Pattern Examiner addresses one of the most pressing issues in whole-genome association studies: how to perform fast and exhaustive gene interaction analysis with hundreds of thousands of markers. brute-force pair-wise comparison is not a practical option. Many multi-locus association analysis methods have been developed, however few offers efficient solutions for dataset this large. One particular method, the Multi-Dimension Reduction (MDR) method, has been frequently used to detect gene interactions. Despite its innovative way to reduce dimensions, it is still extremely difficult, if not impossible, to analyze hundreds of thousands of markers with MDR.

Pattern Examiner is a nonparametric data mining-based method for the detection of multi-locus associations and gene × gene/gene × environment interactions on data collected in population-based case/control studies. This method has two steps: 1) pattern discovery and 2) significance evaluation. In the pattern discovery step, patterns are identified using as input data from the case population only. The extensiveness (and execution time) of the pattern discovery step is controlled by two parameters: the support threshold, which specifies the minimum number of rows a pattern must have; and the locus threshold, which specifies the extent of locus interaction.

In the significance evaluation step, a contingency table is constructed for each pattern to tally its support in the case and control populations ("case support" and "control support", respectively). The p-value is obtained from a chi-square test of independence and then adjusted for multiple testing. A modified Bonferroni correction for multiple testing is applied to each pattern.

High Throughput Biology has several on-going scientific collaborations with prominent universities, research institutes, as well as pharmaceutical companies such as Yale University, Columbia University, Centocor Pharmaceuticals. In these collaborations we have used Pattern Examiner and other genetic analysis tools such as PLINK and Haploview to detect genetic association to diseases or clinical end points. If you are interested in establishing a scientific collaboration, please contact us.