Interrogating causal pathways linking genetic variants, small molecule metabolites, and circulating lipids
Background Emerging technologies based on mass spectrometry or nuclear magnetic resonance enable the monitoring of hundreds of small metabolites from tissues or body fluids. Profiling of metabolites can help elucidate causal pathways linking established genetic variants to known disease risk factors...
Ausführliche Beschreibung
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| Autor*in: |
Shin, So-Youn [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2014 |
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| Anmerkung: |
© Shin et al.; licensee BioMed Central Ltd. 2014. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( |
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| Übergeordnetes Werk: |
Enthalten in: Genome medicine - London : BioMed Central, 2009, 6(2014), 3 vom: 28. März |
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| Übergeordnetes Werk: |
volume:6 ; year:2014 ; number:3 ; day:28 ; month:03 |
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| DOI / URN: |
10.1186/gm542 |
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| Katalog-ID: |
SPR030614910 |
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| 520 | |a Background Emerging technologies based on mass spectrometry or nuclear magnetic resonance enable the monitoring of hundreds of small metabolites from tissues or body fluids. Profiling of metabolites can help elucidate causal pathways linking established genetic variants to known disease risk factors such as blood lipid traits. Methods We applied statistical methodology to dissect causal relationships between single nucleotide polymorphisms, metabolite concentrations, and serum lipid traits, focusing on 95 genetic loci reproducibly associated with the four main serum lipids (total-, low-density lipoprotein-, and high-density lipoprotein- cholesterol and triglycerides). The dataset used included 2,973 individuals from two independent population-based cohorts with data for 151 small molecule metabolites and four main serum lipids. Three statistical approaches, namely conditional analysis, Mendelian randomization, and structural equation modeling, were compared to investigate causal relationship at sets of a single nucleotide polymorphism, a metabolite, and a lipid trait associated with one another. Results A subset of three lipid-associated loci (FADS1, GCKR, and LPA) have a statistically significant association with at least one main lipid and one metabolite concentration in our data, defining a total of 38 cross-associated sets of a single nucleotide polymorphism, a metabolite and a lipid trait. Structural equation modeling provided sufficient discrimination to indicate that the association of a single nucleotide polymorphism with a lipid trait was mediated through a metabolite at 15 of the 38 sets, and involving variants at the FADS1 and GCKR loci. Conclusions These data provide a framework for evaluating the causal role of components of the metabolome (or other intermediate factors) in mediating the association between established genetic variants and diseases or traits. | ||
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| 650 | 4 | |a Conditional Analysis |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Shared Environmental Effect |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Petersen, Ann-Kristin |4 aut | |
| 700 | 1 | |a Wahl, Simone |4 aut | |
| 700 | 1 | |a Zhai, Guangju |4 aut | |
| 700 | 1 | |a Römisch-Margl, Werner |4 aut | |
| 700 | 1 | |a Small, Kerrin S |4 aut | |
| 700 | 1 | |a Döring, Angela |4 aut | |
| 700 | 1 | |a Kato, Bernet S |4 aut | |
| 700 | 1 | |a Peters, Annette |4 aut | |
| 700 | 1 | |a Grundberg, Elin |4 aut | |
| 700 | 1 | |a Prehn, Cornelia |4 aut | |
| 700 | 1 | |a Wang-Sattler, Rui |4 aut | |
| 700 | 1 | |a Wichmann, H-Erich |4 aut | |
| 700 | 1 | |a de Angelis, Martin Hrabé |4 aut | |
| 700 | 1 | |a Illig, Thomas |4 aut | |
| 700 | 1 | |a Adamski, Jerzy |4 aut | |
| 700 | 1 | |a Deloukas, Panos |4 aut | |
| 700 | 1 | |a Spector, Tim D |4 aut | |
| 700 | 1 | |a Suhre, Karsten |4 aut | |
| 700 | 1 | |a Gieger, Christian |4 aut | |
| 700 | 1 | |a Soranzo, Nicole |4 aut | |
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10.1186/gm542 doi (DE-627)SPR030614910 (SPR)gm542-e DE-627 ger DE-627 rakwb eng Shin, So-Youn verfasserin aut Interrogating causal pathways linking genetic variants, small molecule metabolites, and circulating lipids 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Shin et al.; licensee BioMed Central Ltd. 2014. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Background Emerging technologies based on mass spectrometry or nuclear magnetic resonance enable the monitoring of hundreds of small metabolites from tissues or body fluids. Profiling of metabolites can help elucidate causal pathways linking established genetic variants to known disease risk factors such as blood lipid traits. Methods We applied statistical methodology to dissect causal relationships between single nucleotide polymorphisms, metabolite concentrations, and serum lipid traits, focusing on 95 genetic loci reproducibly associated with the four main serum lipids (total-, low-density lipoprotein-, and high-density lipoprotein- cholesterol and triglycerides). The dataset used included 2,973 individuals from two independent population-based cohorts with data for 151 small molecule metabolites and four main serum lipids. Three statistical approaches, namely conditional analysis, Mendelian randomization, and structural equation modeling, were compared to investigate causal relationship at sets of a single nucleotide polymorphism, a metabolite, and a lipid trait associated with one another. Results A subset of three lipid-associated loci (FADS1, GCKR, and LPA) have a statistically significant association with at least one main lipid and one metabolite concentration in our data, defining a total of 38 cross-associated sets of a single nucleotide polymorphism, a metabolite and a lipid trait. Structural equation modeling provided sufficient discrimination to indicate that the association of a single nucleotide polymorphism with a lipid trait was mediated through a metabolite at 15 of the 38 sets, and involving variants at the FADS1 and GCKR loci. Conclusions These data provide a framework for evaluating the causal role of components of the metabolome (or other intermediate factors) in mediating the association between established genetic variants and diseases or traits. Structural Equation Modeling (dpeaa)DE-He213 Bayesian Information Criterion (dpeaa)DE-He213 Mendelian Randomization (dpeaa)DE-He213 Conditional Analysis (dpeaa)DE-He213 Shared Environmental Effect (dpeaa)DE-He213 Petersen, Ann-Kristin aut Wahl, Simone aut Zhai, Guangju aut Römisch-Margl, Werner aut Small, Kerrin S aut Döring, Angela aut Kato, Bernet S aut Peters, Annette aut Grundberg, Elin aut Prehn, Cornelia aut Wang-Sattler, Rui aut Wichmann, H-Erich aut de Angelis, Martin Hrabé aut Illig, Thomas aut Adamski, Jerzy aut Deloukas, Panos aut Spector, Tim D aut Suhre, Karsten aut Gieger, Christian aut Soranzo, Nicole aut Enthalten in Genome medicine London : BioMed Central, 2009 6(2014), 3 vom: 28. März (DE-627)594424275 (DE-600)2484394-5 1756-994X nnns volume:6 year:2014 number:3 day:28 month:03 https://dx.doi.org/10.1186/gm542 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2014 3 28 03 |
| spelling |
10.1186/gm542 doi (DE-627)SPR030614910 (SPR)gm542-e DE-627 ger DE-627 rakwb eng Shin, So-Youn verfasserin aut Interrogating causal pathways linking genetic variants, small molecule metabolites, and circulating lipids 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Shin et al.; licensee BioMed Central Ltd. 2014. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Background Emerging technologies based on mass spectrometry or nuclear magnetic resonance enable the monitoring of hundreds of small metabolites from tissues or body fluids. Profiling of metabolites can help elucidate causal pathways linking established genetic variants to known disease risk factors such as blood lipid traits. Methods We applied statistical methodology to dissect causal relationships between single nucleotide polymorphisms, metabolite concentrations, and serum lipid traits, focusing on 95 genetic loci reproducibly associated with the four main serum lipids (total-, low-density lipoprotein-, and high-density lipoprotein- cholesterol and triglycerides). The dataset used included 2,973 individuals from two independent population-based cohorts with data for 151 small molecule metabolites and four main serum lipids. Three statistical approaches, namely conditional analysis, Mendelian randomization, and structural equation modeling, were compared to investigate causal relationship at sets of a single nucleotide polymorphism, a metabolite, and a lipid trait associated with one another. Results A subset of three lipid-associated loci (FADS1, GCKR, and LPA) have a statistically significant association with at least one main lipid and one metabolite concentration in our data, defining a total of 38 cross-associated sets of a single nucleotide polymorphism, a metabolite and a lipid trait. Structural equation modeling provided sufficient discrimination to indicate that the association of a single nucleotide polymorphism with a lipid trait was mediated through a metabolite at 15 of the 38 sets, and involving variants at the FADS1 and GCKR loci. Conclusions These data provide a framework for evaluating the causal role of components of the metabolome (or other intermediate factors) in mediating the association between established genetic variants and diseases or traits. Structural Equation Modeling (dpeaa)DE-He213 Bayesian Information Criterion (dpeaa)DE-He213 Mendelian Randomization (dpeaa)DE-He213 Conditional Analysis (dpeaa)DE-He213 Shared Environmental Effect (dpeaa)DE-He213 Petersen, Ann-Kristin aut Wahl, Simone aut Zhai, Guangju aut Römisch-Margl, Werner aut Small, Kerrin S aut Döring, Angela aut Kato, Bernet S aut Peters, Annette aut Grundberg, Elin aut Prehn, Cornelia aut Wang-Sattler, Rui aut Wichmann, H-Erich aut de Angelis, Martin Hrabé aut Illig, Thomas aut Adamski, Jerzy aut Deloukas, Panos aut Spector, Tim D aut Suhre, Karsten aut Gieger, Christian aut Soranzo, Nicole aut Enthalten in Genome medicine London : BioMed Central, 2009 6(2014), 3 vom: 28. März (DE-627)594424275 (DE-600)2484394-5 1756-994X nnns volume:6 year:2014 number:3 day:28 month:03 https://dx.doi.org/10.1186/gm542 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2014 3 28 03 |
| allfields_unstemmed |
10.1186/gm542 doi (DE-627)SPR030614910 (SPR)gm542-e DE-627 ger DE-627 rakwb eng Shin, So-Youn verfasserin aut Interrogating causal pathways linking genetic variants, small molecule metabolites, and circulating lipids 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Shin et al.; licensee BioMed Central Ltd. 2014. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Background Emerging technologies based on mass spectrometry or nuclear magnetic resonance enable the monitoring of hundreds of small metabolites from tissues or body fluids. Profiling of metabolites can help elucidate causal pathways linking established genetic variants to known disease risk factors such as blood lipid traits. Methods We applied statistical methodology to dissect causal relationships between single nucleotide polymorphisms, metabolite concentrations, and serum lipid traits, focusing on 95 genetic loci reproducibly associated with the four main serum lipids (total-, low-density lipoprotein-, and high-density lipoprotein- cholesterol and triglycerides). The dataset used included 2,973 individuals from two independent population-based cohorts with data for 151 small molecule metabolites and four main serum lipids. Three statistical approaches, namely conditional analysis, Mendelian randomization, and structural equation modeling, were compared to investigate causal relationship at sets of a single nucleotide polymorphism, a metabolite, and a lipid trait associated with one another. Results A subset of three lipid-associated loci (FADS1, GCKR, and LPA) have a statistically significant association with at least one main lipid and one metabolite concentration in our data, defining a total of 38 cross-associated sets of a single nucleotide polymorphism, a metabolite and a lipid trait. Structural equation modeling provided sufficient discrimination to indicate that the association of a single nucleotide polymorphism with a lipid trait was mediated through a metabolite at 15 of the 38 sets, and involving variants at the FADS1 and GCKR loci. Conclusions These data provide a framework for evaluating the causal role of components of the metabolome (or other intermediate factors) in mediating the association between established genetic variants and diseases or traits. Structural Equation Modeling (dpeaa)DE-He213 Bayesian Information Criterion (dpeaa)DE-He213 Mendelian Randomization (dpeaa)DE-He213 Conditional Analysis (dpeaa)DE-He213 Shared Environmental Effect (dpeaa)DE-He213 Petersen, Ann-Kristin aut Wahl, Simone aut Zhai, Guangju aut Römisch-Margl, Werner aut Small, Kerrin S aut Döring, Angela aut Kato, Bernet S aut Peters, Annette aut Grundberg, Elin aut Prehn, Cornelia aut Wang-Sattler, Rui aut Wichmann, H-Erich aut de Angelis, Martin Hrabé aut Illig, Thomas aut Adamski, Jerzy aut Deloukas, Panos aut Spector, Tim D aut Suhre, Karsten aut Gieger, Christian aut Soranzo, Nicole aut Enthalten in Genome medicine London : BioMed Central, 2009 6(2014), 3 vom: 28. März (DE-627)594424275 (DE-600)2484394-5 1756-994X nnns volume:6 year:2014 number:3 day:28 month:03 https://dx.doi.org/10.1186/gm542 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2014 3 28 03 |
| allfieldsGer |
10.1186/gm542 doi (DE-627)SPR030614910 (SPR)gm542-e DE-627 ger DE-627 rakwb eng Shin, So-Youn verfasserin aut Interrogating causal pathways linking genetic variants, small molecule metabolites, and circulating lipids 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Shin et al.; licensee BioMed Central Ltd. 2014. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Background Emerging technologies based on mass spectrometry or nuclear magnetic resonance enable the monitoring of hundreds of small metabolites from tissues or body fluids. Profiling of metabolites can help elucidate causal pathways linking established genetic variants to known disease risk factors such as blood lipid traits. Methods We applied statistical methodology to dissect causal relationships between single nucleotide polymorphisms, metabolite concentrations, and serum lipid traits, focusing on 95 genetic loci reproducibly associated with the four main serum lipids (total-, low-density lipoprotein-, and high-density lipoprotein- cholesterol and triglycerides). The dataset used included 2,973 individuals from two independent population-based cohorts with data for 151 small molecule metabolites and four main serum lipids. Three statistical approaches, namely conditional analysis, Mendelian randomization, and structural equation modeling, were compared to investigate causal relationship at sets of a single nucleotide polymorphism, a metabolite, and a lipid trait associated with one another. Results A subset of three lipid-associated loci (FADS1, GCKR, and LPA) have a statistically significant association with at least one main lipid and one metabolite concentration in our data, defining a total of 38 cross-associated sets of a single nucleotide polymorphism, a metabolite and a lipid trait. Structural equation modeling provided sufficient discrimination to indicate that the association of a single nucleotide polymorphism with a lipid trait was mediated through a metabolite at 15 of the 38 sets, and involving variants at the FADS1 and GCKR loci. Conclusions These data provide a framework for evaluating the causal role of components of the metabolome (or other intermediate factors) in mediating the association between established genetic variants and diseases or traits. Structural Equation Modeling (dpeaa)DE-He213 Bayesian Information Criterion (dpeaa)DE-He213 Mendelian Randomization (dpeaa)DE-He213 Conditional Analysis (dpeaa)DE-He213 Shared Environmental Effect (dpeaa)DE-He213 Petersen, Ann-Kristin aut Wahl, Simone aut Zhai, Guangju aut Römisch-Margl, Werner aut Small, Kerrin S aut Döring, Angela aut Kato, Bernet S aut Peters, Annette aut Grundberg, Elin aut Prehn, Cornelia aut Wang-Sattler, Rui aut Wichmann, H-Erich aut de Angelis, Martin Hrabé aut Illig, Thomas aut Adamski, Jerzy aut Deloukas, Panos aut Spector, Tim D aut Suhre, Karsten aut Gieger, Christian aut Soranzo, Nicole aut Enthalten in Genome medicine London : BioMed Central, 2009 6(2014), 3 vom: 28. März (DE-627)594424275 (DE-600)2484394-5 1756-994X nnns volume:6 year:2014 number:3 day:28 month:03 https://dx.doi.org/10.1186/gm542 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2014 3 28 03 |
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10.1186/gm542 doi (DE-627)SPR030614910 (SPR)gm542-e DE-627 ger DE-627 rakwb eng Shin, So-Youn verfasserin aut Interrogating causal pathways linking genetic variants, small molecule metabolites, and circulating lipids 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Shin et al.; licensee BioMed Central Ltd. 2014. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Background Emerging technologies based on mass spectrometry or nuclear magnetic resonance enable the monitoring of hundreds of small metabolites from tissues or body fluids. Profiling of metabolites can help elucidate causal pathways linking established genetic variants to known disease risk factors such as blood lipid traits. Methods We applied statistical methodology to dissect causal relationships between single nucleotide polymorphisms, metabolite concentrations, and serum lipid traits, focusing on 95 genetic loci reproducibly associated with the four main serum lipids (total-, low-density lipoprotein-, and high-density lipoprotein- cholesterol and triglycerides). The dataset used included 2,973 individuals from two independent population-based cohorts with data for 151 small molecule metabolites and four main serum lipids. Three statistical approaches, namely conditional analysis, Mendelian randomization, and structural equation modeling, were compared to investigate causal relationship at sets of a single nucleotide polymorphism, a metabolite, and a lipid trait associated with one another. Results A subset of three lipid-associated loci (FADS1, GCKR, and LPA) have a statistically significant association with at least one main lipid and one metabolite concentration in our data, defining a total of 38 cross-associated sets of a single nucleotide polymorphism, a metabolite and a lipid trait. Structural equation modeling provided sufficient discrimination to indicate that the association of a single nucleotide polymorphism with a lipid trait was mediated through a metabolite at 15 of the 38 sets, and involving variants at the FADS1 and GCKR loci. Conclusions These data provide a framework for evaluating the causal role of components of the metabolome (or other intermediate factors) in mediating the association between established genetic variants and diseases or traits. Structural Equation Modeling (dpeaa)DE-He213 Bayesian Information Criterion (dpeaa)DE-He213 Mendelian Randomization (dpeaa)DE-He213 Conditional Analysis (dpeaa)DE-He213 Shared Environmental Effect (dpeaa)DE-He213 Petersen, Ann-Kristin aut Wahl, Simone aut Zhai, Guangju aut Römisch-Margl, Werner aut Small, Kerrin S aut Döring, Angela aut Kato, Bernet S aut Peters, Annette aut Grundberg, Elin aut Prehn, Cornelia aut Wang-Sattler, Rui aut Wichmann, H-Erich aut de Angelis, Martin Hrabé aut Illig, Thomas aut Adamski, Jerzy aut Deloukas, Panos aut Spector, Tim D aut Suhre, Karsten aut Gieger, Christian aut Soranzo, Nicole aut Enthalten in Genome medicine London : BioMed Central, 2009 6(2014), 3 vom: 28. März (DE-627)594424275 (DE-600)2484394-5 1756-994X nnns volume:6 year:2014 number:3 day:28 month:03 https://dx.doi.org/10.1186/gm542 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2014 3 28 03 |
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Shin, So-Youn Petersen, Ann-Kristin Wahl, Simone Zhai, Guangju Römisch-Margl, Werner Small, Kerrin S Döring, Angela Kato, Bernet S Peters, Annette Grundberg, Elin Prehn, Cornelia Wang-Sattler, Rui Wichmann, H-Erich de Angelis, Martin Hrabé Illig, Thomas Adamski, Jerzy Deloukas, Panos Spector, Tim D Suhre, Karsten Gieger, Christian Soranzo, Nicole |
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interrogating causal pathways linking genetic variants, small molecule metabolites, and circulating lipids |
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Interrogating causal pathways linking genetic variants, small molecule metabolites, and circulating lipids |
| abstract |
Background Emerging technologies based on mass spectrometry or nuclear magnetic resonance enable the monitoring of hundreds of small metabolites from tissues or body fluids. Profiling of metabolites can help elucidate causal pathways linking established genetic variants to known disease risk factors such as blood lipid traits. Methods We applied statistical methodology to dissect causal relationships between single nucleotide polymorphisms, metabolite concentrations, and serum lipid traits, focusing on 95 genetic loci reproducibly associated with the four main serum lipids (total-, low-density lipoprotein-, and high-density lipoprotein- cholesterol and triglycerides). The dataset used included 2,973 individuals from two independent population-based cohorts with data for 151 small molecule metabolites and four main serum lipids. Three statistical approaches, namely conditional analysis, Mendelian randomization, and structural equation modeling, were compared to investigate causal relationship at sets of a single nucleotide polymorphism, a metabolite, and a lipid trait associated with one another. Results A subset of three lipid-associated loci (FADS1, GCKR, and LPA) have a statistically significant association with at least one main lipid and one metabolite concentration in our data, defining a total of 38 cross-associated sets of a single nucleotide polymorphism, a metabolite and a lipid trait. Structural equation modeling provided sufficient discrimination to indicate that the association of a single nucleotide polymorphism with a lipid trait was mediated through a metabolite at 15 of the 38 sets, and involving variants at the FADS1 and GCKR loci. Conclusions These data provide a framework for evaluating the causal role of components of the metabolome (or other intermediate factors) in mediating the association between established genetic variants and diseases or traits. © Shin et al.; licensee BioMed Central Ltd. 2014. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( |
| abstractGer |
Background Emerging technologies based on mass spectrometry or nuclear magnetic resonance enable the monitoring of hundreds of small metabolites from tissues or body fluids. Profiling of metabolites can help elucidate causal pathways linking established genetic variants to known disease risk factors such as blood lipid traits. Methods We applied statistical methodology to dissect causal relationships between single nucleotide polymorphisms, metabolite concentrations, and serum lipid traits, focusing on 95 genetic loci reproducibly associated with the four main serum lipids (total-, low-density lipoprotein-, and high-density lipoprotein- cholesterol and triglycerides). The dataset used included 2,973 individuals from two independent population-based cohorts with data for 151 small molecule metabolites and four main serum lipids. Three statistical approaches, namely conditional analysis, Mendelian randomization, and structural equation modeling, were compared to investigate causal relationship at sets of a single nucleotide polymorphism, a metabolite, and a lipid trait associated with one another. Results A subset of three lipid-associated loci (FADS1, GCKR, and LPA) have a statistically significant association with at least one main lipid and one metabolite concentration in our data, defining a total of 38 cross-associated sets of a single nucleotide polymorphism, a metabolite and a lipid trait. Structural equation modeling provided sufficient discrimination to indicate that the association of a single nucleotide polymorphism with a lipid trait was mediated through a metabolite at 15 of the 38 sets, and involving variants at the FADS1 and GCKR loci. Conclusions These data provide a framework for evaluating the causal role of components of the metabolome (or other intermediate factors) in mediating the association between established genetic variants and diseases or traits. © Shin et al.; licensee BioMed Central Ltd. 2014. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( |
| abstract_unstemmed |
Background Emerging technologies based on mass spectrometry or nuclear magnetic resonance enable the monitoring of hundreds of small metabolites from tissues or body fluids. Profiling of metabolites can help elucidate causal pathways linking established genetic variants to known disease risk factors such as blood lipid traits. Methods We applied statistical methodology to dissect causal relationships between single nucleotide polymorphisms, metabolite concentrations, and serum lipid traits, focusing on 95 genetic loci reproducibly associated with the four main serum lipids (total-, low-density lipoprotein-, and high-density lipoprotein- cholesterol and triglycerides). The dataset used included 2,973 individuals from two independent population-based cohorts with data for 151 small molecule metabolites and four main serum lipids. Three statistical approaches, namely conditional analysis, Mendelian randomization, and structural equation modeling, were compared to investigate causal relationship at sets of a single nucleotide polymorphism, a metabolite, and a lipid trait associated with one another. Results A subset of three lipid-associated loci (FADS1, GCKR, and LPA) have a statistically significant association with at least one main lipid and one metabolite concentration in our data, defining a total of 38 cross-associated sets of a single nucleotide polymorphism, a metabolite and a lipid trait. Structural equation modeling provided sufficient discrimination to indicate that the association of a single nucleotide polymorphism with a lipid trait was mediated through a metabolite at 15 of the 38 sets, and involving variants at the FADS1 and GCKR loci. Conclusions These data provide a framework for evaluating the causal role of components of the metabolome (or other intermediate factors) in mediating the association between established genetic variants and diseases or traits. © Shin et al.; licensee BioMed Central Ltd. 2014. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( |
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Petersen, Ann-Kristin Wahl, Simone Zhai, Guangju Römisch-Margl, Werner Small, Kerrin S Döring, Angela Kato, Bernet S Peters, Annette Grundberg, Elin Prehn, Cornelia Wang-Sattler, Rui Wichmann, H-Erich de Angelis, Martin Hrabé Illig, Thomas Adamski, Jerzy Deloukas, Panos Spector, Tim D Suhre, Karsten Gieger, Christian Soranzo, Nicole |
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