Transcriptome-guided amyloid imaging genetic analysis via a novel structured sparse learning algorithm

Abstract

Motivation: Imaging genetics is an emerging field that studies the influence of genetic variation on brain structure and function. The major task is to examine the association between genetic markers such as single-nucleotide polymorphisms (SNPs) and quantitative traits (QTs) extracted from neuroimaging data. The complexity of these datasets has presented critical bioinformatics challenges that require new enabling tools. Sparse canonical correlation analysis (SCCA) is a bi-multivariate technique used in imaging genetics to identify complex multi-SNP–multi-QT associations. However, most of the existing SCCA algorithms are designed using the soft thresholding method, which assumes that the input features are independent from one another. This assumption clearly does not hold for the imaging genetic data. In this article, we propose a new knowledge-guided SCCA algorithm (KG-SCCA) to overcome this limitation as well as improve learning results by incorporating valuable prior knowledge.Results: The proposed KG-SCCA method is able to model two types of prior knowledge: one as a group structure (e.g. linkage disequilibrium blocks among SNPs) and the other as a network structure (e.g. gene co-expression network among brain regions). The new model incorporates these prior structures by introducing new regularization terms to encourage weight similarity between grouped or connected features. A new algorithm is designed to solve the KG-SCCA model without imposing the independence constraint on the input features. We demonstrate the effectiveness of our algorithm with both synthetic and real data. For real data, using an Alzheimer’s disease (AD) cohort, we examine the imaging genetic associations between all SNPs in the APOE gene (i.e. top AD gene) and amyloid deposition measures among cortical regions (i.e. a major AD hallmark). In comparison with a widely used SCCA implementation, our KG-SCCA algorithm produces not only improved cross-validation performances but also biologically meaningful results.Availability: Software is freely available on request.Contact: shenli@iu.edu

Genetic studies of quantitative MCI and AD phenotypes in ADNI: Progress, opportunities, and plans

“…Finally, several amyloid PET studies examined various facets of epistasis or gene-gene interactions. Yan et al [62] performed a transcriptome-guided amyloid imaging genetic analysis and identified bi-multivariate associations between APOE SNPs and brain-wide amyloid imaging measures. Hohman et al [63] examined epistatic relationships between genes involved in amyloid and tau pathophysiology using [ 18 F]Florbetapir PET imaging as the target phenotype.…”

Two-dimensional enrichment analysis for mining high-level imaging genetic associations

“…Due to many-to-one mapping from brain samples to AAL ROIs, there are >1 samples for each ROI. Following [27], samples located in the same ROI were merged using the mean statistics. Probes were then merged to genes using the same strategy.…”

Structured Sparse Low-Rank Regression Model for Brain-Wide and Genome-Wide Associations

“…The assumption of the existence of latent factors in either phenotypes or genotypes has been reported, making imaging-genetic analysis gain accurate estimation [1,15]. Equation (3) may achieve by seeking the low-rank representation of phenotypes and genotypes, but not producing interpretable results and also not touching the issues of non-invertible X T X and over-fitting.…”