Genetics of human metabolism: an update.
Genetics of human metabolism: an update.
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DOI：
10.1093/hmg/ddv263
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发表时间：
2015-10-15
影响因子：
3.5
通讯作者：
Suhre K
中科院分区：
文献类型：
--
作者：
Kastenmüller G;Raffler J;Gieger C;Suhre K
全基因组关联研究与代谢组学（mGWAS）确定遗传影响的代谢型（GIMs），他们的合奏定义每个人的代谢个性的遗传部分。代谢中遗传变异的知识具有许多生物医学和药学兴趣的应用，包括对遗传与临床终点的关联的功能性理解，纠正代谢紊乱的策略设计以及代谢疾病生物标志物的遗传效应修饰剂的鉴定。此外，它已被证明，GIM功能基因组学和代谢组学和新的基因功能和代谢物的身份识别提供了可检验的假设。mGWAS的样本量越来越大，代谢性状组越来越复杂，这使得下游分析更加全面和基于系统。生成的遗传关联的大型数据集现在可以由生物医学研究界挖掘，并为假设驱动的研究提供宝贵的资源。在这篇综述中，我们提供了一个简要的总结mGWAS的关键方面，其次是最近发表的mGWAS的更新。然后，我们讨论了整合和探索mGWAS结果的新方法，并在最近的研究中提出了GIMs的选定应用。
Genome-wide association studies with metabolomics (mGWAS) identify genetically influenced metabotypes (GIMs), their ensemble defining the heritable part of every human's metabolic individuality. Knowledge of genetic variation in metabolism has many applications of biomedical and pharmaceutical interests, including the functional understanding of genetic associations with clinical end points, design of strategies to correct dysregulations in metabolic disorders and the identification of genetic effect modifiers of metabolic disease biomarkers. Furthermore, it has been shown that GIMs provide testable hypotheses for functional genomics and metabolomics and for the identification of novel gene functions and metabolite identities. mGWAS with growing sample sizes and increasingly complex metabolic trait panels are being conducted, allowing for more comprehensive and systems-based downstream analyses. The generated large datasets of genetic associations can now be mined by the biomedical research community and provide valuable resources for hypothesis-driven studies. In this review, we provide a brief summary of the key aspects of mGWAS, followed by an update of recently published mGWAS. We then discuss new approaches of integrating and exploring mGWAS results and finish by presenting selected applications of GIMs in recent studies.