A Dissimilar Approach to Associating Angiotensin Converting Enzyme Polymorphisms

摘要

Angiotensin I converting enzyme (ACE) gene, as a component of Renin-Angiotensin System (RAS), regulates blood pressure as it converts somatic isozyme Angiotensin I into physiologically active peptide Angiotensin II and simultaneously brakes down bradykinins. Over 100 polymorphisms are reported for ACE gene. Most of these polymorphisms having no phenotypic effect relay the attention towards polymorphisms based on insertions (I) or deletions (D) of a 287 bp Alu repeat sequence in 16th intron. There are three possible genotypes for the stated polymorphism: DD, DI or II. There have been a lot of studies searching for direct associations between ACE polymorphisms and performance phenotypes along different sports requiring power or endurance. The previous experiments are based on the performance criteria but direct associations of ACE polymorphisms are not fully understood until today. We believe different approaches may aid scientist to plot the big picture. A sample population of 101 individuals from Bosnia and Herzegovina contributed to sample pool of the initial project. Buccal swabs from 101 samples were collected along with a phenotypic structure and environmental characteristic survey which was filled by each individual himself/herself. Genotypes of the individuals were obtained after isolation, amplification and gel electrophoresis of biological samples collected as buccal swabs. A total of 165 artificial neural network (ANN) models were developed considering the input parameters, possible genotype outputs, applied algorithm and sample size. The aim of developing various ANNs was to validate a possible ACE polymorphism genotype prediction algorithm based on phenotypic and environmental characteristics of individuals, in other words, without any biological testing. A two-layered feed-forward network, with sigmoid hidden neurons was designed to perform the classification of input data. Trainscg (Scaled Conjugate Gradient) activation function was used in hidden layer since classification of data was non-linear. All ANN models were trained with scaled conjugate gradient backpropagation. ANN models differing in the parameters has shown different accuracy in the results. Most outstanding result was observed in the ANN build composed of 2 distinctive layers with 500 neurons in the first and 3 neurons in the second layer. Trained with 70% of samples and verified with 15% of samples and validated with an additional 50 samples. Training set was composed of the following subject parameters; gender, eye color, hair color, height, weight, presence of hypertension in family and presence of cardiovascular diseases in family. The highest prediction accuracy was obtained as 86,6 % training score, 78,6 % testing score and 80,2 % overall score in genotype prediction for ACE polymorphisms. With further development of data collection and high resolution analysis, overall score could be boosted. Also, phenotypical data can be applied as markers of genotypes in ACE polymorphisms.