ABSTRACT
OBJECTIVES: In Qataris, a population characterized by a small size and a high rate of consanguinity, between two-thirds to three-quarters of adults are overweight or obese. We investigated the relevance of 23 obesity-related loci in the Qatari population. METHODS: Eight-hundred-four individuals assessed to be third generation Qataris were included in the study and assigned to 3 groups according to their body mass index (BMI): 190 lean (BMI < 25 kg/m(2)); 131 overweight (25 kg/m(2) ≤ BMI < 30 kg/m(2)) and 483 obese (BMI ≥ 30 kg/m(2)). Genomic DNA was isolated from peripheral blood and genotyped by TaqMan. RESULTS: Two loci significantly associated with obesity in Qataris: the TFAP2B variation (rs987237) (A allele versus G allele: chi-square = 10.3; P = 0.0013) and GNPDA2 variation (rs10938397) (A allele versus G allele: chi-square = 6.15; P = 0.013). The TFAP2B GG genotype negatively associated with obesity (OR = 0.21; P = 0.0031). Conversely, the GNDPA2 GG homozygous genotype associated with higher risk of obesity in subjects of age < 32 years (P = 0.0358). CONCLUSION: We showed a different genetic profile associated with obesity in the Qatari population compared to Western populations. Studying the genetic background of Qataris is of primary importance as the etiology of a given disease might be population-specific.
Subject(s)
Arabs/genetics , Consanguinity , Genetic Loci , Genetic Predisposition to Disease , Obesity/genetics , Adult , Body Mass Index , Female , Humans , Logistic Models , Male , Middle Aged , Phenotype , Polymorphism, Single Nucleotide/genetics , Principal Component Analysis , Qatar , Racial Groups/genetics , Thinness/geneticsABSTRACT
This paper proposes unconstrained functional networks as a new classifier to deal with the pattern recognition problems. Both methodology and learning algorithm for this kind of computational intelligence classifier using the iterative least squares optimization criterion are derived. The performance of this new intelligent systems scheme is demonstrated and examined using real-world applications. A comparative study with the most common classification algorithms in both machine learning and statistics communities is carried out. The study was achieved with only sets of second-order linearly independent polynomial functions to approximate the neuron functions. The results show that this new framework classifier is reliable, flexible, stable, and achieves a high-quality performance.
