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Please use this identifier to cite or link to this item: http://hdl.handle.net/11375/20668
Title: On the origin of obesity: A critical review of biological, environmental, and cultural drivers of genetic risk among human populations
Authors: Qasim, Anila
Advisor: Meyre, David
Department: Health Research Methodology
Keywords: obesity;evolution;natural selection;thrifty genotype hypothesis;pleiotropy;genetics
Publication Date: Nov-2016
Abstract: Genetic predisposition to obesity presents a paradox: how do genetic variants with such a detrimental impact on human health persist through evolutionary time? Numerous hypotheses, for instance the thrifty genotype hypothesis, attempt to explain this phenomenon, yet fail to provide a satisfying answer to the modern obesity epidemic. In this critical review, I appraise existing theories explaining the evolutionary origins of obesity and explore novel biological and sociocultural agents of evolutionary change that may help explain the distribution of obesity and leanness predisposing variants in modern human populations. Gene pleiotropy and adaptations to diverse environmental niches may explain the rise and subsequent selection of obesity risk alleles. The regulation of gene expression by epigenetic mechanisms may serve as a stochastic factor affecting the manifestation of obesity phenotypes. Finally, exposure to malnutrition and disease epidemics in the wake of colonialism, culturally mediated notions of attractiveness and desirability, and diverse mating systems – including forced copulation, consanguinity and polygamy – may play a role in shaping the human genome. In short, I posit that in order to explain ethnic variation in obesity susceptibility, we must examine the origin of physiological adaptations and understand the sociocultural experiences of individuals and populations. As an imperative first step towards the identification of important drivers of obesity gene evolution, this review will inform empirical research focused on testing evolutionary theories by way of population genetics and mathematical modelling. Ultimately, these data will promote a better understanding of the aetiology of obesity and are expected to guide the development of targeted management, treatment, and prevention strategies.
URI: http://hdl.handle.net/11375/20668
Appears in Collections:Open Access Dissertations and Theses

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