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


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.

Download full-text


Available from: Nick Schmansky,
  • Source
    • "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. "
    [Show abstract] [Hide abstract]
    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.
    NeuroImage 01/2015; 109. DOI:10.1016/j.neuroimage.2015.01.029 · 6.36 Impact Factor
  • Source
    • "These AD risk variants have recently been used to examine the genotypic overlap between AD and other types of dementia (Carrasquillo et al., 2014). Some of these variants have been studied with respect to various MRI measures in a mixed study sample of AD patients, mildly cognitive impaired and healthy control subjects (Biffi et al., 2010; Furney et al., 2011). They could also be implemented to explore the impact of genetic determinants of AD on MRI markers of structural brain changes in nondemented community persons. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Whether novel risk variants of Alzheimer's disease (AD) identified through genome-wide association studies also influence magnetic resonance imaging-based intermediate phenotypes of AD in the general population is unclear. We studied association of 24 AD risk loci with intracranial volume, total brain volume, hippocampal volume (HV), white matter hyperintensity burden, and brain infarcts in a meta-analysis of genetic association studies from large population-based samples (N = 8175-11,550). In single-SNP based tests, AD risk allele of APOE (rs2075650) was associated with smaller HV (p = 0.0054) and CD33 (rs3865444) with smaller intracranial volume (p = 0.0058). In gene-based tests, there was associations of HLA-DRB1 with total brain volume (p = 0.0006) and BIN1 with HV (p = 0.00089). A weighted AD genetic risk score was associated with smaller HV (beta ± SE = -0.047 ± 0.013, p = 0.00041), even after excluding the APOE locus (p = 0.029). However, only association of AD genetic risk score with HV, including APOE, was significant after multiple testing correction (including number of independent phenotypes tested). These results suggest that novel AD genetic risk variants may contribute to structural brain aging in nondemented older community persons. Copyright © 2015 Elsevier Inc. All rights reserved.
    Alzheimer's and Dementia 01/2015; 10(4). DOI:10.1016/j.neurobiolaging.2014.12.028 · 12.41 Impact Factor
  • Source
    • "Structured data from these different modalities need to be combined for appropriate statistical analyses that can also control for measured covariates. For example, genetic ancestry may covary with imaging measurements (Biffi et al., 2010), or socio-economic data may covary with cognitive measurements (Hurst et al., 2013). When this information is integrated into the statistical analysis, ancestry admixture effects can be disassociated from effects driven by socio-economic factors. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Clinical research studies generate data that need to be shared and statistically analyzed by their participating institutions. The distributed nature of research and the different domains involved present major challenges to data sharing, exploration, and visualization. The Data Portal infrastructure was developed to support ongoing research in the areas of neurocognition, imaging, and genetics. Researchers benefit from the integration of data sources across domains, the explicit representation of knowledge from domain experts, and user interfaces providing convenient access to project specific data resources and algorithms. The system provides an interactive approach to statistical analysis, data mining, and hypothesis testing over the lifetime of a study and fulfills a mandate of public sharing by integrating data sharing into a system built for active data exploration. The web-based platform removes barriers for research and supports the ongoing exploration of data.
    Frontiers in Neuroinformatics 03/2014; 8:25. DOI:10.3389/fninf.2014.00025 · 3.26 Impact Factor
Show more