BACKGROUND
it is essential to elucidate the relationships between metabolic and genomic diversity to understand the genetic causes of phenotypic variation among natural populations.
visible and chemical variations associated with genomic diversity provide the information required to identify key genes associated with such phenotypic changes  <cit> .
patterns of nucleotide polymorphisms and a population structure figured in a natural population have allowed us to understand the evolutionary significance of genetic variation under the influence of geographic factors or different environments  <cit> .
the technical development of metabolite profiling has provided quantitative phenotypic information on the metabolome among strains and identified genes involved in metabolic networks  <cit> .
therefore, a comprehensive exploration of the correlations between metabolic and genomic diversity, achieved by superimposing data from these two "omics" approaches, could provide information regarding both the broad and specific relationships between metabolo-phenotypes and genotypes.
this information would aid the identification of genetic associations between the metabolic and/or visible phenotypes  <cit> .
metabolite quantitative trait loci  analysis using segregated populations has been applied to various plant species as a popular forward genetics approach  <cit> .
although mqtls analysis has been used to dissect the genetic loci involved in metabolo-phenotypic changes in mapping populations of various organisms, no methods have been developed to identify the correlations between genetic polymorphisms and metabolo-phenotypic differences in divergent accessions or individuals from natural populations.
to date, several attempts have been performed to find correlative relationships between genetic and metabolic diversity in various organisms  <cit> .
it is still unclear whether it is possible to discover such correlations between each metabolite and the polymorphic pattern found at each chromosomal location to explain natural variation.
combinatorial approaches of population genomics coupled with metabolo-phenotyping should play a significant role in the exploration of the association of genetic variation with metabolic changes that have a significant impact on evolution and adaptation  <cit> .
in this study, to assess the correlative relationship between metabolic and genomic diversity found in  <dig> rice accessions selected from the world rice collection, we compared the distance relationships of accessions for these two omics patterns, globally, with marker polymorphisms vs. the metabolomic fingerprints of nmr spectra, as well as locally, for each chromosomal region vs. each spectra.
to perform these analyses, we developed a robust procedure to explore the relationship between metabolic and genomic diversity.
as a result, we present the first correlation map of metabolic and genomic diversity among rice accessions.
RESULTS
to select the appropriate rice accessions for the comparison of metabolic profiles with patterns of genetic polymorphisms, we initially investigated the population structure of the world rice collection.
the accessions of the world rice collection have been classified into three major groups, japonica, indica i, and indica ii  <cit> .
to determine in greater detail the genetic structure of these populations, we performed population structure analysis using structure software on data generated by genotyping  <dig> restriction-fragment-length polymorphism  markers.
this analysis showed that the japonica type accessions were composed of  <dig> sub-groups; temperate, and tropical i, ii, and iii.
for the twoindica groups, the members of indica i were not divided, while those of indica ii were divided into two sub-groups; indica iia and indica iib .
we chose  <dig> rice accessions from  <dig> representative sub-groups to cover a diverse range of cultivated rice populations, on the basis of population structure, to avoid regional bias .
the rflp genotyping data were also used to generate global and local genetic distance matrices along each chromosome in combination with physical allocation data on the rice genome .
soluble extracts of seeds from each accession were analyzed by 1d 1h-nuclear magnetic resonance  analysis to elucidate the metabolic diversity among the accessions .
to assess the global correlation between metabolic and genomic diversity, we calculated spearman's coefficient of correlation  between the distance matrices calculated from the  <dig> - to  <dig> -ppm 1h chemical shifts and genetic distance among accessions with polymorphisms at the  <dig> rflp markers.
we found no correlation between global metabolic diversity and the phylogenetic relationships among the rice accessions .
although the phylogenetic tree of the genetic distance matrix showed the expected  <dig> typical clusters , the metabolic diversity tree showed no identifiable clusters corresponding to the phylogenetic clusters .
this result indicates that there are no relationships between the genomic and metabolic diversity of solution metabolites of rice grain in the comparison of holistic data.
in higher plants, the diversity of the metabolome might tend to be unrelated with genomic diversity.
in sesame, which has been an example for the comparison of metabolic and genomic diversity in higher plants, the relationship patterns generated for the aflps and the seed metabolic profiles also differed  <cit> .
seed metabolic profiles are derived from the transcriptome that is specifically activated in the embryo and endosperm during seed development.
genes involved in the abundance of solution metabolites of rice seed might consist of small members expressed in seed specific transcriptome.
although there have been few examples of the global assessment of correlations between metabolic and genomic diversity in higher plants, the recent updating of the infrastructure for population genomics and metabolomics should allow us to determine correlation patterns at different levels i.e., from genomic segments to small chromosomal regions in each organism along with their evolutionary patterns.
in arabidopsis, a large number of metabolic qtls were identified in a recombinant inbred line  of two parental ecotypes, which had been selected from  <dig> ecotypes based on their metabolic profiles  <cit> .
most of the metabolic variation observed among the ecotypes was successfully mapped using this ril population, indicating that metabolic variation can be associated with simplified polymorphic patterns at specific chromosomal regions.
we then calculated the rs of euclidian distance matrices derived from each  <dig> -ppm integral region of the 1h chemical shift against the genetic distance matrices derived from sets of three adjacent markers along each chromosome .
the results of these local analyses are presented as a 2d correlation map .
to assess the statistical significance of each correlation coefficient value in this dataset, we calculated the p-value for each rs value and determined the relationship between the rs value and the p-value for all combinations.
as a result of this analysis, an rs value of  <dig>  was supported with a p-value of  <dig> e-07; the value of rs at the  <dig> % level of the p-value was between ≥  <dig>  and ≤  <dig> .
we also calculated the correlation coefficient values between each genetic distance matrix of three randomly sampled rflp markers and the metabolic profile of each of the  <dig> nmr spectra in  <dig>  trials to estimate the probability of a high correlation by random chance .
the results showed that the probability of a correlation coefficient rs ≥  <dig>  in the randomized data was less than 1% .
therefore, in this analysis, we applied rs =  <dig>  as the threshold to detect correlative combinations between metabolic and genomic diversity.
the presentation of the correlations as a 2d heat map showed large non-correlative areas studded with correlative areas, which can be explored in omics-wide comparisons.
the heat map showed clear correlations between the profiles of soluble metabolites and local genomic diversity at each chromosomal location.
to identify metabolites, we performed 2d 1h-13c nmr analysis .
diversity in the choline profile  showed a high correlation with genetic diversity around the r <dig> rflp marker, located at  <dig>  cm on chromosome   <dig> .
the lipid methylene signal  showed correlations with polymorphic sites at multiple chromosomal locations, with the highest located around the c <dig> rflp marker at  <dig>  cm on chr.
<dig> , and the second highest around r <dig> at  <dig>  cm on chr.
<dig> .
the correlation peaks corresponding to each of the lipids  with chr.
<dig> , chr.
<dig> , and chr.
<dig>  were in good accordance with the covalent bond networks within molecules of each compound.
furthermore, sucrose  with chr.
<dig>  and chr.
<dig>  also showed good accordance.
similar intramolecular covalent bond networks were reported in nmr experiments of biological metabolite mixtures, including covariance total correlation spectroscopy  analysis of insect venom  <cit>  and tocsy analysis of intact human gut biopsies  <cit> .
local comparison between the dendrogram based on the correlative rflp polymorphisms and nmr spectra showed a similar tree structure between both genetic and metabolic variation among the accessions.
for example, the dendrogram of genetic polymorphisms around the c <dig> rflp marker  and the diversity of the lipid methylene signal indicated the genetic and metabolic divergence between indica i and two other groups, japonica and indica ii .
to display the chromosomal locations of the correlative regions at  <dig> rflp markers, the results of the correlation analysis were superimposed onto a genetic map of rice .
this genetic map should allow us to identify metabolic qtls associated with each of the metabolic abundances allocated on a genetic map.
it would be beneficial if future genetic analyses narrowed down candidate genes and/or isolated such metabolic qtls in combination with other genomic resources for rice such as genetic marker systems and genome annotation databases.
throughout this study, we developed a robust approach to explore the correlation between genomic and metabolomic diversity observed in natural variation, which is shown as a schematic flow in additional file  <dig>  the procedure is summarized as follows:  selection of appropriate accessions based on population structures;  acquisition of the metabolic profile of selected accessions using various types of metabolomics platforms and the calculation of the distance among accessions on the basis of metabolite abundance or spectrum intensities;  genotyping of selected accessions using dna markers covering the whole genome and the calculation of local genetic distance among accessions from genotype data of adjacent marker sets;  to discover the correlative relationships between each metabolite and chromosomal region and the coefficients of correlation for metabolic and genetic distances in all-against-all combinations and to display their distribution on a genetic map.
in this analysis of rice, we did not find a correlation between metabolic and genomic diversity as observed in previously reported studies.
on the other hand, local correlation analysis allowed us to identify small correlative areas mostly in non-correlative spaces.
this suggests that local correlation analysis could be an effective method to discover such correlative areas allocated to chromosomal regions together with linkage maps or variation data of genomic sequences  <cit> .
this study illustrated a new integrative approach to explore the links between metabolomics and genomics.
the procedure could be extended to a higher resolution by using denser polymorphic marker maps in various species.
CONCLUSIONS
this study assessed the correlative relationships between genetic and metabolic diversity among rice accessions.
the data presents the first correlative relationship for the chromosomal distribution of each metabolic profile in rice.
the combinatorial use of genomic and metabolomic data demonstrated here should facilitate the elucidation of complex regulatory networks and those of evolutionary significance in plant metabolic systems.

 it is essential to elucidate the relationships between metabolic and genomic diversity to understand the genetic causes of phenotypic variation among natural populations. visible and chemical variations associated with genomic diversity provide the information required to identify key genes associated with such phenotypic changes. patterns of nucleotide polymorphisms and a population structure figured in a natural population have allowed us to understand the evolutionary significance of genetic variation under the influence of geographic factors or different environments. the technical development of metabolite profiling has provided quantitative phenotypic information on the metabolome among strains and identified genes involved in metabolic networks. therefore, a comprehensive exploration of the correlations between metabolic and genomic diversity, achieved by superimposing data from these two "omics" approaches, could provide information regarding both the broad and specific relationships between metabolo-phenotypes and genotypes. this information would aid the identification of genetic associations between the metabolic and/or visible phenotypes. metabolite quantitative trait loci analysis using segregated populations has been applied to various plant species as a popular forward genetics approach. although mqtls analysis has been used to dissect the genetic loci involved in metabolo-phenotypic changes in mapping populations of various organisms, no methods have been developed to identify the correlations between genetic polymorphisms and metabolo-phenotypic differences in divergent accessions or individuals from natural populations. to date, several attempts have been performed to find correlative relationships between genetic and metabolic diversity in various organisms. it is still unclear whether it is possible to discover such correlations between each metabolite and the polymorphic pattern found at each chromosomal location to explain natural variation. combinatorial approaches of population genomics coupled with metabolo-phenotyping should play a significant role in the exploration of the association of genetic variation with metabolic changes that have a significant impact on evolution and adaptation. in this study, to assess the correlative relationship between metabolic and genomic diversity found in rice accessions selected from the world rice collection, we compared the distance relationships of accessions for these two omics patterns, globally, with marker polymorphisms vs. the metabolomic fingerprints of nmr spectra, as well as locally, for each chromosomal region vs. each spectra. to perform these analyses, we developed a robust procedure to explore the relationship between metabolic and genomic diversity. as a result, we present the first correlation map of metabolic and genomic diversity among rice accessions.  to select the appropriate rice accessions for the comparison of metabolic profiles with patterns of genetic polymorphisms, we initially investigated the population structure of the world rice collection. the accessions of the world rice collection have been classified into three major groups, japonica, indica i, and indica ii. to determine in greater detail the genetic structure of these populations, we performed population structure analysis using structure software on data generated by genotyping restriction-fragment-length polymorphism markers. this analysis showed that the japonica type accessions were composed of sub-groups; temperate, and tropical i, ii, and iii. for the twoindica groups, the members of indica i were not divided, while those of indica ii were divided into two sub-groups; indica iia and indica iib. we chose rice accessions from representative sub-groups to cover a diverse range of cultivated rice populations, on the basis of population structure, to avoid regional bias. the rflp genotyping data were also used to generate global and local genetic distance matrices along each chromosome in combination with physical allocation data on the rice genome. soluble extracts of seeds from each accession were analyzed by 1d 1h-nuclear magnetic resonance analysis to elucidate the metabolic diversity among the accessions. to assess the global correlation between metabolic and genomic diversity, we calculated spearman's coefficient of correlation between the distance matrices calculated from the - to -ppm 1h chemical shifts and genetic distance among accessions with polymorphisms at the rflp markers. we found no correlation between global metabolic diversity and the phylogenetic relationships among the rice accessions. although the phylogenetic tree of the genetic distance matrix showed the expected typical clusters, the metabolic diversity tree showed no identifiable clusters corresponding to the phylogenetic clusters. this result indicates that there are no relationships between the genomic and metabolic diversity of solution metabolites of rice grain in the comparison of holistic data. in higher plants, the diversity of the metabolome might tend to be unrelated with genomic diversity. in sesame, which has been an example for the comparison of metabolic and genomic diversity in higher plants, the relationship patterns generated for the aflps and the seed metabolic profiles also differed. seed metabolic profiles are derived from the transcriptome that is specifically activated in the embryo and endosperm during seed development. genes involved in the abundance of solution metabolites of rice seed might consist of small members expressed in seed specific transcriptome. although there have been few examples of the global assessment of correlations between metabolic and genomic diversity in higher plants, the recent updating of the infrastructure for population genomics and metabolomics should allow us to determine correlation patterns at different levels i.e., from genomic segments to small chromosomal regions in each organism along with their evolutionary patterns. in arabidopsis, a large number of metabolic qtls were identified in a recombinant inbred line of two parental ecotypes, which had been selected from ecotypes based on their metabolic profiles. most of the metabolic variation observed among the ecotypes was successfully mapped using this ril population, indicating that metabolic variation can be associated with simplified polymorphic patterns at specific chromosomal regions. we then calculated the rs of euclidian distance matrices derived from each -ppm integral region of the 1h chemical shift against the genetic distance matrices derived from sets of three adjacent markers along each chromosome. the results of these local analyses are presented as a 2d correlation map. to assess the statistical significance of each correlation coefficient value in this dataset, we calculated the p-value for each rs value and determined the relationship between the rs value and the p-value for all combinations. as a result of this analysis, an rs value of was supported with a p-value of e-07; the value of rs at the % level of the p-value was between ≥ and ≤. we also calculated the correlation coefficient values between each genetic distance matrix of three randomly sampled rflp markers and the metabolic profile of each of the nmr spectra in trials to estimate the probability of a high correlation by random chance. the results showed that the probability of a correlation coefficient rs ≥ in the randomized data was less than 1%. therefore, in this analysis, we applied rs = as the threshold to detect correlative combinations between metabolic and genomic diversity. the presentation of the correlations as a 2d heat map showed large non-correlative areas studded with correlative areas, which can be explored in omics-wide comparisons. the heat map showed clear correlations between the profiles of soluble metabolites and local genomic diversity at each chromosomal location. to identify metabolites, we performed 2d 1h-13c nmr analysis. diversity in the choline profile showed a high correlation with genetic diversity around the r rflp marker, located at cm on chromosome. the lipid methylene signal showed correlations with polymorphic sites at multiple chromosomal locations, with the highest located around the c rflp marker at cm on chr., and the second highest around r at cm on chr.. the correlation peaks corresponding to each of the lipids with chr., chr., and chr. were in good accordance with the covalent bond networks within molecules of each compound. furthermore, sucrose with chr. and chr. also showed good accordance. similar intramolecular covalent bond networks were reported in nmr experiments of biological metabolite mixtures, including covariance total correlation spectroscopy analysis of insect venom and tocsy analysis of intact human gut biopsies. local comparison between the dendrogram based on the correlative rflp polymorphisms and nmr spectra showed a similar tree structure between both genetic and metabolic variation among the accessions. for example, the dendrogram of genetic polymorphisms around the c rflp marker and the diversity of the lipid methylene signal indicated the genetic and metabolic divergence between indica i and two other groups, japonica and indica ii. to display the chromosomal locations of the correlative regions at rflp markers, the results of the correlation analysis were superimposed onto a genetic map of rice. this genetic map should allow us to identify metabolic qtls associated with each of the metabolic abundances allocated on a genetic map. it would be beneficial if future genetic analyses narrowed down candidate genes and/or isolated such metabolic qtls in combination with other genomic resources for rice such as genetic marker systems and genome annotation databases. throughout this study, we developed a robust approach to explore the correlation between genomic and metabolomic diversity observed in natural variation, which is shown as a schematic flow in additional file the procedure is summarized as follows: selection of appropriate accessions based on population structures; acquisition of the metabolic profile of selected accessions using various types of metabolomics platforms and the calculation of the distance among accessions on the basis of metabolite abundance or spectrum intensities; genotyping of selected accessions using dna markers covering the whole genome and the calculation of local genetic distance among accessions from genotype data of adjacent marker sets; to discover the correlative relationships between each metabolite and chromosomal region and the coefficients of correlation for metabolic and genetic distances in all-against-all combinations and to display their distribution on a genetic map. in this analysis of rice, we did not find a correlation between metabolic and genomic diversity as observed in previously reported studies. on the other hand, local correlation analysis allowed us to identify small correlative areas mostly in non-correlative spaces. this suggests that local correlation analysis could be an effective method to discover such correlative areas allocated to chromosomal regions together with linkage maps or variation data of genomic sequences. this study illustrated a new integrative approach to explore the links between metabolomics and genomics. the procedure could be extended to a higher resolution by using denser polymorphic marker maps in various species.  this study assessed the correlative relationships between genetic and metabolic diversity among rice accessions. the data presents the first correlative relationship for the chromosomal distribution of each metabolic profile in rice. the combinatorial use of genomic and metabolomic data demonstrated here should facilitate the elucidation of complex regulatory networks and those of evolutionary significance in plant metabolic systems. 
to assess the correlative relationship between the metabolic and genomic diversity found in rice accessions, we compared the distance matrices for these two "omics" patterns in the rice accessions. we found no correlation between global metabolic diversity and the phylogenetic relationships among the rice accessions by analyzing the distance matrices and the genetic distance on the basis of the rflp markers. however, local correlation analysis between the distance matrices against genetic distance matrices, generated clear correlations at rflp markers. we selected accessions from the world rice collection based on their population structure. although several attempts have been made to find correlative relationships between genetic and metabolic diversity among natural populations in various organisms, it is still unclear whether it is possible to discover such correlations between each metabolite and the polymorphisms found at each chromosomal location. this combinatorial approach will be valuable for exploring the correlative relationships between metabolic and genomic diversity. it is essential to elucidate the relationship between metabolic and genomic diversity to understand the genetic regulatory networks associated with the changing metabolo-phenotype among natural variation and/or populations. to determine the genomic diversity of the rice genome, we genotyped restriction fragment length polymorphism markers to calculate the genetic distance among the accessions. metabolite quantitative trait analysis is a key approach for the identification of genetic loci involved in metabolite variation using segregated populations. it will facilitate the elucidation of complex regulatory networks and those of evolutionary significance in plant metabolic systems.
 it is essential to elucidate the relationship between metabolic and genomic diversity to understand the genetic regulatory networks associated with the changing metabolo-phenotype among natural variation and/or populations. recent innovations in metabolomics technologies allow us to grasp the comprehensive features of the metabolome. metabolite quantitative trait analysis is a key approach for the identification of genetic loci involved in metabolite variation using segregated populations. although several attempts have been made to find correlative relationships between genetic and metabolic diversity among natural populations in various organisms, it is still unclear whether it is possible to discover such correlations between each metabolite and the polymorphisms found at each chromosomal location. to assess the correlative relationship between the metabolic and genomic diversity found in rice accessions, we compared the distance matrices for these two "omics" patterns in the rice accessions.  we selected accessions from the world rice collection based on their population structure. to determine the genomic diversity of the rice genome, we genotyped restriction fragment length polymorphism markers to calculate the genetic distance among the accessions. to identify the variations in the metabolic fingerprint, a soluble extract from the seed grain of each accession was analyzed with one dimensional 1h-nuclear magnetic resonance. we found no correlation between global metabolic diversity and the phylogenetic relationships among the rice accessions by analyzing the distance matrices and the genetic distance on the basis of the rflp markers. however, local correlation analysis between the distance matrices against genetic distance matrices, generated clear correlations at rflp markers.  this combinatorial approach will be valuable for exploring the correlative relationships between metabolic and genomic diversity. it will facilitate the elucidation of complex regulatory networks and those of evolutionary significance in plant metabolic systems. 