Identification of Functional Variants Associated with Obesity in Pakistani Kindred

Author:

Aftab Ayesha1,Jamal Syed Babar2,Bakhtiar Syeda Marriam1

Affiliation:

1. Genetic and Molecular Epidemiology Research Group, Department of Bioinformatics and Biosciences, Faculty of Health and Life Sciences, Capital University of Science and Technology, Islamabad, Pakistan

2. Department of Biological Sciences, National University of Medical Sciences, The Mall Road, 46000, Rawalpindi, Pakistan

Abstract

Background: Obesity is an emerging pandemic considered to be an outcome of change in lifestyle owing to more processed food and the use of mechanical locomotives. Obesity has not only appeared as a problem in the esthetic appearance of an individual rather is a serious health issue due to its associations with various chronic diseases such as coronary and cardiovascular problems, hypertension, osteoarthritis, type-II diabetes mellitus, hyperlipidemia, and certain cancers. It is estimated that 30 percent of the world’s population, i.e. approximately 2.1 billion people, are victims of obesity. In addition to environmental causes, various genes and a group of genes are reported to be increasing the suceptibility of obesity. Objective: Pakistan is a heterogeneous population, an amalgam of various races, therefore, narrowing down the list of obesity-associated genes and their functional variance could help molecular biologists to select potential SNPs in the Pakistani population for molecular diagnosis and treatment. Method: The extraction of a set of obesity-associated genes has been performed by using Polysearch2. SNPs for each gene are retrieved from dbSNP. RegulomeDB and SNPinfo tools have been used for the functional analysis of SNPs retrieved against the Pakistani population. For the prediction of potential deleterious SNPs, SIFT, Polyphen-2, MUTTASTER, MUTASSESSOR, and LRT (likelihood ratio test) are utilized. Functional analysis of potential deleterious SNPs has been performed by studying protein stability and mapping of identified SNPs to protein structure. For the protein stability analysis, I-Mutant and SNPs3D have been used. Results: Four genes FTO, POMC, LEPR, and MC4R and further analysis revealed 3 deleterious SNPs in FTO, 4 in POMC, 1 in LEPR, and 1 in MC4R. Conclusion: This research was designed to identify obesity-associated genes and the most impactful deleterious SNPs in these genes. These findings will be helpful for the molecular biologists and pharmacists to design better and focused diagnosis and treatment strategies.

Publisher

Bentham Science Publishers Ltd.

Subject

General Medicine

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