Genetics of human obesity
Genetics of human obesity
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DOI：
10.1079/pns2005416
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发表时间：
2005-05-01
影响因子：
7
通讯作者：
Clément, K
中科院分区：
文献类型：
--
作者：
Clément, K
新概念和工具的快速发展导致研究人员进行营养相关研究的方式发生了变化。肥胖是由易感基因和环境因素之间的相互作用决定的，但目前还不能详细分析导致这种复杂疾病发展的基因-基因和基因-环境相互作用。本文的目的是提供肥胖遗传学领域现有知识的一些例子，以及正在开发的新策略，旨在研究众多基因对肥胖的相对贡献及其对环境变化的反应。在罕见的单基因肥胖病例中，其中一个主要基因是原因，分子方法已被证明在识别基因负责和定义新的综合征方面非常强大。然而，在常见形式的肥胖症（多基因肥胖症）中，大多数研究分析了基因型-表型关联，有时没有考虑环境因素（饮食，久坐的生活方式）的影响。在限制这种综合方法肥胖的方面是有足够大的样本和生物计算工具的扩展开发访问的问题，没有先验假设的多个相互作用的困难。这种情况正在迅速改变。目前正在编制大型临床数据和DNA数据库以及生物样本库，其中包括更精确的环境信息和患者表型。同时在DNA或RNA水平上研究多个基因的能力也是可能的。最后，生物计算的巨大进步将允许整合这些不同类型的数据（与环境，表型，基因型，基因表达有关），并将提高处理这种复杂疾病的能力。
The rapid development of new concepts and tools has led to a change in the way in which researchers carry out nutrition-related research. Obesity is determined by the interaction between predisposing genetic and environmental aspects, but at present the gene-gene and gene-environment interactions contributing to the development of this complex disease cannot be analysed in detail. The purpose of the present paper is to provide some examples of the knowledge that is available in the field of obesity genetics, and also the new strategies being developed that are aimed at studying the relative contribution of numerous genes to obesity and their responses to environmental changes. In the rare cases of monogenic obesities in which a major gene is the cause the molecular approach has proved extremely powerful in the identification of the genes responsible and in defining new syndromes. However, in the common forms of obesity (polygenic obesity) most studies have analysed genotype-phenotype associations without sometimes taking into account the influence of environmental factors (diet, sedentary lifestyle). Among the aspects limiting this integrated approach to obesity are the difficulty of having large enough samples and the expansion of biocomputing tools developed for accessing the question of multiple interactions with no a priori hypotheses. This picture is rapidly changing. Large databases of clinical data and DNA and biological sample banks with more precise environmental information and patient phenotypes are being compiled. The capacity for studying multiple genes simultaneously at the DNA or RNA levels is also possible. Finally, the tremendous progress in biocomputing will allow the integration of these different types of data (relating to environment, phenotype, genotype, gene expression) and will improve the ability to deal with this complex disease.