Abstract Background Alzheimer’s disease (AD) is a complex neurodegenerative disorder and the most common type of dementia. AD is characterized by a decline of cognitive function and brain atrophy, and is highly heritable with estimated heritability ranging from 60 to 80 $$\%$$ % . The most straightforward and widely used strategy to identify AD genetic basis is to perform genome-wide association study (GWAS) of the case-control diagnostic status. These GWAS studies have identified over 50 AD related susceptibility loci. Recently, imaging genetics has emerged as a new field where brain imaging measures are studied as quantitative traits to detect genetic factors. Given that many imaging genetics studies did not involve the diagnostic outcome in the analysis, the identified imaging or genetic markers may not be related or specific to the disease outcome. Results We propose a novel method to identify disease-related genetic variants enriched by imaging endophenotypes, which are the imaging traits associated with both genetic factors and disease status. Our analysis consists of three steps: (1) map the effects of a genetic variant (e.g., single nucleotide polymorphism or SNP) onto imaging traits across the brain using a linear regression model, (2) map the effects of a diagnosis phenotype onto imaging traits across the brain using a linear regression model, and (3) detect SNP-diagnosis association via correlating the SNP effects with the diagnostic effects on the brain-wide imaging traits. We demonstrate the promise of our approach by applying it to the Alzheimer’s Disease Neuroimaging Initiative database. Among 54 AD related susceptibility loci reported in prior large-scale AD GWAS, our approach identifies 41 of those from a much smaller study cohort while the standard association approaches identify only two of those. Clearly, the proposed imaging endophenotype enriched approach can reveal promising AD genetic variants undetectable using the traditional method. Conclusion We have proposed a novel method to identify AD genetic variants enriched by brain-wide imaging endophenotypes. This approach can not only boost detection power, but also reveal interesting biological pathways from genetic determinants to intermediate brain traits and to phenotypic AD outcomes.
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Abstract Background In Alzheimer’s Diseases (AD) research, multimodal imaging analysis can unveil complementary information from multiple imaging modalities and further our understanding of the disease. One application is to discover disease subtypes using unsupervised clustering. However, existing clustering methods are often applied to input features directly, and could suffer from the curse of dimensionality with high-dimensional multimodal data. The purpose of our study is to identify multimodal imaging-driven subtypes in Mild Cognitive Impairment (MCI) participants using a multiview learning framework based on Deep Generalized Canonical Correlation Analysis (DGCCA), to learn shared latent representation with low dimensions from 3 neuroimaging modalities.
Results DGCCA applies non-linear transformation to input views using neural networks and is able to learn correlated embeddings with low dimensions that capture more variance than its linear counterpart, generalized CCA (GCCA). We designed experiments to compare DGCCA embeddings with single modality features and GCCA embeddings by generating 2 subtypes from each feature set using unsupervised clustering. In our validation studies, we found that amyloid PET imaging has the most discriminative features compared with structural MRI and FDG PET which DGCCA learns from but not GCCA. DGCCA subtypes show differential measures in 5 cognitive assessments, 6 brain volume measures, and conversion to AD patterns. In addition, DGCCA MCI subtypes confirmed AD genetic markers with strong signals that existing late MCI group did not identify.
Conclusion Overall, DGCCA is able to learn effective low dimensional embeddings from multimodal data by learning non-linear projections. MCI subtypes generated from DGCCA embeddings are different from existing early and late MCI groups and show most similarity with those identified by amyloid PET features. In our validation studies, DGCCA subtypes show distinct patterns in cognitive measures, brain volumes, and are able to identify AD genetic markers. These findings indicate the promise of the imaging-driven subtypes and their power in revealing disease structures beyond early and late stage MCI.
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Abstract Background Although genome-wide association studies (GWAS) have successfully located various genetic variants susceptible to Alzheimer’s Disease (AD), it is still unclear how specific variants interact with genes and tissues to elucidate pathologies associated with AD. Summary-data-based Mendelian Randomization (SMR) addresses this problem through an instrumental variable approach that integrates data from independent GWAS and expression quantitative trait locus (eQTL) studies in order to infer a causal effect of gene expression on a trait.
Results Our study employed the SMR approach to integrate a set of meta-analytic cis-eQTL information from the Genotype-Tissue Expression (GTEx), CommonMind Consortium (CMC), and Religious Orders Study and Rush Memory and Aging Project (ROS/MAP) consortiums with three sets of meta-analysis AD GWAS results.
Conclusions Our analysis identified twelve total gene probes (associated with twelve distinct genes) with a significant association with AD. Four of these genes survived a test of pleiotropy from linkage (the HEIDI test).Three of these genes – RP11-385F7.1, PRSS36, and AC012146.7 – have not yet been reported differentially expressed in the brain in the context of AD, and thus are the novel findings warranting further investigation.
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Background: Human brain structural connectivity is an important imaging quantitative trait for brain development and aging. Mapping the network connectivity to the phenotypic variation provides fundamental insights in understanding the relationship between detailed brain topological architecture, function, and dysfunction. However, the underlying neurobiological mechanism from gene to brain connectome, and to phenotypic outcomes, and whether this mechanism changes over time, remain unclear. Methods: This study analyzes diffusion-weighted imaging data from two age-specific neuroimaging cohorts, extracts structural connectome topological network measures, performs genome-wide association studies of the measures, and examines the causality of genetic influences on phenotypic outcomes mediated via connectivity measures. Results: Our empirical study has yielded several significant findings: 1) It identified genetic makeup underlying structural connectivity changes in the human brain connectome for both age groups. Specifically, it revealed a novel association between the minor allele (G) of rs7937515 and the decreased network segregation measures of the left middle temporal gyrus across young and elderly adults, indicating a consistent genetic effect on brain connectivity across the lifespan. 2) It revealed rs7937515 as a genetic marker for body mass index in young adults but not in elderly adults. 3) It discovered brain network segregation alterations as a potential neuroimaging biomarker for obesity. 4) It demonstrated the hemispheric asymmetry of structural network organization in genetic association analyses and outcome-relevant studies. Discussion: These imaging genetic findings underlying brain connectome warrant further investigation for exploring their potential influences on brain-related complex diseases, given the significant involvement of altered connectivity in neurological, psychiatric and physical disorders.more » « less
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Brain imaging genetics studies the genetic basis of brain structures and functionalities via integrating genotypic data such as single nucleotide polymorphisms (SNPs) and imaging quantitative traits (QTs). In this area, both multi-task learning (MTL) and sparse canonical correlation analysis (SCCA) methods are widely used since they are superior to those independent and pairwise univariate analysis. MTL methods generally incorporate a few of QTs and could not select features from multiple QTs; while SCCA methods typically employ one modality of QTs to study its association with SNPs. Both MTL and SCCA are computational expensive as the number of SNPs increases. In this paper, we propose a novel multi-task SCCA (MTSCCA) method to identify bi-multivariate associations between SNPs and multi-modal imaging QTs. MTSCCA could make use of the complementary information carried by different imaging modalities. MTSCCA enforces sparsity at the group level via the G2,1-norm, and jointly selects features across multiple tasks for SNPs and QTs via the L2,1-norm. A fast optimization algorithm is proposed using the grouping information of SNPs. Compared with conventional SCCA methods, MTSCCA obtains better correlation coefficients and canonical weights patterns. In addition, MTSCCA runs very fast and easy-to-implement, indicating its potential power in genome-wide brain-wide imaging genetics.more » « less
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