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Recent progress in genetics, epigenetics and metagenomics unveils the pathophysiology of human obesity

生物 表观遗传学 肥胖 遗传力缺失问题 全基因组关联研究 遗传学 生物信息学 候选基因 基因 内分泌学 单核苷酸多态性 基因型
作者
Marie Pigeyre,Fereshteh Yazdi,Yuvreet Kaur,David Meyre
出处
期刊:Clinical Science [Portland Press]
卷期号:130 (12): 943-986 被引量:357
标识
DOI:10.1042/cs20160136
摘要

In high-, middle- and low-income countries, the rising prevalence of obesity is the underlying cause of numerous health complications and increased mortality. Being a complex and heritable disorder, obesity results from the interplay between genetic susceptibility, epigenetics, metagenomics and the environment. Attempts at understanding the genetic basis of obesity have identified numerous genes associated with syndromic monogenic, non-syndromic monogenic, oligogenic and polygenic obesity. The genetics of leanness are also considered relevant as it mirrors some of obesity's aetiologies. In this report, we summarize ten genetically elucidated obesity syndromes, some of which are involved in ciliary functioning. We comprehensively review 11 monogenic obesity genes identified to date and their role in energy maintenance as part of the leptin–melanocortin pathway. With the emergence of genome-wide association studies over the last decade, 227 genetic variants involved in different biological pathways (central nervous system, food sensing and digestion, adipocyte differentiation, insulin signalling, lipid metabolism, muscle and liver biology, gut microbiota) have been associated with polygenic obesity. Advances in obligatory and facilitated epigenetic variation, and gene–environment interaction studies have partly accounted for the missing heritability of obesity and provided additional insight into its aetiology. The role of gut microbiota in obesity pathophysiology, as well as the 12 genes associated with lipodystrophies is discussed. Furthermore, in an attempt to improve future studies and merge the gap between research and clinical practice, we provide suggestions on how high-throughput ‘-omic’ data can be integrated in order to get closer to the new age of personalized medicine.
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