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Genome-wide association studies with metabolomics

机译:全基因组与代谢组学的关联研究

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摘要

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.
机译:全基因组关联研究(GWAS)分析了表型的遗传成分或疾病的病因。尽管许多GWAS取得了成功,但在发现许多疾病的潜在机制方面进展甚微。代谢组学作为分子表型的读数的使用,使得人们能够发现以前未发现的疾病与信号传导和代谢途径之间的关联。此外,将GWAS与代谢组学信息结合起来,可以同时分析遗传和环境对稳态的影响。在诸如糖尿病,肥胖症和血脂异常的代谢疾病中已经看到最成功的研究。最近,GWAS用代谢组学解释了FADS1,ELOVL2或SLC16A9等基因座与脂质浓度之间的关联。将GWAS与代谢组学(mGWAS)结合使用可提供开发特定诊断和靶向药物所需的强大且定量的信息。这篇综述讨论了GWAS的局限性,并提供了有关代谢组学如何克服这些局限性(以代谢疾病为重点)的示例。

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