# Patent

## Title
Identity elucidation of unknown metabolites

## Abstract

A method of elucidating the identity of an unknown metabolite comprising measuring amounts of known and unknown metabolites in subjects; associating the unknown metabolite with a specific gene from a gene association study; determining a protein associated with the specific gene and analyzing information for the protein; associating the unknown metabolite with concentrations and/or ratios of other metabolites in subjects; obtaining chemical structural data for the unknown metabolite; and using the information obtained to elucidate the identity of the unknown metabolite.

## Background
CROSS REFERENCE TO RELATED APPLICATIONS
This application is a National Stage application filed under Rule 371 based upon PCT/US12/43461 filed Jun. 21, 2012, which claims the benefit of U.S. Provisional Patent Application No. 61/503,673, filed Jul. 1, 2011, the entire content of which is hereby incorporated by reference.


BACKGROUND
The ability to determine the identity of a chemical entity in a complex mixture has a broad range of highly useful applications. The techniques traditionally used in analysis of complex mixtures include chromatography and mass spectrometry. Although both chromatography and mass spectrometry separate a complex mixture into constituent parts, neither technique provides direct identification of the chemical constituents. Rather, the identity of a chemical constituent must be determined based on an analysis of the measured characteristics of the chemical constituent.
As used herein, the term “identification” as applied to chemical entities refers to the high confidence determination of the identity of a chemical entity. An example of identification is the determination that a molecule having 7 carbon atoms, 7 hydrogen atoms, a nitrogen atom, and 2 oxygen atoms is anthranilic acid rather than salicylamide, both of which have the same chemical formula C7H7NO2.
This ability to perform non-targeted analysis, such as initial detection and subsequent recognition of unknown metabolites, has enormous benefits. For example, in a metabolic analysis of cells with and without cancer, if the analysis results show that cancerous cells almost always contain a certain unknown molecule while healthy cells do not; these results give important direction to research for detection or treatment of that cancer.
Metabolomics includes the ability to perform non-targeted analysis, which means that a chemical constituent may be detected and subsequently recognized, even though it may not be identified.
Currently, methods exist to determine the elemental compositions of ions in a mass spectrum. This knowledge greatly reduces the number of possible compounds that could produce a particular mass spectrum. One can conclusively refute as candidate compounds those that provide similar low resolution mass spectra containing a molecular ion or a fragment ion with a different ion composition. Review of the chemical and commercial literature can further limit the probable identity of an analyte to one or a few compounds. However, in many cases the number of compounds with the same composition is large or the chemical classes of such compounds may represent multiple chemical classes. Thus, even when the list of candidates is reduced to only a few compounds, confirmation is time and resource intensive. In many cases the standards for possible candidates cannot be purchased and instead must be synthesized de novo which can be expensive and time consuming.
Therefore a need exists to improve the ability to elucidate the identity of an unknown compound by narrowing the list of candidate compounds to chemicals from the same biochemical class (e.g., amino acids, fatty acids, carbohydrates) and to further limit the candidates within a particular class.
BRIEF SUMMARY
In an aspect of the invention, a method of elucidating the identity of an unknown metabolite comprises measuring amounts of known and unknown metabolites in subjects; associating an unknown metabolite with a specific gene from a gene association study; determining a protein associated with the specific gene and analyzing information for the protein; associating the unknown metabolite with concentrations and/or ratios of other metabolites in subjects using a partial correlation network; obtaining chemical structural data for the unknown metabolite and deriving from the information obtained the identity of the unknown metabolite.
In a feature, the gene association study may be a genome wide association study. In another feature, the specific gene may comprise a single nucleotide polymorphism. In yet another feature, the method may further comprise reviewing the identity and/or characteristics of the other metabolites associated with the specific gene from the gene association study and/or identifying the biochemical pathway with which at least a portion of the other metabolites associated with the specific gene are involved.
In an additional feature, the chemical structural data may be obtained using mass spectrometry. The chemical structural data may also be obtained using nuclear magnetic resonance (NMR). The mass spectrometric data of the unknown metabolite may include mass, molecular formula, fragmentation spectra, and retention time. In a further feature, the information concerning the protein known to be associated with the gene may include function of the protein. In another feature, the protein may perform a metabolic function. The protein may be an enzyme. The substrate of the enzyme may be identified.
In another feature, the information for the protein may include the biochemical pathway for the protein substrate. Further, the information may include alternative biochemical pathways for the substrate. An alternative substrate of the enzyme may be determined. In an additional feature, the protein may be a transporter.
In yet another feature, reviewing the identity and/or characteristics of other metabolites associated with the specific gene from the gene association study and/or metabolites associated using the partial correlation network may include reviewing mass, class of compound, retention time, isotope patterns, fragments, and functionality of other metabolites. Further, the association between the protein and the gene may be the protein being encoded by the gene.
In another aspect of the invention, a method of elucidating the identity of an unknown metabolite comprises measuring amounts of known and unknown metabolites in subjects; associating an unknown metabolite with a specific gene from a gene association study; determining a protein associated with the specific gene and analyzing information for the protein; reviewing the identity and/or characteristics of the other metabolites associated with the specific gene from the gene association study; and/or identifying the biochemical pathway with which at least a portion of the other metabolites associated with the specific gene are involved; obtaining chemical structural data for the unknown metabolite; and deriving from the information obtained the identity of the unknown metabolite.
In yet another aspect of the invention, a method of elucidating the identity of an unknown metabolite comprises measuring amounts of known and unknown metabolites in subjects; associating an unknown metabolite with concentrations and/or ratios of other metabolites in the subjects using a partial correlation network; reviewing the identity and/or characteristics of the other metabolites associated with the unknown metabolite; and/or identifying the biochemical pathway with which at least a portion of the other metabolites associated with the unknown metabolite are involved; obtaining chemical structural data for the unknown metabolite; and deriving from the information obtained the identity of the unknown metabolite. In a feature of this aspect, the method may further comprise associating the unknown metabolite with a specific gene from a gene association study and determining a protein associated with the specific gene and analyzing information for the protein.



BRIEF DESCRIPTION OF THE DRAWINGS
The accompanying figures are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application. In the figures:
FIG. 1 is a Manhattan plot demonstrating the locations across the chromosomes of the human genome (on the X-axis) where there was a statistically significant association of the metabolites (knowns and unknowns). In the plot, the higher the dots, the stronger the genetic association.
FIG. 2 is a graphical representation of a Gaussian Graphical Model (GGM) network showing the most significant direct and second neighbors of X-14205, X-4208 and X-14478.
FIG. 3 is a graphical representation of an association network showing the most significant direct and second neighbors of X-11244. Solid lines denote positive partial correlations. Dashed lines indicate negative partial correlations.
FIG. 4 is a graphical representation of an association network showing the most significant direct and second neighbors of X-12441. Solid lines denote positive partial correlations. Dashed lines indicate negative partial correlations.
FIG. 5 is a graphical representation of an association network showing the most significant direct and second neighbors of X-11421. Solid lines denote positive partial correlations. Dashed lines indicate negative partial correlations.
FIG. 6 is a graphical representation of an association network showing the most significant direct and second neighbors of X-13431. Solid lines denote positive partial correlations. Dashed lines indicate negative partial correlations.
FIG. 7 is a graphical representation of an association network showing the most significant direct and second neighbors of X-11793. Solid lines denote positive partial correlations. Dashed lines indicate negative partial correlations.
FIG. 8 is a graphical representation of a GGM network showing the most significant direct and second neighbors of X-11593.



DETAILED DESCRIPTION
The instant invention relates to a method whereby one or a plurality of unknown components (e.g., compounds, molecules, metabolites, biochemicals) can be identified. Biochemical analysis can be performed to aid in determining the identity of the unknown component. Biochemical analysis involves determining an association or relationship between two components (e.g., metabolites) using a correlation network. For example, a first variable showing a significant partial correlation to a second variable may be said to be associated with the second variable. Genetic analysis can also be used to aid in determining the identity of the unknown component. Genetic analysis includes using the association of the unknown component with a genetic locus or a genetic mutation. The association can be made using a genetic association study. A genetic association can be described as the occurrence of two or more traits in association with one another in a population, wherein at least one of the traits is known to be genetic and wherein the association occurs more often than can be explained by random chance. An exemplary genetic association study is a genome wide association study (GWAS). In addition, chemical structural data for the unknown component may be used to aid in determining the identity of the unknown. For example, data obtained from a mass spectrometer, such as accurate mass or ion fragment information, or data obtained from nuclear magnetic resonance may be used.
Information obtained from the biochemical analysis may be used with chemical structural data to aid in elucidating the identity of the unknown component. Information obtained from the genetic analysis may be used with chemical structural data to aid in elucidating the identity of the unknown component. Additionally, information obtained from both biochemical and genetic analysis may be combined and used with chemical structural data to aid in elucidating the identity of the unknown component.
With regard to the genetic analysis, the association of an unknown component with a gene or a genetic polymorphism can reveal the type of reaction in which the unknown component is involved. For example, GWAS analysis between single nucleotide polymorphisms (SNPs) and an unknown component can be used to reveal the type of reaction (for example, methylation) in which the unknown component is involved. As will be understood by one of ordinary skill in the art, the association of an unknown component with a gene or a genetic polymorphism can provide valuable information in determining the identity of the unknown component.
In an exemplary embodiment, metabolic data (for example, the amount of known and unknown metabolites) may be obtained from biological samples taken from subjects in a population group. For the genetic analysis, the metabolic data can be used to associate an unknown metabolite with a genetic locus or a genetic mutation. One of ordinary skill in the art will understand that genotype information for the subjects is also used in making the genetic association. For biochemical analysis, the metabolic data can be used to determine associations between various metabolites using partial correlation networks, which are also called Gaussian Graphical Models (GGMs). Using the GGMs, an association between metabolites represents a partial correlation between the metabolites. A network can be built by drawing connections for metabolites that are associated. The network can provide an estimate for a pathway in which an unknown metabolite is involved.
In an example wherein the genetic association study is a GWAS, results from the biochemical analysis and the GWAS can be combined to aid in determining the identity of the unknown component. In addition to the information obtained from biological samples for the particular subject pool, publicly available metabolic pathway data can also be used to further narrow the list of possible components. Thus, using genetic and biochemical information and publicly available information enables reducing the list of potential components for an unknown component, keeping only those components that play a role in the biochemical context given by the partial correlation network and that could, at the same time, be direct or indirect substrates or products of the specified enzymatic reaction, as determined using the genetic information. Additionally, chemical structural analysis can be performed to aid in determining the identity of the unknown component. For example, mass spectrometry (MS) data (e.g., accurate mass and chemical formula) for the reduced list of potential unknown components can be compared with that of known components to help determine the identity of the unknown component. While the exemplary genetic association study discussed herein is a GWAS, one of ordinary skill in the art will understand and appreciate that the data used in determining the identity of an unknown component can be obtained with other types of genetic association studies.
Genome Wide Association Study
A GWAS is an example of a genetic association study. In a GWAS, a plurality of genes is interrogated for their association with a phenotype. In other types of genetic association studies, the same type of association can be done with a single genetic locus. GWAS have been used to identify hundreds of disease risk loci.
In a GWAS, the density of genetic markers and the extent of linkage disequilibrium are sufficient to capture a large proportion of the common variation in the human genome in the population under study, and the number of specimens genotyped provides sufficient power to detect variants of modest effect. GWAS can be conducted to rapidly and cost-effectively analyze genetic differences between people with specific illnesses, such as diabetes or heart disease, compared to healthy individuals. The studies can explore the connection between specific genes, known as genotype information, and their observable characteristics or traits, known as phenotype information, and can facilitate the identification of genetic risk factors for the development or progression of disease. It will be understood that disease status is an exemplary phenotype. It will also be understood that a GWAS or other genetic association study may be used to analyze data related to any phenotype. Phenotypes can be binary (e.g., diseased or healthy) or can be continuous variable (e.g., BMI, weight, blood pressure). Exemplary continuous variable phenotypes include blood pressure, BMI, height, metabolite concentration, and medication being taken.
The GWAS takes an approach that involves rapidly scanning markers (such as, a genetic polymorphism (for example, a SNP)) across the complete sets of DNA, or genomes, of many people to find genetic variations associated with a particular phenotype (e.g., disease). In the example wherein the phenotype is a disease, once new genetic associations are identified, researchers can use the information to develop better strategies to detect, treat and prevent the disease being studied. Such studies are particularly useful in finding genetic variations that contribute to common, complex diseases, such as asthma, cancer, diabetes, heart disease and mental illnesses. More specific details regarding performing a GWAS will be described below. One of ordinary skill in the art will understand that many of the steps performed in a GWAS are also used in other types of genetic association studies, but typically on a smaller scale because the entire genome is not being scanned.
To carry out a GWAS, researchers characterize the participants by a phenotype (e.g., diseased vs. non-diseased). Researchers obtain DNA from each participant, usually by drawing a blood sample or by rubbing a cotton swab along the inside of the mouth to harvest cells.
Each person's DNA is then purified from the blood or cells, placed on genotyping chips comprised of genetic markers representing the entire genome and scanned on automated laboratory instruments. In a smaller scale genetic association study, a smaller subset of genetic markers would be analyzed. The instruments survey each participant's genome for the presence of markers of genetic variation. A genetic marker is a DNA sequence with a known location on a chromosome with a variation that can be observed. A genetic marker may be a short DNA sequence comprised of a single nucleotide difference or it may be a longer one such as a repeating sequence of DNA or DNA sequence insertions or sequence deletions. The most widely used genetic markers are called single nucleotide polymorphisms, or SNPs. Other types of genetic markers include AFLPs (Amplified Fragment Length Polymorphisms), RFLPs (Restriction Fragment Length Polymorphisms), SSLP (Simple Sequence Length Polymorphisms), RAPDs (Random Amplification of Polymorphic DNA) and CAPS (Cleaved Amplified Polymorphisms).
If certain genetic variations are found to be significantly more frequent or less frequent in people showing a phenotype (e.g., the disease) compared to people lacking this phenotype (e.g., without the disease), the variations are said to be “associated” with the phenotype (e.g., disease). The associated genetic variations can serve as pointers to the region of the human genome where the phenotype-causing problem resides.
The associated variants themselves may not directly cause the disease. They may just be “tagging along” with the actual causal variants. For this reason, researchers often need to take additional steps, such as sequencing DNA base pairs in the particular region of the genome, to identify the exact genetic change involved in the disease.
Genetically determined metabotypes (GDMs) are identified using genetic associations with metabolites measured in biological samples (e.g., blood, urine, tissue) as functional intermediate phenotypes, and facilitate the ability to understand the relevance of these genetic variants for biomedical and pharmaceutical research.
Information obtained using data from a genetic association study can be used for various purposes. For example, the information obtained can be used to associate an unknown biochemical with a SNP and the associated genetic locus. The information can be used to identify an unknown biochemical based upon the function of the protein encoded by the identified gene. The information can be used to associate a known metabolite with the same SNP and locus, which can facilitate identification of biochemical pathways for the unknown biochemical and identification of the unknown biochemical.
Partial Correlation Networks (Gaussian Graphical Models)
Gaussian graphical models (GGMs) are partial correlation networks, which can provide an estimate for the pathway in which an unknown component (e.g., a metabolite) is involved. For example, GGMs can be used to determine metabolic pathway reactions using metabolic concentrations measured for a sample population. Characteristic patterns in metabolite profiles can be directly linked to underlying biochemical reaction networks. In the GGM, a connection between two variables (e.g., metabolites) represents a so-called partial correlation between the variables. A GGM can be represented by drawing metabolite-metabolite connections for pairs of metabolites (knowns or unknowns) that show a significant partial correlation. Connections based on known reaction links between two metabolites based on public metabolic databases can be added to the network representation to provide more identifying information. Considering the neighboring known metabolites of an unknown in the network provides a good estimate for the pathway in which the unknown is involved.
Gaussian graphical models are created using full-order partial correlation coefficients. The partial correlation coefficient between two variables is given by the Pearson correlation coefficient corrected against all remaining (n−2) variables. Intuitively speaking, the partial correlation means that if a pair of metabolites is still correlated after the correction, the correlation is directly determined by the association of the two metabolites and not mediated by other metabolites in the data set. For example, when metabolite A directly affects metabolite B and metabolite B directly affects metabolite C, A and C are also correlated in terms of a non-partial correlation. However, A and C are not correlated after correcting for the correlations between A/B and B/C.
By focusing on direct effects between metabolites, GGMs group metabolites by their biochemical context when applied to targeted metabolomics data. In the present method, a GGM is used with non-targeted metabolomics data containing both known and unknown metabolites. Hence, in order to estimate the biochemical context of an unknown metabolite using the GGM, the context or pathway in which the known metabolites neighboring the unknown metabolite are involved is considered. For facilitating network interpretation, connections based on known reaction links between two metabolites according to metabolic databases such as the KEGG PATHWAY database can be added.
Gaussian graphical models use linear regression models and are able to discern indirect correlations between metabolites that do not indicate an independent association between the metabolites. Any indirect correlations can be removed from the analysis.
In an exemplary embodiment, a method of elucidating the identity of an unknown metabolite comprises measuring amounts of known and unknown metabolites; associating an unknown metabolite with a specific gene from a gene association study; determining a protein associated with the specific gene and analyzing information for the protein; associating the unknown metabolite with concentrations and/or ratios of other metabolites using a partial correlation network; obtaining chemical structural data for the unknown metabolite; and using the information obtained in order to elucidate the identity of the unknown metabolite. Measuring the amounts of known and unknown metabolites comprises analysis a biological sample (e.g., tissue, blood, or urine) to measure the amounts of the metabolites.
EXAMPLES
In order to identify candidate molecules for unidentified molecular entities that were repeatedly observed in MS-based metabolomics measurements, information gained from the application of two different methods on the same population-based sample set was integrated: (i) genome-wide association analysis between single nucleotide polymorphisms (SNPs) and the MS-based quantitative measurements of the aforementioned known and unidentified molecular entities (in this example, the entities are metabolites), and (ii) partial correlation networks (Gaussian Graphical Models) calculated from the quantitative measurements of known as well as unidentified molecular entities (in this example, the entities are metabolites). The study was based on genome-wide SNP data for a population-based cohort and the quantities measured for known and yet unknown molecules by UPLC-MS/MS or GC-MS in blood serum samples from the same cohort. In this study, the population-based cohort was 1768 individuals comprising 859 male and 909 female genotyped individuals, who were aged 32-81 years at the time of sampling.
In the study, over 250 known biochemicals were analyzed in 60 biochemical pathways in 1700+ serum samples. In addition, over 200 unknown biochemicals were quantified in these samples. Metabolic profiling was performed on fasting serum from participants of the study (n=1,768) using ultrahigh performance liquid-phase chromatography and gas chromatography separation coupled with tandem mass spectrometry. Highly efficient profiling (24 minutes/sample) was achieved with low median process variability (12%) of more than 250 metabolites, covering over 60 biochemical pathways of human metabolism.
While the examples describe an approach wherein the entire genome for the subjects was studied, one of ordinary skill in the art will understand that the same type of analysis can be performed for individual genes or individual genetic polymorphisms. Additionally, one of ordinary skill in the art will understand that the sequence of the steps of the analysis process may vary. Such variation is within the scope of the invention.
Genome-Wide Associations
SNP data: Genotyping was carried out using the Affymetrix GeneChip array 6.0. For the analyses, only autosomal SNPs passing the following criteria were considered: call rate >95%, Hardy-Weinberg-Equilibrium p-value p(HWE)>10−6, minor allele frequency MAF>1%. In total, 655,658 SNPs were left after filtering.
Molecule quantities: The blood serum samples of the 1768 genotyped individuals were screened on known metabolomics platforms (UPLC-MS/MS, GC-MS) providing the relative quantities of (295) known and (224) unknown metabolites in these samples. In order to avoid spurious false positive associations due to small sample sizes, only metabolic traits with at least 300 non-missing values were included and data-points of metabolic traits that lay more than 3 standard deviations off the mean were excluded by setting them to missing in the analysis. 274 known and 212 unknown metabolites passed this filter.
Statistical analysis: The metabolite quantities were log-transformed since a test of normality showed that in most cases the log-transformed distribution was significantly better represented by a normal distribution than when untransformed values were used. The genotypes are represented by 0, 1, and 2 for major allele homozygous, heterozygous, and minor allele homozygous, respectively.
A linear model was employed to test for associations between a SNP and a metabolite assuming an additive mode of inheritance. The tests were carried out using PLINK software (version 1.06) with age and gender as covariates. Based on a conservative Bonferroni correction, associations with p-values <1.6×10−10 meet genome-wide significance. For significant associations of a metabolite (known and unknown) with SNPs within a distance of 106 nucleotides, only the most significant association is reported in Table 1. Table 2 lists all unknown metabolite-SNP associations with p-values below 1×10−5. Thus, in contrast to Table 1, Table 2 includes (i) associations not reaching genome-wide significance and (ii) all associations rather than only the most significant ones for the 10−6 nucleotides window.
The SNPs involved in the most significant associations of SNPs and/or the SNPs in the linkage disequilibrium of the association SNPs with known metabolites have shown to be mostly within or close to genes whose function ‘matches’ the metabolite (e.g., association of a SNP in the gene encoding oxoprolinase with oxoproline quantities). This effect can thus be used to narrow the set of candidate molecules in case of unknown metabolites. For example, this effect can be used for estimating the type of enzymatic conversion (or transport) to which an unknown is related. For this purpose, we performed a GWAS on quantities of the unknown (and known) metabolites from the metabolomics data set described above. In case of significant SNP-unknown associations for which the SNP is located close to or within a gene, the genetic information (such as the substrate specificity of the encoded enzyme or transporter) was used as a constraint for reducing the number of candidate molecules. FIG. 1 is a Manhattan plot for the known and unknown metabolites showing a circle for each metabolite-SNP pair for which the p-value of their association is below 1.0×10−6. The black horizontal line denotes the limit of genome-wide significance. The black triangles represent all associations with p-values lower than 1.0×10−30. The associations of unknown metabolites are plotted in the upper part, the associations of known metabolites are plotted in the lower part of the figure. Table 1 provides a list of metabolites and SNPs with which the metabolites were associated most significantly, in particular, within a window of 106 nucleotides. The SNPs are listed along with their position on the genome (CHR: chromosome; Position: position on the chromosome (base pairs)) and the gene that was annotated for the associating SNP or SNPs within the linkage disequilibrium (LD) using an LD criterion of r2>0.8. Moreover, the number of samples is given for which data on the amount of the metabolite and data on the genotype was available. The columns BETA and P (p-value) contain the results of the additive linear model that was used for testing the association between the metabolite and the SNP. Only genome-wide significant associations are shown in Table 1. Table 2 provides a list of all unknown metabolite-SNP associations with p-values below 1×10−5. Table 3 provides a list of unknown metabolites and shows the metabolite neighbors of the unknown as determined with the GGM network and the best associating SNP for the unknown as determined with the GWAS. Because of size considerations, Tables 2 and 3 are at the end of this description.














TABLE 1








GENE in






CHR
POSITION
SNP
LD >0.8
METABOLITE
#SAMPLES
BETA
P






















1
47170815
rs1078311
CYP4A11
10-undecenoate (11:1n1)
1744
0.05602
1.47E−013


1
75910194
rs12134854
ACADM
hexanoylcarnitine
1732
−0.08111
4.97E−043


1
75910194
rs12134854
ACADM
octanoylcarnitine
1735
−0.08345
2.57E−036


1
75910194
rs12134854
ACADM
X11421
1721
−0.07391
1.90E−027


1
75879263
rs211718
ACADM
decanoylcarnitine
1736
−0.06534
3.77E−021


2
27594741
rs780094
GCKR
mannose
1702
−0.04725
2.06E−024


2
73506136
rs7598396
ALMS1
N-acetylornithine
1717
−0.2137
1.30E−149


2
73506136
rs7598396
ALMS1
X12510
1697
−0.1317
1.53E−056


2
73673616
rs6710438
NAT8
X11787
1722
−0.04063
2.95E−037


2
73672444
rs13391552
NAT8
X12093
948
0.1114
8.86E−022


2
210768295
rs2286963
ACADL
X13431
1453
0.09672
2.68E−033


2
211325139
rs2216405
CPS1
glycine
1721
0.04127
1.28E−015


2
234333309
rs887829
UGT1A
biliverdin
1123
0.1023
5.53E−047


2
234333309
rs887829
UGT1A
bilirubin (Z,Z)
1646
0.1551
1.33E−046


2
234337378
rs6742078
UGT1A
X11530
1701
0.08613
2.12E−038


2
234337378
rs6742078
UGT1A
X11441
1584
0.08834
5.59E−030


2
234333309
rs887829
UGT1A
X11793
1539
0.07591
2.59E−026


2
234337378
rs6742078
UGT1A
X11442
1584
0.08056
1.19E−025


2
234333309
rs887829
UGT1A
bilirubin (E,E)
1694
0.0939
3.04E−024


4
9611763
rs9991278
SLC2A9
urate
1706
−0.02992
4.64E−021


4
22429602
rs358231
GBA3
X11799
1481
0.1373
2.87E−017


4
159850267
rs8396
ETFDH
decanoylcarnitine
1711
−0.05031
2.35E−012


4
187394452
rs4253252
KLKB1
bradykinin, des-arg(9)
1463
0.09777
5.93E−014


5
36025563
rs13358334
UGT3A1
X11445
1642
0.08772
2.36E−012


5
131693277
rs272889
SLC22A4
isovalerylcarnitine
1725
0.04099
9.18E−015


5
131689055
rs273913
SLC22A4
3-dehydrocarnitine
1682
0.0298
1.62E−011


6
160589071
rs316020
SLC22A2
X12798
1629
−0.1748
1.73E−072


6
160484466
rs662138
SLC22A1
isobutyrylcarnitine
1700
−0.06786
5.41E−015


7
99078115
rs10242455
CYP3A5
X12063
1660
−0.2485
1.47E−045


7
99327507
rs17277546
CYP3A4
androsterone sulfate
1728
−0.2435
2.08E−021


7
99327507
rs17277546
CYP3A4
epiandrosterone sulfate
1729
−0.1717
3.35E−015


8
18317580
rs1495743
NAT2
1-methylxanthine
1148
−0.09426
6.10E−016


8
145211510
rs6558295
OPLAH
5-oxoproline
1734
−0.0611
8.36E−051


9
135143696
rs651007
ABO
ADpSGEGDFXAEGGGVR
1692
0.06719
1.00E−015


10
61119570
rs7094971
SLC16A9
carnitine
1724
−0.02185
1.06E−014


10
85443900
rs12413935

X06226
1722
−0.05911
4.09E−011


10
96454720
rs7896133
CYP2C18
X11787
1738
−0.05619
3.97E−026


10
100149126
rs4488133
PYROXD2
X12092
1711
−0.2842
2.24E−281


10
100149126
rs4488133
PYROXD2
X12093
948
−0.1252
1.35E−027


11
18281722
rs2403254
HPS5
alpha-hydroxyisovalerate
1733
−0.05239
2.60E−016


11
61327924
rs174548
FADS1
arachidonate (20:4n6)
1730
−0.0414
9.98E−022


11
61327359
rs174547
FADS1
1-arachidonoylglycerophosphocholine
1584
−0.05788
2.54E−020


11
61327359
rs174547
FADS1
1-linoleoylglycerophosphoethanolamine
1733
0.04499
1.94E−014


11
61366326
rs174583
FADS1
1-arachidonoylglycerophosphoethanolamine
1699
−0.03864
2.37E−013


11
61327359
rs174547
FADS1
eicosapentaenoate (EPA; 20:5n3)
1730
−0.04007
1.49E−010


12
21222816
rs4149056
SLCO1B1
X11529
1138
0.2564
3.28E−081


12
21222816
rs4149056
SLCO1B1
X11538
1736
0.1087
1.35E−037


12
21222816
rs4149056
SLCO1B1
X13429
1230
0.141
4.86E−022


12
21222816
rs4149056
SLCO1B1
X12063
1660
0.1016
5.22E−020


12
21222816
rs4149056
SLCO1B1
X12456
1260
0.08351
8.41E−017


12
21269288
rs4149081
SLCO1B1
X14626
1235
0.08085
2.08E−013


12
55151605
rs2657879
GLS2
glutamine
1732
−0.01542
3.21E−013


12
119644998
rs2066938
ACADS
butyrylcarnitine
1682
0.1983
3.73E−177


15
61209825
rs2652822
LACTB
succinylcarnitine
1474
−0.04431
1.05E−021


16
66883701
rs6499165
SLC7A6
glutaroyl carnitine
1675
0.03404
6.49E−011


17
58919763
rs4343
ACE
X14189
1703
−0.05975
1.48E−016


17
58923464
rs4351
ACE
X14208
1628
−0.05775
4.58E−015


17
58923464
rs4351
ACE
X14205
1470
−0.04795
3.97E−014


17
58916932
rs4325
ACE
X14304
1467
−0.05359
2.68E−012


17
58916932
rs4325
ACE
aspartylphenylalanine
1688
−0.06079
1.05E−011


19
53060346
rs296391
SULT2A1
X11440
1685
−0.1494
1.69E−043


19
53060346
rs296391
SULT2A1
X11244
1676
−0.1464
2.12E−026


22
17352450
rs2023634
PRODH
proline
1733
0.05435
4.32E−021


22
18331271
rs4680
COMT
X11593
1712
0.04945
1.13E−048


22
18331271
rs4680
COMT
X01911
1626
0.07037
5.80E−011


22
23322266
rs5751901
GGT1
cysteine-glutathione disulfide
1598
−0.05311
2.50E−012









Partial Correlation Networks (Gaussian Graphical Models)

In this example, a network was built by drawing metabolite-metabolite connections for pairs of metabolites (knowns or unknowns) that showed a significant partial correlation. To do this network connections based on known reaction links between two metabolites based on public metabolic databases were added. Considering the neighboring known metabolites of an unknown in the network provides a good estimate for the pathway in which the unknown is involved.
The blood serum samples of all 1768 individuals were screened to provide the relative quantities of (295) known and (224) unknown metabolites in the samples. For the calculation of the GGM, the following data preprocessing was applied. All metabolites with more than 20% missing values and all samples with more than 10% missing values were excluded. Remaining missing values were imputed using MICE. MICE stands for Multivariate Imputation by Chained Equations. MICE is a software program used to impute missing values. Multiple imputation is a statistical technique for analyzing incomplete data sets, that is, data sets for which some entries are missing.
Gaussian graphical models were induced by full-order partial correlation coefficients. Additionally, correction was made for SNPs with significant associations to metabolites in the GWAS. Thus, it was expected that the remaining correlations between metabolites were not mediated by metabolite-SNP associations.
By focusing on direct effects between metabolites, GGMs group metabolites by their biochemical context when applied to targeted metabolomics data. In the present method, a GGM is used with non-targeted metabolomics data containing both known and unknown metabolites. Hence, in order to estimate the biochemical context of an unknown metabolite using the GGM, the context or pathway in which the known metabolites neighboring the unknown metabolite are involved is considered. For facilitating network interpretation, connections based on known reaction links between two metabolites according to metabolic databases such as the KEGG PATHWAY database were added.
Gaussian graphical models utilize linear regression models and are thus able to discern indirect correlations between metabolites that do not indicate an independent association between those metabolites and thus remove any indirect correlations from the analysis. If the dataset contained more samples than variables, full-order partial correlations were calculated by a matrix inversion operation. First, regular Pearson product-moment correlation coefficients ρij were calculated as:




  P
  =
  
    
      (
      
        ρ
        ij
      
      )
    
    =
    
      
        
          ∑
          
            k
            =
            1
          
          n
        
        ⁢
        
          
            (
            
              
                x
                ki
              
              -
              
                
                  x
                  _
                
                i
              
            
            )
          
          ⁢
          
            (
            
              
                x
                kj
              
              -
              
                
                  x
                  _
                
                j
              
            
            )
          
        
      
      
        
          
            
              ∑
              
                k
                =
                1
              
              n
            
            ⁢
            
              
                (
                
                  
                    x
                    ki
                  
                  -
                  
                    
                      x
                      _
                    
                    i
                  
                
                )
              
              2
            
          
        
        ·
        
          
            
              ∑
              
                k
                =
                1
              
              n
            
            ⁢
            
              
                (
                
                  
                    x
                    kj
                  
                  -
                  
                    
                      x
                      _
                    
                    j
                  
                
                )
              
              2
            
          
        
      
    
  




Next, partial correlation coefficients were computed as the normalized, negative matrix inverse of this correlation:

Z=(ζij)=−ωij/√{square root over (ωiiωjj)} with (ωij)=P−1 

P-values p(ζij) for each partial correlation were obtained using Fisher's z-transform:




  
    
      z
      ⁡
      
        (
        
          ζ
          ij
        
        )
      
    
    =
    
      
        1
        2
      
      ⁢
      
        ln
        ⁡
        
          (
          
            
              1
              +
              
                ζ
                ij
              
            
            
              1
              -
              
                ζ
                ij
              
            
          
          )
        
      
    
  
  ,
  
    

  
  ⁢
  
    
      p
      ⁡
      
        (
        
          ζ
          ij
        
        )
      
    
    =
    
      (
      
        1
        -
        
          φ
          ⁡
          
            (
            
              
                
                  n
                  -
                  
                    (
                    
                      m
                      -
                      2
                    
                    )
                  
                  -
                  3
                
              
              ·
              
                z
                ⁡
                
                  (
                  
                    ζ
                    ij
                  
                  )
                
              
            
            )
          
        
      
      )
    
  




where φ stands for the cumulative distribution function of the standard normal distribution. In order to account for multiple hypothesis testing, we applied Bonferroni correction, yielding a corrected significance level of




  
    α
    ^
  
  :=
  
    
      0.05
      
        
          n
          ⁡
          
            (
            
              n
              -
              1
            
            )
          
        
        /
        2
      
    
    .
  




Adding connectors from known reactions: Metabolic reactions were imported from three independent human metabolic reconstruction projects: (1) H. sapiens Recon 1 from the BiGG databases (Duarte, et al., 2007), (2) the Edinburgh Human Metabolic Network (EHMN) reconstruction (Ma, et al., 2007) and (3) the KEGG PATHWAY database (Kanehisa & Goto, 2000) as of January 2011.
When adding connectors from known reactions to the GGM, an accurate mapping between the different metabolite identifiers of the respective databases and the identifiers used in the quantitative metabolite data was created. As one of ordinary skill in the art will appreciate, differing forms of biochemical components can represent the same biochemical entity with regard to biochemical pathway. For example, despite the fact that the salt form and the acid form of a metabolite have different names, the salt form of a metabolite will function biochemically the same as the acid form of the metabolite. Accordingly, metabolite identifiers rather than just chemical names are used to create accurate mapping. Database entries referring to whole groups of metabolites, like “phospholipid”, “fatty acid residue” or “proton acceptor” were excluded. Furthermore, metabolic cofactors like “ATP”, “CO2”, and “SO4”, etc. were not considered in the analysis, since such metabolites unspecifically participate in a plethora of metabolic reactions.
Combining the GGM and GWAS Results
After the GGM step, a good estimate on the biochemical context of an unknown was obtained. After the GWAS, a good estimate of the enzymatic reaction or transport in which the unknown was directly or indirectly involved was obtained. Once this information was available, it was used to exclude or favor molecules from the list of molecules having a mass that matches a mass measured for the unknown. Additional information provided by mass spectrometry can be used to aid in determining the identity of the unknown. For example, ion fragmentation information can be used. In the following, we demonstrate the procedure by giving two examples.
Example 1
Previously unidentified biomarker X-14205 was identified using the following procedure.
The mass of the unknown X-14205 was determined in a LC/MS/MS run in positive ionization mode. The mass quantified for this unknown was 311.1.
Following the GGM steps described above, a GGM network for X-14205 was obtained. Metabolites shown to have significant partial correlations to X-14205 are listed in Table 4.







TABLE 4







Metabolites having significant partial correlation with the unknown


metabolite X-14205.









Unknown:
Significant partial correlation to:
p-value





X-14205
X-14478
4.22E−87



DSGEGDFXAEGGGVR (SEQ ID No. 1)
1.89E−35



Cysteine glutathione disulfide
9.59E−33



X-14208
2.25E−24



X-11805
1.49E−17



X-06307
2.24E−16



X-14086
1.34E−12



X-14450
1.73E−12



ADSGEGDFXAEGGGVR (SEQ ID No. 2)
3.29E−09



aspartate
6.52E−09



phenylalanine
3.18E−07



glutamate
4.19E−07



ADpSGEGDFXAEGGGVR (SEQ ID No.
6.26E−07



3)









FIG. 2 provides a GGM network showing the most significant direct and second neighbors of X-14205. Therein, the connectors connect significantly partially correlating metabolites. The connectors are weighted by the degree of significance (specifically, the lower the p-value of the correlation, the thicker the line). In FIG. 2, the starred (*) metabolites denote those that show a significant association with a SNP in the ACE gene.
For X-14205, checking for known reactions from metabolic databases did not provide additional connectors within a distance of two from the unknown. The majority of known metabolites occurring in the GGM of X-14205 are peptides, dipeptides, and amino acids.
In the GWAS analysis, X-14205 was found to associate most significantly with a SNP in the gene encoding the angiotensin I converting enzyme (ACE). This enzyme is known to cut a dipeptide off from the oligopeptide angiotensin I as well as from further oligopeptides. Table 5 shows the most significant hit that was found in the GWAS analysis for X-14205.







TABLE 5







Most significant hit in the GWAS for the unknown X-14205.











Unknown
Best Assoc. SNP
Locus
Enzyme
p-value





X-14205
rs4351
ACE
angiotensin I
3.97E−14





converting enzyme









When the results from the GGM and the GWAS were integrated, it appeared that besides X-14205, the dipeptide aspartylphenylalanine and the unknowns X-14208, X-14189, and X-14304, were also significantly associated with SNPs in ACE. (In FIG. 2 all metabolites associating with ACE SNPs are marked by a starred (*) box.) It was hypothesized that X-14205 is a dipeptide (and also X-14208, X-14189, X-14304). Considering the mass of X-14205, the most probable candidates were Glu-Tyr or Tyr-Glu.
In order to experimentally confirm the hypothesis, the accurate mass of X-14205 was determined. Its neutral mass 310.11712 supported the formula C14H18N2O6, which also fits the two hypothesized dipeptides. For experimental validation, Glu-Tyr and Tyr-Glu from a commercial source were run on a proprietary LC/MS/MS platform. The retention time and the fragmentation spectrum received for Glu-Tyr matched the time and spectrum of X-14205. Thus, using the above-described method, X-14205 was identified by testing only two candidate molecules.
Example 2
Previously unidentified biomarker X-14208 was identified using the following procedure.
The mass of the unknown X-14208 was determined in a LC/MS/MS run in positive ionization mode. The mass quantified for this unknown was 253.1.
Following the GGM steps described above, a GGM network for X-14208 was obtained. Metabolites shown to have significant partial correlations to X-14208, are listed in Table 6.







TABLE 6







Metabolites having significant partial correlation


with the unknown metabolite X-14208.











Unknown:
Significant partial correlation to:
p-value







X-14208
X-14478
 4.67E−153




X-11805
6.14E−62




X-14205
2.25E−24




X-14086
1.83E−14




lysine
5.68E−11










FIG. 2 provides a GGM network showing the most significant direct and second neighbors of X-14208. Therein, the connectors connect significantly partially correlating metabolites. The connectors are weighted by the degree of significance (specifically, the lower the p-value of the correlation, the thicker the line). In FIG. 2, the starred (*) metabolites denote those that show a significant association with a SNP in the ACE gene.
For X-14208, checking for known reactions from metabolic databases did not provide additional connectors within a distance of two from the unknown. The majority of known metabolites occurring in the GGM of X-14208 are peptides, dipeptides, and amino acids.
In the GWAS analysis, X-14208 was found to associate most significantly with a SNP in the gene encoding the angiotensin I converting enzyme (ACE). This enzyme is known to cut a dipeptide off from the oligopeptide angiotensin I as well as from further oligopeptides. Table 7 shows the most significant hit from the GWAS analysis for X-14208.







TABLE 7







Most significant hit in the GWAS for the unknown X-14208.











Unknown
Best Assoc. SNP
Locus
Enzyme
p-value





X-14208
rs4351
ACE
angiotensin I
4.58E−15





converting enzyme





(peptidyl-





dipeptidase A) 1









When the results from the GGM and the GWAS were integrated, it appeared that besides X-14208, the dipeptide aspartylphenylalanine and the unknowns X-14205, X-14189, and X-14304, were also significantly associated with SNPs in ACE. (In FIG. 2 all metabolites associating with ACE SNPs are marked by a starred (*) box.) It was hypothesized that X-14208 is a dipeptide (and also X-14205, X-14189, X-14304). Considering the mass of X-14208, the most probable candidates were Phe-Ser or Ser-Phe.
In order to experimentally confirm the hypothesis, the accurate mass of X-14208 was determined. Its neutral mass 252.11172 supported the formula C12H16N2O4, which also fits the two hypothesized dipeptides. The formula matches more than 1,200 molecular structures, but the prediction of this unknown as a dipeptide narrowed the field to only the two candidate molecules for validation. For experimental validation, Phe-Ser and Ser-Phe from a commercial source were run on a proprietary LC/MS/MS platform. The retention time and the fragmentation spectrum received for Phe-Ser matched the time and spectrum of X-14208. Thus, using the above-described method, X-14208 was identified by testing only two candidate molecules.
Example 3
Previously unidentified biomarker X-14478 was identified using the following procedure.
The mass of the unknown X-14478 was determined in a LC/MS/MS run in positive ionization mode.
Following the GGM steps described above, a GGM network for X-14478 was obtained. Metabolites shown to have significant partial correlations to X-14478, are listed in Table 8.







TABLE 8







Metabolites having significant partial correlation


with the unknown metabolite X-14478.











Unknown:
Significant partial correlation to:
p-value







X-14478
X-14208
 4.67E−153




X-14205
4.22E−87




X-11805
3.95E−55




X-14450
4.67E−14




cysteineglutathionedisulfide
5.67E−14




aspartylphenylalanine
3.51E−10










FIG. 2 provides a GGM network showing the most significant direct and second neighbors of X-14478. Therein, the connectors connect significantly partially correlating metabolites. The connectors are weighted by the degree of significance (specifically, the lower the p-value of the correlation, the thicker the line).
For X-14478, checking for known reactions from metabolic databases did not provide additional connectors within a distance of two from the unknown. The majority of known metabolites occurring in the GGM of X-14478 are peptides, dipeptides, and amino acids.
The GGM network showed partial correlations of X-14478 with peptides, dipeptides and amino acids. It was hypothesized that X-14478 is a peptide, dipeptide or amino acid. Considering the mass of X-14478, the most probable candidate was the dipeptide Phe-Phe.
In order to experimentally confirm the hypothesis, the accurate mass of X-14478 was determined. For experimental validation, Phe-Phe from a commercial source was run on a proprietary LC/MS/MS platform. The retention time and the fragmentation spectrum received for Phe-Phe matched the time and spectrum of X-14478. Thus, using the above-described method, X-14478 was identified by testing only one candidate molecules.
Example 4
Previously unidentified biomarker X-11244 was identified using the following procedure.
The mass of the unknown X-11244 was determined in a LC/MS/MS run in negative ionization mode. The mass quantified for this unknown was 449.1.
Following the GGM steps described above, a GGM network for X-11244 was obtained. Metabolites shown to have significant partial correlations to X-11244, are listed in Table 9.







TABLE 9







Metabolites having significant partial correlation


with the unknown metabolite X-11244.









Unknown:
Significant partial correlation to:
p-value





X-11244
X-11443
 8.93E−113



X-11440
7.62E−93



dehydroisoandrosteronesulfateDHEAS
7.47E−37



epiandrosteronesulfate
6.66E−16



thromboxaneB2
1.12E−12



X-11470
4.12E−09









FIG. 3 provides an illustration of the association network showing the biochemical edges from the GGM, genetic associations from the GWAS, and pathway annotations showing the most significant direct and second neighbors of X-11244. Therein, the connectors connect significantly partially correlating metabolites. The connectors are weighted by the degree of significance (specifically, the lower the p-value of the correlation, the thicker the line).
The majority of known metabolites occurring in the GGM of X-11244 are related to steroid-hormone compounds. Checking for known reactions from metabolic databases did not provide additional connectors within a distance of two from X-11244.
In the GWAS analysis, X-11244 was found to associate most significantly with a SNP in the gene encoding SULT2A1 which is a member of the sulfotransferase family 2A, dehydroepiandrosterone-preferring. Table 10 shows the most significant hit from the GWAS analysis for X-11244.







TABLE 10







Most significant hit in the GWAS for the unknown X-11244.











Unknown
Best Assoc. SNP
Locus
Enzyme
p-value





X-11244
rs296391
SULT2A1;
sulfotransferase
2.12E−26




CRX
family, cytosol-





ic, 2A, dehy-





droepiandro-





sterone





(DHEA)-pre-





ferring, mem-





ber 1; cone-





rod homeobox









When the results from the GGM and the GWAS were integrated, it appeared that besides X-11244, the sulfated steroids related to androsterone and the unknowns X-11440, and X-11443 were also significantly associated with SNPs in SULT2A1. It was hypothesized that X-11244 is a steroid sulfate related to androsterone.
In order to experimentally confirm the hypothesis, the accurate mass of X-11244 was determined. Its neutral mass of 450.13835 supported the formula C19H30O8S2. Using LC/MS/MS in negative ionization mode, the primary loss of a fragment with a nominal mass of 98 and the presence of an ion at 97 m/z were observed in the fragmentation spectrum of X-11244 which indicated the presence of at least one sulfate group in X-11244. For experimental validation, several disulfated androstenes from a commercial source were run on a proprietary LC/MS/MS platform. All demonstrated similar retention times and fragmentation spectra. Among the variants that were tested, 4-androsten-3β,17β-disulfate showed the best match to the retention time and fragmentation spectrum of X-11244. Given that other isomers are also possible, which cannot necessarily be chromatographically resolved, X-11244 was annotated more generically as androstene disulfate.
Example 5
Previously unidentified biomarker X-12441 was identified using the following procedure.
The mass of the unknown X-12441 was determined in a LC/MS/MS run in negative ionization mode. The mass quantified for this unknown was 319.2.
Following the GGM steps described above, a GGM network for X-12441 was obtained. Metabolites shown to have significant partial correlations to X-12441 are listed in Table 11.







TABLE 11







Metabolites having significant partial correlation


with the unknown metabolite X-12441.









Unknown:
Significant partial correlation to:
p-value





X-12441
arachidonate204n6
 1.52E−116



@1arachidonoylglycerophosphocholine
9.39E−13



docosahexaenoateDHA226n3
2.74E−09



X-10810
2.26E−07



dihomolinolenate203n3orn6
6.48E−07









FIG. 4 provides an association network showing the most significant direct and second neighbors of X-12441. Therein, the connectors connect significantly partially correlating metabolites. The connectors are weighted by the degree of significance (specifically, the lower the p-value of the correlation, the thicker the line). In FIG. 4, direct pathway interaction is shown between X-12441 and the known metabolite 12-HETE which has the same mass as X-12441.
In the GGM analysis, one GGM neighbor (arachidonate) was found. FIG. 4 shows that arachidonate has pathway connections to several lipid-related metabolites, including a variety of hydroxyl-arachidonate variants (HETEs). These HETE variants have the chemical formula C20H32O3 and a molecular weight of 320.2351, which matched the mass of X-12441. When the results from the GGM, and pathway analysis were integrated, it was hypothesized that X-12441 was a species of HETE.
In order to experimentally confirm the hypothesis, the accurate mass of X-12441 was determined. Its neutral mass of 320.23430 supported the formula C20H32O3, which also fits the hypothesis of a species of HETE, as this mass matches the chemical composition of HETE to a precision of +/−0.002 Da. For experimental validation, HETE isoforms 5, 8, 9, 11, 12 and 15 from a commercial source were run on a proprietary LC/MS/MS platform. The retention time and the fragmentation spectrum of the 12-HETE isoform matched the time and spectrum of X-12441. Thus, using the above-described method, X-12441 was identified by testing six HETE isoforms and was identified as 12-HETE.
Example 6
Previously unidentified biomarker X-11421 was identified using the following procedure.
The mass of the unknown X-11421 was determined in a LC/MS/MS run in positive ionization mode.
Following the GGM steps described above, a GGM network for X-11421 was obtained. Metabolites shown to have significant partial correlations to X-11421 are listed in Table 12.







TABLE 12







Metabolites having significant partial correlation


with the unknown metabolite X-11421.











Unknown:
Significant partial correlation to:
p-value







X-11421
X-13435
9.93E−56




linoleate182n6
1.55E−45




octanoylcarnitine
1.05E−40




hexanoylcarnitine
1.00E−23










FIG. 5 provides an association network showing the most significant direct and second neighbors of X-11421. This network incorporates GGM, GWAS and pathway associations. The connectors connect significantly partially correlating metabolites. The connectors are weighted by the degree of significance (specifically, the lower the p-value of the correlation, the thicker the line).
For X-11421, checking for known reactions from metabolic databases revealed carnitine species as additional connectors within a distance of two from the unknown. The majority of known metabolites occurring in the GGM of X-11421 are carnitine species.
In the GWAS analysis, X-11421 was found to associate most significantly with a SNP in the gene encoding the acyl-coenzyme A dehydrogenase (ACAD) for medium-chain length fatty acyl residues (ACADM). Table 13 shows the most significant hit from the GWAS analysis for X-11421. When the results from the GGM, GWAS and pathway analyses were integrated, it was hypothesized that X-11421 is a medium-chain length carnitine.







TABLE 13







Most significant hit in the GWAS for the unknown X-11421.











Unknown
Best Assoc. SNP
Locus
Enzyme
p-value





X-11421
rs12134854
ACADM
acyl-CoA
1.90E−27





dehydrogenase,





C-4 to C-12





straight chain









To experimentally confirm the hypothesis generated from the GGM, GWAS and pathway analyses, the accurate mass of X-11421 was determined. The LC/MS/MS analysis experimentally validated the hypothesis since the results showed that the retention time and fragmentation spectrum of X-11421 matched the retention time and fragmentation spectrum of cis-4-decenoyl-carnitine. Thus, using the above-described method, X-11421 was identified as cis-4-decenoyl-carnitine which is a carnitine with 10 carbon atoms and an ω-6 double bond.
Example 7
Previously unidentified biomarker X-13431 was identified using the following procedure.
The mass of the unknown X-13431 was determined in a LC/MS/MS run in positive ionization mode. The mass quantified for this unknown was 302.2.
Following the GGM steps described above, a GGM network for X-13431 was obtained. Metabolites shown to have significant partial correlations to X-13431 are listed in Table 14.







TABLE 14







Metabolites having significant partial correlation


with the unknown metabolite X-13431.









Unknown:
Significant partial correlation to:
p-value





X-13431
@10undecenoate111n1
1.82E−14



@2methylbutyroylcarnitine
6.68E−13



X-12442
5.43E−12



@1palmitoleoylglycerophosphocholine
2.75E−07









FIG. 6 provides an association network showing the most significant direct and second neighbors of X-13431. This network incorporates GGM and GWAS associations. The connectors connect significantly partially correlating metabolites. The connectors are weighted by the degree of significance (specifically, the lower the p-value of the correlation, the thicker the line).
For X-13431, checking for known reactions from metabolic databases did not provide additional connectors within a distance of two from the unknown. The GGM of X-13431 shows an association with a C11 fatty acid.
In the GWA analysis, X-13431 was found to associate most significantly with a SNP in the gene encoding the acyl-coenzyme A dehydrogenase (ACAD) for long-chain length fatty acyl residues (ACADL). ACADL has been shown to alter C9 carnitines. Table 15 shows the most significant hit from the GWAS analysis for X-13431. When the results from the GGM and GWAS analyses were integrated, it was hypothesized that X-13431 is a C9 carnitine.







TABLE 15







Most significant hit in the GWAS for the unknown X-13431.











Unknown
Best Assoc. SNP
Locus
Enzyme
p-value





X-13431
rs2286963
ACADL
acyl-CoA
2.68E−33





dehydrogenase,





long chain









In order to experimentally confirm the hypothesis, the accurate mass of X-13431 was determined. Its neutral mass 301.22476 supported the formula C16H31NO4, which also is consistent with the hypothesis of a C9 carnitine. Exact mass, fragmentation pattern and chromatographic retention time supported the identification of X-13431 as nonanoyl carnitine. Thus, using the above-described method, X-13431 was identified as nonanoyl carnitine.
Example 8
Previously unidentified biomarker X-11793 was identified using the following procedure.
The mass of the unknown X-11793 was determined in a LC/MS/MS run in positive ionization mode. The mass quantified for this unknown was 601.26587.
Following the GGM steps described above, a GGM network for X-11793 was obtained. Metabolites shown to have significant partial correlations to X-11793 are listed in Table 16.







TABLE 16







Metabolites having significant partial correlation


with the unknown metabolite X-11793.











Unknown:
Significant partial correlation to:
p-value







X-11793
bilirubinEE
8.57E−108










FIG. 7 provides an association network showing the most significant direct and second neighbors of X-11793. This network incorporates GGM and GWAS associations. The connectors connect significantly partially correlating metabolites. The connectors are weighted by the degree of significance (specifically, the lower the p-value of the correlation, the thicker the line).
For X-11793, checking for known reactions from metabolic databases did not provide additional connectors within a distance of two from the unknown. The GGM of X-11793 shows an association with three bilirubin steroisoforms.
In the GWAS analysis, X-11793 was found to associate most significantly with a SNP in the gene encoding the UDP glucuronosyltransferase 1 family, polypeptide A. Table 17 shows the most significant hit from the GWAS analysis for X-11793. When the results from the GGM and GWAS analyses were integrated, it was hypothesized that X-11793 is an oxidized bilirubin variant.







TABLE 17







Most significant hit in the GWAS for the unknown X-11793.











Unknown
Best Assoc. SNP
Locus
Enzyme
p-value





X-11793
rs887829
UGT1A
UDP glucuronosyl-
2.59E−26





transferase 1





family, poly-





peptide A









In order to experimentally confirm the hypothesis, the accurate mass of X-11793 was determined. Its neutral mass 600.25859 supported the formula C33H36N4O7, which also is consistent with the hypothesis of an oxidized bilirubin variant. Exact mass, fragmentation pattern and chromatographic retention time supported to identification of X-11793 as an oxidized bilirubin variant. Thus, using the above-described method, X-11793 was identified as an oxidized bilirubin variant.
Example 9
Previously unidentified biomarker X-11593 was identified using the following procedure.
The mass of the unknown X-11593 was determined in a LC/MS/MS run in negative ionization mode. The mass quantified for this unknown was 189.2.
The GGM for X-11593, including its direct and second neighbors, is shown in FIG. 8. In FIG. 8, only connectors having correlation with p-values below 0.001/(n(n−1)/2) are shown. All metabolites with significant partial correlations to X-11593, at a significance level of 0.05, are listed in Table 18. In FIG. 8, broken line (dashed) connectors denote significant partial correlations between the connected metabolites while solid line (gray) connectors represent connections via a known biochemical reaction as provided by metabolic databases such as KEGG. The lines are weighted by p-value, the lower the p-value, the thicker the line. Both known metabolites directly associated with X-11593 belong to the ascorbate degradation pathway.







TABLE 18







Metabolites having significant partial correlation


with the unknown metabolite X-11593.











Unknown:
Significant partial correlation to:
p-value







X-11593
X-01911
8.00E−38




ascorbate
6.59E−20




threonate
6.92E−20




X-12206
1.98E−11




1,5-anhydroglucitol (1,5-AG)
1.26E−07




C-glycosyltryptophan
6.80E−07










In the GWAS analysis, significant associations of X-11593 with SNPs in the gene encoding catechol-O-methyltransferase (COMT) were found. Table 19 shows the most significant hit from the GWAS analysis for X-11593. COMT is an enzyme relevant for the inactivation and degradation of many drugs. COMT O-methylates molecules with catechol like structures.







TABLE 19







Most significant hit in the GWAS for the unknown X-11593.











Unknown
Best Assoc. SNP
Locus
Enzyme
p-value





X-11593
rs4680
COMT
catechol-O-
1.13E−48





methyltransferase









The constraints for X-11593 given by the GGM shown in FIG. 8 and given by the GWAS analysis were combined. According to the GWAS, X-11593 was probably a substrate or a product of O-methylation. The mass differences to the known metabolites neighboring X-11593, namely ascorbate and threonate, was determined. While the mass difference of X-11593 and threonate is 54, X-11593 and ascorbate show a mass difference of 14, which corresponds to the addition of a methyl moiety. These observations made O-methylated ascorbate derivatives good candidates for X-11593.
From an experimental perspective, this hypothesis was supported by the accurate neutral mass 190.04787 determined for X-11593. Based on the accurate mass, the molecular formula for X-11593 was determined to be C7H10O6. In ChemSpider, 93 molecules were described for this formula. Out of the 93 molecules, three molecules represent O-methylated ascorbates. Their structures are shown in Formulas I, II, and III below. Two of the three molecules are methylated at one of the hydroxyl moieties of the 5-ring. The double bond within the 5-ring with its two hydroxyl moieties could “mimic” the corresponding planar substructure in catechol, on which catechol-o-methyltransferase (COMT) is usually working. As such, the molecules of Formulas I and II are more probable candidates for X-11593. Experimentally, the retention time of X-11593 showed a slight shift compared to the time for ascorbate. This shift matches the shift expected for adding a methyl group. Moreover, the primary fragment loss for X-11593 is 60, which is the same as that for ascorbate. The mass loss of 15, also seen for X-11593, is typical for phenols substituted with a —OH and —OCH3. Thus, it was hypothesized that X-11593 is 2-O-methyl ascorbic acid.




Candidate Molecules for X-11593












TABLE 2







UNKNOWN
ASSOCIATING SNP
PVAL_SNP









X01911
rs4680
5.80E−11



X01911
rs165656
3.65E−10



X01911
rs176533
6.39E−07



X01911
rs4633
3.95E−08



X02249
rs867212
1.502E−06 



X02269
rs6583967
4.84E−07



X02973
rs12652460
2.164E−06 



X03003
rs10879287
6.13E−07



X03056
rs4240520
3.03E−08



X03088
rs7329126
1.322E−06 



X03090
rs4952293
9.08E−07



X03094
rs3741298
1.49E−09



X04357
rs1953661
3.804E−06 



X04494
rs9365108
4.653E−06 



X04495
rs7634246
1.246E−06 



X04498
rs3848141
2.59E−07



X04499
rs17076477
1.15E−07



X04500
rs2920861
1.081E−06 



X04515
rs7785988
1.082E−06 



X05426
rs1881514
1.67E−07



X05491
rs16823855
6.43E−07



X05907
rs1635181
3.764E−06 



X06126
rs353807
8.06E−07



X06226
rs12413935
4.09E−11



X06227
rs1695945
6.75E−07



X06246
rs2219008
5.56E−07



X06267
rs17138748
4.229E−06 



X06307
rs9316180
4.93E−09



X06350
rs12028243
6.31E−07



X06351
rs13097461
9.91E−07



X07765
rs4687417
9.67E−07



X08402
rs4902250
1.18E−09



X08766
rs11621845
1.193E−06 



X08988
rs1398806
9.03E−07



X09026
rs9320134
1.32E−07



X09108
rs10972022
1.514E−06 



X09706
rs803422
3.64E−07



X09789
rs2588976
1.49E−07



X10346
rs6432834
1.764E−06 



X10395
rs10497458
2.54E−08



X10419
rs10260816
2.78E−06



X10429
rs6767775
4.028E−06 



X10500
rs16946189
1.76E−06



X10506
rs1284066
9.82E−07



X10510
rs11856508
2.85E−07



X10675
rs2279812
4.77E−07



X10810
rs9610927
1.122E−06 



X11204
rs1545358
1.234E−05 



X11244
rs296391
2.12E−26



X11244
rs17272617
5.87E−13



X11244
rs2910400
4.38E−07



X11244
rs2932766
6.42E−09



X11244
rs2972515
3.14E−07



X11244
rs3745752
2.10E−10



X11244
rs4427918
5.77E−07



X11247
rs2807872
7.14E−08



X11255
rs8179972
1.694E−06 



X11261
rs1247499
8.16E−07



X11299
rs671938
2.372E−06 



X11315
rs7782739
1.325E−06 



X11317
rs4575635
8.02E−08



X11319
rs11118895
2.90E−07



X11327
rs7129081
7.47E−07



X11334
rs16833988
1.725E−06 



X11374
rs457075
3.39E−07



X11381
rs11106542
1.355E−06 



X11412
rs359980
3.19E−07



X11421
rs12134854
1.90E−27



X11421
rs1001160
7.58E−11



X11421
rs10873788
2.05E−18



X11421
rs11161510
3.86E−25



X11421
rs11161511
2.20E−25



X11421
rs11161620
2.71E−19



X11421
rs11163904
1.16E−09



X11421
rs11163924
2.62E−25



X11421
rs1146579
2.28E−22



X11421
rs11579752
2.03E−09



X11421
rs1159215
1.20E−10



X11421
rs12090849
1.00E−18



X11421
rs12123977
8.67E−26



X11421
rs12131344
9.98E−11



X11421
rs12140121
1.13E−17



X11421
rs1250876
5.65E−14



X11421
rs1251079
1.61E−13



X11421
rs1251551
2.25E−12



X11421
rs1251584
5.87E−10



X11421
rs1303870
9.88E−13



X11421
rs1498311
1.35E−10



X11421
rs1689271
9.29E−12



X11421
rs1694419
1.61E−16



X11421
rs17097780
3.05E−08



X11421
rs17647178
2.02E−11



X11421
rs17650138
3.97E−16



X11421
rs1770887
1.36E−15



X11421
rs1796812
1.62E−12



X11421
rs211718
6.38E−27



X11421
rs2792664
3.51E−11



X11421
rs3818855
1.16E−19



X11421
rs5745347
4.19E−11



X11421
rs6699682
8.66E−19



X11421
rs7516477
1.25E−13



X11421
rs7519526
1.51E−11



X11421
rs7547056
9.14E−15



X11421
rs8396
2.01E−07



X11422
rs2406278
2.70E−07



X11423
rs6429032
1.43E−06



X11437
rs7779508
2.761E−06 



X11438
rs174456
3.052E−06 



X11440
rs296391
1.69E−43



X11440
rs17272617
9.63E−24



X11440
rs2910400
1.55E−11



X11440
rs2932766
5.51E−14



X11440
rs2972515
1.60E−12



X11440
rs3745752
1.08E−15



X11440
rs4427918
2.68E−09



X11441
rs6742078
5.59E−30



X11441
rs887829
2.79E−29



X11441
rs10179091
2.27E−18



X11441
rs10197460
2.09E−19



X11441
rs10203853
4.62E−08



X11441
rs11695484
8.25E−23



X11441
rs11891311
7.16E−17



X11441
rs2602380
2.24E−07



X11441
rs2741021
1.23E−14



X11441
rs2741023
1.68E−09



X11441
rs2741045
3.61E−23



X11441
rs3755319
3.31E−20



X11441
rs3806596
1.35E−21



X11441
rs3806597
9.58E−22



X11441
rs4294999
7.55E−21



X11441
rs4663965
1.21E−22



X11441
rs6714634
1.33E−22



X11441
rs6715325
2.07E−14



X11441
rs6736508
1.91E−18



X11441
rs6744284
4.49E−23



X11441
rs6753320
9.87E−18



X11441
rs6759892
2.74E−18



X11441
rs6761246
8.01E−07



X11441
rs7563561
5.68E−18



X11441
rs7564935
3.93E−19



X11441
rs7608175
6.89E−18



X11442
rs6742078
1.19E−25



X11442
rs887829
2.18E−25



X11442
rs10179091
8.51E−18



X11442
rs10197460
4.41E−16



X11442
rs10203853
3.78E−08



X11442
rs11695484
2.27E−21



X11442
rs11891311
3.83E−16



X11442
rs2741021
3.39E−12



X11442
rs2741023
6.41E−08



X11442
rs2741045
2.68E−19



X11442
rs3755319
1.58E−19



X11442
rs3806596
8.71E−21



X11442
rs3806597
8.89E−21



X11442
rs4294999
8.97E−20



X11442
rs4663965
4.82E−22



X11442
rs6714634
2.18E−21



X11442
rs6715325
1.37E−15



X11442
rs6736508
1.77E−15



X11442
rs6744284
1.20E−20



X11442
rs6753320
1.99E−14



X11442
rs6759892
1.76E−15



X11442
rs7563561
8.52E−15



X11442
rs7564935
1.15E−18



X11442
rs7608175
7.05E−15



X11443
rs16845476
4.651E−06 



X11444
rs12466713
3.295E−06 



X11445
rs296391
7.91E−07



X11445
rs13358334
2.36E−12



X11445
rs4149056
5.26E−08



X11445
rs4149081
8.46E−08



X11445
rs10461715
4.34E−11



X11445
rs11045879
1.69E−07



X11445
rs11746242
2.09E−07



X11445
rs2039623
9.49E−07



X11445
rs3756669
4.32E−12



X11445
rs700176
4.63E−07



X11445
rs7715372
2.10E−07



X11445
rs852238
3.66E−08



X11450
rs17325782
1.745E−06 



X11452
rs253444
1.837E−06 



X11469
rs4712963
5.57E−08



X11470
rs879154
4.09E−07



X11478
rs16946426
4.40E−07



X11483
rs10505816
5.91E−08



X11485
rs17361212
5.50E−07



X11491
rs4149056
7.76E−08



X11497
rs17265949
1.60E−07



X11521
rs6082408
5.39E−07



X11529
rs4149056
3.28E−81



X11529
rs4149081
1.93E−71



X11529
rs10841753
1.30E−11



X11529
rs10841791
4.70E−08



X11529
rs11045818
7.26E−12



X11529
rs11045819
3.44E−11



X11529
rs11045821
4.57E−13



X11529
rs11045825
1.36E−12



X11529
rs11045872
6.72E−11



X11529
rs11045879
2.58E−72



X11529
rs11045907
5.73E−12



X11529
rs11045908
2.09E−11



X11529
rs11045913
1.58E−19



X11529
rs11045953
2.17E−07



X11529
rs12372067
3.29E−18



X11529
rs12372111
5.22E−18



X11529
rs12812279
6.55E−13



X11529
rs1463565
8.81E−13



X11529
rs16923647
8.73E−52



X11529
rs2007379
1.66E−07



X11529
rs2169969
5.65E−12



X11529
rs2196019
3.70E−07



X11529
rs2199766
7.47E−07



X11529
rs2291075
5.84E−21



X11529
rs2291076
2.88E−14



X11529
rs2417963
2.10E−15



X11529
rs2900476
6.55E−49



X11529
rs4148984
4.24E−07



X11529
rs4148987
2.62E−07



X11529
rs4148988
4.53E−08



X11529
rs4149035
3.31E−08



X11529
rs4149057
1.52E−37



X11529
rs4149058
1.88E−54



X11529
rs4149069
2.06E−15



X11529
rs4149076
2.31E−22



X11529
rs7313671
 8.1E−08



X11529
rs7965567
1.04E−07



X11529
rs7966613
8.95E−20



X11529
rs7967303
4.47E−07



X11529
rs7975631
2.32E−07



X11529
rs852549
2.87E−07



X11529
rs919840
8.13E−07



X11529
rs999278
1.05E−13



X11530
rs6742078
2.12E−38



X11530
rs887829
2.30E−38



X11530
rs10179091
3.43E−23



X11530
rs10197460
3.15E−21



X11530
rs10203853
3.21E−10



X11530
rs10209214
2.33E−07



X11530
rs11695484
8.23E−33



X11530
rs11891311
2.51E−23



X11530
rs17864661
6.67E−07



X11530
rs2602379
4.21E−08



X11530
rs2602380
3.62E−08



X11530
rs2741021
1.58E−16



X11530
rs2741023
3.64E−10



X11530
rs2741045
4.14E−25



X11530
rs3755319
3.71E−27



X11530
rs3806596
1.78E−28



X11530
rs3806597
5.61E−29



X11530
rs4148328
1.05E−08



X11530
rs4294999
3.20E−27



X11530
rs4663965
1.86E−27



X11530
rs6714634
9.08E−32



X11530
rs6715325
1.74E−21



X11530
rs6736508
5.10E−19



X11530
rs6744284
2.02E−31



X11530
rs6753320
3.05E−17



X11530
rs6754100
4.35E−09



X11530
rs6759892
8.45E−19



X11530
rs6761246
3.97E−09



X11530
rs7563561
1.07E−19



X11530
rs7564935
4.07E−24



X11530
rs7587916
2.64E−09



X11530
rs7608175
1.79E−19



X11537
rs1529294
8.715E−06 



X11538
rs4149056
1.35E−37



X11538
rs4149081
1.54E−34



X11538
rs10841753
2.01E−23



X11538
rs10841791
1.93E−12



X11538
rs11045512
2.53E−09



X11538
rs11045521
2.31E−09



X11538
rs11045598
4.60E−12



X11538
rs11045611
3.81E−12



X11538
rs11045721
6.32E−11



X11538
rs11045767
6.50E−15



X11538
rs11045773
2.87E−15



X11538
rs11045776
4.64E−11



X11538
rs11045787
3.60E−16



X11538
rs11045818
1.94E−28



X11538
rs11045819
5.99E−28



X11538
rs11045821
8.73E−28



X11538
rs11045825
2.02E−27



X11538
rs11045872
7.95E−25



X11538
rs11045879
1.06E−34



X11538
rs11045907
2.03E−20



X11538
rs11045908
9.50E−19



X11538
rs11045913
6.03E−20



X11538
rs11045953
2.09E−12



X11538
rs12366506
4.50E−12



X11538
rs12370666
1.53E−14



X11538
rs12370697
2.17E−14



X11538
rs12431442
2.87E−07



X11538
rs12812279
2.73E−25



X11538
rs16923647
1.32E−26



X11538
rs17328763
2.08E−16



X11538
rs2007379
4.40E−07



X11538
rs2169969
1.22E−25



X11538
rs2199766
1.67E−20



X11538
rs2857468
1.53E−09



X11538
rs2900476
1.05E−16



X11538
rs3794319
3.18E−10



X11538
rs4148984
5.56E−12



X11538
rs4148987
2.55E−10



X11538
rs4148988
1.80E−12



X11538
rs4149057
1.20E−14



X11538
rs4149058
7.42E−18



X11538
rs718545
8.84E−07



X11538
rs7313671
1.10E−12



X11538
rs7962263
3.66E−11



X11538
rs7965567
3.95E−07



X11538
rs7967303
1.78E−11



X11538
rs919840
4.67E−07



X11540
rs10798980
1.642E−06 



X11546
rs17700286
2.58E−06



X11550
rs894282
3.692E−06 



X11552
rs12512174
1.062E−06 



X11568
rs10449290
3.549E−06 



X11593
rs4680
1.13E−48



X11593
rs1034564
5.36E−08



X11593
rs1034565
 3.2E−08



X11593
rs1110478
4.80E−08



X11593
rs1375450
1.68E−07



X11593
rs1544325
6.70E−23



X11593
rs1640299
2.96E−13



X11593
rs165656
1.16E−46



X11593
rs16982844
1.92E−08



X11593
rs17119998
1.62E−07



X11593
rs17120009
2.41E−07



X11593
rs175165
2.17E−11



X11593
rs175197
2.28E−07



X11593
rs175199
8.76E−08



X11593
rs175200
1.74E−08



X11593
rs2073746
1.53E−12



X11593
rs2266943
7.17E−07



X11593
rs385773
7.78E−09



X11593
rs397701
8.14E−07



X11593
rs404060
8.69E−07



X11593
rs4633
9.74E−35



X11593
rs4646312
3.79E−22



X11593
rs4646316
3.14E−08



X11593
rs5748489
1.05E−19



X11593
rs5993875
2.90E−14



X11593
rs5993883
4.85E−16



X11593
rs8185002
2.58E−09



X11593
rs885980
5.18E−09



X11593
rs9332377
3.24E−07



X11593
rs9605063
5.38E−07



X11593
rs9606240
6.32E−10



X11786
rs7251736
3.11E−07



X11787
rs6710438
2.95E−37



X11787
rs7598396
1.66E−35



X11787
rs6753344
7.28E−36



X11787
rs10190002
8.81E−21



X11787
rs10193032
9.46E−13



X11787
rs10496190
1.75E−21



X11787
rs1052161
4.01E−21



X11787
rs1052162
1.94E−34



X11787
rs1083922
4.81E−26



X11787
rs10865398
1.49E−23



X11787
rs11126417
1.16E−22



X11787
rs11688718
9.37E−23



X11787
rs11884776
5.89E−35



X11787
rs11894953
5.26E−19



X11787
rs12052539
7.17E−24



X11787
rs12478346
2.36E−18



X11787
rs12624267
6.27E−23



X11787
rs12713798
3.17E−21



X11787
rs13017182
1.61E−26



X11787
rs13384952
1.11E−33



X11787
rs13386124
1.06E−22



X11787
rs13391552
6.27E−36



X11787
rs1403284
2.15E−35



X11787
rs17008991
2.42E−12



X11787
rs17009372
1.19E−16



X11787
rs17110192
1.08E−21



X11787
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X12798
rs533452
1.85E−11



X12798
rs596881
1.42E−57



X12798
rs667538
5.01E−09



X12816
rs13275783
1.704E−06 



X12830
rs12517012
2.23E−07



X12844
rs465226
1.153E−06 



X12847
rs9517904
4.538E−06 



X12850
rs11019976
3.68E−08



X12851
rs11029926
4.43E−07



X12855
rs3820881
6.136E−06 



X12990
rs2524299
9.74E−08



X13069
rs1958375
1.257E−06 



X13183
rs10935295
3.21E−07



X13215
rs11880261
4.20E−08



X13372
rs1995973
1.996E−06 



X13429
rs4149056
4.86E−22



X13429
rs4149081
6.57E−20



X13429
rs10841753
4.44E−09



X13429
rs11045512
1.13E−07



X13429
rs11045521
2.91E−07



X13429
rs11045773
3.51E−07



X13429
rs11045818
1.97E−09



X13429
rs11045819
5.94E−10



X13429
rs11045821
8.96E−11



X13429
rs11045825
3.79E−10



X13429
rs11045872
8.18E−10



X13429
rs11045879
1.04E−19



X13429
rs11045907
1.13E−09



X13429
rs11045908
4.64E−09



X13429
rs11045913
8.89E−12



X13429
rs12812279
1.11E−11



X13429
rs16923647
1.46E−11



X13429
rs2169969
6.42E−11



X13429
rs2900476
1.82E−16



X13429
rs4149057
5.36E−08



X13429
rs4149058
5.13E−15



X13431
rs2286963
2.68E−33



X13431
rs10932321
1.92E−15



X13431
rs11889646
2.09E−18



X13431
rs1396828
7.98E−16



X13431
rs1472955
8.82E−12



X13431
rs1509569
1.40E−24



X13431
rs2041688
8.58E−19



X13431
rs2539862
3.88E−14



X13431
rs2723222
7.77E−21



X13431
rs2723225
3.03E−22



X13431
rs3764913
6.93E−28



X13431
rs6725084
1.84E−16



X13431
rs6735154
9.39E−19



X13431
rs7557847
1.49E−27



X13431
rs7570090
2.16E−24



X13431
rs7583039
6.99E−19



X13431
rs7593548
2.29E−19



X13431
rs7596691
4.41E−12



X13435
rs2745454
8.22E−07



X13477
rs6753344
1.04E−07



X13496
rs1867237
1.255E−06 



X13548
rs6882355
3.548E−06 



X13549
rs17122693
2.68E−06



X13553
rs17135372
4.136E−06 



X13619
rs1478903
6.588E−06 



X13640
rs7716072
2.288E−06 



X13671
rs4684510
6.78E−07



X13741
rs3014887
1.66E−06



X13859
rs17817518
4.137E−06 



X14056
rs2224768
1.642E−06 



X14057
rs6659821
3.606E−06 



X14086
rs4351
4.02E−09



X14189
rs4351
2.39E−16



X14189
rs4343
1.48E−16



X14189
rs4325
2.27E−16



X14189
rs1029765
9.57E−07



X14189
rs12494751
6.98E−07



X14189
rs4293
4.56E−10



X14189
rs4324
2.70E−15



X14189
rs4329
8.58E−15



X14189
rs4968762
1.93E−07



X14189
rs558240
2.55E−07



X14189
rs6415419
2.44E−07



X14189
rs6468424
1.09E−07



X14189
rs651007
9.76E−08



X14205
rs4351
3.97E−14



X14205
rs4343
2.28E−13



X14205
rs4325
6.40E−13



X14205
rs4324
2.81E−11



X14205
rs4329
1.77E−10



X14205
rs4736744
8.70E−07



X14208
rs4351
4.58E−15



X14208
rs4343
1.92E−13



X14208
rs4325
2.61E−13



X14208
rs11190338
9.54E−07



X14208
rs17061987
3.11E−07



X14208
rs4293
1.92E−09



X14208
rs4324
7.82E−13



X14208
rs4329
7.23E−12



X14208
rs4895946
5.15E−07



X14208
rs4897621
9.22E−07



X14208
rs9375929
3.49E−07



X14304
rs4351
3.87E−12



X14304
rs4343
6.60E−12



X14304
rs4325
2.68E−12



X14304
rs4293
9.95E−08



X14304
rs4324
8.40E−12



X14304
rs4329
7.85E−11



X14304
rs8066722
7.87E−07



X14374
rs1374273
4.19E−07



X14450
rs644045
1.015E−05 



X14473
rs7828363
1.86E−07



X14478
rs7239408
4.673E−06 



X14486
rs10079220
1.79E−07



X14541
rs1026975
9.57E−07



X14588
rs6853408
7.24E−07



X14625
rs6558292
4.18E−09



X14626
rs4149056
2.91E−13



X14626
rs4149081
2.08E−13



X14626
rs10841753
1.02E−07



X14626
rs11045767
 4.9E−07



X14626
rs11045787
5.70E−08



X14626
rs11045818
7.49E−10



X14626
rs11045819
5.60E−10



X14626
rs11045821
1.55E−09



X14626
rs11045825
2.76E−08



X14626
rs11045872
9.22E−10



X14626
rs11045879
4.19E−13



X14626
rs11045907
1.44E−09



X14626
rs11045908
1.23E−07



X14626
rs11045913
8.46E−11



X14626
rs12812279
8.14E−09



X14626
rs16923647
1.42E−08



X14626
rs2169969
3.39E−09



X14626
rs2900476
6.26E−11



X14626
rs4149058
4.05E−09



X14632
rs10484128
7.48E−07



X14658
rs11265831
1.221E−06 



X14662
rs12093439
1.83E−07



X14663
rs7914737
2.42E−07



X14745
rs6560714
5.15E−07



X14977
rs16834673
1.163E−06 























TABLE 3








BEST








ASSO-


UN-


CIATING


KNOWN
SIGNIF GGM CORR PARTNER
PVAL GGM
SNP
LOCUS dbSNP
DESC LOCUS
PVAL SNP







X01911
piperine
3.28E−118
rs4680
COMT
catechol-O-
5.80E−011



X11847
2.41E−047


methyltransferase



X11849
1.09E−038



X11593
2.98E−038



X11485
8.36E−010



X12206
4.77E−007


X02249
@carboxy4methyl5propyl-
5.23E−032
rs867212
GRAMD4

1.50E−006



2furanpropanoateCMPF



eicosapentaenoateEPA205n3
9.64E−021



theobromine
7.53E−009



X02269
3.11E−008


X02269
X11469
0
rs6583967
CYP2C8

4.84E−007



@3carboxy4methyl5propyl-
2.04E−045



2furanpropanoateCMPF



X02249
3.11E−008


X02973
erythrose
1.24E−017
rs12652460


2.16E−006



ascorbateVitaminC
5.26E−011



threonate
5.24E−010



X13619
2.09E−008



X04357
3.85E−007


X03003
X10810
1.66E−010
rs10879287


6.13E−007



erythrose
1.19E−009


X03056
X11422
1.55E−008
rs4240520
PAOX

3.03E−008



isobutyrylcarnitine
1.41E−007



citrulline
7.89E−007


X03088
arginine
6.36E−018
rs7329126


1.32E−006



citrulline
2.02E−009



phosphate
2.00E−007


X03090


rs4952293


9.08E−007


X03094
@2palmitoylglycerophosphocholine
4.97E−013
rs3741298


1.49E−009



cholesterol
1.49E−010



citrate
1.39E−008



@1stearoylglycerophosphoinositol
1.81E−007


X04357
X12786
9.02E−096
rs1953661


3.80E−006



erythrose
4.93E−019



fructose
5.26E−014



threonate
3.04E−010



threonine
8.87E−008



aspartate
1.10E−007



X02973
3.85E−007



@1palmitoylglycerophosphoinositol
6.32E−007


X04494


rs9365108


4.65E−006


X04495
@2aminobutyrate
2.85E−041
rs7634246


1.25E−006



creatine
1.45E−026



@2hydroxybutyrateAHB
2.84E−012



X12244
5.33E−012



X12556
4.26E−008



X13435
5.76E−008


X04498
X05426
5.71E−016
rs3848141
UNC13C

2.59E−007



@2hydroxyisobutyrate
7.67E−009



threitol
6.39E−008



urea
1.99E−007


X04499
X05491
2.48E−019
rs17076477


1.15E−007


X04500


rs2920861


1.08E−006


X04515


rs7785988
LOC100286906

1.08E−006


X05426
X12039
7.82E−037
rs1881514
SPON1

1.67E−007



X04498
5.71E−016



quinate
2.10E−007


X05491
X04499
2.48E−019
rs16823855


6.43E−007



X13372
2.81E−008



erythrose
1.34E−007


X05907
X10395
7.52E−048
rs1635181
THSD7A

3.76E−006



X06226
2.86E−008


X06126
pcresolsulfate
6.57E−074
rs353807


8.06E−007



threitol
4.94E−007


X06226
X10395
1.17E−013
rs12413935
NRG3
neuregulin 3
4.09E−011



X09026
2.58E−012



X05907
2.86E−008


X06227
acetylphosphate
4.28E−084
rs1695945


6.75E−007


X06246
alanine
2.20E−065
rs2219008


5.56E−007



X14977
1.46E−008


X06267
citrulline
1.25E−021
rs17138748
LHFPL3

4.23E−006



X10810
1.26E−007


X06307
X11805
4.70E−042
rs9316180
CPB2

4.93E−009



X14205
2.24E−016



DSGEGDFXAEGGGVR
1.36E−007



X12798
2.74E−007


X06350


rs12028243


6.31E−007


X06351


rs13097461


9.91E−007


X07765


rs4687417
ATP13A5

9.67E−007


X08402
X10510
7.78E−067
rs4902250
SYNE2

1.18E−009


X08766


rs11621845
CCDC88C

1.19E−006


X08988
glycine
2.40E−024
rs1398806


9.03E−007



alanine
1.46E−010


X09026
X10395
2.56E−012
rs9320134


1.32E−007



X06226
2.58E−012


X09108
glutamine
5.89E−012
rs10972022
UBAP1

1.51E−006


X09706
X13619
6.34E−112
rs803422
MTHFD1L

3.64E−007



urea
4.61E−111



@2hydroxyisobutyrate
1.61E−011


X09789
X12253
9.90E−025
rs2588976


1.49E−007



X12039
6.63E−011



@4vinylphenolsulfate
7.35E−011



homostachydrine
3.17E−010


X10346


rs6432834
CSRNP3

1.76E−006


X10395
X05907
7.52E−048
rs10497458


2.54E−008



X06226
1.17E−013



X09026
2.56E−012



ascorbateVitaminC
7.41E−007


X10419
cholesterol
2.70E−065
rs10260816


2.78E−006



X10510
6.57E−028



X10500
9.58E−017



phosphate
1.75E−009



acetylphosphate
1.74E−007


X10429


rs6767775


4.03E−006


X10500
cholesterol
1.67E−019
rs16946189


1.76E−006



X10419
9.58E−017



acetylphosphate
5.44E−008


X10506
glucose
3.69E−014
rs1284066


9.82E−007



lysine
4.32E−014



aspartate
1.22E−013



pyruvate
2.36E−010



X13619
5.15E−009



serine
2.14E−007


X10510
X08402
7.78E−067
rs11856508


2.85E−007



X10419
6.57E−028


X10675


rs2279812


4.77E−007


X10810
hypoxanthine
7.14E−014
rs9610927


1.12E−006



X03003
1.66E−010



X12990
5.78E−008



X06267
1.26E−007



ascorbateVitaminC
1.56E−007



cysteineglutathionedisulfide
1.98E−007



X12441
2.26E−007


X11204
X11327
3.22E−270
rs1545358


1.23E−005



bilirubinEZorZE
2.84E−009



X11809
2.98E−008



octanoylcarnitine
3.09E−007


X11244
X11443
8.93E−113
rs296391
SULT2A1; CRX
sulfotransferase
2.12E−026



X11440
7.62E−093


family, cytosolic,



dehydroisoandrosteronesulfateDHEAS
7.47E−037


2A,



epiandrosteronesulfate
6.66E−016


dehydroepiandros



thromboxaneB2
1.12E−012


terone (DHEA)-



X11470
4.12E−009


preferring,







member 1; cone-







rod homeobox


X11247
X11787
7.05E−007
rs2807872


7.14E−008


X11255
@4vinylphenolsulfate
4.99E−019
rs8179972


1.69E−006



@2methylbutyroylcarnitine
7.18E−019



eicosenoate201n9or11
1.39E−007



@4ethylphenylsulfate
6.60E−007


X11261
linolenatealphaorgamma183n3or6
7.91E−081
rs1247499
C10orf11

8.16E−007



@10undecenoate111n1
4.18E−008



isobutyrylcarnitine
1.31E−007



X11880
3.66E−007


X11299


rs671938
EYS

2.37E−006


X11315


rs7782739


1.33E−006


X11317
X11497
1.58E−032
rs4575635
KLK13

8.02E−008



X12038
1.49E−028



X12524
1.52E−010


X11319
margarate170
1.84E−015
rs11118895


2.90E−007



@3methoxytyrosine
2.14E−013



@10heptadecenoate171n7
3.11E−009



myristoleate141n5
1.04E−008



@10nonadecenoate191n9
1.94E−007


X11327
X11204
3.22E−270
rs7129081


7.47E−007



octanoylcarnitine
6.97E−007


X11334
pipecolate
5.50E−021
rs16833988


1.73E−006



indolelactate
1.52E−008


X11374


rs457075


3.39E−007


X11381
X12798
5.21E−010
rs11106542
CLLU1; CLLU1OS

1.36E−006



X11442
1.09E−007



@5oxoproline
1.45E−007


X11412


rs359980


3.19E−007


X11421
X13435
9.93E−056
rs12134854
ACADM
acyl-CoA
1.90E−027



linoleate182n6
1.55E−045


dehydrogenase,



octanoylcarnitine
1.05E−040


C-4 to C-12



hexanoylcarnitine
1.00E−023


straight chain


X11422
xanthine
9.35E−110
rs2406278
DAB1

2.70E−007



urate
6.50E−018



hypoxanthine
2.81E−015



X03056
1.55E−008


X11423
X12749
2.88E−150
rs6429032
RYR2

1.43E−006


X11437


rs7779508


2.76E−006


X11438
@10undecenoate111n1
4.36E−042
rs174456
FADS3

3.05E−006



@2hydroxyisobutyrate
5.57E−011



@10nonadecenoate191n9
1.09E−009



X11847
1.66E−008



X11538
6.88E−007


X11440
X11244
7.62E−093
rs296391
SULT2A1; CRX
sulfotransferase
1.69E−043



X11445
1.13E−045


family, cytosolic,



X11470
5.45E−011


2A,



epiandrosteronesulfate
5.89E−011


dehydroepiandrosterone



X12844
2.73E−010


(DHEA)-







preferring,







member 1; cone-







rod homeobox


X11441
X11442
0
rs6742078
UGT1A1; UGT1A10;
UDP
5.59E−030



X11530
1.18E−008

UGT1A3; UGT1A4;
glucuronosyltransferase



bilirubinZZ
2.35E−007

UGT1A5; UGT1A6;
1 family,






UGT1A7;
polypeptide A






UGT1A8; UGT1A9


X11442
X11441
0
rs6742078
UGT1A1; UGT1A10;
UDP
1.19E−025



X11530
4.41E−055

UGT1A3; UGT1A4;
glucuronosyltransferase



bilirubinZZ
3.02E−018

UGT1A5; UGT1A6;
1 family,



X11381
1.09E−007

UGT1A7;
polypeptide A






UGT1A8; UGT1A9


X11443
X11244
8.93E−113
rs16845476
LRP1B

4.65E−006



dehydroisoandrosteronesulfateDHEAS
4.10E−106



epiandrosteronesulfate
6.06E−093



X11450
2.17E−037



X12844
3.73E−010


X11444
X11470
5.28E−088
rs12466713
BIRC6

3.30E−006



X12844
1.35E−048



cortisone
3.28E−007



taurolithocholate3sulfate
3.91E−007


X11445
X11440
1.13E−045
rs13358334
UGT3A1
UDP
2.36E−012



pyroglutamine
2.38E−009


glycosyltransferase







3 family,







polypeptide A1


X11450
dehydroisoandrosteronesulfateDHEAS
1.11E−101
rs17325782
NCKAP5

1.75E−006



X11443
2.17E−037



epiandrosteronesulfate
1.61E−011


X11452
X12231
0
rs253444


1.84E−006



piperine
1.38E−031


X11469
X02269
0
rs4712963


5.57E−008


X11470
X11444
5.28E−088
rs879154


4.09E−007



cortisol
1.80E−019



X11440
5.45E−011



X11244
4.12E−009



@1oleoylglycerophosphocholine
1.29E−007



heme
3.11E−007


X11478


rs16946426
GPC5

4.40E−007


X11483


rs10505816


5.91E−008


X11485
X01911
8.36E−010
rs17361212


5.50E−007



X12231
3.80E−009



@1palmitoylglycerophosphocholine
4.10E−007


X11491


rs4149056
SLCO1B1
solute carrier
7.76E−008







organic anion







transporter family,







member 1B1


X11497
X11317
1.58E−032
rs17265949
NOSTRIN

1.60E−007



X14977
2.02E−009



pelargonate90
1.24E−007


X11521


rs6082408


5.39E−007


X11529


rs4149056
SLCO1B1
solute carrier
3.28E−081







organic anion







transporter family,







member 1B1


X11530
bilirubinZZ
8.97E−085
rs6742078
UGT1A1; UGT1A10;
UDP
2.12E−038



X11442
4.41E−055

UGT1A3; UGT1A4;
glucuronosyltransferase



X11441
1.18E−008

UGT1A5; UGT1A6;
1 family,






UGT1A7;
polypeptide A






UGT1A8; UGT1A9


X11537
X11540
0
rs1529294
CNTNAP5

8.72E−006



glucose
1.47E−007


X11538
octadecanedioate
7.60E−059
rs4149056
SLCO1B1
solute carrier
1.35E−037



X12253
4.01E−012


organic anion



X12063
1.19E−011


transporter family,



X11438
6.88E−007


member 1B1


X11540
X11537
0
rs10798980


1.64E−006



choline
2.06E−013



X14977
5.54E−007


X11546


rs17700286


2.58E−006


X11550
pelargonate90
9.78E−035
rs894282


3.69E−006



heme
3.89E−011



bilirubinEZorZE
6.24E−007


X11552
oleamide
8.95E−012
rs12512174
SORBS2

1.06E−006


X11568


rs10449290
PLD5

3.55E−006


X11593
X01911
2.98E−038
rs4680
COMT
catechol-O-
1.13E−048



ascorbateVitaminC
6.59E−020


methyltransferase



threonate
6.92E−020



X12206
1.98E−011



@15anhydroglucitol15AG
1.26E−007



Cglycosyltryptophan
7.68E−007


X11786
pipecolate
1.55E−021
rs7251736
LRRC68

3.11E−007



Nacetylornithine
8.11E−010


X11787
Nacetylornithine
8.24E−035
rs6710438
ALMS1 (NAT8)
Alstrom syndrome 1
2.95E−037



X11247
7.05E−007



uridine
7.06E−007


X11792


rs4253252
KLKB1
kallikrein B,
1.60E−010







plasma (Fletcher







factor) 1


X11793
bilirubinEE
8.57E−108
rs887829
UGT1A1; UGT1A10;
UDP
2.59E−026






UGT1A3; UGT1A4;
glucuronosyltransferase






UGT1A5; UGT1A6;
1 family,






UGT1A7;
polypeptide A






UGT1A8; UGT1A9


X11795
linolenatealphaorgamma183n3or6
4.74E−008
rs9506615


1.39E−006


X11799
stachydrine
1.14E−017
rs358231
GBA3
glucosidase, beta,
2.87E−017



scylloinositol
8.55E−011


acid 3 (cytosolic)



X14086
6.47E−007


X11805
X14208
6.14E−062
rs10475541


1.57E−006



X14478
3.95E−055



X06307
4.70E−042



aspartylphenylalanine
1.39E−021



DSGEGDFXAEGGGVR
9.79E−021



X14205
1.49E−017



X14450
2.95E−011


X11809
bilirubinEE
2.60E−020
rs17008568


8.73E−007



cholesterol
1.74E−009



X11204
2.98E−008



stearoylcarnitine
1.59E−007



bilirubinZZ
1.86E−007



glycerophosphorylcholineGPC
2.51E−007



bilirubinEZorZE
3.37E−007


X11818
X12510
5.22E−039
rs196676


1.31E−007



linolenatealphaorgamma183n3or6
3.01E−007



linoleate182n6
3.83E−007


X11820


rs2298423


3.18E−007


X11826


rs7111693


4.51E−006


X11843


rs690526
WDR72

8.88E−008


X11845


rs10895514


4.09E−006


X11847
X11849
0
rs2432626
SNX29

1.43E−006



X01911
2.41E−047



X12231
1.91E−013



X11438
1.66E−008



@4ethylphenylsulfate
6.53E−007


X11849
X11847
0
rs7227515
THOC1

3.84E−006



X01911
1.09E−038



X12231
1.91E−008



@1stearoylglycerophosphoinositol
1.03E−007


X11850


rs2003334
SLC41A3

3.82E−006


X11852


rs895900
FREM2

1.70E−006


X11858


rs1849474


9.87E−008


X11859
pelargonate90
1.12E−067
rs196703


5.81E−006


X11876


rs13190556


1.77E−006


X11880
X11261
3.66E−007
rs4149056
SLCO1B1
solute carrier
6.73E−007



eicosapentaenoateEPA205n3
4.36E−007


organic anion







transporter family,







member 1B1


X12013


rs10493639


4.28E−006


X12029
X14588
2.05E−026
rs7555956
SPATA17

3.31E−006


X12038
X11317
1.49E−028
rs913112


9.83E−006



X12524
1.10E−016



cholesterol
3.50E−008



X13372
6.56E−008


X12039
quinate
3.05E−054
rs4908527


1.03E−007



X05426
7.82E−037



caffeine
1.07E−018



X12217
2.63E−014



X14473
2.14E−011



X09789
6.63E−011



@3methoxytyrosine
3.43E−009



theophylline
2.32E−008



piperine
1.01E−007


X12056


rs1345015


3.07E−006


X12063
thromboxaneB2
1.08E−015
rs10242455
CYP3A5

1.47E−045



dehydroisoandrosteronesulfateDHEAS
3.96E−013



X11538
1.19E−011



@7alphahydroxy3oxo4cholestenoate7Hoca
1.13E−007


X12092


rs4488133
PYROXD2
pyridine
2.24E−281







nucleotide-







disulphide







oxidoreductase







domain 2


X12093


rs4488133
PYROXD2
pyridine
1.35E−027







nucleotide-







disulphide







oxidoreductase







domain 2


X12094
X12095
0
rs2596210
RYR3

3.32E−007


X12095
X12094
0
rs10928512
TMEM163

2.00E−006


X12100
kynurenine
2.02E−033
rs6151896
MSH3

3.68E−007


X12116


rs704381
PRICKLE2

6.66E−007


X12188


rs10026884
GABRB1

5.59E−006


X12206
X11593
1.98E−011
rs13416390
LRRTM4

6.32E−006



X01911
4.77E−007


X12212


rs4915559
CFHR4

7.43E−007


X12216


rs2736003


2.59E−006


X12217
catecholsulfate
5.67E−185
rs1383950
CSMD1

4.48E−006



X12039
2.63E−014


X12230


rs12504564
TMEM144

1.37E−007


X12231
X11452
0
rs2741110


5.06E−006



X11847
1.91E−013



X11485
3.80E−009



X11849
1.91E−008



@3methyl2oxovalerate
2.30E−007


X12236


rs6083461
ZNF343

9.04E−007


X12244
X04495
5.33E−012
rs10508017
ABCC4

2.88E−009



creatinine
2.54E−007


X12253
X09789
9.90E−025
rs7936703
KCNQ1

3.80E−006



X11538
4.01E−012



betaine
2.93E−007


X12261


rs2576810
PTH2R

6.29E−007


X12405
@3indoxylsulfate
0
rs7564502
LRP1B

3.03E−007



DSGEGDFXAEGGGVR
2.64E−007



@2hydroxypalmitate
6.66E−007


X12407


rs1475525
DAPK1

8.07E−007


X12428


rs3205166
DDX58

3.39E−006


X12441
arachidonate204n6
1.52E−116
rs138832
BRD1

1.02E−006



@1arachidonoylglycerophosphocholine
9.39E−013



docosahexaenoateDHA226n3
2.74E−009



X10810
2.26E−007



dihomolinolenate203n3orn6
6.48E−007


X12442
X13069
5.73E−026
rs2279502


8.86E−008



@5dodecenoate121n7
3.91E−025



myristoleate141n5
1.04E−016



linoleate182n6
1.84E−016



X13431
5.43E−012



laurylcarnitine
1.78E−008



dihomolinoleate202n6
2.14E−008



hypoxanthine
2.26E−008



myristate140
7.54E−008



@2tetradecenoylcarnitine
3.61E−007


X12443


rs13256631
RGS22

3.53E−007


X12450


rs11760020
BMP6

5.31E−008


X12456


rs4149056
SLCO1B1
solute carrier
8.41E−017







organic anion







transporter family,







member 1B1


X12465


rs7723967


2.24E−006


X12510
X11818
5.22E−039
rs7598396
ALMS1 (NAT8?)
Alstrom syndrome 1
1.53E−056



Nacetylornithine
1.25E−037



@10undecenoate111n1
6.15E−008


X12524
X12038
1.10E−016
rs10497004


1.66E−006



X11317
1.52E−010



palmitate160
1.84E−009



X13859
1.08E−007


X12544


rs798598


6.05E−006


X12556
threonine
4.59E−019
rs1550642


6.63E−007



X04495
4.26E−008


X12627
eicosenoate201n9or11
2.05E−007
rs3798720
ELOVL2

1.86E−007


X12644
@1arachidonoylglycerophospho-
1.11E−154
rs6505683


4.49E−007



ethanolamine



@1docosahexaenoylglycerophosphocholine
2.13E−036



docosahexaenoateDHA226n3
3.89E−020



@1linoleoylglycerophosphoethanolamine
1.29E−007


X12645


rs168190


4.09E−007


X12680


rs7477871
PARD3

3.14E−006


X12696
@15anhydroglucitol15AG
7.82E−174
rs1936074


6.65E−007


X12704


rs13129177


7.26E−007


X12711


rs11242244


3.60E−007


X12717


rs6695534
SSBP3

1.64E−006


X12719


rs11670870


1.15E−006


X12726


rs1015150
TFEB

5.66E−006


X12728


rs11831314


2.25E−006


X12729


rs13246970
SDK1

8.53E−008


X12734


rs12725733


1.48E−006


X12740


rs2301920
CARD11

1.90E−006


X12749
X11423
2.88E−150
rs6507247


5.04E−007


X12771


rs802441


6.40E−007


X12776
X13619
4.14E−007
rs6429539


3.89E−006


X12786
X04357
9.02E−096
rs17406291


7.00E−007



lactate
1.06E−008



aspartate
4.79E−007


X12798
@dehydrocarnitine
1.95E−062
rs316020
SLC22A2
solute carrier
1.73E−072



X11381
5.21E−010


family 22 (organic



X06307
2.74E−007


cation







transporter),







member 2


X12816


rs13275783


1.70E−006


X12830


rs12517012


2.23E−007


X12844
X11444
1.35E−048
rs465226
SLC35F1

1.15E−006



X11440
2.73E−010



X11443
3.73E−010



thromboxaneB2
1.48E−009



dehydroisoandrosteronesulfateDHEAS
2.01E−008



epiandrosteronesulfate
1.72E−007


X12847


rs9517904
CLYBL

4.54E−006


X12850


rs11019976
FAT3

3.68E−008


X12851


rs11029926


4.43E−007


X12855


rs3820881
SPATS2L

6.14E−006


X12990
docosahexaenoateDHA226n3
4.56E−037
rs2524299
FADS2

9.74E−008



eicosapentaenoateEPA205n3
1.41E−023



@3carboxy4methyl5propyl-
2.60E−021



2furanpropanoateCMPF



dihomolinolenate203n3orn6
6.15E−020



@1arachidonoylglycerophosphocholine
1.21E−017



X10810
5.78E−008



adrenate224n6
9.70E−008



docosapentaenoaten3DPA225n3
1.21E−007



arachidonate204n6
1.98E−007


X13069
X12442
5.73E−026
rs1958375


1.26E−006


X13183
linoleamide182n6
3.36E−044
rs10935295


3.21E−007



@2stearoylglycerophosphocholine
5.89E−008



oleamide
1.74E−007


X13215


rs11880261
AKT2

4.20E−008


X13372
X05491
2.81E−008
rs1995973


2.00E−006



X12038
6.56E−008



bilirubinEZorZE
9.56E−008



@4ethylphenylsulfate
6.70E−007


X13429


rs4149056
SLCO1B1
solute carrier
4.86E−022







organic anion







transporter family,







member 1B1


X13431
@10undecenoate111n1
1.82E−014
rs2286963
ACADL
acyl-CoA
2.68E−033



@2methylbutyroylcarnitine
6.68E−013


dehydrogenase,



X12442
5.43E−012


long chain



@1palmitoleoylglycerophosphocholine
2.75E−007


X13435
X11421
9.93E−056
rs2745454
C6orf146

8.22E−007



acetylcarnitine
2.03E−017



@2tetradecenoylcarnitine
3.89E−012



hexanoylcarnitine
1.11E−010



X04495
5.76E−008


X13477
Nacetylornithine
6.71E−034
rs6753344
ALMS1 (NAT8?)
Alstrom syndrome 1
1.04E−007


X13496
erythrose
3.87E−008
rs1867237
GOSR2

1.26E−006


X13548
X13549
1.51E−094
rs6882355
EFNA5

3.55E−006


X13549
X13548
1.51E−094
rs17122693
ATL1

2.68E−006


X13553


rs17135372
MCC

4.14E−006


X13619
X09706
6.34E−112
rs1478903


6.59E−006



urea
3.71E−036



asparagine
2.10E−011



X10506
5.15E−009



X02973
2.09E−008



X12776
4.14E−007


X13640


rs7716072
FSTL4

2.29E−006


X13671


rs4684510


6.78E−007


X13741


rs3014887


1.66E−006


X13859
X14625
3.30E−032
rs17817518
RYR3

4.14E−006



X12524
1.08E−007


X14056
X14057
9.43E−141
rs2224768


1.64E−006



bilirubinEE
7.85E−017


X14057
X14056
9.43E−141
rs6659821


3.61E−006


X14086
stachydrine
4.76E−058
rs4351
ACE
angiotensin I
4.02E−009



X14304
1.54E−027


converting



X14189
1.21E−025


enzyme (peptidyl-



X14208
1.83E−014


dipeptidase A) 1



X14205
1.34E−012



@15anhydroglucitol15AG
3.03E−008



DSGEGDFXAEGGGVR
5.77E−007



X11799
6.47E−007


X14189
X14304
1.45E−204
rs4343
ACE
angiotensin I
1.48E−016



X14086
1.21E−025


converting



aspartylphenylalanine
3.74E−016


enzyme (peptidyl-



DSGEGDFXAEGGGVR
3.05E−008


dipeptidase A) 1


X14205
X14478
4.22E−087
rs4351
ACE
angiotensin I
3.97E−014



DSGEGDFXAEGGGVR
1.89E−035


converting



cysteineglutathionedisulfide
9.59E−033


enzyme (peptidyl-



X14208
2.25E−024


dipeptidase A) 1



X11805
1.49E−017



X06307
2.24E−016



X14086
1.34E−012



X14450
1.73E−012



ADSGEGDFXAEGGGVR
3.29E−009



aspartate
6.52E−009



phenylalanine
3.18E−007



glutamate
4.19E−007



ADpSGEGDFXAEGGGVR
6.26E−007


X14208
X14478
4.67E−153
rs4351
ACE
angiotensin I
4.58E−015



X11805
6.14E−062


converting



X14205
2.25E−024


enzyme (peptidyl-



X14086
1.83E−014


dipeptidase A) 1



lysine
5.68E−011


X14304
X14189
1.45E−204
rs4325
ACE
angiotensin I
2.68E−012



X14086
1.54E−027


converting







enzyme (peptidyl-







dipeptidase A) 1


X14374
X14473
2.57E−098
rs1374273


4.19E−007



theobromine
8.53E−045



hippurate
6.51E−017



quinate
3.66E−007


X14450
aspartylphenylalanine
5.43E−067
rs644045


1.02E−005



X14478
4.67E−014



X14205
1.73E−012



X11805
2.95E−011


X14473
X14374
2.57E−098
rs7828363


1.86E−007



quinate
2.66E−017



X12039
2.14E−011



theophylline
1.13E−008



bradykinindesarg9
5.15E−007


X14478
X14208
4.67E−153
rs7239408


4.67E−006



X14205
4.22E−087



X11805
3.95E−055



X14450
4.67E−014



cysteineglutathionedisulfide
5.67E−014



aspartylphenylalanine
3.51E−010


X14486


rs10079220


1.79E−007


X14541


rs1026975
ANK2
ankyrin 2,
9.57E−007







neuronal


X14588
X12029
2.05E−026
rs6853408
CCDC158

7.24E−007



pipecolate
2.88E−013



histidine
1.29E−007


X14625
X13859
3.30E−032
rs6558292
OPLAH

4.18E−009



@5oxoproline
8.59E−028



glucose
3.49E−018


X14626


rs4149081
SLCO1B1
solute carrier
2.08E−013







organic anion







transporter family,







member 1B1


X14632


rs10484128


7.48E−007


X14658


rs11265831


1.22E−006


X14662


rs12093439


1.83E−007


X14663


rs7914737


2.42E−007


X14745


rs6560714


5.15E−007


X14977
X11497
2.02E−009
rs16834673


1.16E−006



X06246
1.46E−008



X11540
5.54E−007

## Claims

1. A method of determining the structure of an unknown metabolite when the accurate mass of the metabolite is known, comprising:
(a) measuring amounts of known and unknown metabolites in subjects;
(b) associating an unknown metabolite with a specific gene using a genome wide association study;
(c) determining a protein associated with the specific gene and analyzing information for the protein;
(d) associating the unknown metabolite with concentrations and/or ratios of other metabolites in the subjects using a partial correlation network;
(e) obtaining chemical structural data for the unknown metabolite using a mass spectrometer; and
(f) correlating the results obtained from steps (a) through (e) to determine the structure of the unknown metabolite.

2. The method of claim 1, wherein the specific gene comprises a genetic polymorphism.

3. The method of claim 1, further comprising reviewing the structure and/or characteristics of other metabolites associated with the specific gene from the genome wide association study and/or identifying the biochemical pathway with which at least a portion of the other metabolites associated with the specific gene are involved prior to performing step (f).

4. The method of claim 1, further comprising reviewing the structure and/or characteristics of the other metabolites associated with the unknown metabolite using the partial correlation network and/or identifying the biochemical pathway with which at least a portion of the other metabolites are involved prior to performing step (f).

5. The method of claim 1, wherein the mass spectrometric data of the unknown metabolite comprises mass, molecular formula, or fragmentation spectra.

6. The method of claim 1, wherein the information concerning the protein known to be associated with the gene includes function of the protein.

7. The method of claim 1, wherein the protein performs a metabolic function.

8. The method of claim 1, wherein the protein is an enzyme.

9. The method of claim 8, wherein the substrate of the enzyme is identified.

10. The method of claim 9, wherein the information includes the biochemical pathway for the substrate.

11. The method of claim 9, wherein the information includes alternative biochemical pathways for the substrate.

12. The method of claim 8, wherein an alternative substrate of the enzyme is determined.

13. The method of claim 12, wherein the information includes the biochemical pathway for the substrate.

14. The method of claim 1, wherein the protein is a transporter.

15. The method of claim 3, wherein reviewing the structure and/or characteristics of the other metabolites associated with the specific gene from the genome wide association study and/or metabolites associated using the partial correlation network includes reviewing mass, class of compound, retention time, isotope patterns, fragments, and functionality of the other metabolites.

16. The method of claim 1, wherein the association between the protein and the specific gene is the protein being encoded by the gene.

17. A method of determining the structure of an unknown metabolite when the accurate mass of the metabolite is known, comprising:
(a) measuring amounts of known and unknown metabolites in subjects;
(b) associating an unknown metabolite with a specific gene from a genome wide association study;
(c) determining a protein associated with the specific gene and analyzing information for the protein;
(d) reviewing the structure and/or characteristics of other metabolites associated with the specific gene from the genome wide association study;
and/or identifying the biochemical pathway with which at least a portion of the other metabolites associated with the specific gene are involved;
(e) obtaining chemical structural data for the unknown metabolite using a mass spectrometer; and
(f) correlating results obtained from steps (a) through (e) to determine the structure of the unknown metabolite.

18. A method of determining the structure of an unknown metabolite when the accurate mass of the metabolite is known, comprising:
(a) measuring amounts of known and unknown metabolites in subjects;
(b) associating an unknown metabolite with concentrations and/or ratios of other metabolites in the subjects using a partial correlation network;
(c) reviewing the structure and/or characteristics of the other metabolites associated with the unknown metabolite; and/or identifying the biochemical pathway with which at least a portion of the other metabolites associated with the unknown metabolite are involved;
(d) obtaining chemical structural data for the unknown metabolite using a mass spectrometer; and
(e) correlating the results obtained from steps (a) through (d) to determine the structure of the unknown metabolite.

19. The method of claim 18, further comprising associating the unknown metabolite with a specific gene from a genome wide association study and determining a protein associated with the specific gene and analyzing information for the protein.