P-SVM Variable Selection for Discovering Dependencies Between Genetic and Brain Imaging Data

The joint analysis of genetic and brain imaging data is the key to understand the genetic underpinnings of brain dysfunctions in several psychiatric diseases known to have a strong genetic component. The goal is to identify associations between genetic and functional or morphometric brain measurements. We here suggest a machine learning method to solve this task, which is based on the recently proposed Potential Support Vector Machine (P-SVM) for variable selection, a subsequent k-NN classification and an estimation of the effect of 'correlations by chance'. We apply it to the detection of associations between candidate single nucleotide polymorphisms (SNPs) and volumetric MRI measurements in alcohol dependent patients and healthy controls.

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