Akademik Veri Yönetim Sistemi
ACTA BIOTHEORETICA, cilt.74, sa.3, 2026 (SCI-Expanded, Scopus)
Classical statistical analysis is a frequently employed methodology in numerous domains of genetic research. In recent times, however, there has been a notable increase in the interest accorded to the deployment of Bayesian statistics in the field of genetics, as it incorporates a priori hypotheses about genetic knowledge into the problem. The potential risk of developing a genetic disease is influenced by the patient's genetics, ethnicity, gender, age, and family history. The objective of this study is there\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$<^>{1}$$\end{document}fore to provide molecular pathologists working with genetic testing with a comprehensive overview of the basic principles of Bayesian analysis and genetic risk assessment. Furthermore, the study aims to develop a computer code that estimates the probability of transmission of genetic traits between generations and performs risk analysis within the framework of Bayesian logical inference. This framework facilitates the calculation of the probability of a specific hypothesis, whether it pertains to disease state or a determination of carrier status, by integrating familial data and/or the results obtained from genetic testing. The present algorithm was utilized for the purpose of evaluating the genetic risk of everyone within a given pedigree, in addition to predicting the likelihood of cystic fibrosis (CF) manifesting in human genetics. This objective was accomplished by employing transition matrices in Markov chains and subsequently calculating the final probability vector of the transmission of genetic traits. The primary function of this tool is to evaluate the genetic susceptibility of individuals within a family history to cystic fibrosis and to predict the probability of developing the condition. The results demonstrate the effectiveness of the proposed algorithm in performing reliable genetic risk assessments for patients and family members with cystic fibrosis disease or other autosomal recessive disorders.