There were no significant positive correlations. Results: fMRI analysis showed a significant negative correlation between LOAD RPS and hippocampal activation (left: PFWE_corrected = 0.005, MNI coordinates x =-39, y =-24, z =-12, right: PFWE_corrected = 0.139, Puncorrected<0.001, MNI coordinates x = 39, y = -18, z = -18) during the neutral encoding phase of SDMT. Influence of RELN on association between LOAD related AD RPS and hippocampal activation was examined separately for five independent Reelin polymorphisms (rs736707, rs362691, rs7341475, rs6943822, and rs4298437.) previously implicated in Alzheimer’s disease or Autism Spectrum Disease using flexible factorial analysis in SPM12. A region of interest analysis was performed using bilateral hippo-parahippocampal masks from the Anatomical Automatic Labeling Atlas. To control for population stratification, 5 MDS components based on 8M SNP genotypes from a GWAS analysis extracted with EIGENSOFT5.01 were included in the analysis as covariates along with age, gender, SNAV, and genotyping batch labels. Association between RPS and hippocampal activation during the neutral encoding phase of the SDMT was tested using SPM12. Odd’s ratios of 22 independent SNPs, with P<1 × 10-5 in Hollingworth’s meta-analysis1 comprising four Alzheimer’s disease GWAS datasets (GERAD1, EADI1, TGEN1, ADNI), spanning the regions of ABCA7, APOC4, APOE, BCAM, BC元, BIN1, C16orf88, CDK1, CEACAM1E, CLPTMI, CLU, CNTN5, CR1, CR2, CUX2, EXOC3L2, IQCK, LRRC68, MS4A4A, MS4A4E, MS4A6A, PICALM, PVR, PVRL2, and TOMM40 genes, were used to calculate the RPS for each individual subject using the approach described by Purcell et al.3. Images were motion-corrected, normalized to MNI space, and spatially smoothed (8mm FWHM) using SPM5. Methods: BOLD functional MRI images (GE 3 T MRI scanner, TR/TE = 2000/28ms, flip angle = 90 deg, FOV = 64圆4, 24 axial slices, 170 volumes) were collected for 265 right-handed Caucasian healthy volunteers (116 male, 149 female) from the age of 18 to 86 years (SD = 14.17) while they performed a simple declarative memory task (SDMT). Studies have shown that normal RELN levels are necessary to prevent abnormal phosphorylation of tau (Ohkubo et al., 2003) and beta-amyloid-induced suppression of long term potentiation and NMDA receptors (Durakoglugil et al., 2009). 2011), on the detrimental effect of LOAD RPS on hippocampal function. To that end, in the current study, we explored the role of polymorphisms in the gene encoding Reelin (RELN), a glycoprotein that has been shown to be critical for neuronal development and synaptic plasticity (Kramer et al.
Identifying mechanisms, particularly genetic mechanisms that confer resilience to the detrimental effect of LOAD related risk genes on brain structure and function could provide a viable avenue to identify novel therapeutic targets for LOAD. While most of these genes have weak effects, using a polygenic risk profile score (RPS) approach – a method that allows exploration of the influence of the cumulative effect of risk alleles - we and others have shown the negative influence of LOAD risk genes on brain structure (Chauhan et al., 2015) and function (Xiao et al., 2015 HBM) even in healthy volunteers. Genome-wide association studies (GWAS) have identified more than 20 genetic loci in addition to APOEɛ4 that are associated with increased risk for LOAD. Mattay Lieber Institute for Brain Development, Baltimore, Maryland, United Statesīackground: Late Onset Alzheimer’s Disease (LOAD) is one of the most common debilitating causes of dementia worldwide with heritability estimates ranging from 50 – 70%. Goldman, Rahul Bharadwaj, Kaitlin Healy, Brad Zoltick, Saumitra Das, Karen Berman, Daniel R.
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