Genetic association studies have become an essential tool for studying the relationship between genotypes and phenotypes. They are necessary for the discovery of disease-causing genetic variants. Here we provide a tool for conducting genetic association studies, which uses statistical learning techniques such as random forests and support vector machines, as well as using Bayesian inference with Bayesian hierarchical models. These techniques are superior to the commonly used (frequentist) statistical approaches, alleviating the multiple hypothesis problems and the need for P value corrections, which often lead to massive numbers of false negatives. Thus, with genphen we provide a framework to compare the results obtained using frequentist methods with those obtained using the more sophisticated methods provided by this tool. The tool also provides a few visualization functions which enable the user to inspect the results of such genetic association study and conveniently select the genotypes which have the highest strength of association with the phenotype.
|Bioconductor views||Bayesian Classification FeatureExtraction Genetics GenomeWideAssociation Regression SequenceMatching Sequencing SupportVectorMachine|
|Maintainer||Simo Kitanovski <[email protected]>|
|License||GPL (>= 2)|
|Package repository||View on GitHub|
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