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.

Keywords: AD biomarker; Imaging genomics studies; Nonlocal method; Spatial correlations; vGWAS.

Fig. 1

The schematic of our proposed method, which includes three main parts: (1) exploiting spatial correlations, (2) performing FVGWAS procedure, and (3) obtaining associated SNPs and Clusters (FVGWAS: fast voxel-wise genome-wide association analysis; GSIS: global sure independence screening procedure).

Fig. 2

The optimized parameters and the selected results. A, the size of the GS window (Ω_G); B, the degree of GS (h); C, the size of the NLM search window (Ω_N1); D, the size of the NLM similar window (Ω_N2); E, the degree of NLM (t); and F, the degree of BM3D (f) (GS: Gaussian; NLM: Nonlocal means; and BM3D: Block-matching and 3D filtering). For simplicity, we denoted the Ω = ∗ as Ω = ∗ × ∗ in the legend of Fig. 2A, C and D.

Fig. 3

Simulation results for the association between SNPs and voxels: the first column shows three different ROI locations with a size of 10 × 10. The second column contains the ROC curves of the nonlocal method and the lack of applied nonlocal method corresponding to the three different ROIs shown in the first column (N-weight: method without nonlocal operation; GS: Gaussian; NLM: Nonlocal means; and BM3D: Block-matching and 3D filtering).

Fig. 4

Simulation results for the association between SNPs and clusters. A: the number of false positive clusters in each causal SNP; B: the size in the number of pixels of false positive clusters in each causal SNP; C: the DOR in each causal SNP; (N-weight: method without nonlocal operation; GS: Gaussian; NLM: Nonlocal means; and BM3D: Block-matching and 3D filtering).

Fig. 5

Manhattan and QQ plots. A and C correspond to the weight setting by the GS function; B and D correspond to the weight setting by BM4D (GS: Gaussian; BM4D: block-matching and 4D filtering).

Fig. 6

The number of significant voxel-locus pairs based on the raw p-values (rawpv) of the Wald-type test statistic at the 10⁻⁵ significance level with the top N₀ = 1000 SNPs. The number of significant voxel-locus pairs based on the corrected p-values (corpv) of the Wald-type test statistic at the 0.5 or 0.8 significance level with the top N₀ = 1000 SNPs. A, B and C correspond to the weights setting by the GS function; D, E and F correspond to the weights setting by BM4D (GS: Gaussian; BM4D: block-matching and 4D filtering).

Fig. 7

ADNI whole-brain GWAS: selected slice maps of −log₁₀(p)-value for significant clusters corresponding to some SNPs within the topN₀. Fig. 8A corresponds to the weight setting by the GS function, and B corresponds to the weight setting by BM4D (GS: Gaussian; BM4D: block-matching and 4D filtering).