Whole genome association study of brain-wide imaging phenotypes for identifying quantitative trait loci in MCI and AD: A study of the ADNI cohort

Approximate score-based testing with application to multivariate trait association analysis

For genome-wide association studies and DNA sequencing studies, several powerful score-based tests, such as kernel machine regression and sum of powered score tests, have been proposed in the last few years. However, extensions of these score-based tests to more complex models, such as mixed-effects models for analysis of multiple and correlated traits, have been hindered by the unavailability of the score vector, due to either no output from statistical software or no closed-form solution at all. We propose a simple and general method to asymptotically approximate the score vector based on an asymptotically normal and consistent estimate of a parameter vector to be tested and its (consistent) covariance matrix. The proposed method is applicable to both maximum-likelihood estimation and estimating function-based approaches. We use the derived approximate score vector to extend several score-based tests to mixed-effects models. We demonstrate the feasibility and possible power gains of these tests in association analysis of multiple and correlated quantitative or binary traits with both real and simulated data. The proposed method is easy to implement with a wide applicability.

HMGCR is a genetic modifier for risk, age of onset and MCI conversion to Alzheimer's disease in a three cohorts study

Several retrospective epidemiological studies report that utilization of 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR) inhibitors called statins at mid-life can reduce the risk of developing sporadic Alzheimer's disease (AD) by as much as 70%. Conversely, the administration of these inhibitors in clinically diagnosed subjects with AD confers little or no benefits over time. Here, we investigated the association between AD and HMGCR rs3846662, a polymorphism known to be involved in the regulation of HMGCR exon 13 skipping, in a founder population and in two distinct mixed North American populations of converting mild cognitively impaired (MCI) subjects (Alzheimer's disease Cooperative study (ADCS) and Alzheimer's disease Neuroimaging Initiative (ADNI) cohorts). Targeting more specifically women, the G allele negative (G-) AD subjects exhibit delayed age of onset of AD (P=0.017) and significantly reduced risk of AD (OR: 0.521; P=0.0028), matching the effect size reported by the apolipoprotein E type 2 variant. Stratification for APOE4 in a large sample of MCI patients from the ADCS cohort revealed a significant protective effect of G negative carriers on AD conversion 3 years after MCI diagnosis (odds ratio (OR): 0.554; P=0.041). Conversion rate among APOE4 carriers with the HMGCR's G negative allele was markedly reduced (from 76% to 27%) to levels similar to APOE4 non-carriers (27.14%), which strongly indicate protection. Conversion data from the independent ADNI cohort also showed significantly reduced MCI or AD conversion among APOE4 carriers with the protective A allele (P=0.005). In conclusion, HMGCR rs3846662 acts as a potent genetic modifier for AD risk, age of onset and conversion.

Impact of the Alzheimer's Disease Neuroimaging Initiative, 2004 to 2014

INTRODUCTION

The Alzheimer's Disease Neuroimaging Initiative (ADNI) was established in 2004 to facilitate the development of effective treatments for Alzheimer's disease (AD) by validating biomarkers for AD clinical trials.

METHODS

We searched for ADNI publications using established methods.

RESULTS

ADNI has (1) developed standardized biomarkers for use in clinical trial subject selection and as surrogate outcome measures; (2) standardized protocols for use across multiple centers; (3) initiated worldwide ADNI; (4) inspired initiatives investigating traumatic brain injury and post-traumatic stress disorder in military populations, and depression, respectively, as an AD risk factor; (5) acted as a data-sharing model; (6) generated data used in over 600 publications, leading to the identification of novel AD risk alleles, and an understanding of the relationship between biomarkers and AD progression; and (7) inspired other public-private partnerships developing biomarkers for Parkinson's disease and multiple sclerosis.

DISCUSSION

ADNI has made myriad impacts in its first decade. A competitive renewal of the project in 2015 would see the use of newly developed tau imaging ligands, and the continued development of recruitment strategies and outcome measures for clinical trials.

Introduction to special issue: Overview of Alzheimer's Disease Neuroimaging Initiative

The Alzheimer's Disease Neuroimaging Initiative (ADNI), designed as a naturalistic longitudinal study to develop and validate magnetic resonance, positron emission tomography, cerebrospinal fluid, and genetic biomarkers for use in AD clinical trials, has made many impacts in the decade since its inception. The initial 5-year study, ADNI-1, enrolled cognitively normal, mild cognitive impairment (MCI) and AD subjects, and the subsequent studies (ADNI-GO and ADNI-2) added early- and late-MCI cohorts. The development of standardized methods allowed comparison of data gathered across multiple sites, and these data are available to qualified researchers without embargo. ADNI data have been used in >600 publications including those describing relationships between biomarkers, improved methods for disease diagnosis and the prediction of future decline, and identifying novel genetic AD risk loci. ADNI has provided a framework for similar initiatives worldwide.

2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception

The Alzheimer's Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer's disease (AD). The initial study, ADNI-1, enrolled 400 subjects with early mild cognitive impairment (MCI), 200 with early AD, and 200 cognitively normal elderly controls. ADNI-1 was extended by a 2-year Grand Opportunities grant in 2009 and by a competitive renewal, ADNI-2, which enrolled an additional 550 participants and will run until 2015. This article reviews all papers published since the inception of the initiative and summarizes the results to the end of 2013. The major accomplishments of ADNI have been as follows: (1) the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers in a multicenter setting; (2) elucidation of the patterns and rates of change of imaging and CSF biomarker measurements in control subjects, MCI patients, and AD patients. CSF biomarkers are largely consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimer's Dis 2006;9(Suppl 3):151-3) and tau-mediated neurodegeneration hypotheses for AD, whereas brain atrophy and hypometabolism levels show predicted patterns but exhibit differing rates of change depending on region and disease severity; (3) the assessment of alternative methods of diagnostic categorization. Currently, the best classifiers select and combine optimum features from multiple modalities, including MRI, [(18)F]-fluorodeoxyglucose-PET, amyloid PET, CSF biomarkers, and clinical tests; (4) the development of blood biomarkers for AD as potentially noninvasive and low-cost alternatives to CSF biomarkers for AD diagnosis and the assessment of α-syn as an additional biomarker; (5) the development of methods for the early detection of AD. CSF biomarkers, β-amyloid 42 and tau, as well as amyloid PET may reflect the earliest steps in AD pathology in mildly symptomatic or even nonsymptomatic subjects and are leading candidates for the detection of AD in its preclinical stages; (6) the improvement of clinical trial efficiency through the identification of subjects most likely to undergo imminent future clinical decline and the use of more sensitive outcome measures to reduce sample sizes. Multimodal methods incorporating APOE status and longitudinal MRI proved most highly predictive of future decline. Refinements of clinical tests used as outcome measures such as clinical dementia rating-sum of boxes further reduced sample sizes; (7) the pioneering of genome-wide association studies that leverage quantitative imaging and biomarker phenotypes, including longitudinal data, to confirm recently identified loci, CR1, CLU, and PICALM and to identify novel AD risk loci; (8) worldwide impact through the establishment of ADNI-like programs in Japan, Australia, Argentina, Taiwan, China, Korea, Europe, and Italy; (9) understanding the biology and pathobiology of normal aging, MCI, and AD through integration of ADNI biomarker and clinical data to stimulate research that will resolve controversies about competing hypotheses on the etiopathogenesis of AD, thereby advancing efforts to find disease-modifying drugs for AD; and (10) the establishment of infrastructure to allow sharing of all raw and processed data without embargo to interested scientific investigators throughout the world.

Opportunities in pharmacogenomics for the treatment of Alzheimer's disease

ABSTRACT 

In Alzheimer's disease (AD), approximately 10–20% of direct costs are associated with pharmacological treatment. Pharmacogenomics account for 30–90% variability in pharmacokinetics and pharmacodynamics. Genes potentially involved in the pharmacogenomics outcome include pathogenic, mechanistic, metabolic, transporter and pleiotropic genes. Over 75% of the Caucasian population is defective for the CYP2D6+2C9+2C19 cluster. Polymorphic variants in the APOE-TOMM40 region influence AD pharmacogenomics. APOE-4 carriers are the worst responders and APOE-3 carriers are the best responders to conventional treatments. TOMM40 poly T-S/S carriers are the best responders, VL/VL and S/VL carriers are intermediate responders and L/L carriers are the worst responders. The haplotype 4/4-L/L is probably responsible for early onset of the disease, a faster cognitive decline and a poor response to different treatments.

Effects of traumatic brain injury and posttraumatic stress disorder on Alzheimer's disease in veterans, using the Alzheimer's Disease Neuroimaging Initiative

Both traumatic brain injury (TBI) and posttraumatic stress disorder (PTSD) are common problems resulting from military service, and both have been associated with increased risk of cognitive decline and dementia resulting from Alzheimer's disease (AD) or other causes. This study aims to use imaging techniques and biomarker analysis to determine whether traumatic brain injury (TBI) and/or PTSD resulting from combat or other traumas increase the risk for AD and decrease cognitive reserve in Veteran subjects, after accounting for age. Using military and Department of Veterans Affairs records, 65 Vietnam War veterans with a history of moderate or severe TBI with or without PTSD, 65 with ongoing PTSD without TBI, and 65 control subjects are being enrolled in this study at 19 sites. The study aims to select subject groups that are comparable in age, gender, ethnicity, and education. Subjects with mild cognitive impairment (MCI) or dementia are being excluded. However, a new study just beginning, and similar in size, will study subjects with TBI, subjects with PTSD, and control subjects with MCI. Baseline measurements of cognition, function, blood, and cerebrospinal fluid biomarkers; magnetic resonance images (structural, diffusion tensor, and resting state blood-level oxygen dependent (BOLD) functional magnetic resonance imaging); and amyloid positron emission tomographic (PET) images with florbetapir are being obtained. One-year follow-up measurements will be collected for most of the baseline procedures, with the exception of the lumbar puncture, the PET imaging, and apolipoprotein E genotyping. To date, 19 subjects with TBI only, 46 with PTSD only, and 15 with TBI and PTSD have been recruited and referred to 13 clinics to undergo the study protocol. It is expected that cohorts will be fully recruited by October 2014. This study is a first step toward the design and statistical powering of an AD prevention trial using at-risk veterans as subjects, and provides the basis for a larger, more comprehensive study of dementia risk factors in veterans.

Hippocampal transcriptome-guided genetic analysis of correlated episodic memory phenotypes in Alzheimer's disease

As the most common type of dementia, Alzheimer's disease (AD) is a neurodegenerative disorder initially manifested by impaired memory performances. While the diagnosis information indicates a dichotomous status of a patient, memory scores have the potential to capture the continuous nature of the disease progression and may provide more insights into the underlying mechanism. In this work, we performed a targeted genetic study of memory scores on an AD cohort to identify the associations between a set of genes highly expressed in the hippocampal region and seven cognitive scores related to episodic memory. Both main effects and interaction effects of the targeted genetic markers on these correlated memory scores were examined. In addition to well-known AD genetic markers APOE and TOMM40, our analysis identified a new risk gene NAV2 through the gene-level main effect analysis. NAV2 was found to be significantly and consistently associated with all seven episodic memory scores. Genetic interaction analysis also yielded a few promising hits warranting further investigation, especially for the RAVLT list B Score.

Massively expedited genome-wide heritability analysis (MEGHA)

The discovery and prioritization of heritable phenotypes is a computational challenge in a variety of settings, including neuroimaging genetics and analyses of the vast phenotypic repositories in electronic health record systems and population-based biobanks. Classical estimates of heritability require twin or pedigree data, which can be costly and difficult to acquire. Genome-wide complex trait analysis is an alternative tool to compute heritability estimates from unrelated individuals, using genome-wide data that are increasingly ubiquitous, but is computationally demanding and becomes difficult to apply in evaluating very large numbers of phenotypes. Here we present a fast and accurate statistical method for high-dimensional heritability analysis using genome-wide SNP data from unrelated individuals, termed massively expedited genome-wide heritability analysis (MEGHA) and accompanying nonparametric sampling techniques that enable flexible inferences for arbitrary statistics of interest. MEGHA produces estimates and significance measures of heritability with several orders of magnitude less computational time than existing methods, making heritability-based prioritization of millions of phenotypes based on data from unrelated individuals tractable for the first time to our knowledge. As a demonstration of application, we conducted heritability analyses on global and local morphometric measurements derived from brain structural MRI scans, using genome-wide SNP data from 1,320 unrelated young healthy adults of non-Hispanic European ancestry. We also computed surface maps of heritability for cortical thickness measures and empirically localized cortical regions where thickness measures were significantly heritable. Our analyses demonstrate the unique capability of MEGHA for large-scale heritability-based screening and high-dimensional heritability profile construction.

Transcriptome-guided amyloid imaging genetic analysis via a novel structured sparse learning algorithm

MOTIVATION

Imaging genetics is an emerging field that studies the influence of genetic variation on brain structure and function. The major task is to examine the association between genetic markers such as single-nucleotide polymorphisms (SNPs) and quantitative traits (QTs) extracted from neuroimaging data. The complexity of these datasets has presented critical bioinformatics challenges that require new enabling tools. Sparse canonical correlation analysis (SCCA) is a bi-multivariate technique used in imaging genetics to identify complex multi-SNP-multi-QT associations. However, most of the existing SCCA algorithms are designed using the soft thresholding method, which assumes that the input features are independent from one another. This assumption clearly does not hold for the imaging genetic data. In this article, we propose a new knowledge-guided SCCA algorithm (KG-SCCA) to overcome this limitation as well as improve learning results by incorporating valuable prior knowledge.

RESULTS

The proposed KG-SCCA method is able to model two types of prior knowledge: one as a group structure (e.g. linkage disequilibrium blocks among SNPs) and the other as a network structure (e.g. gene co-expression network among brain regions). The new model incorporates these prior structures by introducing new regularization terms to encourage weight similarity between grouped or connected features. A new algorithm is designed to solve the KG-SCCA model without imposing the independence constraint on the input features. We demonstrate the effectiveness of our algorithm with both synthetic and real data. For real data, using an Alzheimer's disease (AD) cohort, we examine the imaging genetic associations between all SNPs in the APOE gene (i.e. top AD gene) and amyloid deposition measures among cortical regions (i.e. a major AD hallmark). In comparison with a widely used SCCA implementation, our KG-SCCA algorithm produces not only improved cross-validation performances but also biologically meaningful results.

AVAILABILITY

Software is freely available on request.

Cortical surface biomarkers for predicting cognitive outcomes using group l2,1 norm

Regression models have been widely studied to investigate the prediction power of neuroimaging measures as biomarkers for inferring cognitive outcomes in the Alzheimer's disease study. Most of these models ignore the interrelated structures either within neuroimaging measures or between cognitive outcomes, and thus may have limited power to yield optimal solutions. To address this issue, we propose to use a new sparse multitask learning model called Group-Sparse Multi-task Regression and Feature Selection (G-SMuRFS) and demonstrate its effectiveness by examining the predictive power of detailed cortical thickness measures toward 3 types of cognitive scores in a large cohort. G-SMuRFS proposes a group-level l2,1-norm strategy to group relevant features together in an anatomically meaningful manner and use this prior knowledge to guide the learning process. This approach also takes into account the correlation among cognitive outcomes for building a more appropriate predictive model. Compared with traditional methods, G-SMuRFS not only demonstrates a superior performance but also identifies a small set of surface markers that are biologically meaningful.

GN-SCCA: GraphNet based Sparse Canonical Correlation Analysis for Brain Imaging Genetics

Identifying associations between genetic variants and neuroimaging quantitative traits (QTs) is a popular research topic in brain imaging genetics. Sparse canonical correlation analysis (SCCA) has been widely used to reveal complex multi-SNP-multi-QT associations. Several SCCA methods explicitly incorporate prior knowledge into the model and intend to uncover the hidden structure informed by the prior knowledge. We propose a novel structured SCCA method using Graph constrained Elastic-Net (GraphNet) regularizer to not only discover important associations, but also induce smoothness between coefficients that are adjacent in the graph. In addition, the proposed method incorporates the covariance structure information usually ignored by most SCCA methods. Experiments on simulated and real imaging genetic data show that, the proposed method not only outperforms a widely used SCCA method but also yields an easy-to-interpret biological findings.

Gene interactions and structural brain change in early-onset Alzheimer's disease subjects using the pipeline environment

OBJECTIVE

This article investigates subjects aged 55 to 65 from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to broaden our understanding of early-onset (EO) cognitive impairment using neuroimaging and genetics biomarkers.

METHODS

Nine of the subjects had EO-AD (Alzheimer's disease) and 27 had EO-MCI (mild cognitive impairment). The 15 most important neuroimaging markers were extracted with the Global Shape Analysis (GSA) Pipeline workflow. The 20 most significant single nucleotide polymorphisms (SNPs) were chosen and were associated with specific neuroimaging biomarkers.

RESULTS

We identified associations between the neuroimaging phenotypes and genotypes for a total of 36 subjects. Our results for all the subjects taken together showed the most significant associations between rs7718456 and L_hippocampus (volume), and between rs7718456 and R_hippocampus (volume). For the 27 MCI subjects, we found the most significant associations between rs6446443 and R_superior_frontal_gyrus (volume), and between rs17029131 and L_Precuneus (volume). For the nine AD subjects, we found the most significant associations between rs16964473 and L_rectus gyrus (surface area), and between rs12972537 and L_rectus_gyrus (surface area).

CONCLUSION

We observed significant correlations between the SNPs and the neuroimaging phenotypes in the 36 EO subjects in terms of neuroimaging genetics. However, larger sample sizes are needed to ensure that the effects will be detectable for a reasonable false-positive error rate using the GSA and Plink Pipeline workflows.

Structural Neuroimaging Genetics Interactions in Alzheimer's Disease

This article investigates late-onset cognitive impairment using neuroimaging and genetics biomarkers for Alzheimer's Disease Neuroimaging Initiative (ADNI) participants. Eight-hundred and eight ADNI subjects were identified and divided into three groups: 200 subjects with Alzheimer's disease (AD), 383 subjects with mild cognitive impairment (MCI), and 225 asymptomatic normal controls (NC). Their structural magnetic resonance imaging (MRI) data were parcellated using BrainParser, and the 80 most important neuroimaging biomarkers were extracted using the global shape analysis Pipeline workflow. Using Plink via the Pipeline environment, we obtained 80 SNPs highly-associated with the imaging biomarkers. In the AD cohort, rs2137962 was significantly associated bilaterally with changes in the hippocampi and the parahippocampal gyri, and rs1498853, rs288503, and rs288496 were associated with the left and right hippocampi, the right parahippocampal gyrus, and the left inferior temporal gyrus. In the MCI cohort, rs17028008 and rs17027976 were significantly associated with the right caudate and right fusiform gyrus, rs2075650 (TOMM40) was associated with the right caudate, and rs1334496 and rs4829605 were significantly associated with the right inferior temporal gyrus. In the NC cohort, Chromosome 15 [rs734854 (STOML1), rs11072463 (PML), rs4886844 (PML), and rs1052242 (PML)] was significantly associated with both hippocampi and both insular cortices, and rs4899412 (RGS6) was significantly associated with the caudate. We observed significant correlations between genetic and neuroimaging phenotypes in the 808 ADNI subjects. These results suggest that differences between AD, MCI, and NC cohorts may be examined by using powerful joint models of morphometric, imaging and genotypic data.

Influence of genetic variants in SORL1 gene on the manifestation of Alzheimer's disease

We studied the association of SORL1 single-nucleotide polymorphisms genotypes with measures of pathology in patients with probable Alzheimer's disease (AD) using an endophenotype approach. We included (1) 133 patients from the German Dementia Competence Network (71 ± 8 years; 50% females; Mini Mental State Examination [MMSE], 24 ± 3); (2) 83 patients from the Alzheimer's Disease Neuroimaging Initiative (75 ± 8 years; 45% females; MMSE, 24 ± 2); and (3) 452 patients from the Amsterdam Dementia Cohort 66 ± 8 years; 47% females; MMSE, 20 ± 5). As endophenotype markers we used cognitive tests, cerebrospinal fluid (CSF) biomarkers amyloid-beta, total tau (tau), tau phosphorylated at threonine 181, and hippocampal atrophy. We measured 19 SORL1 SNP alleles. Genotype-endophenotype associations were determined by linear regression analyses. There was an association between rs2070045-G allele and increased CSF-tau and more hippocampal atrophy. Additionally, haplotype-based analyses revealed an association between haplotype rs11218340-A/rs3824966-G/rs3824968-A and higher CSF-tau and CSF-tau phosphorylated at threonine 181. In conclusion, we found that SORL1 SNP rs2070045-G allele was related to CSF-tau and hippocampal atrophy, 2 endophenotype markers of AD, suggesting that SORL1 may be implicated in the downstream pathology in AD.

Functional-mixed effects models for candidate genetic mapping in imaging genetic studies

The aim of this paper is to develop a functional-mixed effects modeling (FMEM) framework for the joint analysis of high-dimensional imaging data in a large number of locations (called voxels) of a three-dimensional volume with a set of genetic markers and clinical covariates. Our FMEM is extremely useful for efficiently carrying out the candidate gene approaches in imaging genetic studies. FMEM consists of two novel components including a mixed effects model for modeling nonlinear genetic effects on imaging phenotypes by introducing the genetic random effects at each voxel and a jumping surface model for modeling the variance components of the genetic random effects and fixed effects as piecewise smooth functions of the voxels. Moreover, FMEM naturally accommodates the correlation structure of the genetic markers at each voxel, while the jumping surface model explicitly incorporates the intrinsically spatial smoothness of the imaging data. We propose a novel two-stage adaptive smoothing procedure to spatially estimate the piecewise smooth functions, particularly the irregular functional genetic variance components, while preserving their edges among different piecewise-smooth regions. We develop weighted likelihood ratio tests and derive their exact approximations to test the effect of the genetic markers across voxels. Simulation studies show that FMEM significantly outperforms voxel-wise approaches in terms of higher sensitivity and specificity to identify regions of interest for carrying out candidate genetic mapping in imaging genetic studies. Finally, FMEM is used to identify brain regions affected by three candidate genes including CR1, CD2AP, and PICALM, thereby hoping to shed light on the pathological interactions between these candidate genes and brain structure and function.

Genetic modification of the relationship between phosphorylated tau and neurodegeneration

BACKGROUND

A subset of individuals present at autopsy with the pathologic features of Alzheimer's disease having never manifest the clinical symptoms. We sought to identify genetic factors that modify the relationship between phosphorylated tau (PTau) and dilation of the lateral inferior ventricles.

METHODS

We used data from 700 subjects enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI). A genome-wide association study approach was used to identify PTau × single nucleotide polymorphism (SNP) interactions. Variance explained by these interactions was quantified using hierarchical linear regression.

RESULTS

Five SNP × PTau interactions passed a Bonferroni correction, one of which (rs4728029, POT1, 2.6% of variance) was consistent across ADNI-1 and ADNI-2/GO subjects. This interaction also showed a trend-level association with memory performance and levels of interleukin-6 receptor.

CONCLUSIONS

Our results suggest that rs4728029 modifies the relationship between PTau and both ventricular dilation and cognition, perhaps through an altered neuroinflammatory response.

Alzheimer's disease biomarker discovery using SOMAscan multiplexed protein technology

Blood proteins and their complexes have become the focus of a great deal of interest in the context of their potential as biomarkers of Alzheimer's disease (AD). We used a SOMAscan assay for quantifying 1001 proteins in blood samples from 331 AD, 211 controls, and 149 mild cognitive impaired (MCI) subjects. The strongest associations of protein levels with AD outcomes were prostate-specific antigen complexed to α1-antichymotrypsin (AD diagnosis), pancreatic prohormone (AD diagnosis, left entorhinal cortex atrophy, and left hippocampus atrophy), clusterin (rate of cognitive decline), and fetuin B (left entorhinal atrophy). Multivariate analysis found that a subset of 13 proteins predicted AD with an accuracy of area under the curve of 0.70. Our replication of previous findings provides further evidence that levels of these proteins in plasma are truly associated with AD. The newly identified proteins could be potential biomarkers and are worthy of further investigation.

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 TOMM40 poly-T rs10524523 variant is associated with cognitive performance among non-demented elderly with type 2 diabetes

The variable length poly-T, rs10524523 ('523') located within the TOMM40 gene, was recently associated with several phenotypes of cognitive function. The short (S) allele is associated with later AD onset age and better cognitive performance, compared to the longer alleles (long and very-long (VL)). There is strong linkage disequilibrium between variants in the TOMM40 and APOE genes. In this study, we investigated the effect of '523' on cognitive performance in a sample of cognitively normal Jewish elderly with type 2 diabetes, a group at particularly high risk for cognitive impairment. Using a MANCOVA procedure, we compared homozygous carriers of the S/S allele (N=179) to carriers of the VL/VL allele (N=152), controlling for demographic and cardiovascular covariates. The S/S group performed better than the VL/VL group (p=0.048), specifically in the executive function (p=0.04) and episodic memory (p=0.050) domains. These results suggest that previous findings of an association of the TOMM40 short allele with better cognitive performance, independently from the APOE variant status, are pertinent to elderly with diabetes.

Computational genetics analysis of grey matter density in Alzheimer's disease

Background

Alzheimer’s disease is the most common form of progressive dementia and there is currently no known cure. The cause of onset is not fully understood but genetic factors are expected to play a significant role. We present here a bioinformatics approach to the genetic analysis of grey matter density as an endophenotype for late onset Alzheimer’s disease. Our approach combines machine learning analysis of gene-gene interactions with large-scale functional genomics data for assessing biological relationships.

Results

We found a statistically significant synergistic interaction among two SNPs located in the intergenic region of an olfactory gene cluster. This model did not replicate in an independent dataset. However, genes in this region have high-confidence biological relationships and are consistent with previous findings implicating sensory processes in Alzheimer’s disease.

Conclusions

Previous genetic studies of Alzheimer’s disease have revealed only a small portion of the overall variability due to DNA sequence differences. Some of this missing heritability is likely due to complex gene-gene and gene-environment interactions. We have introduced here a novel bioinformatics analysis pipeline that embraces the complexity of the genetic architecture of Alzheimer’s disease while at the same time harnessing the power of functional genomics. These findings represent novel hypotheses about the genetic basis of this complex disease and provide open-access methods that others can use in their own studies.

Longitudinal analysis is more powerful than cross-sectional analysis in detecting genetic association with neuroimaging phenotypes

Most existing genome-wide association analyses are cross-sectional, utilizing only phenotypic data at a single time point, e.g. baseline. On the other hand, longitudinal studies, such as Alzheimer's Disease Neuroimaging Initiative (ADNI), collect phenotypic information at multiple time points. In this article, as a case study, we conducted both longitudinal and cross-sectional analyses of the ADNI data with several brain imaging (not clinical diagnosis) phenotypes, demonstrating the power gains of longitudinal analysis over cross-sectional analysis. Specifically, we scanned genome-wide single nucleotide polymorphisms (SNPs) with 56 brain-wide imaging phenotypes processed by FreeSurfer on 638 subjects. At the genome-wide significance level P < 1.8 x 10(9)) or a less stringent level (e.g. P < 10(7)), longitudinal analysis of the phenotypic data from the baseline to month 48 identified more SNP-phenotype associations than cross-sectional analysis of only the baseline data. In particular, at the genome-wide significance level, both SNP rs429358 in gene APOE and SNP rs2075650 in gene TOMM40 were confirmed to be associated with various imaging phenotypes in multiple regions of interests (ROIs) by both analyses, though longitudinal analysis detected more regional phenotypes associated with the two SNPs and indicated another significant SNP rs439401 in gene APOE. In light of the power advantage of longitudinal analysis, we advocate its use in current and future longitudinal neuroimaging studies.

Testing for association with multiple traits in generalized estimation equations, with application to neuroimaging data

There is an increasing need to develop and apply powerful statistical tests to detect multiple traits-single locus associations, as arising from neuroimaging genetics and other studies. For example, in the Alzheimer's Disease Neuroimaging Initiative (ADNI), in addition to genome-wide single nucleotide polymorphisms (SNPs), thousands of neuroimaging and neuropsychological phenotypes as intermediate phenotypes for Alzheimer's disease, have been collected. Although some classic methods like MANOVA and newly proposed methods may be applied, they have their own limitations. For example, MANOVA cannot be applied to binary and other discrete traits. In addition, the relationships among these methods are not well understood. Importantly, since these tests are not data adaptive, depending on the unknown association patterns among multiple traits and between multiple traits and a locus, these tests may or may not be powerful. In this paper we propose a class of data-adaptive weights and the corresponding weighted tests in the general framework of generalized estimation equations (GEE). A highly adaptive test is proposed to select the most powerful one from this class of the weighted tests so that it can maintain high power across a wide range of situations. Our proposed tests are applicable to various types of traits with or without covariates. Importantly, we also analytically show relationships among some existing and our proposed tests, indicating that many existing tests are special cases of our proposed tests. Extensive simulation studies were conducted to compare and contrast the power properties of various existing and our new methods. Finally, we applied the methods to an ADNI dataset to illustrate the performance of the methods. We conclude with the recommendation for the use of the GEE-based Score test and our proposed adaptive test for their high and complementary performance.

Blockade of EphA4 signaling ameliorates hippocampal synaptic dysfunctions in mouse models of Alzheimer's disease

Alzheimer's disease (AD), characterized by cognitive decline, has emerged as a disease of synaptic failure. The present study reveals an unanticipated role of erythropoietin-producing hepatocellular A4 (EphA4) in mediating hippocampal synaptic dysfunctions in AD and demonstrates that blockade of the ligand-binding domain of EphA4 reverses synaptic impairment in AD mouse models. Enhanced EphA4 signaling was observed in the hippocampus of amyloid precursor protein (APP)/presenilin 1 (PS1) transgenic mouse model of AD, whereas soluble amyloid-β oligomers (Aβ), which contribute to synaptic loss in AD, induced EphA4 activation in rat hippocampal slices. EphA4 depletion in the CA1 region or interference with EphA4 function reversed the suppression of hippocampal long-term potentiation in APP/PS1 transgenic mice, suggesting that the postsynaptic EphA4 is responsible for mediating synaptic plasticity impairment in AD. Importantly, we identified a small-molecule rhynchophylline as a novel EphA4 inhibitor based on molecular docking studies. Rhynchophylline effectively blocked the EphA4-dependent signaling in hippocampal neurons, and oral administration of rhynchophylline reduced the EphA4 activity effectively in the hippocampus of APP/PS1 transgenic mice. More importantly, rhynchophylline administration restored the impaired long-term potentiation in transgenic mouse models of AD. These findings reveal a previously unidentified role of EphA4 in mediating AD-associated synaptic dysfunctions, suggesting that it is a new therapeutic target for this disease.

Genetic interactions within inositol-related pathways are associated with longitudinal changes in ventricle size

The genetic etiology of late-onset Alzheimer's disease (LOAD) has proven complex, involving clinical and genetic heterogeneity and gene-gene interactions. Recent genome wide association studies in LOAD have led to the discovery of novel genetic risk factors; however, the investigation of gene-gene interactions has been limited. Conventional genetic studies often use binary disease status as the primary phenotype, but for complex brain-based diseases, neuroimaging data can serve as quantitative endophenotypes that correlate with disease status and closely reflect pathological changes. In the Alzheimer's Disease Neuroimaging Initiative cohort, we tested for association of genetic interactions with longitudinal MRI measurements of the inferior lateral ventricles (ILVs), which have repeatedly shown a relationship to LOAD status and progression. We performed linear regression to evaluate the ability of pathway-derived SNP-SNP pairs to predict the slope of change in volume of the ILVs. After Bonferroni correction, we identified four significant interactions in the right ILV (RILV) corresponding to gene-gene pairs SYNJ2-PI4KA, PARD3-MYH2, PDE3A-ABHD12B, and OR2L13-PRKG1 and one significant interaction in the left ILV (LILV) corresponding to SYNJ2-PI4KA. The SNP-SNP interaction corresponding to SYNJ2-PI4KA was identical in the RILV and LILV and was the most significant interaction in each (RILV: p = 9.13 × 10(-12); LILV: p = 8.17 × 10(-13)). Both genes belong to the inositol phosphate signaling pathway which has been previously associated with neurodegeneration in AD and we discuss the possibility that perturbation of this pathway results in a down-regulation of the Akt cell survival pathway and, thereby, decreased neuronal survival, as reflected by increased volume of the ventricles.

The perfect neuroimaging-genetics-computation storm: collision of petabytes of data, millions of hardware devices and thousands of software tools

The volume, diversity and velocity of biomedical data are exponentially increasing providing petabytes of new neuroimaging and genetics data every year. At the same time, tens-of-thousands of computational algorithms are developed and reported in the literature along with thousands of software tools and services. Users demand intuitive, quick and platform-agnostic access to data, software tools, and infrastructure from millions of hardware devices. This explosion of information, scientific techniques, computational models, and technological advances leads to enormous challenges in data analysis, evidence-based biomedical inference and reproducibility of findings. The Pipeline workflow environment provides a crowd-based distributed solution for consistent management of these heterogeneous resources. The Pipeline allows multiple (local) clients and (remote) servers to connect, exchange protocols, control the execution, monitor the states of different tools or hardware, and share complete protocols as portable XML workflows. In this paper, we demonstrate several advanced computational neuroimaging and genetics case-studies, and end-to-end pipeline solutions. These are implemented as graphical workflow protocols in the context of analyzing imaging (sMRI, fMRI, DTI), phenotypic (demographic, clinical), and genetic (SNP) data.

TOMM40 rs2075650 may represent a new candidate gene for vulnerability to major depressive disorder

Evidence suggests that depression is a risk factor for dementia; however, the relationship between the two conditions is not fully understood. A novel gene (TOMM40) has been consistently associated with Alzheimer's disease (AD), but has received no attention in depression. We conducted a three-level cross-sectional study to investigate the association of the TOMM40 rs2075650 SNP with depression. We recruited a community sample of 1220 participants (571 controls, 649 lifetime depression) to complete a psychiatric background questionnaire, the Brief Symptom Inventory, and Big Five Inventory at Level-1, 243 (102 controls, 97 remitted, 44 currently depressed) to complete a face-to-face clinical interview and neuropsychological testing at Level-2 and 58 (33 controls, 25 remitted) to complete an emotional face-processing task during fMRI at Level-3. Our results indicated that the TOMM40 rs2075650 G allele was a significant risk factor for lifetime depression (p = 0.00006) and, in depressed subjects, was a significant predictor of low extraversion (p = 0.009). Currently depressed risk allele carriers showed subtle executive dysfunction (p = 0.004) and decreased positive memory bias (p = 0.021) together with reduced activity in the posterior (p(FWE) = 0.045) and anterior (p(FWE) = 0.041) cingulate during sad face emotion processing. Our results suggest that TOMM40 rs2075650 may be a risk factor for the development of depression characterized by reduced extraversion, impaired executive function, and decreased positive emotional recall, and reduced top-down cortical control during sad emotion processing.

Genetic analysis of quantitative phenotypes in AD and MCI: imaging, cognition and biomarkers

The Genetics Core of the Alzheimer's Disease Neuroimaging Initiative (ADNI), formally established in 2009, aims to provide resources and facilitate research related to genetic predictors of multidimensional Alzheimer's disease (AD)-related phenotypes. Here, we provide a systematic review of genetic studies published between 2009 and 2012 where either ADNI APOE genotype or genome-wide association study (GWAS) data were used. We review and synthesize ADNI genetic associations with disease status or quantitative disease endophenotypes including structural and functional neuroimaging, fluid biomarker assays, and cognitive performance. We also discuss the diverse analytical strategies used in these studies, including univariate and multivariate analysis, meta-analysis, pathway analysis, and interaction and network analysis. Finally, we perform pathway and network enrichment analyses of these ADNI genetic associations to highlight key mechanisms that may drive disease onset and trajectory. Major ADNI findings included all the top 10 AD genes and several of these (e.g., APOE, BIN1, CLU, CR1, and PICALM) were corroborated by ADNI imaging, fluid and cognitive phenotypes. ADNI imaging genetics studies discovered novel findings (e.g., FRMD6) that were later replicated on different data sets. Several other genes (e.g., APOC1, FTO, GRIN2B, MAGI2, and TOMM40) were associated with multiple ADNI phenotypes, warranting further investigation on other data sets. The broad availability and wide scope of ADNI genetic and phenotypic data has advanced our understanding of the genetic basis of AD and has nominated novel targets for future studies employing next-generation sequencing and convergent multi-omics approaches, and for clinical drug and biomarker development.

The association between TOMM40 gene polymorphism and spontaneous brain activity in amnestic mild cognitive impairment

The outer mitochondria membrane 40 homolog (TOMM40) is thought to be involved in the mitochondrial function and to influence the susceptibility for the development of AD. To determine whether TOMM40 rs157581 polymorphism is a plausible modulator of spontaneous brain activity in amnestic mild cognitive impairment (aMCI) patients, 46 aMCI subjects and 21 healthy controls were recruited and explored. Each individual was firstly genotyped for TOMM40 rs157581 and was further assessed by resting-state functional MRI to evaluate regional brain activity using amplitude low-frequency fluctuation analysis (ALFF). aMCI patients showed decreased ALFF in the left inferior frontal gyrus and insula, and increased ALFF in right posterior cingulate, lingual gyrus and calcarine sulcus. A significant difference in the interaction of "groups × genotypes" was observed in the bilateral superior frontal gyrus, bilateral lingual gyrus, right calcarine sulcus and left cerebellum. These results demonstrated a pattern of change in ALFF values, in which increased and subsequently decreased ALFF values in parallel with the progression of aMCI symptoms. The present study shows for the first time that TOMM40 rs157581 polymorphism may modulate regional spontaneous brain activity and related to the progression of aMCI.

DATA SYNTHESIS AND METHOD EVALUATION FOR BRAIN IMAGING GENETICS

Brain imaging genetics is an emergent research field where the association between genetic variations such as single nucleotide polymorphisms (SNPs) and neuroimaging quantitative traits (QTs) is evaluated. Sparse canonical correlation analysis (SCCA) is a bi-multivariate analysis method that has the potential to reveal complex multi-SNP-multi-QT associations. We present initial efforts on evaluating a few SCCA methods for brain imaging genetics. This includes a data synthesis method to create realistic imaging genetics data with known SNP-QT associations, application of three SCCA algorithms to the synthetic data, and comparative study of their performances. Our empirical results suggest, approximating covariance structure using an identity or diagonal matrix, an approach used in these SCCA algorithms, could limit the SCCA capability in identifying the underlying imaging genetics associations. An interesting future direction is to develop enhanced SCCA methods that effectively take into account the covariance structures in the imaging genetics data.

Estrogen receptor α polymorphisms and the risk of cognitive decline: A 2-year follow-up study

OBJECTIVE

The neuroprotective role of estrogen is supported by biochemical studies, but the results from clinical trials of estrogen replacement therapy on cognitive decline are controversial. One possible missing link might be the interindividual difference in estrogen receptor expression. In this study, the association of estrogen receptor α (ESR1) polymorphisms and cognitive decline was investigated.

METHODS

Chinese older adults (n = 284) were recruited, and the cognitive profile was follow-up over 2-year period. Twenty ESR1 polymorphisms were investigated and correlated with the cognitive decline for the subjects.

RESULTS

Significant association was found between ESR1 polymorphisms (rs9340799 [ESR1+351], rs1801132 [ESR1+975], rs6557171, rs9397456, and rs1884049) and subjects with no dementia (Clinical Dementia Rating, CDR 0) and very mild dementia (CDR 0.5). Several ESR1 polymorphisms were associated with cognitive decline as assessed by Chinese versions of Mini-Mental State Examination and Alzheimer Disease Association Scales-Cognitive Subscale. Different sets of ESR1 polymorphisms were associated with cognitive decline from CDR 0 to 0.5 and CDR 0.5 to 1. ESR1 polymorphisms (rs3853248, rs22334693 [ESR1+397], rs9340799 [ESR1+351], rs9397456, rs1801132 [ESR1+975], rs2179922, rs932477, and rs9341016) were associated with the deterioration of episodic memory among subjects with baseline CDR 0, indicating these polymorphisms might be markers for episodic memory decline at an earlier stage.

CONCLUSION

This study showed association between ESR polymorphisms and cognitive decline or specific areas in cognitive profile. These findings might be useful in identifying individuals at risk for early intervention, and more research is required to elucidate the underlying mechanisms.

EphA4 activation of c-Abl mediates synaptic loss and LTP blockade caused by amyloid-β oligomers

The early stages of Alzheimer's disease are characterised by impaired synaptic plasticity and synapse loss. Here, we show that amyloid-β oligomers (AβOs) activate the c-Abl kinase in dendritic spines of cultured hippocampal neurons and that c-Abl kinase activity is required for AβOs-induced synaptic loss. We also show that the EphA4 receptor tyrosine kinase is upstream of c-Abl activation by AβOs. EphA4 tyrosine phosphorylation (activation) is increased in cultured neurons and synaptoneurosomes exposed to AβOs, and in Alzheimer-transgenic mice brain. We do not detect c-Abl activation in EphA4-knockout neurons exposed to AβOs. More interestingly, we demonstrate EphA4/c-Abl activation is a key-signalling event that mediates the synaptic damage induced by AβOs. According to this results, the EphA4 antagonistic peptide KYL and c-Abl inhibitor STI prevented i) dendritic spine reduction, ii) the blocking of LTP induction and iii) neuronal apoptosis caused by AβOs. Moreover, EphA4-/- neurons or sh-EphA4-transfected neurons showed reduced synaptotoxicity by AβOs. Our results are consistent with EphA4 being a novel receptor that mediates synaptic damage induced by AβOs. EphA4/c-Abl signalling could be a relevant pathway involved in the early cognitive decline observed in Alzheimer's disease patients.

Gene-based GWAS and biological pathway analysis of the resilience of executive functioning

Resilience in executive functioning (EF) is characterized by high EF measured by neuropsychological test performance despite structural brain damage from neurodegenerative conditions. We previously reported single nucleotide polymorphism (SNP) genome-wide association study (GWAS) results for EF resilience. Here, we report gene- and pathway-based analyses of the same resilience phenotype, using an optimal SNP-set (Sequence) Kernel Association Test (SKAT) for gene-based analyses (conservative threshold for genome-wide significance = 0.05/18,123 = 2.8 × 10(-6)) and the gene-set enrichment package GSA-SNP for biological pathway analyses (False discovery rate (FDR) < 0.05). Gene-based analyses found a genome-wide significant association between RNASE13 and EF resilience (p = 1.33 × 10(-7)). Genetic pathways involved with dendritic/neuron spine, presynaptic membrane, postsynaptic density, etc., were enriched with association to EF resilience. Although replication of these results is necessary, our findings indicate the potential value of gene- and pathway-based analyses in research on determinants of cognitive resilience.

Population-based studies of myocardial hypertrophy: high resolution cardiovascular magnetic resonance atlases improve statistical power

BACKGROUND

Cardiac phenotypes, such as left ventricular (LV) mass, demonstrate high heritability although most genes associated with these complex traits remain unidentified. Genome-wide association studies (GWAS) have relied on conventional 2D cardiovascular magnetic resonance (CMR) as the gold-standard for phenotyping. However this technique is insensitive to the regional variations in wall thickness which are often associated with left ventricular hypertrophy and require large cohorts to reach significance. Here we test whether automated cardiac phenotyping using high spatial resolution CMR atlases can achieve improved precision for mapping wall thickness in healthy populations and whether smaller sample sizes are required compared to conventional methods.

METHODS

LV short-axis cine images were acquired in 138 healthy volunteers using standard 2D imaging and 3D high spatial resolution CMR. A multi-atlas technique was used to segment and co-register each image. The agreement between methods for end-diastolic volume and mass was made using Bland-Altman analysis in 20 subjects. The 3D and 2D segmentations of the LV were compared to manual labeling by the proportion of concordant voxels (Dice coefficient) and the distances separating corresponding points. Parametric and nonparametric data were analysed with paired t-tests and Wilcoxon signed-rank test respectively. Voxelwise power calculations used the interstudy variances of wall thickness.

RESULTS

The 3D volumetric measurements showed no bias compared to 2D imaging. The segmented 3D images were more accurate than 2D images for defining the epicardium (Dice: 0.95 vs 0.93, P<0.001; mean error 1.3 mm vs 2.2 mm, P<0.001) and endocardium (Dice 0.95 vs 0.93, P<0.001; mean error 1.1 mm vs 2.0 mm, P<0.001). The 3D technique resulted in significant differences in wall thickness assessment at the base, septum and apex of the LV compared to 2D (P<0.001). Fewer subjects were required for 3D imaging to detect a 1 mm difference in wall thickness (72 vs 56, P<0.001).

CONCLUSIONS

High spatial resolution CMR with automated phenotyping provides greater power for mapping wall thickness than conventional 2D imaging and enables a reduction in the sample size required for studies of environmental and genetic determinants of LV wall thickness.

A novel structure-aware sparse learning algorithm for brain imaging genetics

Brain imaging genetics is an emergent research field where the association between genetic variations such as single nucleotide polymorphisms (SNPs) and neuroimaging quantitative traits (QTs) is evaluated. Sparse canonical correlation analysis (SCCA) is a bi-multivariate analysis method that has the potential to reveal complex multi-SNP-multi-QT associations. Most existing SCCA algorithms are designed using the soft threshold strategy, which assumes that the features in the data are independent from each other. This independence assumption usually does not hold in imaging genetic data, and thus inevitably limits the capability of yielding optimal solutions. We propose a novel structure-aware SCCA (denoted as S2CCA) algorithm to not only eliminate the independence assumption for the input data, but also incorporate group-like structure in the model. Empirical comparison with a widely used SCCA implementation, on both simulated and real imaging genetic data, demonstrated that S2CCA could yield improved prediction performance and biologically meaningful findings.

Identifying genetic associations with MRI-derived measures via tree-guided sparse learning

In recent imaging genetic studies, much work has been focused on regression analysis that treats large-scale single nucleotide polymorphisms (SNPs) and quantitative traits (QTs) as association variables. To deal with the weak detection and high-throughput data problem, feature selection methods such as the least absolute shrinkage and selection operator (Lasso) are often used for selecting the most relevant SNPs associated with QTs. However, one problem of Lasso as well as many other feature selection methods for imaging genetics is that some useful prior information, i.e., the hierarchical structure among SNPs throughout the whole genome, are rarely used for designing more powerful model. In this paper, we propose to identify the associations between candidate genetic features (i.e., SNPs) and magnetic resonance imaging (MRI)-derived measures using a tree-guided sparse learning (TGSL) method. The advantage of our method is that it explicitly models the priori hierarchical grouping structure among the SNPs in the objective function for feature selection. Specifically, two kinds of hierarchical structures, i.e., group by gene and group by linkage disequilibrium (LD) clusters, are imposed as a tree-guided regularization term in our sparse learning model. Experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database show that our method not only achieves better predictions on the two MRI measures (i.e., left and right hippocampal formation), but also identifies the informative SNPs to guide the disease-induced interpretation compared with other reference methods.

Genetic and neuroanatomic associations in sporadic frontotemporal lobar degeneration

Genome-wide association studies have identified single nucleotide polymorphisms (SNPs) that are sensitive for tau or TDP-43 pathology in frontotemporal lobar degeneration (FTLD). Neuroimaging analyses have revealed distinct distributions of disease in FTLD patients with genetic mutations. However, genetic influences on neuroanatomic structure in sporadic FTLD have not been assessed. In this report, we use novel multivariate tools, Eigenanatomy, and sparse canonical correlation analysis to identify associations between SNPs and neuroanatomic structure in sporadic FTLD. Magnetic resonance imaging analyses revealed that rs8070723 (MAPT) was associated with gray matter variance in the temporal cortex. Diffusion tensor imaging analyses revealed that rs1768208 (MOBP), rs646776 (near SORT1), and rs5848 (PGRN) were associated with white matter variance in the midbrain and superior longitudinal fasciculus. In an independent autopsy series, we observed that rs8070723 and rs1768208 conferred significant risk of tau pathology relative to TDP-43, and rs646776 conferred increased risk of TDP-43 pathology relative to tau. Identified brain regions and SNPs may help provide an in vivo screen for underlying pathology in FTLD and contribute to our understanding of sporadic FTLD.

Epistatic genetic effects among Alzheimer's candidate genes

Background

Novel risk variants for late-onset Alzheimer’s disease (AD) have been identified and replicated in genome-wide association studies. Recent work has begun to address the relationship between these risk variants and biomarkers of AD, though results have been mixed. The aim of the current study was to characterize single marker and epistatic genetic effects between the top candidate Single Nucleotide Polymorphisms (SNPs) in relation to amyloid deposition.

Methods

We used a combined dataset across ADNI-1 and ADNI-2, and looked within each dataset separately to validate identified genetic effects. Amyloid was quantified using data acquired by Positron Emission Tomography (PET) with ¹⁸F-AV-45.

Results

Two SNP-SNP interactions reached significance when correcting for multiple comparisons, BIN1 (rs7561528, rs744373) xPICALM (rs7851179). Carrying the minor allele in BIN1 was related to higher levels of amyloid deposition, however only in non-carriers of the protective PICALM minor allele.

Conclusions

Our results support previous research suggesting these candidate SNPs do not show single marker associations with amyloid pathology. However, we provide evidence for a novel interaction between PICALM and BIN1 in relation to amyloid deposition. Risk related to the BIN1 minor allele appears to be mitigated in the presence of the PICALM protective variant. In that way, variance in amyloid plaque burden can be better classified within the context of a complex genetic background. Efforts to model cumulative risk for AD should explicitly account for this epistatic effect, and future studies should explicitly test for such effects whenever statistically feasible.

Ligand-dependent activation of EphA4 signaling regulates the proteolysis of amyloid precursor protein through a Lyn-mediated pathway

Alzheimer's disease is the most common dementia afflicting the elderly in modern society. This disease arises from the neurotoxicity elicited by abnormal aggregates of amyloid-β (Aβ) protein. Such aggregates form through the cleavage of amyloid precursor protein (APP) by β-secretase and the subsequent proteolysis of the APP C-terminal fragment (APP-βCTF or C99) by γ-secretase to yield Aβ and APP intracellular domain (AICD). Recent evidence suggests that C99 and AICD may exert harmful effects on cells, suggesting that the proteolytic products of APP, including Aβ, C99, and AICD, could play a pivotal role in neuronal viability. Here, we demonstrate that ligand-activated EphA4 signaling governs the proteostasis of C99, AICD, and Aβ, without significantly affecting γ-secretase activity. EphA4 induced accumulation of C99 and AICD through a Lyn-dependent pathway; activation of this pathway triggered phosphorylation of EphA4, resulting in positive feedback of C99 and AICD proteostasis. Inhibition of EphA4 by dasatinib, a receptor tyrosine kinase inhibitor, effectively suppressed C99 and AICD accumulation. Furthermore, EphA4 signaling controlled C99 and AICD proteolysis through the ubiquitin-proteasome system. In conclusion, we have identified an EphA4-Lyn pathway that is essential for the metabolism of APP and its proteolytic derivatives, thereby providing novel pharmacological targets for the development of anti-Aβ therapeutics for AD.

Interactions between GSK3β and amyloid genes explain variance in amyloid burden

The driving theoretical framework of Alzheimer's disease (AD) has been built around the amyloid-β (Aβ) cascade in which amyloid pathology precedes and drives tau pathology. Other evidence has suggested that tau and amyloid pathology may arise independently. Both lines of research suggest that there may be epistatic relationships between genes involved in amyloid and tau pathophysiology. In the current study, we hypothesized that genes coding glycogen synthase kinase 3 (GSK-3) and comparable tau kinases would modify genetic risk for amyloid plaque pathology. Quantitative amyloid positron emission tomography data from the Alzheimer's Disease Neuroimaging Initiative served as the quantitative outcome in regression analyses, covarying for age, gender, and diagnosis. Three interactions reached statistical significance, all involving the GSK3β single nucleotide polymorphism rs334543-2 with APBB2 (rs2585590, rs3098914) and 1 with APP (rs457581). These interactions explained 1.2%, 1.5%, and 1.5% of the variance in amyloid deposition respectively. Our results add to a growing literature on the role of GSK-3 activity in amyloid processing and suggest that combined variation in GSK3β and APP-related genes may result in increased amyloid burden.

TOMM40 rs2075650 polymorphism shows no association with neovascular age-related macular degeneration or polypoidal choroidal vasculopathy in a Chinese population

PURPOSE

Age-related macular degeneration (AMD) and Alzheimer disease (AD) are age-related neurodegenerative diseases that share similar environmental risk factors, cellular pathologies, and genetic backgrounds. Recently, the rs2075650 single nucleotide polymorphism in the translocase of outer mitochondrial membrane 40 homolog (TOMM40) gene was identified as a risk factor for AMD and Alzheimer disease. We aimed to examine the associations between the TOMM40 rs2075650 polymorphism and neovascular age-related macular degeneration (nAMD) and polypoidal choroidal vasculopathy (PCV) in a Chinese population.

METHODS

The study consisted of 900 subjects, including 300 controls, 300 cases with nAMD, and 300 cases with PCV. Genomic DNA was extracted from venous blood leukocytes. The allelic variant of rs2075650 was determined with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Differences in the observed genotypic distributions between the case and control groups were tested using chi-square tests, with age and gender adjusted using logistic regression analysis.

RESULTS

The TOMM40 rs2075650 polymorphism was not statistically significantly associated with the nAMD or PCV phenotype (p>0.05). The difference remained insignificant after correction for age and gender differences based on the logistic regression models (p>0.05).

CONCLUSIONS

Our data provide no evidence to support an association of rs2075650 in TOMM40 with nAMD or PCV, suggesting that this gene is unlikely to be a major AMD and PCV susceptibility gene locus in the Chinese population.

TOMM40 intron 6 poly-T length, age at onset, and neuropathology of AD in individuals with APOE ε3/ε3

BACKGROUND

This study investigates the association between TOMM40 poly-T length, age at onset, and neuropathology in individuals with Alzheimer's disease (AD) with the apolipoprotein E (APOE) ε3/ε3 allele.

METHODS

Thirty-two presenilin 1 (PSEN1) mutation carriers with AD, 27 presenilin 2 (PSEN2) mutation carriers with AD, 59 participants with late-onset AD (LOAD), and 168 autopsied subjects from a community-based cohort were genotyped for TOMM40 intron 6 poly-T (rs10524523) length using short tandem repeat assays.

RESULTS

Among AD individuals with PSEN2 mutations, the presence of a long poly-T was associated with an earlier age at onset, whereas there were no such associations for subjects with PSEN1 mutations or LOAD. In community-based participants, the presence of a long poly-T was associated with increased neuritic tangles and a greater likelihood of pathologically diagnosed AD.

CONCLUSION

TOMM40 intron 6 poly-T length may explain some of the variation in age at onset in PSEN2 familial AD and may be associated with AD neuropathology in persons with APOE ε3/ε3.

Mapping the genetic variation of regional brain volumes as explained by all common SNPs from the ADNI study

Typically twin studies are used to investigate the aggregate effects of genetic and environmental influences on brain phenotypic measures. Although some phenotypic measures are highly heritable in twin studies, SNPs (single nucleotide polymorphisms) identified by genome-wide association studies (GWAS) account for only a small fraction of the heritability of these measures. We mapped the genetic variation (the proportion of phenotypic variance explained by variation among SNPs) of volumes of pre-defined regions across the whole brain, as explained by 512,905 SNPs genotyped on 747 adult participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We found that 85% of the variance of intracranial volume (ICV) (p = 0.04) was explained by considering all SNPs simultaneously, and after adjusting for ICV, total grey matter (GM) and white matter (WM) volumes had genetic variation estimates near zero (p = 0.5). We found varying estimates of genetic variation across 93 non-overlapping regions, with asymmetry in estimates between the left and right cerebral hemispheres. Several regions reported in previous studies to be related to Alzheimer's disease progression were estimated to have a large proportion of volumetric variance explained by the SNPs.

The Alzheimer's Disease Neuroimaging Initiative: a review of papers published since its inception

The Alzheimer's Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer's disease (AD). The study aimed to enroll 400 subjects with early mild cognitive impairment (MCI), 200 subjects with early AD, and 200 normal control subjects; $67 million funding was provided by both the public and private sectors, including the National Institute on Aging, 13 pharmaceutical companies, and 2 foundations that provided support through the Foundation for the National Institutes of Health. This article reviews all papers published since the inception of the initiative and summarizes the results as of February 2011. The major accomplishments of ADNI have been as follows: (1) the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers in a multicenter setting; (2) elucidation of the patterns and rates of change of imaging and CSF biomarker measurements in control subjects, MCI patients, and AD patients. CSF biomarkers are consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimers Dis 2006;9(Suppl 3):151-3) and tau-mediated neurodegeneration hypotheses for AD, whereas brain atrophy and hypometabolism levels show predicted patterns but exhibit differing rates of change depending on region and disease severity; (3) the assessment of alternative methods of diagnostic categorization. Currently, the best classifiers combine optimum features from multiple modalities, including MRI, [(18)F]-fluorodeoxyglucose-PET, CSF biomarkers, and clinical tests; (4) the development of methods for the early detection of AD. CSF biomarkers, β-amyloid 42 and tau, as well as amyloid PET may reflect the earliest steps in AD pathology in mildly symptomatic or even nonsymptomatic subjects, and are leading candidates for the detection of AD in its preclinical stages; (5) the improvement of clinical trial efficiency through the identification of subjects most likely to undergo imminent future clinical decline and the use of more sensitive outcome measures to reduce sample sizes. Baseline cognitive and/or MRI measures generally predicted future decline better than other modalities, whereas MRI measures of change were shown to be the most efficient outcome measures; (6) the confirmation of the AD risk loci CLU, CR1, and PICALM and the identification of novel candidate risk loci; (7) worldwide impact through the establishment of ADNI-like programs in Europe, Asia, and Australia; (8) understanding the biology and pathobiology of normal aging, MCI, and AD through integration of ADNI biomarker data with clinical data from ADNI to stimulate research that will resolve controversies about competing hypotheses on the etiopathogenesis of AD, thereby advancing efforts to find disease-modifying drugs for AD; and (9) the establishment of infrastructure to allow sharing of all raw and processed data without embargo to interested scientific investigators throughout the world. The ADNI study was extended by a 2-year Grand Opportunities grant in 2009 and a renewal of ADNI (ADNI-2) in October 2010 through to 2016, with enrollment of an additional 550 participants.

Genome-wide pathway analysis of memory impairment in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort implicates gene candidates, canonical pathways, and networks

Memory deficits are prominent features of mild cognitive impairment (MCI) and Alzheimer's disease (AD). The genetic architecture underlying these memory deficits likely involves the combined effects of multiple genetic variants operative within numerous biological pathways. In order to identify functional pathways associated with memory impairment, we performed a pathway enrichment analysis on genome-wide association data from 742 Alzheimer's Disease Neuroimaging Initiative (ADNI) participants. A composite measure of memory was generated as the phenotype for this analysis by applying modern psychometric theory to item-level data from the ADNI neuropsychological test battery. Using the GSA-SNP software tool, we identified 27 canonical, expertly-curated pathways with enrichment (FDR-corrected p-value < 0.05) against this composite memory score. Processes classically understood to be involved in memory consolidation, such as neurotransmitter receptor-mediated calcium signaling and long-term potentiation, were highly represented among the enriched pathways. In addition, pathways related to cell adhesion, neuronal differentiation and guided outgrowth, and glucose- and inflammation-related signaling were also enriched. Among genes that were highly-represented in these enriched pathways, we found indications of coordinated relationships, including one large gene set that is subject to regulation by the SP1 transcription factor, and another set that displays co-localized expression in normal brain tissue along with known AD risk genes. These results 1) demonstrate that psychometrically-derived composite memory scores are an effective phenotype for genetic investigations of memory impairment and 2) highlight the promise of pathway analysis in elucidating key mechanistic targets for future studies and for therapeutic interventions.

Genetic architecture of resilience of executive functioning

The genetic basis of resilience, defined as better cognitive functioning than predicted based on neuroimaging or neuropathology, is not well understood. Our objective was to identify genetic variation associated with executive functioning resilience. We computed residuals from regression models of executive functioning, adjusting for age, sex, education, Hachinski score, and MRI findings (lacunes, cortical thickness, volumes of white matter hyperintensities and hippocampus). We estimated heritability and analyzed these residuals in models for each SNP. We further evaluated our most promising SNP result by evaluating cis-associations with brain levels of nearby (±100 kb) genes from a companion data set, and comparing expression levels in cortex and cerebellum from decedents with AD with those from other non-AD diseases. Complete data were available for 750 ADNI participants of European descent. Executive functioning resilience was highly heritable (H² = 0.76; S.E. = 0.44). rs3748348 on chromosome 14 in the region of RNASE13 was associated with executive functioning resilience (p-value = 4.31 × 10⁻⁷). rs3748348 is in strong linkage disequilibrium (D' of 1.00 and 0.96) with SNPs that map to TPPP2, a member of the α-synuclein family of proteins. We identified nominally significant associations between rs3748348 and expression levels of three genes (FLJ10357, RNASE2, and NDRG2). The strongest association was for FLJ10357 in cortex, which also had the most significant difference in expression between AD and non-AD brains, with greater expression in cortex of decedents with AD (p-value = 7 × 10⁻⁷). Further research is warranted to determine whether this signal can be replicated and whether other loci may be associated with cognitive resilience.

Hippocampal sclerosis of aging, a prevalent and high-morbidity brain disease

Hippocampal sclerosis of aging (HS-Aging) is a causative factor in a large proportion of elderly dementia cases. The current definition of HS-Aging rests on pathologic criteria: neuronal loss and gliosis in the hippocampal formation that is out of proportion to AD-type pathology. HS-Aging is also strongly associated with TDP-43 pathology. HS-Aging pathology appears to be most prevalent in the oldest-old: autopsy series indicate that 5-30 % of nonagenarians have HS-Aging pathology. Among prior studies, differences in study design have contributed to the study-to-study variability in reported disease prevalence. The presence of HS-Aging pathology correlates with significant cognitive impairment which is often misdiagnosed as AD clinically. The antemortem diagnosis is further confounded by other diseases linked to hippocampal atrophy including frontotemporal lobar degeneration and cerebrovascular pathologies. Recent advances characterizing the neurocognitive profile of HS-Aging patients have begun to provide clues that may help identify living individuals with HS-Aging pathology. Structural brain imaging studies of research subjects followed to autopsy reveal hippocampal atrophy that is substantially greater in people with eventual HS-Aging pathology, compared to those with AD pathology alone. Data are presented from individuals who were followed with neurocognitive and neuroradiologic measurements, followed by neuropathologic evaluation at the University of Kentucky. Finally, we discuss factors that are hypothesized to cause or modify the disease. We conclude that the published literature on HS-Aging provides strong evidence of an important and under-appreciated brain disease of aging. Unfortunately, there is no therapy or preventive strategy currently available.

Epigenetic signature and enhancer activity of the human APOE gene

The human apolipoprotein E (APOE) gene plays an important role in lipid metabolism. It has three common genetic variants, alleles ε2/ε3/ε4, which translate into three protein isoforms of apoE2, E3 and E4. These isoforms can differentially influence total serum cholesterol levels; therefore, APOE has been linked with cardiovascular disease. Additionally, its ε4 allele is strongly associated with the risk of Alzheimer's disease (AD), whereas the ε2 allele appears to have a modest protective effect for AD. Despite decades of research having illuminated multiple functional differences among the three apoE isoforms, the precise mechanisms through which different APOE alleles modify diseases risk remain incompletely understood. In this study, we examined the genomic structure of APOE in search for properties that may contribute novel biological consequences to the risk of disease. We identify one such element in the ε2/ε3/ε4 allele-carrying 3'-exon of APOE. We show that this exon is imbedded in a well-defined CpG island (CGI) that is highly methylated in the human postmortem brain. We demonstrate that this APOE CGI exhibits transcriptional enhancer/silencer activity. We provide evidence that this APOE CGI differentially modulates expression of genes at the APOE locus in a cell type-, DNA methylation- and ε2/ε3/ε4 allele-specific manner. These findings implicate a novel functional role for a 3'-exon CGI and support a modified mechanism of action for APOE in disease risk, involving not only the protein isoforms but also an epigenetically regulated transcriptional program at the APOE locus driven by the APOE CGI.