Fused multi‐modal similarity network as prior in guiding brain imaging genetic association

Abstract

AbstractBackgroundBrain imaging genetics aims to explore the genetic architecture underlying brain structure and functions. Recent studies showed that incorporation of prior knowledge, such as subject diagnosis information and brain regional correlation, can help identify significantly stronger imaging genetic associations. However, sometimes such information may be incomplete or even unavailable. In this project, we explore a new data‐driven prior knowledge that captures the subject‐level similarity matrix derived from fusion of multi‐modal similarity networks. It was built into the sparse canonical correlation analysis model, which is expected to identify brain imaging and genetic markers that contribute to the fused similarity matrix.MethodThe Amyloid and Tau imaging data were downloaded from the ADNI database. We have 800 and 291 subjects for amyloid and tau imaging respectively (Table. 1). SNPs with p<5×10−6 (n = 1080) were extracted from the large‐scale GWAS summary statistics from the IGAP. We built similarity networks for imaging and genetic data individually using normalized mutual information and then fused these network following (Fig. 1). Fused network was then applied as a prior knowledge in the discriminative sparse canonical correlation analysis (DSCCA) model. We also tested other prior knowledge in SCCA model for comparison including diagnosis group network and transcriptomic brain network. All methods were evaluated using ADNI genotype, amyloid and tau imaging data, where nested 5‐fold cross validation with the same set of training and test data was applied for fair comparison.ResultSCCA with fused network and diagnosis group showed better performances over others in both training and testing data set (Table. 2,3). In Fig. 2 are top 10 ROIs identified by SCCA with fused network, whose amyloid deposition are strongly associated with APOE SNP (rs429358). For tau, we identified left and right amygdala with tau accumulation strongly and consistently associated with rs429358 (fig.3).ConclusionAs prior knowledge incorporated into SCCA algorithm, fused similarity network can help improve the performance of imaging genetic association over traditional SCCA and that guided by the brain co‐expression network. It showed comparable performance as diagnosis information and thus could be a potential prior knowledge to consider when lack of diagnosis information.