A Genetic Analytics Approach for Risk Variant Identification to Support Intervention Strategies for People Susceptible t
Obesity is a growing epidemic that has increased steadily over the past several decades. It affects significant parts of the global population and this has resulted in obesity being high on the political agenda in many countries. It represents one of the
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pplied Computing Research Group, Faculty of Engineering and Technology, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK {C.A.Curbelomontanez,B.T.Abdulaimma}@2015.ljmu.ac.uk, {P.Fergus,A.Hussain,D.Aljumeily}@ljmu.ac.uk 2 College of Computer Engineering and Science, Sattam Bin Abdulaziz University, Al-Kharj, Kingdom of Saudi Arabia [email protected]
Abstract. Obesity is a growing epidemic that has increased steadily over the past several decades. It affects significant parts of the global population and this has resulted in obesity being high on the political agenda in many countries. It represents one of the most difficult clinical and public health challenges worldwide. While eating healthy and exercising regularly are obvious ways to combat obesity, there is a need to understand the underlying genetic constructs and pathways that lead to the manifestation of obesity and their susceptibility metrics in specific individuals. In particular, the interpretation of genetic profiles will allow for the identification of Deoxyribonucleic Acid variations, known as Single Nucleotide Polymorphism, associated with traits directly linked to obesity and validated with Genome-Wide Association Studies. Using a robust data science methodology, this paper uses a subset of the TwinsUK dataset that contains genetic data from extremely obese individuals with a BMI ≥ 40, to identify significant obesity traits for potential use in genetic screening for disease risk prediction. The paper posits an approach for methodical risk variant identification to support intervention strategies that will help mitigate long-term adverse health outcomes in people susceptible to obesity and overweight. Keywords: Genetics gwascat
SNPs
Obesity
Data science
R
Bioconductor
1 Introduction According to the World Health Organization (WHO)1, the occurrence of obesity and overweight worldwide has doubled since 1980. More recently, the figures suggest that in 2014 more than 1.9 billion adults were overweight and 600 million were obese. 1
http://www.who.int/.
© Springer International Publishing Switzerland 2016 D.-S. Huang et al. (Eds.): ICIC 2016, Part I, LNCS 9771, pp. 808–819, 2016. DOI: 10.1007/978-3-319-42291-6_80
A Genetic Analytics Approach for Risk Variant Identification
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The condition was initially recognized as a disease in 1948 by the WHO [1] and since then its prevalence has continued to increase making it a global phenomenon and one of the main contributors to poor health. Today it is considered one of the most difficult clinical and public health challenges worldwide [2–4]. It is the leading cause of Type 2 Diabetes, cardiovascular disease, premature death, hypertension, osteoarthritis, stroke and certain cancers [2, 3, 5]. Consequently, it is high on the political agenda of many countries. While North America is considered the most obese continent, the UK is ranked as one of the most obese nations in Europe2. The predisposition to obesity in humans is referred to as polygenic obesity and is considered a
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