Spatial correlations exploitation based on nonlocal voxel-wise GWAS for biomarker detection of AD

Abstract

Potential biomarker detection is a crucial area of study for the prediction, diagnosis, and monitoring of Alzheimer's disease (AD). The voxelwise genome-wide association study (vGWAS) is widely used in imaging genomics studies that is usually applied to the detection of AD biomarkers in both imaging and genetic data. However, performing vGWAS remains a challenge because of the computational complexity of the technique and our ignorance of the spatial correlations within the imaging data. In this paper, we propose a novel method based on the exploitation of spatial correlations that may help to detect potential AD biomarkers using a fast vGWAS. To incorporate spatial correlations, we applied a nonlocal method that supposed that a given voxel could be represented by weighting the sum of the other voxels. Three commonly used weighting methods were adopted to calculate the weights among different voxels in this study. Then, a fast vGWAS approach was used to assess the association between the image and the genetic data. The proposed method was estimated using both simulated and real data. In the simulation studies, we designed a set of experiments to evaluate the effectiveness of the nonlocal method for incorporating spatial correlations in vGWAS. The experiments showed that incorporating spatial correlations by the nonlocal method could improve the detecting accuracy of AD biomarkers. For real data, we successfully identified three genes, namely, ANK3, MEIS2, and TLR4, which have significant associations with mental retardation, learning disabilities and age according to previous research. These genes have profound impacts on AD or other neurodegenerative diseases. Our results indicated that our method might be an effective and valuable tool for detecting potential biomarkers of AD.

Feature Fusion and Detection in Alzheimer’s Disease Using a Novel Genetic Multi-Kernel SVM Based on MRI Imaging and Gene Data

“…RBFOX1 (p-value = 5.84303 × 10 −22 ) is expressed in the brain and related to the brain function that had been confirmed to be related to AD and MCI [27]. It can be seen from Table 4 that the risk AD genes, such as MEIS2 (p-value = 3.0419 × 10 −147 ), DLGAP2 (p-value = 3.57803 × 10 −19 ) and MAGI2 (p-value = 8.108022 × 10 −15 ) have been identified in the AD-HC group [31][32][33]. We suggest that MIR8063 (p-value = 1.827314 × 10 −221 ) may be associated with susceptibility to AD.…”

A Systematic Analysis Revealed the Potential Gene Regulatory Processes of ATRA-Triggered Neuroblastoma Differentiation and Identified a Novel RA Response Sequence in theNTRK2Gene

“…e TFs encoded by STAT5A, MEIS2, and EFRRG have been reported to be related to cognitive abilities [57][58][59]. e TF genes FSOL2, MESI2, NEF2L2, and PRDM2 have been reported to be associated with neurodegenerative diseases [60][61][62][63]. Moreover, the TF genes FSOL2, KLF11, and NEF2L2 play essential roles in the neurodegenerative diseases [64][65][66].…”

Genetic Variability in Molecular Pathways Implicated in Alzheimer's Disease: A Comprehensive Review

“…Alternative splicing mechanism regulator, important in erythropoiesis, is encoded by RNA binding protein fox-1 homolog 1 (RBFOX1) (Ponthier et al, 2006 ). ANK3 rs10761514 was associated with CSF Aβ levels in AD (Huang et al, 2019 ). ANK3 encodes for a membrane protein AnkG, important for spectrin-based anchoring of membrane proteins to the cytoskeleton (Kordeli et al, 1995 ).…”