Brain-Wide Genome-Wide Association Study for Alzheimer's Disease via Joint Projection Learning and Sparse Regression Model

Abstract

In this paper, a Brain-Wide and Genome-Wide Association (BW-GWA) study is presented to identify the associations between the brain imaging phenotypes (i.e., regional volumetric measures) and the genetic variants (i.e., Single Nucleotide Polymorphism (SNP)) in Alzheimer's Disease (AD). The main challenges of this study include the data heterogeneity, complex phenotype-genotype associations, high-dimensional data (e.g., thousands of SNPs), and the existence of phenotype outliers. Previous BW-GWA studies, while addressing some of these challenges, did not consider the diagnostic label information in their formulations, thus limiting their clinical applicability. To address these issues, we present a novel joint projection and sparse regression model to discover the associations between the phenotypes and genotypes. Specifically, to alleviate the negative influence of data heterogeneity, we first map the genotypes to an intermediate imaging-phenotype-like space. Then, to better reveal the complex phenotype-genotype associations, we project both the mapped genotypes and the original imaging phenotypes to a diagnostic-label-guided joint feature space, where the intra-class projected points are constrained to be close to each other. In addition, we use L2,1-norm minimization on both the regression loss function and the transformation coefficient matrices, to reduce the effect of phenotype outliers, and to encourage sparse feature selections of both the genotypes and phenotypes. We evaluate our method using Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and the results show that the proposed method outperforms several state-of-the-art methods in term of average Root-Mean-Square Error (RMSE) of genome-to-phenotype predictions. Besides, the associated SNPs and brain regions found in this study have also been shown AD-related in previously studies, thus verifying the effectiveness and potential of the proposed method in AD pathogenesis study.

BIRNet: Brain image registration using dual-supervised fully convolutional networks

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A Novel Three-Stage Framework for Association Analysis Between SNPs and Brain Regions

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