Genetic resilience to amyloid related cognitive decline

MORE: a multi-omics data-driven hypergraph integration network for biomedical data classification and biomarker identification

High-throughput sequencing methods have brought about a huge change in omics-based biomedical study. Integrating various omics data is possibly useful for identifying some correlations across data modalities, thus improving our understanding of the underlying biological mechanisms and complexity. Nevertheless, most existing graph-based feature extraction methods overlook the complementary information and correlations across modalities. Moreover, these methods tend to treat the features of each omics modality equally, which contradicts current biological principles. To solve these challenges, we introduce a novel approach for integrating multi-omics data termed Multi-Omics hypeRgraph integration nEtwork (MORE). MORE initially constructs a comprehensive hyperedge group by extensively investigating the informative correlations within and across modalities. Subsequently, the multi-omics hypergraph encoding module is employed to learn the enriched omics-specific information. Afterward, the multi-omics self-attention mechanism is then utilized to adaptatively aggregate valuable correlations across modalities for representation learning and making the final prediction. We assess MORE's performance on datasets characterized by message RNA (mRNA) expression, Deoxyribonucleic Acid (DNA) methylation, and microRNA (miRNA) expression for Alzheimer's disease, invasive breast carcinoma, and glioblastoma. The results from three classification tasks highlight the competitive advantage of MORE in contrast with current state-of-the-art (SOTA) methods. Moreover, the results also show that MORE has the capability to identify a greater variety of disease-related biomarkers compared to existing methods, highlighting its advantages in biomedical data mining and interpretation. Overall, MORE can be investigated as a valuable tool for facilitating multi-omics analysis and novel biomarker discovery. Our code and data can be publicly accessed at https://github.com/Wangyuhanxx/MORE.

Resistance and Resilience to Alzheimer's Disease

Dementia is a significant public health crisis; the most common underlying cause of age-related cognitive decline and dementia is Alzheimer's disease neuropathologic change (ADNC). As such, there is an urgent need to identify novel therapeutic targets for the treatment and prevention of the underlying pathologic processes that contribute to the development of AD dementia. Although age is the top risk factor for dementia in general and AD specifically, these are not inevitable consequences of advanced age. Some individuals are able to live to advanced age without accumulating significant pathology (resistance to ADNC), whereas others are able to maintain cognitive function despite the presence of significant pathology (resilience to ADNC). Understanding mechanisms of resistance and resilience will inform therapeutic strategies to promote these processes to prevent or delay AD dementia. This article will highlight what is currently known about resistance and resilience to AD, including our current understanding of possible underlying mechanisms that may lead to candidate preventive and treatment interventions for this devastating neurodegenerative disease.

ZCCHC17 Modulates Neuronal RNA Splicing and Supports Cognitive Resilience in Alzheimer's Disease

ZCCHC17 is a putative master regulator of synaptic gene dysfunction in Alzheimer's disease (AD), and ZCCHC17 protein declines early in AD brain tissue, before significant gliosis or neuronal loss. Here, we investigate the function of ZCCHC17 and its role in AD pathogenesis using data from human autopsy tissue (consisting of males and females) and female human cell lines. Co-immunoprecipitation (co-IP) of ZCCHC17 followed by mass spectrometry analysis in human iPSC-derived neurons reveals that ZCCHC17's binding partners are enriched for RNA-splicing proteins. ZCCHC17 knockdown results in widespread RNA-splicing changes that significantly overlap with splicing changes found in AD brain tissue, with synaptic genes commonly affected. ZCCHC17 expression correlates with cognitive resilience in AD patients, and we uncover an APOE4-dependent negative correlation of ZCCHC17 expression with tangle burden. Furthermore, a majority of ZCCHC17 interactors also co-IP with known tau interactors, and we find a significant overlap between alternatively spliced genes in ZCCHC17 knockdown and tau overexpression neurons. These results demonstrate ZCCHC17's role in neuronal RNA processing and its interaction with pathology and cognitive resilience in AD, and suggest that the maintenance of ZCCHC17 function may be a therapeutic strategy for preserving cognitive function in the setting of AD pathology.

BrainGENIE: The Brain Gene Expression and Network Imputation Engine

In vivo experimental analysis of human brain tissue poses substantial challenges and ethical concerns. To address this problem, we developed a computational method called the Brain Gene Expression and Network-Imputation Engine (BrainGENIE) that leverages peripheral-blood transcriptomes to predict brain tissue-specific gene-expression levels. Paired blood-brain transcriptomic data collected by the Genotype-Tissue Expression (GTEx) Project was used to train BrainGENIE models to predict gene-expression levels in ten distinct brain regions using whole-blood gene-expression profiles. The performance of BrainGENIE was compared to PrediXcan, a popular method for imputing gene expression levels from genotypes. BrainGENIE significantly predicted brain tissue-specific expression levels for 2947-11,816 genes (false-discovery rate-adjusted p < 0.05), including many transcripts that cannot be predicted significantly by a transcriptome-imputation method such as PrediXcan. BrainGENIE recapitulated measured diagnosis-related gene-expression changes in the brain for autism, bipolar disorder, and schizophrenia better than direct correlations from blood and predictions from PrediXcan. We developed a convenient software toolset for deploying BrainGENIE, and provide recommendations for how best to implement models. BrainGENIE complements and, in some ways, outperforms existing transcriptome-imputation tools, providing biologically meaningful predictions and opening new research avenues.

ZCCHC17 modulates neuronal RNA splicing and supports cognitive resilience in Alzheimer's disease

ZCCHC17 is a putative master regulator of synaptic gene dysfunction in Alzheimer's Disease (AD), and ZCCHC17 protein declines early in AD brain tissue, before significant gliosis or neuronal loss. Here, we investigate the function of ZCCHC17 and its role in AD pathogenesis. Co-immunoprecipitation of ZCCHC17 followed by mass spectrometry analysis in human iPSC-derived neurons reveals that ZCCHC17's binding partners are enriched for RNA splicing proteins. ZCCHC17 knockdown results in widespread RNA splicing changes that significantly overlap with splicing changes found in AD brain tissue, with synaptic genes commonly affected. ZCCHC17 expression correlates with cognitive resilience in AD patients, and we uncover an APOE4 dependent negative correlation of ZCCHC17 expression with tangle burden. Furthermore, a majority of ZCCHC17 interactors also co-IP with known tau interactors, and we find significant overlap between alternatively spliced genes in ZCCHC17 knockdown and tau overexpression neurons. These results demonstrate ZCCHC17's role in neuronal RNA processing and its interaction with pathology and cognitive resilience in AD, and suggest that maintenance of ZCCHC17 function may be a therapeutic strategy for preserving cognitive function in the setting of AD pathology.

Comprehensive analysis of dysregulated circular RNAs and construction of a ceRNA network involved in the pathology of Alzheimer's disease in a 5 × FAD mouse model

INTRODUCTION

Alzheimer's disease (AD) causes a decline in cognitive function that poses a significant hazard to human health. However, the exact pathogenesis of AD and effective treatment have both proven elusive. Circular RNAs (circRNAs), which were initially deemed as meaningless non-coding RNAs, have been shown to participate in a variety of physiological and pathological processes. However, the variations and characteristics of circRNAs are not fairly well understood during the occurrence and development of AD.

METHODS

In this study, we performed RNA sequencing analyses, identified circRNA expression profiles, and explored the circRNA-associated competing endogenous RNA (ceRNA) relationship in the hippocampus of five familial AD (5 × FAD) mice with cognitive dysfunction.

RESULTS

The RNA sequencing results identified 34 dysregulated circRNAs in the hippocampus of 5 × FAD mice, including 17 upregulated and 17 downregulated circRNAs. The circRNA-miRNA interaction network for the dysregulated circRNAs was generated, and it was found to include 34 circRNAs and 711 miRNAs. Next, 2067 mRNAs potentially modulated by upregulated circRNA-interacting miRNAs and 2297 mRNAs potentially modulated by downregulated circRNA-interacting miRNAs were identified. Pathway enrichment analyses revealed that the circRNA-miRNA-mRNA network modulated AD development via multiple pathways, such as axon guidance, mitogen-activated protein kinase, and neurotrophin. The associated biological processes were mainly related to neuron projection development, cell morphogenesis, and head development. Their corresponding distributions were especially high in the axon, postsynapse, and neuronal body. We constructed a ceRNA network that included five circRNAs, four miRNAs, and 188 mRNAs. In this network, the differential expressions of three circRNAs (circRNA04655, circRNA00723, and circRNA01891), two miRNAs (miR-3470b and miR-6240), and 13 mRNAs (Vgll3, Nhsl2, Rab7, Tardbp, Vps33b, Fam107a, Tacr1, Ankrd40, Creb1, Snap23, Csnk1a1, Bmi1, and Bfar) in the hippocampus of 5 × FAD mice using qRT-PCR analyses were consistent with the RNA sequencing results. Another one circRNAs (circRNA00747) and two mRNAs (Zfp37 and Polr1e) had similar expression trends to the sequencing data, while circRNA03723 and Mapk10 had deviated expression trends to the sequencing data.

CONCLUSIONS

In conclusion, our study uncovered dysregulated circRNA expression profiles in the hippocampus of 5 × FAD mice, stretched comprehension of ceRNA biology, investigated the potential role of this ceRNA network in pathogenesis and progression, and identified potential biomarkers and therapeutic targets for AD.

Emerging roles of centrosome cohesion

The centrosome, consisting of centrioles and the associated pericentriolar material, is the main microtubule-organizing centre (MTOC) in animal cells. During most of interphase, the two centrosomes of a cell are joined together by centrosome cohesion into one MTOC. The most dominant element of centrosome cohesion is the centrosome linker, an interdigitating, fibrous network formed by the protein C-Nap1 anchoring a number of coiled-coil proteins including rootletin to the proximal end of centrioles. Alternatively, centrosomes can be kept together by the action of the minus end directed kinesin motor protein KIFC3 that works on interdigitating microtubules organized by both centrosomes and probably by the actin network. Although cells connect the two interphase centrosomes by several mechanisms into one MTOC, the general importance of centrosome cohesion, particularly for an organism, is still largely unclear. In this article, we review the functions of the centrosome linker and discuss how centrosome cohesion defects can lead to diseases.

Polygenic resilience scores capture protective genetic effects for Alzheimer's disease

Polygenic risk scores (PRSs) can boost risk prediction in late-onset Alzheimer's disease (LOAD) beyond apolipoprotein E (APOE) but have not been leveraged to identify genetic resilience factors. Here, we sought to identify resilience-conferring common genetic variants in (1) unaffected individuals having high PRSs for LOAD, and (2) unaffected APOE-ε4 carriers also having high PRSs for LOAD. We used genome-wide association study (GWAS) to contrast "resilient" unaffected individuals at the highest genetic risk for LOAD with LOAD cases at comparable risk. From GWAS results, we constructed polygenic resilience scores to aggregate the addictive contributions of risk-orthogonal common variants that promote resilience to LOAD. Replication of resilience scores was undertaken in eight independent studies. We successfully replicated two polygenic resilience scores that reduce genetic risk penetrance for LOAD. We also showed that polygenic resilience scores positively correlate with polygenic risk scores in unaffected individuals, perhaps aiding in staving off disease. Our findings align with the hypothesis that a combination of risk-independent common variants mediates resilience to LOAD by moderating genetic disease risk.

The Neurogenetics Collection: emerging themes and future considerations for the field in Brain

Genomics has emerged over the last two decades as a fundamental approach to understanding the molecular basis of human diseases. This Collection brings together some recent articles published in Brain, selected to illustrate the impact of genomics on neurology and to highlight emerging themes in the neurogenetics space.

Exploring common genetic contributors to neuroprotection from amyloid pathology

Preclinical Alzheimer's disease describes some individuals who harbour Alzheimer's pathologies but are asymptomatic. For this study, we hypothesized that genetic variation may help protect some individuals from Alzheimer's-related neurodegeneration. We therefore conducted a genome-wide association study using 5 891 064 common variants to assess whether genetic variation modifies the association between baseline beta-amyloid, as measured by both cerebrospinal fluid and positron emission tomography, and neurodegeneration defined using MRI measures of hippocampal volume. We combined and jointly analysed genotype, biomarker and neuroimaging data from non-Hispanic white individuals who were enrolled in four longitudinal ageing studies (n = 1065). Using regression models, we examined the interaction between common genetic variants (Minor Allele Frequency >0.01), including APOE-ɛ4 and APOE-ɛ2, and baseline cerebrospinal levels of amyloid (CSF Aβ42) on baseline hippocampal volume and the longitudinal rate of hippocampal atrophy. For targeted replication of top findings, we analysed an independent dataset (n = 808) where amyloid burden was assessed by Pittsburgh Compound B ([11C]-PiB) positron emission tomography. In this study, we found that APOE-ɛ4 modified the association between baseline CSF Aβ42 and hippocampal volume such that APOE-ɛ4 carriers showed more rapid atrophy, particularly in the presence of enhanced amyloidosis. We also identified a novel locus on chromosome 3 that interacted with baseline CSF Aβ42. Minor allele carriers of rs62263260, an expression quantitative trait locus for the SEMA5B gene (P = 1.46 × 10-8; 3:122675327) had more rapid neurodegeneration when amyloid burden was high and slower neurodegeneration when amyloid was low. The rs62263260 × amyloid interaction on longitudinal change in hippocampal volume was replicated in an independent dataset (P = 0.0112) where amyloid burden was assessed by positron emission tomography. In addition to supporting the established interaction between APOE and amyloid on neurodegeneration, our study identifies a novel locus that modifies the association between beta-amyloid and hippocampal atrophy. Annotation results may implicate SEMA5B, a gene involved in synaptic pruning and axonal guidance, as a high-quality candidate for functional confirmation and future mechanistic analysis.

Glucocerebrosidase Mutations and Motor Reserve in Parkinson's Disease

BACKGROUND

The concept of motor reserve explains the individual differences in motor deficits despite similar degrees of nigrostriatal dopamine depletion in Parkinson's disease (PD).

OBJECTIVE

To investigate glucocerebrosidase (GBA) variants as potential determinants of motor reserve for exploratory purposes.

METHODS

A total of 408 patients with drug-naïve PD were enrolled from the Parkinson's Progression Markers Initiative cohort database. All patients underwent SPECT dopamine transporter (DAT) scans and had results for Sanger sequencing of GBA. Parkinsonian motor deficits were assessed using the Movement Disorders Society Unified Parkinson's Disease Rating Scale Part III (MDS-UPDRS-III). We compared MDS-UPDRS-III scores while adjusting for DAT availability in the putamen (i.e., motor reserve) between the PD groups according to the presence of GBA mutations.

RESULTS

Fifty-four (13.2%) patients carried GBA mutations. PD patients with GBA mutations were younger than those without mutations. There were no significant differences in sex, disease duration, years of education, and striatal DAT availability between the PD groups. PD patients with GBA mutations had higher MDS-UPDRS-III scores for the less affected side than those without mutations, despite similar levels of DAT availability in the contralateral putamen. The MDS-UPDRS-III sub-scores of the more affected side did not differ between the two PD groups.

CONCLUSION

The results of this study demonstrated the detrimental effect of GBA variants on individual capacity to cope with PD-related pathologies, with different impacts depending on the motor laterality.

KL-VS heterozygosity is associated with lower amyloid-dependent tau accumulation and memory impairment in Alzheimer's disease

Klotho-VS heterozygosity (KL-VS^het) is associated with reduced risk of Alzheimer’s disease (AD). However, whether KL-VS^het is associated with lower levels of pathologic tau, i.e., the key AD pathology driving neurodegeneration and cognitive decline, is unknown. Here, we assessed the interaction between KL-VS^het and levels of beta-amyloid, a key driver of tau pathology, on the levels of PET-assessed neurofibrillary tau in 551 controls and patients across the AD continuum. KL-VS^het showed lower cross-sectional and longitudinal increase in tau-PET per unit increase in amyloid-PET when compared to that of non-carriers. This association of KL-VS^het on tau-PET was stronger in Klotho mRNA-expressing brain regions mapped onto a gene expression atlas. KL-VS^het was related to better memory functions in amyloid-positive participants and this association was mediated by lower tau-PET. Amyloid-PET levels did not differ between KL-VS^het carriers versus non-carriers. Together, our findings provide evidence to suggest a protective role of KL-VS^het against amyloid-related tau pathology and tau-related memory impairments in elderly humans at risk of AD dementia.

The KL-VS haplotype of the Klotho gene has been associated with reduced risk of Alzheimer’s disease and dementia. Here the authors show an association between the KL-VS haplotype and amyloid-dependent tau accumulation using PET data.

MOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification

To fully utilize the advances in omics technologies and achieve a more comprehensive understanding of human diseases, novel computational methods are required for integrative analysis of multiple types of omics data. Here, we present a novel multi-omics integrative method named Multi-Omics Graph cOnvolutional NETworks (MOGONET) for biomedical classification. MOGONET jointly explores omics-specific learning and cross-omics correlation learning for effective multi-omics data classification. We demonstrate that MOGONET outperforms other state-of-the-art supervised multi-omics integrative analysis approaches from different biomedical classification applications using mRNA expression data, DNA methylation data, and microRNA expression data. Furthermore, MOGONET can identify important biomarkers from different omics data types related to the investigated biomedical problems.

Comparative analysis reveals distinctive epigenetic features of the human cerebellum

Identifying the molecular underpinnings of the neural specializations that underlie human cognitive and behavioral traits has long been of considerable interest. Much research on human-specific changes in gene expression and epigenetic marks has focused on the prefrontal cortex, a brain structure distinguished by its role in executive functions. The cerebellum shows expansion in great apes and is gaining increasing attention for its role in motor skills and cognitive processing, including language. However, relatively few molecular studies of the cerebellum in a comparative evolutionary context have been conducted. Here, we identify human-specific methylation in the lateral cerebellum relative to the dorsolateral prefrontal cortex, in a comparative study with chimpanzees (Pan troglodytes) and rhesus macaques (Macaca mulatta). Specifically, we profiled genome-wide methylation levels in the three species for each of the two brain structures and identified human-specific differentially methylated genomic regions unique to each structure. We further identified which differentially methylated regions (DMRs) overlap likely regulatory elements and determined whether associated genes show corresponding species differences in gene expression. We found greater human-specific methylation in the cerebellum than the dorsolateral prefrontal cortex, with differentially methylated regions overlapping genes involved in several conditions or processes relevant to human neurobiology, including synaptic plasticity, lipid metabolism, neuroinflammation and neurodegeneration, and neurodevelopment, including developmental disorders. Moreover, our results show some overlap with those of previous studies focused on the neocortex, indicating that such results may be common to multiple brain structures. These findings further our understanding of the cerebellum in human brain evolution.

Humans are distinguished from other species by several aspects of cognition. While much comparative evolutionary neuroscience has focused on the neocortex, increasing recognition of the cerebellum’s role in cognition and motor processing has inspired considerable new research. Comparative molecular studies, however, generally continue to focus on the neocortex. We sought to characterize potential genetic regulatory traits distinguishing the human cerebellum by undertaking genome-wide epigenetic profiling of the lateral cerebellum, and compared this to the prefrontal cortex of humans, chimpanzees, and rhesus macaque monkeys. We found that humans showed greater differential CpG methylation–an epigenetic modification of DNA that can reflect past or present gene expression–in the cerebellum than the prefrontal cortex, highlighting the importance of this structure in human brain evolution. Humans also specifically show methylation differences at genes involved in neurodevelopment, neuroinflammation, synaptic plasticity, and lipid metabolism. These differences are relevant for understanding processes specific to humans, such as extensive plasticity, as well as pronounced and prevalent neurodegenerative conditions associated with aging.

Coping with brain amyloid: genetic heterogeneity and cognitive resilience to Alzheimer's pathophysiology

Although abnormal accumulation of amyloid in the brain is an early biomarker of Alzheimer's disease (AD), wide variation in cognitive trajectories during life can be seen in the setting of brain amyloidosis, ranging from maintenance of normal function to progression to dementia. It is widely presumed that cognitive resilience (i.e., coping) to amyloidosis may be influenced by environmental, lifestyle, and inherited factors, but relatively little in specifics is known about this architecture. Here, we leveraged multimodal longitudinal data from a large, population-based sample of older adults to discover genetic factors associated with differential cognitive resilience to brain amyloidosis determined by positron emission tomography (PET). Among amyloid-PET positive older adults, the AD risk allele APOE ɛ4 was associated with worse longitudinal memory trajectories as expected, and was thus covaried in the main analyses. Through a genome-wide association study (GWAS), we uncovered a novel association with cognitive resilience on chromosome 8 at the MTMR7/CNOT7/ZDHHC2/VPS37A locus (p = 4.66 × 10-8, β = 0.23), and demonstrated replication in an independent cohort. Post-hoc analyses confirmed this association as specific to the setting of elevated amyloid burden and not explained by differences in tau deposition or cerebrovascular disease. Complementary gene-based analyses and publically available functional data suggested that the causative variant at this locus may tag CNOT7 (CCR4-NOT Transcription Complex Subunit 7), a gene linked to synaptic plasticity and hippocampal-dependent learning and memory. Pathways related to cell adhesion and immune system activation displayed enrichment of association in the GWAS. Our findings, resulting from a unique study design, support the hypothesis that genetic heterogeneity is one of the factors that explains differential cognitive resilience to brain amyloidosis. Further characterization of the underlying biological mechanisms influencing cognitive resilience may facilitate improved prognostic counseling, therapeutic application, and trial enrollment in AD.

Genetic variants and functional pathways associated with resilience to Alzheimer's disease

Approximately 30% of older adults exhibit the neuropathological features of Alzheimer's disease without signs of cognitive impairment. Yet, little is known about the genetic factors that allow these potentially resilient individuals to remain cognitively unimpaired in the face of substantial neuropathology. We performed a large, genome-wide association study (GWAS) of two previously validated metrics of cognitive resilience quantified using a latent variable modelling approach and representing better-than-predicted cognitive performance for a given level of neuropathology. Data were harmonized across 5108 participants from a clinical trial of Alzheimer's disease and three longitudinal cohort studies of cognitive ageing. All analyses were run across all participants and repeated restricting the sample to individuals with unimpaired cognition to identify variants at the earliest stages of disease. As expected, all resilience metrics were genetically correlated with cognitive performance and education attainment traits (P-values < 2.5 × 10-20), and we observed novel correlations with neuropsychiatric conditions (P-values < 7.9 × 10-4). Notably, neither resilience metric was genetically correlated with clinical Alzheimer's disease (P-values > 0.42) nor associated with APOE (P-values > 0.13). In single variant analyses, we observed a genome-wide significant locus among participants with unimpaired cognition on chromosome 18 upstream of ATP8B1 (index single nucleotide polymorphism rs2571244, minor allele frequency = 0.08, P = 2.3 × 10-8). The top variant at this locus (rs2571244) was significantly associated with methylation in prefrontal cortex tissue at multiple CpG sites, including one just upstream of ATPB81 (cg19596477; P = 2 × 10-13). Overall, this comprehensive genetic analysis of resilience implicates a putative role of vascular risk, metabolism, and mental health in protection from the cognitive consequences of neuropathology, while also providing evidence for a novel resilience gene along the bile acid metabolism pathway. Furthermore, the genetic architecture of resilience appears to be distinct from that of clinical Alzheimer's disease, suggesting that a shift in focus to molecular contributors to resilience may identify novel pathways for therapeutic targets.

The Centrosome Linker and Its Role in Cancer and Genetic Disorders

Centrosome cohesion, the joining of the two centrosomes of a cell, is increasingly appreciated as a major regulator of cell functions such as Golgi organization and cilia positioning. One major element of centrosome cohesion is the centrosome linker that consists of a growing number of proteins. The timely disassembly of the centrosome linker enables centrosomes to separate and assemble a functional bipolar mitotic spindle that is crucial for maintaining genomic integrity. Exciting new findings link centrosome linker defects to cell transformation and genetic disorders. We review recent data on the molecular mechanisms of the assembly and disassembly of the centrosome linker, and discuss how defects in the proper execution of these processes cause DNA damage and genomic instability leading to disease.

Late-Life Physical and Cognitive Activities Independently Contribute to Brain and Cognitive Resilience

BACKGROUND

Active lifestyles are related to better cognitive aging outcomes, yet the unique role of different types of activity are unknown.

OBJECTIVE

To examine the independent contributions of physical (PA) versus cognitive (CA) leisure activities to brain and cognitive aging.

METHODS

Independent samples of non-demented older adults from University of California, San Francisco Hillblom Aging Network (UCSF; n = 344 typically aging) and University of California, Davis Diversity cohort (UCD; n = 485 normal to MCI) completed: 1) self-reported engagement in current PA and CA (UCSF: Physical Activity Scale for the Elderly and Cognitive Activity Scale; UCD: Life Experiences Assessment Form); 2) neuropsychological batteries; and 3) neuroimaging total gray matter volume, white matter hyperintensities, and/or global fractional anisotropy. PA and CA were simultaneously entered into multivariable linear regression models, adjusting for demographic characteristics and functional impairment severity.

RESULTS

Brain outcomes: In UCSF, only PA was positively associated with gray matter volume and attenuated the relationship between age and fractional anisotropy. In UCD, only CA was associated with less white matter hyperintensities and attenuated the relationship between age and gray matter volume. Cognitive outcomes: In both cohorts, greater CA, but not PA, related to better cognition, independent of age and brain structure. In UCSF, CA attenuated the relationship between fractional anisotropy and cognition. In UCD, PA attenuated the association between white matter hyperintensities and cognition.

CONCLUSIONS

Although their specificity was not easily teased apart, both PA and CA are clearly related to better brain and cognitive resilience markers across cohorts with differing educational, racial, and disease statuses. PA and CA may independently contribute to converging neuroprotective pathways for brain and cognitive aging.

Integration of Machine Learning Methods to Dissect Genetically Imputed Transcriptomic Profiles in Alzheimer's Disease

The genetic component of many common traits is associated with the gene expression and several variants act as expression quantitative loci, regulating the gene expression in a tissue specific manner. In this work, we applied tissue-specific cis-eQTL gene expression prediction models on the genotype of 808 samples including controls, subjects with mild cognitive impairment, and patients with Alzheimer's Disease. We then dissected the imputed transcriptomic profiles by means of different unsupervised and supervised machine learning approaches to identify potential biological associations. Our analysis suggests that unsupervised and supervised methods can provide complementary information, which can be integrated for a better characterization of the underlying biological system. In particular, a variational autoencoder representation of the transcriptomic profiles, followed by a support vector machine classification, has been used for tissue-specific gene prioritizations. Interestingly, the achieved gene prioritizations can be efficiently integrated as a feature selection step for improving the accuracy of deep learning classifier networks. The identified gene-tissue information suggests a potential role for inflammatory and regulatory processes in gut-brain axis related tissues. In line with the expected low heritability that can be apportioned to eQTL variants, we were able to achieve only relatively low prediction capability with deep learning classification models. However, our analysis revealed that the classification power strongly depends on the network structure, with recurrent neural networks being the best performing network class. Interestingly, cross-tissue analysis suggests a potentially greater role of models trained in brain tissues also by considering dementia-related endophenotypes. Overall, the present analysis suggests that the combination of supervised and unsupervised machine learning techniques can be used for the evaluation of high dimensional omics data.

Sex Moderates Amyloid and Apolipoprotein ε4 Effects on Default Mode Network Connectivity at Rest

Women are more likely to have Alzheimer's disease (AD) and decline more rapidly once diagnosed despite greater verbal memory early in the disease compared to men—an advantage that has been termed “memory reserve.” Resting state functional MRI (fMRI) investigations demonstrate interactions between sex and AD risk factors in default mode network (DMN) connectivity, a network of brain regions showing progressive dysfunction in AD. Separate work suggests connectivity of left prefrontal cortex (PFC) may correlate with more general cognitive reserve in healthy aging. It is unknown whether left prefrontal functional connectivity with anterior and posterior default mode network (aDMN, pDMN) might differ by sex in AD. This study employed group independent component analysis (ICA) to analyze resting state fMRI data from 158 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) with baseline diagnoses of normal cognition or early mild cognitive impairment (eMCI). pDMN and aDMN were defined on a subject-specific basis; prefrontal areas were selected from the Brodmann atlas (BA 6, 44, 8, and 9). Moderation regression analyses examined whether sex and amyloid PET positivity (A+/–) moderated effects of apolipoprotein ε4 (APOE ε4) on connectivity between left PFC, aDMN, and pDMN; and between aDMN and pDMN. Significant analyses were followed up with partial correlations assessing relationship of connectivity to verbal memory on the Rey Auditory Verbal Learning Test (RAVLT), and with preliminary analyses within NC and eMCI groups separately. Results showed no sex moderation of effects of A+ and APOE ε4 on left prefrontal/DMN connectivity in the full sample. However, sex significantly moderated impact of A+ and APOE ε4 on connectivity between aDMN and pDMN (p < 0.01). Women with an APOE allele (ε4+) and A+ showed greater aDMN/pDMN connectivity than their ε4- counterparts. No significant results were observed in men. Subgroup analyses suggested the aDMN/pDMN finding was true for those with NC, not eMCI. Partial correlations controlling for age and education showed increased aDMN/pDMN connectivity related to better verbal learning in women (p < 0.01) and not men (p = 0.18). In women at risk for AD or in early symptomatic stages who also have evidence of amyloid burden, stronger aDMN/pDMN connectivity may support verbal learning.

Understanding disease progression and improving Alzheimer's disease clinical trials: Recent highlights from the Alzheimer's Disease Neuroimaging Initiative

INTRODUCTION

The overall goal of the Alzheimer's Disease Neuroimaging Initiative (ADNI) is to validate biomarkers for Alzheimer's disease (AD) clinical trials. ADNI is a multisite, longitudinal, observational study that has collected many biomarkers since 2004. Recent publications highlight the multifactorial nature of late-onset AD. We discuss selected topics that provide insights into AD progression and outline how this knowledge may improve clinical trials.

METHODS

We used standard methods to identify nearly 600 publications using ADNI data from 2016 and 2017 (listed in Supplementary Material and searchable at http://adni.loni.usc.edu/news-publications/publications/).

RESULTS

(1) Data-driven AD progression models supported multifactorial interactions rather than a linear cascade of events. (2) β-Amyloid (Aβ) deposition occurred concurrently with functional connectivity changes within the default mode network in preclinical subjects and was followed by specific and progressive disconnection of functional and anatomical networks. (3) Changes in functional connectivity, volumetric measures, regional hypometabolism, and cognition were detectable at subthreshold levels of Aβ deposition. 4. Tau positron emission tomography imaging studies detailed a specific temporal and spatial pattern of tau pathology dependent on prior Aβ deposition, and related to subsequent cognitive decline. 5. Clustering studies using a wide range of modalities consistently identified a "typical AD" subgroup and a second subgroup characterized by executive impairment and widespread cortical atrophy in preclinical and prodromal subjects. 6. Vascular pathology burden may act through both Aβ dependent and independent mechanisms to exacerbate AD progression. 7. The APOE ε4 allele interacted with cerebrovascular disease to impede Aβ clearance mechanisms. 8. Genetic approaches identified novel genetic risk factors involving a wide range of processes, and demonstrated shared genetic risk for AD and vascular disorders, as well as the temporal and regional pathological associations of established AD risk alleles. 9. Knowledge of early pathological changes guided the development of novel prognostic biomarkers for preclinical subjects. 10. Placebo populations of randomized controlled clinical trials had highly variable trajectories of cognitive change, underscoring the importance of subject selection and monitoring. 11. Selection criteria based on Aβ positivity, hippocampal volume, baseline cognitive/functional measures, and APOE ε4 status in combination with improved cognitive outcome measures were projected to decrease clinical trial duration and cost. 12. Multiple concurrent therapies targeting vascular health and other AD pathology in addition to Aβ may be more effective than single therapies.

DISCUSSION

ADNI publications from 2016 and 2017 supported the idea of AD as a multifactorial disease and provided insights into the complexities of AD disease progression. These findings guided the development of novel biomarkers and suggested that subject selection on the basis of multiple factors may lower AD clinical trial costs and duration. The use of multiple concurrent therapies in these trials may prove more effective in reversing AD disease progression.

Embracing polygenicity: a review of methods and tools for psychiatric genetics research

The availability of genome-wide genetic data on hundreds of thousands of people has led to an equally rapid growth in methodologies available to analyse these data. While the motivation for undertaking genome-wide association studies (GWAS) is identification of genetic markers associated with complex traits, once generated these data can be used for many other analyses. GWAS have demonstrated that complex traits exhibit a highly polygenic genetic architecture, often with shared genetic risk factors across traits. New methods to analyse data from GWAS are increasingly being used to address a diverse set of questions about the aetiology of complex traits and diseases, including psychiatric disorders. Here, we give an overview of some of these methods and present examples of how they have contributed to our understanding of psychiatric disorders. We consider: (i) estimation of the extent of genetic influence on traits, (ii) uncovering of shared genetic control between traits, (iii) predictions of genetic risk for individuals, (iv) uncovering of causal relationships between traits, (v) identifying causal single-nucleotide polymorphisms and genes or (vi) the detection of genetic heterogeneity. This classification helps organise the large number of recently developed methods, although some could be placed in more than one category. While some methods require GWAS data on individual people, others simply use GWAS summary statistics data, allowing novel well-powered analyses to be conducted at a low computational burden.