All MetSyn traits are strongly influenced by genetic factors. Most have heritabilities above 40% and a few, such as obesity and high-density lipoprotein (HDL) levels, have heritabilities as high as 70% in some studies4
. But heritability estimates are approximate and they generally make certain assumptions, such as the absence of gene– environment, gene–sex and gene–gene interactions as these are difficult to dissect in human populations. Also, estimates are specific for the particular population studied and will reflect both the diversity of the population and the diversity of the environment8
Until recently, our understanding of the genetics of MetSyn came largely from studies of Mendelian traits in humans or of biochemically defined candidate genes. Although these studies were enormously informative in providing molecular insights into homeostatic mechanisms, they have not explained how genes interact with each other and with the environment.
Studies that began in the early 1990s to identify genes contributing to the common forms of MetSyn traits using linkage analysis were only modestly successful. This was primarily due to the low power and poor resolution of nonparametric linkage analyses, as well as the unexpected complexity of the traits. Whereas linkage analysis of the genes contributing to the traits in rats and mice is straightforward, at least for those genes contributing more than a few percent of the variance of the trait, resolution is very poor and loci generally contain 100 or more genes. As a consequence, successes in identifying genes underlying quantitative trait loci (QTLs) have been few.
GWA studies became feasible following the completion of the human genome sequence, the cataloguing of common variations in human populations and the development of improved SNP genotyping technologies. Association approaches are more powerful and have much better resolution than linkage approaches. Several large studies of traits relevant to MetSyn have been reported (BOX 3
); these have confirmed a number of genes previously identified through candidate gene approaches and have identified many novel genes or loci (reviewed in REFS 9–11
). These include: two common variants that affect fasting glucose levels (glucokinase (hexokinase 4) regulator (GCKR
), and a genomic region containing glucose6phosphatase, catalytic, 2 (G6PC2
) and ATP binding cassette, subfamily B, member 11 (ABCB11
)); two obesity (that is, adiposity) variants (fat mass and obesity associated (FTO
) and melanocortin 4 receptor (MC4R
)); 19 type 2 diabetes loci; and many triglyceride, HDLcholesterol and lowdensity lipoprotein (LDL)-cholesterol loci12–24
. None of the genes identified affect the entire spectrum of MetSyn traits, although some influence several of them (BOX 3
). For example, studies of FTO
show that, although its primary effect is on adiposity, it has secondary effects on insulin sensitivity, adipokine levels and resting metabolic rate25,26
. Additional genetic studies, identification of rare functional variants by high-throughput sequencing and analysis of copy number variation should add to our knowledge24,27–29
Box 3. Human genome-wide association studies: a view of the genetic architecture of MetSyn
Several genome-wide association (GWA) studies relevant to metabolic syndrome (MetSyn), type 2 diabetes and coronary artery disease have confirmed candidate gene associations and have identified a number of novel genes and loci (discussed in the text and reviewed in REFS 9–11
). Examples include:
- Melanocortin 4 receptor, MC4R: this gene was identified in Asian-Indian and European populations for several MetSyn traits17, 127. Rare MC4R loss-of-function variants have previously been associated with hyperphagia and childhood obesity, and experimental studies have identified it as a key regulator of energy balance.
- Fat mass and obesity associated, FTO: two GWA studies of Europeans have associated FTO with body mass index14, 23. Recent studies in rodents suggest that FTO might be co-regulated with an adjacent gene, FTM, and that it exhibits phenotypic overlap with Bardet-Biedl syndrome128.
- MLX interacting protein-like, MLXIPL: in European and Indian–Asian populations this gene is linked to plasma triglyceride levels129. Its protein product coordinates transcriptional regulation of enzymes that channel glycolytic end-products into lipogenesis and energy storage.
- Transcription factor 7-like 2, T-cell specific, HMG-box, TCF7L2: this is one of 19 susceptibility genes for type 2 diabetes. Although originally identified by genetic linkage followed by traditional genetic association130, this association was confirmed by GWA studies24. Many of the type 2 diabetes genes, including TCF7L2, seem to affect pancreatic beta-cell function.
As yet, no genetic factors that encompass all MetSyn traits have been identified. This might simply reflect the lack of power of the analyses, as genes perturbing individual pathways might indirectly contribute to traits such as lipid levels and blood pressure. Some traits, such as blood pressure, have few or no loci that achieve genome-wide significance, and for the others the identified loci explain less than 10% of the variance of the trait. As most MetSyn traits have heritabilities of approximately 50%, genes detectable above the noise of GWA studies explain only a small fraction of the genetic component. This is illustrated in a hypothetical distribution of genes contributing to MetSyn (see figure). Genes identified thus far by GWA studies (shown at left) tend to be those exerting the largest effects, but account for only ~5–10% of the trait variance. The majority of remaining genes (so called ‘dark matter’) will be more difficult to identify owing to their modest effects on MetSyn traits, complex interactions and rare variations.
The loci identified in GWA studies frequently contain several genes in strong linkage disequilibrium, and biochemical or animal model studies might be required to definitively identify which gene is causal. One promising approach to validate susceptibility genes involves network modelling, which allows genes of unknown function to be related to known pathways or clinical traits. For example, the integration of human and mouse genotypic and expression data suggested that sortilin 1 (SORT1
) and cadherin EGF LAG sevenpass G-type receptor 2 (CELSR2
) are susceptibility genes for CVD and hyperlipidaemia30
The loci identified thus far from GWA studies explain less than 10% of the population variance of MetSyn traits. Given the high heritabilities of MetSyn traits, it seems that the GWA study results reported so far have mapped a tiny fraction of their genetic components. This raises the possibility that MetSyn is underpinned by hundreds of genes, each with modest effects, and by many rare mutations not detected in GWA studies (BOX 2
). Genes with such modest effects will be difficult to study using standard genetic approaches, and genetic heterogeneity, interactions and ethnic differences will complicate analyses. Recent sequencing studies designed to identify rare genetic variants involved in common disorders suggest that these are likely to contribute significantly to MetSyn traits (for example, REFS 27,28
A central tenet of MetSyn has been the ‘thrifty gene’ hypothesis — the notion that the repeated famines in human history have selected for alleles that result in obesity during times of plentiful food. Thus, when a famine occurs those individuals with excess fat would be most likely to survive. However, recent data indicating that death from starvation results primarily from infection rather than depletion of fat stores has put the hypothesis into question31
Environmental influences also play a major part in MetSyn: a high-calorie diet and a sedentary life-style are primary environmental contributors (BOX 1
). Environmental factors are difficult to study in humans. Even if diet and exercise could be accurately assessed, interactions with genetic factors would be difficult to study because no two humans, with the exception of identical twins, share the same genetic background. Consequently, few human studies have as yet attempted to tackle gene–gene and gene–environment interactions, and have focused instead on single candidate genes. Whether classical genetic and molecular biology approaches can address these complex interactions is unclear. Alternative systems-based approaches are discussed later in this Review.