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White Matter Lesion Progression

Genome-Wide Search for Genetic Influences
and on behalf of the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium
Originally published 2015;46:3048–3057


Background and Purpose—

White matter lesion (WML) progression on magnetic resonance imaging is related to cognitive decline and stroke, but its determinants besides baseline WML burden are largely unknown. Here, we estimated heritability of WML progression, and sought common genetic variants associated with WML progression in elderly participants from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium.


Heritability of WML progression was calculated in the Framingham Heart Study. The genome-wide association study included 7773 elderly participants from 10 cohorts. To assess the relative contribution of genetic factors to progression of WML, we compared in 7 cohorts risk models including demographics, vascular risk factors plus single-nucleotide polymorphisms that have been shown to be associated cross-sectionally with WML in the current and previous association studies.


A total of 1085 subjects showed WML progression. The heritability estimate for WML progression was low at 6.5%, and no single-nucleotide polymorphisms achieved genome-wide significance (P<5×10−8). Four loci were suggestive (P<1×10−5) of an association with WML progression: 10q24.32 (rs10883817, P=1.46×10−6); 12q13.13 (rs4761974, P=8.71×10−7); 20p12.1 (rs6135309, P=3.69×10−6); and 4p15.31 (rs7664442, P=2.26×10−6). Variants that have been previously related to WML explained only 0.8% to 11.7% more of the variance in WML progression than age, vascular risk factors, and baseline WML burden.


Common genetic factors contribute little to the progression of age-related WML in middle-aged and older adults. Future research on determinants of WML progression should focus more on environmental, lifestyle, or host-related biological factors.


The pathogenesis of white matter lesions (WMLs) on magnetic resonance imaging (MRI) is still incompletely understood. WML burden was shown to be highly heritable.13 Age and hypertension are the main known risk factors for WML but explain only a small proportion of lesion presence and burden.4 Before the era of genome-wide association studies (GWASs), candidate gene studies investigated variants in 19 genes and found associations between WML extent and polymorphisms in the apolipoprotein E (APOE), the methylentetrahydrofolate reductase, the angiotensin-converting enzyme, and the angiotensinogen genes.5,6 Moreover, genetic variants in the NOTCH3 gene may not only play a role in CADASIL (cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy), a monogenic cerebral small vessel disease, but are also likely to be involved in the pathogenesis of age-related WML burden.7 In 2011, the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium8 performed the first GWAS on WML burden in the general population.9 The CHARGE investigators identified 6 single-nucleotide polymorphisms (SNPs) mapping to a locus on chromosome 17q25 to be related to WML burden. The findings of the discovery meta-analyses had been confirmed in an independent sample of 1607 Aging Gene-Environment Susceptibility Reykjavik Study (AGES-Reykjavik) participants and in 1417 and 1677 elderly white participants from the Three-City-Dijon Study (3C-Dijon)9 and the Rotterdam Study III (RS III),10 as well as in a study of WML burden in persons with a clinical ischemic stroke.11 The association was also confirmed in an Asian population including 1190 Japanese persons with a mean age of 66 years.12 The region on chromosome 17 is ≈100-kb long and harbors several genes with diverse functions such as the 2 tripartite motif-containing genes (TRIM65 and TRIM47) the WW domain-binding protein 2 gene (WBP2), the mitochondrial ribosomal protein L38 gene (MRPL38), the Fas-binding factor 1 gene (FBF1), the acyl-coenzyme A oxidase 1 gene (ACOX1), and the Caenorhabditis elegans homolog (UNC13D) gene. Although genetic factors may play an important role in the occurrence of WML in middle-aged to older adults, whether these genes or others influence the further progression of WML is unknown. Using data on WML progression from all the cohorts currently available within the CHARGE consortium, we examined the heritability of WML progression and performed a meta-analysis of GWAS data in 7773 individuals of European descent from 10 cohorts to identify common SNPs that influence the risk for WML progression. We also assessed the relative contribution of genetic factors in predicting WML progression beyond information that can be obtained from baseline clinical and MRI data alone.

Materials and Methods

Study Population

Study participants were from 10 prospective cohort studies collaborating in the CHARGE Consortium8: the AGES-Reykjavik study,13 the Atherosclerosis Risk in Communities (ARIC) study,14 the Austrian Stroke Prevention Study (ASPS),15,16 the Cardiovascular Health Study (CHS),17 the Framingham Heart Study (FHS),18,19 the Prospective Study of Pravastatin in the Elderly at Risk (PROSPER),20,21 the RS II, the RS III,22 the Tasmanian Study of Cognition and Gait (TASCOG),23 and the 3C-Dijon study.24

All participating studies agreed on phenotype harmonization, covariate selection, prespecified analytic plans for within-study analyses, and meta-analysis of results. Each study secured approval from Institutional Review Boards, and all participants provided written informed consent for study participation, MRI scanning, and use of DNA for genetic research.

Participants were eligible for this study if they had genotyping, serial MRI, and lacked a history of transient ischemic attacks, strokes, dementia, or any combination of these conditions. All the individuals in this analysis were whites of European descent. The number of participants and their characteristics in each cohort are shown in Table I in the online-only Data Supplement.

WML Progression Assessment

In each study, eligible participants were invited to undergo serial MRI scans, which were performed and interpreted in a standardized fashion without knowledge of demographic, clinical, or genetic information (Section II in the online-only Data Supplement). Except for ARIC and CHS whose readers used a 10-point scale, readers in the other cohorts measured the volume of WML on each MRI scan. WML progression was defined as absent or present (Section III in the online-only Data Supplement). In brief, WML progression was considered to be present if visual rating increased by at least 1 grade between baseline and follow-up in ARIC and CHS, or if WML volume increased by at least 1 SD of the study-specific mean of volume change in AGES-Reykjavik, ASPS, FHS, PROSPER, RS II, RS III, TASCOG, and 3C-Dijon. WML regression was rare in all studies and was not considered separately in these analyses.


The consortium was formed after individual studies had finalized their GWAS platforms, which differed across studies. All studies used their genotype data to impute to the 2.5 million nonmonomorphic, autosomal, SNPs described in HapMap European population panel. Extensive quality control analyses were performed in each cohort. Details on the genotyping, quality control, and imputation efforts are described in Tables II–IV in the online-only Data Supplement.

Heritability of WML Progression

Heritability of WML progression was calculated based on family structure in FHS, which is the only family study among the participating cohorts. Calculations were based on annual WML change and 2 models were assessed. The first model adjusted for age and sex, whereas the second model additionally adjusted for WML volume at baseline. The ratio of the genetic variance to the phenotypic variance in FHS was determined using variance component models in the Sequential Oligogenic Linkage Analysis Routines (SOLAR) software.25

Genome-Wide Association Analyses

For the GWAS, each study fitted an additive genetic model with a 1 degree-of-freedom trend test relating genotype dosage, 0 to 2 copies of the minor allele, and presence or absence of WML progression. We used logistic regression models to calculate regression estimates with corresponding SEs. Initial analyses were adjusted for age, sex, interval between scans as well as principal components of population structure if appropriate. Subsequent analyses included additional adjustment for WML volume at baseline. In addition, ARIC and CHS also adjusted for study site, and FHS, for familial structure. We then conducted a meta-analysis of logistic regression estimates and SEs using a fixed effects inverse-variance weighting approach with genomic control correction as implemented in METAL.26 There was no evidence of inflated test statistics in the individual cohort analyses (Figures I and II in the online-only Data Supplement). The genome-wide significance threshold was set a priori at 5×10−8. Associations were considered highly suggestive for SNPs with 5×10−8<P<1×10−5.

To evaluate if the lengths of the observational period influenced the effect size of highly suggestive SNPs and WML progression, we performed GWAS meta-analyses separately for cohorts with short mean follow-up and those with long mean follow-up time. Short-term studies were AGES-Reykjavik, PROSPER, and TASCOG. Long-term cohorts were ARIC, ASPS, CHS, and FHS (for follow-up intervals between scans see Table I in the online-only Data Supplement). Identical analyses were also done for younger (ARIC, ASPS, FHS, RS II, and RS III) and older (AGES-Reykjavik, CHS, PROSPER, TASCOG, and 3C-Dijon) cohorts (for mean age of cohorts see Table I in the online-only Data Supplement). The meta-analysis regression coefficients β of the SNPs were then compared between the investigational subsets using a z test.


Key SNPs were functionally annotated with SNPnexus27 and ANNOVAR.28 The SNAP29 web application of the Broad Institute was used to determine linkage disequilibrium (LD). Regional association plots were generated with LocusZoom.30 Furthermore, we used the NCBI Genotype-Tissue Expression eQTL Browser31 to check if a given SNP was associated with a quantitative gene expression trait.

Performance of WML Progression Risk Models

To estimate the relative importance of genetic factors for progression of WML, we compared the explained variance between 3 models in ASPS, CHS, FHS, PROSPER, RS II, RS III, and 3C-Dijon. Model 1 served as the reference and included age, sex, hypertension, diabetes mellitus, current smoking, and the time interval between scans; model 2 included model 1 variables and those SNPs with the lowest P value at each highly suggestive locus according to the present GWAS meta-analysis with adjustment for WML burden at baseline. We also included a missense SNP identified by annotation analyses. Model 3 included all model 2 variables plus genetic polymorphisms that were shown to be associated with WML in previous publications.5,7,9,3243 We included only those SNPs for which an association with WML was reported by at least 2 studies. In case that any of these SNPs was not present in our database, a proxy SNP (r2>0.8) was selected to be included.9 On the basis of this search strategy, we selected ACE5,3234 (rs4343), MTHFR5,35,36 (rs1801133), AGT5,3739 (rs2478539), TRIM479 (rs1055129), TRIM659 (rs3744028), NOTCH37 (rs10404382) and APOE45,4043 genotypes to be included in model 3. APOEε4 status was included as a binary variable (E4+ included the e3/e4 and e4/e4 genotypes; E4− included e2/e2, e2/e3, and e2/e4 genotypes). The 3 models were also calculated with additional adjustment for WML volume at baseline. Models 2 and 3 were adjusted for principal components of population structure if appropriate. The goodness of fit for each model was assessed by Nagelkerke R2.44,45


The analyses included 7773 participants from 10 cohort studies (Table I in the online-only Data Supplement). A total of 4342 (56%) study participants were women, 4030 (52%) were hypertensive, and 690 (8.9%) were diabetic. The mean systolic blood pressure ranged between 121.9 and 157.8 mm Hg, and the mean diastolic blood pressure ranged between 69.6 and 86.5 mm Hg. The mean time interval between the baseline and follow-up MRI was shortest in AGES-Reykjavik with 29.1 months and longest in ARIC with 123.6 months. Overall, WML progression was observed in 1085 (14.0%) study participants with the study-specific mean annual volume increase ranging between 0.2 cm3 and 1.4 cm3. The association between mean WML volume at baseline and mean annual volume increase in our cohorts was moderate (Pearson correlation coefficient r=0.59), but did not reach statistical significance (P=0.095). The relationship for each cohort is displayed in Figure III in the online-only Data Supplement.

Heritability of WML Progression

After adjustment for age and sex, the heritability estimate for WML progression observed in 1368 FHS individuals was 6.5%. Additional adjustment for WML burden at baseline resulted in a heritability estimate of 4.7%.

Genome-Wide Associations of WML Progression

Figure 1 illustrates the meta-analysis results of genome-wide association analyses on WML progression without and with adjustment for WML burden at baseline. In both the meta-analyses, no SNPs achieved genome-wide significance (P<5×10−8). Meta-analysis without adjustment for WML burden at baseline revealed 45 SNPs in 7 loci on 7 chromosomes with a highly suggestive association with WML progression at P<1×10−5 (Table V in the online-only Data Supplement). After additional adjustment for WML burden at baseline, 8 highly suggestive associations were identified (Table VI in the online-only Data Supplement). These SNPs are located in 6 loci on 6 chromosomes. There is an overlap of 4 suggestive loci (1q41, 5q12.1, 12q13.13, and 13q34) between the 2 meta-analysis results. In the meta-analysis without adjustment for WML burden at baseline 35 of 45 suggestive SNPs in total were located at 10q24.32. The SNP rs10883817 had the lowest P value (1.46×10–6) at this locus, with an odds ratio (OR) of 1.27, a mean minor allele frequency of 0.41, and a mean imputation quality score of 0.98. The suggestive variants at locus 10q24.32 were in LD (r2 between 0.336 and 1) and reside either in introns of genes AS3MT, CNNM2, NT5C2, in the intergenic regions between these genes, or in exons of CNNM2 and NT5C2 (Figure 2A).

Figure 1.

Figure 1. Genome-wide Manhattan plot for progression of white matter lesion (WML). The Manhattan plot for the genome-wide association study (GWAS) without adjustment for WML burden at baseline is shown in the top and the Manhattan plot for the GWAS adjusted for WML burden at baseline is shown in the bottom. The plot shows the −log10 (P values) for all single-nucleotide polymorphisms (SNPs) in the analysis against their genomic position. Within each chromosome, shown on the x axis, the results are plotted left to right from the P-terminal end. The red line represents the threshold for genome-wide significance (5×10−8), and the blue line represents the threshold for highly suggestive SNPs (1×10−5).

Figure 2.

Figure 2. Regional association plots for white matter lesion (WML) progression single-nucleotide polymorphisms (SNPs) in loci 10q24.32, 12q13.13, 20p12.1, and 4p15.31. Top, The plots for locus 10q24.32 (A) and 12q13.13 (B) are shown. Bottom, The plots for locus 20p12.1 (C) and 4p15.31 (D) are shown. For loci 10q24.32, 12q13.13, and 20p12.1 associations were determined by WML progression genome-wide association study (GWAS) meta-analysis in the model without adjustment for WML burden at baseline. For locus 4p15.31 the associations were determined by WML progression GWAS meta-analysis in the model with adjustment for WML burden at baseline. Plots are centered on the most significant SNP at a given locus along with the meta-analysis results for SNPs in a region surrounding it (±200 kb). All SNPs are plotted with their meta-analysis P values against their genomic position, with the most significant SNP in the region indicated as a diamond and other SNPs shaded according to their pairwise correlation (r2) with the signal SNP. The blue line represents the estimated recombination rates. Gene annotations are shown as dark blue line. The boxes at the end of the genes show the 5′ and 3′ untranslated regions, whereas boxes within the gene represent exons. Arrows indicate the direction of transcription. The recombintation rate is given in centimorgans per megabase (cM/MB).

At the second locus of interest, 12q13.13, we identified 3 highly suggestive variants in the analysis without adjustment for WML burden at baseline and only 1 with adjustment. In the meta-analysis without adjustment for baseline WML burden, rs4761974 had the lowest P value (8.71×10–7) with an OR of 0.5. After additional adjustment for baseline WML volume the P value of the association changed to 2.94×10–6 (OR=0.5). The SNP had a mean minor allele frequency of 0.057 and a mean imputation quality score of 0.98. Suggestive SNPs at this locus were in high LD (r2 between 0.786 and 1) and are located either in introns or in close proximity to the gene SLC4A8 (Figure 2B).

At the third locus 20p12.1 in the analysis without adjustment for WML burden at baseline, the top SNP was rs6135309 with a P value of 3.69×10–6, an OR of 0.78, a mean minor allele frequency of 0.29, and a mean imputation quality score of 0.97. Both suggestive variants at this locus were in LD (r2=0.642) and reside in introns of MACROD2 (Figure 2C).

The fourth locus suggestive of an association with WML progression was located on chromosome 4p15.31 in the analysis with adjustment for WML burden at baseline. The top SNP at this locus was rs7664442 (P=2.26×10–6; OR=0.65), which had a mean minor allele frequency of 0.079 and a mean imputation quality score of 0.96. These 3 SNPs at 4p15.31 were in perfect LD (r2=1) and are located in the intergenic region near GBA3 (Figure 2D).

Functional annotation of all highly suggestive SNPs and SNPs in LD with them was performed. The 2 suggestive exonic variants at locus 10q24.32, rs2275271 (P=2.26×10–6; OR=0.79), and rs3740387 (P=5.10×10–6; OR=1.25) were synonymous. Two other suggestive SNPs at this locus have been reported in previous GWASs investigating schizophrenia4648 (rs7897654; P=5.26×10–6; OR=0.79 and rs7914558; P=2.14×10–6; OR=1.26). No association between suggestive SNPs and quantitative gene expression traits could be determined.

A comparison of the effect size of suggestive SNPs on WML progression between studies with short- versus long-term follow-up failed to demonstrate significantly increased effect sizes in studies with long-term follow-up. There were also no significant differences in the effect sizes of suggestive SNPs on WML progression between younger and older cohorts (data not shown).

Performance of WML progression risk models

As can be seen from Table, model 1, which included demographics and risk factors, explained between 2.3% and 14.9% of the variance in WML progression in the various cohorts. Among studies that assessed WML progression by volume change, 17.2% to 43.3% of the WML progression variance was explained if baseline WML was included (model 1). In CHS, which used a visual rating scale for assessment of WML progression, only 4.5% of the variance could be explained. Highly suggestive SNPs from the current GWAS and all genetic variants previously described as being related to WML accounted for additional 1.1% to 8.6% (model 2) and 0.8% to 11.7% (model 3) of the variance of WML progression beyond age, sex, vascular risk factors, and baseline WML volume.

Table. Performance of WML Progression Risk Models

Model 1Model 2Model 3Model 2–Model 1Model 3–Model 1
ASPSWithout WML burden adjustment0.1460.2260.2750.0860.117
With WML burden adjustment0.4330.5190.550
CHSWithout WML burden adjustment0.0230.0510.0680.0270.044
With WML burden adjustment0.0450.0720.089
FHSWithout WML burden adjustment0.1130.1300.1430.0110.022
With WML burden adjustment0.1720.1830.194
PROSPERWithout WML burden adjustment0.0570.0950.1430.0720.090
With WML burden adjustment0.3200.3920.410
RS IIWithout WML burden adjustment0.1490.1640.1990.0170.062
With WML burden adjustment0.3910.4080.453
RS IIIWithout WML burden adjustment0.1190.1460.1580.0220.008
With WML burden adjustment0.3970.4190.405
3C-DijonWithout WML burden adjustment0.0540.1060.1180.0440.056
With WML burden adjustment0.2030.2470.259

Data are Nagelkerke R2. Model 1: WML progression ≈ age+sex+time+hypertension+diabetes mellitus+current smoking* without or with adjustment for WML burden at baseline. Model 2: WML progression ≈ age+sex+time+hypertension+diabetes mellitus+current smoking *+GWAS SNPs without or with adjustment for WML burden at baseline. Model 3: WML progression ≈ age+sex+time+hypertension+diabetes mellitus+current smoking *+GWAS SNPs+previously reported SNPs+APOE4 without or with adjustment for WML burden at baseline. 3C-Dijon indicates three-City Dijon study; ASPS, Austrian Stroke Prevention Study; CHS, Cardiovascular Health Study; FHS, Framingham Heart Study; GWAS, genome-wide association study; PROSPER, Prospective Study of Pravastatin in the Elderly at Risk; RS II, Rotterdam Study II; SNP, single-nucleotide polymorphism; and WML, white matter lesion.

*Frequency of current smoking ranged between 5.9% in 3C-Dijon and 24.5% in RS III.


In this first GWAS on WML progression, our data indicate that genetic factors contribute only little to WML progression in the general elderly population. This conclusion relies on 3 major findings. First, the family-based heritability estimate for WML progression was only 6.5% versus a heritability of 55% for baseline WML burden in the same sample.2 Second, our genome-wide analysis yielded no associations at a genome-wide significance level. Third, risk prediction models including highly suggestive SNPs according to the present GWAS meta-analysis plus all genetic polymorphisms that have been shown to be associated with WML burden in previous literature explained only between 0.8% and 11.7% more of the variance of WML progression than that explained by baseline clinical and MRI data. These results for WML progression oppose previous cross-sectional studies in which the contribution of genetic factors for WML burden was substantial.5,7,9,3243 This finding is puzzling because cross-sectionally assessed WML burden and WML progression are both quantitative measures with presumably the same biological basis. In this context it is of note, however, that in our study initial burden of WML predicted annual WML volume increase moderately, but this relationship was not significant. Moreover, for both, brain volume and cognitive functions, similar results of limited genetic variance in longitudinal measures have been shown.4951 In line with our findings on WMLs, these investigations also found that measures of brain atrophy and cognitive performance assessed cross-sectionally are highly heritably but predominantly environmental factors account for the rate of change of these cerebral phenotypes over time.

We identified 4 suggestive loci for WML progression on chromosomes 10q24.32, 12q13.13, 20p12.1, and 4p15.31. None of the SNPs in these loci reached genome-wide significance; however, some of the neighboring genes within these loci have been related to neuropsychiatric or vascular diseases. Locus 10q24.32 includes AS3MT (arsenic [+3 oxidation state] methyltransferase), CNNM2 (Cyclin M2), and NT5C2 (5′-nucleotidase, cytosolic II). These genes have previously been identified to be associated with schizophrenia4648,52,53 and blood pressure5457 in GWAS and replication studies. CNNM2 and NT5C2 were associated with coronary artery disease,58 and CNNM2 was additionally associated with intracranial aneurysm.59 Moreover, a suggestive variant in an intron of AS3MT, rs10748835 (P=2.79×10–6; OR=1.25), is known to modify cognitive function in persons with low-level arsenic exposure.60

The locus at 12q13.13 includes SLC4A8, a sodium bicarbonate cotransporter of the designated solute carrier family 4. It transports sodium and bicarbonate ions across the cell membrane.61,62 The gene is highly expressed in all major regions of the brain and is involved in pH regulation in human neurons.63

MACROD2 (MACRO domain containing 2) at locus 20p12.1 is a protein-coding gene that has been found to be associated with MRI-defined brain infarcts in a previous GWAS.64 This is particularly intriguing as recent studies have suggested new lacunes are most likely to develop in areas of white matter progression.65MACROD2 has also been associated with autistic traits.66

Although we included a large number of participants from 10 cohort studies with longitudinal assessment of WML change, we cannot exclude the possibility that we missed significant associations of WML progression to SNPs with small effect sizes or low-risk allele frequencies. Moreover, 3 other factors need to be considered when interpreting our results. First, we used a binary phenotype based on visual rating or cut-off values of volumetric change to define WML progression. This conservative definition decreased the likelihood of false-positive ratings but, among those with WML at baseline, might have led to an underestimation of WML progression. Second, the average time period between scans among contributing cohorts was 54 months, and despite the fact that single studies had followed their participants for >10 years, it is conceivable that this period was too short to reveal the full impact of genetic factors on WML progression. Our findings that the effect sizes of suggestive GWAS SNPs on WML progression were not significantly larger in cohorts with long-term follow-up versus cohorts with short-term follow-up and in older versus younger cohorts do not support this assumption. Third, we by design concentrated on elderly people. Genetic factors may play a larger role for white matter progression in younger populations.

Our study findings have important implications for future research on age-related white matter changes. They suggest that, although the contribution of genetic factors seems to be large during the initiating phase of white matter damage, the propagating phase of WML seems to be mainly influenced by nongenetic determinants. With the exception of high blood pressure, these nongenetic risk factors for WML progression remain largely unknown.67,68 On the basis of our data, we need to intensify the search for potentially modifiable environmental and lifestyle factors that influence the progression of age-related white matter changes and the associated morbidity. Moreover, although this study focused on the influence of genetics on age-related WML progression, the effects of heritability on injury-induced WML progression are an interesting direction for future studies.


Atherosclerosis Risk in Communities (ARICs) thank the staff and participants of the ARIC study for their important contributions. Austrian Stroke Prevention Study (ASPS) thank the staff and the participants of the ASPS for their valuable contributions. 3C-Dijon thank the staff and the participants of the 3C Study for their important contributions. 3C-Dijon thank Anne Boland (Centre National de Génotypage, Insitut de Génomique, CEA) for her technical help in preparing the DNA samples for analyses. Study concept/design was performed by Dr R. Schmidt, Dr H. Schmidt, Dr S. Seshadri, and Dr DeCarli; data analysis was carried out by Drs Hofer and Cavalieri; article was prepared by Drs Hofer, Cavalieri, R. Schmidt, and H. Schmidt; cohort contributions (alphabetic order): Study concept/design was performed by Aging Gene-Environment Susceptibility Reykjavik Study (AGES-Reykjavik): Drs Launer and Gudnason; ARIC: Drs Fornage and Mosley; ASPS: Drs H. Schmidt and R. Schmidt; Cardiovascular Health Study (CHS): Drs Psaty and Longstreth; Framingham Heart Study (FHS): Dr Seshadri; Prospective Study of Pravastatin in the Elderly at Risk (PROSPER): Drs Buckley, Ford, Stott, Sattar, Westendorp, and Jukema; Rotterdam Study (RS): Dr Ikram; Tasmanian Study of Cognition and Gait (TASCOG): Dr Srikanth; 3C-Dijon: Drs Dufouil and Tzourio; Phenotype data acquisition/QC was performed by AGES-Reykjavik: S. Sigurdsson, Drs van Buchem, and Zijdenbos; ARIC: Drs Shibata, Windham, Gottesman, Heiss, and Mosley; ASPS: Drs H. Schmidt and R. Schmidt; CHS: Drs Psaty and Longstreth; FHS: Drs Beiser and DeCarli; PROSPER: Drs Buckley, Ford, Stott, Sattar, Westendorp, and Jukema; RS: Drs Vernooij, Niessen, and Ikram; TASCOG: Dr Srikanth, Dr Phan, Dr Callisaya, C. Moran, and Dr Beare; 3C-Dijon: Drs Dufouil, Mazoyer, and Tzourio; Genotype data acquisition/QC was performed by AGES-Reykjavik: Dr Smith; ARIC: Dr Fornage; ASPS: Dr Schmidt and P. Freudenberger; CHS: Drs Bis, Lumley, and Taylor; FHS: Dr Wang and Yang; PROSPER: Drs Trompet, Jukema, Slagboom, and Craen; RS: Drs Hofman and van Duijn; TASCOG: Dr Thomson; 3C-Dijon: Drs Wolf, Debette, Chauhan, and Amouyel; Data analysis was carried out by AGES-Reykjavik: Dr Smith; ARIC: Drs Fornage; ASPS: Drs Hofer, Cavalieri, and Töglhofer; CHS: Drs Bis and Lumley; FHS: Drs Wang and Yang; PROSPER: Dr Trompet; RS: Drs Verhaaren and Adams; TASCOG: Dr Thomson; 3C-Dijon: Drs Wolf, Debette, Chauhan, and Schilling; Critical revision of article for Important Intellectual content was done by all the authors.


The online-only Data Supplement is available with this article at

Correspondence to Reinhold Schmidt, MD, Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, Auenbruggerplatz 22, 8036 Graz, Austria. E-mail


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