Abstract
Genome-wide association studies (GWAS) analyze the genetic component of a phenotype or the etiology of a disease. Despite the success of many GWAS, little progress has been made in uncovering the underlying mechanisms for many diseases. The use of metabolomics as a readout of molecular phenotypes has enabled the discovery of previously undetected associations between diseases and signaling and metabolic pathways. In addition, combining GWAS and metabolomic information allows the simultaneous analysis of the genetic and environmental impacts on homeostasis. Most success has been seen in metabolic diseases such as diabetes, obesity and dyslipidemia. Recently, associations between loci such as FADS1, ELOVL2 or SLC16A9 and lipid concentrations have been explained by GWAS with metabolomics. Combining GWAS with metabolomics (mGWAS) provides the robust and quantitative information required for the development of specific diagnostics and targeted drugs. This review discusses the limitations of GWAS and presents examples of how metabolomics can overcome these limitations with the focus on metabolic diseases.
Highlights
In this study the genetic variants were able to explain up to 28% of the metabolic ratio variance
In a recent fascinating study on the genetic impact on the human metabolome and its pharmaceutical implications with Genome-wide association studies (GWAS) and non-targeted metabolomics (GC or liquid chromatography (LC)‐mass spectrometry (MS)/MS), 25 genetic loci showed unusually high penetrance in a population of 1,768 individuals and accounted for up to 60% of the difference in metabolite levels per allele copy
Published records worldwide illustrate the power of metabolomics and GWAS (mGWAS)
Summary
By combining metabolomics as a phenotyping tool with GWAS, the studies gain more precision, standardization, robustness and sensitivity. Published records worldwide illustrate the power of mGWAS. They provide new insights into the genetic mechanisms of diseases that is required for personalized medicine. Abbreviations GC, gas chromatography; GWAS, genome-wide association study; HDL, highdensity lipoprotein; LC, liquid chromatography; LDL, low-density lipoprotein; mGWAS, metabolomics with genome-wide association study; mQLT, metabolite quantitative trait locus; MS, mass spectrometry; MS/MS, tandem mass spectrometer; NMR, nuclear magnetic resonance; PUFA, polyunsaturated fatty acid; QTL, quantitative trait locus; SNP, single nucleotide polymorphism. Competing interests The author has no competing interests to declare
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
