Targeted genetic analysis of cerebral blood flow imaging phenotypes implicates the INPP5D gene

Alzheimer's Disease: Models and Molecular Mechanisms Informing Disease and Treatments

Alzheimer's Disease (AD) is a complex neurodegenerative disease resulting in progressive loss of memory, language and motor abilities caused by cortical and hippocampal degeneration. This review captures the landscape of understanding of AD pathology, diagnostics, and current therapies. Two major mechanisms direct AD pathology: (1) accumulation of amyloid β (Aβ) plaque and (2) tau-derived neurofibrillary tangles (NFT). The most common variants in the Aβ pathway in APP, PSEN1, and PSEN2 are largely responsible for early-onset AD (EOAD), while MAPT, APOE, TREM2 and ABCA7 have a modifying effect on late-onset AD (LOAD). More recent studies implicate chaperone proteins and Aβ degrading proteins in AD. Several tests, such as cognitive function, brain imaging, and cerebral spinal fluid (CSF) and blood tests, are used for AD diagnosis. Additionally, several biomarkers seem to have a unique AD specific combination of expression and could potentially be used in improved, less invasive diagnostics. In addition to genetic perturbations, environmental influences, such as altered gut microbiome signatures, affect AD. Effective AD treatments have been challenging to develop. Currently, there are several FDA approved drugs (cholinesterase inhibitors, Aß-targeting antibodies and an NMDA antagonist) that could mitigate AD rate of decline and symptoms of distress.

SHIP1 and its role for innate immune regulation-Novel targets for immunotherapy

Phosphoinositide-3-kinase/AKT (PI3K/AKT) signaling plays key roles in the regulation of cellular activity in both health and disease. In immune cells, this PI3K/AKT pathway is critically regulated by the phosphoinositide phosphatase SHIP1, which has been reported to modulate the function of most immune subsets. In this review, we summarize our current knowledge of SHIP1 with a focus on innate immune cells, where we reflect on the most pertinent aspects described in the current literature. We also present several small-molecule agonists and antagonists of SHIP1 developed over the last two decades, which have led to improved outcomes in several preclinical models of disease. We outline these promising findings and put them in relation to human diseases with unmet medical needs, where we discuss the most attractive targets for immune therapies based on SHIP1 modulation.

Microglial function, INPP5D/SHIP1 signaling, and NLRP3 inflammasome activation: implications for Alzheimer's disease

Recent genetic studies on Alzheimer's disease (AD) have brought microglia under the spotlight, as loci associated with AD risk are enriched in genes expressed in microglia. Several of these genes have been recognized for their central roles in microglial functions. Increasing evidence suggests that SHIP1, the protein encoded by the AD-associated gene INPP5D, is an important regulator of microglial phagocytosis and immune response. A recent study from our group identified SHIP1 as a negative regulator of the NLRP3 inflammasome in human iPSC-derived microglial cells (iMGs). In addition, we found evidence for a connection between SHIP1 activity and inflammasome activation in the AD brain. The NLRP3 inflammasome is a multiprotein complex that induces the secretion of pro-inflammatory cytokines as part of innate immune responses against pathogens and endogenous damage signals. Previously published studies have suggested that the NLRP3 inflammasome is activated in AD and contributes to AD-related pathology. Here, we provide an overview of the current understanding of the microglial NLRP3 inflammasome in the context of AD-related inflammation. We then review the known intracellular functions of SHIP1, including its role in phosphoinositide signaling, interactions with microglial phagocytic receptors such as TREM2 and evidence for its intersection with NLRP3 inflammasome signaling. Through rigorous examination of the intricate connections between microglial signaling pathways across several experimental systems and postmortem analyses, the field will be better equipped to tailor newly emerging therapeutic strategies targeting microglia in neurodegenerative diseases.

Microglial INPP5D limits plaque formation and glial reactivity in the PSAPP mouse model of Alzheimer's disease

INTRODUCTION

The inositol polyphosphate-5-phosphatase D (INPP5D) gene encodes a dual-specificity phosphatase that can dephosphorylate both phospholipids and phosphoproteins. Single nucleotide polymorphisms in INPP5D impact risk for developing late onset sporadic Alzheimer's disease (LOAD).

METHODS

To assess the consequences of inducible Inpp5d knockdown in microglia of APPKM670/671NL /PSEN1Δexon9 (PSAPP) mice, we injected 3-month-old Inpp5dfl/fl /Cx3cr1CreER/+ and PSAPP/Inpp5dfl/fl /Cx3cr1CreER/+ mice with either tamoxifen (TAM) or corn oil (CO) to induce recombination.

RESULTS

At age 6 months, we found that the percent area of 6E10+ deposits and plaque-associated microglia in Inpp5d knockdown mice were increased compared to controls. Spatial transcriptomics identified a plaque-specific expression profile that was extensively altered by Inpp5d knockdown.

DISCUSSION

These results demonstrate that conditional Inpp5d downregulation in the PSAPP mouse increases plaque burden and recruitment of microglia to plaques. Spatial transcriptomics highlighted an extended gene expression signature associated with plaques and identified CST7 (cystatin F) as a novel marker of plaques.

HIGHLIGHTS

Inpp5d knockdown increases plaque burden and plaque-associated microglia number. Spatial transcriptomics identifies an expanded plaque-specific gene expression profile. Plaque-induced gene expression is altered by Inpp5d knockdown in microglia. Our plaque-associated gene signature overlaps with human Alzheimer's disease gene networks.

INPP5D modulates TREM2 loss-of-function phenotypes in a β-amyloidosis mouse model

The genetic associations of TREM2 loss-of-function variants with Alzheimer disease (AD) indicate the protective roles of microglia in AD pathogenesis. Functional deficiencies of TREM2 disrupt microglial clustering around amyloid β (Aβ) plaques, impair their transcriptional response to Aβ, and worsen neuritic dystrophy. However, the molecular mechanism underlying these phenotypes remains unclear. In this study, we investigated the pathological role of another AD risk gene, INPP5D, encoding a phosphoinositide PI(3,4,5)P3 phosphatase expressed in microglia. In a Tyrobp-deficient TREM2 loss-of-function mouse model, Inpp5d haplodeficiency restored the association of microglia with Aβ plaques, partially restored plaque compaction, and astrogliosis, and reduced phosphorylated tau+ dystrophic neurites. Mechanistic analyses suggest that TREM2/TYROBP and INPP5D exert opposing effects on PI(3,4,5)P3 signaling pathways as well as on phosphoproteins involved in the actin assembly. Our results suggest that INPP5D acts downstream of TREM2/TYROBP to regulate the microglial barrier against Aβ toxicity, thereby modulates Aβ-dependent pathological conversion of tau.

Mitochondrial oxidative stress in brain microvascular endothelial cells: Triggering blood-brain barrier disruption

Blood-brain barrier disruption plays an important role in central nervous system diseases. This review provides information on the role of mitochondrial oxidative stress in brain microvascular endothelial cells in cellular dysfunction, the disruption of intercellular junctions, transporter dysfunction, abnormal angiogenesis, neurovascular decoupling, and the involvement and aggravation of vascular inflammation and illustrates related molecular mechanisms. In addition, recent drug and nondrug therapies targeting cerebral vascular endothelial cell mitochondria to repair the blood-brain barrier are discussed. This review shows that mitochondrial oxidative stress disorder in brain microvascular endothelial cells plays a key role in the occurrence and development of blood-brain barrier damage and may be critical in various pathological mechanisms of blood-brain barrier damage. These new findings suggest a potential new strategy for the treatment of central nervous system diseases through mitochondrial modulation of cerebral vascular endothelial cells.

Pathological Roles of INPP5D in Alzheimer's Disease

Current hypothesis of Alzheimer's disease (AD) postulates that amyloid β (Aβ) deposition in the brain causes tau inclusion in neurons and leads to cognitive decline. The discovery of the genetic association between triggering receptor expressed on myeloid cells 2 (TREM2) with increased AD risk points to a causal link between microglia and AD pathogenesis, and revealed a crucial role of TREM2-dependent clustering of microglia around amyloid plaques that prevents Aβ toxicity to facilitate tau deposition near the plaques. Here we review the physiological and pathological roles of another AD risk gene expressed in microglia, inositol polyphosphate-5-polyphosphatase D (INPP5D), which encodes a phosphoinositide phosphatase. Evidence suggests that its risk polymorphisms alter the expression level and/or function of INPP5D, while concomitantly affecting tau levels in cerebrospinal fluids. In β-amyloidosis mice, INPP5D was upregulated upon Aβ deposition and negatively regulated the microglial clustering toward amyloid plaques. INPP5D seems to exert its function by acting antagonistically at downstream of the TREM2 signaling pathway, suggesting that it is a novel regulator of the protective barrier by microglia. Further studies to elucidate INPP5D's role in AD may help in developing new therapeutic targets for AD treatment.

Identification and Prioritization of PET Neuroimaging Targets for Microglial Phenotypes Associated with Microglial Activity in Alzheimer's Disease

Activation of microglial cells accompanies the progression of many neurodegenerative disorders, including Alzheimer's disease (AD). Development of molecular imaging tools specific to microglia can help elucidate the mechanism through which microglia contribute to the pathogenesis and progression of neurodegenerative disorders. Through analysis of published genetic, transcriptomic, and proteomic data sets, we identified 19 genes with microglia-specific expression that we then ranked based on association with the AD characteristics, change in expression, and potential druggability of the target. We believe that the process we used to identify and rank microglia-specific genes is broadly applicable to the identification and evaluation of targets in other disease areas and for applications beyond molecular imaging.

Deep multiview learning to identify imaging-driven subtypes in mild cognitive impairment

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.

Integrative analysis of summary data from GWAS and eQTL studies implicates genes differentially expressed in Alzheimer's disease

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.

Feature Fusion and Detection in Alzheimer’s Disease Using a Novel Genetic Multi-Kernel SVM Based on MRI Imaging and Gene Data

Voxel-based morphometry provides an opportunity to study Alzheimer’s disease (AD) at a subtle level. Therefore, identifying the important brain voxels that can classify AD, early mild cognitive impairment (EMCI) and healthy control (HC) and studying the role of these voxels in AD will be crucial to improve our understanding of the neurobiological mechanism of AD. Combining magnetic resonance imaging (MRI) imaging and gene information, we proposed a novel feature construction method and a novel genetic multi-kernel support vector machine (SVM) method to mine important features for AD detection. Specifically, to amplify the differences among AD, EMCI and HC groups, we used the eigenvalues of the top 24 Single Nucleotide Polymorphisms (SNPs) in a p-value matrix of 24 genes associated with AD for feature construction. Furthermore, a genetic multi-kernel SVM was established with the resulting features. The genetic algorithm was used to detect the optimal weights of 3 kernels and the multi-kernel SVM was used after training to explore the significant features. By analyzing the significance of the features, we identified some brain regions affected by AD, such as the right superior frontal gyrus, right inferior temporal gyrus and right superior temporal gyrus. The findings proved the good performance and generalization of the proposed model. Particularly, significant susceptibility genes associated with AD were identified, such as CSMD1, RBFOX1, PTPRD, CDH13 and WWOX. Some significant pathways were further explored, such as the calcium signaling pathway (corrected p-value = 1.35 × 10⁻⁶) and cell adhesion molecules (corrected p-value = 5.44 × 10⁻⁴). The findings offer new candidate abnormal brain features and demonstrate the contribution of these features to AD.

Genetic Influence Underlying Brain Connectivity Phenotype: A Study on Two Age-Specific Cohorts

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.

Genome-Wide association study of quantitative biomarkers identifies a novel locus for alzheimer's disease at 12p12.1

BACKGROUND

Genetic study of quantitative biomarkers in Alzheimer's Disease (AD) is a promising method to identify novel genetic factors and relevant endophenotypes, which provides valuable information to deconvolute mechanistic complexity and better understand disease subtypes.

RESULTS

Using the data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we performed a genome-wide association study (GWAS) between 565,373 single nucleotide polymorphisms (SNPs) and 16 key AD biomarkers from 1,576 subjects at four visits. We identified a novel locus rs5011804 at 12p12.1 significantly associated with several AD biomarkers, including three cognitive traits (CDRSB, FAQ, ADAS13) and one imaging trait (fusiform volume). Additional mediation and interaction analyses investigated the relationships among this SNP, relevant biomarkers, and clinical diagnosis, confirming and further elaborating the genetic effects seen in the GWAS.

CONCLUSION

Our GWAS not only affirms key AD genes but also suggests the promising role of the SNP rs5011804 due to its associations with several AD cognitive and imaging outcomes. The SNP rs5011804 has a reported association with adult asthma and slightly affects intracranial volume but has not been associated with AD before. Our novel findings contribute to a more comprehensive view of the molecular mechanism behind AD.

Research on Frequent Itemset Mining of Imaging Genetics GWAS in Alzheimer’s Disease

As an efficient method, genome-wide association study (GWAS) is used to identify the association between genetic variation and pathological phenotypes, and many significant genetic variations founded by GWAS are closely associated with human diseases. However, it is not enough to mine only a single marker effect variation on complex biological phenotypes. Mining highly correlated single nucleotide polymorphisms (SNP) is more meaningful for the study of Alzheimer's disease (AD). In this paper, we used two frequent pattern mining (FPM) framework, the FP-Growth and Eclat algorithms, to analyze the GWAS results of functional magnetic resonance imaging (fMRI) phenotypes. Moreover, we applied the definition of confidence to FP-Growth and Eclat to enhance the FPM framework. By calculating the conditional probability of identified SNPs, we obtained the corresponding association rules to provide support confidence between these important SNPs. The resulting SNPs showed close correlation with hippocampus, memory, and AD. The experimental results also demonstrate that our framework is effective in identifying SNPs and provide candidate SNPs for further research.

Dysregulated phosphoinositide 3-kinase signaling in microglia: shaping chronic neuroinflammation

Microglia are integral mediators of innate immunity within the mammalian central nervous system. Typical microglial responses are transient, intending to restore homeostasis by orchestrating the removal of pathogens and debris and the regeneration of damaged neurons. However, prolonged and persistent microglial activation can drive chronic neuroinflammation and is associated with neurodegenerative disease. Recent evidence has revealed that abnormalities in microglial signaling pathways involving phosphatidylinositol 3-kinase (PI3K) and protein kinase B (AKT) may contribute to altered microglial activity and exacerbated neuroimmune responses. In this scoping review, the known and suspected roles of PI3K-AKT signaling in microglia, both during health and pathological states, will be examined, and the key microglial receptors that induce PI3K-AKT signaling in microglia will be described. Since aberrant signaling is correlated with neurodegenerative disease onset, the relationship between maladapted PI3K-AKT signaling and the development of neurodegenerative disease will also be explored. Finally, studies in which microglial PI3K-AKT signaling has been modulated will be highlighted, as this may prove to be a promising therapeutic approach for the future treatment of a range of neuroinflammatory conditions.

Hippocampal Subregion and Gene Detection in Alzheimer's Disease Based on Genetic Clustering Random Forest

The distinguishable subregions that compose the hippocampus are differently involved in functions associated with Alzheimer's disease (AD). Thus, the identification of hippocampal subregions and genes that classify AD and healthy control (HC) groups with high accuracy is meaningful. In this study, by jointly analyzing the multimodal data, we propose a novel method to construct fusion features and a classification method based on the random forest for identifying the important features. Specifically, we construct the fusion features using the gene sequence and subregions correlation to reduce the diversity in same group. Moreover, samples and features are selected randomly to construct a random forest, and genetic algorithm and clustering evolutionary are used to amplify the difference in initial decision trees and evolve the trees. The features in resulting decision trees that reach the peak classification are the important "subregion gene pairs". The findings verify that our method outperforms well in classification performance and generalization. Particularly, we identified some significant subregions and genes, such as hippocampus amygdala transition area (HATA), fimbria, parasubiculum and genes included RYR3 and PRKCE. These discoveries provide some new candidate genes for AD and demonstrate the contribution of hippocampal subregions and genes to AD.

Genome-wide variant-based study of genetic effects with the largest neuroanatomic coverage

BACKGROUND

Brain image genetics provides enormous opportunities for examining the effects of genetic variations on the brain. Many studies have shown that the structure, function, and abnormality (e.g., those related to Alzheimer's disease) of the brain are heritable. However, which genetic variations contribute to these phenotypic changes is not completely clear. Advances in neuroimaging and genetics have led us to obtain detailed brain anatomy and genome-wide information. These data offer us new opportunities to identify genetic variations such as single nucleotide polymorphisms (SNPs) that affect brain structure. In this paper, we perform a genome-wide variant-based study, and aim to identify top SNPs or SNP sets which have genetic effects with the largest neuroanotomic coverage at both voxel and region-of-interest (ROI) levels. Based on the voxelwise genome-wide association study (GWAS) results, we used the exhaustive search to find the top SNPs or SNP sets that have the largest voxel-based or ROI-based neuroanatomic coverage. For SNP sets with >2 SNPs, we proposed an efficient genetic algorithm to identify top SNP sets that can cover all ROIs or a specific ROI.

RESULTS

We identified an ensemble of top SNPs, SNP-pairs and SNP-sets, whose effects have the largest neuroanatomic coverage. Experimental results on real imaging genetics data show that the proposed genetic algorithm is superior to the exhaustive search in terms of computational time for identifying top SNP-sets.

CONCLUSIONS

We proposed and applied an informatics strategy to identify top SNPs, SNP-pairs and SNP-sets that have genetic effects with the largest neuroanatomic coverage. The proposed genetic algorithm offers an efficient solution to accomplish the task, especially for identifying top SNP-sets.

INPP5D expression is associated with risk for Alzheimer's disease and induced by plaque-associated microglia

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, robust microgliosis, neuroinflammation, and neuronal loss. Genome-wide association studies recently highlighted a prominent role for microglia in late-onset AD (LOAD). Specifically, inositol polyphosphate-5-phosphatase (INPP5D), also known as SHIP1, is selectively expressed in brain microglia and has been reported to be associated with LOAD. Although INPP5D is likely a crucial player in AD pathophysiology, its role in disease onset and progression remains unclear. We performed differential gene expression analysis to investigate INPP5D expression in AD and its association with plaque density and microglial markers using transcriptomic (RNA-Seq) data from the Accelerating Medicines Partnership for Alzheimer's Disease (AMP-AD) cohort. We also performed quantitative real-time PCR, immunoblotting, and immunofluorescence assays to assess INPP5D expression in the 5xFAD amyloid mouse model. Differential gene expression analysis found that INPP5D expression was upregulated in LOAD and positively correlated with amyloid plaque density. In addition, in 5xFAD mice, Inpp5d expression increased as the disease progressed, and selectively in plaque-associated microglia. Increased Inpp5d expression levels in 5xFAD mice were abolished entirely by depleting microglia with the colony-stimulating factor receptor-1 antagonist PLX5622. Our findings show that INPP5D expression increases as AD progresses, predominantly in plaque-associated microglia. Importantly, we provide the first evidence that increased INPP5D expression might be a risk factor in AD, highlighting INPP5D as a potential therapeutic target. Moreover, we have shown that the 5xFAD mouse model is appropriate for studying INPP5D in AD.

Polygenic mediation analysis of Alzheimer's disease implicated intermediate amyloid imaging phenotypes

Mediation models have been employed in the study of brain disorders to detect the underlying mechanisms between genetic variants and diagnostic outcomes implicitly mediated by intermediate imaging biomarkers. However, the statistical power is influenced by the modest effects of individual genetic variants on both diagnostic and imaging phenotypes and the limited sample sizes ofimaging genetic cohorts. In this study, we propose a polygenic mediation analysis that comprises a polygenic risk score (PRS) to aggregate genetic effects ofa set ofcandidate variants and then explore the implicit effect ofimaging phenotypes between the PRS and disease status. We applied our proposed method to an amyloid imaging genetic study of Alzheimer's disease (AD), identified multiple imaging mediators linking PRS with AD, and further demonstrated the promise of the PRS on mediator detection over individual variants alone.

AlzGPS: a genome-wide positioning systems platform to catalyze multi-omics for Alzheimer's drug discovery

BACKGROUND

Recent DNA/RNA sequencing and other multi-omics technologies have advanced the understanding of the biology and pathophysiology of AD, yet there is still a lack of disease-modifying treatments for AD. A new approach to integration of the genome, transcriptome, proteome, and human interactome in the drug discovery and development process is essential for this endeavor.

METHODS

In this study, we developed AlzGPS (Genome-wide Positioning Systems platform for Alzheimer's Drug Discovery, https://alzgps.lerner.ccf.org ), a comprehensive systems biology tool to enable searching, visualizing, and analyzing multi-omics, various types of heterogeneous biological networks, and clinical databases for target identification and development of effective prevention and treatment for AD.

RESULTS

Via AlzGPS: (1) we curated more than 100 AD multi-omics data sets capturing DNA, RNA, protein, and small molecule profiles underlying AD pathogenesis (e.g., early vs. late stage and tau or amyloid endophenotype); (2) we constructed endophenotype disease modules by incorporating multi-omics findings and human protein-protein interactome networks; (3) we provided possible treatment information from ~ 3000 FDA approved/investigational drugs for AD using state-of-the-art network proximity analyses; (4) we curated nearly 300 literature references for high-confidence drug candidates; (5) we included information from over 1000 AD clinical trials noting drug's mechanisms-of-action and primary drug targets, and linking them to our integrated multi-omics view for targets and network analysis results for the drugs; (6) we implemented a highly interactive web interface for database browsing and network visualization.

CONCLUSIONS

Network visualization enabled by AlzGPS includes brain-specific neighborhood networks for genes-of-interest, endophenotype disease module networks for omics-of-interest, and mechanism-of-action networks for drugs targeting disease modules. By virtue of combining systems pharmacology and network-based integrative analysis of multi-omics data, AlzGPS offers actionable systems biology tools for accelerating therapeutic development in AD.

Volumetric GWAS of medial temporal lobe structures identifies an ERC1 locus using ADNI high-resolution T2-weighted MRI data

Medial temporal lobe (MTL) consists of hippocampal subfields and neighboring cortices. These heterogeneous structures are differentially involved in memory, cognitive and emotional functions, and present nonuniformly distributed atrophy contributing to cognitive disorders. This study aims to examine how genetics influences Alzheimer's disease (AD) pathogenesis via MTL substructures by analyzing high-resolution magnetic resonance imaging (MRI) data. We performed genome-wide association study to examine the associations between 565,373 single nucleotide polymorphisms (SNPs) and 14 MTL substructure volumes. A novel association with right Brodmann area 36 volume was discovered in an ERC1 SNP (i.e., rs2968869). Further analyses on larger samples found rs2968869 to be associated with gray matter density and glucose metabolism measures in the right hippocampus, and disease status. Tissue-specific transcriptomic analysis identified the minor allele of rs2968869 (rs2968869-C) to be associated with reduced ERC1 expression in the hippocampus. All the findings indicated a protective role of rs2968869-C in AD. We demonstrated the power of high-resolution MRI and the promise of fine-grained MTL substructures for revealing the genetic basis of AD biomarkers.

Regional imaging genetic enrichment analysis

MOTIVATION

Brain imaging genetics aims to reveal genetic effects on brain phenotypes, where most studies examine phenotypes defined on anatomical or functional regions of interest (ROIs) given their biologically meaningful interpretation and modest dimensionality compared with voxelwise approaches. Typical ROI-level measures used in these studies are summary statistics from voxelwise measures in the region, without making full use of individual voxel signals.

RESULTS

In this article, we propose a flexible and powerful framework for mining regional imaging genetic associations via voxelwise enrichment analysis, which embraces the collective effect of weak voxel-level signals and integrates brain anatomical annotation information. Our proposed method achieves three goals at the same time: (i) increase the statistical power by substantially reducing the burden of multiple comparison correction; (ii) employ brain annotation information to enable biologically meaningful interpretation and (iii) make full use of fine-grained voxelwise signals. We demonstrate our method on an imaging genetic analysis using data from the Alzheimer's Disease Neuroimaging Initiative, where we assess the collective regional genetic effects of voxelwise FDG-positron emission tomography measures between 116 ROIs and 565 373 single-nucleotide polymorphisms. Compared with traditional ROI-wise and voxelwise approaches, our method identified 2946 novel imaging genetic associations in addition to 33 ones overlapping with the two benchmark methods. In particular, two newly reported variants were further supported by transcriptome evidences from region-specific expression analysis. This demonstrates the promise of the proposed method as a flexible and powerful framework for exploring imaging genetic effects on the brain.

AVAILABILITY AND IMPLEMENTATION

The R code and sample data are freely available at https://github.com/lshen/RIGEA.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Deep Multiview Learning to Identify Population Structure with Multimodal Imaging

We present an effective deep multiview learning framework to identify population structure using multimodal imaging data. Our approach is based on canonical correlation analysis (CCA). We propose to use deep generalized CCA (DGCCA) to learn a shared latent representation of non-linearly mapped and maximally correlated components from multiple imaging modalities with reduced dimensionality. In our empirical study, this representation is shown to effectively capture more variance in original data than conventional generalized CCA (GCCA) which applies only linear transformation to the multi-view data. Furthermore, subsequent cluster analysis on the new feature set learned from DGCCA is able to identify a promising population structure in an Alzheimer's disease (AD) cohort. Genetic association analyses of the clustering results demonstrate that the shared representation learned from DGCCA yields a population structure with a stronger genetic basis than several competing feature learning methods.

Brain Imaging Genomics: Integrated Analysis and Machine Learning

Brain imaging genomics is an emerging data science field, where integrated analysis of brain imaging and genomics data, often combined with other biomarker, clinical and environmental data, is performed to gain new insights into the phenotypic, genetic and molecular characteristics of the brain as well as their impact on normal and disordered brain function and behavior. It has enormous potential to contribute significantly to biomedical discoveries in brain science. Given the increasingly important role of statistical and machine learning in biomedicine and rapidly growing literature in brain imaging genomics, we provide an up-to-date and comprehensive review of statistical and machine learning methods for brain imaging genomics, as well as a practical discussion on method selection for various biomedical applications.