BMC Medical Genetics

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Framingham Heart Study genome-wide association: results for
pulmonary function measures
Jemma B Wilk*1, Robert E Walter2,3, Jason M Laramie1,4,
Daniel J Gottlieb2,3,5 and George T O'Connor2,3
Address: 1Department of Neurology, Boston University School of Medicine, Boston, MA, USA, 2Pulmonary Center, Department of Medicine,
Boston University School of Medicine, Boston, MA, USA, 3The National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham,
MA, USA, 4Program in Bioinformatics, Boston University, Boston, MA, USA and 5VA Boston Healthcare System, Boston, MA, USA
Email: Jemma B Wilk* - jwilk@bu.edu; Robert E Walter - walterb@bu.edu; Jason M Laramie - laramiej@bu.edu;
Daniel J Gottlieb - dgottlieb@lung.bumc.bu.edu; George T O'Connor - goconnor@lung.bumc.bu.edu* Corresponding author

Published: 19 September 2007
BMC Medical Genetics 2007, 8():S8

doi:10.1186/1471-2350-8-S1-S8This article is available from: http://www.biomedcentral.com/1471-2350/8/S1/S8© 2007 Wilk 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: Pulmonary function measures obtained by spirometry are used to diagnose chronic obstructive
pulmonary disease (COPD) and are highly heritable. We conducted genome-wide association (GWA) analyses(Affymetrix 100 K SNP GeneChip) for measures of lung function in the Framingham Heart Study.
Methods: Ten spirometry phenotypes including percent of predicted measures, mean spirometry measures over two
examinations, and rates of change based on forced expiratory volume in one second (FEV1), forced vital capacity (FVC),
forced expiratory flow from the 25th to 75th percentile (FEF25–75), the FEV1/FVC ratio, and the FEF25–75/FVC ratio were
examined. Percent predicted phenotypes were created using each participant's latest exam with spirometry. Predicted
lung function was estimated using models defined in the set of healthy never-smokers, and standardized residuals ofpercent predicted measures were created adjusting for smoking status, pack-years, and body mass index (BMI). All
modeling was performed stratified by sex and cohort. Mean spirometry phenotypes were created using data from two
examinations and adjusting for age, BMI, height, smoking and pack-years. Change in pulmonary function over time wasstudied using two to four examinations with spirometry to calculate slopes, which were then adjusted for age, height,
smoking and pack-years.
Results: Analyses were restricted to 70,987 autosomal SNPs with minor allele frequency ≥ 10%, genotype call rate ≥80%, and Hardy-Weinberg equilibrium p-value ≥ 0.001. A SNP in the interleukin 6 receptor (IL6R) on chromosome 1
was among the best results for percent predicted FEF25–75. A non-synonymous coding SNP in glutathione S-transferase
omega 2 (GSTO2) on chromosome 10 had top-ranked results studying the mean FEV1 and FVC measurements from two
examinations. SNPs nearby the SOD3 and vitamin D binding protein genes, candidate genes for COPD, exhibited
association to percent predicted phenotypes.
Conclusion: GSTO2 and IL6R are credible candidate genes for association to pulmonary function identified by GWA.
These and other observed associations warrant replication studies. This resource of GWA results for pulmonary
function measures is publicly available at http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007.

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BMC Medical Genetics 2007, 8:S8Background
Chronic obstructive pulmonary disease (COPD), which
affects approximately six percent of the adult US population [1] and is the fourth most common cause of death in
the US [2], has been defined as "airflow limitation that is
not fully reversible" [3], a definition based on spirometry.
Environmental factors, most notably tobacco smoking,are associated with accelerated longitudinal decline of
pulmonary function and are important causes of COPD.
Several lines of evidence indicate that genetic factors also
contribute to the development of this condition. First,
family studies have revealed increased risk of lung function impairment in smoking first degree relatives of
COPD cases [4] and substantial heritability of spirometry
measures in population based studies [5]. Second, severealpha-1-antitrypsin deficiency due to homozygous mutations of the SERPINA1 (AAT) gene is a documented causeof COPD, although this condition explains only a small
proportion of COPD in the population. Finally, familybased and case-control studies are beginning to reveal
genetic variants other than those of the SERPINA1 genethat are associated with chronic airflow obstruction [6].
Despite this evidence of a genetic basis for susceptibility to
COPD, the specific genetic risk factors underlying mostcases of COPD remain uncertain.
The Framingham Heart Study offers the opportunity to
conduct family-based linkage and association studies
seeking potential genetic factors that influence obstructive(COPD, asthma), restrictive, or developmentally related
lung function impairment. Using spirometry measurements as quantitative phenotypes, we have previously
reported results of genome-wide linkage analyses among
these same families using microsatellite markers spaced
approximately 10 centiMorgans apart [7] and fine-mapping of a promising region on chromosome 6q [8,9]. The
availability of data on over 100,000 single nucleotide polymorphisms (SNPs) throughout the genome now permits
the application of genome-wide association (GWA) testing to the search for genetic risk factors for chronic airflow
limitation. The longitudinal pulmonary function datathat have been obtained over the years of the Framingham
Heart Study, in combination with the 100 K SNP data,
make this a unique resource for the discovery of novel
genetic risk factors for chronic airflow obstruction.

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spirometry was performed without bronchodilator testing.
Measurements as percent of predicted at latest
examination
The spirometry measurements from each participant's latest examination with acceptable pulmonary function data
[10] were used; eligible examinations included Cohort
exams 19, 17, and 13 and Offspring exams 7, 6, 5, and 3.
Predicted values for each lung function measurement
were calculated using cohort and gender-specific regression models predicting spirometry measurements on thebasis of age, age squared, and height squared [11] among
Framingham subjects who were lifetime nonsmokers andhad no history of chronic bronchitis, pulmonary disease,
COPD/emphysema, asthma, or wheezing. The percent ofpredicted value was calculated by dividing the observedby the predicted value. Standardized residuals were thencreated by regressing the percent predicted on current
smoking (y/n), former smoking (y/n), pack-years, andbody mass index (BMI: kg/m2), in cohort and gender-specific models. Forced expiratory volume in one second(FEV1), forced vital capacity (FVC), forced expiratory flow
between the 25th and 75th percentile (FEF25–75), the FEV1/
FVC ratio, and the FEF25–75/FVC ratio were examined as
cross-sectional percent of predicted measures. These phenotypes are referenced in tables preceded by the letters"pp" (for percent predicted), see Table 1 for explanationof phenotype abbreviations.
Mean of measurements at two specified examinations
In previous analyses of the genetics of lung function in the
Framingham families [7,8], we have used the mean of thevalues of each spirometry measure from two specified
examinations. For Cohort participants, spirometry datafrom exam cycles 5 or 6 and cycle 13 were used to generate
the mean value. In Offspring participants, spirometry datafrom cycle 3 and cycle 5 were used to generate the meanvalue. The mean FEV1, FVC, and FEV1/FVC ratio were
adjusted for the effects of age, age2, BMI, height, dummy
variables indicating never, former, or current smoking status, and, for former and current smokers, pack-years.
Standardized residuals were generated separately by sex
and within Cohort or Offspring samples. These phenotypes are referenced in tables as meanfev1, meanfvc, andmeanratio.

Methods
Three types of spirometry phenotypes were evaluated for
GWA: 1) measurements taken at a participant's mostrecent available examination and expressed as a percent ofpredicted; 2) the mean of measurements taken at two
specified examinations; and 3) the annual rate of declineof spirometry measurements derived by calculating theslope of measurements across multiple examinations. All

Annual rate of decline of measurements
Rate of decline phenotypes were defined by fitting a slope
to the spirometry data from different time points. The
examinations incorporated were the same as those eligible
for percent of predicted phenotypes described above.
Slopes were calculated by ordinary least-squares using all
available data. A minimum of two eligible exams wereneeded to calculate a slope. Slopes were adjusted for the

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Table 1: Characteristics of phenotypes studied*PhenotypeNa

Description

Eligible exam cycles
Offspring

CohortAdjustmentb

Heritability

ppfev1

1217

Percent predicted FEV1 for latest exam

7, 6, 5, 3

19, 17, 13

0.36

ppfvc
ppratio1217
1217

7, 6, 5, 3
7, 6, 5, 3

19, 17, 13
19, 17, 13

0.45
0.29

ppfef
ppfefrat

1212
1212

7, 6, 5, 3
7, 6, 5, 3

19, 17, 13
19, 17, 13Same as ppfev1
Same as ppfev1

0.40
0.41

meanfev1

1222

Percent predicted FVC for latest exam
Percent predicted FEV1/FVC for latest
exam
Percent predicted FEF25–75 for latest exam
Percent predicted FEF25–75/FVC for latest
exam
Mean FEV1 from two exams

Predicted defined by age, age2, height, %predicted adjusted for current or former
smoking, pack-years, and BMI
Same as ppfev1
Same as ppfev1

3 and 55 or 6 and 13

0.35

meanfvc
meanratio
fev1slope

1222
1222
1097

Mean FVC from two exams
Mean FEV1/FVC from two exams
Longitudinal slope of FEV1

3 and 5
3 and 57, 6, 5, 3

5 or 6 and 13
5 or 6 and 13
19, 17, 13

fefslope

1059

Longitudinal slope of FEV25–75

7, 6, 5, 3

19, 17, 13

age, age2, BMI, height, current or former
smoking, and pack-years
Same as meanfev1
Same as meanfev1
age, age2, height, height2, pack-years at firstexam, interim pack-years, and sustainedsmoking
Same as fev1slope

0.51
0.25
0.10
0.20

*Results for additional longitudinal slope phenotypes, residual from predicted phenotypes, phenotypes created from spirometry at a single exam, and phenotypes limited tosmokers reporting 10 or more pack-years are available at http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007a) sample size with phenotype out of 1345 with GWA genotypingb) adjustment performed in four separate regression models by sex and Original or Offspring cohort

covariates age, age squared, height, and height squared,
using mean ages and heights from included exams. Slopes
were also adjusted for pack-years at first exam, interim
pack-years, and sustained smoking (y/n). FEV1 and FEF25–
75, the slope phenotypes with the highest heritability,
were studied in the GWA. These phenotypes are referenced in tables as fev1slope and fefslope.
Statistical analysis methods
All SNPs were studied using family-based association tests(FBAT) and generalized estimating equations (GEE) [12](see 100 K Overview). SNP results reported met the criteria of having a minor allele frequency ≥10%, a HardyWeinberg p-value ≥ 0.001, and a call rate ≥ 80%. All
reported FBAT tests also required a minimum of ten
informative families.

Multipoint variance component linkage analysis was
implemented with a subset of 10,588 SNPs and all 612
available microsatellites studied in previous linkage analyses [12]. Multipoint identity-by-descent estimates weregenerated using the software Merlin [13]. Heritability estimates, estimating the proportion of the total phenotypicvariance due to genetic effects, and variance component
linkage analysis were performed using the software
SOLAR [14].
In addition to evaluating all SNP associations with eachphenotype individually, we developed a method to identify SNPs in or near genes that exhibited the strongest
associations (as assessed by p-value) to multiple spirometry phenotypes. For each phenotype, we identified the 200
lowest p-values that met the criteria above and were localized within 60,000 base pairs of the transcription start orstop of a gene. All gene annotations are derived from the

UCSC genome browser May 2004 assembly, build 125
http://genome.ucsc.edu/[15,16]. We evaluated the frequency that a SNP appeared among the 200 lowest p-values in gene regions for the ten phenotypes. This strategy
was based on the hypothesis that SNPs identified to be
associated with multiple spirometry measures are more
likely to reflect a true association with lung function than
SNPs identified to be associated with only a single measurement. SNPs in gene regions that appeared among thelowest 200 p-values in five or more of the phenotypes
studied are reported.
Candidate genes
Genes previously reported in the literature to be associated with spirometry measures or pulmonary disease were
examined to determine whether any available 100 K SNPs
in or near the genes were associated with spirometry phenotypes. Twelve COPD candidate genes studied in the
Boston Early-Onset COPD cohort [17], and the SERPINE2
gene, a novel gene identified through linkage and association with COPD in the same cohort [6], were reviewed.
The previously established COPD gene alpha-1-antitrypsin (SERPINA1) and the cystic fibrosis transmembrane
conductance regulator (CFTR) as well as additional genes
in the class of Glutathione S-transferases (O1, O2, M2, T1,
T2) and surfactant proteins (SFTPA1, SFTPC) werereviewed. In addition, extracellular super oxide dismutase(SOD3) [18,19], interleukin-8 receptor alpha (IL8RA)[20], interleukin-10 (IL10) [21], beta-2 adrenergic receptor (ADRB2) [22], and transforming growth factor beta-1(TGFB1) [23] were examined as COPD candidates. The
GEE and FBAT SNP association results in or within 60
kilobase pairs (kb) of these 27 genes was reviewed. By
specifying a 60 kb distance around the gene to screen
results, we were able to identify SNPs near most candidate

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gene regions, but often the SNP reported does not lie
strictly within the transcription start or stop of the gene ofinterest.

Results
Table 1 reports the heritability estimates for each of theten phenotypes presented. The slope phenotypes havelower heritability than their corresponding cross sectional
phenotypes. FVC has the highest heritability among phenotypes defined using the same method (percent predicted or mean). The percent predicted FEF25–75/FVC ratio
had a higher heritability estimate than either FEV1/FVC
ratio phenotype.
Table 2 reports the SNPs with the lowest p-values for anyof the 10 phenotypes evaluated in GWA analysis. Results
were ranked by p-value and the top 25 SNPs are reported.
In some cases, a SNP had top ranked results for multiple
correlated phenotypes, and thus the p-value for all phenotypes is reported at the ranking position of the best pvalue. Table 2a was ranked by GEE p-value, and Table 2b
was ranked by FBAT p-value. SNP positions are reported
according to NCBI Human Genome Build 35. SNPs in the
Affymetrix set whose chromosome and position are
unknown and SNPs on sex chromosomes are not reported
for association. In total, 70,987 SNPs were considered inassociation analyses. None of these results achieved a conservative threshold for genome-wide significance. All
results, regardless of allele frequency, call rate, or deviation from Hardy-Weinberg are publicly available at http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007.
Only a single SNP among those reported in Tables 2a and2b is a known coding SNP. Among the top ranked GEE pvalues was a non-synonymous coding SNP (rs156697) in
the Glutathione S-transferase omega 2 gene (GSTO2) on
chromosome 10. The SNP was among the top ranked GEE
p-values for association with the mean FEV1 and mean
FVC phenotypes.
Other SNPs reported in Table 2a were localized to intronic
gene regions in COL1A2, ADARB1, SNTG1, RHBDD1,
NID2, IL6R, and SYT10. The best GEE p-value localized tothe untranslated region of SNRPN. SNPs identified by
FBAT p-value, reported in Table 2b, located in introns
were in CCBL2, LRRC9, PAX3, LIPF, MTHFD1L, and
KIAA1797. Additional genes reported in Tables 2a and 2b
were within 60 kb of the associated SNP. SNP rs2906966,
among the top 25 p-values for both tests, is located near
CDRT4. SNPs reported may be in linkage disequilibrium(LD). Among the top six FBAT results, strong LD was
observed between the three chromosome 1 SNPs in
CCBL2, and the three chromosome 14 SNPs in LRRC9,
thus only 2 regions are being identified.

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Linkage to all autosomes and the X chromosome was performed. Table 2c reports all LOD scores above 2.0 withthe 1.5-LOD support interval. The best LOD score
observed is in a region of linkage on chromosome 6q thatwas reported previously using microsatellites in the Framingham families [7,8]. The original LOD score of 2.4 formean FEV1 using genome-wide microsatellites [7] wasincreased to a LOD of 2.89 with the addition of SNP data,
and a LOD of 2.65 was observed in the same region for thepercent predicted FEV1 phenotype. The second highest
LOD score observed was 2.86 for the longitudinal FEF25–
75 phenotype, which was located on the X chromosome.
The percent predicted FEV1 and FVC phenotypes both had
LOD scores over 2.0 on chromosome 4, with overlapping
LOD support intervals centered around 166–170 Mb.
Table 3 reports SNPs that met the quality control criteria
and were among the top 200 p-values for SNPs locatedwithin 60 kb of a gene for at least five of the phenotypes
studied. A SNP in an intron of the interleukin 6 receptor(IL6R) and a SNP near the sodium channel, voltage gated,
type I alpha (SCN1A) gene were among the top ranked
result for six phenotypes. All others were among the top
ranked results for five phenotypes. The genes syntrophin,
gamma 1 (SNTG1) and the chromosome 20 open reading
frame 133 (C20orf133) appear in both the FBAT and GEElists of top results across 5 phenotypes. The two SNPs in
SNTG1 are separated by 104 kb and have low LD (r2 =0.05 and D' = 0.28 in the HapMap CEU sample). The two
C20orf133 SNPs are in strong LD (r2 = 0.78 in HapMap
CEU), though rs10485771 is located 3' of the gene.
Table 4 reports the best p-value observed when examining
SNP results specifically in the regions of the 27 candidate
genes. SNPs reported are within 60 kb of the transcription
start or stop for the gene listed, and sometimes lie withina different gene. Only 20 of the 27 genes of interest had
SNPs in or within 60 kb of the gene. No SNPs were nearenough to the genes GSTM1 and TNF, presented by Hershet al. (2005), and GSTO1, GSTT1, SFTPA1, SFTPC and
IL8RA to be considered. Of the 20 best FBAT results, 12
were p-values less than 0.05, but only 5 were p-values lessthan 0.01. Of the best GEE results, 14 were p-values lessthan 0.05, and 5 were p-values less than 0.01. Three generegions had p-values less than 0.01 for both types of test,
and these were CFTR, GSTO2, and SOD3. The vitamin D
binding protein (GC) region produced an FBAT p-value of
0.0009, though GEE results were weaker.

Discussion
This is the first GWA of quantitative lung function measures to be reported, and it provides an opportunity forboth hypothesis generation and hypothesis testing. We
have identified a number of novel gene regions associatedwith pulmonary function. Associations with these SNPs

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Table 2: GEE, FBAT, and linkage results

2a Top Association results based on GEE p-value
Phenotype

SNP

fev1slope

1. rs3867498

Chr

Physicalposition
15

GEE p-value

22629880

1.36 × 10-06

FBAT p-value

Gene Region

0.07

SNRPN

meanfvc

2. rs441051

793698651

2.16 × 10-06

0.005

COL1A2

meanratio

3. rs2838815

2145454018

2.63 × 10-06

0.0002ADARB1

ppfvc

4. rs1455782

1590851970

4.23 × 10-06

0.18

FLJ32831

108472826

4.32 × 10-06

7.19 × 10-05

1.7 × 10-05

0.000251575144

5.14 × 10-06

0.14

SNTG1

meanfvc

5. rs10516541

4

meanfev1
ppratio

6. rs310558

8

ppfev1

7. rs3820928

2227598971

5.33 × 10-06

0.0005RHBDD1

ppfef

8. rs730532

1451588561

5.89 × 10-06

0.15

NID2

ppfefrat

9. rs808225

1457467669

7.38 × 10-06

0.00081.45 × 10-05

0.007

ppratioppfef

10. rs4129267

1151239337

7.39 × 10-06

0.07

IL6R

ppfev1

11. rs2906966*

17

15272242

8.31 × 10-06

5.03 × 10-05

CDRT4

ppfvc

12. rs357394

7137506642

8.77 × 10-06

0.14

fefslope

13. rs1994169

1233436783

9.55 × 10-06

0.05

SYT10

meanratio

14. rs2225434

2145458574

9.68 × 10-06

0.0004ADARB1

meanfvc

15. rs156697(a)10106029175

9.78 × 10-06

9.42 × 10-05

1.8 × 10-05

0.002

meanfev1

GSTO2

ppfefrat

16. rs564425

1346797096

1.03 × 10-05

0.02

ppfef

17. rs10498441

1451613974

1.06 × 10-05

0.28

NID2

0.26

AK128224

ppratio

18. rs880713

2128847844

1.11 × 10-05

ppfefrat

19. rs811732

1457456738

1.3 × 10-05

0.20

ppfvc

20. rs6558132

829526866

1.58 × 10-05

0.74
0.10

fev1slope

21. rs6972823

73009137

1.69 × 10-05

ppfef

22. rs9285611(b)181895076

1.7 × 10-05

0.07

meanfev1

23. rs10504836

888669709

1.72 × 10-05

0.28
0.12
0.11

meanfev1

24. rs1491520

3193243153

1.77 × 10-05

ppfvc

25. rs9300826

13103001176

1.79 × 10-05

2b Top Association results based on FBAT p-value
Phenotype

SNP

ppratio

1. rs10922530

Chr

Physical position

GEE p-value

FBAT p-value

Gene Region

0.001

8.7 × 10-07

ppfefrat

0.002

3.78 × 10-06

ppfef

0.005

2.36 × 10-05 CCBL2

0.004

1.87 × 10-06

ppratio

0.004

3.59 × 10-06

ppfef

0.01

1.42 × 10-05 CCBL2

0.01

6.15 × 10-06

0.04

4.91 × 10-05 LRRC9

ppfefrat

ppratio

2. rs3753683

3. rs219349

1

1

14

89139273

89139592

59525168

ppfefrat
ppratio

4. rs219391

1459554684

0.02

6.18 × 10-06 LRRC9

ppratio

5. rs219326

14

59512943

0.02

8.51 × 10-06

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Table 2: GEE, FBAT, and linkage results (Continued)

0.06

3.74 × 10-05 LRRC9

0.005

9.12 × 10-06

ppratio

0.005

1.08 × 10-05

ppfef

0.01

5.14 × 10-05 CCBL2

ppfefrat
ppfefrat

6. rs1409149

189141170

ppfvc

7. rs9299191

9110944100

0.009

1.79 × 10-05

meanfvc

8. rs10515289(c)599315845

0.17

2.21 × 10-05

ppfvc

9. rs10498137

2222966943

0.05

2.23 × 10-05 PAX3

ppfef

10. rs3858282

1090424391

0.001

2.92 × 10-05 LIPF

ppratio

11. rs905367

459896363

0.02

2.95 × 10-05

meanfvc

12. rs10495872

237915266

0.05

3.02 × 10-05

fev1slope

13. rs1347222

1280896622

0.01

3.27 × 10-05

ppratio

14. rs2009488

459895895

0.03

3.43 × 10-05

meanfvc

15. rs6481257

1058608558

0.02

3.52 × 10-05

ppratio

16. rs461951

1459608986

0.02

3.63 × 10-05 LRRC9

meanfev1

17. rs491552

6151403169

0.01

3.86 × 10-05 MTHFD1L

meanfev1

18. rs1910137

427087329

0.08

4.04 × 10-05

ppfvc

19. rs2831605(d)2128467064

0.03

4.41 × 10-05
5.03 × 10-05 CDRT4
5.17 × 10-05 ETAA16

ppfev1

20. rs2906966*

17

15272242

8.31 × 10-06

fefslope

21. rs6740919

267531706

0.0009meanfev1

22. rs10498818

6631341290.02

5.25 × 10-05

ppfefrat

23. rs1393593

459907689

0.06

5.44 × 10-05

ppfefrat

24. rs9312080

459891451

0.11

5.63 × 10-05

ppfefrat

25. rs7851363

920748306

0.006

5.7 × 10-05 KIAA1797

Physical position

1.5-LOD interval
start

2c Linkage peaks with LOD score > 2.0
Phenotype

SNP

Chr

1.5-LOD intervalend

LOD score

meanfev1

rs2300081

6168097526

165513097

170788550

fefslope

rs5909594

X(e)118057198

113970434

124311695

2.89
2.86

ppfev1

AGC001b

6170788550

164268152

170788550

2.65

ppratiors10497042

2150675813

148296793

154203261

2.40

meanratio

rs10518669

182811739

74999965

88728520(b)

2.37

ppfef

rs2974490

5113433775

95608129

124687730(c)

2.29
2.29

meanfvc

rs10488908

2121904704

19805361

34461762(d)ppfev1

rs10518032

4

170322764

161768472

181501690

2.28

ppfvc

rs10489542

1225169260

213625196

233478438

2.17

ppfef

rs721411

1736902470

28313357

52519635

2.17

ppfev1

rs753765

1757323782

45345729

59677087

2.17

meanfev1

rs4918762

10114389096

102080914

122214789(a)

2.12

ppfvc

rs10517825

4

166251091

156507719

191091333

2.02*) SNP occurring in both Table 2a and 2ba) SNP associated to meanfvc and meanfev1 in Table 2a under linkage peak for meanfev1 on chromosome 10.
b) SNP associated to ppfef in Table 2a under linkage peak for meanratio on chromosome 1.
c) SNP associated to meanfvc in Table 2b under linkage peak for ppfef on chromosome 5.
d) SNP associated to ppfvc in Table 2b under linkage peak for meanfvc on chromosome 21.
e) X chromosome linkage results are not available online

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Table 3: Top ranked SNPs within 60 kb of a gene and associated with five or more of the phenotypes studied

SNP

Chr

bp position

Gene region

3a GEE
rs4129267
rs7587026
rs3820928
rs445347
rs310558
rs581446
rs10485770

1
2
2
5
8
18
20

151239337
166804257
227598971
53011487
51575144
10646848
13943214

IL6R
SCN1A
RHBDD1NDUFS4
SNTG1
FAM38B
C20orf133

3b FBAT
rs10489030
rs2438345
rs7759033
rs2391996
rs10504106
rs10485771

4
5
6
7
8
20

24521105
90198041
116404207
31487207
51471079
13999103

3' of SOD3
GPR98
FRK
C7orf16SNTG1
C20orf133

and gene regions require replication in other study samples as well as functional studies before any statement
about causality is warranted. Many of the best p-values arelikely to reflect false positive results, and GEE results
exhibited elevated Type I error [12] (see 100 K Overview).
Additional studies of this data will be useful, including
smoking stratified analyses and more sophisticatedapproaches to creating multivariate phenotypes. However, several of the observed associations involve genes forwhich there are plausible biologic rationales for a relation
to lung function phenotypes.
The Glutathione S-Transferase (GST) superfamily genes
are of interest because of their role in metabolism of xenobiotics, such as cigarette smoke. Recently, Hersh et al.
studied GSTP1 and GSTM1 in two independent analysesof COPD and reported null findings. In contrast, a studyof annual change in lung function measures in a population based cohort reported that the GSTT1 deletion alone
or in combination with the GSTM1 deletion influenced
decline in FEV1 in men [24]. Using the Affymetrix 100 K
SNP GeneChip, we have limited ability to directly confirm
or refute the aforementioned findings because no SNPs
were genotyped within 10 kb of the genes.
Here, we show that a non-synonymous SNP in exon 5 of
GSTO2 encoding an Asn142Asp amino acid change isamong the most striking GWA results for mean FEV1 andFVC phenotypes. Both the non-synonymous SNP,
rs156697, and a second SNP, rs156699, located in an
intron exhibited strong association using both FBAT and
GEE tests (r2 between SNPs = 0.8 in HapMap CEU). Linkage results also support the evidence for GSTO2, as thegene's position lies within the confidence interval aroundthe LOD of 2.12 observed on chromosome 10 for mean
FEV1 (Table 2c). GSTO2 is involved in the biotransforma-tion of arsenic, which is a component of cigarette smoke,and may exhibit modest expression in bronchial epithelial cells. Gene expression studies in COS-1 cells demonstrated that the Asp142 variant exhibited 76% of the levelof expressed protein occurring in the wild-type, andexpression levels were further reduced to 15% when the
Asp142 occurred in conjunction with an Ile158 variant[25]. The observation of strong association to a non-synonymous polymorphism with demonstrated effects on
gene expression is compelling. Moreover, this finding in
conjunction with a growing literature on GST gene association with pulmonary phenotypes suggests that a complete evaluation of functional variants in this gene family
may be warranted.
The IL6R SNP was not only among the top 25 p-values forpercent predicted FEF25–75, but also among the top 200 pvalues in gene regions for six of the ten phenotypes presented. IL6R is thought to be expressed in lung, and may
play a role in the immune response. Recently, we haveshown that IL6 levels in blood were associated with
impaired lung function in the Framingham offspringcohort [26]. The IL6 pathway, as a mediator of the inflammatory process, is of interest as it relates to lung function
phenotypes.
The SNP identified in the SOD3 region lies within a hypothetical protein 3' of the SOD3 gene. The non-synonymous SNP in SOD3 that has been reported for association
with COPD (rs1799895) was not included in the HapMap, so we could not determine the extent of LD between
it and the SNPs genotyped in this study. Another exon 3
SNP (rs2536512) located 519 base pairs away fromrs1799895 is present in the HapMap. The associated SNP
identified in this study, rs10489030, is in very low LD
with the SOD3 exon 3 HapMap SNP (D' = 0.32 and r2 =Page 7 of 10(page number not for citation purposes)

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Table 4: Candidate gene evaluation

Gene region

Chr

SNP

Phenotype

FBAT p-value

GEE p-value

Alpha-1-antitrypsin (AAT/SERPINA1)

14

rs2402446

ppfvc
fev1slope

0.02

0.30

Beta-2 adrenergic receptor (ADRB2)

5

rs10484042
rs30329

0.90
0.009

0.01
0.07

cystic fibrosis transmembrane regulator (CFTR)

7

rs9325117
rs213987

0.006

0.04
0.38

Microsomal epoxide hydrolase (EPHX1)

1

rs10487367
rs3738051

Vitamin D binding protein (GC)4

rs423817

0.25
0.01
0.03
0.0009

0.002
0.30
0.04
0.06

Glutathione S-transferase M2 (GSTM2)

1

rs842873
rs542338

Glutathione S-transferase O2 (GSTO2)
Glutathione S-transferase P1 (GSTP1)

1011

rs156697
rs688878

Glutathione S-transferase T2 (GSTT2)

22

rs140289

Heme oxygenase (HMOX1)

22

rs2267331

0.05
0.19
0.43
9.4 × 10-05
0.07
0.39
0.17
0.41
0.07

0.02
0.63
0.20
9.8 × 10-06
0.35
0.02
0.13
0.10
0.80

Interleukin-10 (IL10)1

rs10483190
rs10494879

Matrix metalloproteinase-1 (MMP1)

11

rs495366

Matrix metalloproteinase-9 (MMP9)
Alpha-1-antichymotrypsin (SERPINA3)

20
14

rs2903908
rs10484047

Serine proteinase inhibitor E2 (SERPINE2)

2

rs717610

Surfactant protein B (SFTPB)
Surfactant protein D (SFTPD)
extracellular super oxide dismutase (SOD3)2
104

rs7577293
rs726289
rs10489030

transforming growth factor beta-1 (TGFB1)

19

rs3745295

Tissue inhibitor of metalloproteases-2 (TIMP2)17

rs2889529

0.82
0.06
0.46
0.02
0.20
0.19
0.02
0.10
0.04
0.19
0.02
0.02
0.0005
0.01
0.21
0.74
0.07
0.18

0.008
0.73
0.02
0.13
0.03
0.05
0.03
0.01
0.003
0.001
0.06
0.03
0.02
0.007
0.62
0.18
0.11
0.09

fefslope
meanratio
meanfvc
ppratiofefslope
ppfefrat
ppfefrat
ppfefrat
meanratio
fev1slope
meanfvc
ppfefrat
ppfvc
ppfvc
ppfev1fev1slope
meanratio
fefslope
fev1slope
fefslope
ppfef
ppfefrat
ppfvc
fev1slope
meanfvc
meanratio
meanratio
fefslope
ppfev1
ppfvc
fev1slope
ppfev1
ppfef
meanratio

Best p-value for FBAT and GEE tests reported with specific SNP*phenotype producing result. P-values < 0.01 bolded.

0.005 in HapMap CEU). The low LD between the SOD3
exonic SNP and this 3' SNP, separated by 43.5 kb, does
not suggest a clear replication of association with the
SOD3 gene, but suggests that the genomic region is ofcontinued interest. Also on chromosome 4, a SNP in theregion of the vitamin D binding protein (GC) was associated with the percent predicted FEF25–75/FVC ratio with ap-value of 0.0009 using the FBAT test, but this SNP is in
low LD with the Asp432Gly polymorphism, rs7041 (D' =0.17 and r2 = 0.007 in HapMap CEU).
The SNP identified in the region of SERPINE2 (rs717610)
was not in LD with the six reported SNPs replicating significant associations to COPD [6] that were also available

in HapMap, as the r2 values ranged from 0.002 to 0.008.
Two of the top SNPs (rs3820928, GEE rank #7;
rs10498137, FBAT rank #9) lie within the linkage region
identified for FEV1/FVC in severe early-onset COPD cases[27] that subsequently led to the discovery of the
SERPINE2 associated SNP. SNP rs3820928 was among
those identified with association to five of the phenotypes
studied and lies in the gene RHBDD1 (alias
DKFZp547E052). Not much is known about the functionof this gene, but the associated SNP is located in a region
with LD extending to the adjacent COL4A4 gene. The
rs3820928 exhibited an r2 of 0.81 with two non-synonymous coding SNPs in COL4A4 in the HapMap CEU data.
Defects in Type IV collagen genes have been shown toPage 8 of 10(page number not for citation purposes)

BMC Medical Genetics 2007, 8:S8

influence Goodpasture's syndrome, an autoimmune disease affecting the lung [28], and both COL4A4 and
COL4A3 lie in this region, sharing a common promoter.
These results do not provide a strong replication of theoriginal SERPINE2 SNP associations due to the low LD
between SNPs reported in the literature and the SNPs
from the 100 K data with association. However, the results
suggest that chromosome 2q is of continued interest andmay harbor multiple genes influencing lung function.
Studying lung function measures in a community-based
sample may identify genetic variants associated with lunggrowth and development, susceptibility to obstructive
ventilatory impairment related to asthma, emphysema
and COPD or susceptibility to restrictive ventilatory
impairment due to pulmonary fibrosis or other processes.
The relevance to disease pathogenesis of associations
between SNPs and lung function must be interpreted withcaution, and some of the observed associations may
reflect polymorphisms that protect against ventilatory
impairment by leading to better lung function in early life
or protection against the adverse effects of cigarette smoking. All of the GWA results are publicly available at http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007. Replication of novel results
identified by GWA will be the true test of the value of the
GWA approach to gene discovery.

Conclusion
These publicly available results provide a resource forinvestigators to assess whether their findings of association to pulmonary function phenotypes replicate in the
Framingham population. In addition, we have identified
novel results that warrant replication studies in other populations.

http://www.biomedcentral.com/1471-2350/8/S1/S8

analysis. JML wrote scripts to parse through the GWA
results and facilitate the presentation of results. All
authors read and approved the final manuscript.
Acknowledgements
The authors would like to recognize the Framingham Heart Study participants and the support from NIH/NHLBI Contract NO1-HC25195. Drs.
Wilk and Walter are each supported by their own Young Clinical Scientist
Award from the Flight Attendant Medical Research Institute (FAMRI). The
authors would like to thank Emelia Benjamin, MD, ScM for serving as guest
editor and providing thoughtful comments on the manuscript. A portion ofthis 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 (1S10RR16373601A1).
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.

5.

6.

Abbreviations
BMI = body mass index; COPD = chronic obstructive pulmonary disease; FEV1 = forced expiratory volume in one
second; FBAT = family based association test; FVC = forced
vital capacity; FEF25–75 = forced expiratory flow between
the 25th and 75th percentile; GEE = generalized estimating
equation; GWA = genome-wide association; kb = kilobase
pairs (1000 base pairs); LD = linkage disequilibrium;
SNPs = single nucleotide polymorphisms.

Competing interests

7.

8.

9.

The authors declare that they have no competing interests.

Authors' contributions
JBW created the phenotypic residuals that were analyzed,
reviewed GWA results, and drafted the manuscript text
and tables. REW, DJG, and GTO assessed the quality ofspirometry data, identified exclusion criteria, and defined
the covariates and methods to develop phenotypes for

10.
11.
12.

Mannino DM, Homa DM, Akinbami LJ, Ford ES, Redd SC: Chronic
obstructive pulmonary disease surveillance – United States,
1971–2000. Respiratory care 2002, 47(10):1184-1199.
Hoyert DL, Heron MP, Murphy SL, Kung HC: Deaths: final data for2003. Natl Vital Stat Rep 2006, 54(13):1-120.
Fabbri L, Pauwels RA, Hurd SS: Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive
Pulmonary Disease: GOLD Executive Summary updated
2003. Copd 2004, 1(1):105-141. discussion 103–104.
Silverman EK, Chapman HA, Drazen JM, Weiss ST, Rosner B, Campbell EJ, O'Donnell WJ, Reilly JJ, Ginns L, Mentzer S, Wain J, Speizer FE:
Genetic epidemiology of severe, early-onset chronic
obstructive pulmonary disease. Risk to relatives for airflow
obstruction and chronic bronchitis. American journal of respiratory and critical care medicine 1998, 157(6 Pt 1):1770-1778.
Wilk JB, Djousse L, Arnett DK, Rich SS, Province MA, Hunt SC,
Crapo RO, Higgins M, Myers RH: Evidence for major genes influencing pulmonary function in the NHLBI family heart study.
Genet Epidemiol 2000, 19(1):81-94.
Demeo DL, Mariani TJ, Lange C, Srisuma S, Litonjua AA, Celedon JC,
Lake SL, Reilly JJ, Chapman HA, Mecham BH, Haley KJ, Sylvia JS, Sparrow D, Spira AE, Beane J, Pinto-Plata V, Speizer FE, Shapiro SD, Weiss
ST, Silverman EK: The SERPINE2 Gene Is Associated with
Chronic Obstructive Pulmonary Disease. American journal of
human genetics 2006, 78(2):253-264.
Joost O, Wilk JB, Cupples LA, Harmon M, Shearman AM, Baldwin CT,
O'Connor GT, Myers RH, Gottlieb DJ: Genetic loci influencing
lung function: a genome-wide scan in the Framingham
Study. American journal of respiratory and critical care medicine 2002,
165(6):795-799.
Wilk JB, DeStefano AL, Joost O, Myers RH, Cupples LA, Slater K,
Atwood LD, Heard-Costa NL, Herbert A, O'Connor GT, Gottlieb
DJ: Linkage and association with pulmonary function measures on chromosome 6q27 in the Framingham Heart Study.
Human molecular genetics 2003, 12(21):2745-2751.
Wilk JB, Herbert A, Shoemaker CM, Gottlieb DJ, Karamohamed S:
Secreted modular calcium-binding protein 2 haplotypes areassociated with pulmonary function. American journal of respiratory and critical care medicine 2007, 175(6):554-560.
ATS: Standardization of Spirometry, 1994 Update. American
Thoracic Society. American journal of respiratory and critical care
medicine 1995, 152(3):1107-1136.
Hankinson JL, Odencrantz JR, Fedan KB: Spirometric reference
values from a sample of the general U.S. population. American
journal of respiratory and critical care medicine 1999, 159(1):179-187.
Cupples LA, Arruda HT, Benjamin EJ, D'Agostino RB Sr, Demissie S,
DeStefano AL, Dupuis J, Falls K, Fox CS, Gottlieb DJ, Govindaraju DR,

Page 9 of 10(page number not for citation purposes)

BMC Medical Genetics 2007, 8:S8

13.
14.
15.

16.
17.

18.

19.

20.

21.

22.
23.

24.

25.

26.
27.

28.

Guo CY, Heard-Costa NL, 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 100 K
SNP genome-wide association study resource: Overview of
17 phenotype working group reports. BMC Med Genet 2007,
8(Suppl 1):S1.
Abecasis GR, Cherny SS, Cookson WO, Cardon LR: Merlin – rapid
analysis of dense genetic maps using sparse gene flow trees.
Nature genetics 2002, 30(1):97-101.
Almasy L, Blangero J: Multipoint quantitative-trait linkage analysis in general pedigrees. American journal of human genetics 1998,
62(5):1198-1211.
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 research 2003, 31(1):51-54.
Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM,
Haussler D: The human genome browser at UCSC. Genome
research 2002, 12(6):996-1006.
Hersh CP, Demeo DL, Lange C, Litonjua AA, Reilly JJ, Kwiatkowski
D, Laird N, Sylvia JS, Sparrow D, Speizer FE, Weiss ST, Silverman EK:
Attempted replication of reported chronic obstructive pulmonary disease candidate gene associations. American journalof respiratory cell and molecular biology 2005, 33(1):71-78.
Young RP, Hopkins R, Black PN, Eddy C, Wu L, Gamble GD, Mills
GD, Garrett JE, Eaton TE, Rees MI: Functional variants of antioxidant genes in smokers with COPD and in those with normallung function. Thorax 2006, 61(5):394-399.
Juul K, Tybjaerg-Hansen A, Marklund S, Lange P, Nordestgaard BG:
Genetically increased antioxidative protection anddecreased chronic obstructive pulmonary disease. American
journal of respiratory and critical care medicine 2006, 173(8):858-864.
Stemmler S, Arinir U, Klein W, Rohde G, Hoffjan S, Wirkus N, Reinitz-Rademacher K, Bufe A, Schultze-Werninghaus G, Epplen JT: Association of interleukin-8 receptor alpha polymorphisms withchronic obstructive pulmonary disease and asthma. Genes
and immunity 2005, 6(3):225-230.
Burgess JL, Fierro MA, Lantz RC, Hysong TA, Fleming JE, Gerkin R,
Hnizdo E, Conley SM, Klimecki W: Longitudinal decline in lungfunction: evaluation of interleukin-10 genetic polymorphisms in firefighters. Journal of occupational and environmental
medicine/American College of Occupational and Environmental Medicine
2004, 46(10):1013-1022.
Hegab AE, Sakamoto T, Saitoh W, Massoud HH, Massoud HM, Hassanein KM, Sekizawa K: Polymorphisms of IL4, IL13, and
ADRB2 genes in COPD. Chest 2004, 126(6):1832-1839.
Celedon JC, Lange C, Raby BA, Litonjua AA, Palmer LJ, DeMeo DL,
Reilly JJ, Kwiatkowski DJ, Chapman HA, Laird N, Sylvia JS, Hernandez
M, Speizer FE, Weiss ST, Silverman EK: The transforming growth
factor-beta1 (TGFB1) gene is associated with chronic
obstructive pulmonary disease (COPD). Human molecular
genetics 2004, 13(15):1649-1656.
Imboden M, Downs SH, Senn O, Matyas G, Brandli O, Russi EW,Schindler C, Ackermann-Liebrich U, Berger W, Probst-Hensch NM:
Glutathione S-transferase genotypes modify lung function
decline in the general population: SAPALDIA cohort study.
Respiratory research 2007, 8:2.
Mukherjee B, Salavaggione OE, Pelleymounter LL, Moon I, Eckloff
BW, Schaid DJ, Wieben ED, Weinshilboum RM: Glutathione Stransferase omega 1 and omega 2 pharmacogenomics. Drugmetabolism and disposition: the biological fate of chemicals 2006,
34(7):1237-1246.
Walter RE, Wilk JB, Larson MG, Vasan RS, Keaney JF, Lipinska I,
O'Connor GT, Benjamin EJ: Systemic inflammation and COPD:
The Framingham Heart Study. Chest in press.
Silverman EK, Palmer LJ, Mosley JD, Barth M, Senter JM, Brown A,
Drazen JM, Kwiatkowski DJ, Chapman HA, Campbell EJ, Province
MA, Rao DC, Reilly JJ, Ginns LC, Speizer FE, Weiss ST: Genomewide linkage analysis of quantitative spirometric phenotypes in severe early-onset chronic obstructive pulmonary
disease. American journal of human genetics 2002, 70(5):1229-1239.
Hudson BG, Tryggvason K, Sundaramoorthy M, Neilson EG:
Alport's syndrome, Goodpasture's syndrome, and type IV

http://www.biomedcentral.com/1471-2350/8/S1/S8

collagen.
The New348(25):2543-2556.

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medicine

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