**2. Genomics**

While the global incidence of obesity is on the rise and is thought to be predominantly due to poor diet and sedentary lifestyles, it is also widely accepted that genetic and epigenetic factors also play an important role in obesity development. Twin studies have demonstrated that the heritability of obesity ranges from 40 to 70% [3], which clearly shows an important role of genetics in obesity and could depict which individuals are at higher risk of developing the disease. Therefore, it is empirical that we identify and understand the genetic regulation of obesity which will potentially lead to more targeted therapies to help stem this obesity epidemic.

#### **2.1. Genome‐wide association studies in obesity**

cant advances in the understanding of obesity and metabolism have been made using "omic" technologies. "Omic" techniques involve the detection and identification of molecules within a given biological sample, whether it is derived from cells, a tissue sample or indeed an entire organ or organism. Primarily, omics studies have aimed to identify the genes (genomics), messenger RNA (mRNA) (transcriptomics), proteins (proteomics) and metabolites (metabolomics that encompasses lipids (lipidomics)) of a sample or a group of samples and how they differ from another sample or group. Other omics platforms are also important in the regulation of these pathways including the effect of epigenetics (the epigenome/epigenomics) on the function of genes and the role of the gut microbiota within a host (the microbiome/microbiomics) on metabolite production and energy harvest from food. "Omic" platforms are being utilised by researchers around the world to identify mechanisms that contribute to the development and maintenance of obesity, the evolution of obesity to metabolic diseases such as type 2 diabetes and to try and identify possible therapeutic avenues to treat obesity (**Figure 1**). This chapter focuses on discussing obesity from the level of the genes associated with obesity and their regulation by the epigenome right through to the proteomic, lipidomic and metabolomic level in studies from both human cohorts as well as studies conducted in pre‐clinical models.

While the global incidence of obesity is on the rise and is thought to be predominantly due to poor diet and sedentary lifestyles, it is also widely accepted that genetic and epigenetic factors also play an important role in obesity development. Twin studies have demonstrated that the heritability of obesity ranges from 40 to 70% [3], which clearly shows an important role of

**Figure 1.** Flow chart representation of the various "omic" platforms used to study obesity. Further understanding of each of these stages will not only lead to a greater understanding of the pathogenesis of obesity and identification of therapeutic targets, but could potentially be used for prescription of personalized medicine (prevention and treatment). Data such as what is able to be obtained from lipidomic analysis may be utilized in the clinic to evaluate risk and monitor

**2. Genomics**

disease severity and provide prognostic information.

24 Adiposity - Omics and Molecular Understanding

With the completion of the Human Genome Project, it has been established that there are 20,000–25,000 genes within the human genome [4]. High‐throughput genotyping technology coupled with The HapMap project [5] and 1000 Genomes Project [6] has made it possible to conduct genome‐wide association studies (GWASs) to identify common variations in the genome that may be linked to diseases. The basis of GWAS is the detection of association of linkage disequilibrium (LD) between the causal variants and single‐nucleotide polymorphisms (SNPs), which reduces the number of SNPs required to cover the whole genome. However, this is also a limitation as in the analyses a minor allele frequency of >5% is required, which means that only common SNPs will be identified. The very first large‐scale SNP chip GWAS was performed by the Wellcome Trust Case Control Consortium in 1,924 type 2 diabetic (T2D) cases and 2,938 population controls from European samples and identified several variants associated with T2D phenotypes [7]. They also identified a novel gene, which was associated with obesity, fat mass associated with obesity (*FTO*). *FTO* has been shown to exert its primary effect on T2D risk through its impact on adiposity [8]. Following on from the discovery of *FTO*, a meta‐analysis was performed in 16,876 European subjects and replicated the associations between variants in *FTO* and obesity, and also identified variants within the melanocortin‐4 receptor (*MC4R*) to be associated with fat mass, weight and obesity risk, which have been previously shown to be the leading cause of monogenic severe childhood‐onset obesity [9]. This underscores the merit of GWAS meta‐analyses to validate previous associations as well as identify new regions that may be associated with obesity‐related phenotypes. Since these two studies, there has been a boom in GWAS studies and subsequent obesity susceptibility loci identified. To date, ~200 variants associated with obesity‐related phenotypes have been identified; however, it is postulated that these loci only account for <10% of the variance [10–15]. Although this is quite low, and means that 90% of variance remains to


**Table 1.** Summary of recent genome‐wide meta‐analyses of obesity.

be explained, they have provided us with an emerging wealth of knowledge of the genomic localisation, frequency and effect sizes, and potential functional implications that these loci may have. More recently, a number of meta‐analyses of GWAS data have been performed and have identified new loci associated with body mass index (BMI) (**Table 1**). Surprisingly, these meta‐analyses also replicated previously identified loci, which validate that these loci may be contributing to obesity and underscore the importance of performing GWASs in the first place.
