RT Journal Article SR Electronic T1 Statistical Association Mapping of Population-Structured Genetic Data JF bioRxiv FD Cold Spring Harbor Laboratory SP 069658 DO 10.1101/069658 A1 A. Najafi A1 S. Janghorbani A1 S. A. Motahari A1 E. Fatemizadeh YR 2016 UL http://biorxiv.org/content/early/2016/08/15/069658.abstract AB Association mapping of genetic diseases has attracted extensive research interest during the recent years. However, most of the methodologies introduced so far suffer from spurious inference of the disease-causing sites due to population inhomogeneities. In this paper, we introduce a statistical framework to compensate for this shortcoming by equipping the current methodologies with a state-of-the-art clustering algorithm being widely used in population genetics applications. The proposed framework jointly infers the disease causal factors and the hidden population structures. In this regard, a Markov Chain-Monte Carlo (MCMC) procedure has been employed to assess the posterior probability distribution of the model parameters. We have implemented our proposed framework on a software package whose performance is extensively evaluated on a number of synthetic datasets, and compared to some of the well-known existing methods such as STRUCTURE. It has been shown that in extreme scenarios, up to 10 – 15% of improvement in the inference accuracy is achieved with a moderate increase in computational complexity.