gms | German Medical Science

In genetic epidemiology, we are facing extremely high dimensional data and complex patterns such as gene-gene or gene-environment interactions. For this reason, it is promising to use machine learning instead of classical statistical methods to analyze such data. However, most methods for statistical inference with machine learning test against a marginal null hypothesis and by that cannot handle correlated predictor variables.

Building on the knockoff framework of Candès et al. [1], we propose the conditional predictive impact (CPI), a provably consistent and unbiased estimator of a variables' association with a given outcome, conditional on a reduced set of predictor variables. The method works in conjunction with any supervised learning algorithm and loss function. Simulations confirm that our inference procedures successfully control type I error and achieve nominal coverage probability with greater power than alternative variable importance measures and other nonparametric tests of conditional independence. We apply our method to a gene expression dataset on breast cancer. Further, we propose a modification which avoids the computation of the high-dimensional knockoff matrix and is computationally feasible on data from genome-wide association studies.

The authors declare that they have no competing interests.

The authors declare that an ethics committee vote is not required.