Name
Abstract | ||
|---|---|---|
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 associated 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-associated 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 of improvement in the inference accuracy is achieved with a moderate increase in computational complexity. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TCBB.2017.2786239 | IEEE/ACM transactions on computational biology and bioinformatics |
Keywords | Field | DocType |
Diseases,Sociology,Statistics,Mathematical model,Genomics,Bioinformatics | Data mining,Population,Markov chain Monte Carlo,Computer science,Inference,Markov chain,Posterior probability,Bioinformatics,Cluster analysis,Spurious relationship,Computational complexity theory | Journal |
Volume | Issue | ISSN |
16 | 2 | 1557-9964 |
Citations | PageRank | References |
1 | 0.63 | 2 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amir Najafi | 1 | 5 | 1.01 |
| Sepehr Janghorbani | 2 | 1 | 1.98 |
| Abolfazl S. Motahari | 3 | 255 | 33.01 |
| Emad Fatemizadeh | 4 | 117 | 13.86 |