CR1 genotype is associated with entorhinal cortex volume in young healthy adults

Common risk alleles for schizophrenia within the major histocompatibility complex predict white matter microstructure

Recent research has highlighted the role of complement genes in shaping the microstructure of the brain during early development, and in contributing to common allele risk for Schizophrenia. We hypothesised that common risk variants for schizophrenia within complement genes will associate with structural changes in white matter microstructure within tracts innervating the frontal lobe. Results showed that risk alleles within the complement gene set, but also intergenic alleles, significantly predict axonal density in white matter tracts connecting frontal cortex with parietal, temporal and occipital cortices. Specifically, risk alleles within the Major Histocompatibility Complex region in chromosome 6 appeared to drive these associations. No significant associations were found for the orientation dispersion index. These results suggest that changes in axonal packing - but not in axonal coherence - determined by common risk alleles within the MHC genomic region - including variants related to the Complement system - appear as a potential neurobiological mechanism for schizophrenia.

Effects of complement gene-set polygenic risk score on brain volume and cortical measures in patients with psychotic disorders and healthy controls

Multiple genome-wide association studies of schizophrenia have reported associations between genetic variants within the MHC region and disease risk, an association that has been partially accounted for by alleles of the complement component 4 (C4) gene. Following on previous findings of association between both C4 and other complement-related variants and memory function, we tested the hypothesis that polygenic scores calculated based on identified schizophrenia risk alleles within the "complement" system would be broadly associated with memory function and associated brain structure. We tested this using a polygenic risk score (PRS) calculated for complement genes, but excluding C4 variants. Higher complement-based PRS scores were observed to be associated with lower memory scores for the sample as a whole (N = 620, F change = 8.25; p = .004). A significant association between higher PRS and lower hippocampal volume was also observed (N = 216, R² change = 0.016, p = .015). However, after correcting for further testing of association with the more general indices of cortical thickness, surface area or total brain volume, none of which were associated with complement, the association with hippocampal volume became non-significant. A post-hoc analysis of hippocampal subfields suggested an association between complement PRS and several hippocampal subfields, findings that appeared to be particularly driven by the patient sample. In conclusion, our study yielded suggestive evidence of association between complement-based schizophrenia PRS and variation in memory function and hippocampal volume.

Single Nucleotide Polymorphisms in Alzheimer's Disease Risk Genes Are Associated with Intrinsic Connectivity in Middle Age

BACKGROUND

It is critical to identify individuals at risk for Alzheimer's disease (AD) earlier in the disease time course, such as middle age and preferably well prior to the onset of clinical symptoms, when intervention efforts may be more successful. Genome-wide association and candidate gene studies have identified single nucleotide polymorphisms (SNPs) in APOE, CLU, CR1, PICALM, and SORL1 that confer increased risk of AD.

OBJECTIVE

In the current study, we investigated the associations between SNPs in these genes and resting-state functional connectivity within the default mode network (DMN), frontoparietal network (FPN), and executive control network (ECN) in healthy, non-demented middle-aged adults (age 40 -60; N = 123; 74 females).

METHODS

Resting state networks of interest were identified through independent components analysis using a template-matching procedure and individual spatial maps and time courses were extracted using dual regression.

RESULTS

Within the posterior DMN, functional connectivity was associated with CR1 rs1408077 and CLU rs9331888 polymorphisms (p's < 0.05). FPN connectivity was associated with CR1 rs1408077, CLU rs1136000, SORL1 rs641120, and SORL1 rs689021 (p's < 0.05). Functional connectivity within the ECN was associated with the CLU rs11136000 (p < 0.05). There were no APOE- or PICALM-related differences in any of the networks investigated (p's > 0.05).

CONCLUSION

This is the first demonstration of the relationship between intrinsic network connectivity and AD risk alleles in CLU, CR1, and SORL1 in healthy, middle-aged adults. These SNPs should be considered in future investigations aimed at identifying potential preclinical biomarkers for AD.

A novel mouse model expressing human forms for complement receptors CR1 and CR2

BACKGROUND

The complement cascade is increasingly implicated in development of a variety of diseases with strong immune contributions such as Alzheimer's disease and Systemic Lupus Erythematosus. Mouse models have been used to determine function of central components of the complement cascade such as C1q and C3. However, species differences in their gene structures mean that mice do not adequately replicate human complement regulators, including CR1 and CR2. Genetic variation in CR1 and CR2 have been implicated in modifying disease states but the mechanisms are not known.

RESULTS

To decipher the roles of human CR1 and CR2 in health and disease, we engineered C57BL/6J (B6) mice to replace endogenous murine Cr2 with human complement receptors, CR1 and CR2 (B6.CR2CR1). CR1 has an array of allotypes in human populations and using traditional recombination methods (Flp-frt and Cre-loxP) two of the most common alleles (referred to here as CR1long and CR1short) can be replicated within this mouse model, along with a CR1 knockout allele (CR1KO). Transcriptional profiling of spleens and brains identified genes and pathways differentially expressed between mice homozygous for either CR1long, CR1short or CR1KO. Gene set enrichment analysis predicts hematopoietic cell number and cell infiltration are modulated by CR1long, but not CR1short or CR1KO.

CONCLUSION

The B6.CR2CR1 mouse model provides a novel tool for determining the relationship between human-relevant CR1 alleles and disease.

The multiplex model of the genetics of Alzheimer's disease

Genes play a strong role in Alzheimer's disease (AD), with late-onset AD showing heritability of 58-79% and early-onset AD showing over 90%. Genetic association provides a robust platform to build our understanding of the etiology of this complex disease. Over 50 loci are now implicated for AD, suggesting that AD is a disease of multiple components, as supported by pathway analyses (immunity, endocytosis, cholesterol transport, ubiquitination, amyloid-β and tau processing). Over 50% of late-onset AD heritability has been captured, allowing researchers to calculate the accumulation of AD genetic risk through polygenic risk scores. A polygenic risk score predicts disease with up to 90% accuracy and is an exciting tool in our research armory that could allow selection of those with high polygenic risk scores for clinical trials and precision medicine. It could also allow cellular modelling of the combined risk. Here we propose the multiplex model as a new perspective from which to understand AD. The multiplex model reflects the combination of some, or all, of these model components (genetic and environmental), in a tissue-specific manner, to trigger or sustain a disease cascade, which ultimately results in the cell and synaptic loss observed in AD.

The Radiogenomics of Late-onset Alzheimer Disease

Radiogenomics, defined as the integrated analysis of radiologic imaging and genetic data, is a well-established tool shown to augment neuroimaging in the clinical diagnosis, prognostication, and scientific study of late-onset Alzheimer disease (LOAD). Early work using candidate single nucleotide polymorphisms (SNPs) identified genetic variation in APOE, BIN1, CLU, and CR1 as key modifiers of brain structure and function using magnetic resonance imaging (MRI). More recently, polygenic risk scores used in conjunction with MRI and positron emission tomography have shown great promise as a risk-stratification tool for clinical trials and care-management decisions. In addition, recent work using multimodal MRI and positron emission tomography as proxies of LOAD progression has identified novel risk variants that are enhancing our understanding of LOAD pathophysiology and progression. Herein, we highlight key studies and trends in the radiogenomics of LOAD over the past two decades and their implications for clinical practice and scientific research.

Translating Alzheimer's disease-associated polymorphisms into functional candidates: a survey of IGAP genes and SNPs

The International Genomics of Alzheimer's Project (IGAP) is a consortium for characterizing the genetic landscape of Alzheimer's disease (AD). The identified and/or confirmed 19 single-nucleotide polymorphisms (SNPs) associated with AD are located on non-coding DNA regions, and their functional impacts on AD are as yet poorly understood. We evaluated the roles of the IGAP SNPs by integrating data from many resources, based on whether the IGAP SNP was (1) a proxy for a coding SNP or (2) associated with altered mRNA transcript levels. For (1), we confirmed that 12 AD-associated coding common SNPs and five nonsynonymous rare variants are in linkage disequilibrium with the IGAP SNPs. For (2), the IGAP SNPs in CELF1 and MS4A6A were associated with expression of their neighboring genes, MYBPC3 and MS4A6A, respectively, in blood. The IGAP SNP in DSG2 was an expression quantitative trait loci (eQTL) for DLGAP1 and NETO1 in the human frontal cortex. The IGAP SNPs in ABCA7, CD2AP, and CD33 each acted as eQTL for AD-associated genes in brain. Our approach for identifying proxies and examining eQTL highlighted potentially impactful, novel gene regulatory phenomena pertinent to the AD phenotype.

Developmental roles of microglia: A window into mechanisms of disease

Microglia are engineers of the central nervous system (CNS) both in health and disease. In addition to the canonical immunological roles of clearing damaging entities and limiting the spread of toxicity and death, microglia remodel the CNS throughout life. While they have been extensively studied in disease and injury, due to their highly variable functions, their precise role in these contexts still remains uncertain. Over the past decade, we have greatly expanded our understanding of microglial function, including their essential homeostatic roles during development. Here, we review these developmental roles, identify parallels in disease, and speculate whether developmental mechanisms re-emerge in disease and injury. Developmental Dynamics 248:98-117, 2019. © 2018 Wiley Periodicals, Inc.

Genome-wide significant, replicated and functional risk variants for Alzheimer's disease

Genome-wide association studies (GWASs) have reported numerous associations between risk variants and Alzheimer's disease (AD). However, these associations do not necessarily indicate a causal relationship. If the risk variants can be demonstrated to be biologically functional, the possibility of a causal relationship would be increased. In this article, we reviewed all of the published GWASs to extract the genome-wide significant (p < 5×10-8) and replicated associations between risk variants and AD or AD-biomarkers. The regulatory effects of these risk variants on the expression of a novel class of non-coding RNAs (piRNAs) and protein-coding RNAs (mRNAs), the alteration of proteins caused by these variants, the associations between AD and these variants in our own sample, the expression of piRNAs, mRNAs and proteins in human brains targeted by these variants, the expression correlations between the risk genes and APOE, the pathways and networks that the risk genes belonged to, and the possible long non-coding RNAs (LncRNAs) that might regulate the risk genes were analyzed, to investigate the potential biological functions of the risk variants and explore the potential mechanisms underlying the SNP-AD associations. We found replicated and significant associations for AD or AD-biomarkers, surprisingly, only at 17 SNPs located in 11 genes/snRNAs/LncRNAs in eight genomic regions. Most of these 17 SNPs enriched some AD-related pathways or networks, and were potentially functional in regulating piRNAs and mRNAs; some SNPs were associated with AD in our sample, and some SNPs altered protein structures. Most of the protein-coding genes regulated by the risk SNPs were expressed in human brain and correlated with APOE expression. We conclude that these variants were most robust risk markers for AD, and their contributions to AD risk was likely to be causal. As expected, APOE and the lipoprotein metabolism pathway possess the highest weight among these contributions.

Relationship of Hippocampal Volume to Amyloid Burden across Diagnostic Stages of Alzheimer's Disease

AIMS

To assess how hippocampal volume (HV) from volumetric magnetic resonance imaging (vMRI) is related to the amyloid status at different stages of Alzheimer's disease (AD) and its relevance to patient care.

METHODS

We evaluated the ability of HV to predict the florbetapir positron emission tomography (PET) amyloid positive/negative status by group in healthy controls (HC, n = 170) and early/late mild cognitive impairment (EMCI, n = 252; LMCI, n = 136), and AD dementia (n = 75) subjects from the Alzheimer's Disease Neuroimaging Initiative Grand Opportunity (ADNI-GO) and ADNI2. Logistic regression analyses, including elastic net classification modeling with 10-fold cross-validation, were used with age and education as covariates.

RESULTS

HV predicted amyloid status only in LMCI using either logistic regression [area under the curve (AUC) = 0.71, p < 0.001] or elastic net classification modeling [positive predictive value (PPV) = 72.7%]. In EMCI, age (AUC = 0.70, p < 0.0001) and age and/or education (PPV = 63.1%), but not HV, predicted amyloid status.

CONCLUSION

Using clinical neuroimaging, HV predicted amyloid status only in LMCI, suggesting that HV is not a biomarker surrogate for amyloid PET in clinical applications across the full diagnostic spectrum.

Family History of Alzheimer's Disease and Cortical Thickness in Patients With Dementia

A first-degree family history of Alzheimer's disease reflects genetic risks for the neurodegenerative disorder. Recent imaging data suggest localized effects of genetic risks on brain structure in healthy people. It is unknown whether this association can also be found in patients who already have dementia. Our aim was to investigate whether family history risk modulates regional medial temporal lobe cortical thickness in patients with Alzheimer's disease. We performed high-resolution magnetic resonance imaging and cortical unfolding data analysis on 54 patients and 53 nondemented individuals. A first-degree family history of Alzheimer's disease was associated with left hemispheric cortical thinning in the subiculum among patients and controls. The contribution of Alzheimer's disease family history to regional brain anatomy changes independent of cognitive impairment may reflect genetic risks that modulate onset and clinical course of the disease.

Genes associated with Alzheimer's disease: an overview and current status

Alzheimer's disease (AD) is a progressive, neurodegenerative disease and the most common form of dementia in elderly people. It is an emerging public health problem that poses a huge societal burden. Linkage analysis was the first milestone in unraveling the mutations in APP, PSEN1, and PSEN2 that cause early-onset AD, followed by the discovery of apolipoprotein E-ε4 allele as the only one genetic risk factor for late-onset AD. Genome-wide association studies have revolutionized genetic research and have identified over 20 genetic loci associated with late-onset AD. Recently, next-generation sequencing technologies have enabled the identification of rare disease variants, including unmasking small mutations with intermediate risk of AD in PLD3, TREM2, UNC5C, AKAP9, and ADAM10. This review provides an overview of the genetic basis of AD and the relationship between these risk genes and the neuropathologic features of AD. An understanding of genetic mechanisms underlying AD pathogenesis and the potentially implicated pathways will lead to the development of novel treatment for this devastating disease.

An Alzheimer's Disease Genetic Risk Score Predicts Longitudinal Thinning of Hippocampal Complex Subregions in Healthy Older Adults

Variants at 21 genetic loci have been associated with an increased risk for Alzheimer's disease (AD). An important unresolved question is whether multiple genetic risk factors can be combined to increase the power to detect changes in neuroimaging biomarkers for AD. We acquired high-resolution structural images of the hippocampus in 66 healthy, older human subjects. For 45 of these subjects, longitudinal 2-year follow-up data were also available. We calculated an additive AD genetic risk score for each participant and contrasted this with a weighted risk score (WRS) approach. Each score included APOE (apolipoprotein E), CLU (clusterin), PICALM (phosphatidylinositol binding clathrin assembly protein), and family history of AD. Both unweighted risk score (URS) and WRS correlated strongly with the percentage change in thickness across the whole hippocampal complex (URS: r = -0.40; p = 0.003; WRS: r = -0.25, p = 0.048), driven by a strong relationship to entorhinal cortex thinning (URS: r = -0.35; p = 0.009; WRS: r = -0.35, p = 0.009). By contrast, at baseline the risk scores showed no relationship to thickness in any hippocampal complex subregion. These results provide compelling evidence that polygenic AD risk scores may be especially sensitive to structural change over time in regions affected early in AD, like the hippocampus and adjacent entorhinal cortex. This work also supports the paradigm of studying genetic risk for disease in healthy volunteers. Together, these findings will inform clinical trial design by supporting the idea that genetic prescreening in healthy control subjects can be useful to maximize the ability to detect an effect on a longitudinal neuroimaging endpoint, like hippocampal complex cortical thickness.

HYDRA: Revealing heterogeneity of imaging and genetic patterns through a multiple max-margin discriminative analysis framework

Multivariate pattern analysis techniques have been increasingly used over the past decade to derive highly sensitive and specific biomarkers of diseases on an individual basis. The driving assumption behind the vast majority of the existing methodologies is that a single imaging pattern can distinguish between healthy and diseased populations, or between two subgroups of patients (e.g., progressors vs. non-progressors). This assumption effectively ignores the ample evidence for the heterogeneous nature of brain diseases. Neurodegenerative, neuropsychiatric and neurodevelopmental disorders are largely characterized by high clinical heterogeneity, which likely stems in part from underlying neuroanatomical heterogeneity of various pathologies. Detecting and characterizing heterogeneity may deepen our understanding of disease mechanisms and lead to patient-specific treatments. However, few approaches tackle disease subtype discovery in a principled machine learning framework. To address this challenge, we present a novel non-linear learning algorithm for simultaneous binary classification and subtype identification, termed HYDRA (Heterogeneity through Discriminative Analysis). Neuroanatomical subtypes are effectively captured by multiple linear hyperplanes, which form a convex polytope that separates two groups (e.g., healthy controls from pathologic samples); each face of this polytope effectively defines a disease subtype. We validated HYDRA on simulated and clinical data. In the latter case, we applied the proposed method independently to the imaging and genetic datasets of the Alzheimer's Disease Neuroimaging Initiative (ADNI 1) study. The imaging dataset consisted of T1-weighted volumetric magnetic resonance images of 123 AD patients and 177 controls. The genetic dataset consisted of single nucleotide polymorphism information of 103 AD patients and 139 controls. We identified 3 reproducible subtypes of atrophy in AD relative to controls: (1) diffuse and extensive atrophy, (2) precuneus and extensive temporal lobe atrophy, as well some prefrontal atrophy, (3) atrophy pattern very much confined to the hippocampus and the medial temporal lobe. The genetics dataset yielded two subtypes of AD characterized mainly by the presence/absence of the apolipoprotein E (APOE) ε4 genotype, but also involving differential presence of risk alleles of CD2AP, SPON1 and LOC39095 SNPs that were associated with differences in the respective patterns of brain atrophy, especially in the precuneus. The results demonstrate the potential of the proposed approach to map disease heterogeneity in neuroimaging and genetic studies.

Neuroimaging genetic risk for Alzheimer's disease in preclinical individuals: From candidate genes to polygenic approaches

Better characterization of the preclinical phase of Alzheimer's disease (AD) is needed in order to develop effective interventions. Neuropathological changes in AD, including neuronal loss and the formation of proteinaceous deposits, begin up to 20 years before the onset of clinical symptoms. As such, the emergence of cognitive impairment should not be the sole basis used to diagnose AD nor to evaluate individuals for enrollment in clinical trials for preventative AD treatments. Instead, early preclinical biomarkers of disease and genetic risk should be used to determine most likely prognosis and enroll individuals in appropriate clinical trials. Neuroimaging-based biomarkers and genetic analysis together present a powerful system for classifying preclinical pathology in patients. Disease modifying interventions are more likely to produce positive outcomes when administered early in the course of AD. In this review, we examine the utility of the neuroimaging genetics field as it applies to AD and early detection during the preclinical phase. Neuroimaging studies focused on single genetic risk factors are summarized. However, we particularly focus on the recent increased interest in polygenic methods and discuss the benefits and disadvantages of these approaches. We discuss challenges in the neuroimaging genetics field, including limitations of statistical power arising from small effect sizes and the over-use of cross-sectional designs. Despite the limitations, neuroimaging genetics has already begun to influence clinical trial design and will play a major role in the prevention of AD.

Microglial malfunction: the third rail in the development of Alzheimer's disease

Studies of Alzheimer's disease (AD) have predominantly focused on two major pathologies: amyloid-β (Aβ) and hyperphosphorylated tau. These misfolded proteins can accumulate asymptomatically in distinct regions over decades. However, significant Aβ accumulation can be seen in individuals who do not develop dementia, and tau pathology limited to the transentorhinal cortex, which can appear early in adulthood, is usually clinically silent. Thus, an interaction between these pathologies appears to be necessary to initiate and propel disease forward to widespread circuits. Recent multidisciplinary findings strongly suggest that the third factor required for disease progression is an aberrant microglial immune response. This response may initially be beneficial; however, a maladaptive microglial response eventually develops, fueling a feed-forward spread of tau and Aβ pathology.

Genetics of Alzheimer's disease

Alzheimer's disease (AD) represents the main form of dementia, and is a major public health problem. Despite intensive research efforts, current treatments have only marginal symptomatic benefits and there are no effective disease-modifying or preventive interventions. AD has a strong genetic component, so much research in AD has focused on identifying genetic causes and risk factors. This chapter will cover genetic discoveries in AD and their consequences in terms of improved knowledge regarding the disease and the identification of biomarkers and drug targets. First, we will discuss the study of the rare early-onset, autosomal dominant forms of AD that led to the discovery of mutations in three major genes, APP, PSEN1, and PSEN2. These discoveries have shaped our current understanding of the pathophysiology and natural history of AD as well as the development of therapeutic targets and the design of clinical trials. Then, we will explore linkage analysis and candidate gene approaches, which identified variants in Apolipoprotein E (APOE) as the major genetic risk factor for late-onset, "sporadic" forms of AD (LOAD), but failed to robustly identify other genetic risk factors, with the exception of variants in SORL1. The main focus of this chapter will be on recent genome-wide association studies that have successfully identified common genetic variations at over 20 loci associated with LOAD outside of the APOE locus. These loci are in or near-novel AD genes including BIN1, CR1, CLU, phosphatidylinositol-binding clathrin assembly protein (PICALM), CD33, EPHA1, MS4A4/MS4A6, ABCA7, CD2AP, SORL1, HLA-DRB5/DRB1, PTK2B, SLC24A4-RIN3, INPP5D, MEF2C, NME8, ZCWPW1, CELF1, FERMT2, CASS4, and TRIP4 and each has small effects on risk of AD (relative risks of 1.1-1.3). Finally, we will touch upon the ongoing effort to identify less frequent and rare variants through whole exome and whole genome sequencing. This effort has identified two novel genes, TREM2 and PLD3, and shown a role for APP in LOAD. The identification of these recently identified genes has implicated previously unsuspected biological pathways in the pathophysiology of AD.

A kernel machine method for detecting effects of interaction between multidimensional variable sets: an imaging genetics application

Measurements derived from neuroimaging data can serve as markers of disease and/or healthy development, are largely heritable, and have been increasingly utilized as (intermediate) phenotypes in genetic association studies. To date, imaging genetic studies have mostly focused on discovering isolated genetic effects, typically ignoring potential interactions with non-genetic variables such as disease risk factors, environmental exposures, and epigenetic markers. However, identifying significant interaction effects is critical for revealing the true relationship between genetic and phenotypic variables, and shedding light on disease mechanisms. In this paper, we present a general kernel machine based method for detecting effects of the interaction between multidimensional variable sets. This method can model the joint and epistatic effect of a collection of single nucleotide polymorphisms (SNPs), accommodate multiple factors that potentially moderate genetic influences, and test for nonlinear interactions between sets of variables in a flexible framework. As a demonstration of application, we applied the method to the data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to detect the effects of the interactions between candidate Alzheimer's disease (AD) risk genes and a collection of cardiovascular disease (CVD) risk factors, on hippocampal volume measurements derived from structural brain magnetic resonance imaging (MRI) scans. Our method identified that two genes, CR1 and EPHA1, demonstrate significant interactions with CVD risk factors on hippocampal volume, suggesting that CR1 and EPHA1 may play a role in influencing AD-related neurodegeneration in the presence of CVD risks.

Two Alzheimer's disease risk genes increase entorhinal cortex volume in young adults

Alzheimer's disease (AD) risk genes alter brain structure and function decades before disease onset. Apolipoprotein E (APOE) is the strongest known genetic risk factor for AD, and a related gene, apolipoprotein J (APOJ), also affects disease risk. However, the extent to which these genes affect brain structure in young adults remains unclear. Here, we report that AD risk alleles of these two genes, APOE-ε4 and APOJ-C, cumulatively alter brain volume in young adults. Using voxel-based morphometry (VBM) in 57 individuals, we examined the entorhinal cortex, one of the earliest brain regions affected in AD pathogenesis. Apolipoprotein E-ε4 carriers exhibited higher right entorhinal cortex volume compared to non-carriers. Interestingly, APOJ-C risk genotype was associated with higher bilateral entorhinal cortex volume in non-APOE-ε4 carriers. To determine the combined disease risk of APOE and APOJ status per subject, we used cumulative odds ratios as regressors for volumetric measurements. Higher disease risk corresponded to greater right entorhinal cortex volume. These results suggest that, years before disease onset, two key AD genetic risk factors may exert influence on the structure of a brain region where AD pathogenesis takes root.

The Role of PICALM in Alzheimer's Disease

Alzheimer's disease (AD) is a highly heritable disease (with heritability up to 76%) with a complex genetic profile of susceptibility, among which large genome-wide association studies (GWASs) pointed to the phosphatidylinositol-binding clathrin assembly protein (PICALM) gene as a susceptibility locus for late-onset Alzheimer's disease (LOAD) incidence. Here, we summarize the known functions of PICALM and discuss its genetic polymorphisms and their potential physiological effects associated with LOAD. Compelling data indicated that PICALM affects AD risk primarily by modulating production, transportation, and clearance of β-amyloid (Aβ) peptide, but other Aβ-independent pathways are discussed, including tauopathy, synaptic dysfunction, disorganized lipid metabolism, immune disorder, and disrupted iron homeostasis. Finally, given the potential involvement of PICALM in facilitating AD occurrence in multiple ways, it might be possible that targeting PICALM might provide promising and novel avenues for AD therapy.

Neuroimaging and genetic risk for Alzheimer's disease and addiction-related degenerative brain disorders

Neuroimaging offers a powerful means to assess the trajectory of brain degeneration in a variety of disorders, including Alzheimer's disease (AD). Here we describe how multi-modal imaging can be used to study the changing brain during the different stages of AD. We integrate findings from a range of studies using magnetic resonance imaging (MRI), positron emission tomography (PET), functional MRI (fMRI) and diffusion weighted imaging (DWI). Neuroimaging reveals how risk genes for degenerative disorders affect the brain, including several recently discovered genetic variants that may disrupt brain connectivity. We review some recent neuroimaging studies of genetic polymorphisms associated with increased risk for late-onset Alzheimer's disease (LOAD). Some genetic variants that increase risk for drug addiction may overlap with those associated with degenerative brain disorders. These common associations offer new insight into mechanisms underlying neurodegeneration and addictive behaviors, and may offer new leads for treating them before severe and irreversible neurological symptoms appear.

CR1 in Alzheimer's disease

The complement component receptor 1 gene (CR1), which encodes a type-I transmembrane glycoprotein, has recently been identified as one of the most important risk genes for late-onset Alzheimer's disease (LOAD). In this article, we reviewed the recent evidence concerning the role of CR1 in LOAD. First, we introduced the structure, localization and physiological function of CR1 in humans. Afterward, we summarized the relation of CR1 polymorphisms with LOAD risk. Finally, we discussed the possible impact of CR1 on the pathogenesis of AD including amyloid-β pathology, tauopathy, immune dysfunction and glial-mediated neuroinflammation. We hope that a more comprehensive understanding of the role that CR1 played in AD may lead to the development of novel therapeutics for the prevention and treatment of AD.

Impacts of PICALM and CLU variants associated with Alzheimer's disease on the functional connectivity of the hippocampus in healthy young adults

PICALM rs3851179 and CLU rs11136000 have been recently associated with Alzheimer's disease (AD). Investigating the effects of these genetic variants on the resting-state functional connectivity (rsFC) of the hippocampus may provide new insight into AD pathogenesis. We investigated the main effects and interactions of these two genetic variants on hippocampal rsFC in 283 healthy young adults. The hippocampus showed positive rsFC with the default mode network and negative rsFC with the fronto-parietal network. Risk PICALM G-allele carriers showed weaker negative rsFC compared with AA carriers, whereas risk CLU-CC carriers exhibited stronger positive and negative rsFC than T-allele carriers. There existed complex interactions between PICALM and CLU on the negative rsFC of the hippocampus. Moreover, we found an allele-dependent effect of CLU on hippocampal connectivity when an additive genetic model was applied to CLU. Most of these effects remained significant even after controlling for individual ApoE status. Our results suggest that PICALM and CLU risk genotypes exert differential impacts on the hippocampal rsFC in healthy young subjects. The complex interactions between PICALM and CLU should be considered when investigating the impact of these two genetic variants on the brain.

CLU genetic variants and cognitive decline among elderly and oldest old

The CLU gene is one of the prime genetic candidates associated with Alzheimers disease. In the present study CLU genotypes and haplotypes were associated with baseline cognition and the rate of cognitive decline in two cohorts, the Danish 1905 birth cohort (93 years of age in 1998) and the Longitudinal Study of Aging Danish twins (LSADT) (73-83 year old twins in 1997). Both Mini Mental State Examination (MMSE) and a cognitive composite score was attained up to six times for up to 10 years and analysed using random effects models and vital status. The rs11136000 T allele was associated with better baseline cognitive performance both in the LSADT (effect on intercept: 0.41 95% CI [-0.04; 0.87]) and the 1905 birth cohort (effect on intercept: 0.28 95% CI [0.01; 0.55]), although it did not reach significance in the LSADT cohort. However, the rs11136000 T allele was significantly associated with a steeper decline (effect on slope: -0.06 95% CI [-0.11; -0.01]) in the LSADT cohort, but not in the 1905 birth cohort. Haplotype analyses revealed that carriers of the common rs11136000, rs1532278 and rs9331888 TTC haplotype (36%) in the CLU gene performed cognitively better than non-carriers in the 1905 birth cohort (effect on intercept: 0.50 95% CI [0.12; 0.91]) and carriers of a rare TCC haplotype (1%) performed worse on the cognitive composite score (effect on intercept: -1.51 95% CI [-2.92; -0.06]). The association between the TTC haplotype and better cognitive composite score was higher among those surviving past the age of 98 (p = 0.014), and among these the TTC haplotype was borderline associated with a steep decline (effect on slope: -0.13 95% CI [-0.27; 0.00]). In summery CLU genetic variants associate with cognition in two cohorts, but the genetic effect of CLU seems to regress toward the mean when aging.

Multilocus genetic profiling to empower drug trials and predict brain atrophy

Designers of clinical trials for Alzheimer's disease (AD) and mild cognitive impairment (MCI) are actively considering structural and functional neuroimaging, cerebrospinal fluid and genetic biomarkers to reduce the sample sizes needed to detect therapeutic effects. Genetic pre-selection, however, has been limited to Apolipoprotein E (ApoE). Recently discovered polymorphisms in the CLU, CR1 and PICALM genes are also moderate risk factors for AD; each affects lifetime AD risk by ~ 10–20%. Here, we tested the hypothesis that pre-selecting subjects based on these variants along with ApoE genotype would further boost clinical trial power, relative to considering ApoE alone, using an MRI-derived 2-year atrophy rate as our outcome measure. We ranked subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) based on their cumulative risk from these four genes. We obtained sample size estimates in cohorts enriched in subjects with greater aggregate genetic risk. Enriching for additional genetic biomarkers reduced the required sample sizes by up to 50%, for MCI trials. Thus, AD drug trial enrichment with multiple genotypes may have potential implications for the timeliness, cost, and power of trials.

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ApoE genotype status helps enrich MCI trials, using a structural MRI outcome measure.

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CLU, PICALM and CR1 risk genes boost potential MCI trial power beyond ApoE alone.

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CLU, PICALM and CR1 show significant, aggregate effects on TBM maps of brain atrophy.

Recent advances in imaging Alzheimer's disease

Advances in brain imaging technology in the past five years have contributed greatly to the understanding of Alzheimer's disease (AD). Here, we review recent research related to amyloid imaging, new methods for magnetic resonance imaging analyses, and statistical methods. We also review research that evaluates AD risk factors and brain imaging, in the context of AD prediction and progression. We selected a variety of illustrative studies, describing how they advanced the field and are leading AD research in promising new directions.

Genetic insights in Alzheimer's disease

In the search for new genes in Alzheimer's disease, classic linkage-based and candidate-gene-based association studies have been supplanted by exome sequencing, genome-wide sequencing (for mendelian forms of Alzheimer's disease), and genome-wide association studies (for non-mendelian forms). The identification of new susceptibility genes has opened new avenues for exploration of the underlying disease mechanisms. In addition to detecting novel risk factors in large samples, next-generation sequencing approaches can deliver novel insights with even small numbers of patients. The shift in focus towards translational studies and sequencing of individual patients places each patient's biomaterials as the central unit of genetic studies. The notional shift needed to make the patient central to genetic studies will necessitate strong collaboration and input from clinical neurologists.

Unveiling clusters of RNA transcript pairs associated with markers of Alzheimer's disease progression

Background

One primary goal of transcriptomic studies is identifying gene expression patterns correlating with disease progression. This is usually achieved by considering transcripts that independently pass an arbitrary threshold (e.g. p<0.05). In diseases involving severe perturbations of multiple molecular systems, such as Alzheimer’s disease (AD), this univariate approach often results in a large list of seemingly unrelated transcripts. We utilised a powerful multivariate clustering approach to identify clusters of RNA biomarkers strongly associated with markers of AD progression. We discuss the value of considering pairs of transcripts which, in contrast to individual transcripts, helps avoid natural human transcriptome variation that can overshadow disease-related changes.

Methodology/Principal Findings

We re-analysed a dataset of hippocampal transcript levels in nine controls and 22 patients with varying degrees of AD. A large-scale clustering approach determined groups of transcript probe sets that correlate strongly with measures of AD progression, including both clinical and neuropathological measures and quantifiers of the characteristic transcriptome shift from control to severe AD. This enabled identification of restricted groups of highly correlated probe sets from an initial list of 1,372 previously published by our group. We repeated this analysis on an expanded dataset that included all pair-wise combinations of the 1,372 probe sets. As clustering of this massive dataset is unfeasible using standard computational tools, we adapted and re-implemented a clustering algorithm that uses external memory algorithmic approach. This identified various pairs that strongly correlated with markers of AD progression and highlighted important biological pathways potentially involved in AD pathogenesis.

Conclusions/Significance

Our analyses demonstrate that, although there exists a relatively large molecular signature of AD progression, only a small number of transcripts recurrently cluster with different markers of AD progression. Furthermore, considering the relationship between two transcripts can highlight important biological relationships that are missed when considering either transcript in isolation.