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|
|Date of publication||None|
|Maintainer||Simo Kitanovski <email@example.com>|
|License||GPL (>= 2)|
genotype.saap: SAAP genotype dataset
genotype.saap.msa: SAAP genotype dataset (msa)
genotype.snp: SNP genotype dataset
genotype.snp.msa: SNP genotype dataset (msa)
phenotype.saap: Phenotype dataset
phenotype.snp: Phenotype dataset
plotGenphenBayes: Visualizing the genphen results of runGenphenBayes
plotGenphenRfSvm: Visualizing the genphen results of runGenphenRf or...
plotManhattan: Visualizing genphen results with Manhattan plots
plotSpecificGenotype: Visualizing specific genotypes
runGenphenBayes: Conducting genetic association analysis with Bayesian...
runGenphenRf: Conducting genetic association analysis with random forest
runGenphenSvm: Conducting genetic association analysis with linear support...
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