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Originally Published 10 June 2020
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Common Genetic Variation Indicates Separate Causes for Periventricular and Deep White Matter Hyperintensities

Nicola J. Armstrong, PhD, Karen A. Mather, PhD, Muralidharan Sargurupremraj, PhD, Maria J. Knol, BSc, Rainer Malik, PhD, Claudia L. Satizabal, PhD, Lisa R. Yanek, MPH, … Show All … , Wei Wen, PhD, Vilmundur G. Gudnason, MD, PhD, Nicole D. Dueker, PhD, Lloyd T. Elliott, PhD, Edith Hofer, PhD, Joshua Bis, PhD, Neda Jahanshad, PhD, Shuo Li, MSc, Mark A. Logue, PhD, Michelle Luciano, PhD, Markus Scholz, PhD, Albert V. Smith, PhD, Stella Trompet, PhD, Dina Vojinovic, PhD, Rui Xia, PhD, Fidel Alfaro-Almagro, MSc, David Ames, MD, Najaf Amin, PhD, Philippe Amouyel, MD, PhD, Alexa S. Beiser, PhD, Henry Brodaty, DSc, Ian J. Deary, PhD, Christine Fennema-Notestine, PhD, Piyush G. Gampawar, MSc, Rebecca Gottesman, MD, PhD, Ludovica Griffanti, PhD, Clifford R. Jack Jr, MD, Mark Jenkinson, PhD, Jiyang Jiang, PhD, Brian G. Kral, MD, MPH, John B. Kwok, PhD, Leonie Lampe, MD, David C.M. Liewald, BSc, Pauline Maillard, PhD, Jonathan Marchini, PhD, Mark E. Bastin, DPhil, Bernard Mazoyer, MD, PhD, Lukas Pirpamer, MSc, José Rafael Romero, MD, Gennady V. Roshchupkin, PhD, Peter R. Schofield, PhD, Matthias L. Schroeter, MD, PhD, David J. Stott, MD, Anbupalam Thalamuthu, PhD, Julian Trollor, MD, Christophe Tzourio, MD, PhD, Jeroen van der Grond, PhD, Meike W. Vernooij, MD, PhD, Veronica A. Witte, PhD, Margaret J. Wright, PhD, Qiong Yang, PhD, Zoe Morris, MBBS, Siggi Siggurdsson, MSc, Bruce Psaty, MD, Arno Villringer, MD, Helena Schmidt, MD, PhD, Asta K. Haberg, MD, PhD, Cornelia M. van Duijn, PhD, J. Wouter Jukema, PhD, Martin Dichgans, MD, Ralph L. Sacco, MD, Clinton B. Wright, MD, William S. Kremen, PhD, Lewis C. Becker, MD, Paul M. Thompson, PhD, Thomas H. Mosley, PhD, Joanna M. Wardlaw, MD, M. Arfan Ikram, MD, PhD, Hieab H.H. Adams, MD, PhD, Sudha Seshadri, MD, Perminder S. Sachdev, MD, Stephen M. Smith, PhD, Lenore Launer, PhD, William Longstreth, MD, Charles DeCarli, MD, Reinhold Schmidt, MD, Myriam Fornage, PhD, Stephanie Debette, MD, PhD, and Paul A. Nyquist, MD, MPH https://orcid.org/0000-0001-6078-3543 [email protected]Author Info & Affiliations
Stroke
Volume 51, Number 7
https://doi.org/10.1161/STROKEAHA.119.027544
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Abstract

Background and Purpose:

Periventricular white matter hyperintensities (WMH; PVWMH) and deep WMH (DWMH) are regional classifications of WMH and reflect proposed differences in cause. In the first study, to date, we undertook genome-wide association analyses of DWMH and PVWMH to show that these phenotypes have different genetic underpinnings.

Methods:

Participants were aged 45 years and older, free of stroke and dementia. We conducted genome-wide association analyses of PVWMH and DWMH in 26,654 participants from CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology), ENIGMA (Enhancing Neuro-Imaging Genetics Through Meta-Analysis), and the UKB (UK Biobank). Regional correlations were investigated using the genome-wide association analyses -pairwise method. Cross-trait genetic correlations between PVWMH, DWMH, stroke, and dementia were estimated using LDSC.

Results:

In the discovery and replication analysis, for PVWMH only, we found associations on chromosomes 2 (NBEAL), 10q23.1 (TSPAN14/FAM231A), and 10q24.33 (SH3PXD2A). In the much larger combined meta-analysis of all cohorts, we identified ten significant regions for PVWMH: chromosomes 2 (3 regions), 6, 7, 10 (2 regions), 13, 16, and 17q23.1. New loci of interest include 7q36.1 (NOS3) and 16q24.2. In both the discovery/replication and combined analysis, we found genome-wide significant associations for the 17q25.1 locus for both DWMH and PVWMH. Using gene-based association analysis, 19 genes across all regions were identified for PVWMH only, including the new genes: CALCRL (2q32.1), KLHL24 (3q27.1), VCAN (5q27.1), and POLR2F (22q13.1). Thirteen genes in the 17q25.1 locus were significant for both phenotypes. More extensive genetic correlations were observed for PVWMH with small vessel ischemic stroke. There were no associations with dementia for either phenotype.

Conclusions:

Our study confirms these phenotypes have distinct and also shared genetic architectures. Genetic analyses indicated PVWMH was more associated with ischemic stroke whilst DWMH loci were implicated in vascular, astrocyte, and neuronal function. Our study confirms these phenotypes are distinct neuroimaging classifications and identifies new candidate genes associated with PVWMH only.

Introduction

Radiological white matter hyperintensities (WMH) of presumed ischemic origin are the most prevalent sign of cerebral small vessel disease (SVD) and represent 40% of all SVD disease burden.1 They are detected as incidental lesions on T2-weighted magnetic resonance imaging.1 WMH are associated with increased risk for ischemic and hemorrhagic stroke, cognitive decline, and motor gait disorders.2–6 Two regional classifications, based on their anatomic relationship to the lateral ventricles in the brain, are periventricular WMH (PVWMH) and deep WMH (DWMH).5,7–9 PVWMH have been associated with declines in cognitive performance and increased systolic and arterial pressure, whereas DWMH are linked to body mass index, mood disorders, gait impairment, and arterial hypertension.10–12 This categorization reflects proposed differences in underlying pathophysiology.5,7,8 DWMH lesions occur in the subcortex, areas primarily supplied by long microvessels, with lower estimated blood pressures, possibly subject to damage secondary to hypertension and possibly with consequent hypoperfusion.1,8,13,14 PVWMH are related to alterations in short penetrating microvessels ending in close approximation to larger arterial blood vessels with different vascular architecture such as 2 leptomeningeal layers and enlarged perivascular spaces.1,15 They are hypothesized to be affected more directly by hypertension and risk factors associated with stroke.1,8,13,14
These subclassifications may also reflect differences in associated underlying genetic factors.16 Twin and family studies report that both PVWMH and DWMH have high heritability and genetic correlations.16,17 Recently, genome-wide association analyses (GWAS) for total WMH volume identified a major genetic risk locus on chromosome 17q25.118–21 and several other loci (eg, 10q24, 2p21, 2q33, 6q25.1).19,21,22 However, the genetic determinants of regional WMH burden, specifically DWMH and PVWMH, remain elusive.
We combined all available participants aged 45 and above with both DWMH and PVWMH measurements from the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) and the ENIGMA (Enhancing Neuro-Imaging Genetics Through Meta-Analysis) consortia, and the UKB (UK Biobank). This is the only GWAS to date examining WMH subclassifications. We hypothesized that separating the two WMH subclassifications would mitigate phenotype heterogeneity, allowing us to identify additional risk loci and show that DWMH and PVWMH have different genetic underpinnings and pathophysiology.

Methods

Summary data for this meta-analysis will be available through the database of Genotypes and Phenotypes Cohorts for Heart and Aging Research in Genomic Epidemiology Summary Results site, which can be downloaded via authorized access.

Study Cohorts

Study participants (total N=26 654) were drawn from cohorts in the CHARGE and ENIGMA consortia and the UKB. Detailed Methods are in the Data Supplement. All cohorts followed standardized procedures for participant inclusion, genotype calling, phenotype harmonization, covariate selection, and study-level analysis. Participants were included if they had phenotype, genotype, and covariate data available and were aged 45 years and over without stroke, dementia, or any neurological abnormality at the time of magnetic resonance imaging scanning. All participants provided written informed consent, and each study received ethical approval to undertake this work.

Phenotype and Covariates

The magnetic resonance imaging and WMH extraction methods for each study are detailed in the Data Supplement. In brief, PVWMH and DWMH volumetric data were extracted using automated methods for all studies except HUNT, LBC, and AGES, which used visual rating scales (Table I in the Data Supplement). Hypertension was defined as systolic blood pressure ≥140 mm Hg and diastolic blood pressure ≥90 mm Hg or on current antihypertensive treatment.

Statistical Analysis

Each study fitted linear regression models to test the association of DWMH and PVWMH (continuous measures) with individual single nucleotide polymorphisms (SNPs). Additive genetic effects were assumed, and the models were adjusted for age (years), sex, and intracranial volume (where applicable). In addition, principal components for population stratification and other covariates, such as familial structure, were included if necessary. Models were also fitted with hypertension as an additional covariate.
Fixed-effects, inverse variance–weighted meta-analysis was carried out in METAL,23 with correction for genomic control. Two meta-analyses were carried out: all cohorts excluding UKB (discovery, phase I) and all cohorts (phase II). Post meta-analysis QC was also performed (see in the Data Supplement).

Genetic Correlations With Stroke and Dementia

Cross-trait genetic correlation between the 2 subclassifications of WMH, stroke, and dementia was estimated using LDSC24 on the GWAS summary statistics from phase II, MEGASTROKE (European ancestry only).25 Linkage disequilibrium scores were based on the HapMap3 European reference panel. Regional level correlation was investigated using the GWAS-PW and HESS methods.26,27

Results

Detailed study descriptions are provided in Tables I through III in the Data Supplement. The discovery cohort was comprised of ≈18 234 older adults (≥45 years, 16 studies) and was primarily white, with 736 blacks and 658 Hispanics. The predominantly white UKB was used as the replication cohort (n=8428).
In the discovery analysis (phase I), genome-wide significant associations (P<5×10−8) were observed in the 17q25.1 region for both phenotypes (Tables IV and V in the Data Supplement). Only the PVWMH analysis found additional genome-wide significant associations on chromosomes 2 and 10 (2 regions). Two of these regions had previously been described for total WMH burden (chromosomes 2, NBEAL,19,21 10q24.33, SH3PXD2A21), whereas 10q23.1 had not been described. Adjusting for hypertension made little difference to our findings (Tables VI through VII in the Data Supplement). Replication of the majority of genome-wide significant results for both phenotypes was observed after adjustment for multiple testing (DWMH P<3.6×10−4, PVWMH P<2.76×10−4, Tables VIII and IX in the Data Supplement).
Given the relatively large size of the replication cohort, a combined meta-analysis (phase II) was undertaken using all samples (N≈26 654). Removing either the small subsample of nonwhites or the cohorts with visual ratings did not substantially change the findings (beta value r2>0.93). The phase II GWAS meta-analyses identified 236 for DWMH and 513 genome-wide significant SNPs for PVWMH (Figure 1A, Table 1, Tables X and XI in the Data Supplement, respectively). Figure 1B shows the zoom plot of the single locus identified for DWMH on chr17q25.1. The associations of the identified genome-wide and suggestive associations for each phenotype for the alternate trait are also provided in Tables X and XI in the Data Supplement. The only SNPs genome-wide significant for both phenotypes (n=209) were located on 17q25.1 (Figure 2A).
Table 1. Top Genome-Wide Significant SNP Results From Each Genomic Locus Identified From the Phase II GWAS Meta-Analysis for Deep and PV WMH
WMHrsIDCHRPOSNearest GeneFunction/PositionA1A2Freq (A1)Beta (SE)NDirectionP Value
PVrs374402017q25.173871773TRIM47IntronicAG0.18970.0899 (0.0073)26 438+++−+++?+++++++++++++7.06×10−35
Deeprs3539290417q25.173883918TRIM65IntronicTC0.7981−0.0765 (0.0070)26 642−−−−−−−+−−−−−−−−+−−−−3.99×10−28
PVrs375857510q24.33105454881SH3PXD2AIntronicAG0.49040.0388 (0.0058)26 654+++−++−++++++++++++++2.00×10−11
PV*rs1292852016q24.287237568C16orf95IntergenicTC0.42520.0431 (0.0065)26 327+++++−?−+−−+++++++−++4.22×10−11
PVrs2753506q25.1151016058PLEKHG1IntronicCG0.42020.0374 (0.0057)26 654+−+−+++++−++++++−−−++4.86×10−11
PVrs75968722p16.156128091EFEMP1IntronicAC0.09750.0642 (0.0099)25 730−++++++−+−−+++???++++8.66×10−11
PVrs729345832q33.2204009057NBEAL1IntronicTG0.87400.0529 (0.0087)25 730−+++++++++−+++???+−++1.03×10−9
PVrs572423282p2143073247AC098824.6IntergenicAG0.3317−0.0368 (0.0061)25 730−−−−+−−−−+−−−−???−++−1.85×10−9
PV*rs721327317q21.3143155914NMT1IntronicAG0.66680.0341 (0.0059)26 111+++++−??++−++−+++++++8.89×10−9
PV*rs199348410q23.182222698TSPAN14IntronicTC0.23880.0378 (0.0067)26 654++++−+−−++−++−+++++++1.36×10−8
PVrs1183877613q34111040681COL4A2IntronicAG0.27930.035026 654−+++++−++−+++++++−+−+2.82×10−8
PV*rs17999837q36.1150696111NOS3ExonicTG0.32010.037326 654++++++−−++−++−++−−−−+3.68×10−8
Effect allele is A1. A1 indicates allele 1; A2, allele 2; Chr, chromosome; GWAS, genome-wide association analyses; POS, base pair position; PV, periventricular; and WMH, white matter hyperintensities.
*
These loci have not been previously associated with total WMH.
Figure 1. Phase II genome-wide association analyses meta-analysis. A, Miami plot for periventricular white matter hyperintensities (PVWMH; upper) and deep white matter hyperintensities (DWMH; lower). Dashed line shows genome-wide significance threshold (P<5×10−8). B, Chromosome 17 regional plot of genome-wide significant SNPs for DWMH. Colors of the SNPs indicate the level of linkage disequilibrium with the top SNP (purple), rs35392904.
Figure 2. Overlap between significant SNPs and genes and Circos plots of the chromosome 17 region for deep (DWMH) and periventricular (PVWMH) white matter hyperintensities. A, Overlap between genome-wide significant SNPs (P<5×10−8) for DWMH and PVWMH. B and C, Circos plots for chromosome 17 for both phenotypes, showing two identified regions for PVWMH (B) but only one for DWMH (C). Outer ring shows SNPs <0.05 with the most significant SNPs located towards the outermost ring. SNPs in high linkage disequilibrium (LD) with the independent significant SNPs in each locus are colored in red (r2>0.8)-blue (r2>0.2); no LD (gray). Genomic risk loci are colored in dark blue (second layer). Genes are mapped by chromatin interaction (orange), expression quantitative trait loci (green), or both (red). D, Overlap between significant genes identified by MAGMA for both phenotypes.
Ten chromosomal regions containing 290 genome-wide significant SNPs for PVWMH only were identified on chromosomes 2 (3 regions), 6, 7, 10 (2 regions), 13, 16, and 17q23.1 (Results, Table XI, and Figures I and II in the Data Supplement). Four loci had not been previously reported for associations with total WMH at the genome-wide significant level: (1) 7q36.1 (7.2 kb) containing 2 exonic SNPs in the NOS3 gene; (2) 10q23.1 (50.5 kb) containing 4 intronic SNPs in TSPAN14 & FAM231A; (3) 16q24.2 (1.2 kb) containing 2 intergenic SNPs; (4) 17q21.31 (27.2 kb) containing 8 SNPs, most of which are intronic and in the NMT1 gene. Many of these are expression quantitative trait loci or participate in long-range chromatin interactions (Figure 2B). Further descriptions of the PVWMH findings are found in the Results in the Data Supplement.
As expected, the association of the 17q25.1 locus with both phenotypes was confirmed. The size of this region, including genome-wide significant SNPs only, was similar for both DWMH (236 SNPs, BP 73757836-74025656, Figure 1B) and PVWMH (223 SNPs, BP 73757836-74024711, Figure IA in the Data Supplement). The top results in this locus were rs3744020 for DWMH (P=7.06×10−35, TRIM47 intronic SNP) and rs35392904 for PVWMH (P=3.989×10−28, TRIM65 intronic SNP), which are in high linkage disequilibrium (R2=0.902; Table 1). Many of these SNPs are expression quantitative trait loci or have long-range chromatin interactions (Figure 2B and 2C). For further details, see the Results in the Data Supplement.
Using gene-based tests, 13 genes in the 17q25.1 locus reached genome-wide significance (P<2.66×10−6) with both phenotypes (Table 2, Figure 2D, Tables XII and XIII in the Data Supplement). For PVWMH, an additional 19 genes were identified, covering the majority of regions/loci found in the SNP-based analysis (Figure 2D, Table 2, Table XIII in the Data Supplement). Four genes were located in previously unidentified regions: CALCRL (2q32.1), KLHL24 (3q27.1), VCAN (5q27.1), and POLR2F (22q13.1).
Table 2. Thirty-Two Significant Genes Were Identified for PVWMH Using Gene-Based Tests (P<2.66×106)
GeneCHRSTARTSTOPN SNPsNP PVWMHP DWMH
WBP21773841780738525882824 6823.19×10−261.16×10−21*
TRIM651773876416738930845224 5557.73×10−249.12×10−19*
TRIM471773870242738746561324 1851.70×10−239.04×10−19*
RP11-552F3.121773894726739262105324 3512.15×10−201.76×10−15*
FBF1†1773905655739372215524 3383.98×10−171.23×10−13*
GALK1†1773747675737617923624 3076.34×10−163.23×10−14*
MRPL381773894724739058992124 4817.62×10−151.18×10−13*
UNC13D1773823306738407987323 7883.10×10−141.22×10−13*
UNK17737806817382188612022 7683.28×10−134.85×10−10*
H3F3B1773772515737819742324 0094.43×10−121.41×10−10*
SH3PXD2A1010534828510561530178824 8478.43×10−120.21731
ACOX117739375887397551515124 1987.72×10−111.1×10−9*
EVPL1774000583740235336724 5821.26×10−102.82×10−14*
PLEKHG16150920999151164799102224 9221.59×10−100.011765
WDR12†220373950520387952132223 7532.53×10−100.00104
ICA1L†220364069020373670822423 8438.44×10−100.001301
CARF†220377693720385178615724 0762.41×10−90.001763
NMT1†17431289784318638422124 7667.18×10−80.00034
CDK3†1773996987740020801224 4338.54×10−81.82×10−8*
OBFC1†101056423001056779639925 4611.41×10−70.054127
NOS3†71506880831507116765824 6081.73×10−70.000371
DCAKD†17431007084313847311125 2292.60×10−70.000363
DYDC2†1082104501821278299125 0502.88×10−70.003460
NBEAL1220387960220409110136723 4133.83×10−70.040539
NEURL11010525373610535230929625 0384.84×10−70.098303
MAT1A†1082031576820494406625 2954.90×10−70.002421
TSPAN14†10822139228229287921324 7316.73×10−70.006605
CALCRL†218820785618831318727824 3097.87×10−70.000574
KLHL24†318335335618340226520724 3561.29×10−60.002571
POLR2F†22383486143843792210523 5251.94×10−60.252540
VCAN†5827672848287812231624 2482.52×10−60.065044
COL4A213110958159111165374114024 8762.61×10−60.365300
Chr indicates chromosome; DWMH, deep white matter hyperintensities; and PVWMH, periventricular white matter hyperintensities.
*
Thirteen of these genes (chr17) were also significant for DWMH.
†
Those loci bolded have not been previously associated with total WMH.
Heritability analyses revealed low to moderate heritability for both traits (see Results in the Data Supplement). A high genetic correlation between DWMH and PVWMH was observed (rg=0.927, P=1.1×10−65), indicating a shared genetic architecture. Figure 3 shows the genetic correlations with DWMH, PVWMH, stroke, and Alzheimer disease. Positive genetic correlations with both phenotypes were found for all stroke, ischemic stroke, and SVD. Intracerebral hemorrhage (all types) was correlated with DWMH only. No significant correlations were found with Alzheimer disease (Table XIV in the Data Supplement).
Figure 3. Genetic correlations (rg) between deep white matter hyperintensities (DWMH), periventricular white matter hyperintensities (PVWMH), Alzheimer disease (AD), and stroke phenotypes. Horizontal bars represent SE, and the size of the square corresponds precision. ICH indicates intracranial hemorrhage; and SVD, small vessel disease stroke.
Using GWAS-PW,26 we observed several regions with high probability (>90%) for harboring a shared genetic variant between PVWMH and DWMH (Table XV in the Data Supplement). These regions encompass several genome-wide significant loci that were identified for PVWMH (2p16.1 [EFEMP1], 2q33.2 [CARF and NBEAL], 6q25.1 [PLEKHGI22], 16q24.2 [C16orf95], and 17q25.1 [TRIM47, TRIM65]). Additionally, by using HESS,27 regional level correlation estimates were derived for those regions identified by the Bayesian approach (GWAS-PW).
Finally, we investigated local regions of a shared genetic variant between the WMH subtypes and stroke (Table XV in the Data Supplement). A region on chromosome 7 (encompassing the PVWMH NOS3 exonic SNP) exhibited shared genetic influence of all stroke with both phenotypes. Other regions of shared influence with all stroke were observed for PVWMH only. For the subtypes of stroke, significant regions were identified for DWMH and PVWMH, but none were found for both phenotypes except the chromosome 7 region for ischemic stroke (also identified for all stroke). Similar to the GW level correlation, a positive regional level genetic correlation was observed between the WMH subtypes and stroke (all stroke, all-ischemic, cardio-embolic and small vessel), by using HESS.27

Discussion

In our meta-analyses using all available individuals (N=26 654, phase II), PVWMH had significant independent associations with loci containing genes implicated in large and SVD, as well as ischemic and deep hemorrhagic stroke suggesting a unique genetic and pathophysiological underpinning. Although our phase II GWAS were only slightly larger than the previous biggest GWAS on total WMH burden with 21 079 participants,21 our detection rate of significant SNPs was substantially higher.18,19,21 This improved detection may be the result of reduced heterogeneity by separately analyzing the DWMH and PVWMH phenotypes.
We identified 11 independent loci for PVWMH and one locus for DWMH. Significant genes associated with WMH for the first time in PVWMH include CALCRL, VCAN, TSPAN, and NOS3. Most genes and loci previously reported as significant in total WMH.28–32 were now found to be associated with PVWMH alone, including PLEKHG1,22 SH3PXD2A,25,28,33 and COL4A2.33. Similarly, genes viewed as potential candidates18,19,21 in prior studies we now find to be significantly associated only with PVWMH, including DYDC2 and NEURL1, as well as NMT1, GALK1, H3F3B, UNK, UNC13D, EVPL, ICAL1, WDR12/CARF, NBEAL1, and EFEMP1.
Many of these genes associated with PVWMH affect vascular function or vascular diseases, such as ischemic stroke or coronary artery disease. The NOS3 gene is associated with coronary artery disease, migraine, vascular dysfunction, SVD, and ischemic stroke.22,29,30,34 PLEKHG1 is associated with dementia and ischemic stroke,35 and SH3PXD2A has been previously associated with total WMH and ischemic stroke.19,25
The most notable associated vascular gene is COL4A2 that encodes for a subunit of type IV collagen, which has been associated with SVD, ischemic stroke, intracranial hemorrhage, and coronary artery disease.31,35–38 It is a proposed therapeutic target for the prevention of intracranial hemorrhage.32,39 The association of this vascular gene with PVWMH and deep intracerebral hemorrhage is suggestive of underlying regional gene effects of the COL4A2 gene on the microvasculature affecting the risk of vascular injury in the periventricular region. These include potential weakening of the structural integrity of the regional microvasculature by altered collagen type 4 structural integrity, dysregulated gene expression of COL4A1 and COL4A2, and toxic cytosolic accumulations of COL4A2 within microvascular structural cells.40 When comparing PVWMH and DWMH anatomy, these mechanisms may enhance the direct mechanical effects of hypertension, or the other stroke risk factors, on the unique microvascular structure of the PVWMH region that also has predicted higher ambient blood pressure.1,6,13
We also discovered a new set of putative PVWMH genes. These include: TSPAN14, which encodes one of the tetraspanins which organize a network of interactions referred to as the tetraspanin web, ADAM10, a metalloprotease that cleaves the precursor of cell surface proteins,41 KLHL24 encodes a ubiquitin ligase substrate receptor,42 VCAN encodes a large chondroitin sulfate proteoglycan that is found in the extracellular matrix. In a recent meta-analysis, VCAN was associated with white matter microstructural integrity.43 These candidate genes for PVWMH may influence the immediate tissues surrounding microvessels and may contribute to SVD-associated biological changes.
The only significant locus observed for DWMH was the previously reported total WMH 17q25.1 locus,18,19,21,22 which was also found for PVWMH. This locus contained the SNPs with the largest effect sizes for both phenotypes. The top genome-wide significant hits for DWMH and PVWMH (17q25.1) were either identical with the SNP recently reported by Traylor et al22 for total WMH (PVWMH rs3744020) or in high linkage disequilibrium (R2>0.9) with the previously identified top ranked SNPs in the same locus (rs3744028, Fornage et al,18 rs7214628, Verhaaren et al21). Our identified SNPs were only in moderate linkage disequilibrium (R2≤0.396) with the top SNP (rs3760128) identified in a recent exome association analysis.19 All of these SNPs fall within or between the previously reported TRIM47 and TRIM65 genes.18,21,22,35 This gene-rich locus contains genes that influence glial cell proliferation and have been hypothesized to influence gliosis, which is a histological and magnetic resonance imaging marker of microvascular injury.1 It includes previously identified total WMH genes, such as TRIM47/TRIM65 (glial proliferation, astrocytoma’s),18,21 ACOX1 (cell replication, hepatic cancer)18,19,21 and MRPL38 (protein synthesis).19 Genes associated with neuronal injury or neurodegenerative disorders are also found in the 17q25.1 locus, including CDK3 (neuronal cell death in stroke),44 H3F3B (schizophrenia pathogenesis) and GALK1 (galactosemia).45 Interestingly, 2 genome-wide significant intronic UNC13D SNPs identified in this study and reported previously for total WMH burden,21 rs9894244 and rs7216615, have been reported as expression quantitative trait loci for GALK1 and H3F3B, respectively.46 The PVWMH specific loci also contained genes that potentially influence astrocytic function and gliosis, several previously reported for total WMH. These include NBEAL1,19,21 WDR12,19 NEURL1,18,19,21 CARF,47 and EFEMP1.37 Newly identified PVWMH genes potentially affecting astrocytic functioning include NMT1,48 ICA1L,49 POLR2F, OBFC1, and DYDC2.
Shortcomings of this study include the potential variability due to the different WMH extraction algorithms used, with a minority of samples using visual ratings. However, this is a common problem encountered in this type of study.18,19,21 Although our results suggest improved power and reduction in potential bias through the discrimination of PVWMH from DWMH, the Euclidean methodology used by the majority of studies undoubtedly missed PVWMH lesions outside this boundary. The majority of the participants in this study were white, and hence these results may not apply to other ethnicities. Sex differences have been previously reported but were not examined in the current study.50 For the phase II meta-analysis, we did not have an independent replication cohort. Older adults were included in this study, and the majority of participants had both DWMH and PVWMH and not one or the other. However, selection of individuals with only one subtype of these lesions present may be more appropriate to identify differences but would only be possible in younger cohorts. Future studies should aim to address these shortcomings, including continuing to improve and harmonize WMH measurement methods but also using consistent DWMH and PVWMH measurement methods across studies.

Conclusions

Our study confirms PVWMH and DWMH have distinct and shared genetic architecture. Genetic analyses indicated PVWMH was more associated with ischemic stroke and vascular function (PLEKHG1, SH3PXD2, COL4A2, CALCRL, VCAN, NOS3), whereas DWMH loci were implicated in vascular, astrocyte and neuronal function (TRIM47/TRIM 65, ACOX1, MRPL38, H3F3B, GALK, UNC13D, GALK1). New genes for PVWMH, potentially affecting the extravascular connective tissue, were also identified (TSPAN14, ADAM10, KLHL24, VCAN). Our study confirms that PVWMH and DWMH are distinct neuroimaging classifications and identifies new candidate genes associated with PVWMH only.

Acknowledgments

See in the Data Supplement.

Supplemental Material

File (str_stroke-2019-027544_supp1.pdf)
File (str_stroke-2019-027544_supp2.pdf)

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Published In

Go to Stroke
Go to Stroke
Stroke
Volume 51 • Number 7 • July 2020
Pages: 2111 - 2121
PubMed: 32517579

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© 2020 American Heart Association, Inc.

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History

Received: 11 September 2019
Revision received: 30 March 2020
Accepted: 27 April 2020
Published online: 10 June 2020
Published in print: July 2020

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Keywords

  1. brain
  2. genome-wide association study
  3. neuroimaging
  4. risk factors
  5. white matter

Subjects

  1. Aging
  2. Cognitive Impairment
  3. Genetic, Association Studies
  4. Magnetic Resonance Imaging (MRI)
  5. Vascular Disease

Authors

Affiliations

Nicola J. Armstrong, PhD*
Mathematics and Statistics, Murdoch University, Perth, Australia (N.J.A.).
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Karen A. Mather, PhD*
Centre for Healthy Brain Ageing, School of Psychiatry (K.A.M., W.W., H.B., J.J., A.T., J.T., P.S.S.), University of New South Wales, Sydney, Australia.
Neuroscience Research Australia, Sydney, Australia (K.A.M., P.R.S., A.T.).
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Muralidharan Sargurupremraj, PhD*
University Bordeaux, Inserm, Bordeaux Population Health Research Center, France (M.S., C.T., S.D.).
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Maria J. Knol, BSc
Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands (M.J.K., D.V., N.A., G.V.R., M.W.V., C.M.v.D., M.A.I., H.H.H.A.).
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Rainer Malik, PhD
Institute for Stroke and Dementia Research (ISD), University Hospital, Ludwig-Maximilians-Universität LMU Munich, Germany (R.M., M.D.).
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Claudia L. Satizabal, PhD
Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, UT Health San Antonio, San Antonio, TX (C.L.S., S.S.).
The Framingham Heart Study, MA (C.L.S., A.S.B., J.R.R., S.S.).
Department of Neurology (C.L.S., A.S.B., J.R.R., S.S.), Boston University School of Medicine, MA.
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Lisa R. Yanek, MPH
GeneSTAR Research Program (L.R.Y., B.G.K., L.C.B., P.A.N.), Johns Hopkins University School of Medicine, Baltimore, MD.
View all articles by this author
Wei Wen, PhD
Centre for Healthy Brain Ageing, School of Psychiatry (K.A.M., W.W., H.B., J.J., A.T., J.T., P.S.S.), University of New South Wales, Sydney, Australia.
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Vilmundur G. Gudnason, MD, PhD
Icelandic Heart Association, Kopavogur (V.G.G., S.S.).
University of Iceland, Reykjavik, Iceland (V.G.G., A.V.S.).
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Nicole D. Dueker, PhD
Dr. John T. Macdonald Foundation Department of Human Genetics (R.L.S.), University of Miami, FL.
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Lloyd T. Elliott, PhD
Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, BC, Canada (L.T.E.).
Wellcome Centre for Integrative Neuroimaging (WIN FMRIB) (L.T.E., F.A.-A., L.G., M.J., S.M.S.), University of Oxford, United Kingdom.
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Edith Hofer, PhD
Clinical Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, Austria (E.H., R.S.).
Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Austria (E.H.).
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Joshua Bis, PhD
Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA (J.B., B.P., W.L.).
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Neda Jahanshad, PhD
Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Marina del Rey (N.J., P.M.T.).
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Shuo Li, MSc
Department of Biostatistics, Boston University School of Public Health, Boston, MA (S.L., M.A.L., A.S.B., Q.Y.).
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Mark A. Logue, PhD
Department of Psychiatry and Biomedical Genetics Section (M.A.L.), Boston University School of Medicine, MA.
Department of Biostatistics, Boston University School of Public Health, Boston, MA (S.L., M.A.L., A.S.B., Q.Y.).
National Center for PTSD: Behavioral Science Division, VA Boston Healthcare System, Boston, MA (M.A.L.).
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Michelle Luciano, PhD
Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, United Kingdom (M.L., I.J.D., D.C.M.L., M.E.B., J.M.W.).
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Markus Scholz, PhD
Institute for Medical Informatics, Statistics and Epidemiology (M.S.)
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Albert V. Smith, PhD
University of Iceland, Reykjavik, Iceland (V.G.G., A.V.S.).
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Stella Trompet, PhD
Department of Internal Medicine, Section of Gerontology and Geriatrics (S.T.), Leiden University Medical Center, the Netherlands.
Department of Cardiology (S.T.), Leiden University Medical Center, the Netherlands.
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Dina Vojinovic, PhD
Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands (M.J.K., D.V., N.A., G.V.R., M.W.V., C.M.v.D., M.A.I., H.H.H.A.).
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Rui Xia, PhD
Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, TX (R.X., M.F.).
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Fidel Alfaro-Almagro, MSc
Wellcome Centre for Integrative Neuroimaging (WIN FMRIB) (L.T.E., F.A.-A., L.G., M.J., S.M.S.), University of Oxford, United Kingdom.
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David Ames, MD
National Ageing Research Institute, Parkville, Victoria, Australia (D.A.).
Academic Unit for Psychiatry of Old Age, University of Melbourne, St George’s Hospital, Kew, Australia (D.A.).
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Najaf Amin, PhD
Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands (M.J.K., D.V., N.A., G.V.R., M.W.V., C.M.v.D., M.A.I., H.H.H.A.).
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Philippe Amouyel, MD, PhD
Lille University, Inserm, Institut Pasteur de Lille, RID-AGE - Risk Factors and Molecular Determinants of Aging-Related Diseases and Labex Distalz, France (P.A.).
Lille University, Inserm, CHU Lille, Institut Pasteur de Lille, RID-AGE (P.A.)
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Alexa S. Beiser, PhD
The Framingham Heart Study, MA (C.L.S., A.S.B., J.R.R., S.S.).
Department of Neurology (C.L.S., A.S.B., J.R.R., S.S.), Boston University School of Medicine, MA.
Department of Biostatistics, Boston University School of Public Health, Boston, MA (S.L., M.A.L., A.S.B., Q.Y.).
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Henry Brodaty, DSc
Centre for Healthy Brain Ageing, School of Psychiatry (K.A.M., W.W., H.B., J.J., A.T., J.T., P.S.S.), University of New South Wales, Sydney, Australia.
Dementia Centre for Research Collaboration (H.B.), University of New South Wales, Sydney, Australia.
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Ian J. Deary, PhD
Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, United Kingdom (M.L., I.J.D., D.C.M.L., M.E.B., J.M.W.).
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Christine Fennema-Notestine, PhD
Department of Psychiatry (C.F.-N.), University of California, San Diego, La Jolla, CA.
Center for Behavior Genetics of Aging (C.F.-N.), University of California, San Diego, La Jolla, CA.
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Piyush G. Gampawar, MSc
Gottfried Schatz Research Center (for Cell Signaling, Metabolism and Aging), Medical University of Graz, Austria (P.G.G., H.S.).
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Rebecca Gottesman, MD, PhD
Department of Neurology, Cerebrovascular and stroke Division (R.G.), Johns Hopkins University School of Medicine, Baltimore, MD.
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Ludovica Griffanti, PhD
Wellcome Centre for Integrative Neuroimaging (WIN FMRIB) (L.T.E., F.A.-A., L.G., M.J., S.M.S.), University of Oxford, United Kingdom.
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Clifford R. Jack Jr, MD
Department of Radiology, Mayo Clinic, Rochester, MN (C.R.J.J.).
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Mark Jenkinson, PhD
Wellcome Centre for Integrative Neuroimaging (WIN FMRIB) (L.T.E., F.A.-A., L.G., M.J., S.M.S.), University of Oxford, United Kingdom.
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Jiyang Jiang, PhD
Centre for Healthy Brain Ageing, School of Psychiatry (K.A.M., W.W., H.B., J.J., A.T., J.T., P.S.S.), University of New South Wales, Sydney, Australia.
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Brian G. Kral, MD, MPH
GeneSTAR Research Program (L.R.Y., B.G.K., L.C.B., P.A.N.), Johns Hopkins University School of Medicine, Baltimore, MD.
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John B. Kwok, PhD
School of Medical Sciences (J.B.K., P.R.S.), University of New South Wales, Sydney, Australia.
Brain and Mind Centre - The University of Sydney, Camperdown, NSW, Australia (J.B.K.).
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Leonie Lampe, MD
Department of Neurology, Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany (L.L., V.A.W.).
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David C.M. Liewald, BSc
Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, United Kingdom (M.L., I.J.D., D.C.M.L., M.E.B., J.M.W.).
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Pauline Maillard, PhD
Imaging of Dementia and Aging (IDeA) Laboratory, Department of Neurology, University of California-Davis, Davis, CA (P.M.).
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Jonathan Marchini, PhD
Statistical Genetics and Methods at Regeneron Pharmaceuticals, Inc, New York, NY (J.M.).
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Mark E. Bastin, DPhil
Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, United Kingdom (M.L., I.J.D., D.C.M.L., M.E.B., J.M.W.).
Centre for Clinical Brain Sciences, Edinburgh Imaging, Centre for Cognitive Ageing, University of Edinburgh, United Kingdom (M.E.B., J.M.W.).
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Bernard Mazoyer, MD, PhD
Institut des Maladies Neurodégénératives, University of Bordeaux, France (B.M.).
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Lukas Pirpamer, MSc
Clinical Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, Austria (L.P.).
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José Rafael Romero, MD
The Framingham Heart Study, MA (C.L.S., A.S.B., J.R.R., S.S.).
Department of Neurology (C.L.S., A.S.B., J.R.R., S.S.), Boston University School of Medicine, MA.
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Gennady V. Roshchupkin, PhD
Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands (M.J.K., D.V., N.A., G.V.R., M.W.V., C.M.v.D., M.A.I., H.H.H.A.).
Department of Radiology and Nuclear Medicine (G.V.R., M.W.V., H.H.H.A.)
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Peter R. Schofield, PhD
School of Medical Sciences (J.B.K., P.R.S.), University of New South Wales, Sydney, Australia.
Neuroscience Research Australia, Sydney, Australia (K.A.M., P.R.S., A.T.).
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Matthias L. Schroeter, MD, PhD
LIFE Research Center for Civilization Disease, Leipzig, Germany (M.S.).
Department of Neurology, Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany (M.L.S., A.V.).
Day Clinic for Cognitive Neurology, University Hospital Leipzig, Germany (M.L.S., A.V.).
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David J. Stott, MD
Institute of Cardiovascular and Medical Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, United Kingdom (D.J.S.).
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Anbupalam Thalamuthu, PhD
Centre for Healthy Brain Ageing, School of Psychiatry (K.A.M., W.W., H.B., J.J., A.T., J.T., P.S.S.), University of New South Wales, Sydney, Australia.
Neuroscience Research Australia, Sydney, Australia (K.A.M., P.R.S., A.T.).
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Julian Trollor, MD
Centre for Healthy Brain Ageing, School of Psychiatry (K.A.M., W.W., H.B., J.J., A.T., J.T., P.S.S.), University of New South Wales, Sydney, Australia.
Department of Developmental Disability Neuropsychiatry, School of Psychiatry (J.T.), University of New South Wales, Sydney, Australia.
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Christophe Tzourio, MD, PhD
University Bordeaux, Inserm, Bordeaux Population Health Research Center, France (M.S., C.T., S.D.).
CHU de Bordeaux, Public Health Department, Medical information Department, Bordeaux, France (C.T.).
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Jeroen van der Grond, PhD
Department of Radiology (J.v.d.G.), Leiden University Medical Center, the Netherlands.
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Meike W. Vernooij, MD, PhD
Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands (M.J.K., D.V., N.A., G.V.R., M.W.V., C.M.v.D., M.A.I., H.H.H.A.).
Department of Radiology and Nuclear Medicine (G.V.R., M.W.V., H.H.H.A.)
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Veronica A. Witte, PhD
Collaborative Research Center 1052 Obesity Mechanisms, Faculty of Medicine, University of Leipzig, Germany (V.A.W).
Department of Neurology, Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany (L.L., V.A.W.).
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Margaret J. Wright, PhD
Queensland Brain Institute (M.J.W.), The University of Queensland, St Lucia, QLD, Australia.
Centre for Advanced Imaging (M.J.W.), The University of Queensland, St Lucia, QLD, Australia.
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Qiong Yang, PhD
Department of Biostatistics, Boston University School of Public Health, Boston, MA (S.L., M.A.L., A.S.B., Q.Y.).
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Zoe Morris, MBBS
Neuroradiology Department, Department of Clinical Neurosciences, Western General Hospital, Edinburgh, United Kingdom (Z.M.).
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Siggi Siggurdsson, MSc
Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, UT Health San Antonio, San Antonio, TX (C.L.S., S.S.).
The Framingham Heart Study, MA (C.L.S., A.S.B., J.R.R., S.S.).
Department of Neurology (C.L.S., A.S.B., J.R.R., S.S.), Boston University School of Medicine, MA.
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Bruce Psaty, MD
Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA (J.B., B.P., W.L.).
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Arno Villringer, MD
Department of Neurology, Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany (M.L.S., A.V.).
Day Clinic for Cognitive Neurology, University Hospital Leipzig, Germany (M.L.S., A.V.).
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Helena Schmidt, MD, PhD
Gottfried Schatz Research Center (for Cell Signaling, Metabolism and Aging), Medical University of Graz, Austria (P.G.G., H.S.).
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Asta K. Haberg, MD, PhD
Department of Neuromedicine and Movement Science (A.K.H.), Norwegian University of Science and Technology, Trondheim, Norway.
Department of Radiology and Nuclear Medicine (A.K.H.), Norwegian University of Science and Technology, Trondheim, Norway.
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Cornelia M. van Duijn, PhD
Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands (M.J.K., D.V., N.A., G.V.R., M.W.V., C.M.v.D., M.A.I., H.H.H.A.).
Nuffield Department of Population Health (C.M.v.D.), University of Oxford, United Kingdom.
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J. Wouter Jukema, PhD
Department of Cardiology (J.W.J.), Leiden University Medical Center, the Netherlands.
Einthoven Laboratory for Experimental Vascular Medicine, LUMC, Leiden, the Netherlands (J.W.J.).
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Martin Dichgans, MD
Institute for Stroke and Dementia Research (ISD), University Hospital, Ludwig-Maximilians-Universität LMU Munich, Germany (R.M., M.D.).
German Center for Neurodegenerative Diseases, Munich, Germany (M.D.).
Munich Cluster for Systems Neurology (SyNergy), Germany (M.D.).
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Ralph L. Sacco, MD
Department of Public Health Sciences, Miller School of Medicine (R.L.S.), University of Miami, FL.
Department of Neurology, Miller School of Medicine (R.L.S.), University of Miami, FL.
Evelyn F. McKnight Brain Institute, Department of Neurology (R.L.S.), University of Miami, FL.
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Clinton B. Wright, MD
National Institute of Neurological Disorders and Stroke (C.B.W.), National Institutes of Health, Bethesda, MD.
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William S. Kremen, PhD
Center for Behavior Genetics of Aging (W.S.K.), University of California, San Diego, La Jolla, CA.
Department of Psychiatry (W.S.K.), University of California, San Diego, La Jolla, CA.
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Lewis C. Becker, MD
GeneSTAR Research Program (L.R.Y., B.G.K., L.C.B., P.A.N.), Johns Hopkins University School of Medicine, Baltimore, MD.
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Paul M. Thompson, PhD
Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Marina del Rey (N.J., P.M.T.).
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Thomas H. Mosley, PhD
Department of Geriatric Medicine, Memory Impairment and Neurodegenerative Dementia (MIND) Center, University of Mississippi Medical Center, Jackson (T.H.M.).
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Joanna M. Wardlaw, MD
Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, United Kingdom (M.L., I.J.D., D.C.M.L., M.E.B., J.M.W.).
Centre for Clinical Brain Sciences, Edinburgh Imaging, Centre for Cognitive Ageing, University of Edinburgh, United Kingdom (M.E.B., J.M.W.).
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M. Arfan Ikram, MD, PhD
Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands (M.J.K., D.V., N.A., G.V.R., M.W.V., C.M.v.D., M.A.I., H.H.H.A.).
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Hieab H.H. Adams, MD, PhD
Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands (M.J.K., D.V., N.A., G.V.R., M.W.V., C.M.v.D., M.A.I., H.H.H.A.).
Department of Radiology and Nuclear Medicine (G.V.R., M.W.V., H.H.H.A.)
Department of Clinical Genetics, Erasmus MC, Rotterdam, the Netherlands (H.H.H.A.).
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Sudha Seshadri, MD
Icelandic Heart Association, Kopavogur (V.G.G., S.S.).
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Perminder S. Sachdev, MD
Centre for Healthy Brain Ageing, School of Psychiatry (K.A.M., W.W., H.B., J.J., A.T., J.T., P.S.S.), University of New South Wales, Sydney, Australia.
Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, Australia (P.S.S.).
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Stephen M. Smith, PhD
Wellcome Centre for Integrative Neuroimaging (WIN FMRIB) (L.T.E., F.A.-A., L.G., M.J., S.M.S.), University of Oxford, United Kingdom.
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Lenore Launer, PhD†
Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Intramural Research Program (L.L.), National Institutes of Health, Bethesda, MD.
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William Longstreth, MD†
Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA (J.B., B.P., W.L.).
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Charles DeCarli, MD†
Alzheimer’s Disease Center and Imaging of Dementia and Aging (IDeA) Laboratory, Department of Neurology and Center for Neuroscience University of California at Davis (C.D.).
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Reinhold Schmidt, MD†
Clinical Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, Austria (E.H., R.S.).
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Myriam Fornage, PhD†
Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, TX (R.X., M.F.).
Human Genetics Center, School of Public Health UT, Houston, TX (M.F.).
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Stephanie Debette, MD, PhD†
University Bordeaux, Inserm, Bordeaux Population Health Research Center, France (M.S., C.T., S.D.).
Department of Neurology, CHU de Bordeaux (University Hospital), Bordeaux, France (S.D.).
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Paul A. Nyquist, MD, MPH† https://orcid.org/0000-0001-6078-3543 [email protected]
GeneSTAR Research Program (L.R.Y., B.G.K., L.C.B., P.A.N.), Johns Hopkins University School of Medicine, Baltimore, MD.
Departments of Neurology, Critical Care Medicine, Neurosurgery (P.A.N.), Johns Hopkins University School of Medicine, Baltimore, MD.
Critical Care Medicine Department (P.A.N.), National Institutes of Health, Bethesda, MD.
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John P. Hussman Institute for Human Genomics (N.D.D.), University of Miami, FL.

Notes

*
Drs Armstrong, Mather, and Sargurupremraj shared first authorship.
†
Drs Launer, Longstreth, DeCarli, Reinhold Schmidt, Fornage, Debette, and Nyquist shared senior authorship.
Preprint posted on BioRxiv June 27, 2019. doi: https://doi.org/10.1101/683367.
This manuscript was sent to Eric E. Smith, MD, MPH, Guest Editor, for review by expert referees, editorial decision, and final disposition.
For Sources of Funding and Disclosures, see page 2119.
The Data Supplement is available with this article at Supplemental Material.
Correspondence to: Paul A Nyquist, MD, MPH, 416 Phipps, 600 N Wolfe St, Baltimore, MD 21287. Email [email protected]

Disclosures

Dr Marchini is an employee of, and owns stock and stock options for, Regeneron Pharmaceuticals; Dr Sachdev received personal fees from Biogen; Henry Brodaty, Advisory Board member, Nutricia Australia; Philippe Amouyel, advisor for Foundation Alzheimer non-profit organization and for Genoscreen Biotech company; Christine Fennema-Notestine, received finding from National Institutes of Health Grants R01AG022381; Dr Gottesman, American Academy of Neurology, Associate Editor, Neurology; Drs Thompson and Jahanshad received grant support from Biogen Inc for work unrelated to the contents of this manuscript. Dr DeCarli, consultant to Novartis; Markus Scholz disclosed Pfizer, Inc, grants; Ralph Sacco, grant support from Boehringer Ingelheim, National Institute of Neurological Disorders and Stroke Grants; Dr Wardlaw, grant support from the Medical Research Council, Age UK, Scottish Funding Council, and Row Fogo Trust during the conduct of the study and grant support from Fondation Leducq, Wellcome Trust, Engineering and Physical Sciences Research Council, Chest Heart Stroke Scotland, British Heart Foundation, Stroke Association, Alzheimer’s Society and Alzheimer’s Research UK outside the submitted work. The other authors report no conflicts.

Sources of Funding

This work is supported by the National Institute of Neurological Disorders and Stroke, National Institutes of Health, R01AG059874, P41EB015922, R56AG058854, U54 EB020403, R01AG022381, U54EB020403, R01AG059874, and R01NS115845. Medical Research Council, Age UK, Scottish Funding Council, Row Fogo Trust, The Welcome Trust, Age UK, Cross Council Lifelong Health and Wellbeing Initiative, Leverhulme Trust, National Institute for Health Research, Biotechnology and Biological Sciences Research Council, UK Medical Research Council, Icelandic Heart Association, and the Althingi, Austrian Science Fund (FWF), Australian National Health and Medical Research Council, Austrian National Bank, Anniversary Fund, European Commission FP6 STRP, European Community’s 5th and 7th Framework Program, Netherlands Organization for Scientific Research, Netherlands Consortium for Healthy Aging, Russian Foundation for Basic Research, Russian Federal Agency of Scientific Organizations, Liaison Committee between the Central Norway Regional Health Authority and the Norwegian University of Science and Technology, Norwegian National Advisory Unit for functional magnetic resonance imaging (MRI), Leipzig Research Center for Civilization Diseases (LIFE), Bristol-Myers Squibb, Netherlands Heart Foundation, French National Research Agency (ANR), Foundation Leducq, Joint Programme of Neurodegenerative Disease research, Bordeaux University, Institut Pasteur de Lille, the labex DISTALZ and the Centre National de Génotypage. Deutsche Forschungsgesellschaft (DFG) no. WI 3342/3-1 and grants from European Union, European Regional Development Fund, Free State of Saxony within the framework of the excellence initiative, LIFE-Leipzig Research Center for Civilization Diseases no. 100329290, 713-241202, 14505/2470, 14575/2470. Max Planck Society, State of Saxony, Brain Foundation, Bristol-Myers Squibb, National Health and Medical Research Council (NHMRC) of Australia, NHMRC of Australia, Parkinson’s UK; Medical Research Council - Dementias Platform UK, National Institute on Aging, Bethesda, MD, P30 AG 010129 Full details of funding support for each cohort are detailed in the Data Supplement.

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Common Genetic Variation Indicates Separate Causes for Periventricular and Deep White Matter Hyperintensities
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