Spectral iEEG markers precede SSEP events during surgery for

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Genetic Regulation of Serum Phytosterol Levels and Risk of Coronary Artery Disease
Daniel Teupser, Ronny Baber, Uta Ceglarek, Markus Scholz, Thomas Illig, Christian
Gieger, Lesca M. Holdt, Alexander Leichtler, Karin H. Greiser, Dominik Huster, Patrick
Linsel-Nitschke, Arne Schäfer, Peter S. Braund, Laurence Tiret, Klaus Stark, Dorette
Raaz-Schrauder, Georg M. Fiedler, Wolfgang Wilfert, Frank Beutner, Stephan Gielen,
Anika Großhennig, Inke R. König, Peter Lichtner, Iris M. Heid, Alexander Kluttig, Nour E.
El Mokhtari, Diana Rubin, Arif B. Ekici, André Reis, Christoph Garlichs, Alistair S. Hall,
Gert Matthes, Christian Wittekind, Christian Hengstenberg, Francois Cambien, Stefan
Schreiber, Karl Werdan, Thomas Meitinger, Markus Löffler, Nilesh J. Samani, Jeanette
Erdmann, H. -Erich Wichmann, Heribert Schunkert and Joachim Thiery
Circ Cardiovasc Genet published online Jun 7, 2010;
DOI: 10.1161/CIRCGENETICS.109.907873
Circulation: Cardiovascular Genetics is published by the American Heart Association. 7272 Greenville
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Copyright © 2010 American Heart Association. All rights reserved. Print ISSN: 1942-325X. Online ISSN:
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Genetic Regulation of Serum Phytosterol Levels and Risk of Coronary Artery Disease
Running Title: Teupser et al.; Genetics of serum phytosterols and CAD risk
Daniel Teupser, MD; Ronny Baber, MSc; Uta Ceglarek, PhD; Markus Scholz, PhD; Thomas
Illig, PhD; Christian Gieger, PhD; Lesca M. Holdt, MD; Alexander Leichtle, MD; Karin H.
Greiser, MD; Dominik Huster, MD; Patrick Linsel-Nitschke, MD; Arne Schäfer, PhD; Peter S.
Braund, MSc; Laurence Tiret, PhD; Klaus Stark, PhD; Dorette Raaz-Schrauder, MD; Georg M.
Fiedler, MD; Wolfgang Wilfert, MSc; Frank Beutner, MD; Stephan Gielen, MD; Anika
Großhennig, MSc; Inke R. König, PhD; Peter Lichtner, PhD; Iris M. Heid, PhD; Alexander
Kluttig, PhD; Nour E. El Mokhtari, MD; Diana Rubin, MD; Arif B. Ekici, PhD; André Reis,
MD; Christoph D. Garlichs, MD; Alistair S. Hall, MD; Gert Matthes, MD; Christian Wittekind,
MD; Christian Hengstenberg, MD; Francois Cambien, MD, PhD; Stefan Schreiber, MD; Karl
Werdan, MD; Thomas Meitinger, MD; Markus Löffler, MD; Nilesh J. Samani, FRCP; Jeanette
Erdmann, PhD; H.-Erich Wichmann MD, PhD*; Heribert Schunkert, MD*; Joachim Thiery,
MD*
y
* Contributed equally
Inst of Lab Med, Clin Ch
C
Che
Chem
em & Molecular
Mo
olleccul
ular
ar Diagnostics
Diaagn
gno
osti
tics
ti
cs ((DT
(DT,
D , RB
RB,, UC
UC, L
UC,
LM
LMH,
MH, A
AL,
L G
L,
GMF,
MF,, W
MF
WW, FB, JT), Inst
for Med Informatics, Stats
Ctrr - Dept of Internal
taatss & Epidemiology
Epi
p de
demi
mio
ology
gy (MS,
(MS
M , ML),
MS
ML
L), Dept
Dep
pt of Med
Med III (DH),
(D
DH), Heart
H arrt Ct
He
C
Med/Cardio (SG), Inst off Transfusion
Med
Pathology
Univ
Leipzig,
Traansfu
usiionn M
ed
d ((GM),
GM),
GM
) & In
IInst
st ooff Pa
Path
thol
th
olog
ol
o y (C
og
((CW)
CW)
W) U
niiv L
Le
eiip
pz Germany; Inst
of Epidemiology (TI, CG,
HEW),
Inst
Human
Genetics
TM)
C IMH, HE
EW)
W , & In
nst of H
uman
um
an
nG
en
net
etic
icss (P
ic
(PL,
L, T
L,
M) Helmholtzz Zentrum
München, German Research
sear
se
arch
ch Ctr
Ctr
t for
for Environmental
Env
Env
nvir
iron
onme
on
ment
me
ntal
nt
al Health,
H
Heeal
alth
th, Neuherberg,
N uh
Ne
uher
erbe
er
b rg
be
g, Germany;
Germ
Ge
rman
rm
any;
an
y; Dept
Dep
Dept
ep
pt for Epidemiology
& Preventive Med, Regensburg
Ctr,
Regensburg,
Med Informatics,
gensburg
g
ge
ens
nsbu
burg
bu
rg Univ
Uniiv Med
M d Ct
Me
Ctr
C
r R
Regensburg
egen
eg
enssbu
b rg Ge
Germany
G
rman
rm
any
y (I
(IMH);
(IMH
MH);
MH
) IInst
);
nstt of M
ns
Biometry & Epidemiology, Ludwig-Maximilians-Univ, Munich, Germany (HEW); Inst of Human
Genetics, Klinikum rechts der Isar, Technical Univ, Munich, Germany (PL, TM); Inst of Med
Epidemiology, Biostatistics, & Informatics, Martin-Luther-Univ Halle-Wittenberg (KHG, AK), & Dept of
Med III (KW), Martin-Luther-Univ Halle-Wittenberg, Halle (Saale), Germany; Medizinische Klinik II
(PL-N, AG, JE, HS), & Inst für Med Biometrie und Statistik (AG, IRK) Univ zu Lübeck, Lübeck,
Germany; Inst für Klinische Molekularbiologie & Dept of Internal Med I, Universitätsklinikum
Schleswig-Holstein, Kiel, Germany (AS, DR, SS); Dept of Cardiovascular Sciences, Univ of Leicester,
Glenfield Hospital, Leicester, UK (PSB, NJS); Inst Nat de la Santé et de la Recherche Médicale
(INSERM) Unité Mixte de Recherche UMR_S 525, Univ Pierre et Marie Curie (UPMC) Univ. Paris 06,
Paris, France (LT, FC); Klinik und Poliklinik für Innere Med II, Univ Regensburg, Germany (KS, CH);
Dept of Cardio & Angiology, Univ Hospital Erlangen, Germany (DR-S, CG); Klinik für Innere Medizin,
Kreiskrankenhaus Rendsburg, Rendsburg, Germany (NEEM); Inst of Human Genetics, Univ of ErlangenNuremberg, Erlangen, Germany (ABE, AR); Leeds Inst of Gen, Health & Therapeutics, Univ of Leeds,
Leeds, UK (ASH);
Correspondence: Dr. Daniel Teupser, University Leipzig, Liebigstr. 27, 04103 Leipzig,
E-mail: teupser@medizin.uni-leipzig.de; Telephone +49-341-9722204; Fax +49-341-9722379 or
Dr. Heribert Schunkert, Universität zu Lübeck, Ratzeburger Allee 160, 23538 Lübeck, Germany.
E-mail: heribert.schunkert@uk-sk.de; Telephone +49-451-5002501; Fax: +49-451-5003060.
Journal Subject Codes: [89] Genetics of Cardiovascular Disease, [90] Lipid and Lipoprotein
Metabolism, [109] Clinical Genetics, [146] Genomics
2
Abstract:
Background: Phytosterols are plant-derived sterols, which are taken up from food and can serve
as biomarkers of cholesterol uptake. Serum levels are under tight genetic control. We used a
genomic approach to study the molecular regulation of phytosterol-serum levels and potential
links to coronary artery disease (CAD).
Methods and Results: A genome-wide association study for serum phytosterols (campesterol,
sitosterol, brassicasterol) was conducted in a population-based sample from KORA (n=1495)
with subsequent replication in two additional samples (n=1157 and n=1760). Replicated SNPs
were tested for association with premature CAD in a meta-analysis
nalysis of 11 different
d
samples
comprising a total of 13,764 CAD cases and 13,630 healthy controls.
contrrol
o s.
s Genetic
Gen
eneti variants in the
ranspo
po
ortter
e AB
ABCG
CG
G8 aand
n aatt th
nd
he bl
bloo
oodd gr
oo
grou
oup AB
ou
ABO
O locus
loc
ocus
us were
w
ATP-binding cassettee ttransporter
ABCG8
the
blood
group
significantly
associated with serum
um ph
um
phyt
phytosterols.
ytos
yt
ossteero
rols
l . Ef
ls
Effe
Effects
fectts in ABCG8
fe
ABCG
AB
CG88 w
CG
were
eree inde
er
in
independently
nde
depe
pend
pe
nden
nd
en
ntlly related to SNP
50
25
rs4245791 and rs41360247
3602
36
3602
0247
47 (combined
((co
comb
co
mbin
mb
ined
in
ed pp=1.6x10
=1
1.6x1
6x1
x100-50
aand
nd 66.2x10
.2x1
2x1
x100-25
, re
resp
respectively,
spec
sp
ecti
ec
tive
ti
vely
ve
ly,, n=4412). Serum
ly
campesterol was elevated 12 percent for each rs4245791 T-allele. The same allele was associated
with 40 percent decreased hepatic ABCG8 mRNA expression (p=0.009). Effects at the ABO locus
were related to SNP rs657152 (combined p=9.4x10-13). Alleles of ABCG8 and ABO associated
with elevated phytosterol levels displayed significant associations with increased CAD risk
(rs4245791, OR=1.10, 95%CI 1.06-1.14, p=2.2x10-6; rs657152, OR=1.13; 95%CI 1.07-1.19,
p=9.4x10-6), whereas alleles at ABCG8 associated with reduced phytosterol levels were
associated with reduced CAD risk (rs41360247, OR=0.84, 95%CI 0.78-0.91, p=1.3x10-5).
Conclusion: Common variants in ABCG8 and ABO are strongly associated with serum
phytosterol levels and show concordant and previously unknown associations with CAD.
Key words: Coronary Disease, Genes, Genetics, Lipids
3
Phytosterols such as campesterol and sitosterol are naturally occurring constituents of plants with
close structural similarity to cholesterol. Mammals are unable to synthesize these substances and
thus, the diet is the only source of phytosterols, which are abundant in vegetables, nuts, fruits and
seeds.1 An average Western diet contains approximately 200 - 400 mg phytosterols of which less
than 5% are absorbed. Excretion of phytosterols is mainly by the biliary route.1 Since
phytosterols are exclusively derived from dietary sources and taken up with cholesterol, these
substances can serve as markers of cholesterol uptake.
Supplementation of phytosterols in “functional foods” is widely used for their potential to lower
o
cholesterol by interfering with intestinal cholesterol absorption. In humans, dos
doses
of 0.8 - 4.0 g
daily reduce low-density-lipoprotein levels by 10 - 15%.2 However,
everr, such
such
su
ch food
foo
oodd supplementation
cconcentration
oncentraatio
ion
o of tthese
heese
s sste
t ro
te
ols.
s F
or eexample,
xamp
xa
mple
mp
lee, di
le,
ddietary
ettar
aryy su
upp
pple
leem
can raise the serum co
sterols.
For
supplementation
with 1.1
g phytosterols/day doubled
o bl
oubl
bled
ed ccholesterol
h leestterroll no
ho
norm
normalized
rmal
rm
rmal
aliz
ized
iz
ed sserum
eerrum ccampesterol
ampe
am
pest
pe
stter
erol
rol levels.
lev
e el
els.
s.3
s.
Despite their LDL-lowering
owe
weri
ring
ri
ngg effect,
eeff
ffec
ff
ectt, there
ec
ther
th
eree is increasing
er
iinc
ncre
nc
reas
re
asin
as
ingg concern
in
conc
co
ncer
nc
ernn that
er
that ele
eelevated
leva
le
vate
va
tedd sserum phytosterol
te
levels may inadvertently increase cardiovascular risk.1 A recent study found
that dietary
supplementation with phytosterols not only increases serum levels of the respective sterols but
also affects atherogenesis in mice and leads to increased tissue sterol concentrations in sclerotic
aortic valves of humans.4 Evidence for a pro-atherogenic role of phytosterols is also documented
in patients with sitosterolemia, a rare autosomal disease characterized by massive accumulation
of phytosterols in serum and tissues, who subsequently develop severe premature
atherosclerosis.5 Moreover, some but not all epidemiological studies found an association of
elevated serum phytosterol levels with coronary artery disease (CAD).6-8
Serum phytosterol levels are under strong genetic control with heritability estimates of ~80%.9
Known proteins responsible for controlling serum phytosterol levels include Niemann-Pick C1
Like 1 (NPC1L1) and ATP-binding cassette hemitransporters G5 and G8 (ABCG5, ABCG8).
4
NPC1L1, a proposed drug target for ezetimibe, plays a role in the entry of sterols into
enterocytes,10 whereas ABCG5 and ABCG8 have previously been identified as the genes
responsible for familial sitosterolemia and sterol excretion.5 However, little is known about the
genetic regulation of phytosterol serum levels in the general population and the association of
phytosterol gene variants with CAD. We therefore pursued a genomic approach to first identify
common genetic variants associated with phytosterol serum levels and subsequently tested
whether these variants were associated with CAD.
Methods
ement
nt.
nt.
nt
A detailed description of methods is provided in the online supplement.
Study cohorts
The study design is shown
show
sh
ownn in Figure
ow
F
Fig
igur
ig
uree 1.
ur
1. The
The genome-wide
gen
gen
enoome
me-w
-wid
-w
idee association
id
asso
as
soci
so
ciat
ci
atio
at
ionn st
io
stud
study
udyy w
ud
was carried out in
1644 population-based subjects from the KORA (Cooperative Research in the Region of
Augsburg) S3/F3 study of which 1495 had full phenotypic and genotypic data.11 Replication was
sought in two further population-based studies, i.e. a second, independent sample of the KORA
S3/F3 study of 1157 adults, and the CARLA study (CARdiovascular disease, Living and Ageing
in Halle), comprising 1760 adults.12 Additional replication was performed in 760 healthy blood
donors (18-68 years).13
The association of phytosterol SNPs with CAD was investigated in a meta-analysis of 11
different populations comprising a total of 13,764 CAD cases and 13,630 healthy controls. Basic
characteristics of these study populations are described in the online supplement.
All studies were conducted in accordance with the principles of the Declaration of Helsinki and
were approved by the respective local ethics committees. The utilization of human liver samples
5
obtained from patients who underwent partial liver resection was approved by the ethics
committee of the University of Leipzig (registration number 23-2006).14
Phenotyping
Serum levels of phytosterols (campesterol, sitosterol and brassicasterol) and cholesterol were
determined by liquid chromatography tandem mass spectrometry (LC-MS/MS) as previously
described.15
Genotyping
SNP arrays in the KORA S3/F3 study and the WTCCC CAD stud
study
uddy we
w
were
eree pperformed
ere
er
with the
16,
6, 17
Affymetrix GeneChip®
p® 500
500K
0K Ma
Mapp
Mapping
ppin
pp
ingg Ar
in
A
Array
r y Se
ra
S
Set,
t,16
whereas
wheere
w
r as tthe
hee Aff
A
Affymetrix®
f ym
ymeetrrixx Genome Wide
Human SNP Array 6.0
6 0 was
6.
was employed
em
mpl
ploy
oyed
oy
ed in
in the
the German
Geerrma
mann MI Family
Fam
amil
ilyy Study
il
Stud
St
u y II.
ud
II Genotyping of
II
individual SNPs was
as performed
as
perf
pe
rfor
rf
orme
or
medd using
me
usin
us
ingg iPlex
in
iPle
iP
lexx single
le
sing
si
ngle
ng
le base
bbas
asee primer
as
prim
pr
imer
im
er extension
eext
xten
xt
ensi
en
sion
si
on and
aann MALDI-TOF
(matrix assisted laser desorption/ionization time-of-flight) mass spectrometry (Sequenom, San
Diego, CA, U.S.A.),18 a melting curve based method with a single fluorescently labelled probe on
an ABI PRISM 7900 HT Sequence Detection System (Applied Biosystems, Darmstadt,
Germany),19 and TaqMan allelic discrimination (Applied Biosystems, Darmstadt, Germany).
Gene expression analysis
RNA was isolated from healthy appearing segments of liver samples using the monophasic Trizol
reagent (Invitrogen, Carlsbad, CA). Gene expression of ABCG5, ABCG8 and beta-actin was
determined in an ABI PRISM 7900 HT Sequence Detection System (Applied Biosystems,
Darmstadt, Germany) by TaqMan quantitative RT-PCR.19
6
Stages of genotyping and statistical analysis
First stage genome-wide association study of KORA S3/F3
From 490,032 SNPs, a total of 390,130 were selected based on stringent quality criteria
(inclusion criteria for autosomal SNPs: call rate t 95%, minor allele frequency (MAF) t 1%, Pvalues of exact HWE test t 10-6). Campesterol, sitosterol, brassicasterol and corresponding ratios
normalized to total cholesterol concentrations as well as total cholesterol itself were logtransformed prior to analysis. Models of additive genetic effects and recessive minor allele
effects were calculated adjusting for age, sex and log(BMI). For detection of population
tor
ors ranged between
stratification, we analysed QQ-Plots for all these test statistics. Inflation fact
factors
1.00063 and 1.012, indicating no relevant inflation of test statistics
isticcs (S
((Supplementary
(Sup
Suupppl
plem
em
Figure 1).
Adjustment for the first
fir
fir
irsst three
ee principal
priinc
ncip
ipal
ip
al components
ccom
ompo
om
po
one
n nts did
did not
not
o substantially
sub
ubsttan
anti
tial
ti
a ly change
al
cha
h n the identified
associations, supporting
r g tthe
rting
he aabsence
bssenc
n e of sig
significant
igni
ig
nifi
ni
fica
fi
cant bbias
ca
iass ca
ia
caus
caused
u eed
us
d bby
y po
ppopulation
pula
pu
l t
la
stratification
(Supplementary Table
le 1).
le
11)). In
In addition,
addi
ad
d ti
di
tion
onn, we
we used
use
sedd multivariate
mu
ult
l iv
i ar
aria
iaatee aanalysis
iate
n ly
na
lysi
siss of
si
of vvariance
ariiancce (MANOVA) to
calculate a summary statistic for the combination of both the total phytosterol concentration and
the ratios of total phytosterol and total cholesterol concentration.
Second stage, validation in KORA S3/F3 stage 2
We selected 68 SNPs for further validation in remaining individuals of the KORA S3/F3 study
(n=1157). These included the 65 top SNPs of the list of SNPs ordered by the minimum of the pvalues of all univariate phenotype associations. In addition, three SNPs located in ABCG8 were
genotyped. These include SNP rs4245791, which had initially violated quality criteria (call rate,
HWE) on the 500K Array Set due to misgenotyping, and the two coding SNPs rs11887534
(D19H) and rs4148217 (T400K) not present on the 500k Array Set with known associations with
serum phytosterol levels.9 SNPs were genotyped using the Sequenome assay. From the 68
7
initially selected SNPs, a total of 9 SNPs, including the 4 SNPs located in ABCG8 (rs41360247,
rs4245791, rs11887534 and rs4148217) and 5 additional SNPs showed p-values less than 0.01 in
at least one of the test statistics in the second stage and were selected for the final replication step.
Third stage, validation in CARLA
The 9 SNPs selected in stage 2 were genotyped in n=1760 individuals with full phenotype and
covariate information in the CARLA cohort. For association analyses data were additionally
adjusted for statin treatment. Five SNPs of the total of 9 SNPs selected in the second stage were
finally validated with significance levels below Bonferroni corrected
ected thresholds
dss in at least one of
the test statistics. The set of validated SNPs comprised again
gain aall
lll ffour
ouur S
SNPs in ABCG8
791
91, rs11887534,
rs11188
8875534
34,, rs4148217)
rs41
rs
4148
41
48821
217)
7 and
and one
one SNP
SNP in
in AB
BO (rs657152).
(rs
rs65
rs
6571
65
7 5
(rs41360247, rs4245791,
ABO
a lo
aplo
ap
loty
type
ty
pe aana
naly
na
lysi
ly
siss in C
si
CAR
ARLA
AR
LA
Fine mapping and haplotype
analysis
CARLA
For fine mapping of the ABCG5/8 locus, we genotyped additional SNPs in the haplotype block
containing the four SNPs validated in the third stage from HapMap including flanking and known
coding SNPs in CARLA subjects. After phasing of the data,20 we determined the allelic
association for each of the haplotypes. Finally, we determined the genetic association for the
major haplotype variants determined by rs4952688 and rs11887534 using additive models.
Combined analysis
We calculated a combined effect for the validated SNPs (rs41360247, rs4245791 and rs657152)
which were genotyped in all three stages using regression models which additionally included
cohort assignment variables.
8
DNA sequencing of ABCG5 and ABCG8
DNA sequencing of the intergenic region of ABCG5 and ABCG8 and ~6 kb of the flanking
sequence was performed in DNA from 17 human liver samples using the primers described in
Supplementary Table 2.
Analysis of blood groups
Blood groups were determined by standard immunological testing in the cohort of blood donors
and by genotyping in the CARLA cohort. Association analysis of blood groups was performed by
comparing the sterol phenotypes between the blood group O and
nd the pooled blood
bl
groups A, B
and AB.
Meta-analysis of phytosterol-related
y ost
yto
ster
erol
er
ol-r
ol
-rrellatted
d SNPs
SNP
NPss with
with C
CAD
AD iin
n 11 studies
stu
tudi
dies
di
ess comprising
com
ompr
pris
pr
isin
is
ingg 13,764 coronary
in
nd 113
3,63
6300 he
63
heal
alth
al
thyy co
th
cont
ntro
nt
rols
ro
ls
artery disease cases aand
13,630
healthy
controls
Association of the identified variants in ABCG8 (rs41360247 and rs4245791) and ABO
(rs657152) was performed in a meta-analysis of 11 studies comprising 13,764 coronary artery
disease cases and 13,630 healthy controls. Cases and controls of the single studies were selected
from the same geographic region. Studies were analysed separately using logistic regression
models of additive and recessive heritability. Combined effects were estimated using fixed and
random effects models. Heterogeneity between studies was tested with Q-statistics. No
significant heterogeneities were found. Calculations were performed using the package “meta” of
the R software suite (www.r-project.org). Combined test of Hardy-Weinberg equilibrium was
performed with the help of a stratified test proposed by Troendle et al.21
9
Results
Genome-wide Study of Plasma Phytosterols and Replication
The initial genome-wide analysis using the Affymetrix 500k array identified one single
association at the ATP-binding cassette hemitransporter G8 (ABCG8) gene (rs41360247)
achieving genome-wide significance for phytosterol serum levels (Table 1). One additional SNP
(rs4245791), located 775 bp distal to rs41360247, was also highly significant but had to be
excluded in the initial analysis due to quality problems (Supplementary Table 3). This SNP also
achieved genome-wide significance after re-genotyping using thee Sequenome assay
a
as
(Table 1). A
total of 68 SNPs (Supplementary Table 3) were taken forward for vvalidation
aallid
idat
a io
at
on iin
n additional 1157
RA S3/F3
RA
F sstudy
F3
tud
udyy (S
ud
(Sup
uppl
up
pllem
men
entaary T
able
ab
le 44)) an
aand
d 9 SN
NPs
P aachieving
c
subjects of the KORA
(Supplementary
Table
SNPs
nominal
significance of p<0.01
0 w
01
were
eree ta
er
ttaken
ake
keen fo
forw
forward
war
ardd fo
forr re
rep
replication
pli
lica
cati
ca
tion
ti
on iin
n 17
1760
60 iindividuals
ndiv
nd
ivid
iv
idua
id
u ls ooff the independent
ua
CARLA study (Supplementary
plem
pl
emen
enta
en
tary
ta
ry T
Tab
Table
able
ab
le 55)
5).
).
Fine-mapping and Haplotype Analysis of ABGC5/8
SNPs rs4245791 and rs41360247 at the ABCG8 locus were significantly associated in all three
studies (Table 1, Supplementary Table 6) and were independent of each other (r2=0.03,
Supplementary Table 7). Fine mapping of the haplotype block in CARLA (Figure 2,
Supplementary Table 8) revealed that rs41360247 was in close linkage disequilibrium (r2 = 0.93)
with coding SNP rs11887534 (D19H), which has been associated with phytosterol levels in
previous studies and is known to affect protein structure.9 In addition, SNP rs4952688 was
identified by fine-mapping as a proxy for rs4245791 (r2 = 0.89) with lowest p-values of
association of all SNPs used for fine-mapping. Haplotype analyses of the ABCG8 locus indicated
10
that the effects of rs11887534 (D19H) and rs4952688 on phytosterol levels SNPs were additive
(Supplementary Tables 9, 10; Supplementary Figure 2).
Association of ABCG5/8 SNP rs4952688 with mRNA Expression
One possible mechanism for the association between SNP rs4952688 and serum phytosterol
levels was by affecting expression levels of ABCG5 or ABCG8. To test this hypothesis, we
determined mRNA levels of these genes in 57 patient samples of normal human liver tissue and
observed significantly reduced mRNA expression levels of these two genes in association with
the T allele of rs4952688 (Figure 3) but not with rs41360247 or rs11887534 (D19H).
(D1
D
Sequencing
of the putative intergenic promoter region revealed no SNPs that were
rree aassociated
sssoocciiaate
ted with expression
at the
the responsible
reesppon
onsi
sibl
si
bblle va
vari
r an
antt re
rresides
si
side
dess ou
outs
tsid
ts
de th
his
i rregion
egio
eg
io
on (S
(Sup
upp
up
p
levels, suggesting that
variant
outside
this
(Supplementary
Figure
3).
Association of Phytosterols with ABO Blood Groups
Another novel finding was that in addition to ABCG8, the ABO-gene locus was consistently
associated and also achieved genome-wide significance for association with phytosterol levels in
the combined analysis (Table 1, Supplementary Table 6). The effect of the ABO gene SNP
rs657152 on phytosterol levels was independent of the effects mediated by SNPs in the ABCG8
gene (Supplementary Table 7). The explained variance of serum phytosterols by ABO and
ABCG8 loci was ~10% (Supplementary Table 11). ABO codes for a polymorphic glycosyltransferring enzyme, responsible for the major blood groups. Our studies revealed that rs657152
was tightly linked with the blood group O1 allele (Supplementary Figure 4), coding for a protein
devoid of glycosyltransferase activity. Genetic analysis of blood groups in CARLA and
immunological determination in an independent cohort of blood donors confirmed that the non-
11
functional O allele was associated with decreased phytosterol serum levels (Figure 4,
Supplementary Tables 12, 13).
Meta-analysis of Association of Identified Phytosterol SNPs with CAD Risk
Given the evidence suggesting that elevated phytosterol levels may increase the risk of
atherosclerosis, we next tested the association of variants in ABGC8 (rs41360247, rs4245791)
and ABO (rs657152) with CAD. This was done in a metaanaylsis of 11 different studies
comprising a total of 13,764 CAD cases and 13,630 healthy controls (Figure 5). Detailed results
for each study are presented in Supplementary Figure 5 and Supplementary
upplementary
yT
Tables 14-16. We
found that alleles associated with increased phytosterol levels were
rree ppositively
osit
os
itiv
veelly associated with
y of CAD
D, wh
whil
ille al
ile
aalleles
lele
le
les as
le
aassociated
sooci
c ateed w
ithh re
it
redu
duuc d ph
duce
phyt
y os
yt
o te
tero
r l were associated
increased probability
CAD,
while
with
reduced
phytosterols
with reduced probability
b li
bili
lity
ty ooff CA
CAD
AD (F
(Figure
Fig
igur
uree 5)
ur
5). We aalso
lsoo te
ls
test
tested
sted
st
ed tthe
he ef
effe
effect
f ct ooff iidentified genetic
fe
variants on LDL-cholesterol
oles
ol
este
tero
te
roll le
ro
leve
levels,
vels
ve
ls, si
ls
sinc
since
ncee recent
nc
rece
re
cent
ce
nt st
sstudies
tud
udie
iess ha
ie
have
ve ssho
shown
hown
ho
wn an
an as
asso
association
soci
so
ciat
ci
att
with SNPs in
ABCG5/8 (Aulchenko et al, Kathiresan et al Nat Genet 2009). The latter could be confirmed for
ABCG8 rs41360247 and rs4245791. We also found an association of ABO (rs657152) with LDLcholesterol (Supplementary Figure 6).
Discussion
Our genome-wide analysis and functional studies revealed that ~10% of the variability of serum
phytosterol levels in the normal population is explained by three variants found at the ABCG8
and ABO gene loci. Using this information, we investigated whether genetic variants affecting
phytosterol levels also modulate the risk of CAD. We found that all three polymorphisms
identified to display association with phytosterols were independently associated with CAD.
12
Polymorphisms associated with increased phytosterol serum levels were associated with an
increased risk of CAD, whereas a polymorphism associated with decreased phytosterols was
associated with decreased CAD risk. Thus, our approach using genome-wide analysis of the
intermediate phenotype of serum phytosterols, which is as a maker of cholesterol homeostasis led
to the identification of 3 novel genetic variants modulating CAD risk.
ABCG8 is a plausible candidate for affecting the inherited variability of serum phytosterol levels,
given that the gene encodes the ATP-binding cassette hemitransporter that carries phytosterols
into the bile.1, 2, 5 Indeed, smaller studies previously reported ann association between
beet
the coding
variant D19H in this gene and serum phytosterols,9 a finding that w
wa
was
as cco
confirmed
oonf
nffir
nfi
irm by our data.
nd to affect
afffec
ect th
thee su
susc
scep
sc
ep
pti
t bi
bili
l ty
y for
or ccholesterol
ho
ole
l st
ster
eroll ggal
er
al st
al
all
ston
one disease.22 It was
on
D19H was also found
susceptibility
gall
stone
speculated that the 19H
19
9H variant
vari
va
rian
an
nt may
m y increase
ma
incr
in
c ea
ease
se the
the efficiency
eff
ffic
icie
ic
ieenc
ncyy of
of ssterol
tero
te
rol ex
ro
eexcretion
xcr
creet
cr
into the bile
lumen, causing hypersaturation
bile,
subsequently
leading
stone
formation.23
ersa
er
satu
sa
tura
tu
rati
ra
tion
ti
on of
of th
thee bi
bile
le, su
le
subs
bseq
bs
eque
eq
uent
ue
ntly
nt
ly
y llea
eadi
ea
ding
di
ng ttoo ga
gall
ll sst
t
Indeed, there is published data about an association between the D19H variant and serum
cholesterol levels.24, 25 Moreover, recent genome wide studies identified an association of LDLcholesterol with proxies to D19H and the other ABCG8 variant, rs4245791.26, 27 This effect could
be confirmed in the present study (Supplementary Figure 6), albeit the association of D19H – and
the other variants we identified – with serum cholesterol levels was only weak and effects on
phytosterols remained highly significant after normalization to cholesterol (Table 1) or
adjustment to LDL-cholesterol (Supplementary Table 17).
A novel finding of the present study is that a second genomic effect at the ABCG8 locus adds
independently to the association with serum phytosterol levels. This one was tagged by SNPs
rs4952688/rs4245791 and related to increased liver expression of ABCG5 and of ABCG8
mRNAs. The parallel regulation of two genes suggested that rs4952688/rs4245791 might be
13
linked to a variant, which affects transcriptional activation. However, sequencing of 6kb around
the intergenic region revealed no obvious causative mutations, indicating that other factors
outside this region might be responsible (Supplementary Figure 3).
An unexpected finding was that the ABO blood group gene locus also affects serum phytosterol
levels. The O-allele, which leads to dysfunctional mutations devoid of glycosyltransferase
activity, was associated with significantly reduced phytosterol concentrations. One may speculate
that addition of carbohydrate groups to oligosaccharide chains of proteins might either reduce the
activity of proteins responsible for eliminating sterols or induce
nduce the acti
activity
t
of proteins
responsible for sterol uptake. In this regard, it is of interest that bo
both
th
h A
ABCG5
BC
B
C
and ABCG8
yco
cosylati
t on
ti
on.28 H
However,
owev
ow
ever
ev
err, th
the
he sp
specific
pecif
ific
if
ic bio
bbiological
iolo
io
lo
logi
ogi
gicaal m
me
mechanism
ech
chan
anis
an
ism by which ABO
is
undergo N-linked glycosylation.
alters phytosterol levels
vel
vel
elss is uunclear.
ncle
nc
lear
le
a .29 IInterestingly,
ar
nter
nt
e es
esti
ting
ti
ngly
ng
ly,, itt hhas
ly
as bbeen
eenn pr
ee
prev
previously
rev
evio
ious
io
ussly
y rreported
ep
ep
that serum
cholesterol levels aree sli
sslightly
ligh
li
ghtl
gh
tlyy bu
tl
butt co
cons
consistently
nsis
ns
iste
is
tent
te
ntly
nt
ly
y eele
elevated
leva
le
vate
va
tedd in nnon
te
non-O
on-O
on
-O ssub
subjects.
ubje
ub
ject
je
ctss.30, 31 IInn this regard, it is
ct
of interest that ABO also showed an association with serum total and LDL-cholesterol levels in
our analyses (Table 1, Supplementary Figure 6).
Importantly, the genetic variants associated with serum phytosterols were also associated with
risk of CAD. It should be emphasised that we only tested the associations of these variants with
CAD after their strong association with phytosterol levels became apparent. Therefore, the
significance levels achieved for the association of the variants with CAD can be considered to be
reasonably definitive. Hence the present study adds three additional variants to the evolving list
of genetic markers of this common disease.16, 32, 33 However, our data fall short to prove that these
two associations are causally linked, i.e. that the increase in CAD risk is functionally mediated by
higher phytosterol serum levels, since the identified variants also had a concomitant effect on
14
cholesterol levels. In this regard it is of interest, that early studies demonstrating an effect of
ABO on cholesterol, also showed a somewhat higher 5-year incidence of myocardial infarction
(MI) in non-O carriers, even though these data were on the margin of statistical significance.34
Association between non-O blood group carriership with MI has been recently confirmed in a
meta-analysis of predominantly retrospective studies comprising a total of 8220 cases and
509009 controls.35 Historically, ABO has been one of the first available genetically determined
markers and there are numerous reports of associations with various phenotypes. Some of these
studies had small sample size and showed only modest statistical significance, adding to
scepticism about these findings. However, it is of great interest
est that ABO has
h been recently
associated in a number of hypothesis-free GWA with a diverse
such as
erse sset
e ooff ph
et
pphenotypes
he
pancreatic cancer or plasma
pla
lasma leve
llevels
le
eve
velss ooff ICAM-1.
IC
ICAM
CAM
AM-1
1.36, 37 T
These
heese dat
hese
ddata
ataa su
at
sugg
suggest
ges
estt th
that
hat
a A
ABO
BO effects on these
BO
phenotypes may indeed
e d un
eed
unde
underlie
derl
de
r ie a ccommon
rl
ommo
om
monn me
mo
mech
mechanism
chan
ch
anis
an
issm th
that
at sstill
till
ti
ll nneeds
eeds
ee
eds tto
o be ddetermined.
etee
et
Despite it is not clear from our study whether phytosterols or cholesterol are causally linked with
CAD, our results provide evidence for a role of sterol homoestasis as an effector of CAD since
phytosterols are well established markers of sterol uptake and excretion. In this context it should
be mentioned that we observed additive effects of risk alleles from the three variants on both
phenotypes. In addition, a mechanistic link between phytosterol serum levels and CAD risk
cannot be excluded for several reasons: Firstly, elevated phytosterol levels have been associated
with CAD in previously published studies.4, 6, 7, 38 Secondly, patients with sitosterolemia, a rare
autosomal disease caused by mutations in ABCG5 and ABCG8 display a severe accumulation of
phytosterols in serum and tissues and subsequently develop premature atherosclerosis.5 Thirdly,
deposits of plant-sterols have been found in plaques and degenerated aortic valves of patients
with atherosclerosis.4, 39 Therefore, our findings might have potential public health relevance with
15
regard to the frequent use of phytosterol food supplements, since a substantial number of
individuals with certain genotypes may respond with relatively high phytosterol serum levels
after intake of these additives.3
In summary, this is the first genome-wide association study investigating genetic variability of
serum phytosterol levels in the general population. We identified significant associations of
serum plant sterols with three functional genetic variants. Particularly, our data suggest novel
additive mechanisms for ABCG8 and ABO in regulating serum phytosterol levels which also
n genetic variants
impact serum LDL-cholesterol levels. Moreover, we show that common
associated with serum phytosterol levels affect CAD risk in a conc
concordant
nnccor
o da
dantt ffashion.
dant
a
as
These data
me that
me
th genetic
gen
enet
eetticc variants
var
aria
iaant
ntss af
aaffecting
feecti
t ng ste
tero
te
rol ho
ro
home
meos
me
o ta
tasi
siss pl
si
play
ay a ro
o in susceptibly
show for the first time
sterol
homeostasis
role
to CAD.
Funding Sources: The KORA research platform was initiated and financed by the Helmholtz
Center Munich, which is funded by the German Federal Ministry of Education and Research and
by the State of Bavaria. The KORA GWAS was supported by the German Ministry of Education
and Research through the National Genome Research Network (NGFN). Members of the KORA
Study Group are listed in the online supplement. The CARLA Study was funded in part by a
grant from the German Research Foundation. The German MI Study was supported by the
Deutsche Forschungsgemeinschaft and the German Federal Ministry of Education and Research
(BMBF) in the context of the German National Genome Research Network (NGFN-2 and
NGFN-plus). We are grateful to the WTCCC and the Cardiogenics Consortium for allowing us to
use data from their CAD genome-wide association scans. Cardiogenics is an EU funded
integrated project (LSHM-CT-2006-037593). The Leipzig Heart Study was funded in part by a
grant from the Roland-Ernst-Foundation to D.T.. N.J.S. holds a Chair funded by the British Heart
Foundation. Part of the study was funded by a grant from the German Ministry of Education and
Research through the National Genome Research Network (NGFNplus) to D.T. and J.T. M.S.
was funded by the German Federal Ministry for Education and Research 01KN0702. Part of the
study was funded by a grant of the Medical Faculty, University Leipzig to A.L.
Conflict of Interest Disclosures: None
16
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Table 1: Validation and replication of major genetic associations of serum phytosterol levels
Allelic effect and
p value of association
Cohort
SNP
Gene
Chr
bp position
Alleles
MAF
CR
HWE
CA
SI
BR
KORA S3
500k
rs41360247
ABCG8
2
43927160
T>C
0.067
0.966
0.69
-14%
-10
3.6 x 10
-24%
-15
1.3 x 10
-16%
-12
5.5 x 10
3.3 x 10
-15
(n=1495)
rs4245791
ABCG8
2
43927935
T>C
0.319
0.967
0.31
12%
-17
8.1 x 10
20%
-24
4.6 x 10
14%
-19
2.3 x 10
2.1 x 10
-22
rs657152
ABO
9
133168819
G>T
0.373
0.951
0.83
8%
-5
6.0 x 10
11%
-5
6.5 x 10
7%
-4
8.0 x 10
3.5 x 10
-4
KORA S3
Stage 2
rs41360247
ABCG8
2
43927160
T>C
0.072
0.990
0.63
-14%
-9
5.4 x 10
-20%
-10
5.7 x 10
-15%
-9
5.1 x 10
1.7 x 10
-9
(n=1157)
rs4245791
ABCG8
2
43927935
T>C
0.320
0.976
0.40
15%
-21
3.7 x 10
23%
-27
8.8 x 10
16%
%
-23
23
2
3
9.3 x 10
1.5
1
5 x 10
10
rs657152
ABO
9
133168819
G>T
0.353
0.989
9
0.06
.0
06
8%
-5
1.8
.8 x 10
1
9%
%
-4
8 x 10
1
8.5
6%
%
0.0
0034
0
03
0.0034
4.6 x 10
CARLA
replication
rs41360247
ABCG8
2
43927160
T>C
0.056
0.990
0
0.50
0.5
.5
50
-13%
-13
3%
-9
2.2
2 2 x 10
1
-22%
-22
%
-1
-11
2.7
2
7 x 10
10
-19%
9%
-12
1
8.7
8 7 x 10
1
3.0
0 x 10
10
--10
(n=1760)
rs4245791
ABCG8
2
43927935
T>C
0.326
0.957
7
0.01
0 01
0.0
11%
-18
3 x 10
3.7
0
20%
-23
.3 x 10
1
1.3
15%
5%
-20
.5 x 10
5.5
6 1 x 10
6.1
--25
rs657152
ABO
9
133168819
G>T
0.412
0.984
0.50
7%
-5
2.9 x 10
7%
0.0097
6%
0.0049
Combined
rs41360247
ABCG8
2
43927160
T>C
0.064
-
-
-14%
-25
6.2 x 10
-21%
-32
9.6 x 10
-16%
-28
4.5 x 10
7.7 x 10
-31
(n=4412)
rs4245791
ABCG8
2
43927935
T>C
0.322
-
-
12%
-50
1.6 x 10
21%
-67
2.6 x 10
15%
-55
4.3 x 10
2.2 x 10
-70
rs657152
ABO
9
133168819
G>T
0.383
-
-
8%
-13
9.4 x 10
9%
-8
2.4 x 10
6%
-7
4.9 x 10
2.8 x 10
-10
MANOVA
--26
-5
0.0013
CA/CH
SI/CH
BR/CH
MANOVA/CH
-14%
-12
2.8 x 10
-24%
-18
3.6 x 10
-16%
-14
5.3 x 10
6.5 x 10
-17
11%
-19
6.5 x 10
20%
-27
7.9 x 10
13%
-21
4.2 x 10
9.7 x 10
-25
7%
-6
8.4 x 10
10%
-5
1.6 x 10
7%
-4
3.1 x 10
2.4 x 10
-5
-11%
-7
1.8 x 10
-17%
-9
5.3 x 10
-12%
-7
2.3 x 10
8.3 x 10
-9
13%
-19
1.0 x 10
21%
-26
1.9 x 10
14%
-20
1.5 x 10
6.0 x 10
6%
-4
5.0 x 10
7%
0.0072
4%
0.051
9.5 x 10
-12%
-9
8.0 x 10
-21%
-12
9.6 x 10
-18%
-11
3.5 x 10
2.3 x 10
-10
10%
-20
7.6 x 10
20%
-26
3.8 x 10
15%
-21
7.8 x 10
4.4 x 10
-29
4%
0.0036
4%
0.075
4%
0.085
-12%
-24
1.1 x 10
-21%
-33
3.2 x 10
-16%
-27
3.1 x 10
4.1 x 10
-33
11%
-53
3.2 x 10
20%
-72
1.4 x 10
14%
-56
9.8 x 10
1.1 x 10
-78
6%
-10
2.2 x 10
7%
-7
5.1 x 10
5%
-5
5.0 x 10
3.5 x 10
-27
-4
-0.4%
0.76
0.8%
0.27
0.2%
0.87
-3%
0.028
2%
0.015
2%
0.035
-2%
0.21
0.5%
0.47
3%
0.0085
0.035
-9
Genome wide association in KORA S3, validation in the remaining individuals of KORA S3, replication in the CARLA cohort and combined analysis of the three
SNPs with best p-values of association with phytosterols. CA, campesterol; SI, sitosterol; BR, brassicasterol; CH, cholesterol; MANOVA, multivariate analysis of
CA, SI, BR; CA/CH, campesterol normalized to cholesterol; SI/CH, sitosterol normalized to cholesterol; BR/CH, brassicasterol normalized to cholesterol;
MANOVA/CH, multivariate analysis of CA/CH, SI/CH, BR/CH; bp position refers to NCBI build 36. Alleles, major allele > minor allele; MAF, minor allele
frequency; CR, call rate; HWE, P value of deviation from Hardy-Weinberg equilibrium; p-values of association are given for the additive model for rs41360247 and
rs4245791 and for the recessive model for rs657152. Effects on plasma phytosterol concentrations are shown in Supplementary Table 6.
CH
-2%
0.046
0.9%
0.031
1.5%
0.011
Figure Legends:
Figure 1: Study design. Multi-stage association analyses of SNPs with serum phytosterol levels
(shaded boxes) and metaanalyses of selected phytosterol-SNPs (rs41360247, rs4245791,
rs657152) with CAD ( open box).
Figure 2: Association analysis and LD-plot of ABCG5/ABCG8 with plasma campesterol in the
CARLA cohort (n=1,760). (A) Genomic structure of ABCG5 and ABCG8. Both genes are
located in a head-to-head structure with a short 374 bp intergenic region. (B) –log(P) values of
association of tagging SNPs of the haplotype block with plasma campesterol concentrations. A
total of 32 SNPs were genotyped. cSNPs and spicing variants were force-included. (C) Haplotype
analysis (D’) of SNPs in the CARLA cohort.
Figure 3: Effect of SNP
expression
ABCG5
ABCG8
NP rrs4952688
s49
4952
52
2688 on mRNA expr
prression of AB
ABC
CG5 and ABCG
G in human liver
tissue (n=57). Expression
0.01.
ssio
ss
on levelss were
wer
erre no
nnormalized
ormali
lizedd to
li
o bbeta-actin.
etaet
a-ac
aactiin. * iindicates
ac
ndicat
nd
attes P < 0.
ates
.
Figure 4: Effect of AB
ABO
blood
groups
plasma
A
BO
O bl
bloo
oodd gr
oo
grou
oups
ou
ps on
on pl
plas
asma
as
ma cam
ccampesterol
ampe
am
pest
pe
ster
st
erol
er
ol con
cconcentrations.
once
on
cent
ce
ntra
nt
rati
ra
tion
onss. (A
on
((A)
A CARLA study
(n=1760). (B) Replication
blood
The non-functional
O-allele
i iin bl
d ddonors ((n=760).
760) Th
f
i lO
ll l was consistently
associated with significantly reduced campesterol concentrations, compared to the functional Aand B-alleles (P = 7.6 x 10-5 and
P = 0.011, respectively).
Figure 5: Effect of identified SNPs on plasma campesterol and OR of CAD risk. (A) Fold change
and 95% CI of campesterol levels in the combined analysis of KORA and CARLA for SNPs
located in ABCG8 and ABO genes using additive and recessive models, respectively (n=4,412).
(B) Odds ratio and 95% CI for CAD from the meta-analysis including 13,764 cases and 13,630
controls.
Stage 1: Genome-wide association of serum phytosterol levels
in population-based sample (KORA S3/F3)
n=1,495 (490,032 SNPs/individual)
Stage 2: Validation of serum phytosterol association
in remaining KORA S3/F3
n=1,157 (68 SNPs/individual)
Stage 3: Replication of serum phytosterol assocoation
as
sso
soc
co
oa
attio
on
in CARLA
SNPs/individual)
n=
n=1,760
=
(9 SNPs/
/iin
ndividuall)
Replication
Repl
Re
plic
pl
icat
ic
cat
atio
ion
io
n of p
phy
phytosterol
hyttost
hy
ste
e
donors
associ
association
asso
ciat
atio
ion
n in b
blood
lood
lo
od d
do
o
n=760 (ABO)
Metaanalysis of association between
rs41360247, rs4245791, rs657152 and CAD
(13,764 cases vs. 13,630 controls)
Angio-Lübeck
CARLA
ECTIM
Erlangen
GerMIFSII
GoKard
KORA-B
KORA-MI
LE-Heart
Popgen
WTCCC
2,843 cases vs. 421 controls
145 cases vs. 1,589 controls
1,114 cases vs. 1,154 controls
797 cases vs. 738 controls
rs4148189
rs4952689
rs6544718(V632A)
rs4953028
4 56
rs28517482 (splicing)
C
rs4245795
rs4245794
rs12468591
rs414821
s
2 7 (T400K)
21
(T
T40
4 0K
K
rs4148217
rs
rs49
49
952
5 68
rs4952688
rs67
rs
6733
67
3 45
rs6733452
rrs10174731
rs
1 17
10
7473
rs67
6709
0 90
9
rs6709904
rs34
rs
34
475
7542
243
4 (E238K)
(E2
238K
K
rs34754243
rs
rs10
s10
1022
22
219
1 1
rs10221914
2 3
rs17
rs
17
742
2412
rs17424122
rrs4245791
rs
42
24579
rrs41360247
rs
4136
3602
02
24
ABCG5
rs
s4148
821
2 1 (Y54C)
(Y
Y54C
C
rs4148211
211
rs41
rs
4 48
48210
0 (s
(spl
plic
pl
i ing
ic
g
rs4148210
(splicing)
rs1017
rs
17992
17
rs10179921
rrs4148202
rs
41
4148
14 20
r 11
rs
188
8 75
7 34 (D19H)
(D1
D 9H
H
rs11887534
43
rs38
rs
3806
0 47
4
rs3806471
rrs
4131
3122
31
22
rs4131228
rs414818
8
rs4148185
10 987 6 5
rs42
rs
4 89
42
8923
92
rs4289236
rs4148187
rs4073237
rs4245786
rs1864814
B
rs10439467
-log(P)
A
ABCG8
78 91011 1213
25
20
15
10
5
0
80
*
60
40
0
32
2
22/3
22/3
A
AA
AT/TT
AT/TT
Copies
C
op
ABCG8/
copies
c
o
beta-actin
50
100
20
ABCG8
103
10
103
Copies ABCG5/
copies beta-actin
ABCG5
40
*
30
20
1
0
10
0
32
22/3
AA
A
A
AT/TT
Campesterol (mg/L)
A
6.2
6.0
P = 7.6 x10-5
5.8
5.6
5.4
5.2
623
0
777
237
102
A
B
B
Blood
lood g
group
ro
oup
B
AB
Campesterol (mg/L)
B
5.8
5.6
P = 0.011
5.4
5.2
5.0
301
0
296
111
A
B
Blood group
52
AB
A
ABCG8
rs41360247
ABCG8
rs4245791
ABO
rs657152
P = 6.2x10-25
P = 1.7x10-50
P = 9.4x10-13
0.8
0.9
lower campesterol
1.0
1.1
1.2
higher campesterol
B
ABCG8
rs41360247
ABCG8
rs4245791
ABO
rs657152
P = 2.3x10-6
P = 2.2x10-6
P = 3.9x10-6
0.8
8
0
0.9
.9
9
less CAD
1.0
1
.0
0
1.1
1
.1
1
1.2
1.
.
more CAD
Supplemental Material
Teupser et al., Genetic regulation of serum phytosterol levels and risk of coronary
artery disease
The supplemental materials have the following sections in order:
1. Study cohorts…………………………………………………………………....2
2. Genotyping and gene expression analysis……………………….………..5
3. Statistical analysis……………………………………………………………...7
4. References……………………………………………………………….……..13
5. Supplementary Tables………………………………………………………..16
6. Supplementary Figures…………………………………………………........35
7. Members of the KORA Study Group………………………………………..41
Supplemental Material, Teupser et al
2
1. Study cohorts
Cohorts for phytosterol GWA and replications
The study design is shown in Figure 1. All study subjects used for association of serum
phytosterols were of European descent and recruited in Germany. Genome-wide
analysis was performed in a sample of 1495 subjects with full genotype and phenotype
information from the KORA S3/F3 study, representative of the general population from
the region of Augsburg, Germany, aged 25-69 years (KORA S3/F3 500K). Subjects
were examined in 1994–1995. Recruitment and study procedures of KORA have been
described.1 For validation, data from a subset of 1157 subjects of KORA S3/F3 aged 2574 years was used (KORA stage 2). Replication and fine mapping of the identified loci
was performed in the CARLA study (n=1760), representative of the general population
from the region of Halle (Saale), Germany, aged 45-83 years.2 Additional replication was
performed in a cohort of 760 healthy blood donors (18-68 years) recruited at the Institute
of Transfusion Medicine, University Leipzig.3 All studies were performed according to the
declaration of Helsinki. Population-based studies were approved by institutional review
boards and ethics committees in Leipzig, Munich and Halle (Saale), Germany. The
utilization of human liver samples obtained from patients who underwent liver resection
was approved by the ethics committee of the University of Leipzig (registration number
23-2006).4
Cohorts for CAD metaanalysis
Angio-Lueb.
The
Lübeck
angiographic
study
includes
2,843
patients
with
angiographically proven CAD who underwent cardiac catheterization at the University
Hospital Schleswig-Holstein, Campus Lübeck between 2005 and 2007 (Lübeck
angiographic registry of patients with structural heart disease). Patients were not
selected for particular risk factors or phenotypes. Controls consists of patients with
proven exclusion of CAD from Lübeck (n=421).5
CARLA. n=145 patients with confirmed medical history of myocardial infarction or
coronary artery disease and n=1589 controls selected from the same cohort.2
3
ECTIM. The ECTIM (Etude Cas-Témoin sur l'Infarctus du Myocarde) Study is a casecontrol study of MI based on the MONICA (Multinational MONItoring of trends and
determinants in CArdiovascular disease) project registers in the United-Kingdom,
including Northern Ireland and France. 1,114 MI patients were recruited 3 to 9 months
after the event and had to satisfy the WHO criteria for definite acute MI (category I). In
each center, controls (n=1,154) of similar age and sex were randomly selected in the
areas covered by the MONICA registers.6, 7
Erlangen. The Erlangen cohort included 797 consecutive patients with first appearance
of CAD seen at the Cardiology Department of the University Hospital Erlangen seen
between September 2005 and October 2007. All patients underwent coronary
angiography. In addition, we enrolled 738 healthy controls (with invasive exclusion of
CAD or healthy blood donors). All patients and controls were of German descent. The
study was approved by the institutional ethics committee for human subjects at the
Medical Faculty of the University Erlangen-Nuremberg.8
GerMI FS II. The German Myocardial Infarction Family Study (GerMIFS) II compromises
1222 patients that had a validated myocardial infarction (MI) with a strong genetic
component as documented by an early age of onset (prior to the age of 60 years).5
Patients were identified following their admission for acute treatment of MI or in cardiac
rehabilitation clinics. Population-based controls were derived from the KORA S4 study1
(n=820)
and
through
the
population-based
PopGen
special
control
biobank
(PopGenSPC)9 who were recruited in Schleswig-Holstein (n=587).
GoKard. The cohort included n=966 cases with angiographically proven CAD, who
underwent coronary angiography because of chest pain or any other clinical reason
requiring angiography at the cardiology department at the University of Regensburg
(GoKard). The study was approved by the ethics committee of the University of
Regensburg (Reg Nr. 06211). Population-based controls (n=995) were derived from
participants of the KORA S3/F3 cohort. The controls were independent of the controls
chosen for KORA-MI and KORA-B (see below).
4
KORA-B. The study comprised n=589 patients selected from a myocardial infarction
registry who were <60 years at the time of the event. Population-based controls (n=607)
were derived from participants of the KORA S3/F3 cohort. The controls were
independent of the controls chosen for KORA-MI and GoKard.
KORA-MI. Cases (n =1,504) had a validated MI with early age of onset (prior to the age
of 60 years) and were drawn from the population-based MONICA/KORA MI Registry.10
Patients were identified at their hospital admission for acute treatments of MI.
Population-based controls (n=1,550) were derived from 3,152 randomly selected
participants of the KORA S3/F3 cohort.11 The controls represent a gender and agestratified random sample of all German residents from the same geographical area. The
controls were independent of the controls chosen for KORA-B and GoKard.
LE-Heart. LE-Heart is a cohort study of patients undergoing first coronary angiography
for suspected CAD. Cases (n=469) were patients presenting with >50% stenosis of the
coronary arteries, controls (n=422) were patients with angiographic exclusion of CAD.
The study was approved by the ethics committee of the University of Leipzig (Reg. Nr.
276-2005).
PopGen. The PopGen-CAD sample9 (n = 2,189) comprised unrelated German CAD
patients who were recruited in Schleswig-Holstein, through regional catheterisation
laboratories in the northernmost region in Germany (UK S-H Kiel, local hospitals
Rendsburg, Schleswig, Flensburg, Heide), that have been contacted by the populationbased PopGen biobank (www.popgen.de). The 1,809 male PopGen-controls of the
BAfM (Bundesanstalt für Milchforschung) were selected by age from the general
population via the registration register of the same region.
WTCCC. The Wellcome Trust Case-Control Consortium coronary artery disease
(WTCCC CAD) cohort includes 1926 cases with validated history of CAD before the age
of 66 years. All cases also had a positive family history for CAD in a first degree relative.
2938 population-based subjects or healthy blood donors were used as controls.12,
13
Subjects in both studies were Caucasian of European origin. All studies were approved
by their local ethics committees.
5
2. Genotyping and gene expression analysis
KORA 500K Genotyping
DNA of KORA samples was extracted from EDTA anticoagulated blood using a
commercially available kit (Gentra, Minneapolis MN) according to the manufacturer’s
protocol. Genotyping of 1644 samples of the KORA S3/F3 study was performed using
the Affymetrix Gene Chip Human Mapping 500K Array Set. Genomic DNA was
hybridized in accordance with the manufacturer’s recommendations and genotypes were
called using BRLMM clustering algorithm. Genotyping of the sample has been detailed
in.14 From 490,032 SNPs, a total of 374,370 autosomal SNPs were selected for
subsequent analyses based on stringent quality control criteria. Inclusion criteria were
call rate ≥ 95%, minor allele frequency (MAF) ≥ 1% and P-values of exact HWE test ≥
10-6. The HWE criterion was violated by 13,220 SNPs (2.7%), the MAF criterion by
63,142 (12.9%) and the call rate criterion by 48,469 (9.9%). 115,662 (23.6%) of SNPs
violated at least one of the criteria. For the X-chromosome, these criteria were analyzed
for males and females separately and HWE testing was only performed in females.
SNPs in the pseudoautosomal region were eliminated. For males, a total of 8,164 SNPs
and for females a total of 7,596 SNPs passed all quality criteria.
WTCCC CAD and GerMIFS II Genotyping
Genotyping in the WTCCC CAD study was performed with the Affymetrix® Human
Mapping 500K Array Set12, whereas samples in the GerMIFS II were genotyped with the
Affymetrix® Genome-Wide Human SNP Array 6.0.
Sequenom MALDI TOF MS Genotyping
Genotyping of individual SNPs of KORA samples was performed using iPlex single base
primer extension and MALDI-TOF (matrix assisted laser desorption/ionization time-offlight) mass spectrometry in a 384-well-format (Sequenom, San Diego, CA, U.S.A.) as
described.15 Genotyping was performed by laboratory personnel blinded to case-control
status. Standard genotyping quality control included 10% duplicate samples, testing for
HWE as well as negative samples and revealed no major errors.
6
Melting Curve and TaqMan Based Genotyping
DNA of CARLA samples was isolated using the Qiagen blood kit (Qiagen, Hilden,
Germany). SNP genotyping was performed in an ABI PRISM 7900 HT Sequence
Detection System (Applied Biosystems, Darmstadt, Germany) using a melting curve
based method with a single fluorescently labelled probe as previously described16 or by
TaqMan allelic discrimination according to the manufacturer’s recommendations
(Applied Biosystems, Darmstadt, Germany).
DNA Sequencing of ABCG5 and ABCG8
DNA sequencing of the intergenic region of ABCG5 and ABCG8 and ~6 kb of the
flanking sequence was performed in DNA from 17 human liver samples. DNA was
amplified under standard conditions using the primers described in Supplementary Table
2. Samples were purified and sequencing was performed by standard dye-terminator
chemistry (Applied Biosystems, Darmstadt, Germany).
Gene Expression Analysis
RNA from human liver tissue was isolated using the monophasic Trizol reagent
(Invitrogen, Carlsbad, CA) and reverse transcribed into cDNA with random hexamer
primers using SuperScript II RnaseH- Reverse Transcriptase (Invitrogen). Gene
expression of ABCG5, ABCG8 and beta-actin was determined in an ABI PRISM 7900
HT Sequence Detection System (Applied Biosystems, Darmstadt, Germany) by TaqMan
quantitative RT-PCR in a 384-well format using specific primers and probes.16 Probes
were fluorescently labelled and were selected to span two exons in order to avoid coamplification of genomic DNA. Primers for ABCG5 mRNA were forward 5’
CCCGTACACAGGCATGCTGA 3’, reverse 5’ CTGACTCTCCTGGTCGCTGACA 3’ and
the probe sequence 6FAM-ACGCTGTGAATCTGTTTCCCGTGCTGC-TAMRA. Primers
for ABCG8 mRNA were forward 5’ GGCTGTACACCACTGGTCCATATT 3’, reverse
5’ GTAGATGATGATGTAGGCACAGTGCTC
3’
and
the
probe
sequence
6FAM-CTTTGCCAAGATCCTCGGGGAGCTTCC-TAMRA. mRNA expression levels
were normalized to 103 copies of beta-actin as a housekeeping gene.
7
3. Statistical analysis
First Stage Genome-wide Association Study of KORA S3/F3
Campesterol, sitosterol, brassicasterol and corresponding ratios normalized to total
cholesterol concentrations as well as total cholesterol itself were log-transformed prior to
analysis to achieve a normal distribution. A total of 374370 autosomal SNPs passed
quality criteria, defined as a call rate ≥ 95%, a minor allele frequency (MAF) ≥ 1% and P
values of exact Hardy-Weinberg equilibrium (HWE) test ≥ 10-6. A full set of phenotypes
including co-variates (age, gender, body mass index (BMI)) was available from 1495
probands. Association analysis was calculated for these phenotypes using regression
models adjusting for log(BMI), age and sex. All analyses were performed with and
without normalization of phytosterols to serum cholesterol levels. To account for the high
impact of rs41360247 (intron 3 of ABCG8) in the initial analysis, we decided to perform
an additional adjustment to this SNP for all SNPs residing outside the ABCG5/ABCG8
locus. Models of additive genetic effects and recessive minor allele effects were
calculated. For detection of population stratification, we analysed QQ-Plots for all these
test statistics. Inflation factors17 ranged between 1.00063 and 1.012, indicating no
relevant inflation of test statistics (Supplementary Figure 1). Adjustment for the first three
principal components18 did not substantially change the identifies associations,
supporting the absence of significant bias caused by population stratification
(Supplementary Table 1). In addition, we calculated a summary statistic for the
combination of both the total phytosterol concentration and the ratios of total phytosterol
and total cholesterol concentration as well as by multivariate analysis of variance
(MANOVA). SNPs at the X-chromosome were analysed separately for males and
females. For females, the same models as for autosomal SNPs were calculated. For
males only allelic associations were determined.
We re-typed one SNP rs4245791, located 775 bp distal to rs41360247, which had been
excluded from the initial 500k analysis due to poor call rate (0.883) and HWE violation (p
= 1.3 x 10-42) in spite of a highly significant P-value of association (p = 2.6 x 10-26 for
sitosterol normalized to cholesterol). Re-genotyping using the SNPplex platform
revealed that poor call rate and HWE violation were due to allele-dropout on the 500K
8
Array Set. These parameters were not violated using SNPplex, and the P value of
association of rs4245791 remained highly significant (p = 7.9 x 10-27) (Table 1).
For validation in a second stage of the study we selected the 65 top SNPs of the list of
autosomal SNPs ordered by the minimum of the P-values of all univariate phenotype
associations adjusted for rs41360247. In addition, we included SNPs for which one of
the MANOVA P-values was less than 10-5 and gonosomal SNPs with P-values less than
10-5. This set of SNPs was reduced by selecting tagging SNPs with a cut-off value for
linkage disequilibrium of r2=0.8, resulting in a set of 62 autosomal and 3 gonosomal
SNPs including rs41360247 (Supplementary Table 3). Additionally, 3 SNPs located in
ABCG8 were added: SNP rs4245791 in ABCG8, which had initially violated quality
criteria (call rate, HWE) on the 500K Array Set due to misgenotyping and coding SNPs
rs11887534 (D19H) and rs4148217 (T400K) with known associations with serum
phytosterol levels but not included into the 500k Array Set.19 This brought the total
number of SNPs for replication which were transferred to the second stage of the study
to n=68.
Second Stage, Validation in KORA S3/F3 Stage 2
These 68 SNPs were analysed in the remaining individuals of the KORA S3/F3 cohort
(n=1157) with full information of phenotypes, covariables and genotypes available. Out
of 65 autosomal and 3 gonosomal SNPs selected in the first stage, 55 autosomal and 3
gonosomal SNPs were successfully genotyped. We performed the same statistical
analysis for these SNPs as in the first stage of the study, but in addition to rs41360247,
we also adjusted for SNP rs4245791 which was highly significantly associated with all
phytosterol traits. A total of 9 SNPs, including the 4 SNPs located ABCG8 (rs41360247,
rs4245791, rs11887534 and rs4148217) and 5 additional SNPs showed P-values less
than 0.01 in one of the test statistics and were selected for the final validation step at the
third stage of the study (Supplementary Table 4).
9
Third Stage, Validation in CARLA
The 9 SNPs selected in stage 2 were genotyped in n=1760 individuals with full
phenotype, covariate and genotype information of the CARLA cohort. All SNPs selected
in the second stage were successfully genotyped. We calculated the same models of
association as in stage 2 and additionally adjusted for statin treatment which was
common in the CARLA cohort. Five SNPs of the total of 9 SNPs selected in the second
stage were finally validated with significance levels below Bonferroni corrected
thresholds in at least one of the test statistics. This was the case even when all test
statistics were assumed to be independent resulting in a total of 162 tests performed at
this last stage. The set of validated SNPs comprised again all four SNPs in ABCG8
(rs41360247, rs4245791, rs11887534, rs4148217) and one SNP in ABO (rs657152)
(Supplementary Table 5).
Combined Analysis
We calculated a combined effect for the validated SNPs (rs41360247, rs4245791 and
rs657152) which were genotyped in all three stages of 4412 subjects from KORA S3/F3
500K, KORA S3/F3 stage 2 and CARLA using regression models which additionally
include cohort assignment variables (Table 1).
Fine-mapping and Haplotype Analysis in CARLA
For fine mapping of the ABCG5/8 locus, we selected additional SNPs in the haplotypic
block containing the four SNPs validated in the third stage. For this purpose, we
analysed HapMap data of individuals of European ancestry20 (MAF ≥ 0.01, pairwise r2 ≥
0.8). Known SNPs leading to coding (n = 7) and splice-variants (n = 3) taken from
dbSNP as well as rs3806471 located in the 374 bp intergenic region between ABCG5
and ABCG8 were included. In addition, flanking SNPs on either side of the haplotypic
block were chosen to confirm the block’s margins. 35 SNPs were successfully
genotyped in the CARLA individuals. Two of these SNPs were excluded due to severe
violation of HWE criteria (P < 10-6) and one SNP (rs35648030) was monomorphic.
Heatmaps of linkage disequilibrium of the remaining 32 SNPs were constructed using
10
Haploview 3.32.20 Within this ~50 kb (Figure 2), SNP rs4952688 and coding SNP
rs11887534 (D19H) had the lowest P values of association (P for campesterol = 1.0 x
10-25 and 3.2 x 10-10, respectively) and were tightly linked with the initially identified
SNPs rs4245791 (r2 = 0.89) and rs41360247 and (r2 = 0.93), respectively (Figure 2).
SNPs rs4245791 and rs41360247 at the ABCG8 locus were significantly associated in
all three studies (Table 1; for plasma phytosterol levels see Supplementary Table 6) and
were independent of each other (r2=0.03, Supplementary Table 7). A full set of
associations at ABCG8 is provided in Supplementary Table 8.
Stringent quality criteria were applied for selection of individual haplotypes in the region,
and 4 SNPs with a P-value of less than 0.01 for HWE test and 1 SNP with a MAF of less
than 1% were excluded. In addition, we excluded individuals with more than 25%
genotypes missing, resulting in a total of 1717 individuals with full phenotype, covariate
and haplotype information. We identified a haplotype block containing 21 of the 27
SNP’s considered (Supplementary Table 9). For this block 21 different haplotypes were
detected with an allelic frequency of more than 1%. Estimation of haplotypes was
performed using fastphase 1.2.21 After phasing of the data, we determined the allelic
association for each of the haplotypes (Supplementary Table 10). The major effect with
respect to phytosterol levels was explained by the two SNPs, rs4952688 and
rs11887534 (D19H) which were closely linked with rs4245791 (r2 = 0.89) and
rs41360247 (r2 = 0.93), respectively. Because of perfect linkage disequilibrium with
respect to Lewontin’s D’ we found only 3 possible haplotypes of these SNPs (CA, CT
and GA), where the first nucleotide (C/G) corresponds to rs11887534 (D19H) and the
second nucleotide (A/T) corresponds to rs4952688. The fourth theoretically possible
combination (GT) was not present. The frequencies of these haplotypes were 64% for
CA, 30% for CT and 6% for GA. As shown in Supplementary Figure 3, haplotype CT
was associated with elevated phytosterol levels (dose effect 0.11, p = 2.7 x 10-20),
whereas the GA haplotype was associated with decreased phytosterol levels (dose
effect -0.11, p = 2.8 x 10-6, Supplementary Table 10). The explained variance of these
haplotypes on phytosterol serum levels ranged between 7% for campesterol and 9.6%
for
sitosterol/cholesterol
(Supplementary
Table
11).
Again,
phytosterol-related
phenotypes had been adjusted to age, sex, log(BMI) and statin treatment status.
11
ABO Blood Groups
ABO codes for a polymorphic glycosyl-transferring enzyme, responsible for the major
blood groups, where the O alleles lead to dysfunctional mutations coding for proteins
devoid of glycosyltransferase activity. The O1 allele is caused by a frame-shift mutation,
whereas the O2 allele is caused by an amino acid exchange placing arginine in the
catalytic center rendering the enzyme inactive. In order to investigate the haplotype
structure in the vicinity of the lead SNP rs657152, the available neighbouring SNPs on
the 500K Array Set were used. However, none of the SNPs in the haplotypic block
showed a significant association with serum phytosterol levels below a P value of 0.01
(data not shown). In addition, we found that none of the polymorphisms coding for ABO
blood groups was directly represented on the 500K Array Set. The haplotype structure at
ABO is shown in Supplementary Figure 4. The major alleles are coded by SNPs
rs8176746 (L266M for blood group A vs. B), rs8176747 (G268A for blood group A vs.
B), rs41302905 (G268R for blood group A vs. O2) and rs8176719 (deletion leading to
frame-shift for blood group O1). We genotyped these SNPs in the CARLA cohort and
used the genotyping data to deduce the probands’ blood groups. Interestingly, we found
that the major variant (rs8176719) responsible for the dysfunctional O1-allele was tightly
linked (r² = 0.98, Supplementary Figure 4) with rs657152, identified in our initial analysis.
Both variants (rs8176719 and rs657152) were significantly associated with reduced
campesterol concentrations (p = 2.1 x 10-5 and 3.0 x 10-5 for rs8176719 and rs657152,
respectively). In general, subjects with blood group O had significantly lower
concentrations of campesterol compared to subjects with blood groups A, B, or AB (p =
7.6 x 10-5) (Figure 4A, Supplementary Table 12). The variance of phytosterol levels
explained by major blood groups (O vs. A, B and AB) in the CARLA study ranged
between 1.1% for campesterol and 0.2% for sitosterol/cholesterol (Supplementary Table
11). To replicate our findings, we determined serum phytosterol levels in an independent
cohort of healthy blood donors (n=760). Blood groups were determined by a standard
immunoassay. This replication confirmed that the dysfunctional O-allele was consistently
associated with decreased campesterol concentrations compared to blood groups A, B,
or AB (p = 0.011) (Figure 4B, Supplementary Table 13). These data provided consistent
evidence for a reduction of serum phytosterol levels associated with the non-functional
O-allele.
12
Metaanalysis of Phytosterol-related SNPs with CAD in 11 Studies Comprising 13,764
CAD cases and 13,630 Healthy Controls
Association of the identified variants in ABCG8 and ABO was performed in a
metaanalysis of 11 studies comprising 13,764 coronary artery disease cases and 13,630
healthy controls (Figure 5, Supplementary Figure 5, Supplementary Tables 14, 15, 16).
Cases and controls of the single studies were selected from the same geographic
region. In ABCG8, we tested the association of rs41360247 and rs4245791, whereas for
ABO, rs657152 was used. Studies were analysed separately using logistic regression
models of additive and recessive heritability. The odds-ratio was used as measure of the
within-study effect. Combined effects were estimated using fixed and random effects
models. Heterogeneity between studies was tested with Q-statistics. No significant
heterogeneities were found. Calculations were performed using the package “meta” of
the R software suite (www.r-project.org). Combined test of Hardy-Weinberg equilibrium
was performed with the help of a stratified test proposed by Troendle et al.22 Robustness
of effects was tested by dropping single studies. We also analyzed whether the effects
of single studies are consistent with the meta-effect that is whether the meta-effect is
within the confidence interval of the single study effect. No clues for suspicious single or
combined results were found. Nevertheless, our meta-analysis underlies the usual
limitations of meta-analyses. In particular, potential bias within single study effects and
resulting bias of the meta-effect cannot be completely excluded.
13
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Holle R, Happich M, Lowel H, Wichmann HE. KORA--a research platform for
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Samani NJ, Erdmann J, Hall AS, Hengstenberg C, Mangino M, Mayer B, Dixon
RJ MT, Braund, P WH, Barrett JH, König IR, Stevens S, Szymczak S, Tregouet
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Consortium WTCC, Consortium C, Deloukas P, Tobin MD, Ziegler, A TJ,
Schunkert H. Genome-wide association analysis of coronary artery disease. New
Engl J Med. 2007;357:443-453.
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WTCCC. Genome-wide association study of 14,000 cases of seven common
diseases and 3,000 shared controls. Nature. 2007;447:661-678.
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Doring A, Gieger C, Mehta D, Gohlke H, Prokisch H, Coassin S, Fischer G,
Henke K, Klopp N, Kronenberg F, Paulweber B, Pfeufer A, Rosskopf D, Volzke H,
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Vollmert C, Windl O, Xiang W, Rosenberger A, Zerr I, Wichmann HE, Bickeboller
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Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D.
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16
Supplementary Table 1
Effects and p-values of association of first stage genome-wide association study after adjustment for the first three
principle components
SNP
rs41360247
ABCG8
rs4245791
ABCG8
rs657152
ABO
CA
-15%
2.0x10-10
13%
4.8x10-18
8%
9.1x10-5
SI
-24%
8.8x10-16
21%
3.2x10-24
10%
1.6x10-4
BR
-17%
6.0x10-12
15%
2.9x10-20
7%
1.6x10-3
MANOVA
5.2x10-15
2.2x10-22
5.4x10-4
CA/CH
-14%
1.8x10-12
11%
4.5x10-19
8%
6.6x10-6
SI/CH
-24%
2.8x10-18
20%
6.4x10-26
10%
2.8x10-5
BR/CH
-16%
8.0x10-14
13%
7.6x10-21
7%
3.8x10-4
MANOVA/CH
1.2x10-16
9.2x10-24
2.3x10-5
CH
-0.5%
0.71
1.2%
0.11
-0.04%
0.97
CA, campesterol; SI, sitosterol; BR, brassicasterol; CH, cholesterol; MANOVA, multivariate analysis of CA, SI, BR; CA/CH, campesterol normalized to
cholesterol; SI/CH, sitosterol normalized to cholesterol; BR/CH, brassicasterol normalized to cholesterol; MANOVA/CH, multivariate analysis of CA/CH,
SI/CH, BR/CH; p-values of association are given for the additive model for rs41360247 and rs4245791 and for the recessive model for rs657152.
17
List of primers for ABCG5/8 sequencing
Primer
G5/8Intergen-1fw
G5/8Intergen-1rv
G5/8Intergen-2fw
G5/8Intergen-2rv
G5/8Intergen-3fw
G5/8Intergen-3rv
G5/8Intergen-4fw
G5/8Intergen-4rv
G5/8Intergen-5fw
G5/8Intergen-5rv
G5/8Intergen-6fw
G5/8Intergen-6rv
G5/8Intergen-7fw
G5/8Intergen-7rv
G5/8Intergen-8fw
G5/8Intergen-8rv
G5/8Intergen-9fw
G5/8Intergen-9rv
G5/8Intergen-10fw
G5/8Intergen-10rv
G5/8Intergen-11fw
G5/8Intergen-11rv
Sequence
CACTGCTGCCCAGGCTAGA
GCTGCATTGGCCCTGAAGA
TGGTAATCCAGTGTAGCAGACACTG
AAGACTGGAGAATAATATTTAAAAGTTCATGTAT
AAAGAAAAACGACCAGATAAGATCTGA
TGAAAGAGTATAAAATTCTGCCTAACATG
CCTGAGTACTTTTATATGCCATGGAAC
CCAAACGGACAGGACATTCAGA
AACCTGGCAGATAGCGACTGA
CCAACTGAAGCCACTCTGGG
CAGCAAAGCTGGGCAAATTTT
CAGGAAGTGACCTCAGAGGCCT
AGGACTGTTTCCTGCATGTCAA
CCTGTTAGAGCCACACATGCTG
GTGATGGGTGAGACAGGGTGA
AGCAGAAATGGCAGGGCC
CGATTCAGCCACCACAGCTT
GCATGAGGAGTTTGTGGGTTAAG
TGGCATCTTGGGCACCTG
TCCAACCACCATTGAGGGAT
GAATTTTCTTCCTCCGAAAGATGA
AATATCTGCAGGAGGGATATTAGACAAT
Product size
603
616
616
622
642
631
653
646
621
654
401
18
Details on SNPs selected from 500k analysis stage 1 for validation in stage 2
SNP ID
rs971814
rs6670302
rs10908776
rs2143091
rs17521970
rs17583313
rs11579522
rs1355391
rs41360247
rs4245791
SNP_A-1963469
rs6738590
rs6739734
rs2461741
rs1898906
rs6750111
rs17011226
rs16860868
rs4413348
rs1990805
rs4425233
rs17276327
rs3860694
rs17088961
rs11097119
rs17800095
rs7690517
rs10041522
rs25946
rs1550826
rs6875519
rs2452753
rs2220621
rs3861862
chr
1
1
1
1
1
1
1
1
2
2
2
2
2
2
2
2
3
3
3
3
3
4
4
4
4
4
4
5
5
5
5
5
5
5
additive model
base
hwe
call maf
aa
ab bb ca
si
br
ca/ch si/ch br/ch ma ma/ch ch
5,641
0 0.98 0.452 482 800 328 4.7 3.5 3.4
2.5
2
1.6 2.4
0.8 2.7
56,511
0 0.99 0.244 929 602 97
0 0.3 1.2
0.6
0
2.7 4.5
5.6
1
157,147
0.2
1 0.059 1454 185
4 2.9 4.4 3.3
2.2
3.9
2.6 2.7
2.4 1.1
165,548
0
1 0.401 590 788 265 3.4 2.9 2.2
4.3
3.4
2.6 2.6
3.2
0
188,831
0.1 0.99 0.23 964 584 84 4.9
4 3.1
2.9
2.6
1.4
4
2.5 2.2
194,641
0.8
1 0.444 522 781 338 1.8 2.9 2.1
3.7
4.7
3.9 2.7
4.4 0.8
195,999
0
1 0.195 1067 514 63 2.3 2.6 3.8
1.3
1.9
2.8 2.5
1.7 1.2
222,592
0.7 0.99 0.306 794 669 164 1.9 1.7 1.1
2.5
2.2
1.4 2.8
4.1 0.1
43,985
0.2 0.97 0.067 1382 198
8 9.4 14.9 11.3 11.6 17.4 13.3 14.5
16.2 0.1
43,986 41.9 0.88 0.33 768 408 275 14.7 22.7 17.7 16.7 25.6 19.5 21.4
23.6 0.4
59,471
2.6 0.96 0.173 1059 482 30 2.3
4.4 1.7
2.2
4.6
1.5 4.4
4.6 0.3
64,971
0.1 0.99 0.208 1021 533 71
3 2.9 4.2
1.9
2.1
3 2.6
1.6 1.4
176,073
1.4
1 0.23 960 612 72 4.3
3
5
2.2
1.6
2.8 3.5
1.5 2.8
176,156
0 0.99 0.283 834 663 129 4.3 2.7 4.6
2
1.3
2.4 3.6
1.5 3.2
188,718
0.2 0.99 0.027 1533 87
0 4.2 4.7 3.5
4.2
4.8
3.3 3.5
3.6 0.4
228,683
0.5
1 0.48 433 842 368 1.8 0.7 1.4
3.7
1.4
2.8 2.2
5.3 0.8
22,359
0.9 0.96 0.289 813 625 145 4.4 3.8 4.2
4.9
4.1
4.4 3.2
3.5 0.3
114,563
0.3
1 0.18 1099 494 48 4.5 4.2 3.9
4.4
4.1
3.6 3.4
3.1 0.6
143,816
0.1
1 0.417 561 794 287 4.6 3.4 4.4
2.4
2
2.3 4.3
2.5 3.2
171,672
0.2 0.98 0.154 1151 414 40 1.4 1.2 2.8
2.2
1.7
3.9 3.2
4.6 0.1
190,751
0.1 0.99 0.125 1247 359 24 2.1 0.5 2.6
1.5
0.2
2 4.2
3.4
1
12,002
0.3
1 0.443 502 822 314 2.6 3.3 2.4
0.9
1.9
0.9 2.3
1.3 2.3
68,706
1.5 0.98 0.468 478 761 376 2.5 2.6 1.8
2.2
2.4
1.4 2.1
2.2 0.5
68,719
0.5
1 0.264 899 622 122 2.9 3.6 3.1
1.5
2.5
1.7 2.2
1.3 1.9
88,047
1.7
1 0.113 1297 307 31 2.7 1.7 2.2
4.4
2.5
3.6 1.8
3.3 0.5
109,851
0.5
1 0.273 876 637 130 2.1 1.6 0.6
3.7
2.5
1.3 3.8
6.2 0.5
158,393
0.1 0.99 0.316 755 704 160 2.2 1.7
3
4.1
2.8
5 2.5
4.6 0.6
2,268
0.2
1 0.168 1141 453 49 3.9 3.6 3.2
4.8
4.3
3.9 2.5
3.3
0
14,84
0.7
1 0.451 482 837 320 3.3
3 4.6
4.3
3.6
5.8 4.1
5.7 0.1
14,894
0.2
1 0.346 697 755 192
3 2.4 4.4
3.2
2.5
4.7 4.2
4.6 0.4
75,878
0.1
1 0.28 850 669 125
0 0.2 1.6
0.1
0.1
2.5 6.5
6.7 0.3
86,212
0.4
1 0.322 748 732 163 3.3 3.1 4.9
2.3
2.4
3.9 3.6
2.7 1.1
124,915
0.9 0.96 0.029 1489 84
3 3.8 4.6 3.8
1.5
2.8
1.6 2.4
1.1
3
141,823
1 0.99 0.116 1263 349 15
3 2.7 4.6
1.9
1.9
3.4 3.3
2.2 1.5
recessive model
ca
si
br ca/ch si/ch br/ch ma ma/ch ch
3.7 2.7 2.7
1.7
1.4
1.1 2.1
0.7 2.7
0 0.1 1.2
0.4
0.1
2.4 3.8
4.4 0.6
3.4 5.2 3.9
2.7
4.7
3.1 3.3
3 1.1
3.8 3.5
3
4.8
4.2
3.7
3
4
0
4.1 3.5 2.8
2.1
2.1
1.2 3.1
1.6 2.5
1 1.2 1.1
2.3
2.2
2.3 0.6
1.5 0.9
2.8 3.5 4.7
1.8
2.7
3.5 3.2
2.3 1.2
3.1 2.7
2
3.2
2.8
1.9 4.4
5.4 0.4
9.6
15 12 11.9 17.7
14 14.6
16.5 0.1
14.5 19.9 17 15.4 21.5
17 19.1
20.8 0.8
1.9
4 1.3
1.6
3.9
1 4.2
4.2 0.4
2.9 2.8 4.6
1.5
1.7
3 3.3
2 1.8
3.7 2.3 3.6
1.5
1
1.6 2.4
0.6
3
3.3 1.7 3.3
1.6
0.7
1.7 2.9
1.3 2.5
4.2 4.7 3.5
4.2
4.8
3.3 3.5
3.6 0.4
2.3 1.1 1.9
4.7
2.1
3.7 2.4
5.7 0.9
4 3.6 4.2
4
3.5
4.1 2.5
2.3 0.5
5.3 4.6 4.4
5.2
4.5
4.1 4.5
4.3 0.7
2.9 1.9
3
1.7
1.1
1.8
3
2.1 1.9
1.4 1.5
3
2.1
2
4.1 3.7
5.1 0.1
2.2 0.4 2.9
1.4
0.1
2 5.1
4.1 1.1
4.1 4.6 3.3
1.6
2.7
1.2 3.2
1.7 3.4
4.5 5.5
3
3.8
5.1
2.2 4.6
4.5 0.9
4.6 5.4 4.7
2.8
4
2.9 3.7
2.4 2.2
2.7 1.7 2.2
4.9
2.8
3.9 1.8
3.9 0.8
0.9 0.7 0.1
1.7
1.2
0.4
2
3.1 0.4
1.8 1.4 2.6
4.2
2.7
5.2
2
4.5 1.2
3 3.1 2.4
3.8
3.7
3 1.7
2.3
0
1.9 1.4
3
2.9
2
4.2 2.4
3.5 0.1
1.6 1.3 2.6
1.9
1.4
3 2.4
2.9 0.2
0 0.1 1.4
0.3
0
2.4 4.6
5 0.3
1.8 1.1 3.2
1
0.6
2.2 2.3
1.4
1
3.6 4.2 3.9
1.4
2.4
1.6 2.3
0.9 2.9
2.9 2.6 4.4
2
2
3.4
3
2.1 1.2
rs6898504
rs10068047
rs3734661
rs763415
rs1932107
rs2253833
rs1567725
rs7824014
rs17715553
rs7049110
rs306549
rs657152
rs10508888
rs17727885
rs5019888
rs3026393
rs4756076
rs4766333
rs4466933
rs10847818
rs12435767
SNP_A-2298008
rs6502764
rs4985687
rs197912
rs17202347
rs10406145
rs2585450
rs470094
rs12008496
rs4370708
rs5907655
5
5
6
6
6
7
8
8
8
9
9
9
10
10
11
11
11
12
12
12
14
15
17
17
17
18
19
20
22
X*
X*
X*
143
144,082
90,708
107,745
130,512
106,8
5,561
5,626
88,85
131,343
132,5
133,169
44,528
127,218
18,826
31,769
33,861
5,032
53,633
128,259
61,605
76,335
3,848
5,615
42,345
16,849
4,643
52,181
42,619
40,03
95,669
139,611
0.2
0.3
0
0.4
1
0
0.1
0.1
0.1
0.2
0
0.1
0.4
0.7
0.1
0.1
0.4
0.1
0.7
0.5
0.2
0.2
0.5
0
0.9
0.3
0.3
0.1
0.3
0
0.4
1.4
0.99
0.98
1
1
0.99
0.97
1
0.99
0.98
1
1
0.95
0.99
0.99
0.99
1
1
1
0.99
0.95
1
0.99
0.99
0.99
1
0.98
1
1
1
0.95
1
1
0.262
0.389
0.011
0.184
0.356
0.252
0.198
0.441
0.336
0.13
0.253
0.373
0.09
0.103
0.333
0.49
0.26
0.312
0.074
0.04
0.067
0.112
0.368
0.461
0.357
0.04
0.283
0.467
0.464
0.043
0.498
0.452
19
880
596
1602
1098
661
890
1054
512
708
1246
912
613
1342
1302
729
424
903
774
1386
1447
1429
1282
643
472
693
1482
847
468
478
723
203
234
636
783
37
482
777
601
525
796
722
367
622
736
274
311
719
829
620
708
231
118
209
329
778
808
726
128
652
815
802
66
428
440
107
238
0
61
191
101
63
319
180
30
104
215
10
12
184
391
117
157
5
4
6
18
212
344
224
1
137
361
361
1
200
154
1.9
2.6
3.8
5.7
4.8
4.6
3.2
1.9
0.5
3.3
3.9
2.3
5.8
0.2
1.2
2.4
4.9
2.5
4.7
4.5
4.5
1.4
3.7
0.5
1.3
3.8
4.7
3.5
1.2
0.4
2.8
3.6
1.8
4.8
3.1
3.8
3.7
3.1
4.7
2.1
0.7
4.6
3
2.5
4.2
1.7
3.3
3.8
4.5
0.2
3.8
3.6
3.6
1.8
4.2
0.1
1.5
2.6
3.1
0.8
2.1
0.2
1.9
3.6
2.5
2.5
4.9
3.9
3.9
2.7
3.3
3.4
0.7
3.4
4.3
1.7
5.2
0.2
1.3
2.4
3
1.5
4.3
2.9
3.2
2.9
3.6
0.5
2.6
4.5
2.5
1.8
1.8
0.8
2.3
3.5
4.8
1.6
2.8
3.2
5.5
3.1
3.4
2.9
0.3
2.8
3.1
3
2.2
0
1.2
3.6
2.8
1.5
5.4
5
2.7
2.8
1.6
0.1
2.8
3.3
2.3
1.8
2.3
1
1.7
5.5
3.7
4
2.3
2.2
4
2
5.1
2.9
1.1
4.3
2.4
3
1.9
1.6
3.6
5
3
0.2
4.1
3.8
2.4
2.8
2.5
0.1
2.6
2.3
1.6
0.1
3.3
0
1.2
4.9
5.5
1.5
3.8
1.8
4.2
1.4
3.5
4.7
0.5
2.8
3.5
2.1
1.9
0.5
1.2
3.5
1.3
0.7
4.7
2.9
1.6
4.7
1.6
1.2
4.6
4
0.8
0.6
3.1
0.5
1.3
5
1.7
5.4
4.5
3.2
2.8
4.6
2.8
3.1
4.6
3.4
2.4
1.6
5.5
5.2
3.9
3.3
4.1
5.3
4.1
3.7
3.5
2.3
3.8
3.6
2.4
3.1
3.6
5.4
3.5
4.8
2.1
2.7
5.3
5
3.8
1
3.1
3.4
2.8
4.2
4
3.1
1.5
2.4
2.1
5.3
4.1
4.3
2.5
3.8
5.2
4.4
2.2
3.5
2.3
3.9
4.2
2.3
1.7
3.6
5.2
5.7
1
4.5
1.7
1.7
1.2
2.7
0.2
1.8
0.2
0.2
0.2
1
1
0.1
4.8
0.5
0.1
0.3
2.3
0.9
0.2
0.2
2.6
0.5
3.1
0.8
0.9
0.5
2.8
2.2
0.6
0.6
1.4
0.3
1
1
3.8
4.7
4.6
3.7
2.5
1.1
0.6
2.8
4.1
4.2
5.2
0.3
1.1
1.2
3.3
2.9
3.8
4.2
4.7
1
3.8
0
0.8
3.7
5.1
3.3
0.8
0.4
6
3.2
1.1
2.5
3.1
3.2
3.7
2.8
4
1.5
0.4
4.1
3
4.2
3.6
1.7
3.3
1.4
3.3
0.4
3.1
3.4
3.9
1.4
5.4
0.3
1
2.7
3.5
1.6
1.8
0.2
4.7
3.6
1.2
1
4.9
3.2
4.1
2.6
2.6
2.8
1.3
2.9
4.7
3.1
4.4
0.2
1.1
1.4
1.7
2.1
3.6
2.7
3.5
2.4
3.9
1.6
1.8
4.6
3
1.9
1.9
0.8
4.8
3.6
2.8
0.3
2.8
2.7
4.9
2
3
2.3
0.2
2.5
3.2
5.1
1.8
0.1
1
1.6
2.7
1.4
4.4
5
2.9
2.3
1.5
0.5
1.7
3.2
3
2.3
1.4
1
3.2
5
2.3
1.8
2.3
1.9
3.8
1.6
4.7
2.4
0.9
3.9
2.4
4.8
1.4
1.6
3.6
1.7
2.9
0.1
3.5
3.8
2.6
2.6
3.4
0.8
1.7
2.3
2.1
1
2.5
0
2.8
5.1
2.9
0.3
3.8
1.5
4.2
1.1
3.1
4.6
0.9
2.5
3.8
3.5
1.4
0.4
1
1.8
1.2
0.9
4
3
1.9
4.3
1.6
3
3.1
4
1.3
1
2.9
0.5
2.4
5.2
0.7
3.5
4.5
2.6
2.6
3.4
2.3
2.9
5.2
2.8
2.9
3.5
4.8
5
4.9
0.7
3.2
5
3.3
3.6
3.8
2.2
4.3
4.4
1.4
3.2
3.8
3.4
3.3
4.3
5.2
2.5
3
3.3
3.8
0.9
2.6
2
2.6
4.2
4.4
2.7
1.9
4.6
1.6
5
5
1
2.8
2.9
4.3
4.7
2.5
3.4
2.8
5.2
2.3
2.3
1.9
2.5
4.1
5.1
2.6
3.9
Chr, chromosome; base, base position in Ensembl build 36 in kb; hwe, -log(P) of Hardy-Weinberg equilibrium test; call, call rate; maf, minor allele frequency; aa, number of probands
homozygous for major allele; ab, number of heterozygous probands; bb, number of probands homozygous for minor allele; ca, -log(P) of association for serum campesterol; si, -log(P) of
association for serum sitosterol; br, -log(P) of association for serum brassicasterol; ca/ch, -log(P) of association for serum campesterol normalized to cholesterol; si/ch, -log(P) of association for
serum sitosterol normalized to cholesterol; br/ch, -log(P) of association for serum brassicasterol normalized to cholesterol; ma, -log(P) of multivariate analysis of variance of campesterol,
sitosetrol and brassicasterol; ma/ch, -log(P) of multivariate analysis of variance of campesterol, sitosetrol and brassicasterol normalized to cholesterol; ch, -log(P) of association for serum
cholesterol. Yellow: P<0.0001; Red: P<0.00001; Margenta: P<0.000001. * data on the X-chromosome are only presented for females since males did not show significant results.
1.2
1.7
1.2
2.1
0.2
2.4
0
0.6
0.5
0.8
1.2
0.1
4.8
0.4
0.1
0.1
0.7
1.6
0.1
0
2.6
0.8
3.7
0.7
0.7
0.5
2.4
1.3
0.3
0.5
3.2
0.2
20
Details on SNPs from stage 2 validation
SNP ID
rs971814
rs10908776
rs2143091
rs17521970
rs17583313
rs11579522
rs1355391
rs11887534
rs41360247
rs4245791
rs4148217
SNP_A-1963469
rs6738590
rs6739734
rs2461741
rs1898906
rs6750111
rs17011226
rs4413348
rs1990805
rs4425233
rs17276327
rs3860694
rs17088961
rs11097119
rs7690517
rs10041522
rs25946
rs1550826
rs6875519
rs2452753
rs2220621
rs3861862
rs6898504
chr
1
1
1
1
1
1
1
2
2
2
2
2
2
2
2
2
2
3
3
3
3
4
4
4
4
4
5
5
5
5
5
5
5
5
base
hwe
5,641
0
157,147
0
165,548
0.3
188,831
0.2
194,641
0.5
195,999
0
222,592
2
43,92
0.1
43,927
0.2
43,928
0.4
43,953
0
59,471
0
64,971
0.5
176,073
0
176,156
0.8
188,718
0.2
228,683
0.3
22,359
0.1
143,816
0.3
171,672
0.5
190,751
0.8
12,002
0
68,706
0.5
68,719
0.3
88,047
0.6
158,393
0.1
2,268
0.2
14,84
0.4
14,894
7.8
75,878
0.1
86,212
0.6
124,915
0
141,823
0.3
143
0.1
call
0.97
0.99
0.94
0.95
0.98
0.98
0.99
0.96
0.99
0.98
0.95
0.99
0.98
0.98
0.99
0.98
0.99
0.98
0.98
0.99
0.97
0.98
0.98
0.96
0.99
0.98
0.99
0.98
0.98
0.97
0.99
0.99
0.98
0.99
maf
aa
0.44 352
0.066 998
0.405 380
0.252 617
0.463 335
0.187 752
0.31 526
0.069 966
0.072 988
0.32 516
0.183 733
0.196 737
0.215 691
0.218 693
0.28 602
0.034 1057
0.477 317
0.314 532
0.42 387
0.161 808
0.12 875
0.416 388
0.476 303
0.274 591
0.102 919
0.31 542
0.158 805
0.439 363
0.375 488
0.275 594
0.325 513
0.023 1092
0.102 913
0.262 620
ab
549
140
537
408
548
345
526
145
150
504
331
359
396
387
442
76
559
492
544
298
227
551
582
436
217
484
309
544
440
447
522
52
213
446
additive model
bb ca
si
218 0.1 0.4
0
0.1
0.2
0.3 0.4
0.5
0.6
5 1.3 1.1 0.7
0.6
0.6
0.1 1.2
0.6
0.9
173 0.1 0.5 0.5
0.3
0.8
0.8 0.5
0.4
0.5
73 0.3 0.6 0.5
1.1
1.3
1.2 0.5
0.9
0.7
252 0.9 0.9 1.1
0.8
0.8
0.9 0.2
0.1
0.2
40 0.3 0.2
0
0.1
0.4
0.2 0.9
0.8
0.4
91 0.2 0.3 0.4
0.3
0.1
0.1 0.1
0.1
1.2
4 8.8 9.2 8.6
7.5
8.5
7.1 8.9
8.3
1.4
7 8.3 9.2 8.3
6.7
8.3
6.6 8.8
8.1
1.6
109 20.4 26.1 22
19 25.7 19.8 25.8
26.2
1.8
36 3.1 5.7 3.8
3.3
6.3
4 5.5
6
0.2
44 0.1
0 0.1
0
0
0.1
0
0
0.1
46 0.9 0.8 1.1
1
0.9
1.3 0.2
0.2
0
54 0.5 0.6 0.2
0.5
0.6
0.2 0.5
0.6
0.1
99 1.3 0.8 0.8
0.6
0.4
0.2 1.2
0.6
1.2
0 0.1 0.3
0
0.1
0.2
0 0.2
0.2
0.1
265
0 0.6 0.1
0.1
0.5
0 0.3
0.3
0.2
110 0.3 0.2
0
0.2
0.1
0.1 0.2
0.3
0
206 0.4 0.9 1.4
0.4
1
1.5 1.4
1.4
0.1
34 0.5 0.5 0.3
1.2
1
0.9 0.6
1.4
0.5
21 1.4 1.9 2.5
1.8
2.3
3.1 2.4
2.4
0
198 0.9 0.5 0.6
0.5
0.3
0.3 0.3
0.2
0.5
248 0.1 0.1 0.6
0
0
0.4 0.5
0.4
0.2
88 0.4 0.1 0.1
0.3
0.1
0 0.2
0.2
0.1
8 0.6 0.6 0.1
1.3
1
0.1 1.8
2.2
0.4
111 0.9 0.5 0.2
0.4
0.2
0.1 1.1
1
0.7
26 0.2
0 0.1
0.1
0.1
0 0.2
0.2
0.3
224 0.1 0.2 0.2
0.1
0
0
0
0.1
0.3
204 0.1 0.1
0
0.1
0.1
0.1
0
0.1
0.1
86 0.3
0 0.2
0.1
0.1
0 0.2
0
0.5
112 1.5 1.2 1.6
1.7
1.3
1.6
1
1.4
0.1
0 0.3 0.1 0.1
0.3
0.2
0.1
0
0
0
9 0.1 0.1 0.4
0
0
0.7 0.9
1.1
0.3
76 0.1 0.3 0.1
0.2
0.3
0.2 0.5
0.4
0.1
recessive model
ca
si
0.1
0 0.3
0.4
0.3
0.7 0.1
0.2 0.7
1.1 0.8 0.5
0.3
0.3
0
1
0.5
1
0.2 0.4 0.4
0.4
0.6
0.6 0.1
0.2 0.3
0.6 0.7 0.5
1.2
1.3
1.1 0.3
0.6 0.5
1.6
1 1.2
1.5
1
1 0.6
0.4 0.2
0 0.4 0.2
0.1
0.7
0.4 0.7
0.7 0.3
0.1 0.1 0.2
0.5
0.3
0.4
0
0 1.4
8.9 9.2 8.8
7.9
8.6
7.5 8.8
8.3 1.3
8.2 9.2 8.6
6.9
8.4
7.1 8.7
8.1 1.4
18.5 24.1 20
17 23.6
18 23.3
23.4 1.8
3.6
6 3.9
4
6.7
4 5.5
6.1 0.2
0.1 0.1 0.1
0.2
0.3
0.2
0
0.1 0.3
0.8
1 1.4
1.2
1.3
1.8 0.6
0.6 0.1
0.9 1.1 0.3
0.8
1
0.2
1
1.2 0.2
1.5 1.1 0.6
0.8
0.7
0.1 1.3
1 0.9
0.1 0.3
0
0.1
0.2
0 0.2
0.2 0.1
0.1 0.3
0
0.2
0.3
0.1 0.4
0.3
0
0.1 0.1 0.1
0
0
0.2 0.1
0.1
0
0.5 1.1 1.6
0.6
1.2
1.7 1.5
1.5 0.1
0.6 0.6 0.4
1.5
1.2
0.9 0.8
1.7 0.5
1.1 1.4 1.9
1.7
1.9
2.7 1.8
1.9 0.1
0.5 0.3 0.4
0.5
0.2
0.4 0.2
0.3 0.1
0.1 0.2 0.1
0.4
0.3
0 0.1
0.2 0.2
0.5 0.4 0.2
0.4
0.2
0.1 0.2
0.2 0.3
0.6 0.5 0.1
1.1
0.8
0 1.7
2.1 0.2
1 0.5 0.3
0.4
0.1
0.1 0.9
0.7
1
0.1 0.1
0
0
0.1
0 0.1
0.1 0.2
0.1
0 0.2
0.1
0.2
0 0.1
0.1 0.2
0.3 0.3 0.1
0.3
0.3
0 0.1
0.1 0.1
0.6 0.4 0.5
0.1
0
0.1 0.3
0
1
1.7 1.4 2.1
1.7
1.5
2.1 1.5
1.7 0.1
0.3 0.1 0.1
0.3
0.2
0.1
0
0
0
0.2 0.1 0.2
0
0.1
0.4 0.4
0.6 0.3
0.1 0.1 0.2
0.3
0
0.4 0.1
0.1 0.3
rs10068047
rs3734661
rs763415
rs1932107
rs1567725
rs7824014
rs7049110
rs306549
rs657152
rs5019888
rs4756076
rs4466933
rs10847818
rs12435767
SNP_A-2298008
rs4985687
rs197912
rs17202347
rs10406145
rs2585450
rs470094
rs12008496
rs4370708
rs5907655
5
6
6
6
8
8
9
9
9
11
11
12
12
14
15
17
17
18
19
20
22
X*
X*
X*
144,082
90,708
107,745
130,512
5,561
5,626
131,343
132,5
133,169
18,826
33,861
53,633
128,259
61,605
76,335
5,615
42,345
16,849
4,643
52,181
42,619
40,03
95,669
139,611
0.2
1.1
0.4
0
0
0.3
0.6
0.1
1.2
0.3
1.5
0.5
0
0.8
0.5
0.1
0.3
0.2
0.9
0.1
0.2
0
0,1
0,2
0.99
0.98
0.97
0.99
0.98
0.94
0.98
0.98
0.99
0.98
0.96
0.98
0.98
0.99
0.98
0.98
0.98
0.97
0.99
0.99
0.98
0.91
0.98
0.99
0.437 366 555 221
0.009 1120 18
1
0.184 742 346 33
0.357 473 522 147
0.204 722 368 48
0.452 334 528 229
0.127 871 244 23
0.249 642 420 72
0.353 494 493 157
0.328 519 490 128
0.275 571 474 70
0.083 958 168 10
0.027 1075 62
0
0.068 986 152
2
0.121 878 235 20
0.447 346 567 225
0.354 466 530 136
0.03 1052 68
0
0.278 607 440 99
0.459 337 560 244
0.449 342 572 225
0.04 511 45
0
0.461 175 293 129
0.458 179 292 129
21
0.2
0.1
0.3
0
0.5
1.3
0.1
0.7
3.2
0.2
0.3
0
1.2
0.3
0.3
0.2
0.7
0.3
0.1
0.3
0.3
0.3
0.2
0.3
Yellow: P<0.01; Red: P<0.001. Otherwise see legend to Supplementary Table 3
0.1
0.2
0.3
0.2
1.2
0.8
0.1
0.7
1.8
0.1
0.4
0.2
0.5
0
0.3
0.1
0.2
0.2
0.1
0.1
0.1
0
0.3
0.3
0.1
0.1
0.5
0.4
1.3
0.7
0.3
1.2
1.4
0.1
0.9
0
0.6
0.7
0.1
0.3
0.2
0
0.3
0.4
0.1
0.1
0.2
0.2
0.3
0
0.4
0.1
0.4
0.5
0
0.2
2.1
0.2
0.4
0.2
0.3
0.1
0.3
0
0.2
0.1
0
0.9
0.1
0.4
0.4
0.6
0.2
0.1
0.4
0.3
1.2
0.3
0
0.3
1.1
0.1
0.5
0.4
0
0.3
0.3
0
0.1
0.4
0.3
0.3
0.1
0
0.5
0.5
0
0
0.6
0.6
1.2
0.1
0.2
0.5
0.6
0
1.1
0.2
0
0.2
0.1
0.1
0.1
0.1
0.6
0.9
0.1
0.1
0.1
0.4
0.1
0.1
0
0.4
1.7
0.8
0.1
0.6
3.1
0.1
0.9
0.1
0.8
0.7
0.1
0.1
0.8
1.3
0.8
0.2
0.3
0.3
0.9
0
0.1
0.1
0.1
0.4
1.7
0.3
0.2
0.2
2.2
0.1
1.2
0.1
0.3
0.5
0
0.2
0.7
1.1
0.8
0.4
0.2
0.2
1.1
0
0.1
0.4
0.1
0.3
0.2
1.1
0.1
1
1.2
0
0
0.4
1.7
1
0.2
0.2
0.7
0.3
0.5
0.5
0.3
0
0.1
0.4
0
0.2
0.2
0.1
0.3
0.6
0.2
0.6
4.7
0.1
0.3
0.2
1.2
0.3
0.6
0.4
0.1
0.3
0.2
0
0.2
0.3
0.6
0.3
0.3
0.3
0.1
0
1.2
0.4
0.3
0.6
3.1
0.1
0.6
0.1
0.5
0
0.6
0.3
0.2
0.2
0
0.3
0
0
0.9
0.1
0.3
0.2
0.3
0.2
1.3
0.2
0.5
1.1
2.5
0.1
1.2
0.1
0.6
0.7
0.3
0.6
0.2
0
0.2
0
0.1
0.1
0.2
0.2
0.2
0
0.3
0.1
0.3
0.1
0
0.2
3.3
0.7
0.4
0.1
0.3
0.1
0.6
0.3
0.1
0.1
0.1
0.2
0
0.4
1.1
0.3
0.5
0.1
0.2
0.2
1.3
0.1
0.1
0.3
2.1
0.4
0.7
0.3
0
0.3
0.6
0.2
0.4
0.4
0.2
0.1
0
0
1.4
0.1
0.1
0.1
0.4
0.6
1.2
0.1
0.3
0.6
1.3
0.3
1.4
0.2
0
0.2
0.2
0.5
0.5
0.1
0.7
0.3
0.2
0.1
0.5
0.2
0.4
0.1
0
0.4
2.1
0.3
0.1
0.8
4.3
0.2
1.3
0.2
0.8
0.6
0.1
0.2
0.6
1.3
0.7
0.2
0.2
0.3
0.7
0.2
0.4
0.1
0
0.3
2
0.1
0.3
0.4
3
0.2
1.5
0.1
0.3
0.4
0.1
0.2
0.6
1.1
0.6
0.2
0.1
0.2
0.9
0.2
0.5
0.4
0.2
0.7
0.1
0.9
0.2
0.7
1.5
1.1
0
0.7
1.7
1
0.1
0.1
0.4
0.3
0.8
0.6
0.1
0
0.2
0
22
Details on SNPs from stage 3 validation in CARLA
SNP ID
rs11887534
rs41360247
rs4245791
rs4148217
rs4425233
rs11097119
rs2452753
rs1567725
rs657152
chr base
hwe call maf
aa
ab bb ca
2 43919751 0.1 0.98 0.058 1544 190
6 9.5
2 43927160 0.3 0.99 0.056 1569 182
7 8.7
2 43927935 1.8 0.96 0.326 794 702 203 17.4
2 43952937 0.2 0.98 0.183 1158 529 55 4.3
3 190751500
0 0.97 0.131 1295 392 29 0.7
4 88046712 7.2 0.99 0.094 1423 329
0 0.6
5 86211684 0.9 0.97 0.312 805 770 154 0.4
8
5560754 0.9 0.98 0.189 1139 556 53 1.1
9 133168819 0.3 0.98 0.412 609 829 302 3.7
Yellow: P<0.05 Red: P<0.05/9. Otherwise see legend to Supplementary Table 3
si
11.7
10.6
22.9
5.5
0.4
0.3
1.2
0.2
2.1
additive model
br
ca/ch si/ch br/ch ma ma/ch ch
11.7
9.3 12.6 11.4 10.1
10.7 0.6
11.1
8.1
11 10.5 9.5
9.6 0.7
19.3 19.1 25.4 20.1 24.2
28.4 0.3
3.1
6.6
7.2
4.3 4.5
6.4 0.5
0.1
0.1
0.1
0.2 0.4
0.2 0.9
0
0.4
0.2
0.1 0.5
0.4 0.2
1.1
0.5
1.3
1.2 0.9
0.9
0
0.5
0.4
0.1
0.1 0.8
0.5 1.1
2.5
1.8
1.2
1.2 1.8
0.7
2
recessive model
ca
si
8.8 10.2 11
8.9 11.1
11
9
9.6 0.5
8.1 9.2 11
7.8
9.7 10.1 8.4
8.6 0.6
15.4 19.7 15 16.3 21.5 15.6 20.3
23 0.4
4.2 5.4 2.6
7
7.4
4 4.3
6.5 0.8
0.8 0.2 0.1
0.2
0
0.2 0.5
0.3 0.8
0.6 0.3
0
0.4
0.2
0.1 0.5
0.4 0.2
1.2
2 1.6
0.8
1.9
1.3 1.3
1.2 0.4
0.9 0.2 0.6
0.1
0.1
0.1 0.5
0.3 1.4
4.5
2 2.3
2.4
1.1
1.1 2.9
1.5 2.1
23
Geometric mean and standard error per genotype for phytosterols and cholesterol (in mg/L)
SNP
Allele
rs41360247
Hom. major
T/T
rs4245791
rs657152
CA
SI
BR
5.9
(1.01)
2.3
(1.01)
0.60
(1.01)
Het.
T/C
5.1
(1.01)
1.8
(1.02)
Hom. minor
C/C
4.6
(1.05)
Hom. major
T/T
CA/CH
SI/CH
BR/CH
CH
2.7
(1.005)
1.1
(1.01)
0.28
(1.01)
2162
(1.003)
0.50
(1.02)
2.4
(1.01)
0.85
(1.02)
0.23
(1.02)
2132
(1.01)
1.4
(1.12)
0.46
(1.06)
2.3
(1.05)
0.68
(1.11)
0.22
(1.06)
2038
(1.02)
5.4
(1.01)
2.0
(1.01)
0.54
(1.01)
2.5
(1.01)
0.92
(1.01)
0.25
(1.01)
2141
(1.004)
Het.
T/C
6.1
(1.01)
2.4
(1.01)
0.62
(1.01)
2.8
(1.01)
1.11
(1.01)
0.29
(1.01)
2175
(1.005)
Hom. minor
C/C
6.7
(1.02)
2.8
(1.02)
0.71
(1.02)
3.1
(1.01)
1.31
(1.02)
0.33
(1.02)
2166
(1.01)
Hom. major
G/G
5.6
(1.01)
2.1
(1.01)
0.57
(1.01)
2.6
(1.01)
0.99
(1.01)
0.27
(1.01)
2140
(1.005)
Het.
G/T
6.0
(1.01)
2.3
(1.01)
0.60
(1.01)
2.8
(1.01)
1.06
(1.01)
0.28
(1.01)
2172
(1.004)
Hom. minor
T/T
5.9
(1.01)
2.3
(1.02)
0.59
(1.02)
2.7
(1.01)
1.04
(1.02)
0.27
(1.01)
2172
(1.01)
Values were adjusted for age, sex, log(BMI), statin treatment and study. CA, campesterol; SI, sitosterol; BR, brassicasterol; CH, cholesterol; CA/CH, campesterol normalized to cholesterol;
SI/CH, sitosterol normalized to cholesterol; BR/CH, brassicasterol normalized to cholesterol.
24
Analysis of all significant SNPs of serum phytosterol levels within one regression model
Allelic effects and
p-value of association
SNP
CA
SI
BR
MANOVA
rs41360247
-10%
-14
4.4 x 10
-17%
-18
3.0 x 10
-13%
-15
1.1 x 10
1.3 x 10
-29
rs4245791
11%
-39
2.3 x 10
19%
-52
1.7 x 10
13%
-43
4.1 x 10
2.4 x 10
-55
rs657152
8%
-13
9.4 x 10
9%
-8
2.4 x 10
6%
-7
4.9 x 10
2.8 x 10
-10
CA/CH
SI/CH
BR/CH
MANOVA/CH
-9%
-13
4.4 x 10
-16%
-18
1.5 x 10
-11%
-14
2.1 x 10
2.6 x 10
-31
10%
-41
1.5 x 10
18%
-56
1.3 x 10
12%
-43
1.6 x 10
5.1 x 10
-62
6%
-10
2.2 x 10
7%
-7
5.1 x 10
5%
-5
5.0 x 10
3.5 x 10
-9
CH
-1.6%
0.065
0.8%
0.068
1.5%
0.012
Allelic effects relative to the major allele and corresponding p-values. Analysis is based on the combined data sets of KORA S3 500k, KORA S3 Stage 2 and CARLA replication. Data were
adjusted for age, sex, log(BMI), statin treatment and study. The regression model simultaneously included the additive effects of rs41360247 and rs4245791 and the recessive effect of rs657152.
For rs657152 and phytosterol phenotypes, the results are identical with Table 1 (combined analysis). CA, campesterol; SI, sitosterol; BR, brassicasterol; CH, cholesterol; MANOVA, multivariate
analysis of CA, SI, BR; CA/CH, campesterol normalized to cholesterol; SI/CH, sitosterol normalized to cholesterol; BR/CH, brassicasterol normalized to cholesterol; MANOVA/CH, multivariate
analysis of CA/CH, SI/CH, BR/CH.
25
Details on SNPs from fine-mapping of the ABCG5/8 locus
SNP ID
rs4148189
rs10439467
rs1864814
rs4245786
rs4073237*
rs4148187*
rs4289236*
rs4148185*
rs4131228*
rs3806471*
rs11887534*
rs4148202
rs10179921
rs4148210*
rs4148211*
rs41360247*
rs4245791*
rs17424122*
rs10221914*
rs35648030
rs34754243
rs6709904*
rs10174731
rs6733452*
rs4952688*
rs4148217*
rs12468591*
rs4245794*
rs4245795*
rs4953027
rs4952689*
rs28517482
rs4148227
rs4953028
rs6544718
chr
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
additive model
base
hwe
call maf
aa
ab
bb ca
si
br
ca/ch si/ch br/ch
43901034
0.4 0.98 0.118 1356 353 28 0.3 0.9 0.8
0.4
1
0.8
43901850
0.2 0.97 0.066 1505 215
6 0.9
2 1.7
0.6
1.7
1.3
43902095
0.4 0.97 0.032 1616 109
0 0.6 0.7 0.7
0.6
0.8
0.7
43902624
0.2 0.98 0.233 1018 628 91
0 0.2 0.3
0.1
0.1
0.4
43903376
0.3 0.98 0.07 1502 231
6 0.1 0.8 0.1
0.6
1.4
0.2
43904392
1 0.97 0.384 671 783 271 0.4 0.5 0.8
0.4
0.4
0.7
43907627
0.6 0.98
0.2 1122 541 77 0.4 0.7 0.6
1.2
1.4
1.3
43909826
1.1 0.98 0.396 651 793 289 0.6 0.5 0.6
0.2
0.2
0.3
43911623
0 0.97 0.018 1661
63
0 2.7 3.5 3.1
2.4
3.4
2.8
43919678
1.1 0.99 0.343 774 758 224 1.9 2.6 1.7
1.5
2.3
1.5
43919751
0.1 0.98 0.058 1544 190
6 9.5 11.7 11.7
9.3 12.6 11.4
43921323
4.7 0.95 0.452 549 746 388 0.6 0.5 0.3
0.6
0.5
0.3
43921795
3.4 0.98 0.058 1550 171 15 0.7
1 1.1
0.8
1
1.2
43925143
0.7 0.99 0.409 625 819 306 2.9 3.3 2.6
2
2.7
1.8
43925247
0.9 0.98 0.409 620 806 306 2.8 3.2 2.5
2
2.6
1.8
43927160
0.3 0.99 0.056 1569 182
7 8.7 10.6 11.1
8.1
11 10.5
43927935
1.8 0.96 0.326 794 702 203 17.4 22.9 19.3 19.1 25.4 20.1
43928721
1 0.99 0.055 1572 176
9 3.3 3.5 2.6
6.1
5.4
4.2
43930957
0.2 0.98 0.027 1642
92
0 0.9 1.1 0.7
0.9
1.2
0.7
43932266
0 0.98
0 1747
0
0
0
0
0
0
0
0
43933259
2.1 0.98 0.002 1730
6
1 1.6 1.5
1
0.6
0.9
0.4
43933828
0.3 0.97 0.104 1378 316 21 0.9 0.9 1.1
1.3
1.2
1.4
43936173
4.8 0.97 0.486 500 771 452 2.2 2.3 2.6
2.3
2.5
2.7
43948349
0 0.98 0.017 1689
59
0 2.3 2.4 3.1
1.6
2.4
2.5
43950274
0.4 0.97 0.302 844 711 164
25 31.4 24.5 26.5
34 24.9
43952937
0.2 0.98 0.183 1158 529 55 4.3 5.5 3.1
6.6
7.2
4.3
43953519
0 0.98 0.051 1572 169
4 2.9 2.2 2.4
4.4
3
3.2
43954353
0.3 0.97 0.117 1352 351 26 0.4 0.2 0.1
1
0
0.3
43954375
0.1 0.98 0.07 1502 227
9 1.7 2.3 2.1
1.2
2
1.6
43954898
28 0.94 0.176 1204 339 123 0.6 0.2 0.9
0.1
0.1
0.4
43954934
0 0.98 0.425 576 853 314 0.6 0.7 0.6
0.2
0.4
0.2
43955042
4.4 0.91 0.425 573 707 332
0 0.2 0.3
0.2
0.4
0.5
43955048 123.5 0.97 0.327 595 1124
0 0.4 0.2 0.4
0.7
0.4
0.7
43955331
0.1 0.96 0.446 521 851 335 0.1 0.2 0.2
0.1
0
0.4
43958429
0.4 0.99 0.219 1076 588 91 0.8 0.4 0.8
1
0.5
0.9
ma ma/ch ch
0.6
0.6
0
1.6
1.4 0.5
0.2
0.2 0.1
0.2
0.2 0.2
0.9
0.8 0.8
0.8
0.8 0.1
0.2
0.5 0.8
0.7
0.5 0.6
1.9
2 0.4
3
3.1 0.4
10.1
10.7 0.6
0.3
0.5 0.1
0.7
0.9
0
3.2
2.9 0.9
3.2
3 0.9
9.5
9.6 0.7
24.2
28.4 0.3
2.8
5.6
1
0.4
0.5 0.1
0
0
0
0.8
0.3 1.3
0.3
0.4 0.2
2.7
3.4 0.1
1.3
1.1 0.7
34.2
38.5 0.6
4.5
6.4 0.5
1.5
2.2 0.4
0.7
0.9 0.5
2.2
1.9 0.6
0.8
0.3 1.1
0.5
0.3 0.8
0.3
0.4 0.3
0.1
0.4 0.3
0.2
0.2 0.4
0.3
0.3
0
ca
0.3
0.7
0.6
0.2
0
0.8
0.2
0.9
2.7
2.2
8.8
0.6
1
3.1
2.8
8.1
15.4
2.9
0.9
0
1.5
0.8
4
2.3
19.8
4.2
3
0.4
1.8
0.3
0.2
0.3
0.4
0.5
1.3
si
0.9
1.7
0.7
0
0.8
0.6
0.4
0.6
3.5
2.7
10.2
0.5
1.1
3.2
3
9.2
19.7
3.1
1.1
0
1.2
0.8
3
2.4
25.5
5.4
2.2
0.1
2.1
0
0.5
0.3
0.2
0.7
0.6
recessive model
0.6
0.3
0.9
0.6 0.6
0.6 0.1
1.4
0.4
1.5
1.1 1.3
1.2 0.5
0.7
0.6
0.8
0.7 0.2
0.2 0.1
0.5
0.3
0
0.6 0.2
0.3 0.1
0.2
0.4
1.5
0.1 1.4
1.1 0.8
1
0.6
0.4
0.7 1.1
0.8 0.4
0.6
1
1
1.4 0.1
0.4 1.1
0.6
0.3
0.2
0.2
1
0.4 1.1
3.1
2.4
3.4
2.8 1.9
2 0.4
1.7
1.7
2.3
1.3
3
2.7 0.6
11
8.9 11.1
11
9
9.6 0.5
0.2
0.3
0.3
0 0.4
0.2 0.5
1.3
1.1
1.3
1.5 0.9
1.1
0
2.2
1.9
2.4
1.4 3.2
2.5 1.1
2.1
1.7
2.3
1.3
3
2.4 1.1
11
7.8
9.7 10.1 8.4
8.6 0.6
15 16.3 21.5 15.6 20.3
23 0.4
2.3
5.7
5
3.9 2.3
5 1.1
0.7
0.9
1.2
0.7 0.4
0.5 0.1
0
0
0
0
0
0
0
1.2
0.7
0.8
0.6 0.6
0.2
1
1
1.4
1.1
1.4 0.2
0.4 0.3
4.1
3.7
3
3.7
4
4.5 0.4
3.1
1.6
2.4
2.5 1.3
1.1 0.7
19
21 27.6 19.5 27.2
30.3 0.5
2.6
7
7.4
4 4.3
6.5 0.8
2.5
4.2
2.9
3.2 1.6
2.1 0.3
0.1
0.9
0
0.3 0.6
0.7 0.4
2.2
1.2
1.8
1.6 2.1
1.7 0.7
0.5
0.1
0.3
0.2 0.5
0.2 0.7
0.4
0.1
0.2
0.2 0.5
0.4 0.6
0.3
0.6
0.4
0.5
0
0.2 0.2
0.4
0.7
0.4
0.7 0.1
0.4 0.3
0.1
0.3
0.6
0 0.3
0.2 0.3
1
1.1
0.6
0.8 0.5
0.4 0.2
Yellow: P<0.01; Red: P<0.001. *indicates SNPs which were used for haplotype analysis (Supplementary Table 9). Otherwise see legend to Supplementary Table 3.
26
Geometric mean and standard deviation for carriers of at least one copy of the corresponding haplotype (in mg/L)
Nr haplotype
N
ca
si
br
ca/ch
si/ch
br/ch
ch
1 GTCGTCCTGTTTCAGACTTGG
596 5.57 (1.01) 1.91 (1.02) 0.58 (1.02)
2.72 (1.01) 0.93 (1.02) 0.283 (1.02) 2050 (1.01)
2 GCCATACCATCTCAGTCTTGT
454 6.13 (1.02) 2.26 (1.02) 0.66 (1.02)
2.99 (1.01) 1.10 (1.02) 0.322 (1.02) 2048 (1.01)
3 GTTGTCCTGTTTCAGACTTGG
422 5.64 (1.02) 1.98 (1.02) 0.60 (1.02)
2.80 (1.01) 0.98 (1.02) 0.299 (1.02) 2017 (1.01)
4 GCCATACTGTTTCAGACTTGG
182 5.59 (1.02) 1.97 (1.04) 0.59 (1.03)
2.81 (1.02) 0.99 (1.03) 0.295 (1.03) 1987 (1.02)
5 GCCATACCATTTCAGAATTGT
161 5.45 (1.03) 1.92 (1.04) 0.59 (1.03)
2.70 (1.02) 0.95 (1.03) 0.295 (1.03) 2022 (1.01)
6 GCCATACCATTTCAGAAACGT
153 5.29 (1.03) 1.82 (1.04) 0.56 (1.04)
2.56 (1.03) 0.88 (1.04) 0.271 (1.03) 2066 (1.01)
7 GCCATACCATTTCAGACTTGG
147 5.36 (1.03) 1.80 (1.04) 0.54 (1.04)
2.63 (1.02) 0.87 (1.04) 0.265 (1.03) 2041 (1.02)
8 ACCATACCATTTCAGAATTGG
127 5.77 (1.03) 2.00 (1.04) 0.62 (1.04)
2.71 (1.03) 0.94 (1.04) 0.291 (1.03) 2125 (1.02)
9 GCCATACCATCACAGTCTTGG 107 6.38 (1.03) 2.40 (1.05) 0.70 (1.04)
3.15 (1.03) 1.18 (1.04) 0.345 (1.04) 2026 (1.02)
10 GTTGTACCATCTCGGTCTCAT
82 6.27 (1.04) 2.39 (1.06) 0.68 (1.05)
3.02 (1.03) 1.15 (1.05) 0.326 (1.04) 2075 (1.02)
11 ACCATACCATTTCAGAATTGT
76 5.42 (1.03) 1.68 (1.07) 0.57 (1.05)
2.63 (1.03) 0.81 (1.06) 0.278 (1.04) 2065 (1.02)
12 GCCATACCATCTTAGTCTTGT
75 6.10 (1.04) 2.30 (1.06) 0.66 (1.04)
2.98 (1.04) 1.13 (1.06) 0.324 (1.04) 2043 (1.02)
13 GTTGTACCATCACAGTCTTGG
74 5.88 (1.04) 2.10 (1.06) 0.60 (1.05)
2.99 (1.03) 1.07 (1.05) 0.308 (1.04) 1962 (1.02)
14 GCCATAGCACTTCGGACTTGT
73 4.81 (1.04) 1.48 (1.07) 0.46 (1.05)
2.39 (1.04) 0.74 (1.07) 0.231 (1.05) 2009 (1.02)
15 GCCATACCATCTCGGTCTCAT
69 6.35 (1.04) 2.51 (1.06) 0.73 (1.05)
2.99 (1.04) 1.19 (1.05) 0.345 (1.05) 2120 (1.03)
16 GCCACACCATCTCAGTCTTGG
60 6.48 (1.05) 2.53 (1.06) 0.71 (1.06)
3.11 (1.04) 1.21 (1.06) 0.341 (1.05) 2085 (1.02)
17 GCCATAGCACTTCGAACTCAT
54 4.92 (1.04) 1.60 (1.07) 0.49 (1.07)
2.49 (1.04) 0.80 (1.07) 0.247 (1.06) 1973 (1.03)
18 GCCATACCATCTCAGTCTTGG
45 6.23 (1.05) 2.32 (1.08) 0.68 (1.06)
3.00 (1.04) 1.11 (1.07) 0.327 (1.06) 2076 (1.03)
19 GTCGTCCTGTTTCAGACTTGT
40 5.51 (1.05) 1.79 (1.08) 0.56 (1.08)
2.63 (1.04) 0.85 (1.08) 0.266 (1.07) 2101 (1.02)
20 GCCATACCATTTCAGACTTGT
39 5.82 (1.05) 2.14 (1.08) 0.62 (1.07)
2.71 (1.05) 1.00 (1.07) 0.291 (1.06) 2145 (1.03)
21 GCCATAGCACTTCGGAATTGT
36 5.11 (1.05) 1.54 (1.11) 0.49 (1.08)
2.55 (1.05) 0.77 (1.09) 0.247 (1.07) 2002 (1.04)
362 5.50 (1.02) 1.90 (1.03) 0.60 (1.02)
2.76 (1.02) 0.95 (1.03) 0.299 (1.02) 1995 (1.01)
22 Rare variants
The variants at rs4952688 and rs11887534 are marked red in the haplotype column. Haplotypes containing C/T variant showed high phytosterol concentrations throughout while haplotypes
containing the G/A variant showed low concentrations. Results of association analysis for these haplotypes are shown in Supplementary Table 10. ca, mean and standard error of serum
campesterol (mg/L); si, mean and standard error of serum sitosterol (mg/L); br, mean and standard error of serum brassicasterol (mg/L); ca/ch, mean and standard error of serum campesterol
3
3
normalized to cholesterol (x10 ); si/ch, mean and standard error of serum sitosterol normalized to cholesterol (x10 ); br/ch, mean and standard error of serum brassicasterol normalized to
3
cholesterol (x10 ); ch, mean and standard error of serum cholesterol.
27
Geometric mean and standard error of phytosterol and cholesterol concentrations in the CARLA cohort for all
genotypes of the three haplotype varaints of rs4952688 and rs11887534 (in mg/L)
ca
si
br
ca/ch
si/ch
br/ch
ch
CA/CA (N=698)
5.41
(1.01)
1.83 (1.02)
0.57 (1.02)
2.66 (1.01)
0.90 (1.02)
0.281 (1.02)
2036 (1.01)
CA/CT (N=662)
5.97
(1.01)
2.17 (1.02)
0.64 (1.02)
2.92 (1.01)
1.06 (1.02)
0.314 (1.02)
2043 (1.01)
CT/CT (N=162)
6.76
(1.03)
2.68 (1.04)
0.75 (1.03)
3.30 (1.02)
1.31 (1.04)
0.367 (1.03)
2052 (1.01)
CA/GA (N=135)
4.75
(1.03)
1.46 (1.04)
0.46 (1.04)
2.37 (1.03)
0.72 (1.04)
0.229 (1.03)
2006 (1.02)
GA/GA (N=6)
3.87
(1.12)
0.80 (1.30)
0.37 (1.16)
2.07 (1.14)
0.43 (1.32)
0.198 (1.18)
1866 (1.03)
CT/GA (N=54)
5.73
(1.04)
2.11 (1.06)
0.61 (1.06)
2.79 (1.03)
1.03 (1.05)
0.300 (1.05)
2052 (1.03)
0.13
0.0045
Dose effect C/T
p-value C/T
Dose effect G/A
p-value G/A
0.11
2.7 x 10
0.19
-20
-0.11
2.8 x 10
2.2 x 10
0.14
-24
-0.20
-06
1.4 x 10
1.5 x 10
0.11
-18
-0.17
-7
4.0 x 10
1.9 x 10
0.19
-22
-0.097
-08
5.2 x 10
-06
3.7 x 10
-27
-0.19
4.8 x 10
2.0 x 10
-19
0.54
-0.16
-08
7.7 x 10
-0.013
-08
0.36
Positive dose effect for haplotype C/T and a negative dose effect for haplotype G/A for all phytosterol traits but not for cholesterol. Effects and p-values are shown for the additive model. Date
were adjusted for age, sex, log(BMI) and statin treatment status. ca, mean and standard error of serum campesterol (mg/L); si, mean and standard error of serum sitosterol (mg/L); br, mean and
3
standard error of serum brassicasterol (mg/L); ca/ch, mean and standard error of serum campesterol normalized to cholesterol (x10 ); si/ch, mean and standard error of serum sitosterol
3
3
normalized to cholesterol (x10 ); br/ch, mean and standard error of serum brassicasterol normalized to cholesterol (x10 ); ch, mean and standard error of serum cholesterol.
28
Explained variance of serum phytosterols by ABCG8 and ABO loci
Trait
explained variance (%)
ABCG8 haplotypes
additive model
blood group
O vs. A,B,AB
combined
ca
si
br
ca/ch
si/ch
br/ch
ch
7.00
8.62
7.19
7.66
9.61
7.49
0.05
1.09
0.39
0.51
0.57
0.20
0.22
0.35
8.08
9.03
7.72
8.25
9.83
7.74
0.4
ca, campesterol; si, sitosterol; br, brassicasterol; ch, cholesterol; ca/ch, campesterol normalized to cholesterol; si/ch, sitosterol normalized to cholesterol; br/ch, brassicasterol normalized to
cholesterol.
29
Phytosterol and cholesterol concentrations (mg/L) in relation to blood groups in CARLA (genetic determination)
Blood group
O (N=623)
A (N=777)
B (N=237)
AB (N=102)
p-value O vs. A,AB,B
ca
5.44 (1.01)
5.87 (1.01)
5.72 (1.02)
5.77 (1.03)
7.6x10-5
si
1.91 (1.02)
2.07 (1.02)
1.98 (1.03)
1.98 (1.05)
0.015
br
ca/ch
si/ch
0.58 (1.02)
2.71 (1.01) 0.95 (1.02)
0.63 (1.02)
2.85 (1.01) 1.00 (1.02)
0.61 (1.03)
2.84 (1.02) 0.98 (1.03)
0.60 (1.04)
2.82 (1.03) 0.97 (1.05)
0.0051
0.0020
0.056
br/ch
0.296 (1.02)
0.311 (1.01)
0.311 (1.03)
0.298 (1.04)
0.042
ch
2006 (1.01)
2067 (1.01)
2012 (1.01)
2034 (1.02)
0.056
Geometric mean and standard error of age, sex, log(BMI) and statin treatment status adjusted traits. Phytosterols were also adjusted for rs4245791 and rs41360247. Blood group O showed
reduced phytosterol concentrations while cholesterol concentrations are equal. P-values were calculated for the comparison of blood group O with the pooled blood groups A,B and AB. ca, mean
and standard error of serum campesterol (mg/L); si, mean and standard error of serum sitosterol (mg/L); br, mean and standard error of serum brassicasterol (mg/L); ca/ch, mean and standard
3
3
error of serum campesterol normalized to cholesterol (x10 ); si/ch, mean and standard error of serum sitosterol normalized to cholesterol (x10 ); br/ch, mean and standard error of serum
3
brassicasterol normalized to cholesterol (x10 ); ch, mean and standard error of serum cholesterol.
30
Phytosterol and cholesterol concentrations (mg/L) in blood donors (immunological determination)
Blood group
O (N=301)
A (N=296)
B (N=111)
AB (N=52)
p-value O vs. A,AB,B
ca
5.17
5.49
5.48
5.49
(1.02)
(1.02)
(1.03)
(1.04)
0.011
si
2.26
2.40
2.38
2.39
(1.02)
(1.02)
(1.04)
(1.05)
0.044
br
0.67
0.71
0.72
0.69
(1.02)
(1.02)
(1.03)
(1.05)
0.031
ca/ch
2.84
3.04
3.02
3.01
(1.02)
(1.02)
(1.03)
(1.04)
0.014
si/ch
1.25
1.33
1.32
1.32
(1.02)
(1.02)
(1.04)
(1.05)
0.030
br/ch
0.370
0.393
0.395
0.378
ch
1819
1810
1815
1823
(1.02)
(1.02)
(1.03)
(1.04)
0.021
(1.02)
(1.02)
(1.03)
(1.04)
0.874
Geometric mean and standard error of age, sex and log(BMI) adjusted traits. Blood group O showed reduced phytosterol concentrations while cholesterol concentrations are equal. P-values
were calculated to compare blood group O with the pooled blood groups A,B and AB. ca, mean and standard error of serum campesterol (mg/L); si, mean and standard error of serum sitosterol
3
(mg/L); br, mean and standard error of serum brassicasterol (mg/L); ca/ch, mean and standard error of serum campesterol normalized to cholesterol (x10 ); si/ch, mean and standard error of
3
3
serum sitosterol normalized to cholesterol (x10 ); br/ch, mean and standard error of serum brassicasterol normalized to cholesterol (x10 ); ch, mean and standard error of serum cholesterol.
31
Metaanalysis of association of ABCG8 SNP rs41360247 with CAD
Cases
(n)
2843
Controls
(n)
421
Call
rate
0.937
Cases
MAF
0.060
Controls
MAF
0.064
P-value
(additive)
0.693
OR (95% CI)
(additive)
0.94 (0.69-1.28)
P-value
(recessive)
0.658
OR (95% CI)
(recessive)
0.93 (0.68-1.28)
CARLA
145
1589
0.991
0.046
0.056
0.481
0.81 (0.46-1.44)
0.388
0.76 (0.41-1,41)
ECTIM
1114
1154
0.990
0.053
0.062
0.194
0.85 (0.66-1.09)
0.132
0.82 (0.63-1.06)
Erlangen
797
738
0.995
0.059
0.070
0.217
0.83 (0.62-1.11)
0.173
0.81 (0.60-1.10)
GerMIFS II
1222
1407
1.000
0.056
0.067
0.083
0.82 (0.65-1.03)
0.063
0.80 (0.63-1.01)
GoKard
966
995
0.962
0.056
0.079
0.006
0.70 (0.54-0.90)
0.001
0.63 (0.48-0.83)
KORA-B
589
607
0.924
0.067
0.059
0.431
1.15 (0.81-1.63)
0.372
1.18 (0.82-1.69)
KORA-MI
1504
1550
0.947
0.057
0.078
0.002
0.72 (0.59-0.89)
0.003
0.72 (0.58-0.90)
LE-Heart
469
422
0.988
0.055
0.053
0.870
1.03 (0.69-1.54)
0.828
1.05 (0.68-1.63)
PopGen
2189
1809
0.890
0.054
0.056
0.782
0.97 (0.80-1.19)
0.690
0.96 (0.77-1.19)
WTCCC
1926
2938
0.958
0.053
0.065
0.018
0.81 (0.68-0.96)
0.012
Cohort
Angio-Lueb
Fixed effects
Random effects
13764
13764
13630
13630
0.955
0.955
0.057
0.057
0.065
0.065
1.3 x 10
-5
4.6 x 10
-5
0.84 (0.78-0.91)
0.84 (0.78-0.92)
0.79 (0.65-0.95)
2.3 x 10
-6
0.82 (0.76-0.89)
7.5 x 10
-5
0.83 (0.75-0.91)
Cohorts are described in supplementary methods; MAF, minor allele frequency (minor allele C, major allele T); OR (95% CI), odds ratio and 95% confidence interval using additive and recessive
models, respectively.
32
Metaanalysis of association of ABCG8 SNP rs4245791 with CAD
Cases
(n)
2843
Controls
(n)
421
Call
rate
0.941
Cases
MAF
0.321
Controls
MAF
0.310
P-value
(additive)
0.531
OR (95% CI)
(additive)
1.05 (0.90-1.24)
P-value
(recessive)
0.546
OR (95% CI)
(recessive)
1.07 (0.86-1.32)
CARLA
145
1589
0.957
0.390
0.323
0.030
1.32 (1.03-1.69)
0.033
1.48 (1.03-2.13)
ECTIM
1114
1154
0.947
0.342
0.344
0.898
0.99 (0.87-1.13)
0.661
0.96 (0.81-1.14)
Erlangen
797
738
0.985
0.346
0.303
0.011
1.22 (1.05-1.42)
0.015
1.29 (1.05-1.58)
GerMIFS II
1222
1407
0.965
0.332
0.299
0.011
1.17 (1.04-1.31)
0.013
1.22 (1.04-1.43)
GoKard
966
995
0.971
0.332
0.321
0.484
1.05 (0.92-1.20)
0.932
0.99 (0.83-1.19)
KORA-B
589
607
0.921
0.333
0.313
0.311
1.10 (0.92-1.31)
0.363
1.12 (0.88-1.42)
KORA-MI
1504
1550
0.953
0.343
0.317
0.040
1.12 (1.01-1.25)
0.075
1.14 (0.99-1.32)
LE-Heart
469
422
0.987
0.305
0.303
0.925
1.01 (0.82-1.24)
0.809
1.03 (0.79-1.35)
PopGen
2189
1809
0.983
0.321
0.313
0.418
1.04 (0.95-1.14)
0.432
1.05 (0.93-1.19)
WTCCC
1926
2938
0.997
0.348
0.319
0.003
1.14 (1.04-1.24)
0.009
Cohort
Angio-Lueb
Fixed effects
Random effects
13764
13764
13630
13630
0.968
0.968
0.333
0.333
0.317
0.317
2.2 x 10
-6
7.2 x 10
-6
1.10 (1.06-1.14)
1.10 (1.05-1.14)
1.17 (1.04-1.31)
4.6 x 10
-5
1.11 (1.06-1.17)
2.8 x 10
-4
1.11 (1.05-1.18)
Cohorts are described in supplementary methods; MAF, minor allele frequency (minor allele C, major allele T); OR (95% CI), odds ratio and 95% confidence interval using additive and recessive
33
Metaanalysis of association of ABO SNP rs657152 with CAD
Cases
(n)
2843
Controls
(n)
421
Call
rate
0.944
Cases
MAF
0.409
Controls
MAF
0.400
P-value
(additive)
0.636
OR (95% CI)
(additive)
1.04 (0.89-1.21)
P-value
(recessive)
0.507
OR (95% CI)
(recessive)
1.08 (0.87-1.34)
CARLA
145
1589
0.980
0.432
0.409
0.466
1.10 (0.86-1.40)
0.207
1.27 (0.87-1.86)
ECTIM
1114
1154
0.973
0.331
0.330
0.928
1.01 (0.89-1.14)
0.697
1.03 (0.87-1.22)
Erlangen
797
738
0.972
0.411
0.389
0.221
1.10 (0.95-1.27)
0.501
1.08 (0.87-1.33)
GerMIFS II
1222
1407
0.989
0.420
0.388
0.020
1.14 (1.02-1.27)
0.006
1.26 (1.07-1.47)
GoKard
966
995
0.967
0.404
0.361
0.007
1.20 (1.05-1.36)
0.042
1.21 (1.01-1.46)
KORA-B
589
607
0.937
0.428
0.357
0.001
1.35 (1.14-1.61)
0.002
1.48 (1.16-1.89)
KORA-MI
1504
1550
0.950
0.402
0.385
0.201
1.07 (0.96-1.19)
0.187
1.11 (0.95-1.29)
LE-Heart
469
422
0.991
0.432
0.408
0.309
1.10 (0.91-1.33)
0.388
1.13 (0.86-1.49)
PopGen
2189
1809
0.979
0.417
0.396
0.065
1.09 (0.99-1.19)
0.143
1.10 (0.97-1.26)
WTCCC
1926
2938
0.993
0.360
0.354
0.514
1.03 (0.94-1.12)
0.350
Cohort
Angio-Lueb
Fixed effects
Random effects
13764
13764
13630
13630
0.972
0.972
0.399
0.399
0.377
0.377
5.0 x 10
-6
4.0 x 10
-5
1.09 (1.05-1.13)
1.09 (1.05-1.14)
1.06 (0.94-1.19)
9.4 x 10
-6
1.13 (1.07-1.19)
1.1 x 10
-5
1.13 (1.07-1.19)
Cohorts are described in supplementary methods; MAF, minor allele frequency (minor allele A, major allele C); OR (95% CI), odds ratio and 95% confidence interval using additive and recessive
34
Replication of major genetic associations of serum phytosterol levels in CARLA with additional adjustment to
LDL-cholesterol levels
Allelic effect and
P value of association
Cohort
CARLA
(n=1760)
SNP
Gene
rs41360247
ABCG8
rs4245791
ABCG8
rs657152
ABO
Chr
bp position
MAF
CR
HWE
2
43927160
0.056
0.990
0.50
2
43927935
0.326
0.957
0.016
9
133168819
0.412
0.984
0.50
CA
SI
-12%
2.0 x 10-8
10%
3.8 x 10-18
5%
5.6 x 10-4
-23%
2.0 x 10-10
19%
2.3 x 10-23
5%
0.059
BR
-20%
8.2 x 10-11
14%
2.4 x 10-19
5%
0.027
CA, campesterol; SI, sitosterol; BR, brassicasterol; CH, cholesterol; bp position refers to NCBI build 36. MAF, minor allele frequency; CR, call rate; HWE, P value of deviation from HardyWeinberg equilibrium; P values of association are given for the additive model for rs41360247 and rs4245791 and for the recessive model for rs657152 after additional adjustment to LDLcholesterol.
35
Supplementary Figure 1
Supplementary Figure 1: Q/Q plots for campesterol . (A) Additive model. (B) recessive model.
36
Campesterol (mg/L)
7.0
6.0
5.0
4.0
3.0
2.0
1.0
0
698
662
162
135
6
54
CA/CA CA/CT CT/CT CA/GA GA/GA CT/GA
rs11887534, rs4952688 haplotype
Supplementary Figure 2: Geometric mean and standard error of campesterol for functionally
relevant haplotypes of the ABCG8 locus in the CARLA cohort (n=1760). Haplotype analysis
showed that the variation of phytosterol levels at this locus could be best explained by
haplotypes defined by rs11887534 (C/G) and rs4952688 (A/T). These SNPs were tightly linked
with rs41360247 and rs4245791 (see Figure 1), respectively but showed improved P-values of
association. The CT haplotype was associated with elevated phytosterols (dose effect 0.11, P =
2.7 x 10-20), whereas the GA haplotype was associated with decreased phytosterols (dose
effect -0.11, P = 2.8 x 10-6).
37
rs 4 9 5 2 6 8 8
43952937
rs 4 1 4 8 2 1 7 (T 4 0 0 K )
rs 4 2 4 5 7 9 1
43950274
rs 4 1 3 6 0 2 4 7
43927935
43921355
43927160
43921323
S a m p le ID
43920673
P o s itio n [b p ]
rs 10177200
43920380
C
T
C
H
C
C
H
H
C
C
C
C
H
C
C
C
C
C
C
C
G
C
C
C
C
C
C
C
C
C
H
C
C
C
H
C
C
C
C
A
C
C
C
C
C
C
C
C
C
H
C
C
C
H
C
C
C
Major
Minor
#1
#2
#3
#4
#5
#6
#7
#8
#9
# 10
# 11
# 12
# 13
# 14
# 15
# 16
# 17
T
C
T
T
T
T
T
T
T
T
T
H
T
T
T
T
H
T
T
T
C
T
H
H
H
T
T
H
H
T
H
T
H
T
C
T
T
T
A
T
A
H
H
H
A
A
H
H
A
H
A
H
A
T
A
A
H
C
A
H
C
C
H
C
H
H
C
H
C
C
C
C
C
C
C
C
U 11-1
rs 4148202
43919751
C
T
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
U 10-1
rs 3806470
43919678
43922480
rs 4245789
43918594
rs 10179921
rs11887534 (D 19H )
43918288
G T G G G T C
A G C A A G T
G H G G H G C
G H G G H H H
G T G G H H C
G T G G G T C
G H G G H H H
G H G G H H H
G T G G G T C
G H G G H H C
G T G G G T C
H T H G G H C
G H G G H G T
G T G G G T C
G G G G A G C
G T G G G H C
G H G G H H C
G G G G A G C
G H G G H H C
43921845
rs3806471
43917855
G
A
G
G
G
G
G
G
G
G
G
G
G
G
G
G
G
G
G
rs 4148203
rs 6756629
43917693
G T
A C
H T
H T
G H
G T
H T
H T
G T
H T
G T
G H
H H
G T
A H
G H
H H
A H
H T
43921795
rs 6710544
43917688
G
A
H
H
G
G
H
H
G
H
G
G
H
G
A
G
H
A
H
43921386
U 2-2
43917677
G A
A C
H H
H H
G A
G A
H H
H H
G A
H H
G A
H A
H H
G A
A C
H A
H H
A C
H H
rs 10495909
rs 34381269
43917601
C
T
H
H
C
C
H
H
C
H
C
H
H
C
T
H
H
T
H
U 2-1
rs 35636239
43917566
C
T
C
C
C
C
C
C
C
C
C
H
C
C
C
H
C
C
C
rs 34520479
rs 13425681
43917235
T
A
H
H
T
T
H
H
T
H
T
H
H
T
A
H
H
A
H
A T
G C
H T
H T
A T
A T
H T
H T
A T
H T
A T
G T
H T
A T
G T
H T
H T
G T
H T
U 1A -1
rs 17031700
43917191
rs 6728053
43916857
43917139
U 1-1
43916850
rs 6741243
rs 13425260
43916819
C A C T
T G T C
H H C H
H H H H
C A C T
C A C T
H H C H
H H C H
C A C T
H H H H
C A C T
C H C H
H H C H
C A C T
T G C C
H H C H
H H C H
H G C C
H H C H
43916861
rs 6712582
43916704
T
G
H
H
T
T
H
H
T
H
T
T
H
T
G
H
H
H
H
rs 4953021
43916686
Major
Minor
#1
#2
#3
#4
#5
#6
#7
#8
#9
# 10
# 11
# 12
# 13
# 14
# 15
# 16
# 17
S am p le ID
P o sitio n [b p ]
Supplementary Figure 3: DNA sequence analysis of 6kb of the intergenic region of ABCG5 and
ABCG8 in 17 human liver samples. The region was selected based on a high level of
conservation determined with the Vista browser. SNPs in the region failed (left) to show
significant linkage with SNP rs4245791 or rs4962688 (right). Homozygousity for major alleles is
indicated in blue and homozygousity for minor alleles in orange. “H” stands for heterozygous
samples.
38
A
B
A
B
01
02
1
2
3
4
5
rs8176747 rs41302905 rs8176746 rs8176719
rs657152
SNP-ID
136.131.315 136.131.316 136.131.322 136.132.908 136.139.265
position [bp]
C/G
C/T
G/T
C/C/A
alleles
Gly/Ala
Gly/Arg
Leu/Met
Val/X
-/amino acid change
A/B
A/0
A/B
A/0
-/blood group change
Exon 7
Exon 6
Supplementary Figure 4: (A) Haplotype structure of the ABO locus using the lead SNP
rs657152 (75) and neighboring SNPs from the 500K Array Set. (B) SNPs determining blood
groups and rs657152 and their corresponding LD-plot in CARLA (r2). Position and effect of
the 5 SNPs on amino acids of ABO and blood groups are shown. Minor alleles of SNP 1 and
3 lead to blood group B (blue). Minor alleles of SNP 2 and 4 lead to blood group O2 and O1,
respectively.
39
A
Angio-Lueb
CARLA
ECTIM
Erlangen
GerMIFS II
GoKard
KORA-B
KORA-MI
LE-Heart
PopGen
WTCCC
Fixed effect
Random effects
0.6
B
0.8
1.0
1.2
1.6
Angio-Lueb
CARLA
ECTIM
Erlangen
GerMIFS II
GoKard
KORA-B
KORA-MI
LE-Heart
PopGen
WTCCC
Fixed effect
Random effects
0.8
C
1.0
1.2
1.4
1.6
2.0
1.0
1.2
1.4
1.6
2.0
Angio-Lueb
CARLA
ECTIM
Erlangen
GerMIFS II
GoKard
KORA-B
KORA-MI
LE-Heart
PopGen
WTCCC
Fixed effect
Random effects
0.8
Supplementary Figure 5: Odds ratio and 95% CI in 11 studies of CAD and meta-effects using
fixed effects and random effects models. (A) ABCG8, rs41360247 (B) ABCG8, rs4245791
(C) ABO, rs657152
40
ABCG8
rs41360247
ABCG8
rs4245791
ABO
rs657152
P = 0.039
P = 0.078
P = 0.012
0.8
0.9
lower LDL-cholesterol
1.0
1.1
1.2
higher LDL-cholesterol
Supplementary Figure 6: Fold change and 95% CI of LDL-cholesterol in CARLA for SNPs
located in ABCG8 and ABO genes using additive and recessive models, respectively.
41
Members of KORA Study Group
Cooperative health research in the Region of Augsburg (KORA)
KORA study group consists of H.-Erich Wichmann1,2 (speaker), Rolf Holle3, Jürgen
John3, Thomas Illig2, Christa Meisinger1, Annette Peters1, and their coworkers, who are
responsible for the design and conduct of the KORA studies. The KORA S3/F3 500K
study was conducted by Christian Gieger1,2, Guido Fischer1, Iris M. Heid1,2, Susana
Eyheramendy1,2, Norman Klopp1,2, Peter Lichtner4, Gertrud Eckstein4, Thomas Illig2, H.Erich Wichmann1,2, and Thomas Meitinger4,5
1
Institute of Epidemiology, GSF - National Research Center for Environment and Health,
85764 Neuherberg, Germany.
2
Chair of Epidemiology, IBE, University of Munich, 81377 Munich, Germany.
3
Institute of Health Economics and Health Care Management, GSF-National Research
Centre for Environment and Health, 85764 Neuherberg, Germany.
4
Institute of Human Genetics, GSF National Research Center for Environment and
Health, 85764 Neuherberg, Germany
5
Institute of Human Genetics, Technical University, 81765 Munich, Germany

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