A cross-validation procedure for general pedigrees and matched odds ratio fitness metric implemented for the multifactor dimensionality reduction pedigree disequilibrium test

A roadmap to multifactor dimensionality reduction methods

Complex diseases are defined to be determined by multiple genetic and environmental factors alone as well as in interactions. To analyze interactions in genetic data, many statistical methods have been suggested, with most of them relying on statistical regression models. Given the known limitations of classical methods, approaches from the machine-learning community have also become attractive. From this latter family, a fast-growing collection of methods emerged that are based on the Multifactor Dimensionality Reduction (MDR) approach. Since its first introduction, MDR has enjoyed great popularity in applications and has been extended and modified multiple times. Based on a literature search, we here provide a systematic and comprehensive overview of these suggested methods. The methods are described in detail, and the availability of implementations is listed. Most recent approaches offer to deal with large-scale data sets and rare variants, which is why we expect these methods to even gain in popularity.

Metabolomics of volatile organic compounds in cystic fibrosis patients and controls

In cystic fibrosis (CF), airway inflammation causes an increased production of reactive oxygen species, responsible for degradation of cell membranes. During this process, volatile organic compounds (VOCs) are formed. Measurement of VOCs in exhaled breath of CF patients may be useful for the assessment of airway inflammation. This study investigates whether "metabolomics' of VOCs could discriminate between CF and controls, and between CF patients with and without Pseudomonas colonization. One hundred five children (48 with CF, 57 controls) were included in this study. After exhaled breath collection, samples were transferred onto tubes containing active carbon to adsorb and stabilize VOCs. Samples were analyzed by gas chromatography-time of flight-mass spectrometry to assess VOC profiles. Analysis showed that 1099 VOCs had a prevalence of at least 7%. By using 22 VOCs, a 100% correct identification of CF patients and controls was possible. With 10 VOCs, 92% of the subjects were correctly classified. The reproducibility of VOC measurements with a 1-h interval was very good (match factor 0.90 +/- 0.038). We conclude that metabolomics of VOCs in exhaled breath was possible in a reproducible way. This new technique was able to discriminate not only between CF patients and controls but also between CF patients with or without Pseudomonas colonization.

A general framework for formal tests of interaction after exhaustive search methods with applications to MDR and MDR-PDT

The initial presentation of multifactor dimensionality reduction (MDR) featured cross-validation to mitigate over-fitting, computationally efficient searches of the epistatic model space, and variable construction with constructive induction to alleviate the curse of dimensionality. However, the method was unable to differentiate association signals arising from true interactions from those due to independent main effects at individual loci. This issue leads to problems in inference and interpretability for the results from MDR and the family-based compliment the MDR-pedigree disequilibrium test (PDT). A suggestion from previous work was to fit regression models post hoc to specifically evaluate the null hypothesis of no interaction for MDR or MDR-PDT models. We demonstrate with simulation that fitting a regression model on the same data as that analyzed by MDR or MDR-PDT is not a valid test of interaction. This is likely to be true for any other procedure that searches for models, and then performs an uncorrected test for interaction. We also show with simulation that when strong main effects are present and the null hypothesis of no interaction is true, that MDR and MDR-PDT reject at far greater than the nominal rate. We also provide a valid regression-based permutation test procedure that specifically tests the null hypothesis of no interaction, and does not reject the null when only main effects are present. The regression-based permutation test implemented here conducts a valid test of interaction after a search for multilocus models, and can be applied to any method that conducts a search to find a multilocus model representing an interaction.