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Variable selection is of increasing importance to address the difficulties ofhigh dimensionality in many scientific areas. In this paper, we demonstrate aproperty for distance covariance, which is incorporated in a novel featurescreening procedure together with the use of distance correlation. The approachmakes no distributional assumptions for the variables and does not require thespecification of a regression model, and hence is especially attractive invariable selection given an enormous number of candidate attributes withoutmuch information about the true model with the response. The method is appliedto two genetic risk problems, where issues including uncertainty of variableselection via cross validation, subgroup of hard-to-classify cases and theapplication of a reject option are discussed.

Using distance covariance for improved variable selection with applications to genetic risk models