Background: The key feature of Bayesian methods is their lack of dependence on defaults necessary for classical statistics. Because of the high volume of simulation, Bayesian methods have a high degree of accuracy. They are efficient in data mining and analyzing large volumes of data, and can be upgraded by entering new data.
Objective: We used Bayesian multidimensional scaling (MDS) to analyze the genetic relationships among 11 Iranian ethnic groups based on HLA class II data.
Method: Allele frequencies of three HLA loci from 816 unrelated individuals belonging to 11 Iranian ethnic groups were analyzed by Bayesian MDS using R and WinBUGS software.
Results: like the results of correspondence analysis as a prototype of classical MDS analysis, the results of Bayesian MDS also showed Arabs from Famur, Balochis, Zoroastrians and Jews to be separate from other Iranian ethnic groups. Decreases stress in Bayesian MDS method compared to classical method revealed the accuracy of Bayesian MDS for HLA data analyses.
Conclusion: This study reports the first application of Bayesian multidimensional scaling to HLA data analysis with Nei's DA genetic distances. Stress reduction in Bayesian MDS compared to classical MDS showed that the Bayesian approach can improve the accuracy of genetic data analysis
