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Genetic variation in metabolic phenotypes: study designs and applications

Nature Reviews Genetics volume 13, pages 759–769 (2012)Cite this article

Subjects

Key Points

  • Technical advances in mass spectrometry and NMR spectroscopy enable genome-wide screens to be carried out for the association between genetic variants and hundreds of metabolic traits in a single experiment.

  • Metabolite concentrations are direct readouts of biological processes and can play the part of intermediate phenotypes, providing functional links between genetic variance and disease end points in genome-wide association studies (GWASs).

  • The first GWASs with metabolomics have already discovered many genetic variants in enzyme-, transporter- and other metabolism-related genes that induce major differences in the individual metabolic capabilities of the organism.

  • Knowledge of the genetic basis of human metabolic individuality holds the key to understanding the interactions of genetic, environmental and lifestyle factors in the aetiology of complex disorders.

  • We review emerging insights from recent GWASs with metabolomics and present design considerations for high-throughput metabolomics experiments with metabolic traits in epidemiological and clinical studies.

  • Using ratios between metabolite concentrations can drastically increase the power of a metabolomics study and can provide functional information on the perturbed underlying biochemical pathways.

  • Integration with other biochemical information, including data from other GWASs, can largely improve the value of the study.

  • Current challenges and future directions include the addition of new sample types (other than urine and blood), extension of the metabolite panels, standardization between platforms and the development of adapted statistical and data analysis tools.

Abstract

Many complex disorders are linked to metabolic phenotypes. Revealing genetic influences on metabolic phenotypes is key to a systems-wide understanding of their interactions with environmental and lifestyle factors in their aetiology, and we can now explore the genetics of large panels of metabolic traits by coupling genome-wide association studies and metabolomics. These genome-wide association studies are beginning to unravel the genetic contribution to human metabolic individuality and to demonstrate its relevance for biomedical and pharmaceutical research. Adopting the most appropriate study designs and analytical tools is paramount to further refining the genotype–phenotype map and eventually identifying the part played by genetic influences on metabolic phenotypes. We discuss such design considerations and applications in this Review.

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Figure 1: The metabolic trait as an intermediate phenotype.
Figure 2: Workflow and considerations.

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Acknowledgements

K.S. is supported by 'Biomedical Research Program' funds at Weill Cornell Medical College in Qatar, a program funded by the Qatar Foundation. The statements made herein are solely the responsibility of the authors. The authors thank G. Kastenmüller, A.-K. Petersen and J. Adamski for critical reading of the manuscript. We thank our reviewers for suggestions that led to the improvement of the manuscript.

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Authors and Affiliations

  1. Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar, Education City, Qatar Foundation, Doha, P.O. BOX 24144, Qatar

    Karsten Suhre

  2. Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, Neuherberg, 85764, Germany

    Karsten Suhre

  3. Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, Neuherberg, 85764, Germany

    Christian Gieger

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Correspondence to Karsten Suhre or Christian Gieger.

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FURTHER INFORMATION

Karsten Suhre's homepage

GeneCards

GRAIL

GWAS server

HapMap

Helmholtz Zentrum München (German Research Center for Environmental Health)

HMDB Serum Metabolome

Human Metabolome Database

Ingenuity Systems Pathway Analysis

KEGG

LIPID MAPS Lipidomics Gateway

MassBank

metaP-server

Nature Reviews Genetics Series on Study designs

NHGRI Catalog of Published Genome-Wide Association Studies

Online Mendelian Inheritance in Man (OMIM)

The Pharmacogenomics Knowledge Base (PharmGKB)

Software — Department of Epidemiology and Biostatistics — Karolinska Institutet

Weill Cornell Medical College in Qatar

Glossary

NMR spectroscopy

An experimental technique that identifies molecules by the specific pattern in the chemical shift of specific atoms.

High-performance liquid-phase chromatography

(HPLC). A chromatographic technique used to separate a complex mixture of metabolites. Often used in combination with mass spectrometry.

Metabolomics

The field of identifying metabolites in a biological sample using techniques such as NMR spectrometry and liquid- or gas-phase chromatography coupled with mass spectroscopy. 'Metabonomics' is often synonymously used in connection with NMR-based experiments.

Metabolic traits

Quantitative measures of the concentrations of a specific metabolite.

Genetically influenced metabotypes

(GIMs). Associations between a genetic variant and a metabolic phenotype.

Metabolic individuality

The metabolic capacities of an individual, as defined by the ensemble of all functional genetic variants (genetically influenced metabotypes) in their metabolism-related genes. Historically, Garrod introduced the term 'chemical individuality' to represent this concept.

Metabolome

The ensemble of all small molecules (metabolites) that are processed by the body's enzyme and transporter proteins.

Glycerophosphatidylethanolamines

Glycerophospholipids with ethanolamine head groups.

Q–Q plots

A graphical method for comparing probability distributions. In genome-wide association studies, it is used to verify whether the P values are normally distributed; an over-representation of low P values indicates possible true positive associations.

Additive linear model

A mathematical model used in statistical association analysis; here, it assumes a linear additive effect of the minor alleles on the metabolite concentrations.

Linkage disequilibrium

(LD). A nonrandom association between neighbouring gene variants; it is used to describe a region of high correlation between SNPs.

Glycerophospholipids

Glycerol-based phospholipids are major constituents of the membrane bi-layers and are found in association with low-density lipoprotein (LDL) and high-density lipoprotein (HDL) particles.

Sphingolipids

A class of lipids that contain a backbone of sphingoid bases

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Suhre, K., Gieger, C. Genetic variation in metabolic phenotypes: study designs and applications. Nat Rev Genet 13, 759–769 (2012). https://doi.org/10.1038/nrg3314

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