Identification and Association of Single Nucleotide Polymorphisms of the FTO Gene with Indicators of Overweight and Obesity in a Young Mexican Population.
Alonso Chama-AvilésKarla Lucero Flores-ViverosJorge Alberto Cabrera-AyalaAdriana Aguilar-GalarzaWillebaldo García-MuñozLorenza Haddad-TalancónMa de Lourdes Anzures-CortésClaudia Velázquez-SánchezJorge Luis Chávez-ServínMiriam Aracely Anaya-LoyolaTeresa de Jesús García-GascaVíctor Manuel Rodríguez-GarcíaUlisses Moreno-CelisPublished in: Genes (2023)
(1) Background: obesity is a global public health problem; various factors have been associated with this disease, and genetic factors play a very important role. Previous studies in multiple populations have associated a gene with fat mass and obesity ( FTO ). Thus, the present work aims to identify and determine associations between genetic variants of FTO with indicators of overweight and obesity in the Mexican population. (2) Methods: a total of 638 subjects were evaluated to compile data on body mass index (BMI), the percentage of body fat (%BF), the waist circumference (WC), the serum levels of triglycerides (TG), and food consumption. A total of 175 genetic variants in the FTO gene were sampled by a microarray in the evaluated population, followed by association statistical analyses and comparisons of means. (3) Results: a total of 34 genetic variants were associated with any of the 6 indicators of overweight and obesity, but only 15 showed mean differences using the recessive model after the Bonferroni correction. The present study shows a wide evaluation of FTO genetic variants associated with a classic indicator of overweight and obesity, which highlights the importance of genetic analyses in the study of obesity.
Keyphrases
- body mass index
- weight gain
- genome wide
- copy number
- public health
- insulin resistance
- metabolic syndrome
- weight loss
- type diabetes
- adipose tissue
- genome wide identification
- dna methylation
- gene expression
- autism spectrum disorder
- skeletal muscle
- genome wide analysis
- fatty acid
- deep learning
- muscular dystrophy
- human health