genotype imputation is able to capture such non-linear relationships by estimating the underlying haplotypes . Furthermore, in case of genotype imputation it is sufficient that the relevant haplotypes are present in the reference panel, but the overall allele frequency does not need to match the GWAS allele frequency.The imputation of summary statistics of an untyped SNV is essentially the linear combination of the summary statistics of the tag SNVs . Such a Summary statistics imputation relies on fine tuning of parameters, such as shrinkage of the correlation matrix. Any λ > 0 will make the correlation matrix invertible, but a stronger shrinkage can compensate for estimation error. We hypothesised that optimal shrinkage depends on local LD structure, and sought to optimise λ for each genomic region using the effect sizes of tag SNVs as training data set in a leave-one-out fashion. When looking at null variants, however, maximum shrinkage (λ = 1) usually leads to the smallest RMSE. Therefore, when looking at a region with a mixture of null and associated SNVs, the selected λ will be shifted towards 1 and shrink the estimation of associated SNVs towards 0, which is not ideal.The imputation quality metric ce panel . We notice panel  by accouce panel . The mosS1 FigThis figure shows how many of the null and associated SNVs were categorised into common, low-frequency and rare MAF subgroups, and into well-imputed, medium imputed and badly imputed imputation subgroups. Associated SNVs are presented in the left window, and null SNVs are presented in the right window. MAF category (x-axis), # of SNVs on the y-axis, colour refers to imputation quality category.(PDF)Click here for additional data file.S2 FigThis figure shows the fraction of badly-, medium- and well-imputed SNVs within each MAF subgroup. Null and associated SNVs were categorised into common, low-frequency and rare MAF subgroup, and into well-imputed, medium imputed and badly imputed imputation subgroup. Associated SNVs are presented in the left window, and null SNVs are presented in the right window. MAF category (x-axis), fraction of SNVs on the y-axis, colour refers to imputation quality category. Numbers within the stacked barplot refer to the number of SNVs imputed in each subgroup.(PDF)Click here for additional data file.S3 FigMACHIMPUTE’s info measure used by genotype imputation (y-axis). To avoid clumping of dots, we used tiles varying from grey (few dots) to black (many dots). The identity line is dotted.MACHr^2  (x-axis)(PDF)Click here for additional data file.S4 FigIMPUTE’s info measure used by genotype imputation (x-axis) vs summary statistics imputation (y-axis). To avoid clumping of dots, we used tiles varying from grey (few dots) to black (many dots). The identity line is dotted.(PDF)Click here for additional data file.S5 Figgenotype imputation (blue) and summary statistics imputation (green) for different significance thresholds (α). It includes 95%-confidence intervals in both directions . This figure is a zoom into the bottom-left area of α = 0.05. The vertical, dashed line represents FPR = 0.05. Results are grouped according to MAF (columns) and imputation quality (rows) categories.This figure compares false positive rate (FPR)  versus power (y-axis) for (PDF)Click here for additional data file.S6 Figmax > 0.1 (LD with one of the top variants in the exome [This graph shows the Z-statistics of the exome chip study on the x-axis versus the Z-statistics of SNP-array study on the y-axis. Each dot shows one of the 2′601 variants that had LDhe exome  or HapMahe exome ). To mak(PDF)Click here for additional data file.S7 FigP-values as orange circles and the imputed P-values (using summary statistics imputation) as solid circles, with the colour representing the imputation quality (only log10(P)-value. The horizontal line shows the P-value threshold of 10−6 (dotted) and 10−8 (dashed). Top and bottom window have annotated summary statistics: In the bottom window we mark dots as black if it is are part of the 122 reported hits of [P-value). Variants in orange (plain) are HapMap variants, but were not among the 697 reported hits. Each of the annotated variants is marked for clarity with a bold circle in the respective colour. The genes annotated in the middle window are printed in grey if the gene has a length < 5′000 bp or is an unrecognised gene (RP-).This figure shows three datasets: Results from the HapMap and the exome chip study, and imputed summary statistics. The top window shows HapMap  hits of . In the  hits of  in bold (ZIP)Click here for additional data file.S8 Figlog10(p)-value of the exome chip study on the x-axis and the imputed −log10(p)-value on the y-axis. The first row refers to the highest imputation quality (between 0.7 and 1), with the columns as the different allele frequency categories. The number of dots in each window is marked top left. The vertical and horizontal dotted lines mark the significance threshold of −log10(0.05/111) (dashed). The width of the x-axis is proportional to the range of the y-axis. For MAF and This graph shows for all 111 variants the −(PDF)Click here for additional data file.S9 Figp-values resulting from summary statistics imputation, for associated variants (left window), null variants (right window). The colours refer to the imputation quality categories. Note that the P-value in these plots are not λGC corrected.These QQ-plots show the distribution of (PDF)Click here for additional data file.S10 FigIn the GIANT meta-analysis  the set of SNVs is different in each cohort, allowing us to create a binary ‘missingness’ vector for each SNV recording whether a given individual in the combined population was genotyped for this SNV. For 10′000 randomly selected pairs of nearby SNVs, we compute the correlation between these missingness vectors and plot the density plot. The correlations are usually greater than zero, and often quite close to one, confirming that a ‘missing independently at random’ assumption is not appropriate.(PDF)Click here for additional data file.S11 Figgenotype imputation summary statistics, while the y-axis shows the Z-statistics from summary statistics imputation. The color of each point refers to the imputation quality of summary statistics imputation. Results are grouped according to MAF (columns) and imputation quality (rows) categories and the numbers top-right in each window refers to the number of SNVs represented. The identity line is indicated with a dotted line. The estimation for correlation and slope are noted in the bottom-right corner.The x-axis shows the Z-statistics of the (PDF)Click here for additional data file.S12 Figgenotype imputation summary statistics, while the y-axis shows the Z-statistics from summary statistics imputation. The color of each point refers to the imputation quality of summary statistics imputation. Results are grouped according to MAF (columns) and imputation quality (rows) categories and the numbers top-right in each window refers to the number of SNVs represented. The identity line is indicated with a dotted line. The estimation for correlation and slope are noted in the bottom-right corner.The x-axis shows the Z-statistics of the (PDF)Click here for additional data file.S13 Figgenotype imputation summary statistics, while the y-axis shows the Z-statistics from summary statistics imputation. The color of each point refers to the imputation quality of genotype imputation. Results are grouped according to MAF (columns) and imputation quality (rows) categories and the numbers top-right in each window refers to the number of SNVs represented. The identity line is indicated with a dotted line. The estimation for correlation and slope are noted in the bottom-right corner.The x-axis shows the Z-statistics of the (PDF)Click here for additional data file.S14 Figgenotype imputation summary statistics, while the y-axis shows the Z-statistics from summary statistics imputation. The color of each point refers to the imputation quality of genotype imputation. Results are grouped according to MAF (columns) and imputation quality (rows) categories and the numbers top-right in each window refers to the number of SNVs represented. The identity line is indicated with a dotted line. The estimation for correlation and slope are noted in the bottom-right corner.The x-axis shows the Z-statistics of the (PDF)Click here for additional data file.S15 FigThe x-axis shows the Z-statistics of the conventional estimate, the y-axis the Z-statistics when accounting for missingness (dependent approach). The dotted line marks the genome-wide threshold. There are 11′200′403 variants displayed in a binned fashion.(PDF)Click here for additional data file.S1 Tablesummary statistics imputation. Within each candidate locus, we provide for the top variant the imputation results (.imp), along with conditional analysis results (.cond), the UK Biobank replication , and (if available) the exome chip study results (.exome). filename shows the filename of the locus-zoom plot in SNP.cond.info presents each HapMap SNV used for conditional analysis, including its MAF, LD between the HapMap SNV and the imputed SNV, and a reversed conditional analysis result (HapMap variant conditioned on the imputed SNV). The column Group classifies each row into candidate loci (i) that were reported by [max.P.cond.hm represents the maximum P-value from a conditional analysis performed with each HapMap variant nearby. P = P-value, N = sample size, r2 = imputation quality, eff = effect size, EAF = effect allele frequency, MAF = minor allele frequency. If a candidate locus was not available in the UK Biobank, we provide a replication for a second variant that is in high LD with the primary variant, hence duplicated region numbers for some candidate loci.This table presents details of the 35 candidate loci discovered with orted by  already,(CSV)Click here for additional data file.S2 Tableα = 10−6. The first four columns represent the region number, SNV, P-value from summary statistics imputation and the P-value in the UK Biobank. The three remaining columns are information extracted from GTEx, with the tissue name, gene name and the P-value of the association between the SNV and the gene expression. For each region, we order SNV-gene-tissue associations according to their P-value. # refers to the region number.This table shows SNVs which are significant eQTLs in GTEx . We only(CSV)Click here for additional data file.S3 Table.imp) for all 122 variants shown as “novel” in [.exome). P = P-value, N = sample size, r2 = imputation quality, eff = effect size, EAF = effect allele frequency. 11 variants were not referenced in UK10K or on chromosome X and therefore not imputed (see column ‘comment’). The position corresponds to hg19.This table presents the summary statistics imputation results (ovel” in . The rig(CSV)Click here for additional data file.S1 Appendix(PDF)Click here for additional data file.S2 Appendix(PDF)Click here for additional data file.S3 Appendix(PDF)Click here for additional data file.


Cucumis melo is highly diverse for fruit traits providing wide breeding and genetic research opportunities, including genome-wide association (GWA) analysis. We used a collection of 177 accessions representing the two C. melo subspecies and 11 horticultural groups for detailed characterization of fruit traits variation and evaluation of the potential of GWA for trait mapping in melon. Through genotyping-by-sequencing, 23,931 informative SNPs were selected for genome-wide analyses. We found that linkage-disequilibrium decays at ~100 Kb in this collection and that population structure effect on association results varies between traits. We mapped several monogenic traits to narrow intervals overlapping with known causative genes, demonstrating the potential of diverse collections and GWA for mapping Mendelian traits to a candidate-gene level in melon. We further report on mapping of fruit shape quantitative trait loci (QTLs) and comparison with multiple previous QTL studies. Expansion of sample size and a more balanced representation of taxonomic groups might improve efficiency for simple traits dissection. But, as in other plant species, integrated linkage-association multi-allelic approaches are likely to produce better combination of statistical power, diversity capture and mapping resolution in melon. Our data can be utilized for selection of the most appropriate accessions for such approaches. Cucumis melo L.: Cucurbitaceae) contains a wealth of phenotypic diversity for multiple attributes, especially for fruit traits such as size, shape, external (rind) and internal (flesh) color, sugar content, acidity, texture and aroma1. This wide diversity is the source for ongoing genetic research and breeding aimed at mapping and identifying quantitative trait loci (QTLs) and genes affecting key horticultural and consumers’ preference traits. Numerous genetic studies in recent years focused on fruit quality traits in melon, including fruit size and shape5, flesh color9, rind color10, netting and sutures12, sweetness and aroma14, acidity15 and ripening behavior16. Most of these studies utilized targeted bi-parental populations for the genetic analyses and trait mapping.Melon , double-haploids (DH), introgression lines (ILs) or backcross inbred lines (BILs). While such populations display different attributes with respect to time and cost of creation, statistical power and genetic resolution, they all share the advantages and disadvantages of bi-allelic, non-structured designs.Bi-parental linkage mapping was, to a large extent, the default genetic mapping approach for simple and quantitative traits in plant species where generation time is short and the development of segregating populations is feasible. This approach is based on the analysis of the segregation of polymorphism between the parental lines and their progeny. Various linkage population types are commonly used in plant genetics, such as: F18. Among the advantages of association genetics is the use of existing collections, the ability to simultaneously analyze wide phenotypic diversity resulting from multi-allelic genetic variation, and the exploitation of accumulated historical recombination events. A key limiting factor that inhibited the effective implementation of this approach in plant genetics was the inherent effect of population structure on creation of spurious associations. The development and successful application of statistical and computational tools to control for genetic relatedness21 enhanced the use of this approach in plants. At first, the implementation of association mapping was mostly through the analysis of candidate-genes, due to the insufficient genome-wide marker coverage defined by linkage-disequilibrium (LD) decay profile and genome size24. However, with the growing application of NGS-based approaches for high-density genotyping, the genome wide association study (GWAS) approach is now extensively used for genome-wide genetic dissection of traits in many crop plants26.Approximately 15 years ago, association mapping methodology started to become a valid alternative strategy for trait mapping in plantset al. screened 87 melon accessions with 286 simple sequence repeat (SSR) markers to describe population structure and fruit traits variation27. Leida et al. used a set of 175 melon accessions genotyped with 251 single nucleotide polymorphisms  to describe population structure, LD and candidate gene associations for sugar accumulation and ripening behavior28. Two recent studies used genotyping-by-sequencing (GBS) to genotype melon panels and to describe population structure and LD using genome-wide high density markers coverage 30.Several recent studies in melon have used germplasm collections for characterization of diversity, population structure and for trait mapping. Tomason In the current study a diverse collection of 177 melon accessions was extensively phenotyped for a number of traits and genotyped with 23,931 SNPs using GBS. The main objective was to describe genetic properties of this diversity panel and to test the actual potential and effectiveness of the GWAS approach for trait mapping in melon. Population structure and genome-wide LD patterns are described. Through GWA analysis, we demonstrate the mapping of several Mendelian traits to a narrow interval overlapping with known causative genes, providing a first proof-of-concept for the potential of association genetics for high resolution mapping to a candidate-gene level in melon.melo and agrestis), encompassing 11 horticultural groups  Short tag sequence , (2) Tag sequence contains highly repetitive DNA, (3) The haplotype is too divergent from the reference to align, (4) Structural variation: the tag sequence is not present in the reference genome individual, or (5) Incomplete reference: the tag sequence is not present in the reference genome.The collection was genotyped using GBS approach32 were identified across the 177 melon accessions. Following further filtration to minor allele frequency (MAF) > 0.05 and maximum of 6% missing data per site, 23,931 informative SNP markers were defined as the genotypic dataset in this study. As expected in GBS, SNP densities fluctuate within and between chromosomes, resulting in differential coverage across the genome  was subsequently cloned35. In the current study, female flowers at anthesis were visually characterized for their sex-expression type  is monomorphic, showing only the andromonoecious phenotype, for the rest of our panel the distribution of the phenotypic classes is relatively uniform across the genetic variation and independent of population structure, which is an important attribute for effective association analysis. Using these phenotypes we then performed GWA analysis and found a single significant locus on chromosome 2 associated with this trait (P = 1.4 × 10−12 at mixed linear model (MLM) analysis, Fig. CmACS-7 gene, in which we did not have a SNP in our genotyping set  and andromonoecious contain both female and male (stamens) organs. This trait was previously mapped to the r Figure . Figure r Figure .Figure 4gf, previously mapped to chromosome 97. Recently, the gene CmOr was identified as gf and accounts for most of the orange/non-orange variation in melon9. In order to characterize flesh color variation in the GWAS panel, longitudinal sections of fifteen fruits per accession were scanned and analyzed for multiple characteristics, including flesh color, using the Tomato Analyzer software36  resulted in high proportion of significant SNP effects across all chromosomes . MLM analysis removed most of the significant effects (a mix of mostly spurious and maybe some positive associations) and only the chromosome 9 SNP remained highly significant (P = 3.7 × 10−12) and passed the genome-wide threshold  5%, Fig. CmOr gene . This SNP has MAF of 0.32, it explain ~70% of the flesh color variation across the panel and effectively distinguish between orange and non-orange 9, and obtained 99% match in allelic segregation  on chromosome 10 was identified as causative for the yellow rind phenotype in yellow casaba melon accession 10. We characterized the collection for rind color through visual scoring and selected only smooth rind (without netting that could mask the rind color) accessions that are either yellow or white/cream . Zooming in on this region showed two SNPs at positions 3,541,676 and 3,541,866 (190 bp apart) that are in complete LD (R2 = 1). These SNPs, that showed the strongest association with yellow rind trait, are located 65 Kb and 8 genes away from the CmKFB gene10  and fruit shape index (ratio between length and width) varies between 0.66 and 6.3 (10-fold) across the panel  were found on all chromosomes  on four of these chromosomes . Interestingly, large effects appear in higher frequencies for SNPs with MAF < 0.1  on F3s10.The current study is aimed at establishing melon diversity collection as a permanent germplasm resource for discovery of phenotypic variation and genetic mapping of traits. We performed phenotypic and genotypic analyses to test and demonstrate the genetic resolution and overall effectiveness of this platform. The panel used in this study is composed of 177 accessions that represent large portion of the genetic and phenotypic diversity within Thus, three independent single gene traits were successfully mapped in the current study to narrow genomic intervals, where the most significant SNPs identified for each trait were located within less than 100 Kb windows from the known, previously mapped, causative genes. LD around these mapped trait loci decayed within the expected physical distance calculated at the whole genome level and allowed us to define confidence intervals containing less than 20 annotated open-reading frames (ORFs) in these three examples.5. We analyzed fruit shape variation across the diversity panel and mapped QTLs for this trait. The extended variation and high heritability enabled the identification of multiple significant SNPs across all chromosomes  and orange flesh color9, most likely occurred during sub-speciation as the orange allele is present only in one accession of the ssp. agrestis group in our collection (6%) while the frequency of the Orange allele at the ssp. melo group is 41%. The genotypic data localize the light-orange-flesh ssp. agrestis accession (PI414723) intermediate between the two sub-species, supporting this hypothesis  is shown here as a useful instrument to evaluate the relation between population structure and trait variation and to gain perspective about the history of allelic variation in target traits. For example, one can hypothesize that the sis Fig. . A secon50. The correlation between population structure and trait distribution is defining the expected rate of spurious associations and the impact of correcting for structure and relatedness on distribution of P values21. We show in the current study the varying effect of population structure on GWA results across different traits in melon. The most striking example is ovary hairiness, spreading versus appressed, which determined subdivision of C. melo into two subspecies, ssp. melo (spreading) and ssp. agrestis (appressed)51. As expected, hairiness phenotypic distribution is completely correlated with sub-specific division and therefore excess of significant effects  are found genome-wide  and Multi-parent Advanced Generation InterCrosses (MAGIC) designs were demonstrated as efficient platforms for high resolution trait dissection56. MAGIC design was also recently used in tomato to detect candidate SNPs underlying QTLs57.Population structure and cryptic relatedness may lead to false-positive discoveries in association analyses Figures . Another Figures . The pro Figures  is a cleCucumis melo. The results presented here provide demonstration for the effectiveness of the melon diversity collection and GWAS approach to map simple traits to candidate-gene level. Based on experience and lessons from other crops, the next logical step to promote the efficient dissection of complex traits in melon would be the development of multi-allelic segregating populations that will overcome the inherent limitations of GWAS and can serve as a community resource for broader comparative genetics within the Cucurbitaceae. The genotypic and phenotypic infrastructure laid in this study can support the selection of balanced representative core panel for that purpose as shown for our current 25 founders core panel  and 11 horticultural groups .The Newe-Ya’ar melon diversity collection used in this study is comprised of 177 inbred accessions derived from many countries and encompassing the two melon subspecies  and the non-sweet elongated pickling melon . (2) TAD × DUL population ; cross between an honey-dew line  and an American cantaloupe melon . (3) PI414723 x DUL population11 (99 RILs); a wide cross between a C. melo ssp. agrestis, var momordica accession and an American cantaloupe melon . These populations were grown during several years in the same location (Newe-Yaar) and similar growing regime.The bi-parental populations that were used as reference for phenotypic variation spectrum in this study are created with lines that are part of the diverse collection and are shown in Figure Core panel of 25 founder lines for future multi-allelic segregating populations was selected based on multiple criteria derived from this study: initial tentative set (n = 40) was constructed to represent all the different horticultural groups in the diverse collection . Phenotypic profiles were then used as the second primary factor; the preliminary core set was projected on the distribution of the different traits to ensure phenotypic spectrum is well captured in the core panel  in climacteric accessions. Five mature fruits per plot were sampled in multiple harvests and total of 15 fruits per accession were analyzed. Due to large variation in maturity time across the diverse collection, the field was walked through on a two-daily basis from middle of June till end of July for selective harvest of mature fruits. Melons were weighed and cut longitudinally for external and internal imaging. Internal side of all fruits was scanned using a standard document scanner . Three fruits per plot were used for flesh color measurement (three reads per fruit) using hand colorimeter . Flesh and rind samples were taken as bulks from at least three fruits per plot and immediately frozen in 50 ml tubes in liquid nitrogen, for further biochemical and molecular analyses. Fruit internal scanned images were analyzed using the Tomato-Analyzer softwareTotal genomic DNA extractions were performed on the177 accessions. DNA isolation was performed using the GenEluteTM Plant Genomic DNA miniprep kit . The quality of the DNA was analyzed by ND-1000 Spectrophotometer  and by electrophoresis on agarose gel. The concentration of DNA was estimated using Qubit® 2.0 Fluorometer  and Qubit® dsDNA BR Assay Kit .31. Fragments were sequenced on an Illumina HiSeq. 2500 as 100 bp, single-end reads and aligned to the reference genome of C. melo32 available at https://melonomics.net/files/Genome/Melon_genome_v3.5.1/. TASSEL pipeline v3.0.173 was used for sequence alignment and single nucleotide polymorphism (SNP) calling58. Further filtration was performed using TASSEL v5.2.3359; SNP list was filtered to MAF > 0.05 and maximum of 6% missing data per site.DNA was shipped to the Institute for Genomic Diversity facility at Cornell University for GBS. GBS 96-plex libraries were prepared using the restriction enzyme ApeKI, following an established protocolCmACS-7 gene (MELO3C015444) was genotyped using CAPs marker adapted from Boualem et al.35. A 234 bp amplicon was produced , cleaved with Alu I restriction enzyme and seperated on a 2% agarose gel. For CmOr gene (MELO3C05449) polymorphism was genotyped based on the SNP described by Tzuri et al.9. 500 bp amplicons  were sequenced and aligned across all samples. SNP at position 160 was called. Polymorphism at the CmKFB gene was genotyped using a protocol adapted from Feder et al.10. A 81 bp product was amplified  and a 23 bp indel differentiating between the alleles was scored on a 2.5% agarose gel.Association results for flower sex-expression, flesh color and yellow rind were validated by genotyping polymorphisms at the causative genes: polymorphism at the 59 was used to estimate the relatedness matrix of pairwise kinship (k matrix) from the filtered SNP dataset using the Centered_IBS method60. Intra-chromosomal LD between pairs of sites was calculated in TASSEL on SNP set filtered to MAF > 0.15 to ensure reliable estimates. Phylogenetic tree was built using the neighbor-joining function in MEGA61. An admixture-based clustering model implemented in the software STRUCTURE33 was used to infer the genetic structure of the collection. Ten independent runs for each K value ranging from 1 to 10 were performed with a burn-in length of 100,000 and 100,000 Markov chain Monte Carlo (MCMC) repeats after burn-in. The optimal subpopulations number was calculated from the second order rate of change of likelihood (delta K method)62.TASSEL software (V 5.2.33)k; random effect based on the genetic relatedness across all accessions) and the fourth model was MLM using both population structure (Q matrix) and relatedness (kinshp matrix).This model considered population structure and cryptic relationships thereby minimizing false positives and increasing the statistical power. Supplementary Figure 63. For fruit shape GWA analysis, stringent Bonferroni correction was used to control for multiple comparisons.In this study, four models (run in TASSEL) were used for the association analysis: the first used a generalized linear model (GLM) without any consideration for population structure; the second was GLM + Q where inferred ancestry of individuals (Q matrix) is used as covariate in the model; the third model was mixed linear model (MLM) using kinship matrix (Supplumentarry InformationSupplementary_Table S1Supplementary_Table S2Supplementary_Table S3Supplementary_Table S4


Data presented in this article are related to the research paper entitled “Short-term effects of nitrogen dioxide on hospital admissions for cardiovascular disease in Wallonia, Belgium.”  3 in July and a maximum of 26.9 µg/m3 in January were observed. The coldest months are December, January and February and the hottest months are June, July and August. Temperature and nitrogen dioxide were negatively correlated in the cold period and positively correlated in the warm period.Nitrogen dioxide concentrations showed a strong seasonal pattern with higher levels in the cold period than in the warm period. A minimum of 13.1 µg/m2 and cardiovascular disease. Apart from haemorrhagic stroke, the strongest association between NO2 concentrations and number of hospital admissions is observed at lag 0. For haemorrhagic stroke, the association is strongest with a delay of 2 days. All associations calculated without stratification are statistically significant and range from an excess relative risk of 2.8% for myocardial infarction to 4.9% for haemorrhagic strokes.For the period 2008–2011 there were 113 147 hospital admissions for cardiovascular disease. Forty-five percent of patients were women and 66.5% were 65 and older. Heart rhythm disorders account for the majority of hospital admissions for cardiovascular disease. Our data confirms the existence of an association between NO Specifications TableValue of the data•Extensive data on temporal variations of nitrogen dioxide concentrations.•Extensive data on the lag effect of nitrogen dioxide on different cardiovascular disease across age groups.•The data on short-term effect of air pollution on health could be useful for government and health workers to make decisions that could reduce the risk of disease among the population.•The result from the analysis can be compared with other environmental studies.12 and cardiovascular disease. However, few data are available on the delay between exposure and a health-related event. The aim of the present study is to determine the strength of association between NO2 and cardiovascular health in Wallonia for the period 2008–2011.Many studies have shown a short-term association between NO22.12 and 3 525 000 inhabitants in 2011.The present study was conducted in Wallonia from 1 January 2008 to 31 December 2011. Wallonia is the southern region of Belgium, with an area of 16 844 km2.22 and temperature for the period between 2008 and 2011 were obtained from the ISSeP (Institut scientifique de service public). Temperature data were collected from five measuring stations while NO2 concentrations were recorded by 10 stations spread across the study area. NO2 was analysed by chemoluminescence . Correlation between monitoring stations was analysed using Spearman's rank correlation. More than 75% of the correlation coefficients between stations were higher than 0.75 . The daily counts of hospital admissions for CVD were graded: arrhythmia (ICD9: 426 and 427), acute myocardial infarction , ischemic stroke  and haemorrhagic stroke .The analyses presented below relate to patients admitted in an hospital between 25 and 104 years of age over time. The daily counts of hospital admissions for cardiovascular disease were taken from the’Résumé Hospitalier Minimum’ (RHM) for 42 hospitals within the study region. The RHM is a mandatory register kept by Belgian hospitals containing patient data  and stay data (e.g.: admission date). Clinical admission diagnoses were registered using the ICD-9 codes  account for the majority (62.3%) of hospital admissions for heart rhythm disorders.2.42 was added in the model as a linear term without a delay (lag 0) and with a delay (lag 1 to 6). The lagged variables were introduced in the model separately. The effect of the delay was analysed for the association between NO2 and CVD stratified over age and for the association between NO2 and the hospital admissions for various cardiovascular pathologies. For each subgroup analysed, the lag giving the strongest association was selected. Residuals and partial autocorrelation were checked graphically to ensure the goodness of the model. The sum of the absolute values of the partial autocorrelation function was calculated for each degree of freedom. The analyses were performed without stratification  and with stratification on gender and age in three subgroups: 25–54, 55–64 and ≥ 65 years. Excess relative risk (ERR) and 95% confidence intervals (CI) were calculated using a Poisson regression model and R software 2.15.0  with the mgcv and spline packages. The excesses relative risks for an increase of 10 µg/m3 of NO2 are presented in the tables.A time-series design was used to assess the association between short-term exposure to air pollution and hospital admissions for CVD. The generalized additive model was applied to analyse the data with a quasi-Poisson regression to account for overdispersion 2.5Sensitivity analysis was performed to check the robustness of the model. Analysis using different degrees of freedom of the two natural splines was performed to estimate the effects on the strength of association. This sensitivity analysis was conducted using total daily admissions for CVD. The number of degrees of freedom giving the lowest sum of the absolute values of the partial autocorrelation function was selected.2 and AMI hospital admissions decreases slightly when the number of degrees of freedom of the smoothing function used for seasonal adjustment increases. The sum of the absolute values of the partial autocorrelation function was lowest for 3 df . The season has a similar effect on CVD and heart rhythm disorders but has no effect on myocardial infarction.The associations between NO