A Multi-task Deep Feature Selection Method for Brain Imaging Genetics

TL;DR

This paper introduces a multi-task deep learning approach that simultaneously models complex nonlinear relationships and performs feature selection to identify genetic risk factors from brain imaging data.

Contribution

It proposes a novel multi-task deep feature selection method that combines nonlinear modeling with SNP relevance selection in brain imaging genetics.

Findings

MTDFS outperforms linear regression and single-task methods in identifying QT-SNP relationships.

MTDFS effectively selects relevant genetic factors contributing to brain imaging traits.

The method enhances the understanding of genetic influences on brain imaging phenotypes.

Abstract

Using brain imaging quantitative traits (QTs) to identify the genetic risk factors is an important research topic in imaging genetics. Many efforts have been made via building linear models, e.g. linear regression (LR), to extract the association between imaging QTs and genetic factors such as single nucleotide polymorphisms (SNPs). However, to the best of our knowledge, these linear models could not fully uncover the complicated relationship due to the loci's elusive and diverse impacts on imaging QTs. Though deep learning models can extract the nonlinear relationship, they could not select relevant genetic factors. In this paper, we proposed a novel multi-task deep feature selection (MTDFS) method for brain imaging genetics. MTDFS first adds a multi-task one-to-one layer and imposes a hybrid sparsity-inducing penalty to select relevant SNPs making significant contributions to abnormal imaging QTs. It then builds a multi-task deep neural network to model the complicated associations between imaging QTs and SNPs. MTDFS can not only extract the nonlinear relationship but also arms the deep neural network with the feature selection capability. We compared MTDFS to both LR and single-task DFS (DFS) methods on the real neuroimaging genetic data. The experimental results showed that MTDFS performed better than both LR and DFS in terms of the QT-SNP relationship identification and feature selection. In a word, MTDFS is powerful for identifying risk loci and could be a great supplement to the method library for brain imaging genetics.