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Abstract:
Genome-wide association studies using thousands to hundreds of thousands of single nucleotide polymorphism (SNP)
markers and region-wide association studies using a dense panel of SNPs are already in use to identify disease
susceptibility genes and to predict disease risk in individuals. Because these tasks become increasingly important, three
different data sets were provided for the Genetic Analysis Workshop 15, thus allowing examination of various novel and
existing data mining methods for both classification and identification of disease susceptibility genes, gene by gene or gene
by environment interaction. The approach most often applied in this presentation group was random forests because of its
simplicity, elegance, and robustness. It was used for prediction and for screening for interesting SNPs in a first step. The
logistic tree with unbiased selection approach appeared to be an interesting alternative to efficiently select interesting SNPs.
Machine learning, specifically ensemble methods, might be useful as pre-screening tools for large-scale association studies
because they can be less prone to overfitting, can be less computer processor time intensive, can easily include pair-wise
and higher-order interactions compared with standard statistical approaches and can also have a high capability for
classification. However, improved implementations that are able to deal with hundreds of thousands of SNPs at a time are
required.
