Estudo de associa̧ção genômica ampla para obesidade por meio de florestas aleatórias

Abstract

Obesity is the result of an energy imbalance and has been growing significantly. However, not all individuals respond in the same way to the same environment, which may be due to genetic factors. According to (29), heritability can contribute around 40% to the variation of a disease. Therefore, notions of genetics are extremely important for a better understanding of chronic diseases such as obesity. Most cases of obesity are caused by the combination of several genetic variants of small effect, leading to various methodological and computational challenges. The statistical methods for Genome-Wide Association Studies (GWAS) commonly used for analyses are univariate, considering only one genetic marker (SNP) at a time. Consequently, such methods do not capture relationships between SNPs. The random forest method has been widely applied to biological data, as it supports high-dimensional data and can capture non-linear models based on genotypes. Therefore, the present study aims to identify genetic variants associated with obesity in a multifactorial manner using random forests. Using the importance measure of random forests and selecting the 99th percentile of its distribution, the results identified several SNPs associated with obesity and waist circumference, reinforcing the genetic complexity of these traits. One of the markers found was SNP rs478582, present in both analyses, suggesting a strong influence on the regulation of adiposity and body fat distribution. Many of the genes identified in the study are involved in metabolic and inflammatory processes, highlighting the importance of genetic factors in the predisposition to obesity. This reinforces the need for additional studies to deepen the understanding of the biological mechanisms related to obesity.