Literature DB >> 27001274

Twin metabolomics: the key to unlocking complex phenotypes in nutrition research.

Rebecca Barron1, Kate Bermingham1, Lorraine Brennan1, Eileen R Gibney1, Michael J Gibney1, Miriam F Ryan1, Aifric O'Sullivan2.   

Abstract

Twin studies are a valuable resource for studying phenotypes and their underlying biology. Heritability estimates based on the classic twin model show that genes influence human traits including health, diet, and food choice. Metabolomics is a promising tool in nutrition and health research where complex metabolite profiles reflect the metabolic effects of foods and diets as well as the biological pathways associated with diet-related diseases. In recent years, publications arising from twin research have incorporated metabolomic analysis, providing insights into genetic and environmental influences on metabolomic profiles. This article reviews the application of metabolomics in twin research with a particular focus on nutrition and diet-related diseases. The review begins by describing the classic twin study design, followed by a look at its application in nutrition research. Indeed, there is clear evidence for a genetic influence on dietary intake, regardless of the outcome measure: energy, macronutrients, dietary patterns, or food choice. The latter part of the review introduces metabolomic research showing how twin studies can separate aspects of the metabolome that are strongly influenced by genetics vs those that are more influenced by environment. The combination of metabolomics and twin research brings the promise of untangling gene-environment effects on complex phenotypes such as the metabolome, obesity, and diet-related diseases. For example, metabolomics is used in nutrition research to identify metabolites associated with particular dietary patterns. When combined within a twin study design, heritability of metabolite-dietary pattern associations can be established allowing further insight into complex gene-environment interactions that shape individual metabolomes.
Copyright © 2016 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Dietary intake; Genetic; Metabolomics; Nutrition; Obesity; Twin study

Mesh:

Year:  2016        PMID: 27001274     DOI: 10.1016/j.nutres.2016.01.010

Source DB:  PubMed          Journal:  Nutr Res        ISSN: 0271-5317            Impact factor:   3.315


  3 in total

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  3 in total

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