Whole genome association study of brain-wide imaging phenotypes for identifying quantitative trait loci in MCI and AD: A study of the ADNI cohort

Abstract

A genome-wide, whole brain approach to investigate genetic effects on neuroimaging phenotypes for identifying quantitative trait loci is described. The Alzheimer's Disease Neuroimaging Initiative 1.5 T MRI and genetic dataset was investigated using voxel-based morphometry (VBM) and FreeSurfer parcellation followed by genome-wide association studies (GWAS). One hundred forty-two measures of grey matter (GM) density, volume, and cortical thickness were extracted from baseline scans. GWAS, using PLINK, were performed on each phenotype using quality-controlled genotype and scan data including 530,992 of 620,903 single nucleotide polymorphisms (SNPs) and 733 of 818 participants (175 AD, 354 amnestic mild cognitive impairment, MCI, and 204 healthy controls, HC). Hierarchical clustering and heat maps were used to analyze the GWAS results and associations are reported at two significance thresholds (p<10(-7) and p<10(-6)). As expected, SNPs in the APOE and TOMM40 genes were confirmed as markers strongly associated with multiple brain regions. Other top SNPs were proximal to the EPHA4, TP63 and NXPH1 genes. Detailed image analyses of rs6463843 (flanking NXPH1) revealed reduced global and regional GM density across diagnostic groups in TT relative to GG homozygotes. Interaction analysis indicated that AD patients homozygous for the T allele showed differential vulnerability to right hippocampal GM density loss. NXPH1 codes for a protein implicated in promotion of adhesion between dendrites and axons, a key factor in synaptic integrity, the loss of which is a hallmark of AD. A genome-wide, whole brain search strategy has the potential to reveal novel candidate genes and loci warranting further investigation and replication.

Copyright 2010 Elsevier Inc. All rights reserved.

Figures

Fig. 1

Heat maps of SNP associations with quantitative traits (QTs) at the significance level of p<10−7. GWAS results at a statistical threshold of p<10−7 using QTs derived from FreeSurfer (top) and VBM/MarSBaR (bottom) are shown. −log10(p-values) from each GWAS are color-mapped and displayed in the heat maps. Heat map blocks labeled with “x” reach the significance level of p<10−7. Only top SNPs and QTs are included in the heat maps, and so each row (SNP) and column (QT) has at least one “x” block. Dendrograms derived from hierarchical clustering are plotted for both SNPs and QTs. The color bar on the left side of the heat map codes the chromosome IDs for the corresponding SNPs. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 2

Heat maps of SNP associations with quantitative traits (QTs) at the significance level of p<10−6. GWAS results at a statistical threshold of p<10−6 using QTs derived from FreeSurfer (top) and VBM/MarSBaR (bottom) are shown. −log10(p-values) from each GWAS are color-mapped and displayed in the heat maps. Heat map blocks labeled with “x” reach the significance level of p<10−6. Only top SNPs and QTs are included in the heat maps, and so each row (SNP) and column (QT) has at least one “x” block. Dendrograms derived from hierarchical clustering are plotted for both SNPs and QTs. The color bar on the left side of the heat map codes the chromosome IDs for the corresponding SNPs. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 3

Manhattan and Q–Q plots of genome-wide association study (GWAS) of an example quantitative trait (QT). The QT examined in this analysis is the mean GM density of the right hippocampus (i.e., VBM phenotype RHippocampus, see Table 1) which was calculated using VBM/MarsBaR and adjusted for age, gender, education, handedness and ICV. Shown on the top panel is the Manhattan plot of the p-values (−log10(observed p-value)) from GWAS analysis of the QT. The horizontal lines display the cutoffs for two significant levels: blue line for p<10−6, and red line for p<10−7. Shown on the bottom panel is the quantile–quantile (Q–Q) plot of the distribution of the observed p-values (−log10(observed p-value)) in this sample versus the expected p-values (−log10(expected p-value)) under the null hypothesis of no association. Genomic inflation factor (based on median chi-squared) is 1.01667. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 4

VBM genetics analysis for rs6463843 (NXPH1). A two-way ANOVA was performed on mean GM density maps to compare rs6463843 SNP genotype and baseline diagnostic group within the ADNI cohort. Analysis of the contrast of two genotype groups, GG>TT, is shown (n=715; 166AD(44 TT, 78 GT, 44 GG); 346 MCI (82 TT, 170 GT, 94 GG); 203 HC (35 TT, 105 GT, 63 GG)). Age, gender, education, handedness, and baseline ICV are included as covariates in all comparisons. Shown in the top panel (a) are the results of comparison involving all 715 subjects (i.e., across all the diagnostic groups), which are displayed at a threshold of p<0.01 (corrected with FDR) with minimum cluster size (k)=27. Shown in the bottom panel (b) are the results of comparisons within each of the three baseline diagnostic groups (AD, MCI, and HC), which are displayed at a threshold of p<0.001 (uncorrected), with minimum cluster size (k)=27.

Fig. 5

Refined analysis of sample imaging phenotypes in relation to rs6463843 (NXPH1) and baseline diagnosis. Two-way ANOVAs were applied to examine the effects of rs6463843 (NXPH1) and baseline diagnosis on four target GM density measures: (a–b) left and right hippocampal GMDs, and (c–d) left and right mean medial temporal lobe GMDs. All the analyses included age, gender, education, handedness and baseline ICV as covariates. n=715 subjects were involved: 166 AD (44 TT, 78 GT, 44 GG); 346 MCI (82 TT, 170 GT, 94 GG); 203 HC (35 TT, 105 GT, 63 GG). The p-values for the main effect of diagnosis (DX), the main effect of SNP (SNP), and the interaction effect of SNP-by-diagnosis (DX×SNP) were shown in each plot.