Sample characteristics after QC
After quality control of the genotyping data including the exclusion of 79 participants to avoid potential population stratification confounds, 733 out of 818 ADNI participants remained in the present study. Among these 733 participants, 729 sets of scans were successful in FreeSurfer segmentation and parcellation and were included in GWAS analyses of FreeSurfer phenotypes (56 volumetric and cortical thickness values described in ). Seven hundred fifteen participants had successful VBM processing and were used in GWAS analyses of VBM phenotypes (86 GM density values described in ). shows the demographics information of the sample analyzed for both FreeSurfer and VBM studies. In both samples, gender and education are significantly different (overall p<0.05) among baseline diagnostic groups (HC, MCI, AD). In the subsequent GWAS analyses, baseline age and gender, as well as education, handedness, and baseline ICV are included as covariates.
Table 3 Demographic information and total number of participants involved in each analysis. Of 818 ADNI participants, 733 remained after quality control of the genotyping data and consideration of population stratification. Among these 733 participants, 729 subjects (more ...)
GWAS of imaging phenotypes
For convenience, in this paper, an SNP is described by its rs number together with its respective gene (i.e., the closest gene, as annotated in Illumina's Human610-Quad SNP list). Shown in are all the imaging genetics associations at a significance threshold of p<10−7 (a typical threshold for genome-wide significance), which are discovered by GWAS analysis of 142 imaging phenotypes (i.e., quantitative traits, or QTs).
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. (more ...)
At the p
significance level, 22 strong SNP-QT associations (see blocks labeled with “x” in ) were identified in the GWAS analyses, and five SNPs were involved in these associations. As a well-established AD risk factor (Farrer et al., 1997
), the APOE SNP rs429358 confirmed to have multiple associations with both FreeSurfer QTs and VBM QTs, showing as the most prominent imaging genetics pattern at the significance level of p
. In addition, associations with multiple FreeSurfer QTs were identified for rs2075650 (TOMM40), supporting the recent finding of TOMM40 as a gene adjacent to APOE and an additional contributor to AD (Osherovich, 2009
; Potkin et al., 2009a
). Three additional SNPs were found to have strong associations with one or more VBM QTs: rs6463843 (NXPH1), rs4692256 (LOC391642), and rs10932886 (EPHA4). Further information about these SNPs is available in .
Table 4 Top quantitative trait (QT) loci ranked by the total number of associations at the significance level of p<10−6. Relevant information about top ranked SNPs and their respective genes (i.e., the closet gene, as annotated in Illumina's Human610-Quad (more ...)
A number of imaging phenotypes were identified to have strong associations with target SNPs in the GWAS analyses, suggesting that these values may be sensitive QTs to imaging genetics studies of AD. As expected, both the left and right amygdalar and hippocampal regions were found to be strongly associated with rs429358 (APOE) using volumetric and GM density measures. In addition, rs2075650 (TOMM40) was significantly associated with bilateral hippocampal volume and left amygdalar volume. Additional imaging phenotypes found to be sensitive QTs, include (a) volume measures from the right cerebral cortex and cerebral white matter, (b) cortical thickness measures from left and right inferior parietal gyri, and right middle temporal gyrus, and (c) GM density measures from the left middle orbital frontal gyrus, left precuneus, left superior frontal gyrus, and left and right mean frontal lobe regions (see MeanFrontal definition in ).
Heat maps of clustered associations at a somewhat less stringent significance level (p<10−6) are shown in . As expected, more SNPs and QTs are involved. The top 10 SNPs and their respective genes ranked by the total number of significant QT associations at p<10−6 are shown in . With more SNPs and QTs available in the heat maps, interesting clustering patterns in both the imaging and genetics dimensions were revealed by examining the corresponding dendrograms (i.e., hierarchical clustering results). In the imaging dimension (x-axis), many pairs of left and right measures of the same structure were clustered together, supporting the symmetric relationship between these phenotypes and genetic variation. In addition, regional similarity was also detected including a prominent pattern of multiple orbital frontal measures clustered together in . In the genomic dimension (y-axis), three SNPs from LOC391642 were grouped together in , suggesting an increased likelihood of linkage disequilibrium (LD) effects.
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. (more ...)
Refined analysis for a sample target QT
Subsequent analyses focused on a target QT and a target SNP selected from heat maps in . Shown in are the Manhattan and Q–Q plots of the GWAS for the target QT, right hippocampal GM density (RHippocampus in ). In the Q–Q plot, for most of the p-values, the observed p-values from GWAS are almost the same as the expected p-values from the null hypothesis. There was little or no evidence of systematic bias, which could be caused by factors such as a strong population substructure and genotyping artifacts. The p-values in the upper tail of the distribution do show a significant deviation suggesting strong associations between these SNPs and the QT.
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 ) which was calculated (more ...)
Refined analysis for a sample target SNP
A target SNP, rs6463843 (NXPH1), was selected for detailed imaging analyses since it was the only SNP strongly associated with both left and right hippocampi other than rs429358 (APOE) and rs2075650 (TOMM40). The results of a two-way ANOVA using VBM to compare the effects of baseline diagnostics group and rs6463843 (NXPH1) genotype on global GM density are shown in . After evaluating hippocampal GM density group means for each diagnosis-genotype group, we chose to contrast GG vs. TT (GG>TT) using all participants (n=715; 166 AD (44 TT, 78 GT, 44 GG); 346 MCI (82 TT, 170 GT, 94 GG); 203 HC (35 TT, 105 GT, 63 GG)). As shown in , TT participants had significantly reduced global GM density throughout the brain relative to GG participants (p<0.01 (FDR), k=27). Maximal differences between groups were found in a number of regions known to be associated with AD, including the medial temporal lobe (−36, −30, −17; T=5.20) and frontal (19, 56, −15; T=5.56), parietal (26, −59, 67; T=5.71) and temporal (−59, 2, −30; T=4.81) lobe cortical surfaces. In order to determine whether a particular diagnostic group was responsible for the effects seen in the full sample contrast of GG>TT, we evaluated the same comparison within each baseline diagnostic group (; AD, MCI, HC). The pattern of significant voxels for GG>TT was largest in the AD group, with highly significant clusters in the right hippocampus (31, −26, −15; T=5.34), left medial temporal lobe (−25, −32, −7; T=4.37), and frontal lobe (−35, 49, −13; T=4.33). MCI and HC groups also showed significant voxels in the contrast of GG>TT, with maximum voxels found in the inferior frontal lobe (45, 25, −13; T=3.82) and middle frontal lobe (−25, 6, 62; T=4.58), respectively. The AD panel in showed more prominent patterns, while the MCI and HC panels appeared less structured. This suggested a possible SNP-by-diagnosis interaction effect on brain structure, which is examined below at a more detailed level for several candidate imaging phenotypes. Furthermore, the inclusion of APOE genotype as a covariate did not significantly alter these effects (data not shown).
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, (more ...)
Based on the heat map and VBM results, four GM density measures were further evaluated as phenotypes for additional associations with rs6463843 (NXPH1). As shown in , expected baseline diagnostic differences in left (; F(7,708)=79.4, p<0.001) and right (; F(7,708)=78.4, p<0.001) hippocampal GM density, as well as left (; F(7,708)=60.3, p<0.001) and right (; F(7,708)=59.4, p<0.001) mean medial temporal lobe GM density were found. Pairwise comparisons indicated that AD participants had significantly reduced hippocampal and mean medial temporal lobe GM density relative to both MCI and HC participants (all p<0.001). MCI participants also showed a significantly reduced GM density in all these regions relative to HCs (p<0.001). The main effect of genotype across all participants was also significant for left and right hippocampal GM density (left, F (7,708)=10.4; right, F(7,708)=9.9, both p<0.001) and left and right mean medial temporal lobe GM density (left, F(7,708)=7.9; right, F(7,708)=9.0, both p<0.001). Paired comparisons indicated significantly reduced left and right hippocampal and mean medial temporal lobar GM density in participants with a TT genotype relative to those with a GG genotype in the rs6463843 (NXPH1) SNP (p<0.01). In addition, participants with the TT genotype had significantly reduced left and right mean medial temporal lobe GM density relative to TG heterozygotes (p<0.01). The interaction effect of baseline diagnosis and rs6463843 genotype was also significant for right hippocampal GM density (p<0.05), but not for the other three regions, which suggested that AD patients with TT genotype were particularly vulnerable to increased GM density loss in right hippocampus.
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 (more ...)