APSampler is a tool that allows multi-locus and multi-level association analysis of genotypic and phenotypic data. The goal is to find the allelic sets (patterns) that are associated with phenotype. The main difficulty of such a task is, given the multiple loci and multiple alleles, the number of all possible classifiers tends to be extremely large. Therefore, Monte Carlo Markov Chain method is applied to reduce the space of solutions and sample only from regions where it is likely to find a good classifier. Once a set of classifiers is found, there is a problem to validate the results, and this is done using a number of well known methods. In case of single disease level, the resulting classifier divides the space of healthy and ill individuals, and the result is represented in a classic Fisher table. Odds ratio and Fisher’s p-value are calculated if applicable. Also, Kruskal’s gamma and the corresponding p-value can be calculated. After each pattern in the output is described by a p-values set of different multiple-hypothesis corrections, including permutation tests.
- WIndows / Linux
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Favorov, A.V. et al.
A Markov chain Monte Carlo technique for identification of combinations of allelic variants underlying complex diseases in humans.
Genetics 171, 2113-2121 (2005).