The genetics of adiposity
Introduction
Obesity is a major risk factor of disease, not only posing an enormous burden on people's personal health [1], but also on societies as a whole [2, 3]. Over the past four decades, the prevalence of obesity among adults has nearly quadrupled worldwide [4, 5]. While in most high-income countries the rise in BMI seems to have slowed down as of late, albeit at a high level, in many low-income and middle-income countries the increase continues. Particularly alarming is the global rise in obesity among children and adolescents [4, 5, 6].
Initiatives to prevent obesity or promote weight loss through lifestyle changes have limited success and are often short-lived, both at the community and individual levels [7, 8], suggesting that innate mechanisms, encoded by the genome, also contribute to energy homeostasis [9••]. Estimates of genetic contribution vary by study design and adiposity outcome, but are sufficiently high to warrant gene discovery studies (Table 1).
In the past 10 years, genome-wide association studies (GWASs) have been particularly effective in the identification of genetic loci associated with adiposity outcomes. However, translation of these loci into new biology has been challenging. Here, I review recent progress and insights gained from these discoveries.
Section snippets
Conventional GWAS — Common (MAF ≥ 5%) variants for commonly studied adiposity phenotypes
In 2007, GWASs discovered the first genetic locus in FTO that showed robust association with BMI and obesity risk [10, 11]. More than 500 genetic loci, for a range of adiposity traits, have since been identified (Figure 1). The vast majority of these (92%) were first identified for body mass index (BMI; n = 341 loci), a proxy for overall adiposity, and for BMI-adjusted waist-to-hip ratio (WHRadjBMI; N = 129), a proxy for body fat distribution. Because data on BMI and WHR are easily obtained, sample
Alternative approaches for gene discovery
Conventional GWAS, described above, have been very fruitful, not only because of the large sample, but also because the studied variants are common (MAF > 5%); both are contributors to increased statistical power for discovery. However, several other approaches have identified new loci through leveraging specific phenotypic and genotypic features.
Genetic correlation and Mendelian randomization
Genetic correlation and Mendelian randomization are complementary approaches to assess shared etiology and causal relationships between adiposity and other traits.
Genetic correlation studies correlate SNP-association effects of one trait with those of another trait. SNP-association effects of BMI and WHRadjBMI were found to positively correlate with those of cardiometabolic traits, excessive daytime sleepiness, sleep duration [60, 61, 62], and negatively with those for anorexia nervosa, age at
Genetic information to predict obesity
Historically, genetic tests have been used to provide a genetic diagnosis to patients with rare forms of extreme and early-onset obesity that may be due to a single mutation [84]. For some patients, such a genetic diagnosis has been instrumental in their treatment [85, 86]. As more variants are being discovered and genome sequencing is becoming mainstream, there is a growing expectation that genetic tests will help clinicians predict and diagnose patients’ risks of complex disease, such as
Conclusions and future perspectives
In the past decade, large-scale genome-wide discovery efforts have uncovered numerous new loci that harbor genetic variants associated with adiposity traits. With the advent of additional large population studies (e.g. UK Biobank, Million Veterans Project, All of Us), the number of loci will continue to increase rapidly in coming years. Preliminary analyses suggest that these loci highlight pathways that broadly overlap with the biology previously identified in extreme models of obesity in
Conflict of interest statement
Nothing declared.
References and recommended reading
Papers of particular interest, published within the period of review, have been highlighted as:
• of special interest
•• of outstanding interest
Acknowledgements
Ruth Loos is supported by the National Institutes of Health [R01 DK107786, R01 DK110113, U01HG007417].
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