BMC Medical Genetics

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Genetic correlates of brain aging on MRI and cognitive test
measures: a genome-wide association and linkage analysis in the
Framingham study
Sudha Seshadri*1,2, Anita L DeStefano1,3, Rhoda Au1,2, Joseph M Massaro1,3,4,
Alexa S Beiser1,2,3, Margaret Kelly-Hayes1,2, Carlos S Kase1,2,Ralph B D'Agostino Sr1,4, Charles DeCarli5, Larry D Atwood1,2 andPhilip A Wolf1,2
Address: 1The National Heart Lung and Blood Institute's Framingham Heart Study, Framingham, MA, USA, 2Department of Neurology, Boston
University School of Medicine, Boston, MA, USA, 3Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA,
4Statistics and Consulting Unit, Department of Mathematics, Boston University, Boston, MA, USA and 5The Department of Neurology, Universityof California – Davis, Sacramento, CA, USA
Email: Sudha Seshadri* - suseshad@bu.edu; Anita L DeStefano - adestef@bu.edu; Rhoda Au - rhodaau@bu.edu;
Joseph M Massaro - jmm@bu.edu; Alexa S Beiser - alexab@bu.edu; Margaret Kelly-Hayes - mkhayes@bu.edu; Carlos S Kase - cskase@bu.edu;
Ralph B D'Agostino - ralph@bu.edu; Charles DeCarli - cdecarli@bu.edu; Larry D Atwood - lda@bu.edu; Philip A Wolf - pawolf@bu.edu* Corresponding author

Published: 19 September 2007
<supplement>
S Vasan</editor><title><note>Research</note><p>The Framingham <url>http://www.biomedcentral.com/content/pdf/1471-2350-8-S1-info.pdf</url>
Heart Study 100,000 single nucleotide polymorphisms resource</p> </title> <editor>Emelia</supplement>
J Benjamin, Caroline S Fox, Daniel J Gottlieb, Cashell E Jaquish, Daniel Levy, James B Meigs, Christopher Newton-Cheh, Christopher J O'Donnell and Ramachandran

BMC Medical Genetics 2007, 8(Suppl 1):S15

doi:10.1186/1471-2350-8-S1-S15

This article is available from: http://www.biomedcentral.com/1471-2350/8/S1/S15© 2007 Seshadri et al; licensee BioMed Central Ltd.
This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract
Background: Brain magnetic resonance imaging (MRI) and cognitive tests can identify heritable endophenotypes
associated with an increased risk of developing stroke, dementia and Alzheimer's disease (AD). We conducted a genomewide association (GWA) and linkage analysis exploring the genetic basis of these endophenotypes in a community-based
sample.
Methods: A total of 705 stroke- and dementia-free Framingham participants (age 62 +9 yrs, 50% male) who underwent
volumetric brain MRI and cognitive testing (1999–2002) were genotyped. We used linear models adjusting for first
degree relationships via generalized estimating equations (GEE) and family based association tests (FBAT) in additivemodels to relate qualifying single nucleotide polymorphisms (SNPs, 70,987 autosomal on Affymetrix 100K Human Gene
Chip with minor allele frequency ≥ 0.10, genotypic call rate ≥ 0.80, and Hardy-Weinberg equilibrium p-value ≥ 0.001) tomultivariable-adjusted residuals of 9 MRI measures including total cerebral brain (TCBV), lobar, ventricular and whitematter hyperintensity (WMH) volumes, and 6 cognitive factors/tests assessing verbal and visuospatial memory, visualscanning and motor speed, reading, abstract reasoning and naming. We determined multipoint identity-by-descentutilizing 10,592 informative SNPs and 613 short tandem repeats and used variance component analyses to compute LOD
scores.
Results: The strongest gene-phenotype association in FBAT analyses was between SORL1 (rs1131497; p = 3.2 × 10-6)
and abstract reasoning, and in GEE analyses between CDH4 (rs1970546; p = 3.7 × 10-8) and TCBV. SORL1 plays a role inamyloid precursor protein processing and has been associated with the risk of AD. Among the 50 strongest associations(25 each by GEE and FBAT) were other biologically interesting genes. Polymorphisms within 28 of 163 candidate genes
for stroke, AD and memory impairment were associated with the endophenotypes studied at p < 0.001. We confirmed
our previously reported linkage of WMH on chromosome 4 and describe linkage of reading performance to a marker
on chromosome 18 (GATA11A06), previously linked to dyslexia (LOD scores = 2.2 and 5.1).
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Conclusion: Our results suggest that genes associated with clinical neurological disease also have detectable effects on
subclinical phenotypes. These hypothesis generating data illustrate the use of an unbiased approach to discover novel
pathways that may be involved in brain aging, and could be used to replicate observations made in other studies.

Background

Methods

Age-related neurological diseases such as stroke anddementia represent a substantial population burden, andone in three persons will develop either stroke or dementia in their lifetime [1]. Twin studies suggest that 37–78%
of the variance in the age of onset of Alzheimer's disease(AD), the most common cause of dementia in the elderly,
can be attributed to additive genetic effects [2,3]. Conversely, cognitively healthy aging also has a substantial
genetic basis [4]. Finally ischemic stroke [5-7] and vascular cognitive impairment are also heritable [8]. However,surprisingly few genes have been identified that determine
the risk of developing stroke (PDE4D, ALOX5AP) [9-11]
or Alzheimer's disease (APOE4) [12], in the community as
a whole, that is for persons not from autosomal dominant, early-onset families. One reason may be that studies
to date have been underpowered to detect small effects.
Two additional challenges to a more complete understanding of the genetic basis of these aging related brain
diseases have been the late phenotypic manifestation ofthese conditions and their complex, polygenic mode ofinheritance. Multiple genes interacting with each otherand with environmental factors likely create a complex
gradient of susceptibility to disease. We hypothesized thatstudying the genetic basis for the gradient of susceptibility
underlying AD and stroke, using endophenotypes, would
provide insights into the genetics of these late-onset neurological diseases. Endophenotypes (or intermediate phenotypes) are heritable traits that reveal the actions ofgenes predisposing an individual to develop a disease but
they often manifest years before clinical and pathologicaldiagnostic criteria for the disease are met.

Study sample
The study design, selection criteria and participant demographics of the Framingham Original and Offspringcohorts have been detailed in prior publications [21,22].
A total of 1345 persons, who were members of the 330
largest families across these two cohorts, underwent genotyping using the Affymetrix GeneChip Human Mapping
100K single nucleotide polymorphism (SNP) set. The
Overview provides details of this sample [23]. The study
sample for the current analyses comprised of 705 strokeand dementia-free Framingham Study participants who
were genotyped and had undergone volumetric brain MRI
and/or cognitive testing between 1999 and 2002. Amongthe 1345 eligible persons who were genotyped, 508 persons were excluded since they died prior to their 7th Offspring examination, did not attend this examination,
declined or were unable to complete MRI or cognitive testing, 12 persons were excluded for prevalent stroke (n =12) at the time of MRI and cognitive testing and 11 persons with neurological diseases such as multiple sclerosis
or brain tumor that could impact study phenotypes werealso excluded; all participants were screened, but none
required exclusion for dementia at the time of MRI. Nine
individuals were excluded because covariate information
was not available. This study was approved by the Institutional Review Board of Boston University Medical Center;all participants provided written informed consent
including consent for genetic studies.

Volumetric brain MRI and comprehensive cognitive testing have been used to define heritable, reproducible,
quantitative endophenotypes which in turn relate to therisk of developing dementia or stroke [13-19]. Twin studies have demonstrated substantial heritability of these
endophenotypes [20]. The recent availability of highthroughput platforms permits genome-wide association
studies (GWAS) that incorporate a more comprehensive
and unbiased approach to detect genes with modest phenotypic effects. We present the results of a GWAS of structural and functional phenotypes previously associatedwith cellular and vascular brain aging.

Phenotype definition
The list of study phenotypes is shown in Column 1 ofTable 1.
Volumetric brain MRI
Details of brain MRI acquisition parameters, blinded
image analysis, definition of brain volumes (indexed for
cranial cavity size) and the mean and standard deviation(SD) values for these measures in the larger sample of all
Framingham subjects (n = 2259) who underwent brain
MRI, have been published previously [14,15,24-27].
Mean and SD values and heritability estimates for each ofthese parameters in the current study sample are available
online at http://www.ncbi.nlm.nih.gov/projects/gap/cgibin/study.cgi?id=phs000007. Digital information from
the MRI scans was transferred to a central laboratory
directed by one of the authors (C.D.) for processing andanalysis. Analysis was done blind to the subjects' genotype, demographic and vascular risk factor data. Analyses

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Table 1: Structural (Volumetric MRI) and Functional (Cognitive Testing) Brain Aging Phenotypes
Exam cycle
Phenotypic Trait (AbbreviatedVariable Name)

Number of traits

N

Original Cohort (Exam 26)

Offspring Cohort (Exam 7)

Total Cerebral Brain Volume (ATCBV)

1705

Original Cohort & Offspring data are pooled

Frontal Brain volume (AFBV)
Parietal Brain Volume (APBV)
Occipital Brain Volume (AOBV)
Temporal Brain volume (ATBV)
Hippocampal Volume (AHPV)Lateral Ventricular Volume (ALVV)†
Temporal Horn Volume (LTHBV)†White Matter Hyperintensity Volume(BMRIZLWMHVMV) [Age-, sexspecific Z-score of log-normalized
white matter hyperintensity volume]

1
1
1
1
1
1
1
1

705
705
705
705
327
705
705
705

Factor 1:Verbal Memory (F1)1694Factor 2:Visual Memory andOrganization (F2)
Factor 3: Measure of attention andexecutive function-Trails A and B (F3)
Boston Naming Test (Nam)
Similarities (Sim)
Wide-Range Achievement Test
(WRAT)

1

694

1

694

1
1
1

694
694
694

Covariates Used to Create
Multivariable-Adjusted
Residuals

Volumetric Brain MRI*
Age, age-squared, sex, current
smoking status, diabetes, systolicblood pressure, anti-hypertensive
drugs, atrial fibrillation, EKG-LVH
at Offspring examination 7 andOriginal cohort 26; data fromsex-specific regressions pooled.

Current smoking status, diabetes,
systolic blood pressure, antihypertensive drugs, atrial
fibrillation, EKG-LVH at Offspringexamination 7 and Original
cohort 26; data from sex-specificregressions pooled.

Cognitive Test Performance
Original cohort and Offspring data are pooled

Birth cohort by decade,education, Framingham Stroke
Risk Profile score, plasma
homocysteine concentrations (atthe 20th Original cohort and the
6th Offspring examinations),
apolipoprotein E genotype (ε4+ve/-ve); data from sex-specificregressions were pooled.

*All MRI volumes were expressed as a ratio of total intracranial volume (TCV), trait names used in this table correspond to trait names posted at the website; an 'A' preceding
the trait name refers to the multivariable adjusted residual.
† Log-normalized values of these traits were used. Website http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007.

were done using semi-automated measurements of pixel
distributions based on mathematical modeling of MRI
pixel intensity histograms for cerebrospinal fluid andbrain matter (white matter and gray matter) to determine
the optimal pixel intensity threshold that distinguishedcerebrospinal fluid (CSF) from brain matter. Brain volume was determined in coronal sections by manually outlining the intracranial vault above the tentorium todetermine the total cranial volume (TCV). Next, the skull
and other non-brain tissues were removed from theimage, followed by mathematical modeling to determine
total brain volume (TBV). TBV included the supratentorial
gray and white matter and excluded the CSF. We used theratio of TBV to TCV (Total Cerebral Brain Volume, TCBV)
as a measure of brain volume to correct for differences inhead size. Regional brain volumes were measured as thesum of the segmented right and left lobar volumes for that
region indexed to the intracranial volume; frontal (FBV),
parietal (PBV), occipital (OBV) and temporal (TBV) lobar
brain volumes and the regional brain volume of the hippocampus (based on hand-drawn outlines) were assessed.
Two measures of ventricular volume were used: the lateral
ventricular volume, and the temporal horn volume eachof which was measured as the sum of the volumes for twosides, log-normalized and indexed over TCV. Finally thewhite matter hyperintensity volume was measured as a zscore within 10-year age- and sex-specific categories of thelogarithmically transformed continuous variable (WMH).
All analyses were performed using a custom-designed
image analysis package, QUANTA 6.2, operating on a Sun

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Microsystems (Santa Clara, CA) Ultra 5 workstation. Theinter-rater reliabilities ranged between 0.90 and 0.94 for
TCV, TCB, regional brain and ventricular volumes andwhite matter hyperintensities, and intra-rater reliabilities
average 0.98 across all measures.
Cognitive measures
Subjects were administered a neuropsychological test battery using standard administration protocols and trained
examiners. Details of the tests administered and normative values for the Framingham Original and Offspringcohorts have been previously published [13,28]. Since
individual cognitive tests are scored measured on different
scales and since scores are known to be associated withage and sex, we transformed the variables, separately bysex, to obtain variables that are comparable across tests.
First, natural logarithmic transformations were applied tonormalize raw scores that had a skewed distribution.
Next, each variable was regressed on age and residuals
from these regressions were standardized using a z-scoretransformation. The resulting standardized cognitive testscores were then either summed to create 3 factors, eachcharacterizing a specific cognitive domain: verbal memory(Factor 1, F1), visuospatial memory and organization(Factor 2, F2) and attention and executive function (Factor 3, F3), or were used individually (Similarities [Sim],
Boston Naming Test [BNT] and Wide Range Achievement
Tests [WRAT]). Details of test source and parameters used
to define each individual test and factor are outlined in
Additional data file 1, table 1.
Genotyping
The Overview [23] describes the Affymetrix 100K SNP
GeneChip genotyping http://gmed.bu.edu/about/geno
typing.html and the Marshfield short-tandem repeat genotyping performed by the Mammalian Genotyping
Service
http://research.marshfieldclinic.org/genetics.
Only the SNP data were used for GWA studies whereas
both SNP and STR data were combined for linkage analyses.
Statistical analysis
As detailed in the Overview [23], we used linear models
adjusting for first degree relationships via generalized estimating equations (GEE) and family based association
tests (FBAT). All tests were performed using additive
genetic models to relate qualifying SNPs to multivariableadjusted residuals of the 9 MRI measures and the 6 cognitive factors/tests described earlier. Qualifying SNPs (n =70,897) were defined as autosomal SNPs with genotypiccall rate ≥80%, minor allele frequency ≥10% and in
Hardy-Weinberg equilibrium with p ≥ 0.001. Additionally, for FBAT analyses ≥10 informative families wererequired. For the linkage analyses, we used Merlin software to compute multipoint identity-by-descent utilizing

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10,592 informative SNPs and 613 short tandem repeats
selected to minimize LD [29,30]; we then used maximum
variance component analyses in SOLAR to compute LOD
scores as a measure of linkage [31].
Multivariable-adjusted trait residuals for the phenotypic
traits listed in Table 1 were computed using linear regression and the full set of all Framingham Study participantsin whom the phenotype of interest was available. For the
MRI analyses, residuals were derived from multivariablelinear regressions in SAS [32], adjusting for the variables
that we had previously found were related to MRI measures: age and if appropriate age-squared, current smoking
status, systolic blood pressure in mm Hg, use of antihypertensive drugs and presence or absence of diabetes
mellitus, atrial fibrillation and electrocardiographic left
ventricular hypertrophy. Similarly, residuals were derived
for each cognitive measure from multiple linear regressions and adjusting for the following covariates: birthcohort by decade, education (high school, high school
graduate, some college or college graduate), Framingham
Stroke Risk Profile score, plasma homocysteine concentrations (at the 20th Original cohort and the 6th Offspringexaminations) and apolipoprotein E genotype (ε4 +ve/-ve). Unless otherwise specified, covariate data for all 15
phenotypic measures were drawn from the 26th Original
cohort and the 7th Offspring examinations. Data from sexspecific regressions were pooled for the SNP-phenotype
association and linkage analyses. Winsorized residuals(truncating extreme values at ± 3.5 standard deviations)were used for linkage analysis of phenotypes with departures from normality as assessed by skewness and kurtosis(TBV, temporal horn volume, F1, F2, F3, Sim, BNT and
WRAT).
Presentation of results
We used several strategies to explore the resulting phenotype-SNP association and linkage results. First, we used an
unbiased approach and collated the 50 strongest phenotype-SNP associations (those with the smallest p-value)
including 25 phenotype-SNP associations each for GEE
and FBAT analyses, and all linkage results with a LOD
score > 2.0. All SNPs were annotated using the UCSC
genome browser tables http://genome.ucsc.edu/ [33,34]
to examine if the SNP was within a gene and to identify
this gene.

Next, we examined the data for genes with pleiotropic
effects. We assessed if genes that were associated with
TCBV or WMH at p < 0.001 (as primary structural indicators of cellular and vascular brain damage) were also associated with at least two of the other brain MRI measures(p < 0.01). We also evaluated if genes that were associatedwith lower scores on either F1 or F3 at p < 0.001 (as primary indicators of amnestic, Alzheimer-type and vascular

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cognitive impairment) seemed associated with other cognitive test measures.
Finally, we investigated SNP associations in candidate
genes. There are few candidate genes that have been
directly linked in prior studies to the endophenotypes
described in these analyses. Hence, we investigated genes
previously reported to be associated with stroke, Alzheimer's disease, brain aging and vascular dementia in established databases including the NCBI Gene, PubMed andOMIM databases [35], the Alzforum Alzgene database
http://www.alzforum.org/res/com/gen/alzgene [36], andthe Science of Aging Knowledge Environment genes/intervention
database
http://sageke.sciencemag.org/cgi/
genesdb [37]. All SNPs within 60 kb of the candidate
genes (listed in Additional data file 1, Additional table 2)
were examined for association with the 15 phenotypic
traits described in this paper. Only phenotype-SNP associations with a p-value < 0.001 are described in Table 4.

Results
The brain aging phenotypic traits available in the Framingham Study 100K SNP resource with details of the sample size, statistical transformation and details of thecovariates used for multivariable adjustment of each phenotype are provided in Table 1. The mean age of the 705
subjects was 62 ± 12 years, 46% were male, 79 were fromthe Original Framingham cohort (enrolled in 1948–50)
while 626 belonged to the Offspring cohort. Table 2 (sections a and b) provide the top twenty-five phenotype-SNP
associations ranked in order by lowest p-value for the GEE
and FBAT models and Table 2 (section c) presents the phenotype-SNP associations with LOD scores ≥ 2.0 and thecorresponding 1.5 – LOD support interval. The strongest
phenotype-SNP association in GEE analyses was between
a SNP on the retinal cadherin gene CDH4 and TCBV
(rs1970546; p = 3.7 × 10-8) and this was the only association that achieved genome-wide significance if we applieda conservative Bonferroni correction as detailed in the
Overview (p < 5 × 10-8); in FBAT analyses the strongest
phenotype-gene association was between a SNP on thegene SORL1 (rs1131497; p = 3.2 × 10-6) and performancein Sim, a test of abstract reasoning. Assuming an additive
genetic model, a minor allele frequency of10% and a very
conservative α of 1 × 10-8 we had an 80% power to detect
an effect of 0.52 standard deviations (SD) in a given variable. For TCBV this translates to an effect size of 1.71%
equivalent to8.5 years of brain aging.
We had previously reported high heritability for WMH. In
the current analyses examining associations between individual SNPs and WMH there was one association that wasin the top 50 and others that were in the top 100, but
none were within the arbitrarily chosen cut-off for Table 2
which only details the top 25 phenotype-SNP associa-

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tions. In FBAT analyses, rs1822285 and rs166085, on
chromosomes 11 and 5 respectively, were associated with
WMH (p = 6.4 × 10-5 and 9.3 × 10-5) but these SNPs are
not within known genes. In GEE analyses, two SNPs on
the biologically plausible gene CLDN10 or claudin 10, an
integral membrane protein that is a component of thetight junction, were related to WMH (rs10508012 andrs10508013, p = 3.3 × 10-5 and 4.9 × 10-5). Other extragenic SNPs and SNPs on biologically interesting genes(the glial growth factor NRG1 and the potassium channel
protein KCNMA1) were also associated with WMH with pvalues in the 10-5 to 10-4 range. We again observed thelinkage between WMH and a region on chromosome 4that we had previously reported [38]. Within this linkage
peak (1.5 LOD support interval) were biologically interesting candidate genes such as EVC and EVC1 related to
the Ellis van Creveld syndrome and GRK4, previously
related to salt-sensitive hypertension [39,40].
We observed that performance on the Wide-Range
Achievement Test (WRAT), a test of reading ability, waslinked to a region on chromosome 18p with a maximum
LOD score of 5.1 at rs1846090. The 1.5 LOD support
interval of this linkage peak includes an STR marker,
D18S53, that has been associated with dyslexia in someprior studies [41] although not in others [42]. In the current study the observed LOD score for WRAT at D18S53(GATA11A06) was 2.5.
Table 3 provides all phenotype-SNP associations with a
GEE or FBAT p < 0.001 for a key phenotype identified apriori, and a GEE or FBAT p < 0.01 for at least two otherphenotypes within each of two groups of related phenotypes. These two groups were the brain MRI parameters(with TCBV and WMH as the key phenotypes) and thecognitive tests (run once with F1 and once with F3 as the
key phenotype). If adjacent SNPs were in significant linkage disequilibrium [LD] (r2 > 0.80) results are only presented for the strongest phenotype-SNP association noted
within the LD block. For the MRI parameters, GEE models
identified 10 SNPs and FBAT models identified 7 SNPs
using TCBV as the index phenotype and none using WMH
as the index phenotype; among these were 4 SNPs on
PDE3A and one each on PDE4B and SCN8. For the cognitive phenotypes GEE models identified 7 phenotype-SNP
associations using F1 as the key phenotype and 4 using F3
as the key phenotype; FBAT models did not identify any
phenotype-SNP associations meeting these prespecified
criteria.
We identified 163 potential candidate genes and looked
for phenotype-SNP associations using all SNPs on the
100K Affymetrix gene chip that were within 60 kb of thecandidate gene. 23 genes had no analyzable SNPs within
the 100K Affymetrix gene chip while 140 genes had 1430

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Table 2: Structural and Functional Brain Aging (MRI and Cognitive Testing) Phenotypes† for FHS 100K Project: Results of Associationand Linkage Analyses
2, section a: GEE, Top 25 p-values

Phenotype

SNP

ATCBV

rs1970546

F3

Chromosome

Physical location

GEE p-value

FBAT p-value

Gene Region (within 60 kb)

2059287333

4.0 × 10-80.005

rs2179965*

188514033

1.1 × 10-6

0.013

Nam

rs1155865

467562623

1.6 × 10-6

0.554

F3

rs2832077

21

29062892

1.8 × 10-6

0.007

F2

rs2352904

1448442551

2.1 × 10-6

0.012

F2

rs6914079*

614704344

2.2 × 10-6

0.018

F2

rs9325032

5146395409

2.8 × 10-6

0.008

ALLV

rs2847476

11113696226

3.0 × 10-6

0.001

NNMT

Sim

rs3891355

12

105453162

3.2 × 10-6

0.089

POLR3B, RFX4

ATBV

rs5028798

1134562011

3.3 × 10-6

0.394

EHF

Nam

rs530965

1178742749

3.5 × 10-6

0.119

Nam

rs9303401

1754202944

4.9 × 10-6

0.099

PPM1E

AFBV

rs952700

1199090946

5.7 × 10-6

0.003

CNTN5

F3

rs1031381

11

133593892

6.0 × 10-6

0.075

NCAPD3F2

rs10489896*

1230890353

6.2 × 10-6

0.109

TARBP1WRAT

rs9300212

1233592433

8.2 × 10-6

0.002

Nam

rs1831521

990488911

8.4 × 10-6

0.002

F3

rs934299

2137172672

9.0 × 10-6

0.318

ALTHBVrs360929

4153265305

9.1 × 10-6

0.055

F2

rs2893363

729952294

9.6 × 10-6

0.812

WRAT

rs10502991

1850243287

1.0 × 10-5

0.001

APBV

rs2769965

979048598

1.1 × 10-5

0.012

APBV

rs719435

731324796

1.1 × 10-5

0.188

F1

rs9292769

540433668

1.1 × 10-5

0.163

Nam

rs10506718

1275377929

1.1 × 10-5

0.402

Physical location

GEE p-value

FBAT p-value

CDH4

DIRAS2

C7orf41

CCDC1292, section b: FBAT, Top 25 p-values

Trait

SNP

Chromosome

Gene Region(s) (within 60 kb)

ALLV

rs7124781

1142513374

0.008

2.0 × 10-7

Sim

rs1131497

11

121007955

0.008

3.2 × 10-6

WRAT

rs10506065

1230342307

0.050

5.0 × 10-6

AFBV

rs3852286

7140126618

0.145

6.5 × 10-6

BRAF and MRPS33

WRAT

rs4529807

1022358107

0.013

1.1 × 10-5

DNAJC1

F3

rs847342

1471805791

0.441

1.3 × 10-5

RGS6

SORL1

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Table 2: Structural and Functional Brain Aging (MRI and Cognitive Testing) Phenotypes† for FHS 100K Project: Results of Associationand Linkage Analyses (Continued)
AFBV

rs719775

3

64366493

0.001

1.8 × 10-5

Sim

rs936111

1599376659

0.014

2.1 × 10-5

LRRK1

ATBV

rs2143881

650960846

0.077

2.1 × 10-5

TFAP2B

AHPV

rs9293140

524643203

0.092

2.1 × 10-5

CDH10

AFBV

rs9288446*

2212907533

0.001

2.3 × 10-5

ERBB4

APBV

rs1472962

495949555

0.004

3.1 × 10-5

PDLIM5

ATBV

rs2793772

1399029574

0.047

3.3 × 10-5

CLYBL

F2

rs1333583

1382037151

0.031

3.4 × 10-5

ATBV

rs10497352

2170781278

0.005

3.6 × 10-5

ZNF650F1

rs497836

13

93605509

0.020

3.8 × 10-5

GPC6

APBV

rs6459928

7

158428045

0.271

4.0 × 10-5

VIPR2

AHPV

rs1963442

375872661

0.046

4.3 × 10-5

ZNF717

APBV

rs10503238

84027465

0.002

4.4 × 10-5F2

rs2861215

277958447

0.006

4.7 × 10-5

Nam

rs9311168

337952421

0.067

4.9 × 10-5

CTDSPLF2

rs2029395

2

1.8 × 10-8

0.027

4.9 × 10-5

TTN, FLJ39502

ATCBV

rs10510717

341307494

0.005

5.0 × 10-5

CTNNB1ATBV

rs1433527

2

1.8 × 10-8

0.028

5.1 × 10-5

DDX18

Physical location

1.5 – LOD
support interval
start

1.5 – LOD support
interval end

2, section c: Linkage Peaks with LOD scores ≥ 2.0.

Trait

SNP closest to
linkage peak

Chromosome

LOD score

WATBV†

rs1547275

979548023

76128637

86702472

2.81

WF3†

rs2975420

819534278

12651557

22836499

2.20

WNam†

rs2765241

162439617

59085658

67006164

2.95

WNam†

rs293966

1126536069

21237681

33363547

2.14

WWRAT†

rs10512187

987400439

84893406

110115339

2.04

WWRAT†

rs1328822

1393605666

87815515

97536766

2.50

WWRAT†

rs1846090

1814573728

13423610

19583575

5.10

WWRAT†

rs10518241

19

3540074

18881786189414

2.33

BMRIZLWMH
VMV

rs4426714

450526711059059505355

2.20

BMRIZLWMHV
MV

rs236535

1765788911

59677087

68475624

2.09

Autosomal SNPs with genotypic call rate ≥ 80%, minor allele frequency ≥ 10%, Hardy-Weinberg test p ≥ 0.001, and ≥10 informative families for FBAT. Genes in bold arehighlighted in discussion*Indicates a similar result for this trait was observed (but not shown) for a SNP with r2 = 1 to the reported SNP†Winsorized residuals were analyzed, hence trait names are prefixed with a 'W'; linkage results in bold are highlighted in the discussion

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Table 3: SNP Associations with a GEE or FBAT p-value < 0.001 for selected phenotype and p values < 0.01 for at least two otherphenotypes within selected group of related phenotypes
Selected Phenotype : (p <0.001)
TCBV
TCBV
TCBV
TCBV
TCBV
TCBV
TCBV
TCBV
TCBV
TCBV
Selected Phenotype : (p <0.001)
TCBV
TCBV
TCBV
TCBV
TCBV
TCBV
TCBV
Selected Phenotype : (p <0.001)
F1
F1
F1
F1
F1
F1
F1
F3
F3
F3
F3

SNP

Chr

rs646860
rs7763081
rs1444644
rs10505865
rs1444645
rs1444629
rs303816
rs2827980†
rs9297594†
rs10512927†

1
6
12
12
12
12
12
21
8
5

SNP

Chr

rs7740148
rs6496742
rs2788646
rs10500956†
rs2059943†
rs853256†
rs853260†

6
15
1
11
8
3
3

SNP

Chr

rs4733809
rs3923615
rs10515155
rs1204116
rs708891
rs10515159
rs10506214
rs608825
rs957603
rs10506214†
rs2109479

8
11
17
6
12
17
12
1
15
12
5

Physical Position

60310322
53816274
2045722720453861
20457264
2045417450469752
23524857
120287483
50346833
Physical Position

35063681
89324040
66518974
23435031
107140783
64290504
64289592
Physical Position

1.29E+08
2463810853836943
62462055
1.18E+08
54157692
41397957
2.33E+08
38796960
41397957
56979996

Gene

C1orf87
LRRC1
PDE3A
PDE3A
PDE3A
PDE3A
SCN8A

MRI phenotype showing strongest
association with SNP

ANKS1
PRC1
PDE4B

Phenotype showing strongest
association with SNP
AFBV
ATCBV
ATCBV
AFBV
ATCBV
AFBV
AFBV

Gene

TMEM75LUZP2RNF43KHDRBS2
CCDC60RAD51CEDARADDRAD51

6.5 × 10-4
6.5 × 10-4
8.8 × 10-5
1.4 × 10-4
1.8 × 10-4
2.7 × 10-4
6.1 × 10-4
5.9 × 10-5
1.3 × 10-4
8.5 × 10-4

APBV
ATCBV
ATBV
AFBV
ATBV
ATCBV
ATCBV
ATCBV
AFBV
ATCBV

Gene

FBAT p-value

0.003
6.4 × 10-4
3.3 × 10-4
0.003
1.4 × 10-4
7.5 × 10-4
4.4 × 10-4

Phenotype showing strongest
association with SNP
Sim
F1
F1
F1
F1
F1
F3
F3
F3
F3
F3

GEE p-value

GEE p-value

1.5 × 10-4
1.1 × 10-4
3.6 × 107.3 × 10-4
4.7 × 10-4
3.1 × 10-4
5.8 × 10-4
5.5 × 10-4
1.5 × 10-4
5.8 × 10-4
4.2 × 10-4

Table is ordered by primary phenotype (TCBV, F1 or F3; whether significant phenotype-SNP association was based on GEE or FBAT p-value and then alphabetically by genename.
* Genes in bold and highlighted in discussion; † SNPs were not within 60 KB of a known gene.

analyzable SNPs within 60 kb of the gene. Table 4 shows
the candidate genes and all phenotype-SNP associations
with a GEE or FBAT p-value < 0.001. In this analysis we
included all SNPs regardless of MAF since in prior studies
significant phenotype-SNP associations had been demonstrated for some of these genes with SNPs having MAF <10%.

Discussion
This is the first GWA study of volumetric brain MRI andcognitive phenotypes in a community-based sample ofadults with data drawn from two generations of persons
within the same families. The complete results of the association and linkage analyses are available at our website
http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007. This resource has the potential
to detect novel susceptibility genes for brain aging, toexamine the relevance within humans of promising can-didate gene associations with these diseases reported inanimal models, and to replicate findings observed inother cohort studies. We used several strategies to prioritize phenotype-SNP associations, but there remain otherunique ways of looking at these data that we and others
will continue to explore.
In our untargeted approach of ranking SNP associations
by the strength of the p-value, we found several phenotype-SNP associations within biologically interesting
genes (Table 2). The most exciting was a strong association between two SNPs in or adjacent to the gene SORL1
and performance on tests of abstract reasoning(rs1131497; FBAT p = 3.2 × 10-6 and rs726601; FBAT
p=8.2 X 10-4). SORL1 is an apolipoprotein E receptor,
binds alpha-2-macroglobulin, and is one component of alarge multimeric complex, termed the retromer complex
that is involved in retrograde transport of proteins fromPage 8 of 14(page number not for citation purposes)

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Table 4: Phenotypic Associations With Candidate Genes Previously Related To Stroke, Dementia And Brain MRI Or Cognitive
Function Phenotypes: Phenotype-SNP Associations With A GEE Or FBAT P-Value < 0.001

Gene

Phenotype

SNP

Chr

NGFB
PRSS25

AHPV
ATBV
ATCBV
F1
ATBV
F1
Sim
AHPV
AHPV
AHPV
WRAT
Sim
Sim
F3
ATCBV
ATBV
WRAT
ATCBV
F2
AFBV
BMRIZLWMH
VMV
ATCBV
Nam
F1
AFBV
AFBV
ATBV
APBV
Sim
Sim
ATCBV
F3
WRAT
F1
AFBV
ATBV
ATBV
Sim
ALLV
APBV
ALLV
WRAT
F1
AOBV
APBV
F1
F1
Nam

rs10489531
rs363685
rs363685
rs2105380
rs4689584
rs10517001
rs3796661
rs2870028
rs7678651
rs10516848
rs295973
rs10514882
rs9292216
rs10484657
rs10484657
rs6937001
rs7006733
rs1383893
rs10503906
rs10503919
rs10503926
rs10503927
rs502309
rs2168136
rs10512152
rs1573219
rs7038866
rs628873
rs1261791
rs1131497
rs726601
rs216903
rs2889717
rs2249017
rs7975693
rs10506151
rs10506151
rs10506176
rs10492226
rs1980236
rs724165
rs10520671
rs1024612
rs9303511
rs1158167
rs2326510
rs2007397
rs10483073
rs10483073

1
2
2
4
4
4
4
4
4
4
5
5
5
6
6
6
8
8
8
8
8
8
9
9
9
9
9
9
11
11
11
12
12
12
12
12
12
12
12
12
14
15
17
17
20
20
21
21
21

GRK4
HMGE
APBB2
SNCA

PDE4D

DCDC2
THBS2
GATA4
NRG1

VLDLR
NTRK2

TEK
BACE1
SORL1
VWF
A2M
LRRK2

CNTN1
LTA4H
IGF1
LTB4R2
NTRK3
CCL2
PRKCA
CST3
PRNP
BACE2

Physical Position
115495044
74726491
74726491
3019934
7179291
41051593
91044685
91122391
91125549
91132390
58945866
59170282
59187886
24224829
24224829
169382718
11645399
31844190
32291504
32519284
32660758
32662772
2562909258457784457363
8461717684617461
27162838
116705848
121007955
120986617
5975760
917806838847487
38874945
38957265
38957265
39561067
94906457
101270359
23876069
86347520
29573469
62158093
23526189464921141438062
41499167
41499167

GEE_Pvalue

FBAT_Pvalue

9.7 × 10-4
2.5 × 10-4
5.7 × 10-5
0.276
0.035
1.4 × 10-4
2.6 × 10-4
3.2 × 10-4
1.3 × 10-4
1.6 × 10-4
5.3 × 10-4
6.4 × 10-5
2.4 × 10-4
0.017
6.0 × 10-4
6.0 × 10-4
0.034
0.407
0.187
1.9 × 10-4
8.7 × 10-5
2.0 × 10-4
5.4 × 10-6
1.6 × 10-4
7.6 × 10-5
0.174
0.146
4.9 × 10-4
0.021
0.008
0.046
0.149
9.3 × 10-5
5.5 × 10-4
8.1 × 10-5
3.7 × 10-4
9.7 × 10-4
8.8 × 10-4
6.7 × 10-4
0.046
4.5 × 10-4
6.2 × 10-5
0.231
0.068
6.5 × 10-40.000718
0.030
3.5 × 10-4
1.7 × 10-4

0.046

5.8 × 10-4
1.0 × 10-4
0.009
0.332
0.180
0.102
0.115
0.060

9.5 × 10-4
0.015
0.021
3.1 × 10-4
8.9 × 10-4
4.2 × 10-4
0.288
0.009
0.002
0.157
0.006
0.018
5.6 × 10-4
3.6 × 10-4
0.051
3.1 × 10-4
3.2 × 10-6
8.2 × 10-4
9.5 × 10-4
0.061
0.005
1.6 × 10-4
0.371
0.622
9.3 × 10-4
0.162
0.062
7.0 × 10-4
7.9 × 10-4
0.032
0.064794
2.2 × 10-4

*Genes in bold are highlighted in discussion section.

endosomes to the trans-Golgi network [43,44]. This retromer complex appears to play a crucial role in the transportation of transmembrane proteins implicated in
Alzheimer's disease, such as amyloid precursor protein(APP) and β-site APP cleaving enzyme (BACE1). SORL1
protein is underexpressed in the frontal lobes of persons
with AD compared to controls and the SORL1 gene has
recently been associated with the risk of developing AD in

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BMC Medical Genetics 2007, 8(Suppl 1):S15

6 population samples [45,46]. Only 7 SNPs on or adjacent to the SORL1 gene were evaluated in the 100K
Affymetrix gene chip. One of these SNPs on SORL1 that
was associated with abstract reasoning (rs726601, FBAT p= 8.2 × 10-4, Table 4) was in LD (r2 > 0.8) with SNPs
(rs2282649, rs1010159) strongly associated with AD inthese studies [45,46].
In unbiased analyses, we also identified 3 genes that wereassociated with measures of frontal or parietal brain volume and with tests of executive function and abstract reasoning. These 3 genes, ERBB4, PDLIM5 and RFX4, (FBAT
ranks #11 and 12, GEE rank #9) have each been previously associated with schizophrenia or mood disorders,conditions known to be associated with smaller frontal
brain volumes and poorer performance on tests of executive function, even in unaffected family members [47,48].
ERBB4 is a neuregulin (NRG1) receptor involved in forebrain development and N-methyl-D-aspartate (NMDA)
receptor function. It has been associated with schizophrenia wherein excess of the IVS 12–15C > T has been noted(odds-ratio 2.98) [49,50]. NRG1 itself has been associatedwith schizophrenia in the Icelandic DeCODE population[51] and in other studies [52-54], with accelerated lobar
atrophy [52], and with bipolar disorders [55,56]. As
shown in Table 4, NRG1, like ERBB4, was associated withfrontal brain volume (FBV) in our sample. PDLIM5 polymorphisms have been associated with schizophrenia(rs2433320 and rs2433322) [52,55] and bipolar disorder(rs10008257 and rs2433320) [57]. Additionally the
PDLIM5 protein is a homolog of AD7c-NTP, a neural
thread protein associated with Alzheimer's disease, and isbeing studied as a possible CSF biomarker of AD [58]. A
final group of 3 genes, CDH4, VIPR2, CTNNB1 (GEE rank
#1 and FBAT ranks #17 and 24) have been shown in animal studies to play an important role in neural tract andsynaptic development [59-61]. Using linkage analyses, wewere able to replicate a previous report that dyslexia waslinked to a short-tandem repeat marker D18S53 on chromosome 18p11.2.
We examined pleiotropic effects by identifying SNP associations across two sets of related phenotypes. In theseanalyses, we uncovered a different set of genes, none ofwhich have been related to brain volumes, cognitive function, stroke or dementia in prior population studies.
However, there are biologically interesting genes related
to brain volumes including PDE3A, previously related to
all aspects of thrombosis [62], SCN8A linked to cerebellar
ataxia with mental retardation [63], and PDE4B which has
been associated with schizophrenia [64].
We also evaluated SNPs within some candidate genes previously reported to be associated with stroke and dementia in animal studies or in population samples, andhttp://www.biomedcentral.com/1471-2350/8/S1/S15

observed that several of these SNPs were associated with
MRI and cognitive endophenotypes that increase the riskof these conditions; this gene list is representative but notcomprehensive. Among these genes are PDE4D and
LTA4H that have been previously related to stroke in several population samples [9,10]; NGFB, NTRK2 andNTRK3 (a neural growth factor and two receptors for neural growth factors) genes, previously associated with performance on memory tasks in animal studies [65,66];
BACE1, PRNP and A2M, genes associated with AD in casecontrol or family-based association studies [36,67,68],
VLDLR, a gene previously associated with an increased
risk of dementia in the presence of vascular risk factors[69] and LRRK2, a gene associated with an increased riskof Parkinson's disease in population samples [70], butalso thought to be an enabling gene for tau pathology[71]. There has been only one prior study that directly
related a gene (KIBRA) to one of the phenotypes (verbal
memory) included in the current analyses. We did nothave any SNPs in significant LD with the SNP(rs17070145) described in that study [72]. We have chosen not to include details of the correlation between SNPs
from the 100K and the specific SNP(s) studied within candidate genes by prior investigators since doing so would
have expanded our Table 4 beyond the size and scope of
this article. For example, prior associations of several of
the candidate genes with related clinical disease phenotypes (for example, PDE4D with ischemic stroke, SORL1
with AD) have described allelic heterogeneity. In thesestudies, multiple SNPs and haplotypes within the gene
were associated with the phenotype, even within Caucasian populations [73-75].

Limitations
Our study had several limitations. A healthy survivor bias
is likely as participants in this sample had to survive
beyond 1990 to provide DNA. Further, persons undergoing MRI had to travel to an MRI center, provide informed
consent, and have no contraindication to the study. We
have previously shown that persons undergoing brain
MRI were significantly healthier than the overall sample
of Framingham participants alive at the time [15].
Our sample of 705 related persons may have a limited
power to uncover associations as compared to the larger
sample that includes unrelated subjects (on whom 100K
genotyping was not obtained). This is especially true forhippocampal volumes, which were computed based on
hand-drawn hippocampal outlines; the number of persons in our study dataset with available hippocampal volumes was only 327. Further, we currently have only asingle measure of brain MRI and cognitive tests in thesesubjects. However, all these participants are being restudied with a second cycle of MRI and cognitive testing. The
genes associated with changes in these measures over time

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BMC Medical Genetics 2007, 8(Suppl 1):S15

may be stronger candidate genes for usual and pathological brain aging processes than the genes related in current
analyses to cross-sectional endophenotypes.
The 100K Affymetrix GeneChip provides limited (~30%)
coverage of the genome, with no coverage of several generich areas and key candidate genes such as APOE [76].
However, the forthcoming NHLBI funded 550 K genomewide scan on over 9000 Framingham participants (discussed in the Overview) should permit validation of ourinitial 100K SNP associations in a larger sample and will
provide more dense coverage of the genome. Population
stratification is not a major concern in this study sample
due to the high homogeneity of ancestry (European).
However, for the same reason we cannot detect race or
ethnicity-specific variations in these phenotype-SNP associations. There are significant issues of multiple-testing
which are addressed in the Overview; when testing for
association with all alleles having a minor allele frequency>5%, it has been estimated that 1,000,000 tests are conducted across the entire human genome, hence for an α of
0.05, using a conservative Bonferroni correction (0.05 ×
10-6) only tests with a p value < 5 × 10-8) would be considered significant; however others have argued that this is
too stringent a threshold since it ignores correlation
between individual SNPs [77-79]. We emphasize that thecurrent study is hypothesis-generating and our findings
need to be replicated in other population samples.

Conclusion
The untargeted genome-wide approach to detect genetic
associations with brain aging identified several biologically interesting genes (such as genes previously related to
AD and schizophrenia) as possible novel candidates
related to brain structure and function in middle-aged toelderly populations. Our data also suggest that genes previously associated with clinical disease may be associatedwith clinical endophenotypes known to increase the riskof developing these conditions. Finally, our database willserve as a resource for in silico replication of findings
noted in other population-based samples, and in animalmodels of brain aging, stroke, and neurodegenerative diseases.

Abbreviations
GWAS = Genome-wide association study; FBAT = family
based association testing; GEE = generalized estimating
equations; LOD = logarithm of the odds; SNP = single
nucleotide polymorphism; MRI = Magnetic resonanceimaging; AD = Alzheimer's disease; TCBV = Total Cerebral
Brain Volume; WMH = White matter hyperintensity volume; CSF = Cerebrospinal fluid.

Competing interests
The authors declare that they have no competing interests.

http://www.biomedcentral.com/1471-2350/8/S1/S15

Authors' contributions
SS was involved in phenotype definition and data collection, planned the analyses, drafted and critically revised
the manuscript. ALD planned and conducted the analyses
and assisted in writing and critically revising the manuscript. RA was primarily responsible for the definition of
cognitive phenotypes, supervised the collection of cognitive and MRI phenotypes and assisted in securing funding,planning the analyses and critically revising the manuscript. JMM and ASB were involved in phenotype definition, planning and conducting the analyses and criticallyrevising the manuscript. CSK and MKH helped define andcollect stroke-related phenotypic data and criticallyrevised the manuscript. CD supervised the generation of
MRI measurements, helped plan the analyses andreviewed the manuscript. RBD contributed to phenotypic
definition, planning of analyses and helped criticallyrevise the manuscript. LDA helped plan and conduct theanalyses and critically revised the manuscript. PAW conceived of the Framingham 'MRI, Genetic and Cognitive
Precursors of AD and Dementia' study, obtained funding
for phenotype collection, helped plan the analyses andcritically revised the manuscript. All authors read andapproved the final manuscript.

Additional material
Additional file 1
Details of test source and parameters used to define each individual test
and factor are outlined in Additional Table 1. All SNPs within 60 kb of
the candidate genes are listed in Additional Table 2.
Click here for file
[http://www.biomedcentral.com/content/supplementary/14712350-8-S1-S15-S1.doc]

Acknowledgements
Statistical analyses were conducted using the Boston University Linux Cluster for Genetic Analysis (LinGA) funded by the NIH NCRR (National
Center for Research Resources) Shared Instrumentation grant
1S10RR163736-01A1. We acknowledge the careful genotyping conductedby Drs. Michael Christman, Alan Hebert and colleagues, in the Boston University School of Medicine Genetics Laboratory. NHLBI's Framingham
Heart Study is supported by contract number N01-HC-25195. The phenotypic data included in this paper were collected with funds from the NIA(5R01-AG08122 and 5R01-AG16495) and the NINDS (5R01-NS17950).
We thank Joanne M. Murabito, MD and Kathryn L. Lunetta, PhD who werevaluable collaborators, freely sharing their expertise, and ideas regarding
the genetics of an analogous phenotype: longevity and healthy aging. Above
all, we thank the Framingham Study participants for their ongoing commitment to the study.
This article has been published as part of BMC Medical Genetics Volume 8
Supplement 1, 2007: The Framingham Heart Study 100,000 single nucleotide polymorphisms resource. The full contents of the supplement areavailable online at http://www.biomedcentral.com/1471-2350/8?issue=S1.

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