This repository archives StrBEAM implementation. StrBEAM stands for 'Structured Bayesian Epistasis Association Mapping'. This program is documented in 'Population-Structured Bayesian Epistasis Association Mapping' paper and I presented it in 13-th Seminar on Probability and Stochastic Process of Iranian Statistical Society.
You can build presentation slides in PDF format, by running build.bat script in Slides directory.
Association mapping is an important field of research in bioinformatics and human genomics studies. Various statistical methods have been used to find out genetic regions are associated with traits or phenotypes. One of the methods used in the association mapping is clustering. In this paper, we introduce a clustering method with special application in association mapping. In this method, the inference of disease associated factors of each sub-population is simultaneously performed by clustering assignment of individuals, based on similarity of genetic samples. A framework is proposed to approximate the posterior of the model based on, either Markov Chain Monte Carlo (MCMC), or Variational Bayes (VB) methods, and Bayesian Epistasis Association Mapping (BEAM) model for disease association discovery.
Najafi A., Janghorbani S., Motahari S. A., and Fatemizadeh E. (2019), Statistical Association Mapping of Population-Structured Genetic Data, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 16, 638-649.
Pritchard J. K., Stephens M., and Donnelly P. (2000), Inference of Population Structure Using Multilocus Genotype Data, Genetics Society of America, 155, 945-959.
Raj A., Stephens M., Pritchard J. K. (2014), fastSTRUCTURE: Variational Inference of Population Structure in Large SNP Data Sets, Genetics, 197, 573-589.
Zhang Y. (2012), A Novel Bayesian Graphical Model for Genome-Wide Multi-SNP Association Mapping, Genomic Epidemiology, 36, 36-47.