On the origin of obesity: identifying the biological, environmental and cultural drivers of genetic risk among human populations. (16th November 2017)

• Record Type: Journal Article
• Authors: Qasim, A.; Turcotte, M.; de Souza, R. J.; Samaan, M. C.; Champredon, D.; Dushoff, J.; Speakman, J. R.; Meyre, D.
• Is Part Of: Obesity reviews. Volume 19:Number 2(2018)
• Page Start: 121
• Page End: 149
• Publication Date: 2017-11-16
• Subjects: gene pleiotropy -- genetic predisposition to obesity -- mating systems -- natural selection; Obesity -- Periodicals; 616.398005
• DOI: 10.1111/obr.12625
• Languages: English
• ISSNs: 1467-7881

Abstract

Summary: Genetic predisposition to obesity presents a paradox: how do genetic variants with a detrimental impact on human health persist through evolutionary time? Numerous hypotheses, such as the thrifty genotype hypothesis, attempt to explain this phenomenon yet fail to provide a justification for the modern obesity epidemic. In this critical review, we appraise existing theories explaining the evolutionary origins of obesity and explore novel biological and sociocultural agents of evolutionary change to help explain the modern‐day distribution of obesity‐predisposing variants. Genetic drift, acting as a form of 'blind justice, ' may randomly affect allele frequencies across generations while gene pleiotropy and adaptations to diverse environments may explain the rise and subsequent selection of obesity risk alleles. As an adaptive response, epigenetic regulation of gene expression may impact the manifestation of genetic predisposition to obesity. Finally, exposure to malnutrition and disease epidemics in the wake of oppressive social systems, culturally mediated notions of attractiveness and desirability, and diverse mating systems may play a role in shaping the human genome. As an important first step towards the identification of important drivers of obesity gene evolution, this review may inform empirical research focused on testing evolutionary theories by way of population genetics and mathematical modelling.