A novel model-free feature selection method with FDR control for omics-wide association analysis

Abstract

Omics-wide association analysis is a very important tool for medicine and human health study. However, the modern omics data sets collected often exhibit the high-dimensionality, unknown distribution response, unknown distribution features and unknown complex associated relationships between the response and its explanatory features. Reliable association analysis results depend on an accurate modeling for such data sets. Most of the existing association analysis methods rely on the specific model assumptions and lack effective false discovery rate (FDR) control so that they may not work well. To address these limitations, we firstly apply a single index model for omics data. This model is free in performance of allowing the relationships between the response variable and linear combination of covariates can be connected by any unknown monotonic link function, and both the random error and the covariates can follow any unknown distribution. Then based on this model, we combine rank-based approach and symmetrized data aggregation approach to develop a novel and model-free feature selection method for achieving fine-mapping of risk features while controlling the false positive rate of selection. The analysis results of simulated data show our method possesses effective and robust performance for all the scenarios. The proposed method is also used to analyze a real ocean microbiome data and identifies some casual taxa unreported by the existing finds.