Literature DB >> 22546499

Genome-wide association studies with metabolomics.

Jerzy Adamski1.   

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.

Entities:  

Year:  2012        PMID: 22546499      PMCID: PMC3446262          DOI: 10.1186/gm333

Source DB:  PubMed          Journal:  Genome Med        ISSN: 1756-994X            Impact factor:   11.117


  55 in total

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Journal:  Nat Genet       Date:  2008-01-13       Impact factor: 38.330

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Journal:  PLoS Genet       Date:  2008-11-28       Impact factor: 5.917

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  20 in total

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Authors:  Eugene P Rhee; Jennifer E Ho; Ming-Huei Chen; Dongxiao Shen; Susan Cheng; Martin G Larson; Anahita Ghorbani; Xu Shi; Iiro T Helenius; Christopher J O'Donnell; Amanda L Souza; Amy Deik; Kerry A Pierce; Kevin Bullock; Geoffrey A Walford; Ramachandran S Vasan; Jose C Florez; Clary Clish; J-R Joanna Yeh; Thomas J Wang; Robert E Gerszten
Journal:  Cell Metab       Date:  2013-07-02       Impact factor: 27.287

Review 2.  Metabolomics and diabetes: analytical and computational approaches.

Authors:  Kelli M Sas; Alla Karnovsky; George Michailidis; Subramaniam Pennathur
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Authors:  Michael A Crawford; Yiqun Wang; David E Marsh; Mark R Johnson; Enitan Ogundipe; Ahamed Ibrahim; Hemalatha Rajkumar; S Kowsalya; Kumar S D Kothapalli; J T Brenna
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Review 4.  Metabolomics in rheumatic diseases: desperately seeking biomarkers.

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Review 5.  Obesity Genomics and Metabolomics: a Nexus of Cardiometabolic Risk.

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6.  Genome-wide association studies of 74 plasma metabolites of German shepherd dogs reveal two metabolites associated with genes encoding their enzymes.

Authors:  Pamela Xing Yi Soh; Juliana Maria Marin Cely; Sally-Anne Mortlock; Christopher James Jara; Rachel Booth; Siria Natera; Ute Roessner; Ben Crossett; Stuart Cordwell; Mehar Singh Khatkar; Peter Williamson
Journal:  Metabolomics       Date:  2019-09-06       Impact factor: 4.290

Review 7.  Desaturase and elongase-limiting endogenous long-chain polyunsaturated fatty acid biosynthesis.

Authors:  Ji Yao Zhang; Kumar S D Kothapalli; J Thomas Brenna
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8.  Genetic determinants of metabolism in health and disease: from biochemical genetics to genome-wide associations.

Authors:  Steven L Robinette; Elaine Holmes; Jeremy K Nicholson; Marc E Dumas
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Review 9.  Functional metabolomics: from biomarker discovery to metabolome reprogramming.

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Review 10.  Mixing omics: combining genetics and metabolomics to study rheumatic diseases.

Authors:  Cristina Menni; Jonas Zierer; Ana M Valdes; Tim D Spector
Journal:  Nat Rev Rheumatol       Date:  2017-02-02       Impact factor: 20.543

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