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Large genome-wide association studies (GWAS) have identified many novel genes influencing Alzheimer disease (AD) risk, but most of the genetic variance remains unexplained. We conducted a two-stage GWAS for AD-related quantitative measures of hippocampal volume (HV), total cerebral volume (TCV), and white matter hyperintensities (WMH).
Brain MRI measures of HV, TCV and WMH were obtained from 981 Caucasian and 419 African American AD cases and their cognitively normal siblings in the MIRAGE Study, and from 168 AD cases, 336 individuals with mild cognitive impairment and 188 controls in the ADNI Study. A GWAS for each trait was conducted in the two Caucasian datasets in stage 1. Results from the two datasets were combined by meta analysis. In stage 2, one SNP from each region that was nominally significant in each dataset (p<0.05) and strongly associated in both datasets (p<1.0×10−5) was evaluated in the African American dataset.
Twenty-two markers (14 for HV, 3 for TCV, and 5 for WMH) from distinct regions met criteria for evaluation in stage 2. Novel genome-wide significant associations (p<5.0×10−8) were attained for HV with SNPs in the APOE, F5/SELP, LHFP and GCFC2 gene regions. All of these associations were supported by evidence in each dataset. Associations with different SNPs in the same gene (p<1×10−5 in Caucasians and p<2.2×10−4 in African Americans) were also observed for PICALM with HV, SYNPR with TCV and TTC27 with WMH.
Our study demonstrates the efficacy of endophenotypes for broadening our understanding of the genetic basis of AD.
Difficulties in the search for susceptibility genes for Alzheimer disease (AD) have been attributed to the etiological heterogeneity of the clinically defined disease phenotype.1 Genome-wide association studies (GWAS) using very large samples have increased the number of robust associations to ten genes including APOE,1–3 however these loci account for no more than 35% of the inherited risk of AD.3 Heritable AD-related endophenotypes obtained by magnetic resonance imaging (MRI) provide in vivo measures of neurodegenerative and cerebrovascular brain injury and can serve as intermediate phenotypes for genetic studies of AD.4 The heritability for hippocampal volume (HV) and white matter hyperintensities (WMH) are 0.40 and 0.73, respectively, among elderly male twins,5,6 and MRI measures of cerebrovascular disease and neurodegeneration are highly heritable in AD families.4
Candidate gene studies have revealed genetic associations with several AD-related MRI traits. Cortical structural changes are associated with specific APOE genotypes among non-demented elderly, as ε4 carriers have demonstrably smaller hippocampal volumes than non-ε4 carriers.7 One study observed a correspondence of SNPs and haplotypes from the two AD-associated regions of SORL1 with MRI and neuropathological measures of WMH and HV.8 Association of HV with several variants and haplotypes in the TTR gene has also been reported.9 Genome-wide association studies of structural and volumetric changes10–13 and using a voxel-based approach14 confirmed SNPs in the APOE and TOMM40 genes as markers strongly associated with multiple brain regions including the amygdala and hippocampus, and yielded promising findings (p<10−6) with several other genes (reviewed in 15).
In this paper, we report results from a GWAS for three AD-related MRI measures in a multi ethnic sample.
One group of subjects are participants of the Multi Institutional Research in Alzheimer’s Genetic Epidemiology (MIRAGE) Study, a family-based genetic epidemiological study of AD described in detail elsewhere.16 A second sample was obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) Study. Brain imaging, biological samples, and clinical assessments were longitudinally collected for healthy controls and patients with mild cognitive decline (MCI) and AD. Details regarding subject ascertainment and evaluation have been previously described (http://www.adni-info.org).17 Study protocols were approved by Institutional Review Boards at each recruitment site. Characteristics of the ADNI and MIRAGE subjects included in this study are summarized in Table 1.
In the MIRAGE sample, MR images of the brain were obtained with 1.5 Tesla magnetic field strength scanners using a standard protocol of 3D T1-weighted high resolution sequence, a double spin-echo sequence and a FLAIR sequence. Semi-quantitative measures of bilateral medial temporal (hippocampal) volume (HV), total cerebral volume (TCV) and white matter hyperintensities (WMH) were obtained from these scans and using methods previously described which account for total intracranial volume.18 These measures were designed to be simple to use and have been shown to linearly correlate with image quantification.19 MR scans were evaluated by a single rater blinded to age, gender, and affection status to reduce inter-rater variability common to semi-quantitative methods. TCV and WMH were rated on a scale of 0 to 100. HV was rated on an ordinal scale of 0 to 4 according to previously described methods.20 TCV and HV were scored as damage expressed as a percentage of the overall brain volume. WMH scores were log10 transformed.
Brain MRI scans (1.5 Tesla) were obtained from ADNI subjects as described elsewhere.21 Available longitudinal scans were also analyzed (supplementary Table 1). TCV and HV were measured at the baseline visit and 12 months later using FreeSurfer (http://surfer.nmr.mgh.harvard.edu/), an open-source program that converts MRI data into volumetric measures.11 WMH volume was measured at 0, 6, 12, 18, 24 and 36 months. In order to obtain a normal distribution for analysis, an adjustment value of 0.5 was added and then log10 transformed.
ADNI participants and approximately two-thirds of the MIRAGE subjects were genotyped on the Illumina Infinium Human 610-Quad BeadChip. The remaining MIRAGE subjects were genotyped with the Illumina Infinium HUmanCNV370-Duo BeadChip. Genotyping for the APOE ε2/ε3/ε4 alleles was performed in the MIRAGE dataset using a Roche diagnostics LightCycler® 480 instrument (Roche). APOE genotypes in the ADNI cohort were obtained by pyrosequencing or restriction fragment length polymorphism analysis.
Prior to analysis, SNPs with a call rate less than 98%, with a minor allele frequency (MAF) less than 0.05 or not in Hardy-Weinberg equilibrium (p<10−6) among unaffected, unrelated individuals were excluded. We excluded individual samples with SNP call rates below 98% among the remaining SNPs, or whose gender as determined by analysis of X-chromosome data (performed using PLINK22) was inconsistent with the reported gender. Per-SNP and per-subject call rates were determined within subsets of MIRAGE individuals genotyped on each chip, as well as among all MIRAGE subjects. Individuals and SNPs in MIRAGE were filtered separately based on quality measurements, and then population stratification was checked in each MIRAGE dataset using SNPs that were common across chips. PLINK was used to identify cryptic relatedness within each dataset. All relationships were examined using PREST and corrected if possible when necessary.23 Subjects whose relationship could not be resolved were dropped from further analysis. Population of origin was confirmed using STRUCTURE in the MIRAGE dataset and EIGENSTRAT in the ADNI dataset.24,25 Principal component (PC) analysis implemented in EIGENSTRAT was used to evaluate population substructure within each dataset.
Imputation of genotypes for autosomal SNPs was performed using the Markov Chain Haplotyping (MaCH) software based on the HapMap 2 and 3 reference SNP panels for Caucasians and Yorubans (http://hapmap.ncbi.nlm.nih.gov/).26 We excluded SNPs with low MAF (<1%), not in HWE (p <10−6), and with potential for undetected strand flips (C/G and A/T coding) to ensure consistency of allele frequencies between the test and reference haplotypes and to improve the quality of imputation. Within each dataset, only SNPs imputed with r2 ≥ 0.80 were included in analysis.
The GWAS was carried out in two stages. The MIRAGE Caucasian families and Caucasian subjects in ADNI were included in the first stage and the MIRAGE African American families were added in Stage 2. SNPs were excluded from analyses if the MAF was below 5%. Imputed SNPs were tested for association with each MRI trait using generalized estimating equations assuming an additive genetic model. A quantitative estimate between 0 and 2 representing the dose of the minor allele was assigned to the SNP variable. Regression models also included terms for age at the time of the MRI scan, sex and the first three principal components. The models were not adjusted for disease status so that association signals were not diluted by the correlation of disease status with MRI measures. In the ADNI dataset, hippocampal and cerebral volume measures were also adjusted for total intracranial volume to account for differences in head size. Multiple values for MRI measures in ADNI were treated as sequential repeated measures. Generalized estimating equations implemented in the GEEPACK package within the R statistical programming language (version 12.2.1)27 were used to account for the MIRAGE family-based design and repeated measures in the ADNI dataset. SNP association results obtained from the ADNI and MIRAGE datasets were combined by meta-analysis using the z-score method in METAL.28
In the Stage 1 analysis, SNPs attaining a meta-analysis p-value <1×10−5 and at least nominal significance (p<0.05) in both Caucasian datasets were identified as threshold SNPs. In instances where multiple correlated SNPs (r2>0.8) met threshold criteria, the most significant SNP was included in Stage 2. For SNPs located within a gene, all SNPs within the coding, intronic and promoter regions were included in follow up investigation in the African American sample. Otherwise, if a threshold SNP was located within 100 kb of a gene, then the follow-up region was extended from the SNP position to and including the nearest gene. Primary association tests were performed for threshold SNPs in the African American families with the same model used for the Caucasian families. Secondary analyses were carried out for all other SNPs in the defined regions to allow for lack of polymorphism in the threshold SNPs and different haplotype structures between Caucasians and African Americans. Results from stages 1 and 2 were combined using METAL.
Genotypes were evaluated for 2,131,250 imputed SNPs that passed MAF and imputation quality thresholds in the ADNI (n=692) and MIRAGE Caucasian (n=991) datasets. We observed only modest levels of genomic control inflation for any of the traits in either Caucasian dataset (maximum λ = 1.045 for HV in ADNI), suggesting that the GWAS results were not impacted by population stratification (supplementary Fig 1). Meta-analysis of results from the two discovery cohorts revealed genome-wide significant association between HV and SNPs spanning the F5 and SELP genes (best result with F5 SNP rs3917836, p=5.53×10−9), and with several variants in the APOE region (best result with APOE, p=5.23×10−31) (Fig 1, supplementary Fig 2).
A total of 99 SNPs in 14 distinct regions met threshold criteria for association with HV (supplementary Table 2). Threshold SNPs in F5, GCFC2, and LHFP showed nominal evidence of association in the African American sample, and were genome-wide significant in the combined (stages 1+2) sample (Table 2). The threshold SNP in COL18A1 (rs2838923) was not significantly associated with HV in the African Americans, but the effect direction was the same as in Caucasians and the association with this SNP was more significant after combining the results from both populations (p=7.94×10−7). APOE ε4 was associated with HV in African Americans (p=2.75×10−4) leading to an even more significant result in the combined sample (p=1.58×10−33). Other notable results in the African Americans were observed with non-threshold SNPs in SELP (rs3917854, p=5.70×10−6), NKAIN2 (rs7773205, p=2.14×10−4) and PICALM (rs17148741, p=9.4×10−5). SELP SNP rs3917854 is 8,072 base pairs from the genome-wide significant SNP in F5. There was also strong evidence of association with HV in Caucasians with SNPs in intergenic regions on chromosomes 1 (rs2942354, p=4.71×10−7) and 9 (rs11139399, p=6.67×10−7).
Further scrutiny of the results in the four regions showing genome-wide significance with HV revealed strong corroborative evidence with other SNPs in F5/SELP, LHFP, and in the APOE region (Fig 1). Numerous SNPs within a 40 kb region spanning the proximal portion of F5 and distal portion of SELP were also significant. These 19 SNPs are in very high linkage disequilibrium (r2>0.95) in both Caucasians and African Americans with consistent effect directions, and are all intronic except for rs1018828 (p=1.49×10−9) within the ~2,500 base pair intergenic region (Fig 2). Six LHFP SNPs spanning a 30 kb region in intron 2 approached or exceeded genome-wide significance. The evidence for association of HV with GCFC2 is derived from a solitary imputed SNP (rs2298948). The imputation quality of rs2298948 was high (r>0.99), the minor allele was relatively common in both Caucasians (average MAF=0.31) and African Americans (MAF=0.08), and there was evidence for association in all three datasets. Rs2298948 was moderately correlated with one adjacent SNP (rs7560262, r2=0.43) and less correlated with all other SNPs in this region (r2<0.4).
APOE ε4 was also very strongly associated with TCV in each of the Caucasian datasets yielding a genome-wide significant result in the combined groups (p=4.25×10−10), but not in the African American dataset (Table 2). In fact, the pattern of effect was opposite in the African American sample. SNPs in two regions associated with TCV and five regions associated with WMH met threshold criteria for further analysis (supplementary Table 2). None of the threshold SNPs for either trait attained genome-wide significance in the combined sample (Table 2). However, results for alternate SNPs in the African American sample gave strong support for association of TCV with SYNPR (rs935793, p=7.12×10−5) and for association of WMH with TTC27 (rs3769573, p=2.18×10−4).
Our two-stage genome-wide association study identified highly significant associations between several loci and three brain MRI AD-related traits in two Caucasian samples and one African American sample containing AD, cognitively impaired, and cognitively healthy subjects. Genome-wide significant results were obtained for HV with SNPs in four gene regions including APOE, F5/SELP, LHFP and GCFC2. All of these associations were supported by evidence in each dataset. However, the GCFC2 finding is less certain because it is based on evidence with only a single SNP. Noteworthy associations (p<1×10−6 in Caucasians and p≤2.18×10−4 in African Americans) were also observed with different SNPs in the same gene for SYNPR with TCV and for TTC27 with WMH. With the exception of APOE, there are no reports of association of any of these genes with AD in any studies including very large GWAS.2,3
All of the genome-wide significant results were found for HV only. Hippocampal atrophy is well-known to occur early in the disease and is correlated with impaired memory function as well as neurofibrillary tangle density in the hippocampus.29,30 Previously, we demonstrated in the MIRAGE study that hippocampal atrophy is highly heritable,4 may be a marker of subclinical disease,18 and is associated with particular 3-SNP haplotypes in SORL1 and TTR in Caucasians.8,9 In this much larger dataset, in Caucasians there was only weak evidence of association of HV with individual SORL1 and TTR SNPs (most significant p-values ~ 0.01). However, in African Americans, five SORL1 SNPs were associated with HV at p<1.8×10−3 (most significant SNP: rs4420280, p=1.70×10−5). A prior GWAS of HV in the ADNI study identified sub-genome-wide significant (1×10−7<p<1×10−5) associations with SNPs in TOMM40, CAND1, MAG12, ARSB, PRUNE2 and EFNA5, but that study lacked a replication sample.12 Of these loci, only TOMM40 was significant in our meta analysis of the ADNI and MIRAGE datasets. It is important to note that F5/SELP, LHFP and GCFC2, were weakly associated with AD risk in a large GWAS including 7–8 fold more subjects in the discovery sample than in this study (most significant results: F5, rs6035, p=0.15; SELP, rs3917687, p=0.053; LHFP, rs7333587, p=0.022; GCFC2, rs17741889, p=0.032).3 Two recent large GWAS of HV in adults of European ancestry identified genome-wide significant association with intergenic SNP rs7294919 located between HRK and FBXW8 in chromosome 12q24.31,32 One of these studies comprising prospectively followed population-based cohorts also reported genome-wide significant results with chromosome 12q14 SNPs rs17178006 in MSRB3 and rs6581612 in WIF1.31 These findings suggest that genes influencing hippocampal changes concomitant with AD are different from those associated with aging per se.
Extremely strong evidence of association (p=1.58×10−33) was identified between HV and APOE ε4, a well-established AD risk factor and known determinant of the rate of HV.33,34 The ε4 allele was also very strongly associated with TCV in Caucasians (p=4.25×10−10), a measure that is highly correlated with HV in Caucasians (ADNI: r=0.54, MIRAGE: r=0.64), but not with TCV in African Americans (r=0.16). Highly significant results with adjacent SNPs are unlikely to be evidence of independent contributions from genes other than APOE because of very strong linkage disequilibrium in this region.35 Of note, there was very little evidence of association between APOE ε4 and WMH suggesting that APOE contributes more substantially to AD-related neuronal loss than to cerebrovascular mechanisms associated with AD. This idea is consistent with the preponderance of evidence suggesting that apoE4 has a direct role in production or clearance of amyloid β (Aβ), potentiating Aβ-induced lysosomal leakage and/or activating the endoplasmic reticulum stress response, leading to increased apoptosis.36
We also observed genome-wide significant association of HV with 24 SNPs spanning the proximal portion of F5 and the distal portion of SELP. Support for the most significant SNP in this region (rs6703865 in F5) was derived primarily from the Caucasian datasets (p=6.58×10−9), whereas the most significant SNP in the African dataset (rs3917854, p=5.70×10−6) is located 8,072 base pairs away in SELP. In light of the high LD among the top-ranked SNPs in this region in both Caucasians and African Americans (Fig 2), it is not possible to ascertain whether the functional variant underlying the association peak is in F5 or SELP, or perhaps there are functional variants in both genes.
F5 encodes Factor V which is an essential cofactor of the blood coagulation cascade and functions to allow Factor Xa to activate thrombin. Recently, Sherva et al. observed significant association of F5 SNP rs2213865 with rate of cognitive decline among 331 AD cases in ADNI (p=3.02×10−7),37 but this SNP is located more than 30 kb from the margin of and in a different haplotype block from our association peak (Figure 2). Factor V Leiden, a G169A mutation (rs6025), prevents efficient activation of factor V leading to overproduction of thrombin and excess clotting. This mutation is relatively uncommon in the general population (4% in Caucasians and < 1% in African Americans). In the Rotterdam Study, carriers of this mutation had a significantly increased risk of vascular dementia (OR=4.28, 95% CI=1.26–14.5) and an elevated risk of AD (OR=2.15, 95% CI=0.82–5.63).38 Rs6025, located approximately 10 kb from the edge of our association peak for HV, was not associated with HV in our sample (Fig 2).
P-selectin encoded by SELP, is a 140 KDa granule membrane protein that mediates the interaction of activated endothelial cells or platelets with leukocytes. Stellos et al. observed significantly higher baseline blood levels of P-selectin in AD patients with fast cognitive decline compared to AD with slow cognitive decline during a 1-year follow-up period.39 In another 2-year follow-up study of 72 AD patients and 6 controls, P-selectin levels were decreased in AD and lowest in AD patients with the highest cognitive decline.40 These findings suggest that P-selectin may induce alterations of endothelial regulation and thereby influence AD-related neurodegenerative processes.
Synaptoporin is a synaptic membrane protein of synaptic vesicles and a member of the synaptophysin family which is involved in uptake, storing, docking and regulating release of neurotransmitters. The synaptoporin gene (SYNPR) encodes a highly conserved protein, has two known splicing variants, and is specifically expressed in the brain.41 Clathrin-mediated endocytosis is the major mechanism of vesicle retrieval after neurotransmitter release in the hippocampus.42 Notably, both SYNPR and PICALM (phosphatidylinositol-binding clathrin assembly protein) are involved in clathrin-mediated endocytosis,41,43 and in our study showed significant association of both loci with MRI measures of cerebral degeneration.
The function of GCFC2 is relatively unknown, but a haplotype for this GC-rich sequence DNA-binding factor gene has been associated with dyslexia in a set of Finnish families.44 Similarly, little is known about the Lipoma HMGIC fusion partner gene (LHFP) which encodes a tetraspan transmembrane protein, or TTC27 which is a member of proteins containing a tetratricopeptide repeat domain. Mutations in another LHFP-like gene result in deafness in humans and mice.45,46
Among the nine novel GWAS genes for AD risk,2,3 we detected strong evidence of association of HV and PICALM in both Caucasians and African Americans, albeit with different top-ranked SNPs in the two populations These SNPs also differ from the top-ranked SNPs in AD GWAS in Caucasians and African Americans.2,3,47 Discordance in the association patterns between Caucasians and African Americans could be related to population differences in allele frequencies or LD patterns. Alternatively, the AD risk variants in these genes may differ across populations (i.e., allelic heterogeneity) as we observed previously in SORL1.48
Results of our study should be interpreted cautiously. The MIRAGE and ADNI studies differ in multiple ways. The MIRAGE cohort contains AD subjects and cognitively normal siblings whereas the ADNI cohort includes unrelated subjects, approximately one-half of whom have MCI. Hippocampal and total cerebral volumes in the MIRAGE subjects are semi-quantitative measures whereas more precise quantitative measures were obtained for the ADNI subjects. Thus, effect sizes could not be estimated for the combined group of subjects. In addition, the sample size of the study is relatively small for a GWAS. However, we were able to increase the effective sample by capturing the phenotypic information obtained from ADNI subjects at multiple examinations. We enhanced the power of the sample by using quantitative outcome measures which are more precise and more likely to be directly influenced by specific genes than a complex disease outcome. This is exemplified by the top-ranked findings for PICALM which attained p-values of 4.75×10−6 and 9.39×10−5 in the Caucasian and African American datasets, respectively. By comparison, the top results for the association between PICALM and AD in a GWAS of more than 2,000 cases and 5,300 controls had p-values between 0.01 and 0.001.49 Finally, our study lacks a true replication sample. However, our top results, particularly the genome-wide significant ones, are supported by evidence in studies with very different designs and genetic background.
Our study demonstrates the efficacy of endophenotypes for broadening our understanding of the genetic basis of AD. It is very likely that the volume and specificity of these associations will increase through future studies using larger samples and focused on additional precise structural and functional MRI measures.
Supplementary Figure 1. Q-Q plots for GWAS of MRI traits in the ADNI (left) and MIRAGE (right) studies. A. Hippocampal volume. B. Total cerebral volume. C. White matter hyperintensities.
Supplementary Figure 2. Manhattan plots for GWAS of MRI traits in the Caucasian datasets. Horizontal line indicates genome-wide significance threshold. A. Hippocampal volume. B. Total cerebral volume. C. White matter hyperintensities.
This work was supported by NIH grants R01-AG09029, R01-AG025259, P30-AG13846, P30-AG010129 and K01 AG030514, and the Dana Foundation. Collection and sharing of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data is funded by NIH grants U01 AG024904 and RC2 AG036535 (PI: Michael W. Weiner, MD). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics, Johnson and Johnson, Eli Lilly and Co., Medpace, Inc., Merck and Co., Inc., Novartis AG, Pfizer Inc, F. Hoffman-La Roche, Schering-Plough, Synarc, Inc., and Wyeth, as well as non-profit partners the Alzheimer’s Association and Alzheimer’s Drug Discovery Foundation, with participation from the U.S. Food and Drug Administration. Private sector contributions to ADNI are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles. The National Cell Repository for Alzheimer’s Disease (NIH grant U24 AG021886) provided support for DNA and cell line banking and processing for ADNI. We gratefully acknowledge the contributions of the MIRAGE Study Group whose members are: Drs. Lindsay A. Farrer, Robert C. Green, L. Clinton C. Baldwin, L. Adrienne Cupples, Kathryn Lunetta, and Mark Logue (Boston University); Drs. Abimbola Akomolafe, Allison Ashley, Lorin Freedman, and Elizabeth Ofili (Morehouse School of Medicine); Dr. Helena Chui (University of Southern California); Dr. Charles DeCarli (University of California– Davis); Dr. Ranjan Duara (Mt. Sinai Medical Center, Miami); Drs. Tatiana Foroud and Martin Farlow (Indiana University School of Medicine); Dr. Robert Friedland (University of Louisville); Drs. Rodney Go and Lindy Harrell (University of Alabama-Birmingham); Dr. Alexander Kurz (Technical University, Munich, Germany); Dr. Thomas Obisesan (Howard University); Drs. Helen Petrovitch and Lon White (Pacific Health Research Institute); Dr. Marwan Sabbagh (Sun Health Research Institute); Dr. Dessa Sadovnick (University of British Columbia); and Dr. Magda Tsolaki (University of Aristotle, Thessaloniki, Greece).