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个性化营养中的基因组学:你能“吃你的基因”吗?

Genomics in Personalized Nutrition: Can You "Eat for Your Genes"?

机构信息

Department of Nutritional Sciences, University of Arizona, Tucson, AZ 85719, USA.

The BIO5 Institute, University of Arizona, Tucson, AZ 85719, USA.

出版信息

Nutrients. 2020 Oct 13;12(10):3118. doi: 10.3390/nu12103118.

Abstract

Genome-wide single nucleotide polymorphism (SNP) data are now quickly and inexpensively acquired, raising the prospect of creating personalized dietary recommendations based on an individual's genetic variability at multiple SNPs. However, relatively little is known about most specific gene-diet interactions, and many molecular and clinical phenotypes of interest (e.g., body mass index [BMI]) involve multiple genes. In this review, we discuss direct to consumer genetic testing (DTC-GT) and the current potential for precision nutrition based on an individual's genetic data. We review important issues such as dietary exposure and genetic architecture addressing the concepts of penetrance, pleiotropy, epistasis, polygenicity, and epigenetics. More specifically, we discuss how they complicate using genotypic data to predict phenotypes as well as response to dietary interventions. Then, several examples (including caffeine sensitivity, alcohol dependence, non-alcoholic fatty liver disease, obesity/appetite, cardiovascular, Alzheimer's disease, folate metabolism, long-chain fatty acid biosynthesis, and vitamin D metabolism) are provided illustrating how genotypic information could be used to inform nutritional recommendations. We conclude by examining ethical considerations and practical applications for using genetic information to inform dietary choices and the future role genetics may play in adopting changes beyond population-wide healthy eating guidelines.

摘要

目前,人们可以快速且廉价地获取全基因组单核苷酸多态性 (SNP) 数据,这使得根据个体在多个 SNP 中的遗传变异性来制定个性化饮食建议成为可能。然而,人们对大多数特定的基因-饮食相互作用知之甚少,并且许多感兴趣的分子和临床表型(例如,体重指数 [BMI])涉及多个基因。在这篇综述中,我们讨论了基于个体遗传数据的直接面向消费者的基因检测 (DTC-GT) 和当前精准营养的潜力。我们审查了一些重要问题,例如饮食暴露和遗传结构,涉及外显率、多效性、上位性、多基因性和表观遗传学等概念。更具体地说,我们讨论了它们如何使预测表型以及对饮食干预的反应变得复杂。然后,提供了几个示例(包括咖啡因敏感性、酒精依赖、非酒精性脂肪肝疾病、肥胖/食欲、心血管疾病、阿尔茨海默病、叶酸代谢、长链脂肪酸生物合成和维生素 D 代谢),说明基因型信息如何用于告知营养建议。最后,我们通过检查使用遗传信息告知饮食选择的伦理考虑和实际应用以及遗传学在采用超越人群健康饮食指南的改变方面可能发挥的作用来结束本文。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43c6/7599709/f1338ab9c7f1/nutrients-12-03118-g001.jpg

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