BMC Medical Genetics

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Genetic correlates of longevity and selected age-related
phenotypes: a genome-wide association study in the Framingham
Study
Kathryn L Lunetta1,3, Ralph B D'Agostino Sr1,5, David Karasik4,
Emelia J Benjamin1,2, Chao-Yu Guo1,2, Raju Govindaraju1,2, Douglas P Kiel4,
Margaret Kelly-Hayes1,2, Joseph M Massaro1,5, Michael J Pencina1,5,
Sudha Seshadri1,2 and Joanne M Murabito*1,2
Address: 1The National Heart Lung and Blood Institute's Framingham Heart Study, Framingham, MA, USA, 2Section of General Internal Medicine
and the Departments of Neurology, Cardiology, and Preventive Medicine, Boston University School of Medicine, Boston, MA, USA, 3Departmentof Biostatistics, Boston University School of Public Health, Boston, MA, USA, 4Hebrew Senior Life Institute for Aging Research and Harvard
Medical School, Boston, MA, USA and 5Statistics and Consulting Unit, Department of Mathematics, Boston University, Boston, MA, USA
Email: Kathryn L Lunetta - klunetta@bu.edu; Ralph B D'Agostino - ralph@bu.edu; David Karasik - karasik@hrca.harvard.edu;
Emelia J Benjamin - emelia@bu.edu; Chao-Yu Guo - chaoyu@bu.edu; Raju Govindaraju - drgraju@bu.edu;
Douglas P Kiel - kiel@hrca.harvard.edu; Margaret Kelly-Hayes - mkhayes@bu.edu; Joseph M Massaro - jmm@bu.edu;
Michael J Pencina - mpencina@bu.edu; Sudha Seshadri - suseshad@bu.edu; Joanne M Murabito* - murabito@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):S13

doi:10.1186/1471-2350-8-S1-S13

This article is available from: http://www.biomedcentral.com/1471-2350/8/S1/S13© 2007 Lunetta 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: Family studies and heritability estimates provide evidence for a genetic contribution to variation in thehuman life span.
Methods: We conducted a genome wide association study (Affymetrix 100K SNP GeneChip) for longevity-related traits
in a community-based sample. We report on 5 longevity and aging traits in up to 1345 Framingham Study participantsfrom 330 families. Multivariable-adjusted residuals were computed using appropriate models (Cox proportional hazards,
logistic, or linear regression) and the residuals from these models were used to test for association with qualifying SNPs
(70, 987 autosomal SNPs with genotypic call rate ≥80%, minor allele frequency ≥10%, Hardy-Weinberg test p ≥ 0.001).
Results: In family-based association test (FBAT) models, 8 SNPs in two regions approximately 500 kb apart on
chromosome 1 (physical positions 73,091,610 and 73, 527,652) were associated with age at death (p-value < 10-5). The
two sets of SNPs were in high linkage disequilibrium (minimum r2 = 0.58). The top 30 SNPs for generalized estimating
equation (GEE) tests of association with age at death included rs10507486 (p = 0.0001) and rs4943794 (p = 0.0002),
SNPs intronic to FOXO1A, a gene implicated in lifespan extension in animal models. FBAT models identified 7 SNPs and
GEE models identified 9 SNPs associated with both age at death and morbidity-free survival at age 65 including rs2374983
near PON1. In the analysis of selected candidate genes, SNP associations (FBAT or GEE p-value < 0.01) were identified
for age at death in or near the following genes: FOXO1A, GAPDH, KL, LEPR, PON1, PSEN1, SOD2, and WRN. Top ranked
SNP associations in the GEE model for age at natural menopause included rs6910534 (p = 0.00003) near FOXO3a andrs3751591 (p = 0.00006) in CYP19A1. Results of all longevity phenotype-genotype associations for all autosomal SNPs
are web posted at http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007.

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Conclusion: Longevity and aging traits are associated with SNPs on the Affymetrix 100K GeneChip. None of theassociations achieved genome-wide significance. These data generate hypotheses and serve as a resource for replication
as more genes and biologic pathways are proposed as contributing to longevity and healthy aging.

Background
Genetic factors associated with human longevity andhealthy aging remain largely unknown. Heritability estimates of longevity derived from twin registries and large
population-based samples suggest a significant but modest genetic contribution to the human lifespan (heritability ~15 to 30%) [1-4]. However, genetic influences onlifespan may be greater once an individual achieves age 60
years [5]. Moreover, the reported magnitude of the genetic
contribution to other important aspects of aging such as
healthy physical aging (wellness)[6], physical performance [7,8], cognitive function [9], and bone aging [10] aremuch larger. Both exceptional longevity and a healthy
aging phenotype have been linked to the same region on
chromosome 4 [11,12], suggesting that although longevity per se and healthy aging are different phenotypes, they
may share some common genetic pathways.
A number of potential candidate genes in a variety of biological pathways have been associated with longevity inmodel organisms. Genes involved in the regulation of
DNA repair and genes in the evolutionarily conserved
insulin/insulin-like growth factor signaling pathway[13,14] are emerging as holding great promise in the
future elucidation of the underlying physiology controlling lifespan. Many of these genes have human homologs
and thus have potential to provide insights into human
longevity [15-20]. Although numerous candidate genes
have been proposed, studies in humans are limited andinitial findings often fail replication [21,22]. Morerecently genome-wide association studies (GWAS) havebecome feasible and offer a more comprehensive anduntargeted approach to detect genes with modest phenotypic effects that underlie common complex conditions[23].
We had the opportunity to use the Framingham Heart
Study (FHS) Affymetrix 100K SNP genotyping resource
for a GWAS of longevity and aging-related phenotypes.
The FHS offers the unique advantage of a longitudinalfamily-based community sample with participants who
have been well-characterized throughout adulthood withrespect to prospectively ascertained risk factors and diseases and continuously followed until death. We report
several strategies for 100K SNP associations: 1) a simple
low p-value SNP ranking strategy; 2) SNP selection due toassociations with more than one related phenotype; and3) SNP associations within candidate genes and regions

previously reported to be associated with longevity inmodel organisms or humans.

Methods
Study sample
The genotyped study sample is comprised of 1345 Original cohort (n = 258) and Offspring (n = 1087) participants who are members of the 330 largest FHS families.
The Overview [24] provides further details of this sample.
With respect to aging and longevity traits, 149 deaths
occurred at a mean age at death of 83 years (range 46 to
99 years) and 713 participants achieved age 65 years or
greater. The Boston University Medical Center Institutional Review Board approved the examination content ofOriginal Cohort and Offspring examinations. All participants provided written informed consent at every examination including consent for genetic studies.
Longevity and aging phenotype definitions and residual
creation
Age at death
Both the Original Cohort and the Offspring Cohort
remain under continuous surveillance and all deaths that
occurred prior to January 1, 2005 were included in thisstudy. Deaths were identified using multiple strategies
including routine participant contact for research examinations or health history updates, surveillance at the local
hospital, search of obituaries in the local newspaper, andif needed through use of the National Death Index. Deathcertificates were routinely obtained and all hospital andnursing home records prior to death and autopsy reports(if performed) were requested. In addition, if there wasinsufficient information to determine a cause of death,the next of kin were interviewed by a senior investigator.
All records pertinent to the death were reviewed by an
endpoint panel comprised of three senior investigators.
The date and cause of death (classified as due to coronary
heart disease, stroke, other cardiovascular disease [CVD],
cancer, other causes, or unknown cause) was recorded.

Cox proportional hazards models were used to generatemartingale residuals using the PHREG procedure in SAS
to perform the regression analysis of survival time fromage at study entry to age at death. Models were sex-specificand adjusted for 1) birth cohort and 2) birth cohort, education, current smoking status (yes/no), obesity (bodymass index ≥30 kg/m2), hypertension (blood pressure≥140/90 mmHg or on antihypertensive treatment), elevated cholesterol (cholesterol > 239 mg/dL), diabetes
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BMC Medical Genetics 2007, 8(Suppl 1):S13(fasting blood sugar ≥126 mg/dL, random blood sugar of≥200 mg/dL, or use of insulin or oral hypoglycemic
agents) and comorbidity defined as CVD and cancer. Birthcohort was defined as a categorical variable for all regression models with the following categories based on yearof birth: birth year prior to 1900, 1900 to 1909, 1910 to
1919, 1920 to 1929, 1930 to 1939, 1940 to 1949, and1950 and later. All covariates were measured at study
entry. Residuals from Original Cohort and Offspring participants were pooled.
Morbidity-free survival at age 65 years
Morbidity-free survival was defined as achieving age 65
years free of CVD, dementia, and cancer. CVD events
included angina pectoris, coronary insufficiency, myocardial infarction, heart failure, stroke, transient ischemic
attack (TIA), intermittent claudication and coronary or
CVD death. Suspected CVD events were reviewed by apanel of three investigators who adjudicated events using
previously established criteria in place since study inception [25]. A separate panel of study neurologists determined the presence of stroke or TIA and a team of at least
one neurologist and one neuropsychologist determined
the presence of dementia. Two independent reviewers
examined records for all cancers, and the vast majority ofcancer cases were microscopically confirmed with pathology reports.

Logistic regression models were used to generate devianceresiduals. Models were sex-specific and adjusted for 1)
birth cohort and 2) birth cohort, education, current smoking status, obesity, hypertension, elevated cholesterol, anddiabetes. Covariates were defined as above for age atdeath. All covariates were measured at the examination
closest to the participant attaining age 65 years using a 5
year window around age 65 years. Residuals from Original Cohort and Offspring participants were pooled.
Age at natural menopause
Natural menopause occurred after a woman had ceased
menstruating naturally for one year and the age at natural
menopause was the self-reported age at last menstruation.
Mean age at natural menopause was similar in Original
Cohort and Offspring women and the distribution of naturally menopausal ages in women in the 330 FHS families
was similar to that of women in all 1643 FHS families[26,27]. The mean age at natural menopause in women in
the 100K sample was 50.2 years (range 38 to 57 years) in
Original Cohort women and 49.1 years (range 29 to 60
years) in Offspring women.

Crude age at natural menopause and standardized residuals from multiple linear regressions in SAS [28] that
adjusted age at natural menopause for covariates of interest were used as traits for analysis. Covariates were

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obtained at all attended examinations prior to the onset ofmenopause and included mean number of cigarettes
smoked per day, mean body mass index, parity (0 versus
1 or more live births), and generation (Original Cohort vs.
Offspring).
Walking speed
Walking speed was measured on Original Cohort participants at examination 27 (January 2002 through December 2003, mean age of Original Cohort at exam 27: 86.7
years) and Offspring participants attending an ancillary
study to examination 7 (1999 to 2004, mean age at exam:
62.0 years). Trained technicians timed participants walking at their normal pace on a four meter course twice andsubsequently asked participants to repeat the course walking at a rapid pace. The mean timed fast walk among Offspring participants in the 100K genotyping sample was
2.44 seconds (standard deviation 0.89). The timed fast
walk was used for analysis. Sex-specific linear regression
was used to generate residuals adjusted for age and heightmeasured at the time of the walk.
Biologic age by osseographic scoring system
An osseographic scoring system (OSS) was applied tohand radiographs obtained on original cohort (1967 to
1969, mean age 58.7 years) and offspring participants(1992 to 1993, mean age 51.6 years) [10]. Biologic age
was then defined as the standardized residual between the
OSS predicted age and the actual age. Biologic age definedby this system predicted mortality [10,29], was very heritable (h2 = 0.57 ± 0.06), and a genome-wide linkage analysis was performed with LOD scores >1.8 present onchromosomes 3q, 11p, 16q, and 21q [10]. Sex- andcohort-specific ranked residuals generated from linear
regression of age on log-OSS adjusted for height, bodymass index, menopause, and estrogen therapy, were used
for analysis.
Genotyping
Affymetrix 100K SNP GeneChip genotyping and the
Marshfield STR genotyping performed by the Mammalian
Genotyping Service http://research.marshfieldclinic.org/
genetics are described in the Overview paper [24].
Statistical analysis
The statistical methods for genome-wide linkage andassociation analyses are described in the Overview [24].
Association
All residual traits described above as well as the additional
traits listed in Table 1 were computed using Cox proportional hazards with martingale residuals for survival traits,logistic regression with deviance residuals for dichotomous traits, and linear regression with standard residuals
for quantitative traits. The full set of FHS participants with

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the phenotype were used to create the residuals. The residuals were used to test for association between the genotyped subset of individuals and the SNPs using additive
family-based association test (FBAT) and generalized estimating equations (GEE) models as described in the Overview [24]. A total of 70,987 autosomal SNPs met thecriteria of genotypic call rate ≥80%, minor allele frequency ≥10%, Hardy-Weinberg test p ≥ 0.001, and ≥10
informative families for FBAT. The number of tests with
an FBAT p < 0.001, p < 0.0001, and p < 0.00001 for all
phenotypes was similar to what would be expected under
the assumptions that the 70,987 tested SNPs were independent and there were no true associations. The GEE
tests tended to give an excess of very small p-values overwhat would be expected under these assumptions.
SNP prioritization
We used several strategies to prioritize SNPs associatedwith longevity and aging traits. First, we used an untargeted approach whereby the top 50 SNP associations
ranked according to the strength of the p-value for each
trait were examined. Next, we explored the consistency of
SNP associations across related sets of traits chosen a priori (trait set one: age at death and morbidity-free survival
at age 65 years; trait set two: biologic age and walking
speed). Trait set one was chosen based upon linkage datain humans demonstrating that both longevity and ahealthy aging trait were linked to the same region on chromosome 4 raising the hypothesis that the two phenotypes
may share common genetic pathways [11,12]. The traits
in set two reflect aging with good physical functioning
and thus we postulated that biologic age and walking
speed may have genetic variants in common. We also
investigated SNP associations in candidate genes andregions reported to be associated with longevity identified
from established databases including NCBI [14] using thesearch term "longevity" and the Science of Aging Knowledge Environment genes/intervention database http://
sageke.sciencemag.org/cgi/genesdb[30] choosing genes
potentially related to lifespan in humans.

The SNPs were annotated using the UCSC genome
browser tables using the May 2004 assembly http://
genome.ucsc.edu/[31,32]. All genes within 60 kb of the
top ranked SNPs were identified.

Results
The longevity and aging traits available in the FHS 100K
SNP resource are listed in Table 1. In this report, we consider only five of the traits listed in Table 1: multivariableadjusted age at death, morbidity-free survival at age 65
years, age at natural menopause, walking speed, and biologic age by OSS. These traits include a pooled sample ofOriginal Cohort and Offspring participants, with the
exception of walking speed, which is reported in Off-http://www.biomedcentral.com/1471-2350/8/S1/S13

spring participants only. Details of the sample size andcovariate adjustment for each trait are provided in Table 1.
For each of the five phenotypes, Table 2a and 2b provides
the top five SNPs ranked in order by lowest p-value for the
GEE and FBAT models (all associations can be viewed on
the web http://www.ncbi.nlm.nih.gov/projects/gap/cgibin/study.cgi?id=phs000007). If multiple SNPs in linkage
disequilibrium (LD r2 > 0.80) were included in the top 5,
additional SNPs were included until a set of 5 independent associations were listed. Eight SNPs on chromosome
1 were associated with age at death in the FBAT analysis;
all with p-value < 10-4 and two with p-value < 10-5. The 8
SNPs consisted of two sets of SNPs (rs10493513,
rs10493514, rs6689491, rs6657082, rs1405051) and(rs10493515, rs10493518, rs10493517), clustered in two
regions approximately 500 kb apart. There was exceptionally high LD across this 500 kb region: the minimum r2
between pairs of the eight SNPs was 0.58. The nearest
genes in this region existing in public databases were >500
kb from any of these SNPs [31,32].
There were several additional associations not listed inTable 2a and 2b that were of interest. For age at death in
the GEE analysis, SNP associations ranked numbers 9 and
13 were rs10507486 (p-value 0.000128) and rs4943794(p-value 0.000277), both are intronic FOXO1A SNPs. Forage at natural menopause, top ranked SNP associations in
the GEE model included number 11, rs6910534 (p =0.00003) near FOXO3a and number 18, rs3751591 (p =0.00006) in CYP19A1.
Table 2c presents the LOD scores ≥2.0 and the corresponding 1.5-LOD support interval from genome-wide
linkage for the three quantitative aging traits. None of theregions overlapped with SNPs associated with these aging
traits in the FBAT and GEE analyses. Of note for biologic
age by OSS the linkage peak on chromosome 21 confirmed a prior Framingham Study report using a genomewide scan with 401 microsatellite markers [10].
Table 3 provides all SNP associations with a GEE or FBAT
p < 0.01 for both traits within the two pairs of related
traits. For age at death and morbidity-free survival at age
65 years, FBAT models identified 7 SNPs and GEE models
identified 9 SNPs associated with both traits including
rs2374983 near PON1 (Tables 3a and 3b). For biologic
age by OSS and walking speed, 13 SNPs in FBAT models
and 6 SNPs in GEE models were associated with bothtraits (Tables 3c and 3d).
We identified from the literature 79 potential candidate
genes and regions associated with longevity (see Additional file 1 for listing). Of these, 12 genes had no SNPs
and 67 genes had 1 to 45 SNPs within 60 kb of the gene

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Table 1: Aging and Longevity Phenotypes for Framingham Heart Study 100K Project
Exam cycle(s)Phenotype Subgroup• Trait (variable name on thewebsite*)Number of Traits

N (MV**)

Offspring / Original
CohortAdjustment

Survival Traits: Cox regression
Survival• Age at death (1. deathageX, 2.
deathageMV)

21345 (1166)

Cohort & OffspringpooledCox regression
Sex-specific
1. birth cohort
2. multivariable adjusted for birth cohort, education,smoking, obesity (BMI ≥ 30), CVD risk factors, co-morbidity
measured at exam 1

Categorical traits: Logistic regression• Survival past the ALE (1.
deathpastALEX, 2. deathpastALEMV)

21345 (1166)

Cohort & OffspringpooledMorbidity-free survival (free of
CVD, cancer and dementia)• At age 65 years (1.
morbidityfree65X, 2.
morbidityfree65MVX)

2558 (558)

Cohort & Offspringpooled, exams closest to
age 65 years

Logistic regression
Sex-specific
1. birth cohort
2. multivariable adjusted for birth cohort, education,smoking, obesity (BMI ≥ 30), CVD risk factors, co-morbidity
measured at exam 1
Logistic regression
Sex-specific
1. birth cohort
2. multivariable adjusted for birth cohort, education,smoking, obesity, CVD risk factors measured at exam closest
to age 65 years (within a 5 year horizon)

Quantitative Traits: Linear regression
Reproductive Aging• Age at natural menopause (1.
menoageX, 2. menoageMVX)

2438 (378)

Cohort & Offspringpooled, women only

Cognitive function• MMSE at age 65 years (1. MMSE65X,
2. MMSE65MVX)• MMSE at the specified Offspringexam (1. MMSE5X, 2. MMSE5MVX, 1.
MMSE7X, 2. MMSE7MV, 1.
MMSE5to7X, 2. MMSE5to7MVX)

2593 (462)

Cohort & Offspringpooled, exams at age 65

61038 (913)

Exam 5
Exam 7
Exam 5 & 7
average score

6764

Exam 7
Exam 7 and Exam 27

3714

Offspring and Cohortpooledexam 6/7 and exam 22

Physical Performance• Hand grip (2. handgrip7x, 2.
handgrips727x)• Walking speed (2. walkingspeed7x, 2.
walkingspeed727x)
Biologic Age by Osseographic
Scoring System (1. deltaOSSr, delta
OSSrf, deltaOSSrm)

Linear regression
1. crude
2. multivariable adjusted for smoking, BMI, parity, generation(measured at exams prior to menopause)
Linear regression
Sex-specific
1. birth cohort
2. multivariable adjusted for birth cohort, education, FSRP
measured at exam closest to age 65 years (5 year horizon)
Linear regression
Sex-specific
1. birth cohort
2. multivariable adjusted for birth cohort, education, FSRP;
covariates measured at the specified exam
Linear regression
Sex-specific‡
1. age2. multivariable adjusted for age, height, weight at the
specified exam
Linear regression
Sex- and cohort-specific ranked residuals§
1. multivariable adjusted for age, height, BMI, menopause,
estrogen use

Residuals from these models were used as traits to test for association with SNP genotypes.
* The number preceding the variable name refers to the covariate adjustment in the last column of the table. The website with all results is found at http://
www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007; ** MV = N for multivariable trait‡ cohort- and sex-specific residuals for traits that included both cohort and offspring; §cohort-specific for traits limited to one sex
ALE = average life expectancy, BMI = body mass index, Co-morbidity = cardiovascular disease and cancer, CVD = cardiovascular disease, FSRP = Framingham stroke risk
profile, MMSE = mini-mental state exam, Risk factors = hypertension, diabetes, elevated cholesterolon the 100K Affymetrix GeneChip. There were 2036 SNPs
in the LGV1 region on chromosome 4 previously linked
to exceptional longevity [11]. Table 4 shows the candidate
genes with SNPs associated with an FBAT or GEE p-value< 0.01 for age at death including: FOXO1a, GAPDH, KL,
LEPR, PON1, PSEN1, SOD2, and WRN and for morbidityfree survival at 65 years including:GHR, LEPR, MORF4L1,
PON1, PTH, and WRN. Biologic age by OSS shared 2 SNPs

in common with age at death: rs4943794 intronic to
FOXO1a and rs911847 near SOD2.

Discussion
To our knowledge, this is the first dense GWAS of longevity and aging traits in a community-based sample ofadults from two generations of the same families. Over
1300 men and women have detailed longevity and aging-

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Table 2: Aging and Longevity Phenotypes† for FHS 100K Project: Results of Association and Linkage Analyses

2a. GEE, Top 5 p-values by Phenotype*
Trait

SNP

Chromosome

Age at deathrs1528753
11
rs2371208
7
rs10496799
2
rs10489006
4
rs3757354
6
Morbidity-free survival at age 65
rs1412337
1
rs32566
5
rs10484246
6
rs48318378
rs2639889
16
Age at natural menopause‡
rs10496265
2
rs10496262*
2
rs958672
2
rs291353
1
rs726336
5
Walking speed exam 7
rs7137869
12
rs7662116
4
rs7972859*
12
rs9318312
13
rs1994854
4
rs7718104
5
Biologic age by osseographic scoring systemrs1463605
12
rs7176093
15
rs3772255
3
rs726846
5
rs646983
13

Physical location

GEE p-value

FBAT p-value

90,523,987
81,982,510
139,261,401
31,444,987
16,235,386

8.1 × 10-8
2.6 × 10-6
1.4 × 10-5
3.6 × 10-5
6.4 × 10-5

0.024
0.031
0.735
0.078
0.316

165,350,299
5,845,507
9,559,183
12,756,234
59,680,648

1.8 × 10-9
1.9 × 10-9
8.4 × 10-8
4.7 × 10-7
9.4 × 10-7

0.505
0.323
0.928
0.182
0.903

81,580,466
81,662,782
154,896,075
232,046,939
163,911,906

1.1 × 10-8
3.3 × 10-7
1.9 × 10-6
5.5 × 10-6
1.1 × 10-5

0.001
0.005
0.087
0.035
0.125

118,452,366
154,375,569
118,452,765
74,489,506
78,124,824
122,183,258

6.3 × 10-7
1.9 × 10-5
2.5 × 10-5
5.8 × 10-5
9.4 × 10-5
1.2 × 10-4

0.009
0.016
0.005
0.266
0.280
0.011

30,005,150
84,170,434
157,585,436
136,099,953
29,413,553

7.0 × 10-8
7.4 × 10-6
8.2 × 10-6
1.1 × 10-5
1.2 × 10-5

5.3 × 10-4
0.005
0.085
0.003
0.003

GEE p-value

FBAT p-value

Gene Region (within 60 kb)

NXPH2
MYLIP
DPT

GALNT13
GNG4

CCDC60CCDC60SNX2

KLHL25KCNAB12b. FBAT, Top 5 p-values by Phenotype*
Trait

SNP

Chromosome

Physical location

Age at deathrs10493513
rs10493514*
rs6689491*
rs10493515
rs10493518*
rs10493517*
rs6657082*
rs10498263

1
1
1
1
1
1
1
14

73,091,610
73,092,533
73,064,050
73,527,652
73,572,652
73,570,372
73,065,349
19,285,288

0.640
0.623
0.205
0.225
0.191
0.215
0.224
0.310

1.5 × 10-6
2.8 × 10-6
2.0 × 10-5
2.3 × 10-5
3.6 × 10-5
4.2 × 10-5
5.5 × 10-5
8.3 × 10-5

rs1915501
rs1405051*
rs6459623

4
1
6

28,612,632
73,060,505
18,634,791

0.383
0.176
0.604

1.1 × 10-4
1.4 × 10-4
1.5 × 10-4

Gene Region (within 60 kb)

OR4Q3
OR4M1

IBRDC2

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Table 2: Aging and Longevity Phenotypes† for FHS 100K Project: Results of Association and Linkage Analyses (Continued)

Morbidity-free survival at age 65
rs10509200
10
rs9650366
rs720565
6
rs1192372
2
rs10505239
8
Age at natural menopause†
rs959702
10
rs7165378
15
rs99716110
rs1652841
rs2280585
3
Walking speed exam 7
rs4471448
11
rs336963
5
rs7862683
9
rs9317757
13
rs10501636*
11
rs2340392
3
Biologic age by osseographic scoring systemrs1380703
2
rs324702
4
rs3247354
rs1106184
2
rs604578
18

65,296,567
20,099,022
136,834,657
84,923,204
115,976,403

0.613
0.550
0.014
0.094
0.141

7.0 × 10-5
8.6 × 10-5
9.8 × 10-5
9.9 × 10-5
1.3 × 10-4

2,139,260
69,478,558
130,876,127
91,235,574
64,882,324

0.003
0.006
0.074
0.006
0.884

1.4 × 10-5
6.4 × 10-5
8.6 × 10-5
8.6 × 10-5
1.1 × 10-4

86,760,972
83,005,460
18,257,947
68,458,430
86,789,967
80,440,990

0.175
0.570
0.001
0.252
0.355
0.002

3.8 × 10-6
2.6 × 10-5
4.9 × 10-5
7.8 × 10-5
1.1 × 10-4
1.7 × 10-4

57,852,938
77,094,969
77,062,193
10,914,125
30,923,438

0.008
0.390
0.096
0.006
0.004

1.1 × 10-5
3.3 × 10-5
7.6 × 10-5
8.8 × 10-5
9.5 × 10-5

MAP7

ZNF644

TMEM135
HAPLN1PPEF2PDIA6MAPRE22c Linkage¶ Peaks with LOD scores ≥ 2.0
Trait

SNP closest to
linkage peak

Chromosome

Age at natural menopause
rs1371217
4
rs10509024
10
rs4793513
17
Walking speed, exam 7
rs2769261
1
rs9210552
rs2602044
3
rs8011773
14
rs1362626
16
Biologic age by osseographic scoring system
rs353810
9rs1203981
16
rs2248383
21

Physical location

1.5 – LOD
supportinterval start

1.5 – LOD
supportinterval end

LOD score

182,890,808
56,567,832
66,429,892

178,671,796
36,084,470
60,635,492

186,905,362
70,573,011
69,512,021

2.08
2.39
2.48

113,278,949
233,522,842
109,427,883
96,927,485
4,489,227

107,080,550
229,328,008
102,255,525
96,342,135
205,160

144,332,709
242,141,304
111,604,019
100,389,787
10,344,522

2.30
2.13
3.38
2.69
2.05

86,258,745
205,160
35,151,811

81,441,976
205,160
27,412,716

92,220,168
7,431,239
40,940,879

3.26
2.49
2.22

SNP criteria: Autosomal SNPs with genotypic call rate ≥ 80%, minor allele frequency ≥ 10%, Hardy-Weinberg test p > 0.001, and ≥10 informative
families for FBAT* For each phenotype SNPs are ranked by p-value. A SNP in LD (r2 > 0.8) with a higher ranked SNP, is identified with an asterisk. All SNPs for aphenotype are listed until 5 independent SNPs are identified. Thus, for some phenotypes more than 5 SNPs are listed. For the age at death trait, theFBAT analysis identified two areas on chromosome 1 in LD, with r2 = .5–.6 between the two regions and r2 of nearly 1.0 within the region.
† Multivariable-adjusted trait results are presented‡Trait had <500 participants in the sample.
¶Results limited to traits presented

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Table 3: All Significant SNP Associations (GEE or FBAT p-value < 0.01) for at least Two Traits
3a. FBAT: Age at Death and Morbidity-Free Survival at 65 years
Trait 1

Age atDeath

Trait 2

SNP

Chr

PhysicalPosition

Morbidityfree at 65

rs6682403

1

234,743,324

rs10488907
rs17190837
rs4752977
rs10506274
rs2831154
rs243725*

4
9
11
12
21
21

113,669,709
13,391,548
47,257,005
80,103,932
28,059,331
28,060,803

Gene

ALPK1
MADD

Trait 1 GEE
p-value

Trait 1 FBAT
p-value

Trait 2 GEE
p-value

Trait 2 FBAT
p-value

0.849

0.004

0.106

0.004

0.452
0.010
0.736
0.531
0.323
0.264

0.008
0.009
0.008
0.001
0.008
0.007

0.336
0.097
0.002
0.989
0.358
0.359

0.009
0.004
0.009
0.001
0.006
0.008

*r2 > 0.80 with the preceding SNP
3b. GEE: Age at Death and Morbidity-Free Survival at 65 years
Trait 1

Age atDeath

Trait 2

SNP

Chr

PhysicalPosition

Gene

Trait 1 GEE
p-value

Trait 1 FBAT
p-value

Trait 2 GEE
p-value

Trait 2 FBAT
p-valueMorbidityfree at 65

rs9308261

1

113,603,160

MAGI3

0.009

0.156

0.002

0.900

rs10490518
rs2374983

2
731,223,004
94,516,375

0.009
0.006

0.373
0.980

0.010
0.007

0.948
0.727

rs655883
rs1368850
rs4943116
rs2254191
rs1620210
rs2823322

11
11
13
13
13
21

98,994,584
130,433,518
32,995,650
45,344,403
45,759,488
15,814,903

GALNT14
PPP1R9A/
PON1
CNTN5

0.006
0.004
0.006
0.007
0.001
0.0004

0.390
0.387
0.205
0.425
0.161
0.045

0.001
0.005
0.008
0.004
0.004
0.006

0.293
0.292
0.049
0.116
0.068
0.029

SNP

Chr

PhysicalPosition

Trait 1 GEE
p-value

Trait 1 FBAT
p-value

Trait 2 GEE
p-value

Trait 2 FBAT
p-value

0.135
0.074
0.015
0.172
0.017

0.004
0.006
0.001
0.0002
0.004

0.114
0.054
0.008
0.399
0.011

0.006
0.005
0.002
0.008
0.004

0.024
0.017
0.020
0.308
0.093
0.046
0.083
0.097

0.008
0.003
0.006
0.005
0.009
0.010
0.009
0.004

0.935
0.959
0.033
0.169
0.031
0.027
0.023
0.053

0.006
0.004
0.004
0.009
0.006
0.003
0.001
0.003

STARD13C13orf18

3c. FBAT: Biologic Age and Walking Speed
Trait 1

Trait 2Biologic age
Biologic age
Biologic age
Biologic age
Biologic age

Walking speed
Walking speed
Walking speed
Walking speed
Walking speed

rs873348
rs10520361*
rs31564rs1862345
rs7844834

4
4
5
5
8

178,246,509
178,247,037
135,258,152
148,018,498
11,323,556

Biologic age
Biologic age
Biologic age
Biologic age
Biologic age
Biologic age
Biologic age
Biologic age

Walking speed
Walking speed
Walking speed
Walking speed
Walking speed
Walking speed
Walking speed
Walking speed

rs952658
rs6487366
rs7135493
rs10492036
rs1978945
rs2165723*
rs10492651*
rs9301112*

12
12
12
12
13
13
13
13

20,756,568
23,994,617
28,134,847
124,728,934
105,641,257
105,641,610
105,641,634
105,642,018

Gene

IL9
HTR4
C8orf12|C8or
f13
SLCO1C1
SOX5* r2 > 0.8 with the preceding SNP (calculated if the distance is <250,000 base pairs)
3d. GEE: Biologic Age and Walking Speed
Trait 1

Trait 2

SNP

Chr

PhysicalPosition

Biologic Age
Biologic Age
Biologic Age
Biologic Age
Biologic Age
Biologic Age

Walking speed
Walking speed
Walking speed
Walking speed
Walking speed
Walking speed

rs1474827
rs10231641
rs310575
rs10520603
rs7166323*
rs2215921

6
7
8
15
15
16

134,886,011
119,166,342
51,603,257
84,170,955
84,171,745
9,604,834

Gene

SNTG1

Trait 1 GEE
p-value

Trait 1 FBAT
p-value

Trait 2 GEE
p-value

Trait 2 FBAT
p-value

0.007
0.004
0.008
0.009
0.009
0.007

0.746
0.077
0.209
0.008
0.012
0.049

0.000
0.009
0.003
0.003
0.003
0.001

0.233
0.278
0.007
0.050
0.065
0.316

* r2 > 0.8 with the preceding SNP (calculated if the distance is <250,000 base pairs)

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Table 4: All Significant SNP Associations with Selected Longevity Candidate Genes* (FBAT or GEE p-value < 0.01)
Trait

Gene

SNP

Chr

PhysicalPosition

FBAT p-value

GEE p-value

SNP function

SNP position relative
to gene (up to 60 kb)

Age at deathFOXO1ars4943794
rs10507486
rs4764600
rs683907
rs687045
rs1475398
rs1343981
rs10493379
rs2154380
rs6669117
rs2374983
rs362356
rs911847
rs2543600
rs719756
rs1171278
rs3790426
rs1383636
rs2374983
rs854523
rs10500784
rs2725369
rs1923249
rs4943794
rs256014
rs1002666
rs911847
rs1927914
rs9322361
rs6433083
rs2918418
rs10515522
rs2833485
rs728546
rs6700734

13
13
12
13
13
1
1
1
1
1
7
14
6
8
5
1
1
15
7
7
11
8
13
13
5
2
6
9
6
2
5
5
21
16
1

40,071,408
40,084,501
6,472,241
32,522,175
32,522,889
65,695,278
65,757,349
65,757,948
65,769,462
65,773,093
94,516,375
72,708,382
160,039,379
30,969,282
42,761,386
65,700,167
65,755,040
76,893,275
94,516,375
94,542,884
13,530,401
30,970,566
40,041,881
40,071,408
137,930,983
169,303,525
160,039,379
117,544,279
152,551,257
169,324,821
142,703,566
142,738,587
32,000,796
68,013,029
169,362,468

0.068
0.043
0.833
0.009
0.007
0.069
0.031
0.015
0.004
0.050
0.980
0.005
0.358
0.182
0.003
0.042
0.460
0.458
0.727
0.850
0.010
0.113
0.006
0.009
0.101
0.001
0.024
0.007
0.124
0.232
0.005
0.004
0.008
0.00450.003

0.000280.00013
0.005
0.507
0.712
0.005
0.006
0.004
0.003
0.007
0.006
0.130
0.005
4.2 × 10-6
0.676
0.003
0.002
0.007
0.007
0.007
0.990
0.003
0.004
0.016
0.005
0.008
0.009
0.401
0.0089
0.006
0.081
0.084
0.507
0.533
0.029

IntronIntronLocus/intron
IntronIntronUntranslated
IntronIntronIntronIntronIntronIntronUnknown
Unknown
Unknown
Untranslated
IntronUnknown
IntronIntronUnknown
Unknown
IntronIntronIntronIntronUnknown
Locus
IntronIntronIntronIntronLocus/intron
Unknown
Intron

in
in
near
in
in
in
in
in
in
in
near
in
near
near
near
in
in
near
near
in
near
near
in
in
in
in
near
near
in
in
in
in
in
near
in

GAPDH†KL
LEPR

Morbidity-free survival at age 65

PON1
PSEN1
SOD2
WRN‡
GHR
LEPR
MORF4L1
PON1

Biologic Age by OSS

Walking speed

PTH
WRN‡
FOXO1a
HSPA9
LASS6
SOD2
TLR4
ESR1
LASS6
NR3C1
SOD1
TERF2
FASLG

*79 genes identified from NCBI, SAGE ke, and GenAge databases; 12 genes with no SNPs on 100K chip; 67 genes with 1–45 SNPs on 100K chip; LGV1 2036 SNPs on 100K
chip, results for this region available on the web†The most strongly associated SNP near GAPDH is actually closer to MRPL51‡The most strongly associated SNP near WRN is actually closer to PURG

related phenotypes and 100K SNP genotyping results
available on the web. This resource has the potential todetect novel susceptibility genes for human longevity andaging and to examine the relevance of promising candidate gene associations reported in animal models tohuman aging. We describe several strategies to prioritize
SNP associations in this unique resource to enhance thediscovery of various genes and pathways that contributeto the control of human longevity. Furthermore, FHS
investigators are part of the NIA sponsored Longevity
Consortium http://www.longevityconsortium.org which
offers the opportunity of collaboration with other investigators to replicate important findings in additional
cohorts.
In our untargeted approach of ranking SNP associations
by the strength of the p-value, 2 intronic FOXO1a SNPs
were associated with age at death. One of these SNPs
(rs4943794) also was associated with biologic age by OSS
in our a priori evaluation of select candidate genes. FOXO

(forkhead box group O) transcription factors are targets ofinsulin-like signaling and are involved in a diverse set ofphysiological functions including DNA repair and resistance to oxidative stress [33,34]. Further, FOXO plays arole in lifespan extension in C. elegans and Drosophila [35].
Studies of this gene in humans are limited; two case-control studies have not identified an association between
FOXO1a and longevity [36,37]. However, the prospective
population-based Leiden 85-plus Study found that
FOXO1a was associated with increased mortality attributable to diabetes related deaths in participants aged 85
years and older [38]. The Leiden 85-plus Study also
reported that genetic variation causing a reduction in
insulin/IGF-1 signaling resulted in improved old age survival among women [20]. However, that report examinedother genes in the insulin/insulin-like signaling pathway
and did not specifically examine FOXO1a. Finally, theuntargeted approach to SNP selection also identified a
SNP near FOXO3a associated with age at natural menopause. This gene has been implicated in oocyte death,

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BMC Medical Genetics 2007, 8(Suppl 1):S13

depletion of functioning ovarian follicles, and infertility
in mice [39,40] and thus represents a plausible candidate
gene for menopause. Most positive common gene variantdisease association studies have failed replication [41]including reports on exceptional longevity. Haplotypebased fine mapping of the region on chromosome 4linked to human longevity initially suggested the MTP
gene, a gene important in lipoprotein synthesis, was associated with longevity [21]. However, this association
failed replication in a French cohort of long-lived individuals and subsequent case-control studies of nonagenarians [22,42]. Beekman, et al [43] found neither linkage tochromosome 4 nor association with the MTP gene andlongevity among nonagenarians in the Leiden Longevity
Study. Meta-analyses implicated admixture of the control
sample in the original report as an explanation for the presumed false-positive association. Thus, our findings arehypothesis generating and their importance can not be
determined without evidence of consistent replication inother populations.
We examined pleiotropic effects by identifying SNP associations across two pairs of related traits. One SNP near
PON1 emerged as associated with both age at death andmorbidity-free survival. Surprisingly, there were relatively
few SNPs associated with both traits; prior work had suggested that longevity per se and healthy aging may sharecommon genetic pathways [11,12]. However, morbidityfree survival was measured at age 65 years, it is possiblethat as our participants age morbidity-free survival
defined at age 75 or 85 years will share additional SNP
associations with our longevity trait, age at death. A SNP
near SOX5, a gene potentially related to musculoskeletal
function was associated with both biologic age by OSS
and walking speed.
Our strategy of selecting SNPs in candidate genes andregions previously reported to be associated with longevity yielded interesting findings. For age at death, we identified SNPs in or near several genes including KL, LEPR,
PON1, SOD2, and WRN. Defects in the WRN gene are thecause of Werner Syndrome, an autosomal recessive disorder characterized by premature aging. A longitudinal
study of ageing Danish twins recently reported a possible
association between a successful aging trait and 3 SNPs in
the WRN gene [44]. We were unable to determine if our
SNP (rs2725369) was in LD with the SNPs in the priorreport because the SNPs were not included in HapMap.
Mutations in the KL (Klotho) gene in the mouse lead to a
syndrome resembling human aging [45-47]. There has
been one report linking a functional variant of the KL geneto human longevity [15]. Thus, results from this GWAS
may direct resources to the most relevant candidate genes
and pathways for further investigation in humans.

http://www.biomedcentral.com/1471-2350/8/S1/S13

Several important limitations merit comment. First, we
acknowledge that there may be a survival bias as participants in this sample had to survive to provide DNA (firstsystematic DNA collection began 1995) and hence arelikely healthier than the full FHS sample. To ameliorate
this issue, we adjusted for covariates using the full Framingham sample, and used the residual traits for the subset
of individuals genotyped using the 100K Affymetrix GeneChip to test for association with the SNPs using linear
regression models. Residual traits from Cox and logistic
models typically are not ideally distributed for linear
regression models, but our adjustment method using thefull sample precludes the testing of SNP associations withage at death and morbidity-free survival using Cox andlogistic models. Second, the 100K Affymetrix GeneChip
provides limited coverage of the genome; many of our apriori candidate genes did not have any SNP coverage on
the chip. For example, several genes that have been studied in model organisms or even in humans such as ACE,
Lamin A, SIRT2 and SIRT3, had no SNPs within 60 kb of
the gene on the 100K Affymetrix GeneChip. However,genotyping is near-complete for the NHLBI funded 550 K
genome-wide scan on all FHS participants. This will enable deeper exploration of our initial 100K SNP associations in a larger sample with denser coverage of the
genome. Third, in this analysis we did not examine epistasis or gene-environment interactions which may modify
the associations in this study. Importantly, this study ishypothesis generating. Our findings need to be replicatedin other samples.

Conclusion
In summary, the untargeted genome-wide approach todetect genetic associations with longevity and aging traits
provides an opportunity to identify novel biologic pathways related to lifespan control. GWAS also have thepotential to direct investigators of human aging to themost promising candidate gene associations and biologic
pathways reported to regulate lifespan in animal models.
Enhancing our understanding of the mechanisms responsible for aging may in turn identify directions for healthpromotion and disease prevention efforts in middle-aged
and older adults so that older persons can enjoy moretime in good health. These data generate hypotheses
regarding novel biologic pathways contributing to longevity and healthy aging and serve as a resource for replication of findings from other population-based samples.

Abbreviations
CVD = cardiovascular disease; FBAT = family-based association test; FHS = Framingham Heart Study; GEE = generalized estimating equations; GWAS = genome-wide
association study; LD = linkage disequilibrium; LOD =logarithm of the odds; NCBI = National Center Biotechnology Information; OSS = osseographic scoring system;

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BMC Medical Genetics 2007, 8(Suppl 1):S13

SNP = single nucleotide polymorphism; TIA = transientischemic attack.

http://www.biomedcentral.com/1471-2350/8/S1/S13

5.
6.

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

Authors' contributions
All authors have made substantial contributions to conception and design or acquisition of phenotypic data.
JMM, KL, EJB, CG, DK, DPK, JMM, MJP, RBD contributed
to the analysis and interpretation of data. JMM, KL, EJB,
DK, DPK, SS have been involved in drafting the manuscript or revising it critically for important intellectual
content. All authors read and approved the final manuscript.

7.

8.
9.

10.
11.

Additional material
12.

Additional file 1
Candidate Gene List for FHS 100K Longevity and Aging Traits
Click here for file
[http://www.biomedcentral.com/content/supplementary/14712350-8-S1-S13-S1.doc]

13.
14.
15.

Acknowledgements
NHLBI's Framingham Heart Study is supported by contract number N01HC-25195. This work was also supported in part by R01 AR/AG 41398 andAG028321.

16.

FHS 100K analyses were conducted using the Boston University.

17.

Linux Cluster for Genetic Analysis (LinGA) funded by the NIH NCRR
(National Center for Research Resources) Shared Instrumentation grant
1S10RR163736-01A1 http://www.bu.edu/dbin/sph/departments/biostatis
tics/linga_publications.php.

18.
19.

We thank the FHS participants for their ongoing participation and dedication to the study making this work possible.
20.
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.

References
1.

2.

3.

4.

Herskind AM, McGue M, Holm NV, Sorensen TI, Harvald B, Vaupel
JW: The heritability of human longevity: a population-based
study of 2872 Danish twin pairs born 1870–1900. HumGenet
1996, 97(3):319.
Iachine IA, Holm NV, Harris JR, Begun AZ, Iachina MK, Laitinen M,
Kaprio J, Yashin AI: How heritable is individual susceptibility todeath? The results of an analysis of survival data on Danish,
Swedish and Finnish twins. TwinRes 1998, 1(4):196.
Ljungquist B, Berg S, Lanke J, McClearn GE, Pedersen NL: The effectof genetic factors for longevity: a comparison of identical andfraternal twins in the Swedish Twin Registry. JGerontolA BiolSciMedSci 1998, 53(6):M441.
McGue M, Vaupel JW, Holm N, Harvald B: Longevity is moderately heritable in a sample of Danish twins born 1870–1880.
JGerontol 1993, 48(6):B237.

21.

22.

23.
24.

Hjelmborg JV, Iachine I, Skytthe A, Vaupel JW, McGue M, Koskenvuo
M, Kaprio J, Pedersen NL, Christensen K: Genetic influence onhuman lifespan and longevity. HumGenet 2006, 119(3):312.
Reed T, Dick DM: Heritability and validity of healthy physicalaging (wellness) in elderly male twins. TwinRes 2003, 6(3):227.
Carmelli D, Kelly-Hayes M, Wolf PA, Swan GE, Jack LM, Reed T,
Guralnik JM: The contribution of genetic influences to measures of lower-extremity function in older male twins. JGerontolA BiolSciMedSci 2000, 55(1):B49.
Reed T, Fabsitz RR, Selby JV, Carmelli D: Genetic influences andgrip strength norms in the NHLBI twin study males aged 59–
69. AnnHumBiol 1991, 18(5):425.
Swan GE, Carmelli D, Reed T, Harshfield GA, Fabsitz RR, Eslinger PJ:
Heritability of cognitive performance in aging twins. The
National Heart, Lung, and Blood Institute Twin Study. ArchNeurol 1990, 47(3):259.
Karasik D, Hannan MT, Cupples LA, Felson DT, Kiel DP: Genetic
contribution to biological aging: the Framingham Study.
JGerontolA BiolSciMedSci 2004, 59(3):218.
Puca AA, Daly MJ, Brewster SJ, Matise TC, Barrett J, Shea-Drinkwater
M, Kang S, Joyce E, Nicoli J, Benson E, Kunkel LM, Perls T: A
genome-wide scan for linkage to human exceptional longevity identifies a locus on chromosome 4. ProcNatlAcadSciUSA
2001, 98(18):10505.
Reed T, Dick DM, Uniacke SK, Foroud T, Nichols WC: Genomewide scan for a healthy aging phenotype provides support fora locus near D4S1564 promoting healthy aging. JGerontolA
BiolSciMedSci 2004, 59(3):227.
Cheng CL, Gao TQ, Wang Z, Li DD: Role of insulin/insulin-likegrowth factor 1 signaling pathway in longevity. World JGastroenterol 2005, 11(13):1891.
Katic M, Kahn CR: The role of insulin and IGF-1 signaling in longevity. Cell MolLife Sci 2005, 62(3):320.
Arking DE, Atzmon G, Arking A, Barzilai N, Dietz HC: Association
between a functional variant of the KLOTHO gene and highdensity lipoprotein cholesterol, blood pressure, stroke, andlongevity. CircRes 2005, 96(4):412.
Bellizzi D, Rose G, Cavalcante P, Covello G, Dato S, De Rango F,
Greco V, Maggiolini M, Feraco E, Mari V, Franceschi C, Passarino G,
De Benedictis G: A novel VNTR enhancer within the SIRT3
gene, a human homologue of SIR2, is associated with survival
at oldest ages. Genomics 2005, 85(2):258.
Browner WS, Kahn AJ, Ziv E, Reiner AP, Oshima J, Cawthon RM,
Hsueh WC, Cummings SR: The genetics of human longevity.
AmJMed 2004, 117(11):851.
Hadley EC, Rossi WK: Exceptional survival in human populations: National Institute on Aging perspectives and programs. MechAgeing Dev 2005, 126(2):231.
Rose G, Dato S, Altomare K, Bellizzi D, Garasto S, Greco V, Passarino
G, Feraco E, Mari V, Barbi C, Bonafe M, Franceschi C, Tan Q, BoikoS, Yashin AI, De Benedictis G: Variability of the SIRT3 gene,
human silent information regulator Sir2 homologue, andsurvivorship in the elderly. ExpGerontol 2003, 38(10):1065.
van Heemst D, Beekman M, Mooijaart SP, Heijmans BT, Brandt BW,
Zwaan BJ, Slagboom PE, Westendorp RG: Reduced insulin/IGF-1
signalling and human longevity. Aging Cell 2005, 4(2):79.
Geesaman BJ, Benson E, Brewster SJ, Kunkel LM, Blanche H, Thomas
G, Perls TT, Daly MJ, Puca AA: Haplotype-based identification ofa microsomal transfer protein marker associated with thehuman lifespan. ProcNatlAcadSciUSA 2003, 100(24):14115.
Nebel A, Croucher PJ, Stiegeler R, Nikolaus S, Krawczak M, Schreiber
S: No association between microsomal triglyceride transfer
protein (MTP) haplotype and longevity in humans. ProcNatlAcadSciUSA 2005, 102(22):7906.
Hirschhorn JN, Daly MJ: Genome-wide association studies forcommon diseases and complex traits. NatRevGenet 2005,
6(2):95.
Cupples LA, Arruda H, Benjamin EJ, D'Agostino RB Sr, Demissie S,
DeStefano AL, Dupuis J, Falls K, Fox CS, Gottlieb DJ, Govindaraju DR,
Guo CY, Heard-Costa N, Hwang SJ, Kathiresan S, Kiel DP, Laramie
JM, Larson MG, Levy D, Liu CY, Lunetta KL, Mailman MD, Manning
AK, Meigs JB, Murabito JM, Newton-Cheh C, O'Connor GT, O'Donnell CJ, Pandey MA, Seshadri S, Vasan RS, Wang ZY, Wilk JB, Wolf
PA, Yang Q, Atwood LD: The Framingham Heart Study 100K
SNP genome-wide association study resource: Overview ofPage 11 of 12(page number not for citation purposes)

BMC Medical Genetics 2007, 8(Suppl 1):S13

25.

26.
27.

28.
29.

30.
31.

32.
33.
34.
35.
36.

37.

38.

39.
40.
41.
42.

43.

44.

45.

17 phenotype working group reports. BMC Med Genet 2007,
8(Suppl 1):S1.
Survival following initial cardiovascular disease events: 30
year follow-up. In The Framingham Heart Study: An epidemiologicalinvestigation of cardiovascular disease Edited by: Kannel WB, Wolf PA,
Garrison RJ. Bethseda, MD: NHLBI, NIH; 1988.
Murabito JM, Yang Q, Fox C, Wilson PW, Cupples LA: Heritabilityof age at natural menopause in the Framingham Heart
Study. J Clin Endocrinol Metab 2005, 90(6):3427-3430.
Murabito JM, Yang Q, Fox CS, Cupples LA: Genome-wide linkage
analysis to age at natural menopause in a community-based
sample: the Framingham Heart Study. Fertil Steril 2005,
84(6):1674-1679.
SAS/STAT software, Version 8.2. SAS System for Windows.
Copyright 1999–2001. SAS Institute Inc. Cary, NC, USA; 1999.
Karasik D, Demissie S, Cupples LA, Kiel DP: Disentangling thegenetic determinants of human aging: biological age as analternative to the use of survival measures. JGerontolA BiolSciMedSci 2005, 60(5):574.
2006 [http://sageke.sciencemag.org/cgi/genesdb].
Karolchik D, Baertsch R, Diekhans M, Furey TS, Hinrichs A, Lu YT,
Roskin KM, Schwartz M, Sugnet CW, Thomas DJ, Weber RJ, Haussler
D, Kent WJ: The UCSC Genome Browser Database. Nucleic
Acids Res 2003, 31(1):51.
Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM,
Haussler D: The human genome browser at UCSC. Genome
Res 2002, 12(6):996.
Barthel A, Schmoll D, Unterman TG: FoxO proteins in insulin
action and metabolism. Trends EndocrinolMetab 2005, 16(4):183.
Lam EW, Francis RE, Petkovic M: FOXO transcription factors:
key regulators of cell fate. BiochemSoc Trans 2006, 34(Pt 5):722.
Hwangbo DS, Gershman B, Tu MP, Palmer M, Tatar M: DrosophiladFOXO controls lifespan and regulates insulin signalling in
brain and fat body. Nature 2004, 429(6991):562.
Bonafe M, Barbieri M, Marchegiani F, Olivieri F, Ragno E, Giampieri C,
Mugianesi E, Centurelli M, Franceschi C, Paolisso G: Polymorphic
variants of insulin-like growth factor I (IGF-I) receptor andphosphoinositide 3-kinase genes affect IGF-I plasma levels
and human longevity: cues for an evolutionarily conserved
mechanism of life span control. JClinEndocrinolMetab 2003,
88(7):3299.
Kojima T, Kamei H, Aizu T, Arai Y, Takayama M, Nakazawa S, Ebihara
Y, Inagaki H, Masui Y, Gondo Y, Sakaki Y, Hirose N: Association
analysis between longevity in the Japanese population andpolymorphic variants of genes involved in insulin and insulinlike growth factor 1 signaling pathways. ExpGerontol 2004,
39(11–12):1595.
Kuningas M, Magi R, Westendorp RG, Slagboom PE, Remm M, vanHeemst D: Haplotypes in the human Foxo1a and Foxo3agenes; impact on disease and mortality at old age. EurJHumGenet 2007, 15(3):294.
Brenkman AB, Burgering BM: FoxO3a eggs on fertility and aging.
Trends Mol Med 2003, 9(11):464-467.
Castrillon DH, Miao L, Kollipara R, Horner JW, DePinho RA: Suppression of ovarian follicle activation in mice by the transcription factor Foxo3a. Science 2003, 301(5630):215-218.
Hirschhorn JN, Lohmueller K, Byrne E, Hirschhorn K: A comprehensive review of genetic association studies. Genet Med 2002,
4(2):45-61.
Bathum L, Christiansen L, Tan Q, Vaupel J, Jeune B, Christensen K:
No evidence for an association between extreme longevity
and microsomal transfer protein polymorphisms in a longitudinal study of 1651 nonagenarians. EurJHumGenet 2005,
13(10):1154.
Beekman M, Blauw GJ, Houwing-Duistermaat JJ, Brandt BW, Westendorp RG, Slagboom PE: Chromosome 4q25, microsomal transfer protein gene, and human longevity: novel data and ameta-analysis of association studies. JGerontolA BiolSciMedSci
2006, 61(4):355.
Sild M, Koca C, Bendixen MH, Frederiksen H, McGue M, Kolvraa S,
Christensen K, Nexo B: Possible associations between successful aging and polymorphic markers in the Werner generegion. AnnNYAcadSci 2006, 1067:309.
Kuro-o M, Matsumura Y, Aizawa H, Kawaguchi H, Suga T, Utsugi T,
Ohyama Y, Kurabayashi M, Kaname T, Kume E, Iwasaki H, Iida A, Shiraki-Iida T, Nishikawa S, Nagai R, Nabeshima YI: Mutation of the

http://www.biomedcentral.com/1471-2350/8/S1/S13

46.

47.

mouse klotho gene leads to a syndrome resembling ageing.
Nature 1997, 390(6655):45.
Kurosu H, Yamamoto M, Clark JD, Pastor JV, Nandi A, Gurnani P,
McGuinness OP, Chikuda H, Yamaguchi M, Kawaguchi H, Shimomura
I, Takayama Y, Herz J, Kahn CR, Rosenblatt KP, Kuro-o M: Suppression of aging in mice by the hormone Klotho. Science 2005,
309(5742):1829.
Masuda H, Chikuda H, Suga T, Kawaguchi H, Kuro O: Regulation ofmultiple ageing-like phenotypes by inducible klotho gene
expression in klotho mutant mice. MechAgeing Dev 2005,
126(12):1274.

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