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Integration of Genome-Wide SNP Data and Gene-Expression Profiles Reveals Six Novel Loci and Regulatory Mechanisms for Amino Acids and Acylcarnitines in Whole Blood.

Burkhardt R, Kirsten H, Beutner F, Holdt LM, Gross A, Teren A, Tönjes A, Becker S, Krohn K, Kovacs P, Stumvoll M, Teupser D, Thiery J, Ceglarek U, Scholz M - PLoS Genet. (2015)

Bottom Line: In conclusion, our integrative analysis of SNP, gene-expression and metabolite data points to novel genetic factors that may be involved in the regulation of human metabolism.At several loci, we provide evidence for metabolite regulation via gene-expression and observed overlaps with GWAS loci for common diseases.These results form a strong rationale for subsequent functional and disease-related studies.

View Article: PubMed Central - PubMed

Affiliation: LIFE Leipzig Research Center for Civilization Diseases, University of Leipzig, Leipzig Germany; Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital Leipzig, Leipzig, Germany.

ABSTRACT
Profiling amino acids and acylcarnitines in whole blood spots is a powerful tool in the laboratory diagnosis of several inborn errors of metabolism. Emerging data suggests that altered blood levels of amino acids and acylcarnitines are also associated with common metabolic diseases in adults. Thus, the identification of common genetic determinants for blood metabolites might shed light on pathways contributing to human physiology and common diseases. We applied a targeted mass-spectrometry-based method to analyze whole blood concentrations of 96 amino acids, acylcarnitines and pathway associated metabolite ratios in a Central European cohort of 2,107 adults and performed genome-wide association (GWA) to identify genetic modifiers of metabolite concentrations. We discovered and replicated six novel loci associated with blood levels of total acylcarnitine, arginine (both on chromosome 6; rs12210538, rs17657775), propionylcarnitine (chromosome 10; rs12779637), 2-hydroxyisovalerylcarnitine (chromosome 21; rs1571700), stearoylcarnitine (chromosome 1; rs3811444), and aspartic acid traits (chromosome 8; rs750472). Based on an integrative analysis of expression quantitative trait loci in blood mononuclear cells and correlations between gene expressions and metabolite levels, we provide evidence for putative causative genes: SLC22A16 for total acylcarnitines, ARG1 for arginine, HLCS for 2-hydroxyisovalerylcarnitine, JAM3 for stearoylcarnitine via a trans-effect at chromosome 1, and PPP1R16A for aspartic acid traits. Further, we report replication and provide additional functional evidence for ten loci that have previously been published for metabolites measured in plasma, serum or urine. In conclusion, our integrative analysis of SNP, gene-expression and metabolite data points to novel genetic factors that may be involved in the regulation of human metabolism. At several loci, we provide evidence for metabolite regulation via gene-expression and observed overlaps with GWAS loci for common diseases. These results form a strong rationale for subsequent functional and disease-related studies.

No MeSH data available.


Related in: MedlinePlus

Network of discovered loci, eQTLs and metabolites.Significant relationships between genetic loci (top SNPs), gene-expression in PBMCs and metabolite levels in whole blood are displayed. Line thickness corresponds to amount of explained variance (Lightblue = genetic loci without triangles, darkblue = genetic loci with triangles, lightgreen = cis-regulated genes, darkgreen = trans-regulated genes, light orange = raw metabolites, darkorange = metabolite ratios). An interactive html-document document of the network can be found in the supplement material.
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pgen.1005510.g004: Network of discovered loci, eQTLs and metabolites.Significant relationships between genetic loci (top SNPs), gene-expression in PBMCs and metabolite levels in whole blood are displayed. Line thickness corresponds to amount of explained variance (Lightblue = genetic loci without triangles, darkblue = genetic loci with triangles, lightgreen = cis-regulated genes, darkgreen = trans-regulated genes, light orange = raw metabolites, darkorange = metabolite ratios). An interactive html-document document of the network can be found in the supplement material.

Mentions: We then integrated information from SNP-metabolite (mQTL), SNP-gene expression (eQTL) and expression-metabolite associations to form association triangles. A triangle is defined by a triple of SNP, transcript and metabolite showing pair-wise associations (see methods for details). We constructed a network of all pairs of associations and their strengths (see Fig 4) to illustrate the multiple relationships between associated genetic loci, genes and metabolites. An interactive html-document to explore the network is provided as supplement material (S4 Fig). Certain overlaps with previously reported molecular interactions exist. These known relationships are summarized in S11 Table. We identified 177 relations containing 21 unique primary associations between features analysed in our study. Additionally, we identified 16 unique molecules potentially connecting features analysed in our study. As expected, these molecules include Proinsulin and Ubiquitin.


Integration of Genome-Wide SNP Data and Gene-Expression Profiles Reveals Six Novel Loci and Regulatory Mechanisms for Amino Acids and Acylcarnitines in Whole Blood.

Burkhardt R, Kirsten H, Beutner F, Holdt LM, Gross A, Teren A, Tönjes A, Becker S, Krohn K, Kovacs P, Stumvoll M, Teupser D, Thiery J, Ceglarek U, Scholz M - PLoS Genet. (2015)

Network of discovered loci, eQTLs and metabolites.Significant relationships between genetic loci (top SNPs), gene-expression in PBMCs and metabolite levels in whole blood are displayed. Line thickness corresponds to amount of explained variance (Lightblue = genetic loci without triangles, darkblue = genetic loci with triangles, lightgreen = cis-regulated genes, darkgreen = trans-regulated genes, light orange = raw metabolites, darkorange = metabolite ratios). An interactive html-document document of the network can be found in the supplement material.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4581711&req=5

pgen.1005510.g004: Network of discovered loci, eQTLs and metabolites.Significant relationships between genetic loci (top SNPs), gene-expression in PBMCs and metabolite levels in whole blood are displayed. Line thickness corresponds to amount of explained variance (Lightblue = genetic loci without triangles, darkblue = genetic loci with triangles, lightgreen = cis-regulated genes, darkgreen = trans-regulated genes, light orange = raw metabolites, darkorange = metabolite ratios). An interactive html-document document of the network can be found in the supplement material.
Mentions: We then integrated information from SNP-metabolite (mQTL), SNP-gene expression (eQTL) and expression-metabolite associations to form association triangles. A triangle is defined by a triple of SNP, transcript and metabolite showing pair-wise associations (see methods for details). We constructed a network of all pairs of associations and their strengths (see Fig 4) to illustrate the multiple relationships between associated genetic loci, genes and metabolites. An interactive html-document to explore the network is provided as supplement material (S4 Fig). Certain overlaps with previously reported molecular interactions exist. These known relationships are summarized in S11 Table. We identified 177 relations containing 21 unique primary associations between features analysed in our study. Additionally, we identified 16 unique molecules potentially connecting features analysed in our study. As expected, these molecules include Proinsulin and Ubiquitin.

Bottom Line: In conclusion, our integrative analysis of SNP, gene-expression and metabolite data points to novel genetic factors that may be involved in the regulation of human metabolism.At several loci, we provide evidence for metabolite regulation via gene-expression and observed overlaps with GWAS loci for common diseases.These results form a strong rationale for subsequent functional and disease-related studies.

View Article: PubMed Central - PubMed

Affiliation: LIFE Leipzig Research Center for Civilization Diseases, University of Leipzig, Leipzig Germany; Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital Leipzig, Leipzig, Germany.

ABSTRACT
Profiling amino acids and acylcarnitines in whole blood spots is a powerful tool in the laboratory diagnosis of several inborn errors of metabolism. Emerging data suggests that altered blood levels of amino acids and acylcarnitines are also associated with common metabolic diseases in adults. Thus, the identification of common genetic determinants for blood metabolites might shed light on pathways contributing to human physiology and common diseases. We applied a targeted mass-spectrometry-based method to analyze whole blood concentrations of 96 amino acids, acylcarnitines and pathway associated metabolite ratios in a Central European cohort of 2,107 adults and performed genome-wide association (GWA) to identify genetic modifiers of metabolite concentrations. We discovered and replicated six novel loci associated with blood levels of total acylcarnitine, arginine (both on chromosome 6; rs12210538, rs17657775), propionylcarnitine (chromosome 10; rs12779637), 2-hydroxyisovalerylcarnitine (chromosome 21; rs1571700), stearoylcarnitine (chromosome 1; rs3811444), and aspartic acid traits (chromosome 8; rs750472). Based on an integrative analysis of expression quantitative trait loci in blood mononuclear cells and correlations between gene expressions and metabolite levels, we provide evidence for putative causative genes: SLC22A16 for total acylcarnitines, ARG1 for arginine, HLCS for 2-hydroxyisovalerylcarnitine, JAM3 for stearoylcarnitine via a trans-effect at chromosome 1, and PPP1R16A for aspartic acid traits. Further, we report replication and provide additional functional evidence for ten loci that have previously been published for metabolites measured in plasma, serum or urine. In conclusion, our integrative analysis of SNP, gene-expression and metabolite data points to novel genetic factors that may be involved in the regulation of human metabolism. At several loci, we provide evidence for metabolite regulation via gene-expression and observed overlaps with GWAS loci for common diseases. These results form a strong rationale for subsequent functional and disease-related studies.

No MeSH data available.


Related in: MedlinePlus