White matter hyperintensities (WMH) are commonly identified on MRI, and their burden increases with age. These MRI findings cannot be considered benign accompaniments of aging because their burden in the elderly is associated with impairments in cognition, mobility, and mood and with an increased risk of subsequent stroke, dementia, and death. Small-vessel angiopathy is presumed to play a major causal role given associations with vascular risk factors, especially hypertension, but the precise pathophysiologic pathways responsible for accumulation of WMH with aging remain obscure. Genetics likely is key, and the heritability of WMH is higher than other MRI findings. The hope is that understanding the genetic underpinnings of WMH may lead to a better understanding of these pathways and thus novel means to prevent the clinical consequences of WMH. Genome-wide association studies are one of the initial steps in coming to that understanding. Here, we describe a multiethnic genome-wide association study including 21 079 middle-aged to elderly participants from 29 population-based cohorts, who were free of dementia and stroke. The findings not only support a vascular cause, further highlighting the relationship between blood pressure and WMH burden, but also suggest a central role of neuroinflammation in WMH, possibly involving pathological mechanisms related to microglial activation and common to gliomas. More work is needed to learn whether these findings will lead to the discovery of interventions, beyond those directed at vascular risk factors, to prevent the development of WMH and their clinical consequences.
Multiethnic Genome-Wide Association Study of Cerebral White Matter Hyperintensities on MRI
Circulation: Cardiovascular Genetics
Abstract
Background—
The burden of cerebral white matter hyperintensities (WMH) is associated with an increased risk of stroke, dementia, and death. WMH are highly heritable, but their genetic underpinnings are incompletely characterized. To identify novel genetic variants influencing WMH burden, we conducted a meta-analysis of multiethnic genome-wide association studies.
Methods and Results—
We included 21 079 middle-aged to elderly individuals from 29 population-based cohorts, who were free of dementia and stroke and were of European (n=17 936), African (n=1943), Hispanic (n=795), and Asian (n=405) descent. WMH burden was quantified on MRI either by a validated automated segmentation method or a validated visual grading scale. Genotype data in each study were imputed to the 1000 Genomes reference. Within each ethnic group, we investigated the relationship between each single-nucleotide polymorphism and WMH burden using a linear regression model adjusted for age, sex, intracranial volume, and principal components of ancestry. A meta-analysis was conducted for each ethnicity separately and for the combined sample. In the European descent samples, we confirmed a previously known locus on chr17q25 (P=2.7×10−19) and identified novel loci on chr10q24 (P=1.6×10−9) and chr2p21 (P=4.4×10−8). In the multiethnic meta-analysis, we identified 2 additional loci, on chr1q22 (P=2.0×10−8) and chr2p16 (P=1.5×10−8). The novel loci contained genes that have been implicated in Alzheimer disease (chr2p21 and chr10q24), intracerebral hemorrhage (chr1q22), neuroinflammatory diseases (chr2p21), and glioma (chr10q24 and chr2p16).
Conclusions—
We identified 4 novel genetic loci that implicate inflammatory and glial proliferative pathways in the development of WMH in addition to previously proposed ischemic mechanisms.
Introduction
Cerebral white matter hyperintensities (WMH) are common in the aging population and are associated with an increased risk of stroke, vascular cognitive impairment, dementia, and death.1 The prevalence and severity of WMH increase with advancing age and the presence of vascular risk factors, notably hypertension.2 The pathophysiology of WMH is poorly understood but likely reflects ischemic or degenerative damage to the small vessels of the brain, leading to chronic cerebral hypoperfusion and myelin rarefaction.3 Perivascular inflammation is a prominent pathological feature in WMH,4 and WMH burden has been associated with circulating biomarkers of inflammation, including high-sensitivity C-reactive protein, interleukin-6, lipoprotein-associated phospholipase A2, and myeloperoxidase.5,6
Clinical Perspective on p 409
Twin and family studies suggest that the heritability of WMH is 55% to 80%.7–9 Yet, few genetic variants have been identified and they explain only a small proportion of the phenotypic variance,10 suggesting that additional variants remain to be discovered. The first meta-analysis of genome-wide association studies (GWAS) on WMH burden identified a new locus on Chr17q25,11 which has been replicated in several studies.12–14 It comprised 9361 individuals of European descent and used genome-wide genotype data imputed to the HapMap2 reference panel.11 In recent years, the 1000 Genomes reference panel has become available for genotype imputation, enabling the study of millions of single-nucleotide polymorphisms (SNPs), including low-frequency variants. Furthermore, additional studies with brain MRI data have obtained genome-wide genotype data, including studies in populations of African, Hispanic, and Asian descent. Here, we conducted a meta-GWAS of WMH burden based on 1000 Genomes imputation data in 21 079 individuals from 4 ethnic groups. To gain pathophysiological insights, we also investigated the joint effect on WMH burden of genetic loci for high blood pressure levels, a strong predictor of WMH burden, and for Alzheimer disease and stroke, which, both, have comorbid loads of WMH.
Subject and Methods
Study participants were from 29 population-based cohorts. All participating studies worked cooperatively to address issues related to phenotype harmonization and covariate selection and to develop analytic plans for within-study GWAS analyses and for meta-analyses of results. Each study received institutional review board approval of its consent procedures, examination and surveillance, DNA collection and use, and data access and distribution. All participants in this study gave written informed consent for study participation, MRI scanning, and use of DNA. Details of cohort recruitment, risk factor assessment, phenotyping, and genotyping are described in the Data Supplement. Briefly, participants were excluded if they lacked information on MRI or genotypes or if they had clinical dementia or stroke. If data on clinical stroke were missing in a given cohort, the presence of MRI infarcts extending into the cortical grey matter was used as an exclusion criterion.
MRI Scans
In each study, MRI scans were performed and interpreted in a standardized fashion, without reference to demographic or clinical information. The field strength of the scanners used ranged from 0.5 to 3.0 Tesla. T1- and T2-weighted scans in the axial plane were obtained for all participants. These were complemented by either scans obtained with fluid attenuation inversion recovery or proton density sequences to allow better separation of WMH and cerebrospinal fluid. A validated automated segmentation method (23 cohorts) or a validated visual grading scale (6 cohorts) was used to quantify WMH burden. Details of the applied WMH quantification method per cohort can be found in the Data Supplement.
Genotyping and Imputation
As described in the Data Supplement, the participating studies used different genotyping platforms and performed extensive quality control analyses. Briefly, participant-specific quality controls filters were applied based on missing call rate, cryptic relatedness, sex mismatch, principal component analysis, and number of Mendelian errors per individual (for studies with family data). SNP-specific quality controls included filters for call rate, minor allele frequency Hardy–Weinberg equilibrium, differential missingness by outcome or genotype, and imputation quality. After quality control analysis, genotype data in each study were used to impute to the 1000 Genomes reference panel (version available in the Data Supplement). Because not all versions of 1000 Genomes that were used included copy number variations, we only analyzed SNPs, which totaled 14 227 402.
Statistical Analyses and Meta-Analysis
Statistical analyses were performed similar to the previous meta-GWAS on WMH burden.8 Analyses were performed separately by ethnic group. Briefly, within each study, we evaluated the relationship between each SNP and WMH burden using a linear regression model, assuming an additive genetic effect.11 WMH burden was expressed as ln(WMH burden+1) to reduce the skewness of its distribution. We adjusted for age, sex, intracranial volume and, if indicated, principal components of ancestry. No adjustment for intracranial volume was performed in studies that used a visual grading scale because these scales take head size into account. Atherosclerosis Risk In Communities Study (ARIS) and Cardiovascular Health Study (CHS) also adjusted for study site, and Framingham Heart Study (FHS) adjusted for familial structure (Data Supplement).
We performed a weighted Z score–based fixed-effect meta-analysis implemented in the METAL software.15 We chose this methodology for several reasons: first, the measures of WMH were not expressed on the same scale in the various studies, thus a random-effect meta-analysis was not possible. Second, the focus of our meta-analyses was to identify new loci for WMH, thus we sought to maximize power of our study. Fixed-effect models have been shown to be more powerful than random-effect models even in the presence of between-study heterogeneity.16 Third, Senn stated that “the choice of fixed effects or random effects meta-analysis should not be made on the basis of perceived heterogeneity but on the basis of purpose.”17 Our purpose was to identify new associations rather than accurately estimating effect size of well-validated variants, which would need to account for possible between-population heterogeneity. For each SNP, the z-statistic, derived from the P value and direction of effect, was weighted by the effective sample size, which is the product of the sample size and the ratio of the empirically observed dosage variance to the expected binomial dosage variance for imputed SNPs. A combined estimate was obtained by summing the weighted z-statistics and dividing by the summed weights. Before meta-analysis, SNPs were filtered out within each cohort if they had a poor imputation quality (r2>0.3), a minor allele frequency <0.005, and an effective sample size <50. The genomic control parameter was calculated and used to remove any residual population stratification within cohort and in the combined meta-analyses. We performed meta-analyses for each ethnicity separately and also combined results in a multiethnic meta-analysis, correcting for genomic inflation.
To gain a better understanding of each genome-wide significant locus (P<5×10−8), we performed a step-wise analysis to examine whether additional variants at the identified loci were independently associated with WMH burden, after adjusting for the effects of the most significant SNP. Each study repeated the primary analyses adjusting for the top-SNP at each of the significant loci (European ancestry sample only), and the results were then meta-analyzed as described above.
To study whether identified SNPs may cause damage of protein function, we used the prediction tools PolyPhen-218 and SIFT.19 To examine whether identified SNPs had an impact on gene regulation, we used a heuristic scoring system implemented in RegulomeDB.20
In secondary analyses, we studied the joint effect of loci for WMH-related traits. We extracted SNPs from the meta-analysis that have been reported to be associated with blood pressure,21 Alzheimer disease,22 and stroke23–25 and meta-analyzed their effects using a weighted Z score method.26 Additional details are provided in the in the Data Supplement.
Results
Demographic and clinical characteristics of the participating cohorts are shown in the Data Supplement (Table I in the Data Supplement). In total, we included 17 936 individuals of European descent, 1943 African descent, 795 individuals of Hispanic descent, and 405 individuals of Asian descent (204 Chinese and 201 Malays). There was no evidence of test statistic inflation in the individual cohort analyses or the ethnic-specific and overall meta-analyses (Table I in the Data Supplement).
Table 1 shows the genome-wide significant loci (P<5×10−8) in the meta-analyses of the overall sample and of each ethnic group. Manhattan-plots are displayed in the Figure II in the Data Supplement. In the European descent samples, we identified 3 regions with genome-wide significant SNPs: on chr17q25 (top-SNP: rs7214628, P=2.7×10−19); on chr10q24 (top-SNP: rs72848980, P=1.6×10−9); and on chr2p21 (top-SNP: rs11679640; P=4.4×10−8; Table 1). In the samples of African, Hispanic, and Asian descent, no variant reached genome-wide significance likely because of limited power. In the multiethnic analyses, we identified 2 additional regions, on chr1q22 (top-SNP: rs2984613, P=2.0×10−8) and chr2p16 (top-SNP: rs78857879, P=1.5×10−8; Table 1). Directions of effect for these SNPs in each of the cohorts are shown in Table II in the Data Supplement and information on suggestive loci (P<1.0×10−5) in Table III in the Data Supplement.
| Locus | SNP | Chr:Position (hg19) | Nearest Gene | P Value | RA | RAFEUR | RAFAFR | RAFHIS | RAFASN | ||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Total (n=21 079) | EUR (n=17 936) | AFR (n=1943) | HIS (n=795) | ASN (n=405) | |||||||||
| 17q25.1 | rs7214628 | 17:73882148 | TRIM65 | +5.1E−19 | +2.7E−19 | +0.12 | +0.11 | −0.32 | G | 0.19 | 0.40 | 0.28 | 0.13 |
| 10q24.33 | rs72848980 | 10:105319409 | NEURL (intron) | +2.6E−09 | +6.3E−09 | +0.09 | +0.41 | −0.31 | G | 0.80 | 0.96 | 0.93 | 0.97 |
| rs7894407 | 10:105176179 | PDCD11 (intron) | +2.6E−08 | +1.6E−09 | −0.36 | +4.4E−02 | −0.46 | T | 0.65 | 0.80 | 0.69 | 0.61 | |
| rs12357919 | 10:105438112 | SH3PXD2A (intron) | +1.5E−08 | +1.9E−08 | +0.36 | +0.31 | +1.00 | T | 0.81 | 0.95 | 0.92 | 0.96 | |
| rs7909791 | 10:105613178 | SH3PXD2A (intron) | +2.9E−09 | +1.7E−08 | +0.33 | +0.29 | +0.09 | A | 0.34 | 0.35 | 0.32 | 0.16 | |
| 2p16.1 | rs78857879 | 2:56135099 | EFEMP1 (intron) | +1.5E−08 | +2.9E−07 | +2.2E−02 | +0.18 | −0.67 | A | 0.10 | 0.02 | 0.05 | 0.04 |
| 1q22 | rs2984613 | 1:156197380 | PMF1−BGLAP (intron) | +2.0E−08 | +1.4E−05 | +6.5E−05 | +1.5E−02 | −0.80 | C | 0.65 | 0.72 | 0.69 | 0.68 |
| 2p21 | rs11679640 | 2:43141485 | HAAO | +2.1E−06 | +4.4E−08 | −0.37 | −0.79 | −0 0.74 | C | 0.80 | 0.84 | 0.85 | 0.98 |
Loci with corresponding P value are given for the association with white matter hyperintensities burden. The sign indicates the direction of the effect of the risk allele. Multiple single-nucleotide polymorphisms at the same locus indicate independent associations. AFR indicates African descent; ASN, Asian descent; Chr, chromosome; EUR, European descent; HIS, Hispanic descent; RA, risk allele; RAF, risk allele frequency; and SNP, single-nucleotide polymorphism.
The chr17q25 locus contained 147 genome-wide significant SNPs in the meta-analysis of the European descent samples (Figure). The top-SNP from chr17q25 (rs7214628) lies close (2.9 kb) to TRIM65 and is in high linkage disequilibrium (LD) with rs3744028, reported in our previous GWAS (r2=0.99).11 Analyses of association adjusting for the effect of rs7214628 were performed to determine whether secondary signals were present across the region. None of the 147 SNPs remained genome-wide significant after accounting for the effect of rs7214628 (Figure III in the Data Supplement). Ten were nominally significant (P<0.05), including 4 intronic variants and 1 missense variant in ACOX1, 3 intronic variants and 1 variant near FBF1, and 1 intronic variant in MRPL38, but would not survive a multiple testing significance threshold. Functional annotation of the genome-wide significant SNPs in the chr17q25 region identified 7 missense variants, 4 eQTLs influencing gene expression of TRIM47, 10 SNPs with a likely functional impact on gene regulation (RegulomeDB score ≤3), and 6 synonymous or intronic SNPs with high levels of evolutionary conservation. Association of these SNPs with WMH burden in each ethnic group is shown in Table 2. The direction of association was generally consistent in Europeans, Hispanics, and blacks but was opposite in Asians. This pattern was also observed across the larger set of 147 genome-wide significant SNPs, suggesting possible heterogeneity of effects in Asian populations. Among the putatively functional SNPs, those with the strongest LD with rs7214628 in Europeans were the TRIM47 eQTL rs3744017 and the putatively regulatory SNP rs3744020, located in an intron of TRIM47. Interestingly, these 2 SNPs had also the lowest P value in blacks (rs3744017, P=0.08; rs3744020, P=0.09). We observed a nominally significant association (P<0.05) for the regulatory SNP rs1551619 in Hispanics. This SNP was in moderately high LD with rs7214628 in both Europeans and Hispanics (r2=0.74).
| Chr | Position (hg19) | SNP | Putative Function and Location | RA | LD With rs7214628 (EUR) | P Value | RAFEUR | RAFAFR | RAFHIS | RAFASN | |||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| EUR | AFR | HIS | ASN | ||||||||||
| 17 | 73827205 | rs1135688 | Missense (K867E, UNC13D) | C | 0.32 | +1.7E−08 | +0.60 | +0.22 | −0.12 | 0.31 | 0.81 | 0.50 | 0.37 |
| 17 | 73839366 | rs3744009 | Regulatory (RDB=3a) (intronic, UNC13D) | T | 0.29 | +1.8E−10 | −0.89 | +0.22 | −0.23 | 0.26 | 0.44 | 0.31 | 0.15 |
| 17 | 73841285 | rs2410859 | Regulatory (RDB=2b) (5′-UTR UNC13D) | C | 0.44 | +1.9E−11 | +0.48 | +0.16 | −0.20 | 0.32 | 0.82 | 0.51 | 0.38 |
| 17 | 73841702 | rs9900122 | Regulatory (RDB=2b) (3′-UTR, WBP2) | C | 0.44 | +1.5E−11 | −0.98 | +0.19 | −0.21 | 0.32 | 0.76 | 0.48 | 0.38 |
| 17 | 73844748 | rs2290771 | Regulatory (RDB=2b) (intronic, WBP2) | G | 0.46 | +8.1E−11 | +0.39 | +0.17 | −0.17 | 0.32 | 0.82 | 0.50 | 0.16 |
| 17 | 73847613 | rs936393 | Regulatory (TRIM47 eQTL; RDB=1f) (intronic, WBP2) | G | 0.86 | +2.7E−18 | +0.74 | +0.96 | −0.46 | 0.19 | 0.26 | 0.21 | 0.14 |
| 17 | 73851113 | rs55868394 | Regulatory (RDB=2b) (intronic, WBP2) | A | 0.63 | +1.5E−13 | −0.87 | +0.22 | −0.34 | 0.13 | 0.03 | 0.08 | 0.09 |
| 17 | 73852008 | rs936394 | Regulatory (RDB=2b) (5′-UTR, WBP2) | A | 0.89 | +6.0E−18 | +0.62 | +0.65 | −0.41 | 0.19 | 0.26 | 0.21 | 0.14 |
| 17 | 73865657 | rs9894383 | Regulatory (TRIM47 eQTL; RDB=2b) (4.6kb 3′ of TRIM47) | G | 0.91 | +7.6E−18 | +0.18 | +0.34 | −0.30 | 0.19 | 0.59 | 0.35 | 0.14 |
| 17 | 73871467 | rs3744017 | Regulatory (TRIM47 eQTL; RDB=1f) (intronic, TRIM47) | A | 0.93 | +6.7E−18 | +0.09 | +0.16 | −0.27 | 0.19 | 0.29 | 0.23 | 0.13 |
| 17 | 73871773 | rs3744020 | Regulatory (RDB=2a) (intronic, TRIM47) | A | 0.93 | +4.1E−18 | +0.10 | +0.16 | −0.29 | 0.19 | 0.29 | 0.22 | 0.13 |
| 17 | 73873394 | rs9908862 | Regulatory (RDB=2b), Conserved (intronic, TRIM47) | G | 0.73 | +7.9E−16 | +0.21 | +0.13 | −0.31 | 0.14 | 0.50 | 0.28 | 0.12 |
| 17 | 73874071 | rs4600514 | Missense (R187W, TRIM47) | A | 0.74 | +6.3E−16 | +0.20 | +0.11 | −0.30 | 0.14 | 0.32 | 0.21 | 0.12 |
| 17 | 73874138 | rs4072479 | Conserved, synonymous (A164A, TRIM47), regulatory (RDB=2b) | C | 0.72 | +5.6E−15 | +0.43 | +0.21 | −0.30 | 0.14 | 0.44 | 0.26 | 0.12 |
| 17 | 73885805 | rs1551619 | Regulatory (RDB=2b) (3′-UTR, TRIM65) | T | 0.74 | +2.2E−14 | +0.24 | +4.4E−02 | −0.34 | 0.23 | 0.33 | 0.27 | 0.13 |
| 17 | 73886888 | rs3760128 | Missense (L509P, TRIM65) | G | 0.46 | +6.9E−12 | +0.65 | +0.12 | −0.06 | 0.33 | 0.82 | 0.51 | 0.20 |
| 17 | 73888427 | rs7222757 | Missense (V222G, TRIM65) | C | 0.56 | +1.3E−14 | −0.95 | +0.34 | −0.07 | 0.28 | 0.71 | 0.45 | 0.20 |
| 17 | 73922941 | rs2305913 | Missense (R151G, FBF1) | C | 0.41 | +4.7E−11 | +0.92 | +0.13 | −2.1E−02 | 0.34 | 0.76 | 0.50 | 0.19 |
| 17 | 73926121 | rs1135889 | Missense (G65V, FBF1) | A | 0.29 | +9.5E−11 | −0.79 | +0.16 | −4.9E−03 | 0.23 | 0.19 | 0.18 | 0.13 |
| 17 | 73949540 | rs1135640 | Missense (I312M, ACOX1) | G | 0.41 | +3.3E−10 | +0.88 | −0.17 | −7.0E−03 | 0.35 | 0.67 | 0.49 | 0.19 |
AFR indicates African descent; ASN, Asian descent; Chr, chromosome; EUR, European descent; HIS, Hispanic descent; LD, linkage disequilibrium; RA, risk allele; RAF, risk allele frequency; and SNP, single-nucleotide polymorphism.
The chr10q24 locus contained 12 genome-wide significant SNPs in the meta-analysis of the European descent samples and 7 SNPs in the overall meta-analysis. These mapped to a 555-kb region from base pair position 105080575 to 105635537 (Figure). The top-SNP from chr10q24 (rs7894407) lies within an intron of PDCD11. Analyses accounting for the effects of rs7894407 revealed that the SNPs in SH3PXD2A(rs12357919, P=2.7×10−3; rs4630220, P=2.7×10−3; rs7909791, P=3.9×10−7), and NEURL (rs72848980, P=1.9×10−3) were independently associated with WMH burden (Figure III in the Data Supplement). Ivn the multiethnic meta-analysis, rs72848980 (NEURL) had the lowest P value within the chr10q24 locus. These 4 SNPs were in low LD with rs7894407 (r2 between 0 and 0.33), and in moderate to low LD with each other (Table IV in the Data Supplement). Functional annotation of the genome-wide significant SNPs identified a missense variant in TAF5 (rs10883859, Ser/Ala). The exonic variant in CALHM1 (rs4918016) was synonymous. Annotation of the genome-wide significant SNPs for predicted function on gene regulation identified 2 SNPs (RegulomeDB score ≤3): rs12357919 located in an intron of SH3PXD2A; and rs729211 located in the 3′untranslated region of CALMH1, and identified as an eQTL influencing gene expression of USMG5. rs11191772 was a highly conserved intronic SNP in SH3PXD2A. Association of these SNPs with WMH burden in each ethnic group is shown in Table 3. rs729211, rs4918016, and rs11191772, identified in the multiethnic meta-analyses, show trends toward nominal significance in blacks and Hispanics.
| Chr | Position (hg19) | SNP | Putative Function and Location | RA | LD with rs7894407(EUR) | P Value | RAFEUR | RAFAFR | RAFHIS | RAFASN | |||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| EUR | AFR | HIS | ASN | ||||||||||
| 10 | 105128134 | rs10883859 | Missense (S130A, TAF5) | T | 0.64 | +1.2E−08 | −0.13 | +0.09 | −0.27 | 0.67 | 0.75 | 0.67 | 0.57 |
| 10 | 105214932 | rs729211 | Regulatory (USMG5 eQTL, RDB=1f) (3′-UTR, CALHM1) | T | 0.65 | +1.7E−07 | +0.21 | +0.08 | −0.69 | 0.67 | 0.63 | 0.62 | 0.61 |
| 10 | 105218254 | rs4918016 | Conserved, synonymous (P85P, CALHM1) | C | 0.66 | +8.1E−08 | +0.38 | +0.06 | −0.71 | 0.67 | 0.80 | 0.70 | 0.61 |
| 10 | 105438112 | rs12357919 | Regulatory (RDB=2b) (intronic, SH3PXD2A) | T | 0.11 | +1.9E−08 | +0.36 | +0.31 | 0.99 | 0.81 | 0.95 | 0.92 | 0.96 |
| 10 | 105459834 | rs11191772 | Conserved (intronic, SH3PXD2A) | T | 0.04 | +1.0E−06 | +0.07 | +0.22 | 0.17 | 0.60 | 0.66 | 0.61 | 0.43 |
AFR indicates African descent; ASN, Asian descent; Chr, chromosome; EUR, European descent; HIS, Hispanic descent; LD, linkage disequilibrium; RA, risk allele; RAF, risk allele frequency; and SNP, single-nucleotide polymorphism.
The chr2p16 locus contained 1 genome-wide significant SNP (rs78857879) in the multiethnic meta-analysis. This SNP maps to an intron of EFEMP1 (Figure) and was predicted to have a putatively functional impact on gene regulation (RegulomeDB score=3a). This SNP was nominally significant in the groups of European and African descent (P=2.9×10−7 and 2.2×10−2, respectively; Table 1).
The chr1q22 locus contained 1 genome-wide significant SNP (rs2984613) in the multiethnic meta-analysis. Although the association of rs2984613 with WMH burden was only nominally significant in individuals of European, African, and Hispanic descent (P=2.4×10−5, 1.4×10−5, and 1.5×10−2, respectively), it reached genome-wide significance in the multiethnic meta-analysis combining all ethnic groups. This SNP is located in an intron of PMF1/PMF1-BGLAP (Figure).
The chr2p21 locus contained 1 genome-wide significant SNP (rs11679640) near HAAO (122 kb) and THADA (316 kb) in individuals of European descent only (Table 1; Figure). The association of rs11679640 with WMH burden was no longer genome-wide significant in the overall meta-analysis and showed opposite direction of effect in the other ethnic groups (Table 1), suggesting possible heterogeneity by ethnicity. Although a genome-wide significant SNP for multiple sclerosis (rs6718520)22 is also nearby (184 kb), this SNP was not in LD with rs11679640 and was not significantly associated with WMH burden in our study.
To gain additional pathophysiological insights, we investigated whether genetic loci identified for WMH-related traits are related to WMH burden.
We showed that genetic loci for blood pressure were significantly related to a higher WMH burden (P for systolic blood pressure <0.0001; P for diastolic blood pressure=0.007). We did not find a significant association between WMH and loci for Alzheimer disease (P=0.12) or stroke (P=0.46, in opposite direction).
Discussion
We performed a meta-analysis of genome-wide association studies in samples of European, African, Hispanic, and Asian descent. We identified 4 novel loci on chr10q24, chr2p21, chr1q22, and chr2p16 and confirmed a previously identified locus on chr17q25. Three of the 4 novel loci (chr10q24, chr1q22, and 2p16.1) were associated with WMH burden in >1 ethnic group. In addition, we showed that genetic loci influencing systolic blood pressure and diastolic blood pressure are associated with WMH burden.
Strengths of our study include the large and diverse sample, the population-based setting, and the comprehensive set of common genetic variants examined. However, several limitations must be acknowledged. First, the use of different WMH quantification methods has constrained our analytic methodology to the meta-analysis of P values, which is less powerful and prevented us to determine an estimate of effect size for the associated SNPs. Second, we had a limited sample size of blacks, Hispanics, and Asians. This limited sample size has reduced our ability to identify new variants in these populations and to replicate findings from the larger European sample. However, the inclusion of these groups in a multiethnic meta-analysis permitted the identification of 2 additional loci, albeit likely because of increased sample size. Finally, we used different versions of the 1000 Genomes reference panel for genotype imputation and did not study copy number variations.
We confirmed the association of the locus on chr17q25 in individuals of European descent. The genome-wide significant SNPs in this locus include all previously reported SNPs.11 However, the most significantly associated SNP in this analysis (rs7214628) was not previously identified. It lies 2.9 kb away from TRIM65 and in strong LD with rs3744028 from the original report. Analyses accounting for the effects of rs7214628 showed a strong attenuation of effects for all genome-wide significant SNPs, suggesting little evidence for multiple independent association signals in this region. Several genome-wide significant SNPs in the chr17q25 locus are missense variants in the UNC13D, TRIM47, TRIM65, FBF1, and ACOX1 genes. In addition, several SNPs were predicted to have a functional impact on gene regulation, including 2 eQTLs of the TRIM47 gene. The direction of associations of SNPs at this locus was generally consistent among populations of European, Hispanic, and African descent but not Asians. Power to detect genetic effects in ethnic groups other than Europeans was limited. However, SNPs potentially affecting regulation of TRIM47 and TRIM65 showed the strongest associations in this region in Hispanics and blacks, whereas SNPs encoding missense mutations in FBF1, ACOX1, and TRIM65 were nominally associated in Asians.
The novel locus on chr10q24 contained genome-wide significant SNPs in introns of PDCD11, NEURL, and SH3PXD2A, TAF5, and CALHM1, of which PDCD11, NEURL, and SH3PXD2A were shown to be independent from each other. PDCD11 encodes the programmed cell death 11 and is involved in T-cell–induced apoptosis.27 It is expressed in glial cells,28 which make up a large proportion of the white matter. NEURL encodes the neuralized homolog (Drosophila), an E3 ubiquitin ligase, which has been implicated in malignant brain tumors.29,30 NEURL reportedly causes apoptosis and downregulates NOTCH target genes in medulloblastoma.29 NEURL maps to a region that is frequently deleted in astrocytoma.30 The SNP in NEURL was also nominally associated in Hispanics in the same direction (P=0.04). The SNP in PDCD11 only showed significant associations in individuals of European descent. SH3PXD2A, which codes for SH3 and PX domain-containing protein 2A, has also been implicated in gliomas.31 In addition, it has been reported to be involved in amyloid-β neurotoxicity32 and implicated in Alzheimer disease.33 TAF5 contained a missense variant, although without predicted damage on protein function. TAF5 codes for transcription initiation factor TFIID subunit 5, which is involved in the initiation of transcription by RNA polymerase II. CALHM1 codes for calcium homeostasis modulator 1, which influences calcium homeostasis and increases cerebral amyloid-β (Aβ) peptide production. Interestingly, a missense variant of CALHM1 (rs2986017) has been associated with late-onset Alzheimer disease and Creutzfeldt–Jakob disease,34,35 but this SNP was only nominally associated with WMH burden (in the same direction) in our study (P=2.5×10−2 in Europeans and P=3.5×10−2 in the total group). The genome-wide significant SNP rs729211, located in the 3′untranslated region of CALHM1, had a predicted functional impact on USMG5 gene expression. USMG5 encodes a small subunit of the mitochondrial ATP synthase, which is phylogenetically conserved and is thought to have a role in cellular energy metabolism.
The novel locus on chr2p21 that reached genome-wide significance in Europeans but not the overall group was located near HAAO. HAAO codes for 3-hydroxyanthranilate 3,4-dioxygenase, which catalyzes the synthesis of quinolinic acid (QUIN) from 3-hydroxyanthranilic acid. QUIN is an excitotoxin whose toxicity is mediated by its ability to activate glutamate N-methyl-d-aspartate receptors. QUIN has been implicated in neuroinflammatory diseases and may participate in the pathogenesis of Parkinson disease, Alzheimer disease, and Huntington disease.36–39 Within the brain, QUIN is produced and released by infiltrating macrophages and activated microglia, which are prominent during neuroinflammation.36
The novel genome-wide significant SNP on chr1q22 is located in an intron of the read-through PMF1-BGLAP sequence, which encodes a variant isoform of the polyamine-modulated factor 1 (PMF1). PMF is a member of a kinetochore-associated multiprotein complex, involved in chromosomal alignment and segregation during mitosis.40 Moreover, it is a cofactor for the regulation of expression of the rate-limiting enzyme in the catabolic pathway of polyamine metabolism.41 Polyamines are important regulators of cell growth and cell death, and epigenetic modification of PMF1 has been implicated in cancer.42 The SNP identified in our analysis (rs2984613) was also identified in a GWAS of nonlobar intracerebral hemorrhage.43 In one study involving two of the cohorts included in this work, WML burden was associated with an increased risk of intracerebral hemorrhage.44 Both intracerebral hemorrhage and WMH share common risk factors, such as hypertension, and may share common underlying pathological mechanisms involving microangiopathy. Our finding supports such a hypothesis.
The locus on chr2p16 contained its top-hit in the intron of EFEMP1, which codes for EGF containing fibulin-like extracellular matrix protein 1. EFEMP1 is uniquely upregulated in malignant gliomas (different grades) and promotes tumor cell motility and invasion.45 It encodes a novel soluble activator of Notch signaling that antagonizes DLL3, an autocrine inhibitor or Notch, and promotes tumor cell survival and invasion in a Notch-dependent manner.46 EFEMP1 was originally cloned from senescent fibroblasts derived from a patient with Werner syndrome a disease of premature aging with diffuse structural abnormalities in the brain white matter.47,48
Intriguingly, 3 of the 5 regions significantly associated with WMH burden and 1 suggestive locus contained variants in genes implicated in malignant brain tumors of the white matter that involve glial cells (TRIM47, NEURL, SH3PXD2A, EFEMP1, and NBEAL1). Although these tumors can appear as WMH on MRI,49 given the population-based setting of the participating studies, the exclusion criteria used in WMH quantification, as well as the very low incidence of gliomas (<5 per 100 000 persons per year),50 the presence of unrecognized glioma cases is very unlikely to explain these associations. However, our findings suggest that WMH in aging and glioma may share common pathophysiological mechanisms, perhaps involving glial cell activation, apoptosis, or both. The role of microglia in white matter injury has been demonstrated in several animal models. For example, activated microglia have a critical role in the formation of the excitotoxic white matter lesion in a mouse model of periventricular leukomalacia.51 In the rat 2-vessel occlusion model, microglial activation was shown to be an early marker of subsequent white matter injury52 and may contribute to induce apoptosis of oligodendrocytes in the white matter of these animals.53
In addition to the identification of novel WMH loci, we showed that loci for blood pressure were also associated with WMH burden. This further establishes the role of blood pressure in WMH. We were not able to identify effects of loci for Alzheimer disease and stroke on WMH. Pathological processes other than those affecting WMH may be stronger determinants of Alzheimer disease and therefore variants identified to date may capture mostly other mechanisms leading to Alzheimer disease. Similarly, stroke is heterogeneous and the stroke risk variants tested here are not those reflecting small-vessel disease stroke subtypes. Shared mechanisms between WMH and stroke are expected mostly for these subtypes.
In summary, in a meta-analysis of genome-wide association studies in individuals of European, African, Hispanic, and Asian descent, we identified 4 novel loci and confirmed a previous locus. Furthermore, we also report significant associations of blood pressure loci with WMH burden. Although additional fine mapping at each of the identified loci will be needed to uncover the causal genes and variants, a unifying hypothesis emerging from this work suggests a central role of neuroinflammation, possibly involving pathological mechanisms related to microglial activation and common to gliomas. Additional work will be needed to establish the importance of these findings in understanding the cause and pathophysiology of WMH and bring us closer to reducing WMH burden and its associated clinical manifestations.
Acknowledgments
We thank the staff and participants of each of the studies for their important contributions. The ASPS authors thank Birgit Reinhart for her long-term administrative commitment and Ing Johann Semmler for the technical assistance at creating the DNA-bank. The ERF study is grateful to P. Veraart for her help in genealogy, J. Vergeer for the supervision of the laboratory work, and P. Snijders for his help in data collection. The Genetic Epidemiology Network of Arteriopathy (GENOA) authors acknowledge the contributions of the Mayo Clinic and the University of Texas Health Sciences Center (Eric Boerwinkle, Megan L. Grove) for genotyping of the GENOA participants. The Lothian Birth Cohort 1936 (LBC1936) study authors thank the nurses and staff at the Wellcome Trust Clinical Research Facility (http://www.wtcrf.ed.ac.uk), where subjects were tested and the genotyping was performed. The Study of Health in Pomerania (SHIP) authors are grateful to Mario Stanke for the opportunity to use his server cluster for single-nucleotide polymorphism imputation as well as to Holger Prokisch and Thomas Meitinger (HelmholtzZentrum München) for genotyping of the SHIP-TREND cohort. The authors of the Rotterdam Study thank Pascal Arp, Mila Jhamai, Marijn Verkerk, Lizbeth Herrera, and Marjolein Peters for their help in creating the genome-wide association studies database, and Karol Estrada and Maksim V. Struchalin for their support in creation and analysis of imputed data. Three-City Dijon (3C-Dijon) study authors thank A. Boland (Centre National de Génotypage) for her technical help in preparing the DNA samples for analyses.
CLINICAL PERSPECTIVE
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Appendix
From the Departments of Epidemiology (B.F.J.V., H.H.A., A.H., C.A.I.-V., B.A.O., M.S., A.G.U., M.W.V., C.M.v.D., M.A.I.), Radiology (B.F.J.V., H.H.A., A.v.d.L., W.J.N., M.W.V., M.A.I.), Neurology (C.A.I.-V., M.S., J.C.v.S., M.A.I.), Medical Informatics (W.J.N.), Internal Medicine (A.G.U.), and Clinical Chemistry (A.G.U.), Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands; Inserm U897, Epidemiology and Biostatistics, Bordeaux, France (S.D., G.C., C.D., C.T., C.W.), CNRS-CEA UMR5296 (F.C., B.M.), Department of Neurology, Bordeaux University Hospitals, Bordeaux, France (S.D.); University of Bordeaux, France (S.D., G.C.); Department of Neurology (S.D., A.S.B., V.C., S.S.S.) and Department of Biostatistics, School of Public Health (A.S.B., J.J.W., Q.Y.), Boston University School of Medicine, MA; Cardiovascular Health Research Unit, Departments of Medicine (J.C.B., S.R.H., B.M.P.), Epidemiology (S.R.H., B.M.P., W.T.L.), Health Services (B.M.P.), Biostatistics (K.M.R.), and Neurology (W.T.L.), University of Washington, Seattle; Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor (J.A.S., W.Z., S.L.R.K.); Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore (M.K.I., J.L., T.Y.W.); Department of Ophthalmology, National University Health System (M.K.I., J.L., T.Y.W.) and Memory Aging and Cognition Centre (M.K.I., C.P.L.H.C., S.H.), National University of Singapore, Singapore, Singapore; John P. Hussman Institute for Human Genomics, Miami, FL (A.H.B., S.H.B.); Department of Human Genetics, Dr. John T. Macdonald Foundation (S.H.B.), Neuroscience Program (S.H.B., C.B.W.), Department of Epidemiology and Public Health Sciences (C.B.W., R.L.S.), Department of Neurology (C.B.W., R.L.S.), Evelyn F. McKnight Brain Institute, Miller School of Medicine, University of Miami, FL (C.B.W., R.L.S.); Rush University Medical Center, Chicago, IL (K.B.R., N.T.A., D.A.E.); Centre for Cognitive Ageing and Cognitive Epidemiology, Psychology (L.M.L., I.J.D., D.C.M.L., M.L., J.M.W.) and Brain Research Imaging Centre, Scottish Imaging Network: A Platform for Scientific Excellence Collaboration, Centre for Clinical Brain Sciences (J.M.W.), University of Edinburgh, Edinburgh, United Kingdom; Department of Neurology (S.B.), G.H.Sergievsky Center, Taub Institute for Research on Alzheimer’s Disease and Aging Brain (A.M.B., R.M.), Columbia University Medical Center, New York, NY; Departments of Radiology (M.A.v.B., J.v.d.G.), Molecular Epidemiology (M.B., J.D., P.E.S.), Gerontology and Geriatrics (A.J.M.d.C.), Cardiology (S.T., J.W.J.), Medical Statistics and Bioinformatics (H.-W.U.), Leiden University Medical Center, Leiden, The Netherlands; The Icelandic Heart Association, Kopavogur, Iceland (A.V.S., V.G., S.S.); Department of Diagnostic Radiology and Neuroradiology (K.H.), Institutes for Community Medicine (M.H.), Clinical Chemistry and Laboratory Medicine (M.N.), Interfaculty Institute for Genetics and Functional Genomics (A.T.), and Department of Psychiatry and Psychotherapy (H.J.G.), University Medicine Greifswald, Greifswald, Germany; Division of Biomedical Statistics and Informatics (M.d.A., E.J.A.), Department of Neurology (D.S.K.), Division of Nephrology and Hypertension (S.T.T.), Mayo Clinic, Rochester, MN; National Heart, Lung, & Blood Institute’s Framingham Heart Study, Framingham, MA (A.S.B., V.C., J.J.W., Q.Y., S.S.S.); Human Genetics Center (E.B., M.F.) and Institute of Molecular Medicine (M.F.), University of Texas Health Science Center at Houston; Department of Radiology, Perelman School of Medicine, University of Pennsylvania Health System, Philadelphia (R.N.B., M.H.); Department of Neurology, Harvard University, Cambridge, MA (P.L.D.J.); Brigham and Women’s Hospital, Boston, MA (P.L.D.J.); Department of Neurology, College of Physicians and Surgeons and Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY (M.S.V.E.); Institute of Molecular Biology and Biochemistry (P.F., A.M.T.), Clinical Division of Neurogeriatrics, Department of Neurology (E.H., R.S.), Institute for Medical Informatics, Statistics and Documentation (E.H.), Medical University Graz, Graz, Austria; Department of Neurology, School of Medicine, Johns Hopkins University, Baltimore, MD (R.F.G., H.S.); Faculty of Medicine, University of Iceland, Reykjavik, Iceland (V.G.); Department of Epidemiology, School of Public Health, University of North Carolina at Chapel Hill (G.H.); Division of Preventive Medicine, University of Alabama, Birmingham (C.E.L.); Alzheimer’s Disease Center, Imaging of Dementia and Aging (IdeA) Laboratory, Department of Neurology, Center for Neuroscience, University of California, Davis (O.O.M., C.C.D.); Laboratory of Neurogenetics (M.N.), Laboratory of Epidemiology, Demography, and Biometry (L.J.L.), National Institute of Aging, The National Institutes of Health, Bethesda, MD; Faculty of Applied Sciences, Delft University of Technology, Delft, The Netherlands (W.J.N.); Group Health Research Institute, Group Health Cooperative, Seattle, WA (B.M.P.); Division of Genomic Outcomes, Department of Pediatrics, Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute (J.I.R.), Harbor-UCLA Medical Center, Torrance; Department of Neurology, University Medicine of Greifswald, Greifswald, Germany (J.I.R., B.v.S.); Division of Epidemiology and Community Health, University of Minnesota, Minneapolis (P.J.S.); Kaiser Permanente Division of Research, Oakland, CA (S.S.S.); Institute of Cardiovascular and Medical Sciences, Faculty of Medicine, University of Glasgow, Glasgow, United Kingdom (D.J.M.S.); Research Centre for Stroke and Dementia, St. George’s, University of London, London, United Kingdom (M.T.); Interuniversity Cardiology Institute of the Netherlands, Utrecht, The Netherlands (S.T., J.W.J.); Division of Geriatrics/Gerontology (B.G.W.), Department of Medicine, University of Mississippi Medical Center, Jackson (T.H.M.); German Center for Neurodegenerative Diseases (DZNE), Rostock/Greifswald, Germany (K.W.); Biospective Inc, Montreal, Quebec, Canada (A.Z.); and Inserm, U744, Université Lille 2, Institut Pasteur de Lille, Centre Hospitalier Régional Universitaire de Lille, Lille, France (P.A.).
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© 2015 American Heart Association, Inc.
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History
Received: 27 August 2014
Accepted: 23 January 2015
Published online: 7 February 2015
Published in print: April 2015
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Sources of Funding
Age, Gene/Environment Susceptibility-Reykjavik Study (AGES-Reykjavik) study has been funded by National Institutes of Health (NIH) contract N01-AG-1-2100, the National Institute on Aging (NIA) Intramural Research Program, Hjartavernd (the Icelandic Heart Association), and the Althingi (the Icelandic Parliament). The study is approved by the Icelandic National Bioethics Committee, VSN: 00 to 063. Atherosclerosis Risk In Communities Study (ARIC) was performed as a collaborative study supported by National Heart, Lung, and Blood Institute (NHLBI) contracts (HHSN268201100005C, HSN268201100006C, HSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN26820 1100011C, and HHSN268201100012C), R01HL70825, R01HL087641, R01HL59367, and R01HL086694; National Human Genome Research Institute contract U01HG004402; and NIH contract HHSN268200625226C. Infrastructure was partly supported by grant No UL1RR025005, a component of the NIH and NIH Roadmap for Medical Research. This project was also supported by NIH R01 grants HL084099 and NS087541 to MF. Austrian Stroke Prevention Study (ASPS): the research reported in this article was funded by the Austrian Science Fond (FWF) grant number P20545-P05 and P13180. The Medical University of Graz supports the databank of the ASPS. Cardiovascular Health Study (CHS) was supported by NHLBI contracts HHSN268201200036C, HHSN268200800007C, N01HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086, HHSN268200960009C, N01HC15103; and NHLBI grants HL080295, HL087652, HL105756, HL103612, and HL120393 with additional contribution from the National Institute of Neurological Disorders and Stroke (NINDS). Additional support was provided through AG023629 and AG15928 from the NIA. A full list of principal CHS investigators and institutions can be found at CHS-NHLBI.org/. The provision of genotyping data was supported, in part, by the National Center for Advancing Translational Sciences, CTSI grant UL1TR000124, and the National Institute of Diabetes and Digestive and Kidney Disease Diabetes Research Center grant DK063491 to the Southern California Diabetes Endocrinology Research Center. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Chicago Health and Aging Project (CHAP) study was funded by NIH grant R01-AG-09966 and R01-AG-11101 for the parent CHAP study. The genetic analysis was supported by NIH grant R01-AG-030146. This study was approved by Rush University Medical Center Internal Review Board. Coronary Artery Risk Development in Young Adults Study (CARDIA) is conducted and supported by the NHLBI in collaboration with the University of Alabama at Birmingham (HHSN268201300025C and HHSN268201300026C), Northwestern University (HHSN268201300027C), University of Minnesota (HHSN268201300028C), Kaiser Foundation Research Institute (HHSN268201300029C), and Johns Hopkins University School of Medicine (HHSN268200900041C). CARDIA is also partially supported by the Intramural Research Program of the National Institute on Aging (NIA) and an intra-agency agreement between NIA and NHLBI (AG0005). This article has been reviewed by CARDIA for scientific content. Genotyping of the CARDIA participants and statistical data analysis was partially supported by National Institutes of Health R01 grants HL084099 and NS087541 to MF. Epidemiology of Dementia in Singapore (EDIS), The Singapore Malay Eye Study (SiMES), and the Singapore Chinese Eye. Study (SCES) are funded by National Medical Research Council (grants 0796/2003, IRG07nov013, IRG09nov014, STaR/0003/2008, and CG/SERI/2010) and Biomedical Research Council (grants 09/1/35/19/616), Singapore. The Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore provided services for genotyping. The Epidemiology of Dementia in Singapore study is supported by the National Medical Research Council, Singapore (NMRC/CG/NUHS/2010 [grant no: R-184-006-184-511]). Dr Ikram received additional funding from the Singapore Ministry of Health’s National Medical Research Council (NMRC/CSA/038/2013). Erasmus Rucphen Family (ERF) study as a part of the European Special Populations Research Network (EUROSPAN) was supported by European Commission FP6 STRP grant number 018947 (LSHG-CT-2006-01947) and also received funding from the European Community’s Seventh Framework Programme (FP7/2007–2013)/grant agreement HEALTH-F4-2007–201413 by the European Commission under the programme Quality of Life and Management of the Living Resources of 5th Framework Programme (no. QLG2-CT-2002-01254). The ERF study was further supported by ENGAGE consortium and CMSB. High-throughput analysis of the ERF data was supported by joint grant from Netherlands Organisation for Scientific Research and the Russian Foundation for Basic Research (NWO-RFBR 047.017.043). Framingham Heart Study (FHS) was supported by the NHLBI’s Framingham Heart Study (contract no. N01-HC-25195) and its contract with Affymetrix, Inc, for genotyping services (contract no. N02-HL-6-4278). A portion of this research used the Linux Cluster for Genetic Analysis (LinGA-II) funded by the Robert Dawson Evans Endowment of the Department of Medicine at Boston University School of Medicine and Boston Medical Center. This study was also supported by grants from the National Institute of Neurological Disorders and Stroke (R01 NS17950), the NHLBI (R01 HL093029) and the National Institute of Aging (P30 AG10129, R01s AG033193, AG08122, and AG16495). Genetic Epidemiology Network of Arteriopathy (GENOA) support was provided by the NHLBI (HL054464, HL054457, HL054481, HL071917, and HL87660) and the National Institute of Neurological Disorders and Stroke (NS041558) of the NIH. Leiden Longevity Study (LLS) has received funding from the European Union’s Seventh Framework Programme (FP7/2007–2011) under grant agreement no 259679. This study was supported by a grant from the Innovation-Oriented Research Program on Genomics (SenterNovem IGE05007), the Centre for Medical Systems Biology, and the Netherlands Consortium for Healthy Ageing (grant 050-060-810), all in the framework of the Netherlands Genomics Initiative, Netherlands Organization for Scientific Research (NWO), UnileverColworth and by BBMRI-NL, a Research Infrastructure financed by the Dutch government (NWO 184.021.007). Lothian Birth Cohort 1936 (LBC1936) was funded by the Age UK’s Disconnected Mind programme (http://www.disconnectedmind.ed.ac.uk) and also by Research Into Ageing (Refs. 251 and 285). The whole genome association part of the study was funded by the Biotechnology and Biological Sciences Research Council (BBSRC; Ref. BB/F019394/1). Analysis of the brain images was funded by the Medical Research Council Grants G1001401 and 8200. The imaging was performed at the Brain Research Imaging Centre, The University of Edinburgh (http://www.bric.ed.ac.uk), a centre in the SINAPSE Collaboration (http://www.sinapse.ac.uk). The work was undertaken by The University of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology (http://www.ccace.ed.ac.uk), part of the cross council Lifelong Health and Wellbeing Initiative (Ref. G0700704/84698). Funding from the BBSRC, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Medical Research Council, and Scottish Funding Council through the SINAPSE Collaboration is gratefully acknowledged. Northern Manhattan Study (NOMAS) is supported by the NINDS (grants R37 NS29993 and K02 NS 059729). Genome-wide data were supported by the Evelyn F. McKnight Brain Institute. PROspective Study of Pravastatin in the Elderly at Risk (PROSPER) study was supported by an investigator initiated grant obtained from Bristol-Myers Squibb. Dr J. W. Jukema is an Established Clinical Investigator of the Netherlands Heart Foundation (grant 2001 D 032). Support for genotyping was provided by the seventh framework program of the European commission (grant 223004) and by the Netherlands Genomics Initiative (Netherlands Consortium for Healthy Aging grant 050-060-810). Rotterdam Study (RS I, RS II, RS III): the generation and management of GWAS genotype data for the Rotterdam Study is supported by the Netherlands Organisation of Scientific Research NWO Investments (no. 175.010.2005.011, 911-03-012). This study is funded by the Research Institute for Diseases in the Elderly (RIDE2; 014-93-015), the Netherlands Genomics Initiative (NGI)/NWO project nr. 050-060-810. Further funding was received from the Netherlands Heart Foundation (2009B102) and a Veni-grant (916.13.054). The Rotterdam Study is funded by Erasmus Medical Center and Erasmus University, Rotterdam, Netherlands Organization for the Health Research and Development (ZonMw), the RIDE, the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam. Study of Health in Pomerania (SHIP and SHIP-TREND) is supported by the German Federal Ministry of Education and Research (grants 01ZZ9603, 01ZZ0103, and 01ZZ0403). Genome-wide data and MRI scans were supported by the Federal Ministry of Education and Research (grant 03ZIK012) and a joint grant from Siemens Healthcare, Erlangen, Germany, and the Federal State of Mecklenburg–West Pomerania. The University of Greifswald is a member of the Center of Knowledge Interchange program of the Siemens AG and the Caché Campus program of the InterSystems GmbH. SHIP-TREND-0: This cohort is part of the Community Medicine Research net (CMR) of the University of Greifswald, which is funded by the German Federal Ministry of Education and Research and the German Ministry of Cultural Affairs, as well as by the Social Ministry of the Federal State of Mecklenburg–West Pomerania. CMR encompasses several research projects that share data from the population-based Study of Health in Pomerania (SHIP; see URLs). MRI scans were supported by a joint grant from Siemens Healthcare, Erlangen, Germany, and the Federal State of Mecklenburg–West Pomerania. The SHIP-TREND cohort was supported by the Federal Ministry of Education and Research (grant 03ZIK012). Three-City Dijon Study (3C-Dijon Study) conducted under a partnership agreement between the Institut National de la Santé et de la Recherche Médicale (INSERM), the Victor Segalen–Bordeaux II University, and Sanofi-Aventis. The Fondation pour la Recherche Médicale funded the preparation and initiation of the study. The 3C Study is also supported by the Caisse Nationale Maladie des Travailleurs Salariés, Direction Générale de la Santé, Mutuelle Générale de l’Education Nationale, Institut de la Longévité, Conseils Régionaux of Aquitaine and Bourgogne, Fondation de France, and Ministry of Research–INSERM Programme Cohortes et collections de données biologiques. Lille Génopôle received an unconditional grant from Eisai. This work was supported by the National Foundation for Alzheimer Disease and Related Disorders, the Institut Pasteur de Lille and the Centre National de Génotypage. Washington Heights-Inwood Columbia Aging Project (WHICAP) acknowledge the following grants for supporting this study: P01-AG007232, R01-AG037212, and NIH R01-AG034189.
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