Interrogating causal pathways linking genetic variants, small molecule metabolites, and circulating lipids

So Youn Shin, Ann Kristin Petersen, Simone Wahl, Guangju Zhai, Werner Römisch-Margl, Kerrin S. Small, Angela Döring, Bernet S. Kato, Annette Peters, Elin Grundberg, Cornelia Prehn, Rui Wang-Sattler, H. Erich Wichmann, Martin H. de Angelis, Thomas Illig, Jerzy Adamski, Panos Deloukas, Tim D. Spector, Karsten Suhre, Christian Gieger & 1 others Nicole Soranzo

Research output: Contribution to journalArticle

15 Citations (Scopus)

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.

Original languageEnglish
Article number25
JournalGenome Medicine
Volume6
Issue number3
DOIs
Publication statusPublished - 28 Mar 2014
Externally publishedYes

Fingerprint

Lipids
Single Nucleotide Polymorphism
Mendelian Randomization Analysis
Serum
Inborn Genetic Diseases
Genetic Loci
Metabolome
Body Fluids
LDL Lipoproteins
HDL Cholesterol
Mass Spectrometry
Magnetic Resonance Spectroscopy
Technology
Population

ASJC Scopus subject areas

  • Molecular Medicine
  • Molecular Biology
  • Genetics
  • Genetics(clinical)

Cite this

Shin, S. Y., Petersen, A. K., Wahl, S., Zhai, G., Römisch-Margl, W., Small, K. S., ... Soranzo, N. (2014). Interrogating causal pathways linking genetic variants, small molecule metabolites, and circulating lipids. Genome Medicine, 6(3), [25]. https://doi.org/10.1186/gm542

Interrogating causal pathways linking genetic variants, small molecule metabolites, and circulating lipids. / 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 H.; Illig, Thomas; Adamski, Jerzy; Deloukas, Panos; Spector, Tim D.; Suhre, Karsten; Gieger, Christian; Soranzo, Nicole.

In: Genome Medicine, Vol. 6, No. 3, 25, 28.03.2014.

Research output: Contribution to journalArticle

Shin, SY, Petersen, AK, Wahl, S, Zhai, G, Römisch-Margl, W, Small, KS, Döring, A, Kato, BS, Peters, A, Grundberg, E, Prehn, C, Wang-Sattler, R, Wichmann, HE, de Angelis, MH, Illig, T, Adamski, J, Deloukas, P, Spector, TD, Suhre, K, Gieger, C & Soranzo, N 2014, 'Interrogating causal pathways linking genetic variants, small molecule metabolites, and circulating lipids', Genome Medicine, vol. 6, no. 3, 25. https://doi.org/10.1186/gm542
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 H. ; Illig, Thomas ; Adamski, Jerzy ; Deloukas, Panos ; Spector, Tim D. ; Suhre, Karsten ; Gieger, Christian ; Soranzo, Nicole. / Interrogating causal pathways linking genetic variants, small molecule metabolites, and circulating lipids. In: Genome Medicine. 2014 ; Vol. 6, No. 3.
@article{7b17fea80a2049718035ce0e87da4f0e,
title = "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.",
author = "Shin, {So Youn} and Petersen, {Ann Kristin} and Simone Wahl and Guangju Zhai and Werner R{\"o}misch-Margl and Small, {Kerrin S.} and Angela D{\"o}ring and Kato, {Bernet S.} and Annette Peters and Elin Grundberg and Cornelia Prehn and Rui Wang-Sattler and Wichmann, {H. Erich} and {de Angelis}, {Martin H.} and Thomas Illig and Jerzy Adamski and Panos Deloukas and Spector, {Tim D.} and Karsten Suhre and Christian Gieger and Nicole Soranzo",
year = "2014",
month = "3",
day = "28",
doi = "10.1186/gm542",
language = "English",
volume = "6",
journal = "Genome Medicine",
issn = "1756-994X",
publisher = "BioMed Central",
number = "3",

}

TY - JOUR

T1 - Interrogating causal pathways linking genetic variants, small molecule metabolites, and circulating lipids

AU - Shin, So Youn

AU - Petersen, Ann Kristin

AU - Wahl, Simone

AU - Zhai, Guangju

AU - Römisch-Margl, Werner

AU - Small, Kerrin S.

AU - Döring, Angela

AU - Kato, Bernet S.

AU - Peters, Annette

AU - Grundberg, Elin

AU - Prehn, Cornelia

AU - Wang-Sattler, Rui

AU - Wichmann, H. Erich

AU - de Angelis, Martin H.

AU - Illig, Thomas

AU - Adamski, Jerzy

AU - Deloukas, Panos

AU - Spector, Tim D.

AU - Suhre, Karsten

AU - Gieger, Christian

AU - Soranzo, Nicole

PY - 2014/3/28

Y1 - 2014/3/28

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84902006099&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84902006099&partnerID=8YFLogxK

U2 - 10.1186/gm542

DO - 10.1186/gm542

M3 - Article

VL - 6

JO - Genome Medicine

JF - Genome Medicine

SN - 1756-994X

IS - 3

M1 - 25

ER -