PT  - JOURNAL ARTICLE
AU  - A. Najafi
AU  - S. Janghorbani
AU  - S. A. Motahari
AU  - E. Fatemizadeh
TI  - Statistical Association Mapping of Population-Structured Genetic Data
AID  - 10.1101/069658
DP  - 2016 Jan 01
TA  - bioRxiv
PG  - 069658
4099  - http://biorxiv.org/content/early/2016/08/15/069658.short
4100  - http://biorxiv.org/content/early/2016/08/15/069658.full
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.