SD1. Based on a performance evaluation of the HRPF and GWAS results, we demonstrate that high-throughput phenotyping has the potential to replace traditional phenotyping techniques and can provide valuable gene identification information. The combination of the multifunctional phenotyping tools HRPF and GWAS provides deep insights into the genetic architecture of important traits.Even as the study of plant genomics rapidly develops through the use of high-throughput sequencing techniques, traditional plant phenotyping lags far behind. Here we develop a high-throughput rice phenotyping facility (HRPF) to monitor 13 traditional agronomic traits and 2 newly defined traits during the rice growth period. Using genome-wide association studies (GWAS) of the 15 traits, we identify 141 associated loci, 25 of which contain known genes such as the Green Revolution semi-dwarf gene,  Next-generation sequencing technology has made the generation of huge amounts of genetic data possible, but phenotype characterization remains slow and difficult. Here the authors develop a high-throughput phenotyping facility for rice that is able to accurately identify and characterize traits related to morphology, biomass and yield. The advent of next-generation sequencing technology has had a major impact on genomics in a short period of time4Oryza sativa landrace) is one of the most important food crops worldwide and has served as a model plant with many advantages, including its abundant natural variation891012There is a large gap between the present linear increase in global food production and the predicted demandIn the present study, we develop a high-throughput rice phenotyping facility (HRPF) that is able to elucidate traits related to morphology, biomass and yield during the rice growth period and after harvest. Using a combination of HRPF and GWAS, we demonstrate that high-throughput phenotyping has the potential to replace traditional phenotyping and serves as a novel tool for studies of plant genetics, genomics, gene characterization and breeding.To enable high-throughput and automatic phenotypic screening of rice germplasm resources and populations throughout the growth period and after harvest , a phenoAfter harvest, the rice yield-related traits  and the prediction error sum of squares (PRESS statistic)15To extract the green leaf area, shoot fresh weight and shoot dry weight, half of the rice samples were randomly selected as a training set for model construction, and the prediction performance of the model was evaluated using the testing set and cross-validation. To select effective predictors for these three traits, all possible regressions were performed using Akaike’s information criterion, the adjusted coefficient of determination  from rice-core germplasm resources were evaluated with the YTS, and 68 accessions were randomly selected and measured manually to estimate the measurement accuracy of the YTS. The O. sativa accessions for 15 traits. In contrast to previous related studies, these traits were measured automatically by the RAP and YTS instead of performing manual measurements8P value thresholds were 1.21E–06 and 6.03E–08  for the entire populationP value thresholds with clear peak-like signals were considered. With the significance threshold set, we identified 57 loci, including 15 loci associated with four traits measured by the RAP and 42 loci with five traits measured by the YTS  located at bp 2,578,017 on chromosome 1 was associated with shoot fresh weight, shoot dry weight and green leaf area at the late tillering stage. Lead SNPs at 22 associated loci passing the suggestive threshold for seven traits  and lead SNPs at another 3 loci with clear peak-like association signals that failed to pass but were close to the suggestive threshold were linked to known related genes . Among tP value thresholds adopted, 12 and 15 associated loci were detected by manual and RAP measurements, respectively. For the raw measurements, however, only two associated loci were detected. For the three traits, 8 of 12 loci detected by manual measurement were also detected by the RAP, whereas only one locus was detected by the raw measurement. We used the GWAS results for the three traits at the late booting stage as an illustration to provide a detailed comparison  prediction, noticeable improvement was achieved by adding morphological features or texture features to the model. Model AM generally performed better than Model AT, with the exception of shoot fresh weight at the late tillering stage. This finding indicated that morphological features were more significant in predicting rice biomass than texture features. The reason that Model AT outperformed Model AM in predicting fresh weight at the late tillering stage may be that the overlap was not significant and the influence of specific organ weight exceeded that of the overlap. After booting, the overlap was more influential. Except for dry weight prediction at the late booting stage, Model ATM showed no noticeable improvement in performance over Model AM. This was because differences in growth status among individual plants during the late booting stage are larger than that during the other three growth periods. Similar conclusions were observed for green leaf area prediction. Noticeable improvement was achieved by adding morphological features or texture features to the model. In addition, Model AM generally performed better than Model AT. The colour of panicles is very similar to that of leaves; thus, the extracted regions of images included both panicles and leaves. To address this problem, a texture feature was added to the model to help reflect the variation and the distribution of the grey level in the image. The addition of the texture feature significantly improved the predictive performance of the model.From the comparison of the GWAS results with the three different phenotyping methods, we found that the RAP provided a relatively more complete representation than manual measurements in dissecting genetic architecture, and that raw measurement did not have sufficient power to study relatively complex traits such as shoot fresh/dry weight and green leaf area. Compared with the use of only the original features, the optimized model plus the original features will benefit the dissection of the genetic architecture of complex traits. Moreover, eliminating the G × E effects was the first and key step in our phenotyping experiment. All the rice accessions were planted in the greenhouse under the same conditions, and each pot was loaded with equivalent soil and fertilizer, as shown in Although genomics has been advancing very rapidly, traditional plant phenotyping lags far behind current genotyping techniques such as sequencing. To relieve this bottleneck, our work describes a combination of high-throughput phenotyping and GWAS to unlock genetic information coded in the rice genome that controls complex traits and demonstrates the feasibility of replacing laborious manual phenotyping with objective, efficient and non-destructive phenotyping tools such as an HRPF. Our study also demonstrates that for complex traits (such as shoot fresh/dry weight and green leaf area), the RAP better dissected the gene architecture of phenotypic traits than did the raw measurement of these traits. In addition to the traditional traits identified by manual measurement, novel traits  can be specifically phenotyped using the HRPF. With appropriate modifications to image analysis, we anticipate that the combination of the HRPF and GWAS can be used for a wide spectrum of other plant species to determine genetic architecture and provide insights into basic biological processes. As a replacement for traditional phenotyping, the HRPF represents a novel tool that can facilitate major advances in plant functional genomics and crop breeding.O. sativa landrace and elite accessions were genotyped (k-nearest neighbor (LD-KNN) algorithm was used instead of the KNN algorithm, which has been previously reportedIn our study, 533 enotyped . The basAs shown in As shown in A), 25 morphological features and 7 texture features, were extracted (The specified imaging techniques used in the RAP and YTS are shown in xtracted . As showN) was calculated using the GEC software toolN) and significant (0.05/N) P value thresholds were set to control the genome-wide type 1 error rate1718P value thresholds were 1.21E–06 and 6.03E–08  for the entire population. The LD statistic r2 based on haplotype frequencies was calculated using PlinkP value threshold were further clumped to remove the dependent SNPs caused by LD (r2>0.25) using the clumping function in Plink36In our association panel containing 529 accessions, a total of 4,358,600 SNPs  were used in our GWAS for 15 traits. A mixed-model approach was implemented using the factored spectrally transformed linear mixed models (FaST-LMM) programmeW.Y., Z.G., C.H., L.D. and G.C. designed the research, performed the experiments, analysed the data and wrote the manuscript. N.J., W.F. and H.F. also performed the experiments. W.X., X.L. and G.W. provided the rice materials and sequence data. Q.Luo. and Q.Z. initiated the project on the construction of a phenotyping platform. Q.Liu. and L.X. supervised the project, designed the research and wrote the manuscript.How to cite this article: Yang, W. et al. Combining high-throughput phenotyping and genome-wide association studies to reveal natural genetic variation in rice. Nat. Commun. 5:5087 doi: 10.1038/ncomms6087 (2014).Supplementary Figures 1-10, Supplementary Tables 1-17, Supplementary Notes 1-5 and Supplementary ReferencesGenome-wide association loci exceeding the suggestive P value threshold in our GWAS using RAP/YTSThe basic information of 533 accessions.The detailed operating procedures of RAPThe detailed operating procedures of YTS.


The F1 parents had different genetic backgrounds resulting from reciprocal interpopulational crosses between low-elevation (L) and high-elevation (H) populations: L × L, L × H, H × L, and H × H. Paternity analysis of the F2 progeny using molecular genetic markers showed that 91.3% of the fathers were located in surrounding stands of the F1 planting site . The remaining fathers were assigned to F1 parents of the L × L cross-type. This indicates that the high-elevation genome in the F1 parents was not inherited by the F2 population via pollen flow. The timing of autumn cold acclimation in the F2 progeny depended on the cross-type of the F1 mother. The progeny of H × H mothers showed less damage in freezing tests than the progeny of other cross-types. Statistical modeling supported a linear effect of genome origin. In the best model, variation in freezing damage was explained by the proportion of maternally inherited high-elevation genome. These results suggest that autumn cold acclimation was partly explained by the additive effect of the responsible maternal genome. Thus, the offspring that inherited a greater proportion of the high-elevation genome developed cold hardiness earlier. Genome-based variation in the regulation of autumn cold acclimation matched the local climatic conditions, which may be a key factor in elevation-dependent adaptation.Boreal coniferous species with wide geographic distributions show substantial variation in autumn cold acclimation among populations. To determine how this variation is inherited across generations, we conducted a progeny test and examined the development of cold hardening in open-pollinated second-generation (F Seasonal growth cycles are well described for plant taxa distributed in boreal, sub-boreal, and temperate climates. In particular, for evergreen coniferous species, cold acclimation often occurs before temperatures drop below freezing . During Abies sachalinensis Mast.  has revealed an adaptive cline in the timing of autumn cold acclimation along an elevational gradient . The elevational distribution extends from 100 to 1600 m a.s.l. This species survived in a glacial refugium in Hokkaido, and had become a dominant species by the time of the last glacial maximum, 20000 years ago  progeny for testing the genetic basis of variation in phenological traits. The maternal population (F1) was produced by reciprocal crosses between two distinct ecotypes: a population inhabiting low elevations and a population inhabiting high elevations. The genetic background of the maternal F1 trees varied among cross-types , and the trees were planted at a single site in order to ensure their exposure to the same environment. Thus, we could evaluate the effects of genome inheritance by examining the traits of the F2 population. Using the F2 progeny, we conducted (1) paternity analysis based on microsatellite markers and (2) statistical modeling of freezing damage. Our objectives were to (1) measure variation in the timing of autumn cold acclimation of the F2 progeny and (2) establish whether this variation could be explained by the genomic background inherited from the low-elevation or high-elevation populations.In the present study, we used open pollination to obtain second-generation . In the UTHF, natural sub-boreal forests cover about 150 km2 of the south-western slope of Mt. Dairoku (1459 m a.s.l.), with an elevational gradient of more than 1200 m. With increasing elevation, dramatic changes occur in soil type, snow depth, and forest vegetation, including changes in the prevalence of the bamboo species, Sasa senanensis and Stigmella kurilensis (We conducted this study in the University of Tokyo Hokkaido Forest (UTHF), located in central Hokkaido, northern Japan  to the high-elevation zone (1200 m) in this area. In 1979, natural populations at 400–530 m and 1100–1200 m were selected as low-elevation (L) and high-elevation (H) populations, respectively, and artificial reciprocal crossing was performed by Kurahashi . We refer to these progeny as the F1 population. Seedlings of the F1 population were grown in an outdoor nursery at a lowland site of the UTHF. In 1986, the F1 individuals were transplanted to the lowland planting site . At the planting site, the progeny were arrayed in grids at a spacing of 1.2 m, assigning one block (2 columns × 10 rows) to each cross-treatment. Unreplicated blocks were used to avoid contamination among treatments  were provided by Sakhalin fir, urahashi . Within urahashi . Pollen eatments . In tota1 trees were alive and some of them were in a reproductive stage. Our field surveys during the flowering and seed-maturing seasons showed that 22 trees flowered that year at the F1 planting site (Table 1). We collected open-pollinated seeds from the flowering trees on September 6th and 9th, 2009. We selected 13 F1 mother trees that produced a sufficient number of progeny (>20 cones) to use in this study . The progeny (F2 population) were grouped into four types according to the maternal cross category: L × L, L × H, H × L, and H × H . Collected seeds were stored at -3°C until use.In 2009, 23 years after planting, 353 F2 progeny. Before the trial, viable seeds were selected from all collected seeds using soft X-ray photography . In the spring of 2010, the viable seeds were subjected to a seed stratification treatment and were sown into the UTHF nursery . One seed was placed in each 4-cm grid of a 1.0 m × 1.2 m block to maintain the identity of all germinated seedlings. The four blocks were separated by buffer spaces. To avoid edge effects for the F2 progeny, control seeds were also sown at the edge of the rows and columns. In total, 2112 seeds were sown in this trial (four blocks × 22 rows × 24 columns). The seedlings were raised until the end of the second growing season. As shown in Table 2, the cross-type differences of the mother of the F2 progeny were reflected in some functional traits, such as the germination rates and 2-year heights.We conducted a common garden trial of the F2 progeny, we performed molecular genetic analysis using nuclear microsatellite (nSSR) and chloroplast microsatellite (cpSSR) markers. In the present study, we used four nSSRs: As08, As16, As32 . These seedlings were germinated in an indoor growth chamber.Samples selected for SSR genotyping consisted of the 22 flowering F1 trees and the 80 F2 progeny. DNA was extracted using the DNeasy Plant Mini Kit , and the PCR reactions were performed using the Multiplex PCR Kit  following the protocol of For the DNA extractions, foliage was collected from the 22 candidate F1 trees as candidate pollen parents of the F2 progeny because no other trees were flowering at the F1 planting site. However, trees outside of the F1 population may have also served as pollen parents because the F1 planting site was surrounded by mature artificial forests. The origin of these artificial forests was a local lowland forest (200–300 m) that was different from the forests used to create the F1 population.We considered the 22 flowering F2 progeny using four nSSRs. Using CERVUS 3.0 (2 progeny to their known seed parents (mothers) and 22 candidate F1 pollen parents (fathers). The most likely father was estimated using maximum-likelihood assignment from the possible allele combinations (genotypes) of the parents. When none of the potential F1 donors were assigned as fathers, we considered pollen contamination and genotyping errors as possible causes. Genotyping errors can lead to inflated estimates of outside pollen flow . When a complete match was not found, we assumed the pollen came from outside of the F1 planting site.After assigning fathers using CERVUS, we performed a simple exclusion procedure using three cpSSRs. The F1 population, and seeds sired by fathers outside the studied F1 population. We also estimated the composition of the F1 trees among four cross-types, based on the genotyping results.Combining the results from CERVUS and the exclusion procedure, we estimated the proportion of selfed seeds, seeds sired by the F2 progeny three times during the autumn season using the method of -2 s-1 during the day). We measured freezing damage using the visual scoring method of 2 seedlings from each F1 mother tree. For some maternal trees with fewer seedlings, we used seven seedlings. In total, 605 seedlings were used in the freezing test (121 seedlings × 5 test conditions).We conducted freezing tests on the F1 involved freezing at -15°C in Test 1, T2 involved freezing at -30°C in Test 2, and T3 involved freezing at -30°C in Test 3. For each condition, we used a nested ANOVA to study the effect of maternal cross-type and the effect of mother trees on freezing damage in the F2 progeny, using the following model:Because no freezing damage was observed in the -15°C treatment in Tests 2 and 3, we excluded these data from subsequent analysis. Therefore, three test conditions were defined: TYijk is the freezing damage score of the k-th progeny  of the j-th mother tree  in the i-th cross-type , μ is the general mean, Ci is the effect of the i-th cross-type, Mj (Ci) is the effect of the j-th mother tree nested in the i-th cross-type, and Eijk is the residual error.where 2 progeny is affected by the genetic inheritance, test conditions, and mother tree. Therefore, we constructed the full model with all assumed effects, as follows:We also evaluated the effect of low-elevation and high-elevation plant genomes on freezing damage using the following statistical model that includes all test conditions. We assumed that freezing damage in the FYijkl is the freezing damage score of the k-th progeny of the j-th mother tree in the i-th cross-type under the l-th test condition , μ, Ci and Mj (Ci) are the same as in Model 1, and Tl is the effect of the l-th test condition. In this full model, we regard the model element C as representing the effect of the plant genome (genetic effect). Then, in the analysis, C, T, and their interaction (C × T) are treated as fixed effects, whereas M (C) and its interaction with T are treated as random effects. Because damage is an ordinal variable  to describe the freezing damage score. Therefore, we used the following four types of the genetic effects as C in Models 2–5:We then assessed which types of variables were appropriate for the effect of the plant genome Model 3:Categorical effect Model 4:LH” for L × H; “HybHL” for H × L)Categorical interaction effect .C. The highest value was assigned to the H × H cross-type, whereas the lowest value, 0, was assigned to the L × L cross-type. Model 2 should show a good fit if freezing damage was closely related to the amount of high-elevation genome inherited from the maternal trees . In Model 3, C was set to one of four categorical values indicating the cross-type of the maternal trees. Model 3 should fit well if the maternal origin is important, but the proportion of high-elevation genome is not. Model 4 included a genome interaction effect. As described above, C was set to one of three categorical values: NoHyb, HybLH, or HybHL. In Model 5, we included both the cross-type linear effect and genome interaction effect. C was partitioned into the combination of the variables used in Models 2 and 4.In Model 2, the proportion of high-elevation genome inherited from the maternal parent was used. Numeric values indicating the linear effect of the maternal genome origin were assigned to 2 progeny. All statistical analyses in this study were conducted using R 3.1.2 (We used a model selection procedure to exclude the variables that did not improve the model fit. The Akaike Information Criterion (AIC) was used to consider goodness-of-fit and number of parameters . Within  R 3.1.2 .N) was 15.0, ranging from 7 for As08 to 21 for As16 and NFH15. For the cpSSR markers, the numbers of haplotypes of Pt30141, Pt30204, and Pt71936 were 15, 13, and 7, respectively. The genotypes of the 22 F1 candidate pollen parents differed from each other . Genetic diversity statistics for the four nSSR markers are summarized for the F1 and F2 materials in Table 3. The genetic characteristics of the F2 progeny were consistent among their maternal groups. The average number of alleles in the F2 progeny was smaller than that in the F1 trees, which was caused by maternal sharing . In contrast, their observed heterozygosity (HO) was higher in the F2 progeny than in the F1 trees, resulting in a negative fixation index (FIS).All seven SSR markers were polymorphic. For the nSSR markers, the average number of alleles per locus , and 91.3% (73 samples), respectively.Paternity analysis using four nSSR markers showed that 12 F2 progeny ranged from 0 to 5 for treatment T1 (-15°C in Test 1) and treatment T2 . In T1, the most frequent damage scores for the progeny belonging to the L × L and H × H cross-types were 5 (30.0%) and 0 (45.2%), respectively. For the hybrid cross-types (L × H and H × L), a score of 1 was most common (26.7% for L × H and 36.7% for H × L). In T2, the most frequent damage score for the H × H cross-type was 1 (51.6%). In contrast, high damage scores (4 and 5) were often observed for the L × L, L × H, and H × L cross-types. In T3 (-30°C in Test 3), most progeny showed no damage, regardless of the cross-type .Freezing damage scores in the FM (C)] for treatment T1 (Table 4). For treatment T2, a significant difference was detected among maternal cross-types (C), but not among mother trees [M (C)]. For treatment T3, however, statistical analysis was not possible due to limited variation in freezing damage score, as shown in Figure 2.The ANOVA indicated a significant difference among mother trees within cross-types [C × T and M (C) × T should be excluded from Models 2, 3, and 5 (Table 5). In Model 4, the C effect was also excluded. This indicates the effect of the mother trees assumed in Model 4  was not useful for predicting freezing damage in the F2 progeny. A model comparison based on AIC values revealed that Model 2, which included the cross-type linear effect, was the best model (Table 5). The effects included in this model were C, T, and M (C). The estimated coefficients of these variables are shown in Table 6. A significant negative effect was observed for C, indicating that the freezing damage tended to be lower in the progeny that inherited a higher proportion of the high-elevation genome.Model selection for each of the four candidate models (Models 2–5) indicated that the interaction terms A. sachalinensis early in the study period, but was barely observed in the final test conducted in November . This demonstrates the development of freezing tolerance over time. In addition, freezing damage differed among maternal cross-types of the F2 progeny, even though the mother trees were grown in the same environment . Genetic variation in autumn phenology was relatively clear. Seedlings that inherited a greater proportion of the high-elevation genome displayed earlier cold acclimation. We used visual scoring, but alternative quantitative evaluations  are also used as indicators of cold hardiness (r = 0.762) between our scoring method and chlorophyll fluorescence  in A. sachalinensis  indicated a high level of pollen flow from outside pollen sources (91.3%). High levels of pollen contamination have been reported to be a severe problem in seed orchard management  and small size (Table 1). As suggested by 1 planting site must be lower than that from the surrounding mature plantations. Therefore, the results obtained by paternity analysis are reasonable.The small paternal contribution from the Fnagement . When a 1 trees are mismatched, outside pollination will occur rarely. Indeed, A. sachalinensis at the plantation forest at 1100 m was delayed by approximately 2 weeks compared with that at 530 m. If the difference in flowering phenology is genetically controlled, the timing of female flowering of F1 trees derived from a crossing among high-elevation parents may differ from that of male flowering of local (low-elevation) trees. However, the flowering phenology of the F1 trees which were planted at the same environment overlapped, as described by A. sachalinensis  increases and the fixation index (FIS) decreases when mating occurs between local and non-local populations . A large decrease in FIS was also detected between the F1 and F2 generations, although there were only small differences within the F2 (Table 2). The negative FIS in F2 indicated that the chances of mating between an F1 mother and its relatives were low. These results support our conclusions concerning pollen flow from outside the F1 planting site.Evidence of frequent outcrossing was also seen in the genetic parameters obtained using nSSRs. Observed heterozygosity  were all derived from the L × L cross-type. This indicates that the high-elevation plant genome from the F1 population was not inherited by the F2 population to any great degree.We concluded that only a few FTable 5). In this model, variation in autumn cold acclimation was explained by variation in the maternal genome composition. The F2 progeny that inherited a greater proportion of the high-elevation genome showed earlier cold acclimation.Our modeling analysis revealed that the regulation of autumn cold acclimation could be well explained by the linear effect of the genome origin. The model using the expected proportion of high-elevation genome as a fixed effect was selected as the best model  was related to the daylength and/or thermal conditions during fertilization and seed maturation . In comparison with models considering such effects, the model including only the effect of genome origin showed the best fit. This result could be partly explained by the fact that all maternal trees were exposed to the same environmental conditions. Moreover, in the absence of a specific cross-type effect, it would be reasonable to expect that the autumn cold acclimation of A. sachalinensis would show a linear pattern based on the proportion of low-elevation and high-elevation plant genomes.Because of epigenetic phenomena, such as maternal effects and ‘after’ effects, the prediction of phenotypic values for quantitative traits can be a complex process . In someturation . Moreoveturation . Sykes eA. sachalinensis, clinal variation in the timing of autumn cold acclimation along an elevational gradient is one of the elevation-dependent adaptive traits. Each population adapts to the local climate, with a trade-off between the avoidance of freezing damage and extension of the growth period (This interpopulational regulation of autumn phenology may be one of the factors driving the local adaptation of conifers . For A. h period . The genA. sachalinensis, it may be difficult to track rapid climate change because of the mismatch of locally adapted autumn phenology. However, the presence of adaptive genes and their potential for climatic adaptation have not been sufficiently examined. Future studies may be needed to evaluate this subject in detail by applying powerful molecular techniques, such as the analysis of quantitative trait loci and/or genome scanning.The genetic control of ecologically relevant traits is important in forest management. Potential genetic changes in future generations must be taken into consideration, particularly considering projections of rapid global warming . If a taThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


These updates include: (i) Up to August 2015, we collected 2479 unique publications from PubMed and other resources; (ii) We further curated moderate SNP-trait associations  from each original publication, and generated a total of 252 530 unique TASs in all GWASdb v2 collected studies; (iii) We manually mapped 1610 GWAS traits to 501 Human Phenotype Ontology (HPO) terms, 435 Disease Ontology (DO) terms and 228 Disease Ontology Lite (DOLite) terms. For each ontology term, we also predicted the putative causal genes; (iv) We curated the detailed sub-populations and related sample size for each study; (v) Importantly, we performed extensive function annotation for each TAS by incorporating gene-based information, ENCODE ChIP-seq assays, eQTL, population haplotype, functional prediction across multiple biological domains, evolutionary signals and disease-related annotation; (vi) Additionally, we compiled a SNP-drug response association dataset for 650 pharmacogenetic studies involving 257 drugs in this update; (vii) Last, we improved the user interface of website.Genome-wide association studies (GWASs), now as a routine approach to study single-nucleotide polymorphism (SNP)-trait association, have uncovered over ten thousand significant trait/disease associated SNPs (TASs). Here, we updated GWASdb (GWASdb v2,  Ten years’ efforts on genome-wide association study (GWAS) have produced large numbers of human genetic variants that are associated with hundreds of medical traits and diseases. The world of GWAS is evolving rapidly with improved technologies such as high-density genotyping array and next generation sequencing (NGS) ,2. New dAlthough GWAS Catalog  and otheIn this update, we systematically collected TASs, as well as detailed information for their effect size and investigated population, from published GWASs. We performed deep and high-quality curation for moderate effect TASs according to related materials of each publication. GWASdb v2 also introduces a batch of new features including new ontology mapping, multi-level annotation, causal gene prediction and an updated interactive user interface.P-value < 1.0 × 10−5) from GWAS Catalog, PheGenI and HuGE . In GWASdb v2, we further added reported effect sizes to characterize SNP-trait associations besides of P-values, such as odds ratio/beta, 95% CI information, text remark of P-value, and risk allele. For each GWAS, in addition to its PubMed ID or Analysis ID (studies from PheGenI), we also provide detailed annotation on sample size and ancestry of initial stage if recorded (known as sub-populations in GWASdb v2). Then, we categorized these sub-populations into eight ethnogeographic super-populations, namely European/Caucasian (EUR), African (AFR), East Asian (ASN), Native American (AMR), Hispanic/Latino (HIS), Middle Eastern (MEA), South Asian (SAN), Oceania (OCN) and ambiguous samples (OTHER), and recorded the corresponding sample size information (Supplementary Methods).Similar to last version, we systematically curated moderately significant TASs from related documents of each original GWAS publication. Generally, we collected TASs by using a http://jjwanglab.org/mapin/, unpublished), which could calculate the similarities between strings, to map various trait/disease descriptions from different GWASs to several well-defined ontology systems, including Disease Ontology (DO)  or several studies involved measurement of particular molecules which are well-recognized biomarkers or risk factors for certain diseases (e.g. ‘Lipoprotein A level’ as a risk factor for coronary heart disease). Meanwhile, ontologies provide a computer friendly structure for semantic integration of biomedical diseases and phenotype terminologies. Therefore, a high quality mapping of natural language descriptions to formal and consistent ontology systems is an essential but challenging task. To this end, we used in-house software MapIn (ogy (DO) , Human Pogy (DO) . After aP-values, then selected the top 200 (commonly used number in PrixFixe) SNPs to perform causal gene prediction. The exact SNP number was used if it is <200 for a certain trait/disease (Supplementary Methods).In this update version, we also provided causal gene prediction for each ontology trait/disease using a recent algorithm PrixFixe . Causal http://www.diygenomics.org/webapp/pharma_data.php), which reports variants for 200 drugs from various references. Drugs were defined according to records from DrugBank . We retrieved correlated SNPs in linkage disequilibrium (LD) with this TAS based on eleven HapMap I+II+III populations or four super populations from 1000 Genomes Project. Gene and genomic element data were downloaded from UCSC and GENCODE. We utilized ENCODE data to annotate the functional elements at the SNP position. Since GWAS TASs could map to genomic regions that are responsible for distinct biological functions, we also used sophisticated software to predict the functional consequences for different types of TASs across different biological domains, including transcription factor binding and gene regulation (GWAS3D ), microRCompared with the previous GWASdb version, we improved the GWASdb v2 architecture by combining jQuery plugins  with a Perl-based web framework Catalyst. Annotation information were either stored in MySQL database or flat files indexed by Tabix . We usedGWASdb v2 significantly extends the data content by deep manual curation and comprehensive resources integration. Compared with existing databases, GWASdb v2 covers the highest number of GWAS publications in the field (Supplementary Table S3). The extensive data volume for moderate effect SNPs will facilitate the finding of more associations that imply important biological function. Also, we updated well-organized trait/disease-ontology mappings including HPO, DO and DOLite, which will assist an effective trait organization. For mapped ontology terms, GWASdb v2 provides putative causal genes. Moreover, GWASdb v2 now clearly classifies each GWAS to respective sub- and super-populations, which will benefit researchers in studying population-specific traits. In addition, GWASdb v2 has collected a SNP-drug response dataset which could potentially benefit for pharmacogenetic research. Last but not least, GWASdb v2 compiles a complete annotation in both interactive web pages and local datasets. These annotation and visualization functions will help users pinpoint the functional attributes of TASs. Detailed improvements in GWASdb v2 since last publication version is shown in Supplementary Table S4.P-value < 5.0 × 10−8 and 257 422 had the moderate effect size with P-value < 1.0 × 10−3). Apart from SNPs collected from existing GWAS resources , GWASdb v2 further curated 266 338 TASs by ourselves. GWASdb v2 contained 252 530 unique TASs. Among them, about 17% were reported by more than one cohort (i.e. different populations) and over 21% were associated with more than one trait/disease (according to DOLite mapping results), suggesting that many GWAS SNPs have shared association across human populations and are relevant to multiple genetic traits. Also, we observed an unbalanced distribution  for the number of TASs in three major human populations  although current GWASs tend to investigate more worldwide populations. Majority of TASs are associated with several top investigated traits/diseases , including nervous system disorders , metabolic disorders , cardiovascular diseases  and immunological diseases (e.g. Systemic lupus erythematosus and lymphoma). The genomic distribution of GWASdb v2 TASs  showed that 105 893 of them locate in the intergenic region and most of remaining genic TASs (92.8%) come from intronic region, indicating the regulatory role of these non-coding genetic variants.Based on August 2015 version of GWASdb v2, 2479 unique GWASs have been collected and curated, which totally included 297 670 SNP-trait/disease associations . This result is consistent with previous studies that GWAS SNPs are enriched in functional elements , it is unfair to directly compare the data volume with them. We alternatively compared the database features in different aspects (Supplementary Table S3). In general, the strengths of GWASdb v2 lie in the following aspects: (i) GWASdb v2 is the largest resource that collects the most GWAS publications; (ii) GWASdb v2 provides manually curated and high-quality SNPs which have less significance but potentially association effects; (iii) GWASdb v2 supports a couple of useful and comprehensive embedded functions . Therefore, to the best of our knowledge, GWASdb v2 is the most comprehensive database in the GWAS community.Since different GWAS resources follow different SNP collection criteria  for users to quickly inspect TASs of interest. It also offers a batch query function to allow users to upload a SNP list containing either SNP IDs or genomic coordinates. A query job will run in the backend, and users can provide their emails or keep the job URL to retrieve their results, which is downloadable and contains detailed GWAS information and SNP annotation. In the front page of GWASdb v2, a circos Manhattan plot shows summary of the latest GWASdb v2 TASs in the whole genome. Users can also enter into the single chromosome view by clicking corresponding chromosome cytoband. The basic statistics of GWASdb v2 are shown in the right tab of the front page, such as regional distribution and SNP type distribution in genic region. We improved the GWASdb v2 result page and made it more compact and integrative compared with previous version. We used an interactive panel to display the TASs in region of interest, users can perform moving, zooming and clicking operations to visualize the region within this panel Figure . The rigP-values.To simplify the visualization of trait/disease ontology mapping, we embedded the whole ontology of HPO and DO to one page coupled with query function by the tree viewer and search box Figure . To querftp://jjwanglab.org/GWASdb/) or remotely retrieved by Tabix. Besides, we compiled a number of RESTful interfaces to quickly redirect users to their interested SNPs, genes, regions or traits.GWASdb v2 TASs, ontology mapping and annotation table can be downloaded via FTP  and whole genome sequencing (WGS), which possess more advantages than GWAS chips to detect low frequency disease-associated variants, are currently applied to decipher the genetic association on many complex diseases  and willIdentification of the true causal and functional variants from a GWAS leading SNP is usually a tough work, which requires expensive and time-consuming experiments. Even though a few statistical methods can facilitate the fine mapping of true causal hits ,54,55, iGWASdb now not only focuses on collection of TASs with significant or moderate effects, but also pays more attention on comprehensive annotation and prioritization of these variants. A batch of algorithms can accurately predict the deleteriousness of non-synonymous mutations that directly alter protein sequences, but for other types of mutations such as variants in regulatory regions, effective algorithms and statistical methods are still in urgent need. Recent works combine multiple genomic data in scoring and prioritizing functional non-coding variants –60, howe


Avena sativa L.) remain relatively under-researched compared to other small grain species. This study explores the historic context of current elite germplasm, including phenotypic and genetic characterization, with a particular focus on identifying under-utilized areas. A diverse panel of cultivated oat accessions was assembled from the USDA National Small Grains Collection to represent a gene pool relatively unaffected by twentieth century breeding activity and unlikely to have been included in recent molecular studies. The panel was genotyped using an oat iSelect 6K beadchip SNP array. The final dataset included 759 unique individuals and 2,715 polymorphic markers. Some population structure was apparent, with the first three principal components accounting for 38.8% of variation and 73% of individuals belonging to one of three clusters. One cluster with high genetic distinctness appears to have been largely overlooked in twentieth century breeding. Classification and phenotype data provided by the Germplasm Resources Information Network were evaluated for their relationship to population structure. Of the structuring variables evaluated, improvement status (cultivar or landrace) was relatively unimportant, indicating that landraces and cultivars included in the panel were all sampled from a similar underlying population. Instead, lemma color and region of origin showed the strongest explanatory power. An exploratory association mapping study of the panel using a subset of 2,588 mapped markers generated novel indications of genomic regions associated with awn frequency, kernels per spikelet, lemma color, and panicle type. Further results supported previous findings of loci associated with barley yellow dwarf virus tolerance, crown rust (caused by Puccinia coronata f. sp. avenae) resistance, days to anthesis, and growth habit (winter/spring). In addition, two novel loci were identified for crown rust resistance.Population structure and genetic architecture of phenotypic traits in oat ( Avena sativa L., 2n = 6x = 42) has played an important role in the development of agriculture around the world, but received less investment from the plant and agricultural research sectors during the twentieth century than other small grain crops such as wheat and barley (Oat (d barley . The oatd barley .Evidence from existing studies of mid- to late-20th century samples of oat genotypes suggest that cultivated oat populations show high diversity. In a study of 83 North American oat cultivars using restriction fragment length polymorphisms (RFLPs), polymorphism was found to be 54.8% in oat as compared to 5% in wheat and 28% in barley , supportHordeum vulgare L.), genotypes fall into distinct sub-populations based on row number (2 vs. 6), growth habit (winter vs. spring) and geographical origin  representing cultivars collected before 1930, and landraces collected through the 19th and 20th centuries. Genotyping was performed using a SNP array developed for hexaploid oat by A. sativa L. accessions were selected from among the 9,283 available from the NSGC in 2013. The sample was composed of (1) 383 accessions selected from either cultivars or from accessions of uncertain improvement status that had entered the NSGC before 1930; and (2) 617 accessions selected from among all available landraces. Cultivars that entered the NSGC before 1930 are likely to be representative of the same population structure as the landraces, because improved varieties of that era were largely selections from landraces or selections from crosses combining two to four landraces  protocol  with mod2.Genotypes for 4,975 SNPs were acquired using an oat iSelect 6K beadchip array  at the USDA-ARS Genotyping Laboratory at Fargo, ND, USA. SNP genotype calls were made with GenomeStudio v2011.1  with GenCall set at 0.15. In addition, SNP calls were manually inspected and adjusted in GenomeStudio. Multi-allelic and monomorphic SNPs were eliminated, as well as those with poor genotype calls due to weak signal or ambiguous clustering. There were 2,715 SNPs with no more than 5% missing calls and/or heterozygosity, and at least 5% minor allele frequency (MAF). Genotypic data is deposited in the public database T3/oatSingle nucleotide polymorphism data were additionally used to screen for duplicates within the panel. Three hundred and twenty-four lines were identified as duplicates with one or more other lines at a threshold of 99.5% identity. A single line was retained from each set of duplicates based on completeness of genotype data. An additional accession was removed due to unidentified problems in the genotype coding. The final sample had 759 accessions.3 on September 22, 2014 and are representative of the entire NSGC accession rather than the single plants used for genotyping. For that reason, phenotypes recorded in GRIN as a range or as mixed were considered missing data. In all but rare cases, GRIN data are based on unreplicated experiments. Country of origin was used to assign each accession to a region of origin as defined by the United Nations Statistics Division4.Phenotypes were downloaded from the GRINhttp://www.ars-grin.gov/npgs/searchgrin.html. In brief: Awn frequency, hullessness of the threshed grain, amount of plant lodging near maturity, panicle density, panicle type, shattering, and straw breakage at maturity were assessed by visual observation. Beta glucan, lipid, and protein content of the kernel were expressed on a percent dry weight basis. Reaction to BYD virus was evaluated on a 1-9 scale with 1 = resistant and 9 = susceptible. Crown rust (Puccinia coronata) reaction was to a naturally occurring mixture of races in the Saint Paul, MN buckthorn nursery. Reactions rated highly resistant, resistant, moderately resistant to resistant, moderately resistant, moderately resistant to moderately susceptible, moderately susceptible, moderately susceptible to susceptible, and susceptible were converted to a numerical score from 0 to 7 for analysis. Crown rust severity was assessed as the percentage of leaf area affected per plot. Reaction to greenbug (Schizaphis graminus) was assessed as the percent of the resistant check ‘Andrew’. Growth habit was assessed after spring planting in Aberdeen, ID, USA as facultative, spring, winter, or a mix of types. However, we used only those deemed spring or winter for analysis. Days to anthesis was recorded as the Julian calendar date at which 50% of spikes are fully emerged, following spring sowing. Kernels per spikelet and spikelets per panicle were averaged over five panicles. Lemma color was evaluated using a Foss GrainCheckTM 2312 Analyzer . Lemma color was coded based on GC color intensity where 1 < 46.00 (black), 4 = 46.01 to 57.99 (gray), 6 = 58.00 to 71.99 (red), 9 = 72.00 to 82.99 (yellow), and 8 > 83.00 (amber/white). Panicle length was taken as the average distance (in cm) between the lowest node and the tip of the apical spike over five panicles. Plant height was taken as the average distance (in cm) from the ground to the panicle tops. Reaction to smut was recorded as the amount of the combination of covered smut (Ustilago kolleri) and loose smut (U. avanae) expressed as a percent of the susceptible check. Straw color was recorded as 1 = black, 2 = blue, 3 = brown, 5 = purple, 7 = tan, 8 = white/amber, and 9 = yellow. Test weight of grain was reported in pounds per bushel. Yield was recorded as the weight of harvested grain in grams per unreplicated 2.44 m row.Detailed descriptions of phenotyping methods are available at Principal component analysis (PCA) was performed in TASSEL v.4.0 . PrincipK value (number of population clusters) were run for K = 1 to 12. Burn-in was set to 10,000 and Monte-Carlo Markov Chain replications (MCMC) to 25,000 after burn-in. Values of L(K) (log posterior probability of the data) returned by STRUCTURE were averaged across simulations for each K value. Following the method of L(K) between values of K (ΔK) was created. A further run with burn-in = 100,000 and 100,000 MCMC replications was performed to assign individuals to population clusters using the value of K inferred from the initial run. A threshold of 80% genetic membership coefficient was used to assign genotypes to a specific cluster, below which they were designated ‘admixed’.STRUCTURE software version 2.3.4  was usedUsing the relationship matrix function in JMP Genomics, distance-based hierarchical cluster analysis was done by first generating a similarity matrix based on allele sharing for each accession pair using the subset of SNPs without missing genotype calls. Accessions were then grouped by the Fast Ward hierarchical clustering procedure.Distribution of phenotypic or origin categories across inferred population clusters was explored by calculating expected distribution based on random allocation of category members to clusters, and calculating over- or underrepresentation based on deviation from expected values as a proportion of group membership. Observations were generally noted if they exceeded 15% deviation from expected values. Statistical significance of results was tested by the chi-squared test. For evaluation of patterns in year of collection, accessions were grouped by decade of collection to avoid excessively small cell sizes in the analysis. Because classification as ‘fall sown’ vs. ‘spring sown’ is not undertaken in GRIN, classification as ‘red’ vs. ‘common’ type and winter vs. spring growth habit were evaluated as possible substitutes.N > 80 accessions in the panel.For the majority of GRIN phenotypes, observations for each accession were taken in a single study environment with no accessions common to multiple study/environments which could be used to estimate environmental effects. Because of this, association analyses were performed within study environments and only for those with Single nucleotide polymorphism locations were taken as those on the oat consensus map version 3.1 reported by p ≤ 1.93 × 10-5, calculated by applying the Bonferroni correction method with an experiment-wise alpha level set at 0.05.Genotype-phenotype association was evaluated using a mixed linear model (MLM) procedure in TASSEL v4  under thA. sativa collection, the panel compiled for this study had proportionally fewer cultivars and accessions of uncertain improvement status compared with landraces. This distribution is undoubtedly due to the fact that we limited our selection of cultivars to those entered before 1930. Within the landraces, the present sample maintains the collection year and region of origin distributions of the NSGC  showed a rapid reduction in ΔK, normally regarded as a signal that the true value of K has been reached, between K = 3 and 4. A further run of STRUCTURE was used to assign accessions in the panel to three population clusters (Table 1). Seventy-three percent of accessions exceeded the 80% membership threshold and could be assigned to a specific cluster, while 27% were considered admixed. Cluster 1 was more divergent from 2 or 3 than they were from each other, based on indicators from both STRUCTURE  and hierarchical clustering . Cluster 2 contained the fewest members but showed the highest within-group gene diversity of all clusters (Table 1). Overlap between cluster assignments based on PCA, hierarchical clustering, and model-based analyses confirmed that all detected the same population structure. STRUCTURE-assigned groupings were used for further evaluation of origin and phenotypic trait distribution across population clusters.The first three principal components (PC) accounted for 23.6, 11.8, and 3.4% of marker variation, respectively . The first five PC together accounted for 43.3% of genetic variation among accessions. A plot of the eigenvalues of the first 25 PC suggested that the first three PC account for most of the population structure in this panel (not shown). Similar results were obtained from the model-based analysis where the plot of Table 2). Assignment to the STRUCTURE groupings accounted for the largest proportion of among-group genetic variance. Lemma color and region of origin groups showed some importance as explanatory variables, while growth habit and classification as ‘red’ or ‘common’ oat were far less important. Improvement status groups accounted for only 2.5% of variance, suggesting that that landraces, unimproved material, and cultivars are all drawn from the same underlying population structure.AMOVA was used to compare a range of variables for their power to explain genetic variability in the panel (p < 0.01).Improvement status, region of origin (collection site), growth habit , lemma color, and ‘red’ vs. ‘common’ classification were explored for their association with STRUCTURE population clusters. The chi-squared test showed non-random distributions for all traits studied (Table 3); cluster 2 as widely distributed across Central Asia and Europe; and cluster 3 as predominantly European and North American. North American accessions, which account for 18.8% of panel members, are overrepresented in the admixed group and underrepresented in cluster 1. Cluster 1 was almost entirely composed of landraces (Table 4). ‘Spring’ growth habit was predominant across the whole panel (78% of rated entries), but those accessions designated ‘winter’ appeared highly disproportionately in cluster 1, somewhat disproportionately in cluster 2, and were underrepresented in cluster 3 (Table 4).Cluster 1 was characterized as coming predominantly from Western Asia , Southern Europe and Northern Africa (Table 4). Black oats were in excess in the admixed group, while gray lemma color was in excess in clusters 1 and 2 and red color in cluster 1. There was imperfect correspondence between accessions with red lemma color and those classified as ‘red’ type oats: 61% of accessions with red lemma color were classified as ‘common’ oat, and 38% of all oats classified as ‘red’ type had yellow, gray or black lemma color. A PCo plot colored by the ‘red’/‘common’ classification  suggested that ‘red’ types cover a similar extent of genetic space to that covered by ‘common’ accessions, but are concentrated in cluster 1 (38% overrepresentation at p < 0.01). Cluster 1 consists of 59% ‘red’ oats, and 54% of all accessions classified as ‘red’ type belong to this cluster.Evaluation of lemma color showed that yellow-hulled oats were disproportionately likely to come from cluster 3, which was also less likely to be a source of black, gray, or red lemma colors .Cluster 1 showed substantially higher mean kernel weight than other clusters (Table 6). Supplementary Figures Sample size and phenotypic distributions (mean and standard deviation) of traits used in association analyses are presented in Supplementary Tables Table 6; Supplementary Figure p-value in Urbana 85 (p = 0.036), providing a true replicate for this effect.A cluster of SNPs at 70.2 to 70.5 cM on Mrg17 was associated with reaction to BYDV evaluated in Urbana 84, but not in Urbana 85  of this marker-trait association was observed in Aberdeenr 84 (not shown). The same marker was also associated at p < 1.0 × 10-4 with crown rust reaction and severity in Saint Paul 97, and with awn frequency in Aberdeen 83.A QTN for days to anthesis (heading date/flowering time) was detected on Mrg02 at 60.4 cM in Aberdeen 83 , whereas those carrying the minor allele were more likely to be either red (31.6%) or black (21.1%).Studies of the global gene pool of cultivated oat have generally reported weak structure. PC1-5 accounted for 23.8% of genetic variation in the 635-member CORE panel of elite cultivars , and 22.The lack of correspondence between these subgroups and classification as ‘landrace’ or ‘cultivar’ (explaining only 2.5% of genetic variation in AMOVA) reflects the early historical era during which cultivars in the panel were collected. Cultivars in the present study all entered the NSGC prior to 1930. In the late nineteenth and early twentieth centuries, oat cultivars tended to be selections from landraces rather than artificial crosses, and breeding programs with highly managed germplasm pools did not yet exist.Table 5), which is both an important center of diversification for Avena sp and a region where they are less important as a cultivated crop (Lemma color was strongly associated with population structure as quantified by AMOVA (13.8%). Indications from earlier quantitative work e.g., . One posted crop . AccessiRegion of origin (collection site) accounted for 13.1% of genetic variation by AMOVA, a similar proportion to that captured by lemma color. Its importance to population structure in the present study echoes the findings of The population structure of the germplasm panel used in our study clearly shows a genetic cluster that has largely been overlooked in 20th century breeding. Cluster 1 was characterized by high genetic distinctness and relatively low internal diversity, with a high proportion of red and winter oats. A similar cluster was identified in a previous survey of global oat germplasm by r2> 0.10 was observed within linkage groups at an average distance between markers of 0.44 cM. The same value was estimated as 2.5 cM by The power and resolution of GWAS is influenced by LD between markers in the population studied, and every population is associated with its own unique pattern of LD. In a worldwide panel of 635 elite breeding lines, The same authors suggested in their study that a minimum of one marker every cM be used for GWAS in oat. The use of historical phenotypic data in these analyses resulted in what can be considered several small studies of genotype-phenotype association, each with an independent sample evaluated in a single environment  and without replications. Small sample size, lack of replication within or across environments and the relatively small number of markers available represent limitations on the power of the present study to reliably detect small to moderate SNP effects on phenotype, so we suggest that results should be interpreted as exploratory, with the primary purpose of highlighting genomic regions of interest for future research. For these exploratory GWAS, we chose to apply the same MLM approach to all genotype-phenotype analyses. It is likely that a more nuanced approach in future efforts would improve statistical power to detect QTL for some phenotypes, especially in the case of a skewed distribution or a genetic architecture characterized by rare variants and moderate effect sizes . Given tFigure 4 (Mrg17 and 20) and Supplementary Figures Association mapping analyses conducted for the present study address several traits which have not been part of recent molecular work in oat and thus represent an initial attempt to discover their underlying genetics. The inclusion of a broad range of traits presents opportunities to look at relationships between them. The recently completed oat consensus map also permits the examination of our findings together with previously identified marker-trait associations. A visualization of the annotated oat genome in Figure 4). The region appears to be homologous with one carrying markers for a major BYD tolerance locus identified in separate oat populations by Ryd3 is a major gene for BYD tolerance in barley which has been mapped to a genetic interval corresponding to a segment of rice chromosome Os02  and Pc91, respectively . No candidate genes could be found for seedling resistance markers on Mrg23 and Mrg01, though they could exist among the many Pc genes yet to be mapped.The present study identified five SNPs associated with oat seedling resistance on Mrg01, Mrg08, Mrg20, Mrg23 and Mrg28. Candidate genes for SNPs on Mrg08 and Mrg28 are ectively , 2015. TFor protection of oats against crown rust, oat breeders have tended to turn to race specific seedling resistance conferred by single genes such as those described above; but the crown rust pathogen is highly variable and has been shown to quickly overcome such resistance . Recent Pc38 which situate it at approximately 70 cM on Mrg02  for crown rust reaction and severity and one marker significant for days to anthesis (GMI_ES03_c7453_413). A marker for heading date of unvernalized plants identified by Vrn1 by Pc48. One explanation for such findings is that developmental stage of the plant influences its susceptibility to the crown rust pathogen.Up to three loci have been observed to control lemma color, depending upon the specific cross, with interactions between loci including epistatic dominance  and complementarity  . Furtherp = 0.002) was observed for marker BA_grs_c8269_158 on Mrg17 at 34 cM, just distal to the SNPs associated with awn frequency on that linkage group.Awn formation is associated with lemma color in certain genetic backgrounds . We wereper se (rate of plant development), in particular since the vernalization requirement of oat is often facultative and weak . This region contains heading date QTL regions mapped in the KO and OT populations  in Aberdeen 83 co-locates with a second marginal association with growth habit in Aberdeen 96 and days to anthesis in Aberdeen 84  with heading date response to vernalization observed in the Kanota × Ogle population and with heading date after early planting in the field observed in the Ogle × TAM O-301 population  can be viewed with more confidence. However, we found no evidence of association with growth habit (or days to anthesis) on linkage groups Mrg09, Mrg12, or Mrg20, all of which have been reported to carry QTL mapped by both linkage and association and which carry candidate genes mapped by sequence homeology .Numerous regions of the oat genome have been implicated in the control of flowering time, vernalization response, and photoperiod response (summarized in Significant associations were not identified in the present study for groat beta glucan, groat lipid, groat protein, plant height, test weight, lodging, yield, panicle length and density, smut reaction, straw breakage, shattering, greenbug reaction, and bundle weight. Previous studies have mapped QTL for groat beta glucan , groat lCultivated oat is a widely adapted cereal with greater ecological range than either wheat or barley . High moThe present study assembled a panel of historic and unimproved oat accessions from the NSGC with the goal of sampling germplasm that predates the influence of major breeding advances in the 20th century as a complement to similarly diverse but more recent panels. Population structure in this panel appeared to be stronger than that identified in previous studies. Primary explanatory variables were region of origin and lemma color. While ‘red’/‘common’ type or winter/spring growth habit classifications were relatively uninformative, available evidence was insufficient to establish whether they could be considered equivalent to the ‘fall sown’/‘spring sown’ classification found to account for structure in more recent oat panels.Most accessions in the panel could be assigned to one of three population clusters. One of these clusters was more genetically distant from the other two and contained a disproportionately high concentration of accessions classified as ‘red’ type, winter, with red or gray lemmas and/or originating from Western Asia. This cluster appears to have been largely overlooked during twentieth century breeding and may be a valuable source of germplasm for future breeding work, particularly given its high average kernel weight.Ryd3. Six loci were associated with crown rust reaction, most of which appear to be linked with previously reported genes. Loci on Mrg01 and Mrg23 are reported here for the first time.Association mapping analyses conducted as part of the present study generated the first evidence of chromosomal regions controlling awn frequency, lemma color, kernels per spikelet and panicle type. Other evidence of association generally supported indications from previous studies. A single locus was associated with tolerance to BYD and may be orthologous to barley gene per se. At the same time, genetic relationships between pairs of traits, such as plant height and lodging or yield and heading date, are imperfectly understood and will require well-designed experiments to resolve. More thorough phenotyping of this nature could greatly strengthen characterization of oat germplasm and explanations of population genetic diversity.For future work, findings of the present study suggest the value of a more detailed approach to phenotyping and classification in cultivated oat. Key examples are the measurement of heat tolerance and cold tolerance, as well as the use experimental designs which separate tests of vernalization requirement, photoperiod response and flowering time KEK initiated and organized the study; HB provided germplasm and phenotype information; SC performed genotyping work; LW, KEK, and JMB performed data analyses; LW drafted the manuscript, and KEK, JMB, and BAY contributed material; all authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The multiplex-case and control design in which multiple cases are sampled from the same family is considered. In such studies phenotype information of the un-genotyped relatives might be available. We intend to use additional family information when performing genetic association tests. A score test is revisited to provide a flexible framework to accommodate various genetic models and to improve power of the association test by adding available family information. The proposed test accounts for correlations induced by multiple cases from the same pedigree, directly deals with X-linked SNPs in mixed-sex-related samples, and incorporates additional phenotypic information such as the number of (un-genotyped) siblings and parents with similar symptoms by assigning the weights to (genotyped) multiplex cases. In addition, the score test directly incorporates posterior probabilities of imputed genotypes, which leads to an efficiency measure that reflects imputation uncertainty on the test conducted. The proposed test is applied to real applications for illustration. Its efficiency is demonstrated via simulations. We investigate the use of family-based samples for conducting case–control association analysis. The family-based association tests such as the FBAT  are robuWhen cases are ascertained via multiple affected individuals within pedigrees, the ascertainment issue should be addressed. It is argued that the ascertainment event depends on the phenotype but is conditionally independent of the genotype given disease outcome. The retrospective likelihood, therefore, is appropriate under selection , 45. Usimore powerful quasi-likelihood score (MQLS) test. This extension of the QLS test incorporates additional phenotypic information of relatives who are not genotyped. That is, the phenotype data of un-genotyped family members are used to give corresponding weights to the (genotyped) multiplex cases. Note that both QLS and MQLS tests are based on the best linear unbiased estimator of allele frequency. These are more suited for related samples from a large complex pedigree for which maximum likelihood estimation is impractical  , 27. Thementclass2pt{minimLet The elements of h et al.  can be u of MACH , which i of MACH .Suppose that we have QLS test  also treFor case population, parental genotypes are generated assuming random mating and Hardy–Weinberg equilibrium for minor allele frequencies of 0.01, 0.05, 0.07, 0.10, and 0.30.Conditional on parental genotypes we generate genotypes of 3 offspring assuming Mendelian transmission.Using the logistic model, disease indicators for offspring were generated. The model we considered was Since we are dealing with a complex phenotype we also included some residual familial correlation due to the polygenic or environmental sources in our disease model. The broad sense heritability can be written as For control population the steps (a) and (b) are performed with exception that in the step (b) only the genotypes of one individual are generated.Each sampling experiment under additive and recessive genetic model and various ascertainment schemes consisted of 1000 independent replicates: 500 cases 500 controls for the ascertainment schemes ASP1, ASP2, ASP3, and 600 cases and 600 controls for the Mixed ascertainment.For each replicate, we tested for an association using (i) the score test (ST) and (ii) where appropriate the score test that includes phenotypic information of un-genotyped relatives , two affected and one unaffected (ASP2), and three affected siblings (ASP3). For testing we select 1 genotyped individual from each family as cases and use the score test, which is the Cochran-Armitage test for trend in proportions , 11. In For generating X-linked genotypes in the step 1, maternal genotypes are generated as above. For paternal genotypes, only one allele is generated, which follows the Bernoulli distribution; the other allele is fixed as Y, which contributes to determine the gender of offspring in the step 2.Type I error rates are reported for each statistic, where the type I error rate is the proportion of significant replicates at the nominal level of 5 % out of the total number of replicates. With 1000 replicates and a true type I error rate of 5 %, 95 % of the empirical estimates are expected to fall between 0.036 and 0.064. In Table Table mentclass2pt{minimmentclass2pt{minimTable For association of X-linked SNPs and illustration of using imputed probabilities of the genotypes, we apply the proposed test to data from the Leiden Longevity Study (LLS) , 41.http://www.hapmap.org) release 22, build 36 of the CEU sample. The imputation program IMPUTE2 (http://mathgen.stats.ox.ac.uk/impute/impute_v2.html) was used.In the Leiden Longevity Study (LLS), long-lived families are investigated for parameters contributing to the longevity phenotype. Families were included if at least two long-lived siblings were alive and fulfilled the age criterion of 89 years or older for men and 91 years or older for women. In total, 944 long-lived proband siblings were included with a mean age of 94 years (range 89–104), 1671 offspring , and 744 partners . Nonagenarian siblings were genotyped using Illumina660W  and their partners were genotyped using Illumina660W or OmniExpress . GenomeStudio was used for genotyping calling algorithm. Sample call rate was p value = 8.47EFor association of X-linked SNPs, we consider an affected sibling pair (ASP) and control design: 933 long-lived subjects from 420 nonagenarian sibling pairs are served as cases and 741 partners of offspring as controls. Additional phenotypic information (current age or age at death) for un-genotyped (or deceased) family members  was included: 404 long-lived relatives. Here, to take into account different expectancies in the different birth cohorts, we used life tables for the Dutch population to define the longevity phenotype, the top 10 % of the specific birth cohorts . In Tablribed in , the ratImputing SNPs that are not directly genotyped but are present on a reference panel such as the HapMap usually results in not one imputed value but three probabilities of the possible genotype value, 0, 1, or 2 for each individual. To deal with genotype uncertainty, one can choose the “best” genotype—genotype with the largest posterior probability, or one can use expected genotype counts (genotype dosages) as in . By calcoss=0 in . We cons}Σloss≥0 . For extinfo of IMPUTE, which is the default threshold for ensuring imputation quality. As shown in Table For illustration purpose, we chose an extreme case: a SNP, rs17183864 (chromosome 6), with MAF 1 % and the post-imputation measure  of MACH  or info DIO2) was identified as an osteoarthritis susceptibility gene. The common coding variant  in the DIO2 gene showed significant association. Information about the number of siblings and parents with similar symptoms was available: 30 % of the genotyped affected siblings have no missing (un-genotyped) affected siblings, 30 % have one missing affected sibling. The maximum number of missing affected siblings is 8 (one family). Regarding affected parents, 16 and 60 % of the ASPs had two and one affected un-genotyped parents, respectively. Using this extra information, Callegaro et al. proposed the allele-sharing statistics for genetic linkage analysis to account for the family history, and this considerably increased the evidence of linkage in the surrounding of the DIO2 susceptibility locus [The Genetics osteoARthritis and progression (GARP) study consists of 187 Caucasian sibling pairs and four trios of Dutch origin affected by symptomatic and radiographic OA at multiple sites . OsteoarThe question arises whether this strategy could successfully be adapted to a genetic association study. We consider a case–control association study: the 380 cases from the GARP study and the control population from the Leiden Longevity Study described in the previous section . The conDIO2 (78 cM), FLRT2 (82 cM), and CALM1 (89 cM) were located. First, the linkage region of 75–95 cM was converted to the physical map, 79147631–95189589 bp, using a build GRCh37 genetic map (ftp://ftp.ncbi.nlm.nih.gov/hapmap/recombination/2011-01_phaseII_B37/). The GWAS data for both the GARP and LLS study were available, and the genotype imputation was performed using IMPUTE2 (http://mathgen.stats.ox.ac.uk/impute/impute_v2.html) based on the reference panel from the February 2012 release of the 1000 Genomes project (ftp://ftp.1000genomes.ebi.ac.uk/) and genome build 37. For the respective linkage region ca. 200K SNPs were available. We performed the association analysis, and the following filtering was employed: MAF in controls Suggestive evidence for linkage was observed on chromosome 14q32.11 , whereianalysis , the \doDIO2 gene. In [http://csg.sph.umich.edu/LAMP) [DIO2 SNPs rs12885300 and rs225014 with women with advanced symptomatic hip OA was found.Next we revisited the common coding variant, rs225014, in the gene. In , joint ldu/LAMP) . A signiam study ; the C ap value. For this specific SNP the magnitude of the In the current association study as shown in Table When testing for weak genetic effects, besides the efforts to increase sample size, it is desirable to develop statistical methods that more effectively detect an association. Through simulation we showed that sampling genotyped cases from the high-risk families increases the power, even if only one case was sampled. To further increase the efficiency of the study, the multiplex-case and control design was considered, in which genotype frequencies between cases, each of whom was sampled from multiple affected families, and unrelated controls were compared. The primary reason for choosing this design would be that familial cases are enriched for genetic factors and therefore may be more informative for genetic research, particularly in the presence of genetic heterogeneity and phenocopies. For genetic linkage analysis Callegaro et al. proposed allele-sharing statistics using information on family history . In the The first issue related to the proposed test is the ascertainment of the cases. The retrospective likelihood is considered to adjust for ascertainment by conditioning on the phenotypes of family members. For a general phenotype, a score statistic to test for an additive effect of a diallelic locus on phenotype is the genotype–phenotype covariance; the score statistic can be conveniently applied in both prospective and retrospective manners , 28. TheSecondly, the methods should allow for familial correlations due to sampling-related individuals. The variance of the score statistic can be readily modified to account for familial relationships based on kinship coefficients. To detect recessive effects of a SNP, however, the calculation of correlation coefficient depends on kinship coefficients as well as allele frequency. Another important extension is to construct a test for X-linked SNPs in mixed-sex-related samples. Combining the two statistics obtained by applying an allele-based test to males and females separately is not a valid test for family data. In this work we adapted the score test using X-linked correlation matrices.Thirdly, to incorporate the extra phenotypic information, the weighting scheme similar to allele-sharing statistics for genetic linkage analysis of  is appliAs shown in Sect. naive estimator of genotype frequency the best linear unbiased estimator [Another benefit of employing our methods would be the use of the weight to pinpoint the extreme families for further investigation. The weights mentclass2pt{minimstimator  can be ustimator .In the Genome-Wide Association Studies (GWAS) era, genotype imputation has become an essential tool. The imputation of genotypes allows investigators to test association at un-genotyped genetic markers and to combine results across studies that rely on different genotyping platforms. Combining the GWAS results for meta-analysis, using family data under strong ascertainment and using case–control data, can be inefficient and difficult. Here, our test can be of great use, since the score statistics from the individual studies can be easily combined. Another aspect of popularity of imputation is that more and more SNPs are imputed using external information of reference haplotypes from the HapMap and 1000 Genomes Project. The ever-increasing number of tests to be performed calls for a tractable flexible approach such as score test.In conclusion, incorporating positive family history in the test statistic appeared to be a powerful strategy. Especially, the power of association using rare SNPs can be much improved by adding phenotypic information of un-genotyped family members.


Recent studies are starting to show that genetic control over stochastic variation is a key evolutionary solution of single celled organisms in the face of unpredictable environments. This has been expanded to show that genetic variation can alter stochastic variation in transcriptional processes within multi-cellular eukaryotes. However, little is known about how genetic diversity can control stochastic variation within more non-cell autonomous phenotypes. Using an Arabidopsis reciprocal RIL population, we showed that there is significant genetic diversity influencing stochastic variation in the plant metabolome, defense chemistry, and growth. This genetic diversity included loci specific for the stochastic variation of each phenotypic class that did not affect the other phenotypic classes or the average phenotype. This suggests that the organism's networks are established so that noise can exist in one phenotypic level like metabolism and not permeate up or down to different phenotypic levels. Further, the genomic variation within the plastid and mitochondria also had significant effects on the stochastic variation of all phenotypic classes. The genetic influence over stochastic variation within the metabolome was highly metabolite specific, with neighboring metabolites in the same metabolic pathway frequently showing different levels of noise. As expected from bet-hedging theory, there was more genetic diversity and a wider range of stochastic variation for defense chemistry than found for primary metabolism. Thus, it is possible to begin dissecting the stochastic variation of whole organismal phenotypes in multi-cellular organisms. Further, there are loci that modulate stochastic variation at different phenotypic levels. Finding the identity of these genes will be key to developing complete models linking genotype to phenotype. Systems biology is largely based on the principal that the link between genotype and phenotype is deterministic, and, if we know enough, can be predicted with high accuracy. In contrast, recent work studying transcription within single celled organisms has shown that the genotype to phenotype link is stochastic, i.e. a single genotype actually makes a range of phenotypes even in a single environment. Further, natural variation within genes can lead to each allele displaying a different phenotypic distribution. To test if multi-cellular organisms also display natural genetic variation in the stochastic link between genotype and phenotype, we measured the metabolome, growth and defense metabolism within an Arabidopsis RIL population and mapped quantitative trait loci. We show that genetic variation in the nuclear and organeller genomes influence the stochastic variation in all measured traits. Further, each trait class has distinct genetics underlying the stochastic variance, showing that there are different mechanisms controlling the stochastic genotype to phenotype link for each trait. Further work is necessary to identify the mechanisms underpinning the stochastic nature of the genotype to phenotype link. The link between genotype and phenotype is often considered to be deterministic such that a single genotype functions to yield a specific phenotypic value. This deterministic relationship is a central tenet of the desire to develop predictive models allowing an organism's phenotype to be forecasted upon knowing its specific genotype. This deterministic hypothesis is supported by research showing that cells limit stochastic noise/variance in genetic, metabolic, and signaling networks through network topology, a characteristic that is known as network robustness Arabidopsis thaliana loci that are known to be under natural selection suggesting that the stochastic aspects of these loci may impart an evolutionary benefit While a deterministic link between genotype and phenotype is the most frequently studied aspect of evolution and genetics, there is growing research showing the potential evolutionary benefit of a stochastic link between genotype and phenotype. A stochastic link between phenotype and genotype allows an individual genotype to generate a range of phenotypes within a specific environment and causes the portfolio effect wherein the fitness of a specific genotype is determined by the range of phenotypes that it can obtain Similar to transcriptional networks, metabolic networks are thought to be highly structured to maximize deterministic relationships and minimize stochastic variance that could disconnect pathways and potentially generate toxic intermediates Arabidopsis thaliana. Arabidopsis is a key organism in the study of complex traits including the genetic programming of stochastic variation through the use of systems biology and quantitative genomics approaches To empirically measure the potential for genetic variation to control stochastic variation within the metabolomic network, we measured metabolome variation in a recombinant inbred line (RIL) population of Using a replicated, randomized sampling design, we measured metabolome variation in the Kas x Tsu RIL population and compared the quantitative genetics for average metabolite accumulation versus the stochastic variation Arabidopsis thaliana, we used a previous analysis of quantitative variation of the average metabolism and growth within the Kas x Tsu RIL population To test if genetic variation affects stochastic noise in the metabolome and growth of the higher plant As previously found using the Arabidopsis transcriptome, the heritability for the metabolite CV was higher than that for the average metabolite accumulation 1 Table , . ThMapping per line CV of growth in comparison to average growth identified a number of average QTLs and only two CV loci for growth  and S5. The Kas x Tsu population was explicitly established as a reciprocal population with approximately half of the lines having the Kas organellar genomes and the remaining RILs having the Tsu organellar genome. Thus, we explicitly tested if genetic variation within the organellar genomes influenced phenotypic variation in the metabolome, growth, and defense by adding the organellar genome as a term in our single marker linear models  and S5. Intriguingly, 49 of 96 polymorphisms within the mitochondria are found within genes in the NADH complex or in cytochrome C function In contrast to the metabolome, none of the growth traits had either the average or per line CV significantly influenced by the organellar genomic variation. Defense chemistry per line CV also was less affected by the organellar variation with only 5 of 19 phenotypes showing a significant link to organellar genomic variation  and S5. Using the average values for growth, defense chemistry and the metabolome, we had previously shown that there was extensive epistasis in this population linking the nuclear and organellar genomes Arabidopsis thaliana RILs, we mapped genetic loci controlling stochastic variance in the metabolome, defense chemistry and growth of a multi-cellular eukaryote . AdditioWithin this study, we show that it is possible to identify genetic loci in both the nuclear and organelles that lead to altered stochastic variation in all measured phenotypes from individual metabolites to whole plant growth. Further, these loci differ from trait to trait, suggesting that we are not identifying generic variance loci as might be expected if they were affecting global mechanisms like HSP90. Instead, these CV loci affect specific genetic networks that are distinct for each trait. This suggests that there may be stochastic specific loci for each plant trait. For instance, numerous natural and induced mutant screens and surveys have been conducted in Arabidopsis to determine the genes controlling the phenotypic average To directly estimate the CV for each individual metabolites accumulation as a separate phenotype within the Kas x Tsu RIL population 2g/σ2p, where σ2g was estimated for both the RIL genotypes and cytoplasmic genotypes and σ2p was the total phenotypic variance for a trait gmeb) was: c =  the Kas or Tsu cytoplasm; g =  the 1…316 for the 316 RILs, e =  experiment 1 or 2. This allowed cytoplasmic effects to be directly tested in the C term and each RIL genotype (G) nested within the appropriate cytoplasmic class, either Kas or Tsu. Experiment was treated as a random term within the model to better parse the variation. All resulting variance estimates, P-values and heritability terms are presented (2g for RIL was pulled from the g(C c)G term while σ2g for cytoplasmic variation was pulled directly from the cMmC  term. We used mean CV values for each RIL for further analysis as we had a randomized complete block design with no missing lines  for each phenotype as H =  σresented . σ2g fong lines .We used the previously reported genetic map for these lines of the Kas × Tsu RIL population t marker .QTL clusters were identified using a QTL summation approach where the position of each QTL for each trait was plotted on the chromosome by placing a 1 at the peak of the QTL. This was then used to sum the number of traits that had a detected QTL at a given position using a 5cM sliding window across the genome To further assess the potential of structured technical or biological variation to influence our analysis, we conducted a permutation analysis wherein we randomized the line to metabolome links within each of the four randomized blocks. This maintains any correlative structure between the metabolites within a metabolomic sample that may have been caused by structured technical or biological error. We then recalculated CV and mean within each RIL using the randomized phenotype data and used this to re-conduct the entire QTL analysis as described above. 100 permutations of the entire dataset identified a maximum of only 53 metabolomic CV QTL identified across the 559 metabolites in any given permutation which lead to no hotspots being identified. This suggests that the observed hotspots are not caused by structured error within the metabolomics samples.g at marker m was shown as gmy. The model (Additive Model) for each metabolite in each line (ygm) was: To directly test the additive effect of each identified QTL cluster, we used an ANOVA model containing the markers most closely associated with each of the significant QTL clusters as individual main effect terms. For each metabolite the average accumulation in lines of genotype g =  Kas(1) or Tsu(2); m = 1, …,11. The main effect of the markers was denoted as M involving 15 markers (m). The cytoplasmic genome was included as an additional marker to test for cytoplasmic genome effects. We independently tested the average metabolite accumulation and CV of each metabolite as a separate phenotype with the appropriate model using lm function implemented in the R/car package, which returned all P values, Type III sums-of-squares for the complete model and each main effect. The results using the average metabolite accumulation are presented (where resented  separateresented . QTL mairesented  and S5. g at marker m was shown as gmy. The model (Pairwise epistasis model) for each metabolite in each line (ygm) was: To test directly for epistatic interactions between the detected QTLs, we conducted an ANOVA using the pairwise epistasis model. We used this pairwise epistasis model per metabolite because we had previous evidence that RIL populations have a significant false negative QTL detection issue and wanted to be inclusive of all possible significant loci g =  Kas(1) or Tsu(2); m = 1, …,14 and n was the identity of the second marker for an interaction. The main effect of the markers was denoted as M having a model involving 15 markers. The cytoplasmic genome was included as an additional single-locus marker to test for interactions between the cytoplasmic and nuclear genomes. We independently tested the average metabolite accumulation and CV of each metabolite as a separate phenotype with the appropriate model using lm function implemented in the R/car package, which returned all P values, Type III sums-of-squares for the complete model and each main effect. The results using the average metabolite accumulation are presented  and average (grey) phenotype across Kas x Tsu RIL populations. A frequency plot shows the estimated heritability's ascribed to the nuclear (solid lines) and organellar (dotted) genomes across all the metabolites. A. Aliphatic Glucosinolate phenotypes. B. Indolic Glucosinolate phenotypes. C. Growth phenotypes.(TIF)Click here for additional data file.S2 FigRelationship between metabolite average and CV across the RILs. Shown is a hexbin plot of the relationship between the mean and CV of each metabolite in each RIL across the entire dataset. The resolution of the plot is set to 50 bins.(TIF)Click here for additional data file.S3 FigLack of correlation in QTL number and effect for CV and Mean metabolite accumulation. A. Shown is the number of QTLs for a given metabolite for both CV and mean. The size of the pie's is proportionate to the number of metabolites present in that specific grouping. No significant correlation was found using either spearman or pearson tests. B. For metabolites where the CV and mean QTLs 1 LOD interval overlapped, the estimated additive effect on CV and mean are plotted. No significant correlation was found using either spearman or pearson tests.(TIF)Click here for additional data file.S4 FigEffect of CV hotspots on CV across the metabolomic network. A map of central metabolism was created in cytoscape and used to plot the estimated additive effect of genetic variation of each metabolite CV hotspot on the affected primary metabolites. A red box shows increased metabolite accumulation when the line contains the Kas cytoplasmic genome while green shows increased metabolite accumulation when the line contains the Tsu cytoplasmic genome. White boxes are metabolites that were detected but not significantly influenced by the cytoplasmic genome and grey boxes are metabolites that were not detected. Each page represents a unique metabolite CV hotspot.(PDF)Click here for additional data file.S5 FigQTL hotspots for defense and growth average and CV. The number of metabolites for which a QTL was detected within a 5 cM sliding window is plotted against the genetic location of the metabolite QTLs in cM. Metabolite average QTLs are shown in grey and CV in black. A. Aliphatic Glucosinolate phenotypes. B. Indolic Glucosinolate phenotypes. C. Growth phenotypes.(TIF)Click here for additional data file.S6 FigEpistatic interactions of Glucosinolate phenotypes using both the average and CV QTL hotspots. The bar plots show the number of pairwise epistatic interactions per locus for Aliphatic (A) and Indole (B) glucosinolate CV and Average using the respective QTL hotspots for each phenotype. Grey bars show the analysis with the Average QTL hotspots and black bars represent the CV QTL hot spots. Only the interactions significantly affecting 10% or more metabolites were considered. The width of the bars is scaled to the percentage of metabolites significantly affected by the main effect of that locus as shown.(TIF)Click here for additional data file.S7 FigEpistatic interactions of growth phenotypes using both the average and CV QTL hotspots. The bar plots show the number of pairwise epistatic interactions per locus for growth CV and Average using the respective QTL hotspots for each phenotype. Grey bars show the analysis with the Average QTL hotspots and black bars represent the CV QTL hot spots. Only the interactions significantly affecting 10% or more metabolites were considered. The width of the bars is scaled to the percentage of metabolites significantly affected by the main effect of that locus as shown.(TIF)Click here for additional data file.S8 FigDifferential normality of CV and Mean across the RILs. For each metabolite, the skewness and kurtosis was measured for both CV and mean across the RILs. The distribution of these values across the metabolites for both CV and mean(AV) are shown.(TIF)Click here for additional data file.S1 TableEstimation of Heritability for Metabolite CV. The results of the linear model analyzing the variation of CV across the Experiments and two estimatible genomes (Organellar and Nuclear) are shown.(XLSX)Click here for additional data file.S2 TableAverage per line CV for all phenotypes. Shown is the average per line CV for each RIL for each phenotype as estimated from the ANOVA.(XLSX)Click here for additional data file.S3 TableQTLs identified for per line CV for all phenotypes. The position and estimated effect size for each identified QTL for each phenotype is presented.(XLSX)Click here for additional data file.S4 TableResults of single marker ANOVA model testing QTL effects for average phenotypes. Results of the single marker validation modeling using the QTL hotspots found in this analysis with the average phenotypes. Metabolites are at the start with defense compounds and growth at the bottom of the table.(XLSX)Click here for additional data file.S5 TableResults of single marker ANOVA model testing QTL effects for per line CV phenotypes. Results of the single marker validation modeling using the QTL hotspots found in this analysis with the per line CV phenotypes. Metabolites are at the start with defense compounds and growth at the bottom of the table.(XLSX)Click here for additional data file.S6 TableP values of Pairwise epistasis tests using ANOVA for all average phenotypes. Results of the pairwise epistasis analysis using the average phenotypes and QTLs validated from the single marker ANOVA. Only P values for the model are shown in this table. Metabolites are at the start with defense compounds and growth at the bottom of the table.(XLSX)Click here for additional data file.S7 TableType III Sums of squares for Pairwise epistasis tests using ANOVA for all average phenotypes. Results of the pairwise epistasis analysis using the average phenotypes and QTLs validated from the single marker ANOVA. Only type III Sums-of-square values for the model are shown in this table. Metabolites are at the start with defense compounds and growth at the bottom of the table.(XLSX)Click here for additional data file.S8 TableP values of Pairwise epistasis tests using ANOVA for all per line CV phenotypes. Results of the pairwise epistasis analysis using per line CV phenotypes and QTLs validated from the single marker ANOVA. Only P values for the model are shown in this table. Metabolites are at the start with defense compounds and growth at the bottom of the table.(XLSX)Click here for additional data file.S9 TableType III Sums of squares for Pairwise epistasis tests using ANOVA for all per line CV phenotypes. Results of the pairwise epistasis analysis using per line CV phenotypes and QTLs validated from the single marker ANOVA. Only type III Sums-of-Squares values for the model are shown in this table. Metabolites are at the start with defense compounds and growth at the bottom of the table.(XLSX)Click here for additional data file.