genetic regulation of serum phytosterol levels and … regulation of serum phytosterol levels and...

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DOI: 10.1161/CIRCGENETICS.109.907873 published online Jun 7, 2010; Circ Cardiovasc Genet

Erdmann, H. -Erich Wichmann, Heribert Schunkert and Joachim Thiery Schreiber, Karl Werdan, Thomas Meitinger, Markus Löffler, Nilesh J. Samani, Jeanette Gert Matthes, Christian Wittekind, Christian Hengstenberg, Francois Cambien, Stefan

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Gieger, Lesca M. Holdt, Alexander Leichtler, Karin H. Greiser, Dominik Huster, Patrick Daniel Teupser, Ronny Baber, Uta Ceglarek, Markus Scholz, Thomas Illig, Christian

Genetic Regulation of Serum Phytosterol Levels and Risk of Coronary Artery Disease

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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*

* Contributed equally

Inst of Lab Med, Clin Chem & Molecular Diagnostics (DT, RB, UC, LMH, AL, GMF, WW, FB, JT), Inst for Med Informatics, Stats & Epidemiology (MS, ML), Dept of Med II (DH), Heart Ctr - Dept of Internal Med/Cardio (SG), Inst of Transfusion Med (GM), & Inst of Pathology (CW) Univ Leipzig, Germany; Instof Epidemiology (TI, CG, IMH, HEW), & Inst of Human Genetics (PL, TM) Helmholtz Zentrum München, German Research Ctr for Environmental Health, Neuherberg, Germany; Dept for Epidemiology & Preventive Med, Regensburg Univ Med Ctr, Regensburg, Germany (IMH); Inst of Med Informatics, 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 Erlangen-Nuremberg, 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: [email protected]; Telephone +49-341-9722204; Fax +49-341-9722379 or Dr. Heribert Schunkert, Universität zu Lübeck, Ratzeburger Allee 160, 23538 Lübeck, Germany. E-mail: [email protected]; 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

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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 of 11 different samples

comprising a total of 13,764 CAD cases and 13,630 healthy controls. Genetic variants in the

ATP-binding cassette transporter ABCG8 and at the blood group ABO locus were significantly

associated with serum phytosterols. Effects in ABCG8 were independently related to SNP

rs4245791 and rs41360247 (combined p=1.6x10-50 and 6.2x10-25, respectively, n=4412). Serum

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

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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

cholesterol by interfering with intestinal cholesterol absorption. In humans, doses of 0.8 - 4.0 g

daily reduce low-density-lipoprotein levels by 10 - 15%.2 However, such food supplementation

can raise the serum concentration of these sterols. For example, dietary supplementation with 1.1

g phytosterols/day doubled cholesterol normalized serum campesterol levels.3

Despite their LDL-lowering effect, there is increasing concern that elevated serum phytosterol

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).

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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

A detailed description of methods is provided in the online supplement.

Study cohorts

The study design is shown in Figure 1. The genome-wide association study 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

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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 study were performed with the

Affymetrix GeneChip® 500K Mapping Array Set,16, 17 whereas the Affymetrix® Genome Wide

Human SNP Array 6.0 was employed in the German MI Family Study II. Genotyping of

individual SNPs was performed using iPlex single base primer extension and 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

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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 95%, minor allele frequency (MAF) 1%, P-

values of exact HWE test 10-6). Campesterol, sitosterol, brassicasterol and corresponding ratios

normalized to total cholesterol concentrations as well as total cholesterol itself were log-

transformed 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

stratification, we analysed QQ-Plots for all these test statistics. Inflation factors ranged between

1.00063 and 1.012, indicating no relevant inflation of test statistics (Supplementary Figure 1).

Adjustment for the first three principal components did not substantially change the identified

associations, supporting the absence of significant bias caused by population stratification

(Supplementary Table 1). In addition, we used multivariate analysis of variance (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 p-

values 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

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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 thresholds in at least one of

the test statistics. The set of validated SNPs comprised again all four SNPs in ABCG8

(rs41360247, rs4245791, rs11887534, rs4148217) and one SNP in ABO (rs657152).

Fine mapping and haplotype analysis in 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.

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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 the pooled blood groups A, B

and AB.

Meta-analysis of phytosterol-related SNPs with CAD in 11 studies comprising 13,764 coronary

artery disease cases and 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

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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 the Sequenome assay (Table 1). A

total of 68 SNPs (Supplementary Table 3) were taken forward for validation in additional 1157

subjects of the KORA S3/F3 study (Supplementary Table 4) and 9 SNPs achieving nominal

significance of p<0.01 were taken forward for replication in 1760 individuals of the independent

CARLA study (Supplementary Table 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

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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). Sequencing

of the putative intergenic promoter region revealed no SNPs that were associated with expression

levels, suggesting that the responsible variant resides outside this region (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 glycosyl-

transferring 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-

rs11887534 (D1DDDDDDDDDD

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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 Tables 14-16. We

found that alleles associated with increased phytosterol levels were positively associated with

increased probability of CAD, while alleles associated with reduced phytosterols were associated

with reduced probability of CAD (Figure 5). We also tested the effect of identified genetic

variants on LDL-cholesterol levels, since recent studies have shown an association 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 LDL-

cholesterol (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.

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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 an association between the coding

variant D19H in this gene and serum phytosterols,9 a finding that was confirmed by our data.

D19H was also found to affect the susceptibility for cholesterol gall stone disease.22 It was

speculated that the 19H variant may increase the efficiency of sterol excretion into the bile

lumen, causing hypersaturation of the bile, subsequently leading to gall stone formation.23

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 LDL-

cholesterol 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

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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 the activity of proteins

responsible for sterol uptake. In this regard, it is of interest that both ABCG5 and ABCG8

undergo N-linked glycosylation.28 However, the specific biological mechanism by which ABO

alters phytosterol levels is unclear.29 Interestingly, it has been previously reported that serum

cholesterol levels are slightly but consistently elevated in non-O subjects.30, 31 In this regard, it is

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

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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 that ABO has been recently

associated in a number of hypothesis-free GWA with a diverse set of phenotypes such as

pancreatic cancer or plasma levels of ICAM-1.36, 37 These data suggest that ABO effects on these

phenotypes may indeed underlie a common mechanism that still needs to be determined.

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

est that ABO h

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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

impact serum LDL-cholesterol levels. Moreover, we show that common genetic variants

associated with serum phytosterol levels affect CAD risk in a concordant fashion. These data

show for the first time that genetic variants affecting sterol homeostasis play a role in susceptibly

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

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16

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ABCG5 d ABCG8 J Cl I 2002 110 605 609



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en JC,CC,C,C,C,C,C,C,CC,C,C,C,C,C,C,C,,,, H HHH H Hobobobobobobbbbobobbobbobbsbsbsbsbsbsbsbsbsbsbsbssbsbbsb HHHH HH

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PA, Berzuini C, Bernardinelli L, Peyvandi F, Tubaro M, Celli P, Ferrario M, Fetiveau R,

Marziliano N, Casari G, Galli M, Ribichini F, Rossi M, Bernardi F, Zonzin P, Piazza A,

Mannucci PM, Schwartz SM, Siscovick DS, Yee J, Friedlander Y, Elosua R, Marrugat J,

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21

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Table 1: Validation and replication of major genetic associations of serum phytosterol levels

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.

Allelic effect and p value of association

Cohort SNPGene Chr bp position Alleles

MAF CR HWE CA SI BR MANOVA CA/CH SI/CH BR/CH MANOVA/CH CH

KORA S3 500k

rs41360247ABCG8 2 43927160 T>C

0.067 0.966 0.69 -14%3.6 x 10-10

-24%1.3 x 10-15

-16%5.5 x 10-12 3.3 x 10-15 -14%

2.8 x 10-12-24%

3.6 x 10-18-16%

5.3 x 10-14 6.5 x 10-17 -0.4%0.76

(n=1495) rs4245791ABCG8 2 43927935 T>C

0.319 0.967 0.31 12%8.1 x 10-17

20%4.6 x 10-24

14%2.3 x 10-19 2.1 x 10-22 11%

6.5 x 10-1920%

7.9 x 10-2713%

4.2 x 10-21 9.7 x 10-25 0.8%0.27

rs657152ABO 9 133168819 G>T

0.373 0.951 0.83 8%6.0 x 10-5

11%6.5 x 10-5

7%8.0 x 10-4 3.5 x 10-4 7%

8.4 x 10-610%

1.6 x 10-57%

3.1 x 10-4 2.4 x 10-5 0.2%0.87

KORA S3 Stage 2

rs41360247ABCG8 2 43927160 T>C

0.072 0.990 0.63 -14%5.4 x 10-9

-20%5.7 x 10-10

-15%5.1 x 10-9 1.7 x 10-9 -11%

1.8 x 10-7-17%

5.3 x 10-9-12%

2.3 x 10-7 8.3 x 10-9 -3%0.028

(n=1157) rs4245791ABCG8 2 43927935 T>C

0.320 0.976 0.40 15%3.7 x 10-21

23%8.8 x 10-27

16%9.3 x 10-23 1.5 x 10-26 13%

1.0 x 10-1921%

1.9 x 10-2614%

1.5 x 10-20 6.0 x 10-27 2%0.015

rs657152ABO 9 133168819 G>T

0.353 0.989 0.06 8%1.8 x 10-5

9%8.5 x 10-4

6%0.0034 4.6 x 10-5 6%

5.0 x 10-47%

0.00724%

0.051 9.5 x 10-4 2%0.035

CARLAreplication

rs41360247ABCG8 2 43927160 T>C

0.056 0.990 0.50 -13%2.2 x 10-9

-22%2.7 x 10-11

-19%8.7 x 10-12 3.0 x 10-10 -12%

8.0 x 10-9-21%

9.6 x 10-12-18%

3.5 x 10-11 2.3 x 10-10 -2%0.21

(n=1760) rs4245791ABCG8 2 43927935 T>C

0.326 0.957 0.01 11%3.7 x 10-18

20%1.3 x 10-23

15%5.5 x 10-20 6.1 x 10-25 10%

7.6 x 10-2020%

3.8 x 10-2615%

7.8 x 10-21 4.4 x 10-29 0.5%0.47

rs657152ABO 9 133168819 G>T

0.412 0.984 0.50 7%2.9 x 10-5

7%0.0097

6%0.0049 0.0013 4%

0.00364%

0.0754%

0.085 0.035 3%0.0085

Combined rs41360247ABCG8 2 43927160 T>C

0.064 - - -14%6.2 x 10-25

-21%9.6 x 10-32

-16%4.5 x 10-28 7.7 x 10-31 -12%

1.1 x 10-24-21%

3.2 x 10-33-16%

3.1 x 10-27 4.1 x 10-33 -2%0.046

(n=4412) rs4245791ABCG8 2 43927935 T>C

0.322 - - 12%1.6 x 10-50

21%2.6 x 10-67

15%4.3 x 10-55 2.2 x 10-70 11%

3.2 x 10-5320%

1.4 x 10-7214%

9.8 x 10-56 1.1 x 10-78 0.9%0.031

rs657152ABO 9 133168819 G>T

0.383 - - 8%9.4 x 10-13

9%2.4 x 10-8

6%4.9 x 10-7 2.8 x 10-10 6%

2.2 x 10-107%

5.1 x 10-75%

5.0 x 10-5 3.5 x 10-9 1.5%0.011

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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 rs4952688 on mRNA expression of ABCG5 and ABCG8 in human liver

tissue (n=57). Expression levels were normalized to beta-actin. * indicates P < 0.01.

Figure 4: Effect of ABO blood groups on plasma campesterol concentrations. (A) CARLA study

(n=1760). (B) Replication in blood donors (n=760). The non-functional O-allele was consistently

associated with significantly reduced campesterol concentrations, compared to the functional A-

and 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.

N G

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A A

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Stage 1: Genome-wide association of serum phytosterol levelsin population-based sample (KORA S3/F3)

n=1,495 (490,032 SNPs/individual)

Stage 2: Validation of serum phytosterol associationin remaining KORA S3/F3

n=1,157 (68 SNPs/individual)

Stage 3: Replication of serum phytosterol assocoationin CARLA

n=1,760 (9 SNPs/individual)

Replication of phytosterolassociation in blood donors

n=760 (ABO)

Metaanalysis of association betweenrs41360247, rs4245791, rs657152 and CAD

(13,764 cases vs. 13,630 controls) Angio-Lübeck 2,843 cases vs. 421 controlsCARLA 145 cases vs. 1,589 controlsECTIM 1,114 cases vs. 1,154 controlsErlangen 797 cases vs. 738 controlsGerMIFSII 1,222 cases vs. 1,407 controlsGoKard 966 cases vs. 995 controlsKORA-B 589 cases vs. 607 controlsKORA-MI 1,504 cases vs. 1,550 controlsLE-Heart 469 cases vs. 422 controlsPopgen 2,189 cases vs. 1,809 controlsWTCCC 1,926 cases vs. 2,938 controls

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Cop

iesABCG8/

103

copi

esbeta-actin

* *

32 22/3 32 22/3

60

20

A AT/TT AA

p o

2

20

AAAA AAAAATTTTT/////TTTTTTTTTT AAAAAAAAAA

110

00000

CCCoopppp

111110000033333

ccccco

2 2222222222/////33333 32

20



6.0

5.8

5.6

6.2

5.4

A0 B AB

P = 7.6 x10-5

Blood group

Cam

pest

erol

(mg/

L)

5.2623 777 237 102

A0 B AB

Cam

pest

erol

(mg/

L) 5.8

5.6

5.4

5.0

P = 0.011

Blood group

5.2301 296 111 52

A

B

ABBBBBBBBBBBBBBBBBBBB

102

A0 B ABA0 B ABBBBBBlllllooooooooooddddd gggggroouuuuuppppp



lower campesterol higher campesterol

0.8 0.9 1.0 1.1 1.2

P = 6.2x10-25

P = 1.7x10-50

P = 9.4x10-13

less CAD more CAD

0.8 0.9 1.0 1.1 1.2

P = 2.3x10-6

P = 2.2x10-6

P = 3.9x10-6

A

B

rs41360247ABCG8

rs4245791

rs657152ABO

ABCG8

rs41360247ABCG8

rs4245791

rs657152ABO

ABCG8

P

P

P

0.88888 00000.99999 11111.0000 11111.11111 1..

P

P



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 25-

74 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




ECTIM. The ECTIM (Etude Cas-Témoin sur l'Infarctus du Myocarde) Study is a case-

control 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).




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 age-

stratified 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 population-

based 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.




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-of-

flight) 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.


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 co-

amplification 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.




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


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).




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


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.




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.




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.




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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 CA SI BR MANOVA CA/CH SI/CH BR/CH MANOVA/CH CH rs41360247

ABCG8 -15%

2.0x10-10 -24%

8.8x10-16 -17%

6.0x10-12 5.2x10-15

-14%

1.8x10-12 -24%

2.8x10-18 -16%

8.0x10-14 1.2x10-16

-0.5% 0.71

rs4245791 ABCG8

13% 4.8x10-18

21% 3.2x10-24

15% 2.9x10-20

2.2x10-22

11%

4.5x10-19 20%

6.4x10-26 13%

7.6x10-21 9.2x10-24

1.2% 0.11

rs657152 ABO

8% 9.1x10-5

10% 1.6x10-4

7% 1.6x10-3

5.4x10-4

8%

6.6x10-6 10%

2.8x10-5 7%

3.8x10-4 2.3x10-5

-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.

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List of primers for ABCG5/8 sequencing

Primer Sequence Product size G5/8Intergen-1fw CACTGCTGCCCAGGCTAGA G5/8Intergen-1rv GCTGCATTGGCCCTGAAGA 603

G5/8Intergen-2fw TGGTAATCCAGTGTAGCAGACACTG G5/8Intergen-2rv AAGACTGGAGAATAATATTTAAAAGTTCATGTAT 616

G5/8Intergen-3fw AAAGAAAAACGACCAGATAAGATCTGA G5/8Intergen-3rv TGAAAGAGTATAAAATTCTGCCTAACATG 616

G5/8Intergen-4fw CCTGAGTACTTTTATATGCCATGGAAC G5/8Intergen-4rv CCAAACGGACAGGACATTCAGA 622

G5/8Intergen-5fw AACCTGGCAGATAGCGACTGA G5/8Intergen-5rv CCAACTGAAGCCACTCTGGG 642

G5/8Intergen-6fw CAGCAAAGCTGGGCAAATTTT G5/8Intergen-6rv CAGGAAGTGACCTCAGAGGCCT 631

G5/8Intergen-7fw AGGACTGTTTCCTGCATGTCAA G5/8Intergen-7rv CCTGTTAGAGCCACACATGCTG 653

G5/8Intergen-8fw GTGATGGGTGAGACAGGGTGA G5/8Intergen-8rv AGCAGAAATGGCAGGGCC 646

G5/8Intergen-9fw CGATTCAGCCACCACAGCTT G5/8Intergen-9rv GCATGAGGAGTTTGTGGGTTAAG 621

G5/8Intergen-10fw TGGCATCTTGGGCACCTG G5/8Intergen-10rv TCCAACCACCATTGAGGGAT 654

G5/8Intergen-11fw GAATTTTCTTCCTCCGAAAGATGA G5/8Intergen-11rv AATATCTGCAGGAGGGATATTAGACAAT 401


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Supplementary Table 3 Details on SNPs selected from 500k analysis stage 1 for validation in stage 2

additive model recessive model SNP ID chr base hwe call maf aa ab bb ca si br ca/ch si/ch br/ch ma ma/ch ch ca si br ca/ch si/ch br/ch ma ma/ch ch rs971814 1 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 3.7 2.7 2.7 1.7 1.4 1.1 2.1 0.7 2.7 rs6670302 1 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 0 0.1 1.2 0.4 0.1 2.4 3.8 4.4 0.6 rs10908776 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 3.4 5.2 3.9 2.7 4.7 3.1 3.3 3 1.1 rs2143091 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 3.8 3.5 3 4.8 4.2 3.7 3 4 0 rs17521970 1 188,831 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 4.1 3.5 2.8 2.1 2.1 1.2 3.1 1.6 2.5 rs17583313 1 194,641 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 1 1.2 1.1 2.3 2.2 2.3 0.6 1.5 0.9 rs11579522 1 195,999 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 2.8 3.5 4.7 1.8 2.7 3.5 3.2 2.3 1.2 rs1355391 1 222,592 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 3.1 2.7 2 3.2 2.8 1.9 4.4 5.4 0.4 rs41360247 2 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 9.6 15 12 11.9 17.7 14 14.6 16.5 0.1 rs4245791 2 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 14.5 19.9 17 15.4 21.5 17 19.1 20.8 0.8 SNP_A-1963469 2 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 1.9 4 1.3 1.6 3.9 1 4.2 4.2 0.4 rs6738590 2 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 2.9 2.8 4.6 1.5 1.7 3 3.3 2 1.8 rs6739734 2 176,073 1.4 1 0.23 960 612 72 4.3 3 5 2.2 1.6 2.8 3.5 1.5 2.8 3.7 2.3 3.6 1.5 1 1.6 2.4 0.6 3 rs2461741 2 176,156 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 3.3 1.7 3.3 1.6 0.7 1.7 2.9 1.3 2.5 rs1898906 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 4.2 4.7 3.5 4.2 4.8 3.3 3.5 3.6 0.4 rs6750111 2 228,683 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 2.3 1.1 1.9 4.7 2.1 3.7 2.4 5.7 0.9 rs17011226 3 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 4 3.6 4.2 4 3.5 4.1 2.5 2.3 0.5 rs16860868 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 5.3 4.6 4.4 5.2 4.5 4.1 4.5 4.3 0.7 rs4413348 3 143,816 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 2.9 1.9 3 1.7 1.1 1.8 3 2.1 1.9 rs1990805 3 171,672 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 1.4 1.5 3 2.1 2 4.1 3.7 5.1 0.1 rs4425233 3 190,751 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 2.2 0.4 2.9 1.4 0.1 2 5.1 4.1 1.1 rs17276327 4 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 4.1 4.6 3.3 1.6 2.7 1.2 3.2 1.7 3.4 rs3860694 4 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 4.5 5.5 3 3.8 5.1 2.2 4.6 4.5 0.9 rs17088961 4 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 4.6 5.4 4.7 2.8 4 2.9 3.7 2.4 2.2 rs11097119 4 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 2.7 1.7 2.2 4.9 2.8 3.9 1.8 3.9 0.8 rs17800095 4 109,851 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 0.9 0.7 0.1 1.7 1.2 0.4 2 3.1 0.4 rs7690517 4 158,393 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 1.8 1.4 2.6 4.2 2.7 5.2 2 4.5 1.2 rs10041522 5 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 3 3.1 2.4 3.8 3.7 3 1.7 2.3 0 rs25946 5 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 1.9 1.4 3 2.9 2 4.2 2.4 3.5 0.1 rs1550826 5 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 1.6 1.3 2.6 1.9 1.4 3 2.4 2.9 0.2 rs6875519 5 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 0 0.1 1.4 0.3 0 2.4 4.6 5 0.3 rs2452753 5 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 1.8 1.1 3.2 1 0.6 2.2 2.3 1.4 1 rs2220621 5 124,915 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 3.6 4.2 3.9 1.4 2.4 1.6 2.3 0.9 2.9 rs3861862 5 141,823 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 2.9 2.6 4.4 2 2 3.4 3 2.1 1.2


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rs6898504 5 143 0.2 0.99 0.262 880 636 107 1.9 1.8 2.5 4.8 3.7 5.5 1.7 5.3 1.7 1 1.1 1.2 2.8 2.3 2.9 0.7 3 1.2 rs10068047 5 144,082 0.3 0.98 0.389 596 783 238 2.6 4.8 2.5 1.6 4 1.5 5.4 5 1.7 1 2.5 1 0.3 1.8 0.3 3.5 3.3 1.7 rs3734661 6 90,708 0 1 0.011 1602 37 0 3.8 3.1 4.9 2.8 2.3 3.8 4.5 3.8 1.2 3.8 3.1 4.9 2.8 2.3 3.8 4.5 3.8 1.2 rs763415 6 107,745 0.4 1 0.184 1098 482 61 5.7 3.8 3.9 3.2 2.2 1.8 3.2 1 2.7 4.7 3.2 3.2 2.7 1.9 1.5 2.6 0.9 2.1 rs1932107 6 130,512 1 0.99 0.356 661 777 191 4.8 3.7 3.9 5.5 4 4.2 2.8 3.1 0.2 4.6 3.7 4.1 4.9 3.8 4.2 2.6 2.6 0.2 rs2253833 7 106,8 0 0.97 0.252 890 601 101 4.6 3.1 2.7 3.1 2 1.4 4.6 3.4 1.8 3.7 2.8 2.6 2 1.6 1.1 3.4 2 2.4 rs1567725 8 5,561 0.1 1 0.198 1054 525 63 3.2 4.7 3.3 3.4 5.1 3.5 2.8 2.8 0.2 2.5 4 2.6 3 4.7 3.1 2.3 2.6 0 rs7824014 8 5,626 0.1 0.99 0.441 512 796 319 1.9 2.1 3.4 2.9 2.9 4.7 3.1 4.2 0.2 1.1 1.5 2.8 2.3 2.4 4.6 2.9 4.2 0.6 rs17715553 8 88,85 0.1 0.98 0.336 708 722 180 0.5 0.7 0.7 0.3 1.1 0.5 4.6 4 0.2 0.6 0.4 1.3 0.2 0.9 0.9 5.2 4.4 0.5 rs7049110 9 131,343 0.2 1 0.13 1246 367 30 3.3 4.6 3.4 2.8 4.3 2.8 3.4 3.1 1 2.8 4.1 2.9 2.5 3.9 2.5 2.8 2.7 0.8 rs306549 9 132,5 0 1 0.253 912 622 104 3.9 3 4.3 3.1 2.4 3.5 2.4 1.5 1 4.1 3 4.7 3.2 2.4 3.8 2.9 1.9 1.2 rs657152 9 133,169 0.1 0.95 0.373 613 736 215 2.3 2.5 1.7 3 3 2.1 1.6 2.4 0.1 4.2 4.2 3.1 5.1 4.8 3.5 3.5 4.6 0.1 rs10508888 10 44,528 0.4 0.99 0.09 1342 274 10 5.8 4.2 5.2 2.2 1.9 1.9 5.5 2.1 4.8 5.2 3.6 4.4 1.8 1.4 1.4 4.8 1.6 4.8 rs17727885 10 127,218 0.7 0.99 0.103 1302 311 12 0.2 1.7 0.2 0 1.6 0.5 5.2 5.3 0.5 0.3 1.7 0.2 0.1 1.6 0.4 5 5 0.4 rs5019888 11 18,826 0.1 0.99 0.333 729 719 184 1.2 3.3 1.3 1.2 3.6 1.2 3.9 4.1 0.1 1.1 3.3 1.1 1 3.6 1 4.9 5 0.1 rs3026393 11 31,769 0.1 1 0.49 424 829 391 2.4 3.8 2.4 3.6 5 3.5 3.3 4.3 0.3 1.2 1.4 1.4 1.6 1.7 1.8 0.7 1 0.1 rs4756076 11 33,861 0.4 1 0.26 903 620 117 4.9 4.5 3 2.8 3 1.3 4.1 2.5 2.3 3.3 3.3 1.7 2.7 2.9 1.2 3.2 2.8 0.7 rs4766333 12 5,032 0.1 1 0.312 774 708 157 2.5 0.2 1.5 1.5 0.2 0.7 5.3 3.8 0.9 2.9 0.4 2.1 1.4 0.1 0.9 5 2.9 1.6 rs4466933 12 53,633 0.7 0.99 0.074 1386 231 5 4.7 3.8 4.3 5.4 4.1 4.7 4.1 5.2 0.2 3.8 3.1 3.6 4.4 3.5 4 3.3 4.3 0.1 rs10847818 12 128,259 0.5 0.95 0.04 1447 118 4 4.5 3.6 2.9 5 3.8 2.9 3.7 4.4 0.2 4.2 3.4 2.7 5 3.8 3 3.6 4.7 0 rs12435767 14 61,605 0.2 1 0.067 1429 209 6 4.5 3.6 3.2 2.7 2.4 1.6 3.5 2.2 2.6 4.7 3.9 3.5 2.9 2.6 1.9 3.8 2.5 2.6 SNP_A-2298008 15 76,335 0.2 0.99 0.112 1282 329 18 1.4 1.8 2.9 2.8 2.8 4.7 2.3 3.5 0.5 1 1.4 2.4 2.3 2.6 4.3 2.2 3.4 0.8 rs6502764 17 3,848 0.5 0.99 0.368 643 778 212 3.7 4.2 3.6 1.6 2.5 1.6 3.8 2.3 3.1 3.8 5.4 3.9 1.5 3.4 1.6 4.3 2.8 3.7 rs4985687 17 5,615 0 0.99 0.461 472 808 344 0.5 0.1 0.5 0.1 0.1 1.2 3.6 3.9 0.8 0 0.3 1.6 0.5 0.8 3 4.4 5.2 0.7 rs197912 17 42,345 0.9 1 0.357 693 726 224 1.3 1.5 2.6 2.8 2.6 4.6 2.4 4.2 0.9 0.8 1 1.8 1.7 1.7 3.1 1.4 2.3 0.7 rs17202347 18 16,849 0.3 0.98 0.04 1482 128 1 3.8 2.6 4.5 3.3 2.3 4 3.1 2.3 0.5 3.7 2.7 4.6 3.2 2.3 4 3.2 2.3 0.5 rs10406145 19 4,643 0.3 1 0.283 847 652 137 4.7 3.1 2.5 2.3 1.6 0.8 3.6 1.7 2.8 5.1 3.5 3 3 2.1 1.3 3.8 1.9 2.4 rs2585450 20 52,181 0.1 1 0.467 468 815 361 3.5 0.8 1.8 1.8 0.1 0.6 5.4 3.6 2.2 3.3 1.6 1.9 2.3 1 1 3.4 2.5 1.3 rs470094 22 42,619 0.3 1 0.464 478 802 361 1.2 2.1 1.8 2.3 3.3 3.1 3.5 5.2 0.6 0.8 1.8 1.9 1.4 2.5 2.9 3.3 4.1 0.3 rs12008496 X* 40,03 0 0.95 0.043 723 66 1 0.4 0.2 0.8 1 0 0.5 4.8 5.7 0.6 0.4 0.2 0.8 1 0 0.5 4.3 5.1 0.5 rs4370708 X* 95,669 0.4 1 0.498 203 428 200 2.8 1.9 2.3 1.7 1.2 1.3 2.1 1 1.4 6 4.7 4.8 3.2 2.8 2.4 5.2 2.6 3.2 rs5907655 X* 139,611 1.4 1 0.452 234 440 154 3.6 3.6 3.5 5.5 4.9 5 2.7 4.5 0.3 3.2 3.6 3.6 5 5.1 5.2 2.5 3.9 0.2 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.


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Supplementary Table 4 Details on SNPs from stage 2 validation additive model recessive model SNP ID chr base hwe call maf aa ab bb ca si br ca/ch si/ch br/ch ma ma/ch ch ca si br ca/ch si/ch br/ch ma ma/ch ch rs971814 1 5,641 0 0.97 0.44 352 549 218 0.1 0.4 0 0.1 0.2 0.3 0.4 0.5 0.6 0.1 0 0.3 0.4 0.3 0.7 0.1 0.2 0.7 rs10908776 1 157,147 0 0.99 0.066 998 140 5 1.3 1.1 0.7 0.6 0.6 0.1 1.2 0.6 0.9 1.1 0.8 0.5 0.3 0.3 0 1 0.5 1 rs2143091 1 165,548 0.3 0.94 0.405 380 537 173 0.1 0.5 0.5 0.3 0.8 0.8 0.5 0.4 0.5 0.2 0.4 0.4 0.4 0.6 0.6 0.1 0.2 0.3 rs17521970 1 188,831 0.2 0.95 0.252 617 408 73 0.3 0.6 0.5 1.1 1.3 1.2 0.5 0.9 0.7 0.6 0.7 0.5 1.2 1.3 1.1 0.3 0.6 0.5 rs17583313 1 194,641 0.5 0.98 0.463 335 548 252 0.9 0.9 1.1 0.8 0.8 0.9 0.2 0.1 0.2 1.6 1 1.2 1.5 1 1 0.6 0.4 0.2 rs11579522 1 195,999 0 0.98 0.187 752 345 40 0.3 0.2 0 0.1 0.4 0.2 0.9 0.8 0.4 0 0.4 0.2 0.1 0.7 0.4 0.7 0.7 0.3 rs1355391 1 222,592 2 0.99 0.31 526 526 91 0.2 0.3 0.4 0.3 0.1 0.1 0.1 0.1 1.2 0.1 0.1 0.2 0.5 0.3 0.4 0 0 1.4 rs11887534 2 43,92 0.1 0.96 0.069 966 145 4 8.8 9.2 8.6 7.5 8.5 7.1 8.9 8.3 1.4 8.9 9.2 8.8 7.9 8.6 7.5 8.8 8.3 1.3 rs41360247 2 43,927 0.2 0.99 0.072 988 150 7 8.3 9.2 8.3 6.7 8.3 6.6 8.8 8.1 1.6 8.2 9.2 8.6 6.9 8.4 7.1 8.7 8.1 1.4 rs4245791 2 43,928 0.4 0.98 0.32 516 504 109 20.4 26.1 22 19 25.7 19.8 25.8 26.2 1.8 18.5 24.1 20 17 23.6 18 23.3 23.4 1.8 rs4148217 2 43,953 0 0.95 0.183 733 331 36 3.1 5.7 3.8 3.3 6.3 4 5.5 6 0.2 3.6 6 3.9 4 6.7 4 5.5 6.1 0.2 SNP_A-1963469 2 59,471 0 0.99 0.196 737 359 44 0.1 0 0.1 0 0 0.1 0 0 0.1 0.1 0.1 0.1 0.2 0.3 0.2 0 0.1 0.3 rs6738590 2 64,971 0.5 0.98 0.215 691 396 46 0.9 0.8 1.1 1 0.9 1.3 0.2 0.2 0 0.8 1 1.4 1.2 1.3 1.8 0.6 0.6 0.1 rs6739734 2 176,073 0 0.98 0.218 693 387 54 0.5 0.6 0.2 0.5 0.6 0.2 0.5 0.6 0.1 0.9 1.1 0.3 0.8 1 0.2 1 1.2 0.2 rs2461741 2 176,156 0.8 0.99 0.28 602 442 99 1.3 0.8 0.8 0.6 0.4 0.2 1.2 0.6 1.2 1.5 1.1 0.6 0.8 0.7 0.1 1.3 1 0.9 rs1898906 2 188,718 0.2 0.98 0.034 1057 76 0 0.1 0.3 0 0.1 0.2 0 0.2 0.2 0.1 0.1 0.3 0 0.1 0.2 0 0.2 0.2 0.1 rs6750111 2 228,683 0.3 0.99 0.477 317 559 265 0 0.6 0.1 0.1 0.5 0 0.3 0.3 0.2 0.1 0.3 0 0.2 0.3 0.1 0.4 0.3 0 rs17011226 3 22,359 0.1 0.98 0.314 532 492 110 0.3 0.2 0 0.2 0.1 0.1 0.2 0.3 0 0.1 0.1 0.1 0 0 0.2 0.1 0.1 0 rs4413348 3 143,816 0.3 0.98 0.42 387 544 206 0.4 0.9 1.4 0.4 1 1.5 1.4 1.4 0.1 0.5 1.1 1.6 0.6 1.2 1.7 1.5 1.5 0.1 rs1990805 3 171,672 0.5 0.99 0.161 808 298 34 0.5 0.5 0.3 1.2 1 0.9 0.6 1.4 0.5 0.6 0.6 0.4 1.5 1.2 0.9 0.8 1.7 0.5 rs4425233 3 190,751 0.8 0.97 0.12 875 227 21 1.4 1.9 2.5 1.8 2.3 3.1 2.4 2.4 0 1.1 1.4 1.9 1.7 1.9 2.7 1.8 1.9 0.1 rs17276327 4 12,002 0 0.98 0.416 388 551 198 0.9 0.5 0.6 0.5 0.3 0.3 0.3 0.2 0.5 0.5 0.3 0.4 0.5 0.2 0.4 0.2 0.3 0.1 rs3860694 4 68,706 0.5 0.98 0.476 303 582 248 0.1 0.1 0.6 0 0 0.4 0.5 0.4 0.2 0.1 0.2 0.1 0.4 0.3 0 0.1 0.2 0.2 rs17088961 4 68,719 0.3 0.96 0.274 591 436 88 0.4 0.1 0.1 0.3 0.1 0 0.2 0.2 0.1 0.5 0.4 0.2 0.4 0.2 0.1 0.2 0.2 0.3 rs11097119 4 88,047 0.6 0.99 0.102 919 217 8 0.6 0.6 0.1 1.3 1 0.1 1.8 2.2 0.4 0.6 0.5 0.1 1.1 0.8 0 1.7 2.1 0.2 rs7690517 4 158,393 0.1 0.98 0.31 542 484 111 0.9 0.5 0.2 0.4 0.2 0.1 1.1 1 0.7 1 0.5 0.3 0.4 0.1 0.1 0.9 0.7 1 rs10041522 5 2,268 0.2 0.99 0.158 805 309 26 0.2 0 0.1 0.1 0.1 0 0.2 0.2 0.3 0.1 0.1 0 0 0.1 0 0.1 0.1 0.2 rs25946 5 14,84 0.4 0.98 0.439 363 544 224 0.1 0.2 0.2 0.1 0 0 0 0.1 0.3 0.1 0 0.2 0.1 0.2 0 0.1 0.1 0.2 rs1550826 5 14,894 7.8 0.98 0.375 488 440 204 0.1 0.1 0 0.1 0.1 0.1 0 0.1 0.1 0.3 0.3 0.1 0.3 0.3 0 0.1 0.1 0.1 rs6875519 5 75,878 0.1 0.97 0.275 594 447 86 0.3 0 0.2 0.1 0.1 0 0.2 0 0.5 0.6 0.4 0.5 0.1 0 0.1 0.3 0 1 rs2452753 5 86,212 0.6 0.99 0.325 513 522 112 1.5 1.2 1.6 1.7 1.3 1.6 1 1.4 0.1 1.7 1.4 2.1 1.7 1.5 2.1 1.5 1.7 0.1 rs2220621 5 124,915 0 0.99 0.023 1092 52 0 0.3 0.1 0.1 0.3 0.2 0.1 0 0 0 0.3 0.1 0.1 0.3 0.2 0.1 0 0 0 rs3861862 5 141,823 0.3 0.98 0.102 913 213 9 0.1 0.1 0.4 0 0 0.7 0.9 1.1 0.3 0.2 0.1 0.2 0 0.1 0.4 0.4 0.6 0.3 rs6898504 5 143 0.1 0.99 0.262 620 446 76 0.1 0.3 0.1 0.2 0.3 0.2 0.5 0.4 0.1 0.1 0.1 0.2 0.3 0 0.4 0.1 0.1 0.3


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rs10068047 5 144,082 0.2 0.99 0.437 366 555 221 0.2 0.1 0.1 0.3 0.2 0 0.1 0.1 0.1 0 0.3 0.3 0.2 0.5 0.1 0.4 0.4 0.5 rs3734661 6 90,708 1.1 0.98 0.009 1120 18 1 0.1 0.2 0.1 0 0.1 0 0.1 0.1 0.4 0.2 0.3 0.2 0 0.1 0.1 0.1 0.1 0.4 rs763415 6 107,745 0.4 0.97 0.184 742 346 33 0.3 0.3 0.5 0.4 0.4 0.6 0 0.1 0.1 0.2 0.1 0.3 0.3 0.2 0.4 0 0 0.2 rs1932107 6 130,512 0 0.99 0.357 473 522 147 0 0.2 0.4 0.1 0.3 0.6 0.4 0.4 0.3 0.1 0 0.2 0.1 0.2 0.6 0.4 0.3 0.7 rs1567725 8 5,561 0 0.98 0.204 722 368 48 0.5 1.2 1.3 0.4 1.2 1.2 1.7 1.7 0.2 0.3 1.2 1.3 0.3 1.3 1.2 2.1 2 0.1 rs7824014 8 5,626 0.3 0.94 0.452 334 528 229 1.3 0.8 0.7 0.5 0.3 0.1 0.8 0.3 1.1 0.6 0.4 0.2 0.1 0.1 0.1 0.3 0.1 0.9 rs7049110 9 131,343 0.6 0.98 0.127 871 244 23 0.1 0.1 0.3 0 0 0.2 0.1 0.2 0.1 0.2 0.3 0.5 0 0.1 0.3 0.1 0.3 0.2 rs306549 9 132,5 0.1 0.98 0.249 642 420 72 0.7 0.7 1.2 0.2 0.3 0.5 0.6 0.2 1 0.6 0.6 1.1 0.2 0.3 0.6 0.8 0.4 0.7 rs657152 9 133,169 1.2 0.99 0.353 494 493 157 3.2 1.8 1.4 2.1 1.1 0.6 3.1 2.2 1.2 4.7 3.1 2.5 3.3 2.1 1.3 4.3 3 1.5 rs5019888 11 18,826 0.3 0.98 0.328 519 490 128 0.2 0.1 0.1 0.2 0.1 0 0.1 0.1 0 0.1 0.1 0.1 0.7 0.4 0.3 0.2 0.2 1.1 rs4756076 11 33,861 1.5 0.96 0.275 571 474 70 0.3 0.4 0.9 0.4 0.5 1.1 0.9 1.2 0 0.3 0.6 1.2 0.4 0.7 1.4 1.3 1.5 0 rs4466933 12 53,633 0.5 0.98 0.083 958 168 10 0 0.2 0 0.2 0.4 0.2 0.1 0.1 0.4 0.2 0.1 0.1 0.1 0.3 0.2 0.2 0.1 0.7 rs10847818 12 128,259 0 0.98 0.027 1075 62 0 1.2 0.5 0.6 0.3 0 0 0.8 0.3 1.7 1.2 0.5 0.6 0.3 0 0 0.8 0.3 1.7 rs12435767 14 61,605 0.8 0.99 0.068 986 152 2 0.3 0 0.7 0.1 0.3 0.2 0.7 0.5 1 0.3 0 0.7 0.1 0.3 0.2 0.6 0.4 1 SNP_A-2298008 15 76,335 0.5 0.98 0.121 878 235 20 0.3 0.3 0.1 0.3 0.3 0.1 0.1 0 0.2 0.6 0.6 0.3 0.6 0.6 0.2 0.1 0.1 0.1 rs4985687 17 5,615 0.1 0.98 0.447 346 567 225 0.2 0.1 0.3 0 0 0.1 0.1 0.2 0.2 0.4 0.3 0.6 0.3 0.2 0.5 0.2 0.2 0.1 rs197912 17 42,345 0.3 0.98 0.354 466 530 136 0.7 0.2 0.2 0.2 0.1 0.1 0.8 0.7 0.7 0.1 0.2 0.2 0.1 0.4 0.5 0.6 0.6 0.4 rs17202347 18 16,849 0.2 0.97 0.03 1052 68 0 0.3 0.2 0 0.1 0.4 0.1 1.3 1.1 0.3 0.3 0.2 0 0.1 0.4 0.1 1.3 1.1 0.3 rs10406145 19 4,643 0.9 0.99 0.278 607 440 99 0.1 0.1 0.3 0 0.3 0.6 0.8 0.8 0.5 0.2 0 0.2 0.1 0.2 0.7 0.7 0.6 0.8 rs2585450 20 52,181 0.1 0.99 0.459 337 560 244 0.3 0.1 0.4 0.9 0.3 0.9 0.2 0.4 0.5 0 0.3 0 0.2 0.1 0.3 0.2 0.2 0.6 rs470094 22 42,619 0.2 0.98 0.449 342 572 225 0.3 0.1 0.1 0.1 0.1 0.1 0.3 0.2 0.3 0.2 0 0.1 0 0 0.2 0.2 0.1 0.1 rs12008496 X* 40,03 0 0.91 0.04 511 45 0 0.3 0 0.1 0.4 0 0.1 0.3 0.2 0 0.3 0 0.1 0.4 0 0.1 0.3 0.2 0 rs4370708 X* 95,669 0,1 0.98 0.461 175 293 129 0.2 0.3 0.2 0.4 0.5 0.1 0.9 1.1 0.1 0.6 0.9 0.2 1.1 1.4 0.5 0.7 0.9 0.2 rs5907655 X* 139,611 0,2 0.99 0.458 179 292 129 0.3 0.3 0.2 0.6 0.5 0.4 0 0 0.4 0.3 0.1 0.2 0.3 0.1 0.2 0.2 0.2 0

Yellow: P<0.01; Red: P<0.001. Otherwise see legend to Supplementary Table 3


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Supplementary Table 5 Details on SNPs from stage 3 validation in CARLA additive model recessive model SNP ID chr base hwe call maf aa ab bb ca si br ca/ch si/ch br/ch ma ma/ch ch ca si br ca/ch si/ch br/ch ma ma/ch ch rs11887534 2 43919751 0.1 0.98 0.058 1544 190 6 9.5 11.7 11.7 9.3 12.6 11.4 10.1 10.7 0.6 8.8 10.2 11 8.9 11.1 11 9 9.6 0.5 rs41360247 2 43927160 0.3 0.99 0.056 1569 182 7 8.7 10.6 11.1 8.1 11 10.5 9.5 9.6 0.7 8.1 9.2 11 7.8 9.7 10.1 8.4 8.6 0.6 rs4245791 2 43927935 1.8 0.96 0.326 794 702 203 17.4 22.9 19.3 19.1 25.4 20.1 24.2 28.4 0.3 15.4 19.7 15 16.3 21.5 15.6 20.3 23 0.4 rs4148217 2 43952937 0.2 0.98 0.183 1158 529 55 4.3 5.5 3.1 6.6 7.2 4.3 4.5 6.4 0.5 4.2 5.4 2.6 7 7.4 4 4.3 6.5 0.8 rs4425233 3 190751500 0 0.97 0.131 1295 392 29 0.7 0.4 0.1 0.1 0.1 0.2 0.4 0.2 0.9 0.8 0.2 0.1 0.2 0 0.2 0.5 0.3 0.8 rs11097119 4 88046712 7.2 0.99 0.094 1423 329 0 0.6 0.3 0 0.4 0.2 0.1 0.5 0.4 0.2 0.6 0.3 0 0.4 0.2 0.1 0.5 0.4 0.2 rs2452753 5 86211684 0.9 0.97 0.312 805 770 154 0.4 1.2 1.1 0.5 1.3 1.2 0.9 0.9 0 1.2 2 1.6 0.8 1.9 1.3 1.3 1.2 0.4 rs1567725 8 5560754 0.9 0.98 0.189 1139 556 53 1.1 0.2 0.5 0.4 0.1 0.1 0.8 0.5 1.1 0.9 0.2 0.6 0.1 0.1 0.1 0.5 0.3 1.4 rs657152 9 133168819 0.3 0.98 0.412 609 829 302 3.7 2.1 2.5 1.8 1.2 1.2 1.8 0.7 2 4.5 2 2.3 2.4 1.1 1.1 2.9 1.5 2.1

Yellow: P<0.05 Red: P<0.05/9. Otherwise see legend to Supplementary Table 3


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Geometric mean and standard error per genotype for phytosterols and cholesterol (in mg/L)

SNP Allele CA SI BR CA/CH SI/CH BR/CH CH

rs41360247 Hom. major T/T

5.9 (1.01)

2.3 (1.01)

0.60 (1.01)

2.7 (1.005)

1.1 (1.01)

0.28 (1.01)

2162 (1.003)

Het. T/C

5.1 (1.01)

1.8 (1.02)

0.50 (1.02)

2.4 (1.01)

0.85 (1.02)

0.23 (1.02)

2132 (1.01)

Hom. minor C/C

4.6 (1.05)

1.4 (1.12)

0.46 (1.06)

2.3 (1.05)

0.68 (1.11)

0.22 (1.06)

2038 (1.02)

rs4245791 Hom. major T/T

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)

rs657152 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.


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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 CA/CH SI/CH BR/CH MANOVA/CH CH

rs41360247 -10% 4.4 x 10-14

-17% 3.0 x 10-18

-13% 1.1 x 10-15 1.3 x 10-29 -9%

4.4 x 10-13 -16%

1.5 x 10-18 -11%

2.1 x 10-14 2.6 x 10-31 -1.6% 0.065

rs4245791 11% 2.3 x 10-39

19% 1.7 x 10-52

13% 4.1 x 10-43 2.4 x 10-55 10%

1.5 x 10-41 18%

1.3 x 10-56 12%

1.6 x 10-43 5.1 x 10-62 0.8% 0.068

rs657152 8% 9.4 x 10-13

9% 2.4 x 10-8

6% 4.9 x 10-7 2.8 x 10-10 6%

2.2 x 10-10 7%

5.1 x 10-7 5%

5.0 x 10-5 3.5 x 10-9 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.


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Details on SNPs from fine-mapping of the ABCG5/8 locus

additive model recessive model SNP ID chr base hwe call maf aa ab bb ca si br ca/ch si/ch br/ch ma ma/ch ch ca si br ca/ch si/ch br/ch ma ma/ch ch rs4148189 2 43901034 0.4 0.98 0.118 1356 353 28 0.3 0.9 0.8 0.4 1 0.8 0.6 0.6 0 0.3 0.9 0.6 0.3 0.9 0.6 0.6 0.6 0.1 rs10439467 2 43901850 0.2 0.97 0.066 1505 215 6 0.9 2 1.7 0.6 1.7 1.3 1.6 1.4 0.5 0.7 1.7 1.4 0.4 1.5 1.1 1.3 1.2 0.5 rs1864814 2 43902095 0.4 0.97 0.032 1616 109 0 0.6 0.7 0.7 0.6 0.8 0.7 0.2 0.2 0.1 0.6 0.7 0.7 0.6 0.8 0.7 0.2 0.2 0.1 rs4245786 2 43902624 0.2 0.98 0.233 1018 628 91 0 0.2 0.3 0.1 0.1 0.4 0.2 0.2 0.2 0.2 0 0.5 0.3 0 0.6 0.2 0.3 0.1 rs4073237* 2 43903376 0.3 0.98 0.07 1502 231 6 0.1 0.8 0.1 0.6 1.4 0.2 0.9 0.8 0.8 0 0.8 0.2 0.4 1.5 0.1 1.4 1.1 0.8 rs4148187* 2 43904392 1 0.97 0.384 671 783 271 0.4 0.5 0.8 0.4 0.4 0.7 0.8 0.8 0.1 0.8 0.6 1 0.6 0.4 0.7 1.1 0.8 0.4 rs4289236* 2 43907627 0.6 0.98 0.2 1122 541 77 0.4 0.7 0.6 1.2 1.4 1.3 0.2 0.5 0.8 0.2 0.4 0.6 1 1 1.4 0.1 0.4 1.1 rs4148185* 2 43909826 1.1 0.98 0.396 651 793 289 0.6 0.5 0.6 0.2 0.2 0.3 0.7 0.5 0.6 0.9 0.6 0.6 0.3 0.2 0.2 1 0.4 1.1 rs4131228* 2 43911623 0 0.97 0.018 1661 63 0 2.7 3.5 3.1 2.4 3.4 2.8 1.9 2 0.4 2.7 3.5 3.1 2.4 3.4 2.8 1.9 2 0.4 rs3806471* 2 43919678 1.1 0.99 0.343 774 758 224 1.9 2.6 1.7 1.5 2.3 1.5 3 3.1 0.4 2.2 2.7 1.7 1.7 2.3 1.3 3 2.7 0.6 rs11887534* 2 43919751 0.1 0.98 0.058 1544 190 6 9.5 11.7 11.7 9.3 12.6 11.4 10.1 10.7 0.6 8.8 10.2 11 8.9 11.1 11 9 9.6 0.5 rs4148202 2 43921323 4.7 0.95 0.452 549 746 388 0.6 0.5 0.3 0.6 0.5 0.3 0.3 0.5 0.1 0.6 0.5 0.2 0.3 0.3 0 0.4 0.2 0.5 rs10179921 2 43921795 3.4 0.98 0.058 1550 171 15 0.7 1 1.1 0.8 1 1.2 0.7 0.9 0 1 1.1 1.3 1.1 1.3 1.5 0.9 1.1 0 rs4148210* 2 43925143 0.7 0.99 0.409 625 819 306 2.9 3.3 2.6 2 2.7 1.8 3.2 2.9 0.9 3.1 3.2 2.2 1.9 2.4 1.4 3.2 2.5 1.1 rs4148211* 2 43925247 0.9 0.98 0.409 620 806 306 2.8 3.2 2.5 2 2.6 1.8 3.2 3 0.9 2.8 3 2.1 1.7 2.3 1.3 3 2.4 1.1 rs41360247* 2 43927160 0.3 0.99 0.056 1569 182 7 8.7 10.6 11.1 8.1 11 10.5 9.5 9.6 0.7 8.1 9.2 11 7.8 9.7 10.1 8.4 8.6 0.6 rs4245791* 2 43927935 1.8 0.96 0.326 794 702 203 17.4 22.9 19.3 19.1 25.4 20.1 24.2 28.4 0.3 15.4 19.7 15 16.3 21.5 15.6 20.3 23 0.4 rs17424122* 2 43928721 1 0.99 0.055 1572 176 9 3.3 3.5 2.6 6.1 5.4 4.2 2.8 5.6 1 2.9 3.1 2.3 5.7 5 3.9 2.3 5 1.1 rs10221914* 2 43930957 0.2 0.98 0.027 1642 92 0 0.9 1.1 0.7 0.9 1.2 0.7 0.4 0.5 0.1 0.9 1.1 0.7 0.9 1.2 0.7 0.4 0.5 0.1 rs35648030 2 43932266 0 0.98 0 1747 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 rs34754243 2 43933259 2.1 0.98 0.002 1730 6 1 1.6 1.5 1 0.6 0.9 0.4 0.8 0.3 1.3 1.5 1.2 1.2 0.7 0.8 0.6 0.6 0.2 1 rs6709904* 2 43933828 0.3 0.97 0.104 1378 316 21 0.9 0.9 1.1 1.3 1.2 1.4 0.3 0.4 0.2 0.8 0.8 1 1.4 1.1 1.4 0.2 0.4 0.3 rs10174731 2 43936173 4.8 0.97 0.486 500 771 452 2.2 2.3 2.6 2.3 2.5 2.7 2.7 3.4 0.1 4 3 4.1 3.7 3 3.7 4 4.5 0.4 rs6733452* 2 43948349 0 0.98 0.017 1689 59 0 2.3 2.4 3.1 1.6 2.4 2.5 1.3 1.1 0.7 2.3 2.4 3.1 1.6 2.4 2.5 1.3 1.1 0.7 rs4952688* 2 43950274 0.4 0.97 0.302 844 711 164 25 31.4 24.5 26.5 34 24.9 34.2 38.5 0.6 19.8 25.5 19 21 27.6 19.5 27.2 30.3 0.5 rs4148217* 2 43952937 0.2 0.98 0.183 1158 529 55 4.3 5.5 3.1 6.6 7.2 4.3 4.5 6.4 0.5 4.2 5.4 2.6 7 7.4 4 4.3 6.5 0.8 rs12468591* 2 43953519 0 0.98 0.051 1572 169 4 2.9 2.2 2.4 4.4 3 3.2 1.5 2.2 0.4 3 2.2 2.5 4.2 2.9 3.2 1.6 2.1 0.3 rs4245794* 2 43954353 0.3 0.97 0.117 1352 351 26 0.4 0.2 0.1 1 0 0.3 0.7 0.9 0.5 0.4 0.1 0.1 0.9 0 0.3 0.6 0.7 0.4 rs4245795* 2 43954375 0.1 0.98 0.07 1502 227 9 1.7 2.3 2.1 1.2 2 1.6 2.2 1.9 0.6 1.8 2.1 2.2 1.2 1.8 1.6 2.1 1.7 0.7 rs4953027 2 43954898 28 0.94 0.176 1204 339 123 0.6 0.2 0.9 0.1 0.1 0.4 0.8 0.3 1.1 0.3 0 0.5 0.1 0.3 0.2 0.5 0.2 0.7 rs4952689* 2 43954934 0 0.98 0.425 576 853 314 0.6 0.7 0.6 0.2 0.4 0.2 0.5 0.3 0.8 0.2 0.5 0.4 0.1 0.2 0.2 0.5 0.4 0.6 rs28517482 2 43955042 4.4 0.91 0.425 573 707 332 0 0.2 0.3 0.2 0.4 0.5 0.3 0.4 0.3 0.3 0.3 0.3 0.6 0.4 0.5 0 0.2 0.2 rs4148227 2 43955048 123.5 0.97 0.327 595 1124 0 0.4 0.2 0.4 0.7 0.4 0.7 0.1 0.4 0.3 0.4 0.2 0.4 0.7 0.4 0.7 0.1 0.4 0.3 rs4953028 2 43955331 0.1 0.96 0.446 521 851 335 0.1 0.2 0.2 0.1 0 0.4 0.2 0.2 0.4 0.5 0.7 0.1 0.3 0.6 0 0.3 0.2 0.3 rs6544718 2 43958429 0.4 0.99 0.219 1076 588 91 0.8 0.4 0.8 1 0.5 0.9 0.3 0.3 0 1.3 0.6 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.


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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)22 Rare variants 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)

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

normalized to cholesterol (x103); si/ch, mean and standard error of serum sitosterol normalized to cholesterol (x103); br/ch, mean and standard error of serum brassicasterol normalized to

cholesterol (x103); ch, mean and standard error of serum cholesterol.


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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)

Dose effect C/T 0.11 0.19 0.14 0.11 0.19 0.13 0.0045 p-value C/T 2.7 x 10-20 2.2 x 10-24 1.5 x 10-18 1.9 x 10-22 3.7 x 10-27 2.0 x 10-19 0.54 Dose effect G/A -0.11 -0.20 -0.17 -0.097 -0.19 -0.16 -0.013 p-value G/A 2.8 x 10-06 1.4 x 10-7 4.0 x 10-08 5.2 x 10-06 4.8 x 10-08 7.7 x 10-08 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

standard error of serum brassicasterol (mg/L); ca/ch, mean and standard error of serum campesterol normalized to cholesterol (x103); si/ch, mean and standard error of serum sitosterol

normalized to cholesterol (x103); br/ch, mean and standard error of serum brassicasterol normalized to cholesterol (x103); ch, mean and standard error of serum cholesterol.


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Explained variance of serum phytosterols by ABCG8 and ABO loci

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.

Trait explained variance (%) ABCG8 haplotypes

additive model

explained variance (%) blood group O vs. A,B,AB

explained variance (%) combined

ca 7.00 1.09 8.08 si 8.62 0.39 9.03 br 7.19 0.51 7.72 ca/ch 7.66 0.57 8.25 si/ch 9.61 0.20 9.83 br/ch 7.49 0.22 7.74 ch 0.05 0.35 0.4


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Phytosterol and cholesterol concentrations (mg/L) in relation to blood groups in CARLA (genetic determination)

Blood group ca si br ca/ch si/ch br/ch ch O (N=623) 5.44 (1.01) 1.91 (1.02) 0.58 (1.02) 2.71 (1.01) 0.95 (1.02) 0.296 (1.02) 2006 (1.01)A (N=777) 5.87 (1.01) 2.07 (1.02) 0.63 (1.02) 2.85 (1.01) 1.00 (1.02) 0.311 (1.01) 2067 (1.01)B (N=237) 5.72 (1.02) 1.98 (1.03) 0.61 (1.03) 2.84 (1.02) 0.98 (1.03) 0.311 (1.03) 2012 (1.01)AB (N=102) 5.77 (1.03) 1.98 (1.05) 0.60 (1.04) 2.82 (1.03) 0.97 (1.05) 0.298 (1.04) 2034 (1.02)p-value O vs. A,AB,B 7.6x10-5 0.015 0.0051 0.0020 0.056 0.042 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

error of serum campesterol normalized to cholesterol (x103); si/ch, mean and standard error of serum sitosterol normalized to cholesterol (x103); br/ch, mean and standard error of serum

brassicasterol normalized to cholesterol (x103); ch, mean and standard error of serum cholesterol.


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Phytosterol and cholesterol concentrations (mg/L) in blood donors (immunological determination)

Blood group ca si br ca/ch si/ch br/ch ch O (N=301) 5.17 (1.02) 2.26 (1.02) 0.67 (1.02) 2.84 (1.02) 1.25 (1.02) 0.370 (1.02) 1819 (1.02)A (N=296) 5.49 (1.02) 2.40 (1.02) 0.71 (1.02) 3.04 (1.02) 1.33 (1.02) 0.393 (1.02) 1810 (1.02)B (N=111) 5.48 (1.03) 2.38 (1.04) 0.72 (1.03) 3.02 (1.03) 1.32 (1.04) 0.395 (1.03) 1815 (1.03)AB (N=52) 5.49 (1.04) 2.39 (1.05) 0.69 (1.05) 3.01 (1.04) 1.32 (1.05) 0.378 (1.04) 1823 (1.04)p-value O vs. A,AB,B 0.011 0.044 0.031 0.014 0.030 0.021 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

(mg/L); br, mean and standard error of serum brassicasterol (mg/L); ca/ch, mean and standard error of serum campesterol normalized to cholesterol (x103); si/ch, mean and standard error of

serum sitosterol normalized to cholesterol (x103); br/ch, mean and standard error of serum brassicasterol normalized to cholesterol (x103); ch, mean and standard error of serum cholesterol.


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Metaanalysis of association of ABCG8 SNP rs41360247 with CAD

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.

Cohort Cases (n)

Controls (n)

Call rate

Cases MAF

Controls MAF

P-value (additive)

OR (95% CI) (additive)

P-value (recessive)

OR (95% CI) (recessive)

Angio-Lueb 2843 421 0.937 0.060 0.064 0.693 0.94 (0.69-1.28) 0.658 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 0.79 (0.65-0.95)

Fixed effects 13764 13630 0.955 0.057 0.065 1.3 x 10-5 0.84 (0.78-0.91) 2.3 x 10-6 0.82 (0.76-0.89) Random effects 13764 13630 0.955 0.057 0.065 4.6 x 10-5 0.84 (0.78-0.92) 7.5 x 10-5 0.83 (0.75-0.91)


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Metaanalysis of association of ABCG8 SNP rs4245791 with CAD

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


Cohort Cases (n)

Controls (n)

Call rate

Cases MAF

Controls MAF

P-value (additive)


P-value (recessive)


Angio-Lueb 2843 421 0.941 0.321 0.310 0.531 1.05 (0.90-1.24) 0.546 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 1.17 (1.04-1.31)



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Metaanalysis of association of ABO SNP rs657152 with CAD

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


Cohort Cases (n)

Controls (n)

Call rate

Cases MAF

Controls MAF

P-value (additive)


P-value (recessive)


Angio-Lueb 2843 421 0.944 0.409 0.400 0.636 1.04 (0.89-1.21) 0.507 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 1.06 (0.94-1.19)



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Replication of major genetic associations of serum phytosterol levels in CARLA with additional adjustment to LDL-cholesterol levels

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 Hardy-Weinberg 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 LDL-cholesterol.

Allelic effect and P value of association

Cohort SNP Gene Chr bp position MAF CR HWE CA SI BR

CARLA

rs41360247 ABCG8 2 43927160 0.056 0.990 0.50 -12%

2.0 x 10-8 -23%

2.0 x 10-10 -20%

8.2 x 10-11

(n=1760) rs4245791 ABCG8 2 43927935 0.326 0.957 0.016 10%

3.8 x 10-18 19%

2.3 x 10-23 14%

2.4 x 10-19

rs657152 ABO 9 133168819 0.412 0.984 0.50 5%

5.6 x 10-4 5%

0.059 5%

0.027


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Supplementary Figure 1: Q/Q plots for campesterol . (A) Additive model. (B) recessive model.

Supplementary Figure 1




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).

CA/CA CA/CT CT/CT CA/GA GA/GA CT/GA

7.06.05.04.03.0

0

rs11887534, rs4952688 haplotype

2.01.0C

ampe

ster

ol (m

g/L)

698 662 162 135 6 54





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.


Supplemental Material, Teupser et al 37 Sa

mpl

e ID

rs49

5302

1rs

6712

582

rs13

4252

60U

1-1

rs67

2805

3rs

6741

243

rs17

0317

00rs

1342

5681

U1A

-1rs

3452

0479

rs35

6362

39rs

3438

1269

U2-

1U

2-2

rs67

1054

4rs

1049

5909

rs67

5662

9rs

3806

471

rs11

8875

34 (D

19H

)rs

4245

789

rs38

0647

0rs

4148

202

rs10

1772

00

Pos

ition

[bp]

4391

6686

4391

6704

4391

6819

4391

6850

4391

6857

4391

6861

4391

7139

4391

7191

4391

7235

4391

7566

4391

7601

4391

7677

4391

7688

4391

7693

4391

7855

4391

8288

4391

8594

4391

9678

4391

9751

4392

0380

4392

0673

4392

1323

4392

1355

Major T C A C T A T T C C G A G G T G G T G G G T CMinor G T G T C G C A T T A C A A C A A G C A A G T# 1 H H H C H H T H C H H H H H T G G H G G H G C# 2 H H H H H H T H C H H H H H T G G H G G H H H# 3 T C A C T A T T C C G A G G H G G T G G H H C# 4 T C A C T A T T C C G A G G T G G T G G G T C# 5 H H H C H H T H C H H H H H T G G H G G H H H# 6 H H H C H H T H C H H H H H T G G H G G H H H# 7 T C A C T A T T C C G A G G T G G T G G G T C# 8 H H H H H H T H C H H H H H T G G H G G H H C# 9 T C A C T A T T C C G A G G T G G T G G G T C# 10 T C H C H G T H H H H A G G H G H T H G G H C# 11 H H H C H H T H C H H H H H H G G H G G H G T# 12 T C A C T A T T C C G A G G T G G T G G G T C# 13 G T G C C G T A C T A C A A H G G G G G A G C# 14 H H H C H H T H H H H A G G H G G T G G G H C# 15 H H H C H H T H C H H H H H H G G H G G H H C# 16 H H G C C G T A C T A C A A H G G G G G A G C# 17 H H H C H H T H C H H H H H T G G H G G H H C

rs41

4820

3rs

1017

9921

U10

-1U

11-1

4392

1386

4392

1795

4392

1845

4392

2480

C C C CT T G AC C C CC H C CC C C CC C C CC H C CC H C CC C C CC C C CC C C CC C H HC H C CC C C CC C C CC C H HC C C CC C C CC C C C

Sam

ple

ID

rs41

3602

47

rs42

4579

1

rs49

5268

8

rs41

4821

7 (T

400K

)

Pos

ition

[bp]

4392

7160

4392

7935

4395

0274

4395

2937

Major T T A CMinor C C T A# 1 T T A H# 2 T H H C# 3 T H H C# 4 T H H H# 5 T T A C# 6 T T A H# 7 T H H H# 8 T H H C# 9 T T A H# 10 H H H C# 11 T T A C# 12 T H H C# 13 T T A C# 14 T C T C# 15 H T A C# 16 T T A C# 17 T T H C



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.


Supplementary Figure 4A

B 1 2 3 4 5SNP-ID rs8176747 rs41302905 rs8176746 rs8176719 rs657152position [bp] 136.131.315 136.131.316 136.131.322 136.132.908 136.139.265alleles C/G C/T G/T C/- C/Aamino acid change Gly/Ala Gly/Arg Leu/Met Val/X -/-blood group change A/B A/0 A/B A/0 -/-

01

B

02

A ExonExon 77 ExonExon 66



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



A

B

C

0.6 0.8 1.0 1.2 1.6

Angio-LuebCARLA

ECTIM

Erlangen

GerMIFS II

GoKard

KORA-B

KORA-MI

LE-Heart

PopGen

WTCCC

Fixed effect

Random effects

0.8 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 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



Supplementary Figure 6: Fold change and 95% CI of LDL-cholesterol in CARLA for SNPslocated in ABCG8 and ABO genes using additive and recessive models, respectively.

0.8 0.9 1.0 1.1 1.2

rs41360247ABCG8

rs4245791

rs657152ABO

ABCG8

lower LDL-cholesterol higher LDL-cholesterol



P = 0.039

P = 0.078

P = 0.012




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

1Institute of Epidemiology, GSF - National Research Center for Environment and Health,

85764 Neuherberg, Germany. 2Chair of Epidemiology, IBE, University of Munich, 81377 Munich, Germany. 3Institute of Health Economics and Health Care Management, GSF-National Research

Centre for Environment and Health, 85764 Neuherberg, Germany. 4Institute of Human Genetics, GSF National Research Center for Environment and

Health, 85764 Neuherberg, Germany 5Institute of Human Genetics, Technical University, 81765 Munich, Germany



genetic regulation of serum phytosterol levels and … regulation of serum phytosterol levels and...

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