Article

Genetic Variation and Neuroimaging Measures in Alzheimer Disease

Center for Human Genetic Research, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
Archives of neurology (Impact Factor: 7.42). 06/2010; 67(6):677-85. DOI: 10.1001/archneurol.2010.108
Source: PubMed

ABSTRACT

To investigate whether genome-wide association study (GWAS)-validated and GWAS-promising candidate loci influence magnetic resonance imaging measures and clinical Alzheimer's disease (AD) status.
Multicenter case-control study of genetic and neuroimaging data from the Alzheimer's Disease Neuroimaging Initiative.
Multicenter GWAS. Patients A total of 168 individuals with probable AD, 357 with mild cognitive impairment, and 215 cognitively normal control individuals recruited from more than 50 Alzheimer's Disease Neuroimaging Initiative centers in the United States and Canada. All study participants had APOE and genome-wide genetic data available.
We investigated the influence of GWAS-validated and GWAS-promising novel AD loci on hippocampal volume, amygdala volume, white matter lesion volume, entorhinal cortex thickness, parahippocampal gyrus thickness, and temporal pole cortex thickness.
Markers at the APOE locus were associated with all phenotypes except white matter lesion volume (all false discovery rate-corrected P values < .001). Novel and established AD loci identified by prior GWASs showed a significant cumulative score-based effect (false discovery rate P = .04) on all analyzed neuroimaging measures. The GWAS-validated variants at the CR1 and PICALM loci and markers at 2 novel loci (BIN1 and CNTN5) showed association with multiple magnetic resonance imaging characteristics (false discovery rate P < .05).
Loci associated with AD also influence neuroimaging correlates of this disease. Furthermore, neuroimaging analysis identified 2 additional loci of high interest for further study.

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    • "An increasing body of work shows an interaction between APOE and sex in AD risk which may be explained by the influence of estrogen levels acting in concert with APOE (Altmann, et al., 2014,Stone, et al., 1997). APOE ε4 was not associated with hippocampal volume in Healthy Older conflicting with some previous reports (Biffi, et al., 2010,Lind, et al., 2006,Reiman, et al., 1998,Wishart, et al., 2006) but in agreement with recent findings from other large scale studies, including both measures of volume and atrophy (Ferencz, et al., 2013,Manning, et al., 2014). Population studies of older people are likely to have a proportion of individuals with MCI, which may be the source of identified APOE association in conflicting reports. "
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    ABSTRACT: INTRODUCTION: Reduction in hippocampal and amygdala volume measured via structural MRI is an early marker of Alzheimer’s disease (AD). Whether genetic risk factors for AD exert an effect on these subcortical structures independent of clinical status has not been fully investigated. METHODS: We examine whether increased genetic risk for AD influences hippocampal and amygdala volumes in case-control and population cohorts at different ages, in 1674 Older (aged >53yrs; 17% AD, 39% MCI) and 467 young (16-30 yrs) adults. RESULTS: An AD polygenic risk score (PRS) combining common risk variants excluding APOE, and a SNP in TREM2, were both associated with reduced hippocampal volume in healthy older adults and those with mild cognitive impairment (MCI). APOE ε4 was associated with hippocampal and amygdala volume in those with AD and MCI, but was not associated in healthy older adults. No associations were found in young adults. DISCUSSION: Genetic risk for AD affects the hippocampus before the clinical symptoms of AD, reflecting a neurodegenerative effect prior to clinical manifestations in older adults.
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    • "Of the remaining genes, PICALM rs3851179, CR1 rs1408077, and BIN1 rs7561528 showed an effect on entorhinal atrophy (Biffi et al., 2010). Of these 3 variants, PICALM rs3851179 was also associated with hippocampal atrophy (Biffi et al., 2010). An association with amyloid deposition has been described for ABCA7 rs3764650 (Shulman et al., 2013), ABCA7 rs3752246 (Hughes et al., 2014), BIN1 rs744373 (Hohman et al., 2013), CR1 rs6701713 (Shulman et al., 2013), CR1 rs3818361 (Thambisetty et al., 2013), CR1 rs6656401, and CLU rs3818361 (Hohman et al., 2013). "

    Full-text · Article · Nov 2015
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    • "We therefore used these 21 genes as our candidate gene set and extracted all the SNPs on the coding regions as well as 20kb up/downstream of each of these genes in the ADNI data set. Some of these genes, e.g., BIN1, CR1 and PICALM, have been associated with quantitative imaging phenotypes , such as hippocampal volume, amygdala volume and entorhinal cortical thickness, in ADNI [Biffi et al., 2010; Bralten et al., 2011; Furney et al., 2010; Weiner et al., 2013]. Table 1 lists the 21 genes and the final number of SNPs located on them after preprocessing and quality control. "
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    ABSTRACT: 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 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 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. Copyright © 2015 Elsevier Inc. All rights reserved.
    Full-text · Article · Jan 2015 · NeuroImage
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