MOTIVATION: Alzheimer's disease (AD) typically progresses gradually for ages rather than suddenly. Thus, staging AD progression in different phases could aid in accurate diagnosis and treatment. In addition, identifying genetic variations that influence AD is critical to understanding the pathogenesis. However, staging the disease progression and identifying genetic variations is usually handled separately. RESULTS: To address this limitation, we propose a novel sparse multi-stage multi-task mixed-effects collaborative longitudinal regression method (MSColoR). Our method jointly models long disease progression as a multi-stage procedure and identifies genetic risk factors underpinning this complex trajectory. Specifically, MSColoR models multi-stage disease progression using longitudinal neuroimaging-derived phenotypes and associates the fitted disease trajectories with genetic variations at each stage. Furthermore, we collaboratively leverage summary statistics from large genome-wide association studies to improve the powers. Finally, an efficient optimization algorithm is introduced to solve MSColoR. We evaluate our method using both synthetic and real longitudinal neuroimaging and genetic data. Both results demonstrate that MSColoR can reduce modeling errors while identifying more accurate and significant genetic variations compared to other longitudinal methods. Consequently, MSColoR holds great potential as a computational technique for longitudinal brain imaging genetics and AD studies. AVAILABILITY AND IMPLEMENTATION: The code is publicly available at https://github.com/dulei323/MSColoR.

Brain imaging genetics offers insights into the genetic basis of brain structure and function by exploring the relationships between genetic variations and neuroimaging features, with canonical correlation association learning as a vital and effective tool. However, imaging large cohorts affected by specific brain diseases entails significant costs. To tackle this challenge, we introduced a novel bi-multivariate sparse canonical correlation association method based on summary statistics from large GWAS (S-SCCA). S-SCCA leverages effect sizes obtained from these datasets to identify genetic variants associated with complex traits, including those influenced by pleiotropy, while simultaneously identifying imaging factors related to the disease under study. Moreover, we have implemented a rapid optimization strategy to circumvent computational burdens while identifying disease-associated risk factors within genetic variations across the entire chromosome. We assessed S-SCCA against conventional SCCA using a neuroimaging genetic dataset from the Alzheimer’s Disease Neuroimaging Initiative. Results showed that S-SCCA demonstrated comparable or superior modeling performance and feature selection capabilities. Furthermore, we applied S-SCCA to two summary statistics datasets from two large GWAS, where original imaging and genetic data were inaccessible. S-SCCA replicated the genetic loci identified by GWAS and additional meaningful variants. Additionally, it revealed bi-multivariate relationships between imaging QTs and SNPs, indicating its powerful modeling capability. These findings highlight the promise of S-SCCA as a practical bi-multivariate learning technique in brain imaging genetics, circumventing the need for sensitive individual-level imaging and genetic data, thereby enhancing its potential for broader applicability and accessibility in biomedical studies.

Discovering critical genetic biomarkers of Alzheimer’s disease (AD) by detecting the complex associations between genotypes (i.e. single nucleotide polymorphism, SNP) and phenotypes (i.e. quantitative trait, QT) is a long-standing and beneficial task for the diagnosis and the follow-up treatment of patients. The function of genes and their relationships with phenotypes are extremely complex. A lot of imaging genetic methods have been designed to uncover the association between brain imaging QTs and SNPs. However, most of them are focused on the effect of a single SNP, which may have limited ability due to the oligogenic or polygenic characteristic of AD. In this paper, we propose a deep reconstruction bidirectional association with feature selection (DRBA-FS) method to explore the multi-SNPmulti-QT associations. In this method, the co-effect of multiple AD-related genetic variations is identified and aggregated, and their high-level genetic associations to brain imaging QTs are jointly modeled. Experiment results on real neuroimaging genetic data from Alzheimer’s Disease Neuroimaging Initiative (ADNI) show that the identified biomarkers are all related to AD. Interestingly, our method can learn the joint effect of multiple AD-related genetic variations across the genome, and thus has significant potential in understanding the genetic mechanism of AD.

Brain imaging genetics investigates the complex relationships between genetic variations and brain imaging quantitative traits (QTs). However, existing approaches primarily focus on the main effects of genetic variations, potentially neglecting the crucial role of epistasis that explains the missing heritability of brain disorders. Unfortunately, this issue has been largely unexploited. In this paper, to fill this gap, we present Multi-Task feature interaction-aware Sparse Canonical Correlation Analysis (MTfiSCCA) to identify disease-related main effect and epistasis of risk genetic factors on multimodal neuroimaging phenotypes simultaneously. To ensure stability and interpretation, we use innovative sparsity-inducing penalties to identify biomarkers that make significant contributions. Additionally, we develop an efficient optimization algorithm to solve the proposed method, which converges to a local optimum. Experimental results on the Alzheimer’s disease neuroimaging initiative (ADNI) dataset show that our MTfiSCCA method achieves higher canonical correlation coefficients (CCC) and better feature selection subsets such as disease-related biomarkers compared to the state-of-the-art methods. Furthermore, MTfiSCCA reveals interpretable epistasis among genetic variations implicated in AD, offering novel insights into the underlying pathogenic mechanisms of brain disorders such as Alzheimer’s disease (AD).

Alzheimer’s disease (AD) is a very complex neurodegenerative disease. Generally, different diagnostic groups could exhibit discriminative and specific patterns, including the single nucleotide polymorphisms (SNPs), brain imaging quantitative traits (QTs), as well as their associations, which may facilitate the comprehensive understanding of AD. However, most existing methods cannot guarantee to identify discriminative or class-specific biomarkers or both of them. To overcome this shortcoming, we propose a sparse multi-task contrastive and discriminative learning approach (MTCDA) to jointly learn the discriminative and specific patterns for multiple diagnostic groups. MTCDA can identify the class-relevant and discriminative SNP-QTs associations, and relevant SNPs, imaging QTs underpinning this relationship. We introduce an efficient algorithm to solve the proposed method which converges to a local optimum. The experimental results on Alzheimer’s Disease Neuroimaging Initiative (ADNI) show that MTCDA can obtain higher canonical correlation coefficients, classification accuracy and better feature selection results than state-of-the-art methods, which demonstrates the potential of our method for multi-class brain imaging genetics.