BMC Medical Genetics

BioMed Central

Introduction

Open Access

The Framingham Heart Study 100K SNP genome-wide association
study resource: overview of 17 phenotype working group reports
L Adrienne Cupples*1,2, Heather T Arruda1, Emelia J Benjamin1,2,3,4,Ralph B D'Agostino Sr1,2,5, Serkalem Demissie1,2, Anita L DeStefano1,2,3,Josée Dupuis1,2, Kathleen M Falls6, Caroline S Fox1,7, Daniel J Gottlieb3,8,
Diddahally R Govindaraju1,3, Chao-Yu Guo1,5, Nancy L Heard-Costa1,3, ShihJen Hwang1,7, Sekar Kathiresan1,9,10,11, Douglas P Kiel12, Jason M Laramie3,13,Martin G Larson1,5, Daniel Levy1,3,7, Chun-Yu Liu2, Kathryn L Lunetta1,2,
Matthew D Mailman14, Alisa K Manning2, James B Meigs15,16,
Joanne M Murabito1,3, Christopher Newton-Cheh1,9,11,
George T O'Connor1,3, Christopher J O'Donnell1,7,11, Mona Pandey1,
Sudha Seshadri1,3, Ramachandran S Vasan1,3,4, Zhen Y Wang14,
Jemma B Wilk1,3, Philip A Wolf1,3, Qiong Yang1,2 and Larry D Atwood1,2,3
Address: 1National Heart Lung and Blood Institute's Framingham Heart Study, Framingham, MA, USA, 2School of Public Health, Boston
University, Boston, MA, USA, 3School of Medicine, Boston University, Boston, MA, USA, 4Whitaker Cardiovascular Institute, Boston University,
Boston, MA, USA, 5Department of Mathematics and Statistics, Boston University, Boston, MA, USA, 6Department of Molecular and Cellular
Biology, Harvard University, Cambridge, MA, 7National Heart, Lung and Blood Institute, Bethesda, MD, USA, 8VA Boston Healthcare System,
Boston, MA, USA, 9Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA, 10Cardiovascular
Disease Prevention Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA, 11Cardiology Division, Massachusetts
General Hospital and Harvard Medical School, Boston, MA, USA, 12Hebrew SeniorLife: Institute for Aging Research and Harvard Medical School,
Boston, MA, USA, 13Bioinformatics Program, Boston University, Boston, MA, USA, 14National Center for Biotechnology Information, Bethesda,
MD, USA, 15General Medicine Division, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA and 16Department ofMedicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
Email: L Adrienne Cupples* - adrienne@bu.edu; Heather T Arruda - harruda@bu.edu; Emelia J Benjamin - emelia@bu.edu;
Ralph B D'Agostino - ralph@bu.edu; Serkalem Demissie - demissie@bu.edu; Anita L DeStefano - adestef@bu.edu;
Josée Dupuis - dupuis@bu.edu; Kathleen M Falls - falls@morgan.harvard.edu; Caroline S Fox - foxca@nhlbi.nih.gov;
Daniel J Gottlieb - gottlieb@bu.edu; Diddahally R Govindaraju - drgraju@bu.edu; Chao-Yu Guo - chao-yu.guo@childrens.harvard.edu;
Nancy L Heard-Costa - nheard@bu.edu; Shih-Jen Hwang - hwangs2@nhlbi.nih.gov; Sekar Kathiresan - skathiresan@partners.org;
Douglas P Kiel - kiel@hrca.harvard.edu; Jason M Laramie - laramiej@bu.edu; Martin G Larson - mlarson@bu.edu; Daniel Levy - levyd@nih.gov;
Chun-Yu Liu - liuchunyu2002@yahoo.com; Kathryn L Lunetta - klunetta@bu.edu; Matthew D Mailman - mm888r@nih.gov;
Alisa K Manning - amanning@bu.edu; James B Meigs - jmeigs@PARTNERS.ORG; Joanne M Murabito - murabito@bu.edu;
Christopher Newton-Cheh - CNEWTONCHEH@PARTNERS.ORG; George T O'Connor - goconnor@lung.bumc.bu.edu;
Christopher J O'Donnell - codonnell@nih.gov; Mona Pandey - mpandey@bu.edu; Sudha Seshadri - suseshad@bu.edu;
Ramachandran S Vasan - vasan@bu.edu; Zhen Y Wang - wangzhe@mail.nih.gov; Jemma B Wilk - jwilk@bu.edu;
Philip A Wolf - pawolf@bu.edu; Qiong Yang - qyang@bu.edu; Larry D Atwood - lda@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):S1

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

This article is available from: http://www.biomedcentral.com/1471-2350/8/S1/S1© 2007 Cupples 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.

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Abstract
Background: The Framingham Heart Study (FHS), founded in 1948 to examine the epidemiology
of cardiovascular disease, is among the most comprehensively characterized multi-generational
studies in the world. Many collected phenotypes have substantial genetic contributors; yet mostgenetic determinants remain to be identified. Using single nucleotide polymorphisms (SNPs) froma 100K genome-wide scan, we examine the associations of common polymorphisms withphenotypic variation in this community-based cohort and provide a full-disclosure, web-based
resource of results for future replication studies.
Methods: Adult participants (n = 1345) of the largest 310 pedigrees in the FHS, many biologicallyrelated, were genotyped with the 100K Affymetrix GeneChip. These genotypes were used toassess their contribution to 987 phenotypes collected in FHS over 56 years of follow up, including:
cardiovascular risk factors and biomarkers; subclinical and clinical cardiovascular disease; cancer
and longevity traits; and traits in pulmonary, sleep, neurology, renal, and bone domains. We
conducted genome-wide variance components linkage and population-based and family-based
association tests.
Results: The participants were white of European descent and from the FHS Original andOffspring Cohorts (examination 1 Offspring mean age 32 ± 9 years, 54% women). This overview
summarizes the methods, selected findings and limitations of the results presented in theaccompanying series of 17 manuscripts. The presented association results are based on 70,897
autosomal SNPs meeting the following criteria: minor allele frequency ≥ 10%, genotype call rate
≥ 80%, Hardy-Weinberg equilibrium p-value ≥ 0.001, and satisfying Mendelian consistency. Linkage
analyses are based on 11,200 SNPs and short-tandem repeats. Results of phenotype-genotype
linkages and associations for all autosomal SNPs are posted on the NCBI dbGaP website at http://
www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007.
Conclusion: We have created a full-disclosure resource of results, posted on the dbGaP website,from a genome-wide association study in the FHS. Because we used three analytical approaches to
examine the association and linkage of 987 phenotypes with thousands of SNPs, our results must
be considered hypothesis-generating and need to be replicated. Results from the FHS 100K projectwith NCBI web posting provides a resource for investigators to identify high priority findings for
replication.

Background
Cardiovascular diseases are major illnesses among Americans, affecting about a third of the population (79 million
with prevalent disease) and resulting in more than
870,000 cardiovascular disease deaths annually [1]. Cardiovascular disease and its risk factors have substantial
genetic contributors [2-11]. Numerous reports from the
Framingham Heart Study (FHS) have documented that
coronary heart disease [12,13], blood pressure [14-16],
lipids [17-20], diabetes [21-23] and weight [24,25] havesubstantial heritability and linkage/association to specificgenomic regions. To evaluate the genetic contributors tothese phenotypes, the Framingham Heart Study conducted a genome-wide scan of 1345 study participants in
two generations, using genotyping from the 100K Affymetrix GeneChip Human Mapping Set.
In this manuscript, we summarize the strategies that we
pursued to conduct the 100K genome-wide study, providing an overview for a series of 17 companion manuscripts(Table 1 of the Overview) describing associations withspecific collections of traits [26-42]. The primary purposeof this project was to generate hypotheses regarding
genetic factors that may contribute to the wide spectrumof phenotypic variables collected in the FHS through agenome-wide approach. More specifically, we primarily
hypothesized that common genetic variants contributing
to phenotypic variation can be detected through agenome-wide association study (GWAS) and that genetic
loci contributing to phenotypic variation can be detected
through linkage. Each manuscript also examines whether
the 100K analyses replicated previously reported associations with consistent evidence from the literature for somespecific traits. The main purpose of this series of publications is to describe the association results made available
for investigators and to direct readers to their free availability in the database of Genotype and Phenotype(dbGaP) public repository http://www.ncbi.nlm.nih.gov/
projects/gap/cgi-bin/study.cgi?id=phs000007
attheNational Center for Biotechnology Information (NCBI),
where these comprehensive results are posted and may bebrowsed in the context of multiple genomic tracks includPage 2 of 19(page number not for citation purposes)

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ing Entrez Gene, RefSeq, dbSNP, genetic markers, andOMIM. The deposition of these data in a public repository
is consistent with the long tradition of publishing preliminary results from the FHS to benefit the wider scientificcommunity.
To organize the evaluation of the rich resource of data collected over nearly 60 years of follow up, we established aset of "Phenotype Working Groups" that included clinicians, epidemiologists, geneticists, and biostatisticians.
These groups specified the traits to be studied, along withcovariate adjustment and subgroups for analyses. In all,987 phenotypes were examined for association, 835 forlinkage. Some phenotypes are the same trait with different
covariate adjustments, at different examinations or evaluated in different subgroups. For example, many traits were
evaluated with both age and sex adjustment as well aswith additional multivariable adjustments, yielding morethan one phenotype for analysis. Each manuscript in thisseries provides a platform for the web posted results. Not
every trait is described in the manuscripts; rather, the purpose of each manuscript is to introduce the trait areas andto present a brief summary of the results. In the present
manuscript, we describe the general approach to analysisof the traits, provide an overview of some results, and discuss the limitations of the studies.

Methods
Study sample
The Framingham Heart Study (FHS) began in 1948 withrecruitment of 5209 men and women (2336 men and2873 women) between the ages of 28 and 62 years in the
town of Framingham, Massachusetts, about 20 miles westof Boston [43-46]. These individuals were recruited
through a two-thirds systematic sample of the households
of Framingham, Massachusetts. Although not initiallyintended as a family study, many households consisted of
spouse pairs (1644 pairs). The primary purpose of theStudy was to follow individuals over time for development of cardiovascular disease events to evaluate theinterplay among multiple risk factors that lead to disease
and their individual and joint effects. The participants in
the Original Cohort have been examined every two years
since.

In 1971, an Offspring Cohort of 5124 men and women,
who were adult children of Original Cohort members or
were spouses of these offspring, was recruited and has
been examined every four to eight years since [47,48]. The
subjects in this report are drawn from the largest 310 pedigrees in these two generations. The participants wererecruited without regard to phenotypes. Thus, the Offspring Cohort of 5124 (2483 men and 2641 women) was
recruited by inviting all offspring of the spouse pairs(2616 and 34 stepchildren), the offspring spouses (1576)

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and, additionally, those offspring (898) of singleton Original Cohort members with elevated lipid levels. Further
information regarding recruitment can be seen in Cupples
et al. [49] and Dawber [43].
In the late 1980s and through the 1990s, DNA was collected from living study participants. As many of the Original Cohort members were deceased by that time, theseDNAs were mostly collected in Offspring Study participants. During the mid- to late-1990s, 1702 DNA samples
were genotyped by the Mammalian Genotyping Service inthe largest 330 two-generation pedigrees consisting of
2885 Framingham Study participants. These pedigrees
were used for linkage analyses of blood pressure [15], lipids [17,50], body mass index [25] and a wide variety ofother traits [51-56]. The numbers of relative pairs amongthe 1345 subjects both genotyped and phenotyped in thisstudy are 435 parent-offspring pairs, 988 sib pairs, 300
avuncular pairs and 634 first-cousin pairs. Among the1087 Offspring Cohort participants, who were the only
participants evaluated in some analyses, there were 936
sib pairs, 63 avuncular pairs and 612 first-cousin pairs.
Original Cohort study subjects return to the Study every
two years for a detailed medical history, physical examination and laboratory tests. The Original Cohort subjects are
currently in their 29th examination. Participants in the
Offspring Cohort return every 4 to 8 years for similar
examinations and the 8th examination is currently underway.
In the early 2000s, a family DNA plate set with 1,399 participants
from
these
330
pedigrees
http://
www.nhlbi.nih.gov/about/framingham/policies/
index.htm was established. Only subjects with lymphoblast cell lines were included on the plate set, although a
substantial number of the DNA samples on the plate set
were derived from whole blood or buffy coat. The family
plate set was used for genotyping of the Affymetrix 100K
GeneChip. After cleaning the genotyping data, the study
sample comprised 1345 FHS participants, 278 from theOriginal and 1087 Offspring Cohorts.
Phenotype definition & methods
Given the breadth of phenotypes, we established 8 larger
Phenotype Working Groups, overseeing 17 discrete phenotypic domains, as follows: 1. Blood pressure, arterial
stiffness, echocardiography, endothelial function andexercise testing; 2. Metabolic traits including anthropometry, glycemic traits and lipids; 3. Pulmonary function andsleep; 4. Systemic biomarkers, including inflammatory
and thrombotic factors; 5. Subclinical atherosclerosis; 6.
Renal and endocrine function; 7. Longevity and aging,including brain and bone aging phenotypes; 8. Cardiovascular disease outcomes, cancer, electrocardiography andPage 3 of 19(page number not for citation purposes)

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Table 1: Papers published in the Framingham Heart Study 100K SeriesFirst Author

Title

Ramachandran S. Vasan

Genome-wide association of echocardiographic dimensions, brachial artery endothelial function and treadmill
exercise responses in the Framingham Heart Study
Framingham Heart Study 100K Project: genome-wide associations for blood pressure and arterial stiffness
Genome-wide association study for subclinical atherosclerosis in major arterial territories in the NHLBI's
Framingham Heart Study
Framingham Heart Study 100K project: genome-wide associations for cardiovascular disease outcomes
Genome-wide association study of breast and prostate cancer in the NHLBI's Framingham Heart Study
Genome-wide association study of electrocardiographic and heart rate variability traits: the Framingham Heart Study
Framingham Heart Study genome-wide association: results for pulmonary function measures
Genome-wide association of sleep and circadian phenotypes
A genome-wide association for kidney function and endocrine-related traits in the NHLBI's Framingham Heart Study
Genome-wide association with select biomarker traits in the Framingham Heart Study
Genome-wide association and linkage analyses of hemostatic factors and hematological phenotypes in the
Framingham Heart Study
Genetic correlates of longevity and selected age-related phenotypes: a genome-wide association study in the
Framingham Study
Genome-wide association with bone mass and geometry in the Framingham Heart Study
Genetic correlates of brain aging on MRI and cognitive test measures: a genome-wide association and linkage analysis
in the Framingham study
Genome-wide association with diabetes-related traits in the Framingham Heart Study
A genome-wide association study for blood lipid phenotypes in the Framingham Heart Study
Genome-wide association to body mass index and waist circumference: the Framingham Heart Study 100K project

Daniel Levy
Christopher J. O'Donnell
Martin G. Larson
Joanne M. Murabito
Christopher Newton-ChehJemma B. Wilk
Daniel J. Gottlieb
Shih-Jen Hwang
Emelia J. Benjamin
Qiong Yang
Kathryn L. Lunetta
Douglas P. Kiel
Sudha Seshadri
James B. Meigs
Sekar Kathiresan
Caroline S. Fox

heart rate variability. In addition, we established a statistical and analytical methodology group. These groups wereconvened by the FHS Genetic Steering Committee todefine phenotypes to be evaluated, including the covariates used in analyses, to review results of linkage and association analyses, to foster communication among various
Framingham investigators who were working on different
traits, and to suggest possible follow-up strategies.
For the 100K genome-wide project, each Working Groupdefined the phenotypes to be studied. Since most traits
have well established factors that contribute to their variation, each group created a set of residuals from multivariable regression models accounting for the primary
known covariates, in order to control for confounding
from these variables and to increase the ability to detect
genetic signals. For quantitative traits, the adjusted standardized residuals were generated using linear regression
models. For qualitative traits, we used a variety of
approaches including Cox proportional hazards with
Martingale residuals for time-to-event (survival) traits andlogistic regression with deviance residuals for dichotomous traits. These methods are described below. In somecases, several different covariate adjustments were used
for a single trait. Each manuscript describes the specificadjustments that were applied. We used residuals from
regression models that included all subjects with traits ineach Cohort, rather than limiting analyses to those who
were genotyped, to produce residuals based on all subjects
with phenotypic values, regardless of availability of genotypic data. This approach avoids potential biases in covari-ate adjustment based only upon the subset of individuals
with both genotype and phenotype data and produces
robust estimates of covariate effects.
Genotyping methods
Genomic DNA derived from whole blood or buffy coat
was phenol-chloroform extracted and DNA from immortalized lymphoblast cell lines was salt-precipitate
extracted. Genotyping of the 100K SNPs in FHS families
was performed through an ancillary study to Drs. Michael
Christman and Alan Herbert at Boston University Schoolof Medicine in the Department of Genetics and Genomics
using the GeneChip Human Mapping 100K set fromAffymetrix, following the manufacturer's protocol as previously described [57]. Genotypes were determined using
the Dynamic Modeling (DM) algorithm [58]. For linkage
analyses, we also included microsatellites that had been
genotyped by the NHLBI Mammalian Genotyping Service, Center for Medical Genetics, Marshfield Medical
Research
Foundation
http://research.marshfieldclinic.org/genetics. A set of 401 microsatellite markers)[59], covering the genome at an average density of one
marker every 10 cM and with an average heterozygosity of
0.77, were genotyped in 1702 subjects in the mid to late
1990s (Screening Set v. 8))[60]. An additional 190 participants on the Family Plate Set were genotyped later withmicrosatellites using Screening Set v.13 and some additional microsatellites were also genotyped in the FHS
Genetics Laboratory. With the addition of these microsatellites and changes in the marker sets from Set 8, there
were 613 microsatellite markers available for analysis.

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Statistical analysis methods
Data cleaning
A total of 1380 individuals were successfully genotyped.
First, familial relationships were checked using thesib_kin utility in the Aspex software package [61]. Because
this study focused on participants of families, nine individuals were excluded as they no longer had biologic relatives in the sample. Twenty-six individuals were excluded
due to inconsistencies; the majority of these individuals
were found to have an excessive number of Mendelian
errors as identified by the software PedCheck, Version 1.1[62]. Others were excluded for having a relationshipinconsistency, for a sex discrepancy or for a low genotyping call rate. Mendelian inconsistencies were resolved byremoving the genotypes of all individuals within nuclear
families in which the error occurred. These steps left 1345
individuals with genotypes available for analyses.

For Hardy-Weinberg equilibrium (HWE) testing, we randomly selected one individual per family to form a sample of unrelated individuals. Then, for each of the 100K
SNPs, the observed genotype frequencies were comparedto those expected under HWE using an exact chi-square
test statistic [63] implemented in the Genetics package[64] in R, Version 1.2.0 [65]. To guard against a result thatmight depend upon an unusual selection of individuals,
we repeated this process of a random selection of subjects
ten times and computed the geometric mean of the ten pvalues for the ten random samples of individuals as the
final p-value for HWE tests for each SNP. Tests for HWE
that indicate a SNP is far from HWE suggest that the SNPmay have issues with genotyping error.
We found 38,062 SNPs with MAF <10% (of which 3084
autosomal SNPs were monomorphic), an additional
2346 SNPs with genotyping call rates <80% and still
another 1595 with HWE p-value < 0.001. We used a genotyping call rate cutpoint of 80% in part because of theuse of the less accurate DM algorithm. SNPs with low
MAF, low genotyping call rates and inadequate HWE produced unstable results, so they were excluded from ourassociation results reported in this set of manuscripts,
leaving 70,987 SNPs. Results for all autosomal SNPs arereported on the dbGaP website, regardless of MAF, genotyping call rates or HWE p-values; however, filters forthese factors are provided on the dbGaP website.
Linkage analyses
Both microsatellites previously genotyped by the Mammalian Genotyping Service and SNPs from the 100K wereused to calculate identity by descent probabilities. We
constructed genetic maps using all microsatellite NCBI
genetic markers with Marshfield genetic location available
and whose physical order and genetic order were consistent. Using this NCBI Marshfield map as our skeleton, we

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applied linear interpolation from physical to genetic distance to obtain approximate genetic locations (in centiMorgans) for all SNPs in the 100K set with known
physical location.
Because current linkage analysis software cannot handle
the marker density available from a 100K scan, we
selected a subset of 10,592 SNPs to supplement 613
genome scan microsatellite markers available on 1886
members of the largest 330 Framingham families. We
selected SNPs to minimize linkage disequilibrium (LD)because current linkage software assumes that markers arein linkage equilibrium, and violation of this assumption
has been shown to create spurious linkage evidence in certain contexts [66,67]. Thus, for calculation of identity bydescent (IBD) probabilities for linkage analyses we used
SNPs with a call rate of at least 85%, HWE p-value > 0.05
and more informative markers with MAF > 5%. We iteratively identified SNP pairs with LD measure D' > 0.5, as
estimated from HapMap data, and eliminated the SNP
that was least informative for linkage (lowest MAF). We
started with SNP pairs most closely located (physical distance) and continued until no pairs of SNPs had a D'
measure exceeding 0.5. The final set of 10,592 SNPs combined with the 613 microsatellites were checked for excess
recombination using MERLIN, Version 0.10.2 [68], and 4
SNPs and 1 microsatellite were omitted from linkage analyses based on a high number of possible errors, leaving a
total of 11,200 markers to perform linkage analysis(10,588 SNPs + 612 short tandem repeats).
Variance component linkage analyses were performed onresiduals of up to 1341 individuals in 310 full pedigrees.
Four of the 1345 subjects were the only person in a pedigree and were excluded from linkage analyses since they
contributed no information. Multipoint probabilities of
IBD between relative pairs were computed at each genetic
marker location with the program MERLIN, Version
0.10.2. Due to size limitations for exact identity bydescent (IBD) multipoint computation in MERLIN software [68], the 310 full pedigrees were broken into 356
smaller pedigrees. The hypothesis of "no linkage at a specific genomic location" was tested by comparing models
incorporating an effect of a putative quantitative trait
locus (QTL) in complete linkage to the genetic marker, inthe form of multipoint IBD sharing probabilities at thelocus, to models incorporating only polygenic effects
without a QTL effect. At each genetic location, a LOD
score was computed as the logarithm to base 10 of thelikelihood ratio of the locus-specific model to the polygenic model using the program SOLAR, Version 3.0.4[69]. Allele frequencies were estimated by simple allele
counts. In Framingham family data, which were collectedfrom randomly sampled pedigrees, we have found theallele frequency estimates by simple allele counting

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closely match those calculated by maximum likelihood
methods accounting for familial correlations.
Association testing
We applied population-based and family-based methods
to test for association between the 100K SNPs and residual phenotypes using an additive model unless otherwise
specified. We used family-based association test methods,
implemented in the program FBAT, Version 1.5.5 [70,71],
to test for differences in probability of transmission of agenotype from parents to offspring based on phenotype,as a test of linkage and association. FBAT has limited
power because it requires association within families, andmany families are non-informative. However, because
FBAT examines association only within families, the typeI error rate is not affected by population stratificationbias)[72,73]. We did not report results if the number of
informative families was fewer than 10.

For the population-based approach, we used generalized
estimating equation (GEE) [74] regression models to test
for association between the 100K SNPs and each residual
phenotype while taking into account the correlation
among related individuals. We implemented the GEE
approach by breaking families into sibships and used an
exchangeable working correlation matrix to account forcorrelation within each sibship. Parental correlations with
their children were not considered in these analyses. The
analyses were performed using the gee program package,
Version 4.13-10 [75] in R [65]. The GEE association test is
a population-based approach that uses all individuals
with both genotype and phenotype, regardless of genotype configuration within a family. Therefore, it isexpected to be a powerful test of association if population
stratification bias is not believed to be an issue, as in the
FHS [76].

Results
Participant characteristics
Of the 1345 subjects who satisfied appropriate familial
relationships and who were considered in the presentation of results in these manuscripts, 258 were Original
Cohort participants (90 men and 168 women) and 1087
were Offspring Cohort participants (527 men and 560
women). Table 2 of the Overview presents descriptive
information on these participants at enrollment (examination one). The Offspring and Cohort participants
included on the family plates had lower mean age thanother examination 1 participants, as these subjects needed
to survive to the mid 1990s to provide DNA. We note that
we used residuals based upon all subjects, as opposed toonly those who were genotyped. Thus, the phenotypes
reflect deviations of these subjects based on regressions
for the full sample of subjects and are thus representativeof the full sample.

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Format of the FHS 100K manuscripts
We present 17 manuscripts, each displaying selected
results for an epidemiologically related group of traits.
Table 1 of the Overview presents the title and first authorof each manuscript. The web resource displaying genetic
association and linkage results is available at the NCBI
dbGaP website, http://www.ncbi.nlm.nih.gov/projects/
gap/cgi-bin/study.cgi?id=phs000007. Each manuscript
describes the traits that were studied and presents someresults. These manuscripts are not intended to be comprehensive and generally do not include results for all phenotypes and covariate adjustment schemes that were studied
and presented on the website. Full listings of all traits evaluated are provided in Additional file 1 (phenotypes for
population-based GEE analyses), Additional file 2 (phenotypes for family-based FBAT analyses) and Additional
file 3 (phenotypes for linkage analyses), including url
links to the corresponding analytical results on the NCBI
dbGaP website. To facilitate the reading of these manuscripts, we have used a common format for all manuscripts. Table 1 of each manuscript presents a generaldescription of the phenotypes that were evaluated. Table
2 of each manuscript displays the top results (lowest pvalues) from GEE analyses, the top results (lowest p-values) from FBAT analyses and linkage results where the
LOD score was 2 or more. Whereas top association results
are based solely on p-value rank, the Working Groups also
applied various additional strategies to identify SNPs thatthe group would prioritize to pursue further. For Table 3of each manuscript, the groups devised schema to summarize results for related traits, grouping phenotypic traits
within biologically plausible domains, or traits examinedlongitudinally. Each manuscript provides a description of
the strategy employed and the results for its Table 3.
Finally, Table 4 in each manuscript lists some SNPs thatare the same as or correlated with genetic variants in genesthat have been reported in the literature to be associatedwith the manuscript's phenotypes and indicates whether
our results replicate those reports. Physical locations of
the SNPs are provided according to NCBI Build35,
whereas the dbGaP website uses a more recent version.
Thus, the physical locations reported in the manuscripts
may differ from those on the website. Each manuscript
provides criteria for choosing which results were reported.
SNP allele frequencies and distribution
Allele frequencies for the 100K Affymetrix GeneChip in
the Framingham sample are displayed in Figure 1. About
38% have MAF < 10% and are not considered in the seriesof manuscripts, although they are included on the dbGaP
website. Among SNPs with MAF ≥ 10%, there were large
numbers between 10–25% and were somewhat evenly
spread over the range from 25–50% MAF. Many SNPs on
the Affymetrix Chip are not near genes (Figure 2). About
30,000 with MAF ≥ 10% are within 5 kb of a gene; another

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Table 2: Description of Framingham Heart Study Subjects in 100K Genome-Wide Scan.
Baseline Data at Exam 1 for Original (1948–1951) and Offspring (1971–1975) Cohorts

Age, years(Limits)
Body Mass Index, kg/m2
Obese (BMI 30 ≥kg/m2), %Systolic Blood Pressure, mm Hg
Diastolic Blood Pressure, mm Hg
Antihypertensive Medication, %Hypertension, %Current Smoking, %Blood Glucose, mg/dL
Prevalent Diabetes, %Total Cholesterol, mg/dL
HDL Cholesterol, mg/dL
Lipid Lowering Medication, %Hemoglobin, g/dL
Forced Vital Capacity, dLPrevalent CVD, %Original Cohort Men
N = 90

Original Cohort Women
N = 168

Offspring Cohort Men
N = 527

Offspring Cohort Women
N = 560

35 ± 4(30–46)25.7 ± 3.2
7.8
129 ± 1381 ± 90
22.2
61.1
78 ± 130220 ± 44
N/A
014.3 ± 1.2
40 ± 7035 ± 4(29–48)23.9 ± 3.66.0
120 ± 1276 ± 80
8.9
42.3
78 ± 110
194 ± 37
N/A
012.3 ± 1.1
28 ± 6031 ± 10(11–62)26.0 ± 3.9
13.3
124 ± 1481 ± 10
1.7
15.0
40.0
102 ± 100.6
192 ± 3744.9 ± 11.0
0.2
15.5 ± 1.0
45 ± 85.5

32 ± 10(5–59)
23.6 ± 4.3
8.2
115 ± 1475 ± 10
1.4
6.8
41.3
97 ± 90.4
185 ± 3656.4 ± 13.9
0.5
13.5 ± 1.0
32 ± 5
2.6

Abbreviations: BMI = body mass index; HDL = high density lipoprotein; CVD = cardiovascular disease.

10,000 with MAF < 10% are within 5 kb. The remaining
SNPs are further away from known genes.
P-value distribution
The results displayed on the NCBI dbGaP http://
www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007 include all autosomal SNPs,
regardless of genotyping call rate, HWE p-value or MAF;
adjustable filters for these factors are provided. In ourmanuscripts, we present results for SNPs that satisfy genotyping call rates ≥ 80%, HWE p-value ≥ 0.001, and MAF≥ 10%. The proportion of SNPs satisfying a genotyping
call rate of ≥80% is 97.4%; 91% of SNPs satisfied a callrate of ≥90%.

We expect only a small percentage of all tested SNPs to betruly associated with any phenotype. Therefore, to obtainan approximation of the null distribution of p-values, we
examined the distribution of p-values for 415 phenotypes
from the Metabolic Working Group and 14 CVD event
phenotypes. If one assumes that only a few true associations exist for each phenotype, these p-value distributions
approximate the null distribution, because only a few
SNPs out of the large total number tested would be
expected to exceed any critical value due to true associations. Table 3 of the Overview displays the proportion of
p-values among all SNPs below specific nominal alphalevels, summarized (mean, minimum and maximum)
across all phenotypes in the trait group for GEE and FBAT
results.
Many of the phenotypes in the Metabolic Working Group
were approximately normally distributed (about 90% had
absolute value of skewness <1 and about 80% had abso-lute value of kurtosis <2) and thus may reflect the situation for which the assumptions of the analytical methods
were generally satisfied. We display two sets of results inTable 3 of the Overview for these phenotypes, those usedin the publication of the manuscripts with the number of
SNPs equal to 70,987 and the larger set of results displayed on the website with the number of SNPs equal to~100–103 K. The difference in the number of SNPs evaluated for GEE and FBAT results arises from those SNPs thatare uninformative for FBAT analyses (those with sufficiently rare minor allele so that fewer than 10 nuclear families were informative for transmission). The p-value
distributions suggest that FBAT p-values generally follow
the expected null distribution, assuming that nearly all
results are false positives, and may actually be somewhat
conservative. In contrast, the GEE p-values exhibit anexcess of small p-values, especially for smaller nominalalpha levels. For example, for SNPs reported in the manuscripts, the average proportion of SNPs for a phenotype
with p-value below specified alpha levels ranged from 1.3
to 19 times greater than the nominal level (1.3 times
larger for nominal alpha of 0.01, 19 for nominal alpha of
10-7 and 10 for 10-8). The excess is higher for the full set of
SNPs reported on the website. Here we found that the
average proportion ranged from 1.2 times larger for nominal level of 0.05 to 19 times greater for nominal level of
10-5 and 2500 times greater for nominal level 10-8.
The CVD phenotypes represent an extreme case, as thephenotypes were residuals from survival models, weregenerally bimodal, and do not satisfy general assumptions
for normality. We see the same general pattern that we
observed for the Metabolic Working Group phenotypes
with somewhat conservative FBAT tests and excess num-

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Table 3: Proportion of p-values falling below nominal levels for selected traits
SNPs with MAF ≥ 10%, HWE p ≥ 0.001 and Genotyping Call Rate ≥ 80%
Nominal alpha level

Metabolic Traits*
Mean† FBATMin† FBATMax† FBAT
Mean GEEMin GEE
Max GEE
CVD Events*
Mean FBATMin FBATMax FBAT
Mean GEEMin GEE
Max GEE# SNPs

0.05

0.01

0.001

1 × 10-4

1 × 10-5

1 × 10-6

1 × 10-7

1 × 10-8

70987

0.050
0.046
0.056
0.058
0.051
0.081

0.010
0.008
0.012
0.013
0.010
0.022

8.5 × 10-4
4.4 × 10-4
1.5 × 10-3
1.6 × 10-3
9.2 × 10-4
3.3 × 10-3

7.3 × 10-50
2.7 × 10-4
2.3 × 10-4
5.6 × 10-5
6.6 × 10-4

5.1 × 10-60
5.6 × 10-5
3.7 × 10-50
1.4 × 10-4

6.1 × 10-70
4.2 × 10-5
7.9 × 10-60
8.5 × 10-5

1.4 × 10-70
2.8 × 10-5
1.9 × 10-60
5.6 × 10-5000
1.0 × 10-70
1.4 × 10-5

0.050
0.047
0.053
0.054
0.046
0.062

0.009
0.006
0.010
0.012
0.009
0.015

6.6 × 10-4
1.7 × 10-4
9.4 × 10-4
1.4 × 10-3
7.9 × 10-4
2.5 × 10-3

4.4 × 10-50
8.5 × 10-5
1.8 × 10-4
4.2 × 10-5
5.9 × 10-4

4.0 × 10-60
1.4 × 10-5
2.4 × 10-50
1.3 × 10-4

000
6.7 × 10-60
5.6 × 10-5000
2.7 × 10-60
2.8 × 10-5000
6.7 × 10-70
1.4 × 10-5

70987

70987

70987

All SNPs posted on website http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007
Metabolic Traits*
Mean FBATMin FBATMax FBAT
Mean GEEMin GEE
Max GEE
CVD Events*
Mean FBATMin FBATMax FBAT
Mean GEEMin GEE
Max GEE

100584

101944

101060103194

0.050
0.046
0.054
0.061
0.052
0.082

0.009
0.008
0.011
0.015
0.012
0.025

7.7 × 10-4
4.4 × 10-4
1.3 × 10-3
2.4 × 10-3
1.5 × 10-3
1.1 × 10-2

6.3 × 10-50
2.2 × 10-4
5.5 × 10-4
1.9 × 10-4
6.8 × 10-3

4.3 × 10-60
5.0 × 10-5
1.9 × 10-4
3.0 × 10-5
4.3 × 10-3

5.7 × 10-70
3.0 × 10-5
8.4 × 10-50
2.6 × 10-3

1.2 × 10-70
2.0 × 10-5
4.3 × 10-50
1.7 × 10-3000
2.5 × 10-50
9.4 × 10-4

0.048
0.043
0.052
0.059
0.050
0.078

0.008
0.005
0.010
0.016
0.011
0.033

5.9 × 10-4
1.2 × 10-4
9.1 × 10-4
3.4 × 10-3
1.1 × 10-3
1.4 × 10-2

3.5 × 10-50
9.9 × 10-5
1.1 × 10-3
1.3 × 10-4
7.1 × 10-3

3.3 × 10-60
1.0 × 10-5
4.7 × 10-4
2.9 × 10-5
3.6 × 10-3000
2.0 × 10-40
1.6 × 10-3000
9.5 × 10-50
7.8 × 10-4000
4.4 × 10-50
3.3 × 10-4

* There were 415 Metabolic Traits and 14 CVD Events from which these descriptive statistics were calculated using the # SNPs indicated. # SNPs = average number of SNPs
across all traits in the Trait Group; †Mean = average proportion of SNPs below nominal level across phenotypes in the trait group; Min = minimum proportion below nominal
level across phenotypes in the trait group; Max = maximum proportion below nominal level across phenotypes in the trait group.

bers of small p-values for GEE tests. As might be expected,
the CVD phenotypes revealed larger excesses of small GEE
p-values, with the average proportion of SNPs falling
below specified alpha levels ranging from 1.08 (5.4% fornominal 5% with SNPs reported in the manuscripts) to
2.4 times greater for nominal level of 10-5 and 67 times
greater for nominal 10-8 than expected. Thus, GEE p-values need to be interpreted with care, as SNPs in the lowest
p-value range have the potential to be especially enrichedwith false positives.
We also examined the dependence of the p-value distribution for GEE results on the genotyping call rate for the
Metabolic Working Group phenotypes. Our sample was
not ascertained on trait status; so genotyping failures werelikely to be randomly distributed. Therefore, one might
expect that the effect of genotyping error on type I error
would be more modest than for case-control studies)[77].

As expected, we continued to find an excess of small p-values, despite increasingly stringent call rate thresholds.
More importantly, we found that this excess occurred
regardless of call rate. For example, for nominal alpha of
0.001 and genotyping call rate > 95%, we found that theratio of the number of observed to expected significant
results ranged from 1.6 for MAF in the range of (0.2, 0.5)
to 7.0 for MAF in the range (0, 0.05). Similarly, for callrate less than 80% we found similar ratios of 1.6 to 8.1,
respectively. For nominal alpha of 10-6 we found this ratio
varied from 9.5 to 614 for call rate > 95% and 6.8 to 667
for call rate < 80%. Thus, we used a liberal genotyping callrate of > 80% for presentation of results in our manuscripts to err on the side of including a result rather thannot, even though we expect nearly all results to be false
positives.

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Table 4: Power of the population-based association approach (GEE test) for a SNP with MAF = 0.1

Nominal Type I Error
Model

0.05

0.01

0.001

10-4

10-5

10-6

10-7

10-8

GEE
GEE
GEE
GEE
GEE

0.977
1
1
1
1

0.919
1.00
1
1
1

0.78
0.99
1
1
1

0.60
0.97
0.998
1
1

0.43
0.91
0.992
1
1

0.27
0.84
0.985
1
1

0.17
0.72
0.957
0.996
1

0.10
0.59
0.918
0.991
1

~80% subjects have phenotype(~20% missing at random): Sample size = 1076
1%
0.24
GEE
0.934
0.836
0.631
2%
0.33
GEE
1.00
0.99
0.97
3%
0.41
GEE
1
1
0.998
4%
0.47
GEE
0.998
0.998
0.998
5%
0.53
GEE
1
1
1

0.427
0.93
0.993
0.998
1

0.261
0.82
0.976
0.991
1

0.149
0.66
0.938
0.984
1

0.074
0.51
0.85
0.971
1

0.041
0.37
0.753
0.949
0.99

~60% subjects have phenotype(~40% missing at random): Sample size = 783
1%
0.24
GEE
0.879
0.737
2%
0.33
GEE
0.992
0.96
3%
0.41
GEE
1
1
4%
0.47
GEE
1
1
5%
0.53
GEE
1
1

0.281
0.744
0.945
0.998
0.997

0.137
0.591
0.874
0.981
0.996

0.055
0.433
0.754
0.95
0.982

0.023
0.293
0.602
0.884
0.964

0.008
0.186
0.467
0.797
0.931

SNP QTL Heritability

Effect Size (SD)*100% phenotype available (n = 1345)
1%
0.24
2%
0.33
3%
0.41
4%
0.47
5%
0.53

0.48
0.88
0.982
1
0.999

*SD: Standard Deviation. The effect size is expressed in unit of standard deviation of the phenotype.

Figure 3 displays observed GEE (blue) and FBAT (red) pvalues versus expected p-values (straight line) on a negative logarithm scale for mean fasting plasma glucose andmean high density lipoprotein cholesterol, calculated
from Offspring measurements over exams 1 to 7. We seethat FBAT p-values tend to be less significant than
expected (conservative) whereas GEE p-values tend to be
more significant than expected (liberal), especially forsmaller expected p-values. While we would expect most pvalues to fall on the line if there were no genetic associations, p-values that reflect true associations will be more
extreme (smaller) than expected. In looking at the figure
for mean fasting glucose, SNPs represented by the blue
dots (GEE) far above the expected line on the right handside of the figure may represent true associations withmean fasting plasma glucose. The plot for mean fasting
HDL cholesterol also suggests that there may be some true
positives, as even a few FBAT p-values are more extreme
than expected.
As in any GWAS, we expect that most results with small pvalues are false positives. The p-value distributions support this notion and further suggest that the GEE results
may have more false positives than one would expect.
Table 2 in each manuscript ranks results by p-value, buteach paper also pursues its own strategy to identify which
results may be more worthy of follow up in Table 3 ofeach manuscript, usually by considering evidence fromseveral sources such as correlated traits.
Power estimations for population-based association
approach
To assess the power of the population-based association
approach with GEE, we simulated a trait following a normal distribution with 30% polygenic heritability in thissample of 1345 subjects. We generated a SNP with MAF
0.10 and assumed that the SNP was the QTL with an additive effect and QTL heritability varying from 1% to 5%.
We also varied the proportion of phenotyped individuals
from 60% to 100%, as some traits were not available in all
subjects genotyped. The phenotype and genotype data
were simulated using SOLAR simqtl, Version 3.0.4. We
tested the association between the SNP and the trait using
GEE. One thousand replicates were performed for eachscenario. The results are displayed in Table 4 of the Overview. For a conservative alpha level such as 10-8, we havemore than 80% power to detect a SNP explaining 4% ormore total phenotypic variation when 60% or more individuals are phenotyped. With higher MAF, the power
remains similar for the same QTL heritability (data notshown). Thus, we have sufficient power to detect SNPs
explaining ≥4% or more of the phenotypic variance using
the population-based GEE association test approach, controlling for multiple testing for a single trait. The effect size
for a specific QTL heritability, defined as the increase/

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46 to 48
42 to 44

Minor Allele Frequency(Percent)

38 to 40
34 to 36
30 to 32
26 to 28
22 to 24
18 to 20
14 to 16
10 to 126 to 82 to 4
Zero
0

2

4

6

8

10

12

Percent

Figure 1 of Minor Allele Frequency for SNPs in Framingham Sample displayed on dbGaP Website
Distribution
Distribution of Minor Allele Frequency for SNPs in Framingham Sample displayed on dbGaP Website. The percentage of SNPs (X axis) with MAF of zero and in ranges (0,2], (2,4], ..., (48,50] percent (Y axis) in the Framingham sample of1345 subjects is detailed. For example, approximately 2.5–3% of SNPs in the Affymetrix 100K GeneChip had MAF of zero in
the Framingham sample and about 4% had MAF greater than 16 percent and less than or equal to 18 percent. This distribution
represents SNPs described on the dbGaP website. The manuscripts only include SNPs with MAF of 10% or more.

decrease of the phenotype value with one copy incrementof the allele tested, depends on the MAF of the SNP. For
example, for a SNP explaining 4% of the phenotypic variation, the effect size is 0.47 times the phenotypic standard
deviation (SD) for a MAF of 0.1, and 0.28 SD for a MAF of
0.5.
Synthetic strategies
Beyond simple examination of individual p-values for single tests, the authors of each manuscript developed theirown synthetic strategies to prioritize SNPs that may be
worthy of follow up (results shown in Table 3 of eachmanuscript). Many strategies considered results among
similar traits. For example, the Subclinical Working
Group created four subgroups of traits: (1) ankle brachialindex phenotypes, (2) common carotid IMT phenotypes,(3) internal carotid IMT phenotypes, and (4) multidetector computed tomography (MDCT) coronary calcification

phenotypes [29]. Within each trait group, SNPs wereranked according to the proportion of traits with p < 0.01
for both FBAT and GEE in the group. Table 3 in the Subclinical manuscript displays the top 5 ranked SNPs withhighest proportions of significant traits and lowest mean
GEE p-values for each trait group [29]. The Lipids Subgroup of the Metabolic Working Group decided to focus
on four phenotypes measured in Offspring subjects: apolipoprotein A-I levels measured at exam 4, small low-density lipoprotein as measured by nuclear magnetic
resonance, mean high-density lipoprotein cholesterol levels over 7 exams, and mean log triglyceride levels over 7
exams [39]. Presenting only those results where at least 3of the 4 traits had GEE p-value < 0.01, the SNPs wereranked according to geometric mean of these four traits[39]. The manuscript presenting results for neurologicaltraits focused on specific phenotypes within subgroups

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p-values by comparison (4.37*10-06 by GEE and 0.0045
by FBAT) [39]. However, there were many instances forwhich there was no evidence of linkage in the setting ofsignificant association. Additionally, each manuscript
describes linkage results that are in accord with previously
published linkage results.

Distribution of SNPs by MAF and Distance from a Gene
50000

Number of SNPs

40000

30000

20000

10000

0
5 kb

(5,20] kb(20,60] kb(60,120] kb

120 kb

Distance from nearest known geneNon polymorphic

 10%

> 10%

FigureDistribution
from
known
2 of
genes
Affymetrix 100K GeneChip SNPs by distanceDistribution of Affymetrix 100K GeneChip SNPs by
distance from known genes. The X axis is the distancefrom known genes and the Y axis is the number of SNPs
according to each distance. Blue represents monomorphic
SNPs, maroon is for SNPs with MAF < 10% and green is for
SNPs with MAF ≥ 10%.

with p < 0.001 in FBAT or GEE results and also other traits
within the subgroup with p < 0.01 [37].
Some results of interest
The main results for each Working Group are presented in
the individual manuscripts of this series. Here we highlight some results that address some of our expectations.
Overlap of linkage and association results
Whereas strategies for genetic studies have been undergoing substantial changes in recent years, partly due tochanges in the laboratory, we hypothesized that genomic
regions that harbor significant linkage results would also
contain significant association results. One example of theconcordance of linkage and association results was noted
for monocyte chemoattractant protein-1 (MCP1), an
inflammatory marker, where a region of linkage (LOD =4.96 at chromosome 1, 159 Mb) for MCP1 concentrations
also contains two SNPs on chromosome 1 within the 1.5
support interval for the linkage peak and within 60 kb of
the genes OR10J1 and FCER1A or OR10J3 (rs4128725
and rs2494250 with p-values in the 10-8 range by FBAT,≤10-12 by GEE) [33]. We found a similar result for C-reactive protein with a LOD score of 3.28 on chromosome 1
and two significant SNPs (rs2794520 (p = 2.83*10-8) andrs2808629 (p = 3.19*10-8)) within the 1.5 support interval of this peak [33]. Whereas Lp(a) had the highest LOD
score (LOD = 23.0) at 159.4 Mb, a SNP (rs1591375)
located at 160.7 Mb near this peak had somewhat modest

SNPs overlapping across phenotypes
We did not expect the same SNPs to appear in many manuscripts, as cardiovascular disease is complex and involves
a large and varied number of pathways for its development. In contrast, some manuscripts report on correlated
traits. Thus, we examined overlap among the top 500
SNPs associated with the phenotypes across 3 Metabolic
Working Groups: glycemic/diabetes phenotypes, lipid
phenotypes and obesity phenotypes. Of 11 SNPs found in
more than one group, none were found among the top
500 SNPs in all three groups. However, 7 SNPs were
found in the glycemia and obesity groups, 2 in glycemia
and lipid groups and 2 in lipid and obesity groups [3840].
Replication of prior associations
In Table 4 of each manuscript, we investigated whether
our results replicated previous reports in the literature.
The 100K chip does not contain many SNPs in wellknown lipid genes, such as APOE. On the other hand, we
found that SNP rs7007797 in the LPL gene was associatedwith both HDL and triglycerides [39]; we replicated recent
findings of association of a SNP in the TCF7L2 gene withdiabetes [38]. Strong statistical support was found for the
association of factor VII concentrations with SNP
rs561241 on chromosome 13 (4*10-16) [34], which
resides near the factor VII gene and is in complete linkage
disequilibrium (r2 = 1) with the Arg/Gln FVII SNP previously shown to account for 9% of the total phenotypicvariance [78]. Similarly, we found associations of circulating levels of C-reactive protein with a SNP in the geneencoding C-reactive protein [33]. Two SNPs in SORL 1, agene recently related to the risk of Alzheimer's disease[79], were found to be associated with performance on
tests of abstract reasoning (rs1131497; FBAT p = 3.2 ×
10-6; rs726601; FBAT p = 8.2 × 10-4) [37]. We found that
SNPs (rs2543600 and rs27225364) near the WRN genethat causes premature aging are associated with age atdeath and morbidity-free survival at age 65 years [35]. The
LD between these SNPs and those previously reported in
the WRN gene is unknown as the previously reported
SNPs are not in the HapMap. We found that SNP
rs2478518 in the AGT gene was associated with both
systolic and diastolic blood pressure [28]. The associationof common variation at the NOS1AP locus with electrocardiographic QT interval duration was replicated with pvalues ranging from 0.0001–0.0009 for 4 partially correlated SNPs [27]. In contrast, a number of results reported

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Figure High
Observed
Fasting
3 versus
Density
Expected
Lipoprotein
p-valuesin (-log
Offspringbase Exams
10 scale)
1 tofor7Mean(Right)Fasting Glucose in Offspring Exams 1 to 7 (Left) and for Mean
Observed versus Expected p-values (-log base 10 scale) for Mean Fasting Glucose in Offspring Exams 1 to 7
(Left) and for Mean Fasting High Density Lipoprotein in Offspring Exams 1 to 7 (Right). Blue dots are for GEE andred dots for FBAT.

in the literature were not replicated in our results. For
example, we did not find significant SNPs in ACE associated with blood pressure [28]. PPARG P12A (rs1801282)
was not associated with diabetes or related traits including
body mass index [38]. Some of these 'negative' results may
be due to low power or low LD between a SNP reported in
the literature and SNPs on the 100K chip.
We have also identified a few results that are biologicallycompelling, although replication of the SNP association is
warranted. For example, we found that a SNP(rs1158167) near the CST3 gene was highly associatedwith serum cystatin-C levels (GEE p-value = 8.5*10-09)[42]. This SNP explained 2.5% of the variation in serum
cystatin-C levels in our data and has been previously
reported to be associated with cystatin-C. These results arepresented in more detail in the Renal Endocrine Working
Group manuscript [42].
Replication of results from other genome-wide studies
While we have been preparing this series of manuscripts,
several genome-wide studies have been published [8086]. Some results in our analyses support results reported
in these recent studies. For example, we find significant

associations for coronary heart disease, cardiovascular disease and coronary artery calcium [29,30] in the same
chromosomal region on 9p recently reported to be associated with myocardial infarction by Helgadottir et al. [82]and McPherson et al. [83]. While our results need to becompared more closely with results being reported byother genome-wide studies, this example provides evidence that our results replicate strong associations fromother genome-wide studies.

Discussion
We have presented a brief description of the methods anda few selected results derived from analyses of the 100K
Affymetrix GeneChip with a large number of FHS traits,
ranging from CVD events and subclinical measures to traditional cardiovascular risk factors of diabetes, lipid levels, blood pressure and also including more novel
biomarker measures that reflect modern hypotheses, suchas the role of inflammatory pathways in the developmentof CVD. We have also reported on a number of neurological, renal, cancer and aging traits, including longevity(age at death) and bone mass and structure. None of thesemanuscripts provide a comprehensive report. Rather, thepurpose of this set of manuscripts is to provide a brief

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summary of the results and to introduce readers to thedata posted on the dbGaP website http://
www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007. We note that the genotypes inthis sample have also been evaluated by Drs. Michael
Christman and Alan Herbert. Some of their results arereported on line as described by Herbert et al. [87].
Several aspects of our investigation merit comment. First,
the present investigation represents a comprehensive
GWAS analysis of numerous phenotypes in a large community-based cohort. To our knowledge, it is the largest
GWAS performed in an observational cohort in terms ofthe number of phenotypes analyzed and web posted. Second, we exploited the phenotypic diversity and richnessof the Framingham Offspring Study database to analyze a
set of phenotypes that were for the most part collected bydetailed, direct measurements of study participants. Further, many of the phenotypes are quantitative traits. Phenotypes have been broadly categorized into seventeen
different domains for manuscripts in this supplement. It
is noteworthy that key risk factor phenotypes, such as
blood pressure and lipid levels, were collected at multiple
examinations, and thus we were able to conduct analyses
using time-averaged traits, maximizing the scientific yieldfrom the longitudinal prospective design of our cohort
study. Further, several recently collected phenotypes, inparticular biomarkers and imaging measures, were collected using highly reproducible, state-of-the-art modalities. Correlated phenotypes facilitated the assessment ofpleiotropy by seeking associations of SNPs with such phenotypes. These investigations occurred primarily among
the variables in each individual manuscript. Finally, formost phenotypes, there was evidence for a significant heritable component from FHS or other studies. We acknowledge that some phenotypic domains may represent
analytical constructs, rather than truly distinct groups
from a biological standpoint.
Third, we have web-posted the results of all analyses on
autosomes on dbGaP, including results without statisticalevidence of association, so that investigators world-wide
can access the data freely and mine them in silico forhypothesis generation, inclusion in meta-analysis, anddirect comparisons with their own results. In addition to
the freely posted aggregate results, participant-specificgenotypic and phenotypic data are available for distribution for further analyses to approved scientific investigators world-wide via the NCBI/NHLBI and consistent with
Framingham Study data distribution policies (see http://
www.nhlbi.nih.gov/about/framingham/policies/
index.htm). For the purpose of publication, reference tothese analyses may be made by referring to either theappropriate manuscript or the specific URL for webposted data. Fourth, the simultaneous and full-disclosure

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

release (on the web) of all association and linkage resultsof phenotypes encompassing at least 17 different domains
in a cohesive and comprehensive manner signifies the tremendous teamwork of numerous FHS investigators, statisticians, programmers, and others. Most importantly, thiseffort would not be possible without the full cooperationand commitment of the FHS participants, who continue
to attend Study examinations in an effort to further thescientific knowledge of factors that lead to heart disease
and other traits.
Fifth, as in any genome-wide association study with alarge number of SNPs, most results that are considered
statistically significant by a conventional p < 0.05 may befalsely positive; so it is difficult to decide what results areimportant. Not only do we have a large number of statistical tests for each phenotype, but we also have numerous
phenotypes. Thus, considering multiple testing in theinterpretation of results is of paramount importance.
There are several approaches to address the issue of multiple testing, such as Bonferroni correction, permutation
testing and false discovery rates. To conduct permutation
testing for all of the traits that we considered is prohibitively time-consuming, particularly in preserving heritability of the traits with family data. Further, withcorrelated traits it is difficult to decide what traits should
be included in a permutation testing strategy. One
approach to controlling the false-positive rate in genomewide association studies is to set a stringent threshold for
declaring statistical significance. According to the reportof the International HapMap Consortium, complete testing of common variants (MAF > 0.05) in each 500 kb isequivalent to performing 150 independent tests in white
populations of European descent [88]. Using this guideand given that there are about 3000 Mb in the humangenome, we would estimate that there are approximately
900,000–1,000,000 independent tests if testing all common variants in the genome. A conservative Bonferroni
correction using this number of tests (0.05/1,000,000)
yields an approximate threshold of genome-wide significance to be 5*10-8. Thus, for a single trait, one could use
this threshold. Several results do fall below this threshold(Table 5 of the Overview). In considering these results, wenote that our sample size of 1345 biologically related subjects is relatively small for detecting genetic variants ofmodest effect. We also note that we have a large numberof correlated traits, including the same traits with different
covariate adjustments. Further, we have already observedthat our GEE results have an excess number of small p-values. Thus, we are hesitant to regard any result reported in
our manuscripts as significant at a genome-wide level. We
believe these findings are best regarded as hypothesis-generating. The determination of what constitutes genomewide significance is challenged both by theoretical considerations as well as practical ones. Without pursuing more

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computationally intensive analyses, it is thus difficult toprovide specific advice regarding what SNPs are mostimportant. It may be safer to assume that most of thesmall p-values are likely to be false positives and that
replication of our results in other independent samples
is of critical importance. We proceed with presentationof full-disclosure results to encourage readers to pursue
such studies.

Eighth, coverage of LD is incomplete with the 100K scan.
Nicolae et al. report that the Affymetrix 100K GeneChip
includes fewer SNPs in coding and more SNPs in intergenic regions than represented on the HapMap [90]. Further, our sample size is modest. These two facts combined
likely limit the power for detection of associations withseveral traits in these data. For instance, while we notedmodest to high heritability of numerous phenotypes,
underscoring the contribution of additive genetic effects
to interindividual variation in these traits, we did not findsignificant low p-values for several heritable traits in relation to the SNPs evaluated. Factors contributing to thisobservation included both the limited coverage of theAffymetrix 100K GeneChip as well as the possibility thatsome of the less significant p-values (example between
0.05 and 10-5) may represent true positive findings. The
limited power to detect SNPs of small effect sizes offeredby the analysis of our relatively modest sample size of~1300 participants contributes to this phenomenon as
well; we only have high power to detect a SNP explaining
4% or more of the phenotypic variance in the populationbased GEE association test; the power of FBAT and variance component linkage analysis is even lower.

Sixth, we note that use of the 80% genotyping call rate isunusually liberal by today's standards in GWAS. We used
this threshold in these manuscripts to be inclusive, rather
than exclusive, in a first look such as this. We recognizethat this threshold may permit consideration of someresults that could be spurious due problems with genotyping. However, a limitation of our genotypes is that the
genotype calls were made with the DM algorithm, whichis less precise than those that have recently been introduced. At this time, we are unable to apply more accurate,
reliable genotyping calls [89], as we do not have access tothe source data. Further, we found that the choice of the
80% threshold versus a more conservative one had little
effect upon p-value distributions. Finally, all results,
regardless of genotyping call rate, are posted on the
dbGaP website and thus, investigators can evaluate forthemselves what they believe to be the more valid resultsfrom this study.

Additionally, for several of the analyzed phenotypes we
did not observe any overlap between the top SNP-phenotype associations noted in GEE and FBAT analyses. The
inherent differences in the two analytical methods especially in the context of the modest sample sizes, particularly for FBAT with small numbers of informative trios,
may contribute to this phenomenon. FBAT is limited by
the number of informative transmissions and although
we suspect that there is little population stratification in
our sample [76], GEE is limited by potential bias due tostratification. Furthermore, for several phenotypes the
SNPs associated with the top LOD scores in linkage analyses were not among the top 50 SNPs in association analyses (GEE or FBAT).

Seventh, in our analyses we found that the GEE resultsappear to have an excess of significant results. We suspectthat one reason is low MAF. Also, given the small sample
of at most 1345 subjects, we would expect only 13–14
individuals to have the minor homozygote. Thus, we limited the results that we present in the manuscripts to those
SNPs with MAF = 10%. Further analyses have indicatedthat use of a linear mixed effects model such as incorporating a SNP as a covariate in a regression model withproper correlation structure for the error terms that fully
represent the familial correlations remedies this problem
and has a valid type I error rate in simulated data.

Table 5: Associations achieving nominal genome wide significance, p < 5*10-8 across the 17 phenotype working groups
Phenotype working
group/manuscript

Trait

SNP rs ID*

Chr

Physical location (bp)

GEE P-value

FBAT P-value

IN/NEAR gene

Select biomarkers [33]

Monocyte chemoattractant protein-1
Monocyte chemoattractant protein-1
C-reactive protein average exams 2,6,7
C-reactive protein average exams 2,6,7
Cystatin C
28-year mean fasting plasma glucose
Epworth sleepiness scale
Total Cerebral Brain Volume (ATCBV)
Factor VII

rs2494250
rs4128725
rs2794520
rs2808629
rs1158167
rs2722425
rs1823068
rs1970546
rs561241

1
1
1
1
20
8
5
20
13

156,091,324
156,219,032
156,491,889
156,489,869
23,526,189
40,603,396
58,711,806
59287333
112,808,035

1.0*10-14
3.7*10-12
2.8*10-8
3.2*10-8
8.5*10-09
2.0*10-8
2.5*10-8
4.0*10-8
4.5*10-16

3.5*10-8
3.3*10-8
4.3*10-5
4.8*10-5
0.006
0.005
0.069
0.005
3.4*10-4

FCER1A, OR10J3
OR10J1CRP
CRP
CST9L|CST9|CST3
ZMAT4
PDE4D
CDH4
F7

Kidney/Endocrine [42]Diabetes [38]
Sleep and circadian [26]
Neurology [37]Hemostatic factors [34]

The following phenotype working groups did not have any traits achieving nominal genome-wide significance: echocardiography, flow-mediated dilation and exercise tolerance
testing; blood pressure and tonometry; subclinical cardiovascular disease; cardiovascular outcomes; cancer; electrocardiography and heart rate variability; pulmonary function
testing; aging; bone; lipids; obesity

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Ninth, we were limited in our ability to replicate genetic
variants previously reported to be associated with phenotypes in our database because specific coverage of suchgenetic variation in these candidates was limited in the
Affymetrix 100K GeneChip. We view such analyses as
more illustrative of the potential utility of our GWAS,
rather than as definitive evidence for or against an association described with a putative candidate gene in the published literature.

the role of common genetic variation in the developmentof cardiovascular disease and its risk factors.

Our data do suggest several interesting biological candidates among the SNPs most strongly associated with different traits in the various analytical approaches. The
strongest and most clear-cut of the associations were forthose phenotypes that represent the direct protein productof a gene. Examples include the association of CRP concentrations with SNPs in the CRP gene (Benjamin et al. inthis series [33]) and factor VII levels with SNP rs561241
on chromosome 13 (Yang et al. in this series [34]). Thus,
while it is difficult to point to any result as definitive,
those results for which we find some evidence of replication of associations found in the literature are regarded as
worthy of further research.

Competing interests

Abbreviations
FBAT = family-based association test; GEE = generalized
estimating equations; GWAS = Genome wide association
study; IBD = identity by descent; LD = linkage disequilibrium; LOD = logarithm of the odds; SNP = single nucleotide polymorphism.

Dr. Meigs currently has research grants from GlaxoSmithKline and Wyeth, and serves on safety or advisory boards
for GlaxoSmithKline, Merck, and Lilly.

Authors' contributions
All authors have made substantial contributions to conception and design, or acquisition of data, or analysis andinterpretation of data; LAC, EJB, SD, ALD, JD, KF, CSF,
DJG, SK, DPK, MGL, DL, KLL, MDM, JBM, CNC, GTO,CJO, SS, RSV, JBW, QY and LDA have been involved indrafting the manuscript or revising it critically for important intellectual content; and All authors have given final
approval of the version to be published.

Finally, the Framingham Study participants were white ofEuropean descent and predominantly middle-aged to elderly. Hence, the genetic associations may not be generalizable to other ethnicities/races or to younger individuals.

In addition, MDM, ZYW: imported, performed QC, processed, organized, and displayed all results within the
NCBI dbGaP.

Conclusion

Additional material

In summary, the results from the FHS 100K associationand linkage studies described herein and posted on the
NCBI website provide a GWAS resource for investigators.
We have presented a description of the methods and general strategies used for analysis of the 100K Affymetrix
GeneChip in relation to a broad range of traits measured
in the FHS. Brief descriptions of results of these analyses
are provided a series of 17 manuscripts, with results for all
autosomal SNPs genotyped successfully displayed at
http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007. Interested investigators can also
access the data through a standing protocol, described at
http://www.nhlbi.nih.gov/about/framingham/policies/
index.htm. Key to interpretation of these results is replication and evaluation of these results in other cohorts andultimately, functional studies. We encourage investigators
to examine the results and to pursue the genetic signals
therein in their own cohorts. In the near future we will
provide results and data from approximately 550,000
SNPs on more than 9000 participants from three generations in the FHS SNP Health Association Resource(SHARe) project. Data will be available to qualified investigators through an application process to dbGaP. It is ourhope that the results from these two genome-wide association studies will lead to a much deeper understanding of

Additional file 1
Phenotypes for population-based GEE analyses.
Click here for file
[http://www.biomedcentral.com/content/supplementary/14712350-8-S1-S1-S1.pdf]

Additional file 2
Phenotypes for family-based FBAT analyses.
Click here for file
[http://www.biomedcentral.com/content/supplementary/14712350-8-S1-S1-S2.pdf]

Additional file 3
Phenotypes for linkage analyses.
Click here for file
[http://www.biomedcentral.com/content/supplementary/14712350-8-S1-S1-S3.pdf]

Acknowledgements
We are most grateful to the Framingham Heart Study participants who
have committed so much of their time and devotion to this research. This
research was support by a contract from NHLBI N01-HC 25195 (LAC, HA,
EJB, RBD, ALD, JD, KF, DRG, CYG, NHC, MGL, CYL, KLL, JMM, MP, RSV,JBW, PAW, QY, LDA), by NHLBI K24 HL 04334 (RSV), by NIA 5R01-

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BMC Medical Genetics 2007, 8(Suppl 1):S1

AG08122 (SS, PAW), by NIA 5R01-AG16495 (SS, PAW, LDA), by NINDS
5R01-NS17950 (SS, PAW, ALD, EJB), by NHLBI RO1 HL076784 and NIA
1R01 AG028321 (EJB, JD, KL, MGL, RS, VSR), by NHLBI HL54776 (LAC,
SD, AKM) and by an American Diabetes Association Career Development
Award (JBM, JD, LAC, AKM, CYL). A portion of the research was 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.
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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