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RESEARCH ARTICLES◥

HEART DISEASE

Rare variant in scavenger receptor BIraises HDL cholesterol and increasesrisk of coronary heart diseasePaolo Zanoni,1* Sumeet A. Khetarpal,1* Daniel B. Larach,1*William F. Hancock-Cerutti,1,2 John S. Millar,1 Marina Cuchel,1

Stephanie DerOhannessian,1 Anatol Kontush,2 Praveen Surendran,3

Danish Saleheen,3,4,5 Stella Trompet,6,7 J. Wouter Jukema,7,8 Anton De Craen,6

Panos Deloukas,9 Naveed Sattar,10 Ian Ford,11 Chris Packard,12

Abdullah al Shafi Majumder,13 Dewan S. Alam,14 Emanuele Di Angelantonio,3

Goncalo Abecasis,15 Rajiv Chowdhury,3 Jeanette Erdmann,16 Børge G. Nordestgaard,17

Sune F. Nielsen,17 Anne Tybjærg-Hansen,18 Ruth Frikke Schmidt,19 Kari Kuulasmaa,20

Dajiang J. Liu,21 Markus Perola,20,22 Stefan Blankenberg,23,24 Veikko Salomaa,20

Satu Männistö,20 Philippe Amouyel,25 Dominique Arveiler,26 Jean Ferrieres,27

Martina Müller-Nurasyid,28,29 Marco Ferrario,30 Frank Kee,31 Cristen J. Willer,32

Nilesh Samani,33,34 Heribert Schunkert,35 Adam S. Butterworth,3

Joanna M. M. Howson,3 Gina M. Peloso,36 Nathan O. Stitziel,37 John Danesh,3,9

Sekar Kathiresan,36 Daniel J. Rader,1† CHD Exome+ Consortium,‡CARDIoGRAM Exome Consortium, Global Lipids Genetics Consortium

Scavenger receptor BI (SR-BI) is the major receptor for high-density lipoprotein (HDL)cholesterol (HDL-C). In humans, high amounts of HDL-C in plasma are associated with alower risk of coronary heart disease (CHD). Mice that have depleted Scarb1 (SR-BIknockout mice) have markedly elevated HDL-C levels but, paradoxically, increasedatherosclerosis. The impact of SR-BI on HDL metabolism and CHD risk in humans remainsunclear. Through targeted sequencing of coding regions of lipid-modifying genes in 328individuals with extremely high plasma HDL-C levels, we identified a homozygote for a loss-of-function variant, in which leucine replaces proline 376 (P376L), in SCARB1, the geneencoding SR-BI. The P376L variant impairs posttranslational processing of SR-BI andabrogates selective HDL cholesterol uptake in transfected cells, in hepatocyte-like cellsderived from induced pluripotent stem cells from the homozygous subject, and in mice.Large population-based studies revealed that subjects who are heterozygous carriers ofthe P376L variant have significantly increased levels of plasma HDL-C. P376L carriers havea profound HDL-related phenotype and an increased risk of CHD (odds ratio = 1.79, which isstatistically significant).

The strong inverse association betweenamounts of high-density lipoprotein (HDL)cholesterol (HDL-C) and coronary heartdisease (CHD) risk has generated interestin a potential causal relationship between

HDL metabolism and CHD. However, clinicaltrials with drugs that raise HDL-C levels, niacinand cholesteryl ester transfer protein (CETP)inhibitors, have produced disappointing results(1). Furthermore, recent studies of human geneticvariants that are associated with HDL-C levelshave generally failed to show association withCHD (2, 3).Most notably, a loss-of-function variantin LIPG, a gene encoding an endothelial lipasethat, in the heterozygous state, raises HDL-Cby ~5 mg/dl, was found to have no associationwith CHD (4). Although these previous studiessuggest that higher HDL-C levels may not becausally protective against CHD, we reasonedthat additional human genetic analyses might

provide mechanistic insight into the complexrelationship between HDL and CHD.The scavenger receptor class BI (SR-BI), encoded

by the gene SCARB1, was discovered to be anHDL receptor two decades ago (5). SR-BI pro-motes the selective uptake of HDL cholesterylesters (HDL-CEs) into cells, particularly hepato-cytes and steroidogenic cells (5, 6). In mice, over-expression of SR-BI in the liver reduces levels ofHDL-C (7–10), and genetic deletion of SR-BI re-sults in higher HDL-C levels (11–13). Remarkably,these geneticmanipulations inmice have effectson atherosclerosis opposite to those predicted byhuman epidemiological data: Overexpression re-duces atherosclerosis despite the lower HDL-Clevels (14–16), and gene deletion increases athero-sclerosis despite the higher HDL-C levels (17–20).One potential explanation relates to the flux ofcholesterol from macrophages through the re-verse cholesterol transport (RCT) pathway; SR-BI

overexpression increases macrophage RCT, andSR-BI knockout reduces macrophage RCT (21).The human relevance of these observations hasbeen unclear.

Identification of SCARB1 P376Lhomozygote and association withextremely high HDL-C

We hypothesized that humans with extremelyhigh levels of HDL-Cmay harbor loss-of-functionvariants in SCARB1 and undertook a targeted re-sequencing discovery experiment in 328 partic-ipants with very high HDL-C (>95th percentile,meanHDL-C of 106.8mg/dl) and a control groupof 398 subjects with lowHDL-C (<25th percentile,mean HDL-C of 30.4 mg/dl). In this cohort, wesequenced the exons of ~990 genes locatedwithin300 kb of each of the 95 loci with significant as-sociations (P < 5 × 10−8) with plasma lipid levelsidentified by the Global Lipids Genetics Consor-tium as of 2010 (22). Among the high HDL-Csubjects, we identified a homozygote for SCARB1P376L (g.125284671 G>A, c.1127 C>T, p.P376L,rs74830677), a 67-year-old female with anHDL-Cof 152 mg/dl, and confirmed this finding bySanger sequencing. This subject harbored no mu-tations in other high HDL-C genes such as CETPand LIPG. In addition to this homozygote, fourP376L heterozygotes were identified by targetedsequencing in the high HDL-C group; no hetero-zygotes were found in the low HDL-C group (P =0.008, Fisher’s exact test).To identify additional P376L carriers, we geno-

typed an expanded cohort of very high versus lowHDL-C subjects. Among 524 additional subjectswith very highHDL-C (meanHDL-C 95.0mg/dl),we identified 11 heterozygotes for P376L; whereasamong 758 subjects with lowHDL-C (meanHDL-C 33.5 mg/dl), we identified 3 heterozygotes. Intotal, our combined sequencing and genotypingfor discovery of the P376L variant showed thatthis variant is significantly overrepresented in sub-jects with high HDL-C [minor allele frequency(MAF) = 0.010 in high HDL-C versus 0.0013 inlow HDL-C controls, P = 0.000127, Fisher’s exacttest, Table 1].Because this variant is present on the exome

array,we expandedour analysis to theGlobal LipidGenetics Consortium exome array data in >300,000individuals. The P376L variant was very rare inthis population (MAF of ~0.0003). It was signifi-cantly associated with higher HDL-C levels with arelatively large effect size (beta = 8.4 mg/dl; P =1.4× 10−15).Notably, this variantwasnot associatedwith plasma levels of low-density lipoprotein cho-lesterol (LDL-C) or triglycerides (TGs) (table S1).Thus, we conclude that SCARB1 P376L is asso-ciated specifically with elevated HDL-C levels.

HDL-related phenotypes ofSCARB1 P376L homozygoteand heterozygotes

We next recruited the P376L homozygote, eightheterozygous carriers, and both high HDL-C andnormal HDL-C noncarrier controls for deepphenotyping of HDL metabolism and relatedtraits. All of the P376L study participants were of

RESEARCH

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European ancestry, almost exclusively of Ashke-nazi Jewish descent. Clinical characteristics andlipid profiles of the subjects are reported in Table2. Fast protein liquid chromatography (FPLC)analysis of plasma lipoproteins confirmed theincrease in large HDL particles in the homo-zygote (Fig. 1A). Cholesterol and apolipoproteinA-I (apoA-I) levels in HDL were significantlyincreased in the homozygote and heterozygotes

compared with controls, but HDL apoA-II levelswere not elevated (Table 2 and Fig. 1B). Therewere no differences between P376L carriers andcontrols in the absolute amount of HDL free cho-lesterol or the ratio of free-to-esterified cholesterolin their HDL (Fig. 1C). P376L heterozygotes had a2.8-fold increase and the homozygote a 6.1-foldincrease in large HDL-2b particles compared withnoncarrier controls (Fig. 1D). There was moreapoA-I (Fig. 1E and fig. S2) and apoC-III (Fig.1F) in largeHDL particles in the homozygote andheterozygous carriers. Cholesterol efflux capacitywas similar in carriers and controls (Fig. 1G). Incontrast to the infertility phenotype of Scarb1-deficient female mice (18), the P376L homozygotehad two healthy children and reported no fertilityimpairment.We also did not observe the steroido-genic or platelet phenotypes reported in Scarb1-deficient mice (see supplementary materials).

SCARB1 P376L results in completeloss of function of SR-BI

Given the profoundHDLphenotype of the P376Lcarriers, we sought to understand the impact ofthe variant on SR-BI function. We generatedinduced pluripotent stem cells (iPSCs) using pe-ripheral bloodmononuclear cells from the P376Lhomozygote and a noncarrier control. We nextdifferentiated these cells into hepatocyte-likecells (HLCs) to study HDL metabolism in thesetting of endogenous cellular SCARB1 expression.HLCs differentiated through this protocol reca-pitulate phenotypes of cultured primary hepato-cytes such as albumin and VLDL (very low densitylipoprotein) secretion (23–26). The cell lines fromthe control donor and the P376L homozygous sub-ject demonstrated expression of hepatocyte-specificgenes ALB (albumin) and AFP (alpha-fetoprotein)and exhibited comparableSCARB1gene expression(fig. S3). Compared with control iPSC hepatocytelines, those from the P376L homozygote demon-strated a profound reduction in selective choles-terol uptake from HDL in vitro (Fig. 2A). Similarresults were observed in experiments with COS7cells transfected with plasmids expressing wild-type (WT) or the P376L variant of SCARB1 (fig. S3,

A and B), along with defective binding to HDL invitro at 4°C (fig. S4, C and D).To evaluate the physiological impact of the

P376L variant onHDL-C levels and catabolism invivo, we used adeno-associated virus (AAV) vec-tors to direct hepatic overexpression ofWTSR-BIor the P376L variant inmice with depleted Scarb1[Scarb1 knockout (KO) mice]. The two groups ofmice demonstrated similar hepatic expressionlevels of Scarb1mRNA (fig. S5A) andSR-BI protein(fig. S5B). Mice expressing WT Scarb1 demon-strated a robust 73% decrease in HDL-C. In con-trast, mice expressing the P376L variant had noreduction in HDL-C; their HDL-C levels werecomparable to those in the control AAV-null in-jected mice (Fig. 2B). Although the clearance of125I-labeled HDL protein was not different amongthe three groups, the clearance of [3H]HDL-CEwas much slower in mice expressing the P376Lvariant compared with those expressing WT SR-BI andwas comparable to that in the controlmice(Fig. 2, C andD). SelectiveHDL-CE clearance fromplasma was increased by WT SR-BI but was un-detectable in the P376L-expressing mice (Fig. 2Eand fig. S5C), as was hepatic uptake of [3H]CE at24 hours (fig. S5D). This indicates that the P376Lsequence variant results in complete loss of thecanonical function of SR-BI—namely, selectiveuptake of HDL-CE.We hypothesized that the markedly reduced

HDL-CE uptake could be because of aberrant pro-cessing of the P376L SR-BI protein, which leadsto impaired cell surface localization. To test this,we isolated cell surface proteins from COS7 cellstransfected with WT and P376L SR-BI using bio-tinylation and found markedly reduced cell sur-face SR-BI in the P376L transfected cell lysatesafter streptavidin cell surface protein pull-downassays (fig. S4E). Given that SR-BI undergoes N-glycosylation in the endoplasmic reticulumconcom-itant with proper folding, we hypothesized thataltered posttranslational modification may under-lie its reduced cell surface localization (27–29).Wemeasured the molecular weights of SR-BI formsafter endoglycosidase-H (Endo-H) treatment oftransfected COS7 (fig. S4E) and iPSC-derivedHLC

SCIENCE sciencemag.org 11 MARCH 2016 • VOL 351 ISSUE 6278 1167

1Departments of Genetics and Medicine, Division of TranslationalMedicine and Human Genetics, Perelman School of Medicine,University of Pennsylvania, Philadelphia, PA 19104, USA.2INSERM UMR 1166 ICAN, Université Pierre et Marie Curie Paris6, Hôpital de la Pitié, Paris, France. 3CardiovascularEpidemiology Unit, Department of Public Health and PrimaryCare, University of Cambridge, Cambridge, UK. 4Department ofBiostatistics and Epidemiology, Perelman School of Medicine,University of Pennsylvania, Philadelphia, PA 19104, USA. 5Centrefor Non-Communicable Diseases, Karachi, Pakistan.6Department of Gerontology and Geriatrics, Leiden UniversityMedical Center, Leiden, Netherlands. 7Department of Cardiology,Leiden University Medical Center, Leiden, Netherlands. 8TheInteruniversity Cardiology Institute of the Netherlands, Utrecht,Netherlands. 9Wellcome Trust Sanger Institute, GenomeCampus, Hinxton, UK. 10Institute of Cardiovascular and MedicalSciences, British Heart Foundation, Glasgow CardiovascularResearch Centre, University of Glasgow, Glasgow, UK.11Robertson Center for Biostatistics, University of Glasgow,Glasgow, UK. 12Glasgow Clinical Research Facility, WesternInfirmary, Glasgow, UK. 13National Institute of CardiovascularDiseases, Sher-e-Bangla Nagar, Dhaka, Bangladesh.14International Centre for Diarrhoeal Disease Research,Mohakhali, Dhaka, Bangladesh. 15Center for Statistical Genetics,Department of Biostatistics, University of Michigan School ofPublic Health, Ann Arbor, MI 48109, USA. 16Institute forIntegrative and Experimental Genomics, University of Lübeck,Lübeck 23562, Germany. 17Department of Clinical Biochemistry,Herlev Hospital, Copenhagen University Hospital, Herlev,Denmark. 18Copenhagen University Hospital, University ofCopenhagen, Copenhagen, Denmark. 19Department of ClinicalBiochemistry, Rigshospitalet, Copenhagen University Hospitals,Copenhagen, Denmark. 20Department of Health, NationalInstitute for Health and Welfare, Helsinki, Finland. 21Departmentof Public Health Sciences, College of Medicine, PennsylvaniaState University, Hershey, PA 17033, USA. 22Institute ofMolecular Medicine FIMM, University of Helsinki, Helsinki,Finland. 23Department of General and Interventional Cardiology,University Heart Center Hamburg, Hamburg, Germany.24University Medical Center Hamburg-Eppendorf, Hamburg,Germany. 25Department of Epidemiology and Public Health,Institut Pasteur de Lille, Lille, France. 26Department ofEpidemiology and Public Health, University of Strasbourg,Strasbourg, France. 27Department of Epidemiology, ToulouseUniversity-CHU Toulouse, Toulouse, France. 28Institute ofGenetic Epidemiology, Helmholtz Zentrum München–GermanResearch Center for Environmental Health, Neuherberg,Germany. 29Department of Medicine I, Ludwig-Maximilians-University Munich, Munich, Germany. 30Research Centre inEpidemiology and Preventive Medicine, Department of Clinicaland Experimental Medicine, University of Insubria, Varese, Italy.31UKCRC Centre of Excellence for Public Health, QueensUniversity, Belfast, Northern Ireland. 32Department ofComputational Medicine and Bioinformatics, Department ofHuman Genetics, and Department of Internal Medicine,University of Michigan, Ann Arbor, MI 48109, USA. 33Departmentof Cardiovascular Sciences, University of Leicester, Leicester,UK. 34National Institute for Health Research (NIHR) LeicesterCardiovascular Biomedical Research Unit, Glenfield Hotel,Leicester, UK. 35Deutsches Herzzentrum München, TechnischeUniversität München, Munich, Germany. 36Broad Institute andCenter for Human Genetic Research, Massachusetts GeneralHospital, Boston, MA 02114, USA. 37Department of Medicine,Division of Cardiology, Department of Genetics, and theMcDonnell Genome Institute, Washington University School ofMedicine, St. Louis, MO 63110, USA.*These authors contributed equally to this work. †Correspondingauthor. E-mail: [email protected] ‡For each consortiumand study, authors and affiliations are listed in the supplementarymaterials.

Table 1. Association of SCARB1 P376L with HDL-C in high versus low HDL-C cohorts. Carriersof the P376L variant were ascertained from the Penn High HDL Study through two approaches,

targeted sequencing of the SCARB1 gene in a total of 726 subjects (328 high HDL-C and 398 low

HDL-C subjects) and genotyping on the exome array (Illumina) in an additional 1282 subjects (524 high

HDL-C subjects and 758 low HDL-C subjects). The association of the P376L variant with the high HDL-Ccohort from both approaches individually and combined together was tested using Fisher’s exact test.

N, number of participants; NonC, noncarriers; Het, heterozygotes; Hom, homozygotes.

Discovery cohort

High HDL-C

(>95th percentile) (N)

Low HDL-C

(<25th percentile) (N) Association

(P)Total NonC Het Hom Total NonC Het Hom

Targeted sequencing

of SCARB1328 323 4 1 398 398 0 0 0.008398

. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .

Exome array genotyping 524 513 11 0 758 755 3 0 0.005296. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .

Combined 852 836 15 1 1156 1153 3 0 0.000127. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .

RESEARCH | RESEARCH ARTICLES

lysates, as well as mouse liver lysates expressingWT or mutant SR-BI (Fig. 2, F and G). Higher-molecular-weight forms representN-glycosylationmodified Endo-H–resistant and partially sensitiveforms at the cell surface after modification byalpha-mannosidase II in the Golgi apparatus (28).

In the iPSC-derived differentiated HCLs from theP376L homozygote (Fig. 2F), we found much lesstotal cellular SR-BI in themutant cell lines relativeto that of WT cells, despite comparable SCARB1gene expression (fig. S3C). After Endo-H treatment,the SR-BI from SCARB1 WT cell and liver lysates

across models was predominantly the partiallysensitive form, along with small amounts of thefully resistant form. In contrast, the SR-BI from celland tissue lysates across P376L-expressing groupswas all the immature, fully Endo-H–sensitive form(Fig. 2, F and G, and fig. S4F). Together, these data


Table 2. Characteristics of SCARB1 P376L carriers and controls re-cruited for deep phenotyping. Demographic, plasma lipid, and apolipo-

protein traits measured from one P376L homozygote, eight heterozygotes,and noncarrier controls from subjects identified from sequencing or geno-

typing of the Penn High HDL Study cohort for deep phenotyping. Lipid

measurements from plasma were performed using an autoanalyzer. Whereapplicable, data are presented as means ± SD. Numbers correspond to

groups for comparison.Group 1, normal HDL-C controls; group 2, high HDL-Ccontrols; group 3, SCARB1 P376L heterozygotes.Tested: ANOVA or chi-square.

Groups: Comparison between groups by number with Tukey’s multiple com-

parison. *Significant at P < 0.05. **Significant at P < 0.05 by chi-square but not

ANOVA. Dash indicates no significant comparison. BMI, body mass index; PTA,phosphotungstate precipitation method; VLDL, very low density lipoprotein;

Lp(a), lipoprotein a.

MeasureGroup

P376L Hom

Significance

1 2 3 Tested Groups

Number of subjects 11 10 8 1 -.. .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .

Age (years) 61.6 (9.7) 64.2 (12.5) 67.5 (15.3) 65 n.s. -.. .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .

Sex (M/F) 6/5 5/5 6/2 0/1 n.s.** -.. .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .

BMI (kg/m2) 26.4 (2) 22.9 (1.3) 25.6 (3.9) 21 * 1/2.. .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .

TC (mg/dl) 185.8 (22.3) 215.8 (29.9) 228 (33.2) 280 * 1/3.. .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .

Glucose (mg/dl) 93.5 (2.9) 91.6 (7.0) 98.8 (5.3) 86 n.s. -.. .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .

LDL-C (mg/dl) 109.1 (17.3) 97.4 (21.6) 116.6 (27.1) 109 n.s. -.. .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .

HDL-C (PTA) (mg/dl) 51 (11.4) 110.1 (19.8) 86.9 (19.9) 152 * 1/2, 1/3, 2/3.. .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .

TG (mg/dl) 121.2 (35) 71.5 (32.3) 99.5 (23.7) 57 * 1/2.. .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .

Alcohol >1/day (n) 4 4 2 0 n.s.** -.. .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .

VLDL-C (mg/dl) 26.9 (8.8) 19 (6.2) 23.1 (9.2) 13 n.s. -.. .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .

Lp(a) (mg/dl) 22.3 (18.8) 19 (22.7) 15.9 (21.2) 17 n.s. -.. .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .

apoA-I (mg/dl) 172.2 (33.3) 241.7 (41.2) 229.6 (36.1) 327 * 1/2, 1/3.. .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .

apoA-II (mg/dl) 40.5 (7) 49.5 (11.5) 46.6 (5.5) 45 n.s. -.. .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .

apoB (mg/dl) 99.7 (13.4) 82.8 (17.1) 95.9 (18.2) 92 n.s. -.. .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .

apoC-II (mg/dl) 4.32 (1.55) 6.09 (2.69) 4.49 (2.17) 5.3 n.s. -.. .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .

apoC-III (mg/dl) 11.4 (4.3) 15.5 (6.9) 13.7 (2.7) 16.1 n.s. -.. .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .

apoE (mg/dl) 4.52 (0.89) 6.03 (1.86) 4.94 (1.12) 6.4 * 1/2.. .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .

Fig. 1. HDL composition and functionality in a SCARB1 P376L homozygote, heterozygous carriers, and controls. (A) FPLC fractionation of plasmalipoproteins from the P376L homozygote subject (red) and from a control with normal HDL-C. (B) Cholesterol, apoA-I, and apoA-II content in total HDL. (C) Freecholesterol (FC) and esterified cholesterol (CE) in total HDL (left) and the FC/CE ratio in total HDL (right). (D) HDL subclass concentrations after separation bydensity-gradient ultracentrifugation. (E) ApoA-I content in the same HDL subclasses. (F) ApoC-III content in the same HDL subclasses. (G) Cholesterol effluxcapacity from macrophages of the THP-1 cell line. All data are reported as means ± SD.


are consistent with amodel in which the P376Lsequence variant alters the endogenous post-translational N-glycosylation of SR-BI to preventeither transit from the ER to the Golgi or fur-ther posttranslational modifications in the Golgi,which ultimately result in reduced cell surfaceexpression.

SCARB1 P376L is associatedwith increased risk of CHDin humans

Despite a profound increase in HDL-C, SR-BI de-ficiency inmice causes accelerated atherosclerosis(17–20). The relationship of reduced SR-BI func-tion to atherosclerotic cardiovascular disease inhumans has not been established. The P376Lhomozygous subject did not have clinical CHD,but her carotid intimal-medial thickness (cIMT)was 0.789mm (left-right average), which is in the

>75th percentile for females of her age; in addi-tion, she had detectable plaque throughout theleft internal carotid artery and at the bifurcationof her right internal carotid artery. cIMTmeasure-mentswere not significantly different in the P376Lheterozygotes compared with both groups of con-trols (fig. S8), but because of small sample size, thestatistical power is limited.To achieve greater statistical power to address

this question, we performed a meta-analysis oflarge exome array genotyping studies of CHDcases and healthy controls to determine the re-lationship of the P376L variant with risk of CHD(Table 3). Among 16 sample sets from two con-sortia [the CARDIoGRAM Exome Consortiumand the CHD Exome+ Consortium], we testedthe association between P376L carrier status andCHD in 137,995 individuals. Across 49,846 CHDcases and 88,149 CHD controls, we found that

P376L carriers had a significantly higher risk ofCHD compared with noncarriers [odds ratio fordisease among carriers = 1.79; P= 0.018] (Table 3).Thus, carriers of this SCARB1 P376L variant havesignificantly increased HDL-C levels and a sig-nificantly increased risk of CHD.

Discussion

Studies ofmice haveprovided important insightsinto the effects of SR-BI on HDL metabolism,RCT, and atherosclerosis. These studies revealedthat overexpression of SR-BI reduces HDL-C(7–10) and reduces atherosclerosis (14–16), where-as gene deletion of SR-BI increases HDL-C (11–13)and accelerates atherosclerosis (17–20). The clin-ical relevance of these findings has remaineduncertain, however. Studies of injected labeledHDL-CE in humans suggested that the majorityof the HDL-CE was transported to the liver via


Fig. 2. SCARB1 P376L is a null variant in vitro and in vivo. (A) [3H]Cholesterol ether (CEt) uptake (left) and selective cholesterol uptake fromHDL(right) in iPSC-derived HLCs from the P376L homozygote versus a noncarriercontrol. Cells were incubated with [3H]CEt and 125I-labeled tyramine cellobiose(TC) dual-labeled human HDL. All values are normalized to relative ALB geneexpression in each cell line. All data represent mean values for wells ofrespective cell lines ± SD. (B) PlasmaHDLcholesterol levels before and 12 daysafter AAVadministration to Scarb1 KOmice. (C) [3H]Cholesterol ether (CEt)clearance (left) and fractional catabolic rate (right) from plasma of Scarb1KO mice injected with null or SR-BI AAVs after administration of [3H]CE/125I-labeled TC dual-labeled human HDL. (D) 125I-labeled TC clearance (left) andfractional catabolic rate (right) from plasma after administration of dual-labeled HDL. (E) Selective cholesterol uptake in mice expressing null, SR-BI

WT, or P376L measured by relative differences in 3H- and 125I-labeled frac-tional catabolic rates. (F) Sensitivity to Endo-H in P376L homozygous versusnoncarrier iPSC-derived HLCs. Cell lysates of each genotype were treatedwith Endo-H to remove complex N-linked glycans from mature forms ofproteins and then immunoblotted for SR-BI. Molecular weights of differentforms of SR-BI after Endo-H treatment are given on the left. (G) SR-BI Endo-Hsensitivity from liver lysates from mice expressing null, SR-BI WT, or SR-BIP376L AAV. Lysates were treated with Endo-H, followed by immunoblotting forSR-BI. Molecular weights of different forms of SR-BI after Endo-H treatmentare given on the left. (A)Mean values for wells of respective cell lines ± SD; [(B)to (E)] means ± SD for each of the three groups. *P < 0.05; ** P < 0.01; ***P <0.001 by analysis of variance (ANOVA) [(B) and (C)]; plasma clearance,unpaired t test (E).


CETP-mediated exchange to apoB-containing lipo-proteins rather than by direct uptake from HDLby the liver (30), which brings into question theimportance of hepatic SR-BI in human physiology.Common genetic variants near the SCARB1 locuswere found to be significantly associated withplasma HDL-C levels, which suggests that SR-BImay play a role in HDL metabolism in humans(22, 31). A family with a rare SCARB1 variant inwhich serine replaces proline 297 (P297S) was re-ported in which the heterozygous carriers of thevariant had modestly elevated HDL-C levels (31).However, the variant retains substantial SR-BIactivity, no homozygotes were identified, the ap-parent effect on HDL-C was modest, and therewas insufficient power to address its effects onatherosclerosis.Through sequencing of subjects with extremely

high plasma levels ofHDL-C, we identified a homo-zygote for a P376L variant in SR-BI. Our comple-mentary approaches consistently demonstratedthat this variant confers virtually complete loss offunction of SR-BI. Our results demonstratemanysimilarities in the consequences of SR-BI deficiencyonHDL composition betweenmice and humans,including a shift toward large, buoyant HDL par-ticles and a significant increase in apoA-I, butnot apoA-II, in plasma and HDL (12, 32, 33). Thehomozygote is a woman who had two healthy

children without fertility issues or delivery com-plications, which suggests that, in humans, SR-BIdeficiency may not impair reproductive functionin the same manner as it does in mice (18, 34). Inmice, SR-BI–mediated CE uptake from HDL is acritical process underlying steroid hormone syn-thesis in adrenal and gonadal tissues, and SR-BIdeficiency alters adrenal cholesterol content, im-pairs adrenal glucocorticoid response under stress,and can lead to fasting-induced hypoglycemia(6, 35, 36). We did not observe any differencesin fasting glucose, serum cortisol, adrenocortico-tropic hormone, or female gonadal hormones inP376L heterozygous subjects versus controls, andwe saw only a modest increase in testosterone inmale P376L heterozygotes relative to noncarriers.We postulate that differences in expression orcapacity for up-regulation of apoB-containinglipoprotein receptors relative to SR-BI betweenmouse models and humans in steroidogenic tis-sues may account, at least partially, for the lackof recapitulation of some of the phenotypes ofSR-BI deficiency in mice. We also observed nodifferences in platelet levels, cholesterol content,and activation from the P376L carriers, despitereports of thrombocytopenia and altered plateletactivity in Scarb1 KOmice (31). These results sug-gest a relatively different contribution of SR-BI toplatelet function betweenmice and humans. Note

that the phenotypes of human SCARB1 P376Lhomozygote (elevated HDL-C and large HDL par-ticles but relatively normal steroidogenesis, re-productive viability, and platelet function) arecomparable to those observed inmice lacking PDZdomain containing 1 (PDZK1), an adaptor pro-tein for SR-BI (37).Perhaps the most important finding of our

study is that, despite the elevation in HDL-C,P376L carriers exhibit increased risk of CHD, asdo Scarb1 KO mice. Our results are consistentwith a growing theme inHDLbiology that steady-state concentrations of HDL-C are not causallyprotective against CHD and that HDL functionand cholesterol fluxmay bemore important thanabsolute levels. Using an in vivo assay of macro-phage RCT, we previously showed that Scarb1KO mice have impaired macrophage RCT eventhough they have elevatedHDL-C levels (21). Ourresults suggest that reduced hepatic SR-BI func-tion in humans causes impaired RCT, which leadsto increased risk of CHDdespite elevation inHDL-C levels. However, SR-BI is also expressed in vas-cular cell types, including endothelial cells, vascularsmooth muscle cells, and macrophages, whereit could have protective effects against athero-sclerosis as well (38, 39). Our results are alsoconsistent with the previously suggested concept(39) that up-regulation or enhancement of SR-BI


Table 3. Meta-analysis of association of SCARB1 P376L variant withCHD. CHD cases and healthy controls across the CARDIoGRAM Exome Consor-

tiumandCHDExome+Consortiumweregenotyped for theSCARB1P376Lvariantby using the exome array. BioVU, Vanderbilt University Medical Center Bio-

repository; BHF, British Heart Foundation; GoDARTS-CAD,Genetics of Diabetes

and Audit Research Tayside Study; MHI, Montreal Heart Institute; North German,GermanNorthCoronaryArteryDiseaseStudy;Ottawa,OttawaHeart Study; PAS,

Premature Atherosclerosis Study—Academic Medical Center—Amsterdam;

Penn, University of Pennsylvania CHD Cohort; South German, German South

Coronary Artery Disease Study; WHI-EA, Women's Health Initiative—European

American Cohort; CCHS, Copenhagen City Heart Study; CIHDS/CGPS, Copenha-

gen Ischemic Heart Disease Study/Copenhagen General Population Study; EPIC-

CVD, EuropeanProspective Investigation intoCancer andNutrition—CardiovascularDiseaseStudy;MORGAM,MOnicaRisk,Genetics,ArchivingandMonographProject;

PROSPER, ProspectiveStudyof Pravastatin in theElderly at RiskStudy;WOSCOPS,

West of Scotland Coronary Prevention Study.The association of the P376L variant

with CHD cases was determined using a Mantel-Haenszel fixed-effects meta-analysis; results were odds ratio = 1.79; P = 0.018.

Consortium or study cohortP376L carriers Total Frequency

Cases Controls CHD cases Controls Cases Controls

CARDIoGRAM Exome Consortium.. .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. .

BioVU 6 10 4587 16546 0.0013 0.0006.. .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. .

BHF 1 0 2833 5912 0.0004 0.. .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. .

GoDARTS-CAD 1 0 1568 2772 0.0006 0.. .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. .

MHI 0 4 2483 8085 0 0.0005.. .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. .

North German 0 1 4464 2886 0 0.0004.. .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. .

Ottawa 0 1 1024 2267 0 0.0004.. .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. .

PAS 1 1 728 808 0.0014 0.0012.. .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. .

Penn 3 0 683 156 0.0044 0.. .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. .

South German 4 0 5255 2921 0.0008 0.. .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. .

WHI-EA 8 29 2860 14929 0.0028 0.0019.. .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. .

CHD Exome+ Consortium.. .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. .

CCHS 1 1 2020 6087 0.0003 0.0001.. .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. .

CIHDS/CGPS 4 3 8079 10367 0.0003 0.0001.. .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. .

EPIC-CVD 4 2 9810 10970 0.0002 0.0001.. .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. .

MORGAM 0 0 2153 2118 0 0.. .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. .

PROSPER 1 0 640 638 0.0008 0.. .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. .

WOSCOPS 0 0 659 687 0 0.. .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. .

.. .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. .

Total 34 52 49846 88149 0.00068 0.00059.. .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. ... ... .. ... .. ... ... .. ... ... .. .


could be a novel therapeutic approach to reduc-ing CHD risk in the general population.

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ACKNOWLEDGMENTS

We appreciate the participation and support of participants of the deepclinical phenotyping studies. We thank E. Mohler for assistance ininterpretation of cIMT results and J. Billheimer and E. Pashos for helpfuldiscussions. We also acknowledge J. Tabita-Martinez for expertassistance with clinical phenotyping studies. This work was supportedin part by an award from the National Center for Research Resources(grant TL1RR024133) and National Center for Advancing TranslationalSciences of the NIH (grant TL1R000138) to support patientrecruitment. D.B.L. was supported by a fellowship from the Doris DukeCharitable Foundation. S.K. has financial relationships with Novartis,Aegerion, Bristol-Myers Squibb, Sanofi, AstraZeneca, Alnylam, Eli Lilly,Leerink Partners, Merck, Catabasis, Regeneron Genetic Center, SanTherapeutics, and Celera. H.S. has financial relationships with MSDSharp and Dohme, Sanofi-Aventis, and Amgen. S.B. has financialrelationships with Boehringer Ingelheim, Bayer, Novartis, Roche, andThermo Fisher. N.S has financial relationships with Amgen, Sanofi,Astrazeneca, and MSD Sharp and Dohme. A.K. has a financialrelationship with Amgen. J.D. has a financial relationship with Novartis.A.T.-H. has financial relationships with Eli Lilly and LGC Genomics.Sequencing data have been deposited in GenBank (SRX1458096).

Genotyping data have been deposited in the Gene Expression Omnibus(GSE76065).

SUPPLEMENTARY MATERIALS

www.sciencemag.org/content/351/6278/1166/suppl/DC1Materials and MethodsSupplementary Text

Figs. S1 to S7Table S1References (40–76)Consortia and Study Author Lists

1 September 2015; accepted 7 January 201610.1126/science.aad3517

CHEMICAL PHYSICS

Wavelike charge densityfluctuations and van der Waalsinteractions at the nanoscaleAlberto Ambrosetti,1,2 Nicola Ferri,1 Robert A. DiStasio Jr.,3* Alexandre Tkatchenko1,4*

Recent experiments on noncovalent interactions at the nanoscale have challenged the basicassumptions of commonly used particle- or fragment-based models for describing van derWaals (vdW) or dispersion forces. We demonstrate that a qualitatively correct description ofthe vdW interactions between polarizable nanostructures over a wide range of finite distancescan only be attained by accounting for the wavelike nature of charge density fluctuations.By considering a diverse set of materials and biological systems with markedly differentdimensionalities, topologies, and polarizabilities, we find a visible enhancement in thenonlocality of the charge density response in the range of 10 to 20 nanometers. Thesecollective wavelike fluctuations are responsible for the emergence of nontrivial modificationsof the power laws that govern noncovalent interactions at the nanoscale.

The assembly of complex nanostructuresand biological systems from simpler build-ing blocks is often driven by noncovalentvan der Waals (vdW) or dispersion interac-tions that arise from electrodynamic corre-

lations between instantaneous charge fluctuationsin matter (1, 2). The influence of vdW forces ex-tends well beyond binding energies and encom-passes the structural (3, 4), mechanical (5, 6),spectroscopic (7), and even electronic (8) signa-tures of condensed matter. A common way tocharacterize vdW interactions is by power laws inthe distanceD between two or more objects (e.g.,atoms, molecules, nanostructures, surfaces, orsolids); themost familiar is arguably the Lennard-Jones potential, which is characterized by a short-range repulsive wall with a D–12 dependence anda long-range attractive tail with aD–6 dependence.Even a slight variation in these power laws canhave a profound impact on observed propertiesand therefore demands an accurate, physicallysound theoretical description.Thus far, both our conceptual understanding

of vdW interactions and the quantitative modelswidely used for describing these quantummechan-ical phenomena are primarily rooted in low-orderintermolecular perturbation theory (IPT), wherein

vdW binding originates from the interactionsbetween transient local multipoles (9), and mac-roscopic Lifshitz theory (10). Although IPT-basedapproaches have had enormous success in describ-ing vdW binding in (small) gas-phase molecularsystems (11, 12), recent advanced experimentaltechniques have produced several findings thatare challenging the basic assumptions of IPT andmacroscopic approaches for nanostructured ma-terials, and are strongly indicative that even ourqualitative understanding of these interactions isincomplete and needs to be substantially revised(13). Examples of such experimental observationsinclude (i) ultra–long-range vdW interactions ex-tending up to tens of nanometers into heteroge-neous dielectric interfaces (14, 15), (ii) completescreening of the vdW interaction between anatomic force microscope (AFM) tip and a SiO2

surface by the presence of one or more layers ofgraphene adsorbed on the surface (16), (iii) super-linear sticking power laws for the self-assemblyof metallic clusters on carbon nanotubes with in-creasing surface area (17), and (iv) nonlinear in-creases in the vdWattraction betweenhomologousmolecules and an Au(111) surface as a function ofmolecular size (18). Satisfactory theoretical expla-nations for these experimental findings eitherrequire ad hoc modifications to IPT [(iii) and(iv)] or are inherently outside the domain of ap-plicability of IPT [(i) and (ii)].To address these issues, we note that the spa-

tial extent of the instantaneous charge densityfluctuations responsible for vdW interactions de-pends rather sensitively on the nature and char-acter of the occupied-to-virtual transitions of the


1Fritz-Haber-Institut der Max-Planck-Gesellschaft, D-14195Berlin, Germany. 2Dipartimento di Fisica e Astronomia,Università degli Studi di Padova, 35131 Padova, Italy.3Department of Chemistry and Chemical Biology, CornellUniversity, Ithaca, NY 14853, USA. 4Physics and MaterialsScience Research Unit, University of Luxembourg, L-1511Luxembourg.*Corresponding author. E-mail: [email protected] (R.A.D.);[email protected] (A.T.)


DOI: 10.1126/science.aad3517, 1166 (2016);351 Science et al.Paolo Zanoni

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Supplementary Materials for

Rare variant in scavenger receptor BI raises HDL cholesterol and increases risk of coronary heart disease

Paolo Zanoni, Sumeet A. Khetarpal, Daniel B. Larach, William F. Hancock-Cerutti,

John S. Millar, Marina Cuchel, Stephanie DerOhannessian, Anatol Kontush, Praveen Surendran, Danish Saleheen, Stella Trompet, J. Wouter Jukema,

Anton De Craen, Panos Deloukas, Naveed Sattar, Ian Ford, Chris Packard, Abdullah al Shafi Majumder, Dewan S. Alam, Emanuele Di Angelantonio,

Goncalo Abecasis, Rajiv Chowdhury, Jeanette Erdmann, Børge G. Nordestgaard, Sune F. Nielsen, Anne Tybjærg-Hansen, Ruth Frikke Schmidt, Kari Kuulasmaa,

Dajiang J. Liu, Markus Perola, Stefan Blankenberg, Veikko Salomaa, Satu Männistö, Philippe Amouyel, Dominique Arveiler, Jean Ferrieres, Martina Müller-Nurasyid, Marco Ferrario, Frank Kee, Cristen J. Willer, Nilesh Samani, Heribert Schunkert, Adam S. Butterworth, Joanna M. M. Howson, Gina M. Peloso, Nathan O. Stitziel,

John Danesh, Sekar Kathiresan, Daniel J. Rader,* CHD Exome+ Consortium, CARDIoGRAM Exome Consortium, Global Lipids Genetics Consortium

*Corresponding author. E-mail: [email protected]

Published 11 March 2016, Science 351, 1166 (2016) DOI: 10.1126/science.aad3517

This PDF file includes

Materials and Methods Supplementary Text Figs. S1 to S7 Table S1 Full Reference List Consortia and Study Author Lists

Acknowledgments and Contributors

Author Contributions to the Research Article:

P.Z. and S.A.K. performed and analyzed data from cell and animal experiments. S.A.K. and S.D.

analyzed results of Penn sequencing and genotyping studies. M.C. and D.B.L. assisted in design

and recruitment of subjects for deep clinical phenotyping studies. W.F.H-C. and A.K. performed

lipoprotein characterization studies and cholesterol efflux capacity assays. S.A.K. and J.S.M.

performed platelet cholesterol measurement assays. P.S. D.S., S.T., J.W.J., A.D.C., P.D., N.S.,

I.F., C.P. A a.S.M., D.S.A., E.D.A., G.A., R.C., J.E., B.G.N., S.F.N., A.T.H., R.F.S., K.K., D.L.,

M.P., S.B., V.S., S.M., P.A., D.A., J.F., M.M.-N., M.F., F.K., C.J.W., N.S., H.S., A.S.B.,

J.M.M.H., G.M.P., N.O.S., J.D., and S.K. contributed exome array genotyping data and analysis.

D.J.R. funded, conceived and designed the study. P.Z., S.A.K. and D.J.R. wrote the manuscript

with input from all of the authors.

Listing of consortia members who contributed to this Research Article:

______________________________________________________________________________

CHD Exome+ Consortium

Primary Investigator: Adam S. Butterworth, John Danesh, Joanna M. M. Howson, Danish

Saleheen, Praveen Surendran

Contributing groups to CHD Exome+ Consortium and Primary Investigators from these groups:

Copenhagen City Heart Study/CIHDS/CGPS

Sune F. Nielsen, Børge G. Nordestgaard, Ruth Frikke-Schmidt, Anne Tybjærg-Hansen

EPIC-CVD

Adam S. Butterworth, John Danesh, Sarah Watson

PROSPER

Anton De Craen, Stella Trompet, J. Wouter Jukema

WOSCOPS

Ian Ford, Christopher J. Packard, Naveed Sattar

PROMIS

Danish Saleheen, John Danesh

MORGAM

Philippe Amouyel, Dominique Arveiler, Stefan Blankenberg, Marco Ferrario, Jean Ferrieres,

Frank Kee, Kari Kuulasmaa, Satu Männistö, Markus Perola, Veikko Salomaa, Martina Müller-

Nurasyid

BRAVE

Abdulla al Shafi Majumder, Emanuele Di Angelantonio, Rajiv Chowdhury, John Danesh

______________________________________________________________________________

Global Lipids Genetics Consortium

Adam Butterworth, Alanna C. Morrison, Albert V. Smith, Alex P. Reiner, Alexessander Couto

Alves, Aliki-Eleni Farmaki, Alisa Manning, Allan Linneberg, Andrew P. Morris, Anette Varbo,

Ani Manichaikul, Aniruddh P. Patel, Anna Dominiczak, Anne Langsted, Anne Tybjærg-Hansen,

Anne U. Jackson, Annette Peters, Anubha Mahajan, Asif Rasheed, Audrey Y. Chu, Børge G.

Nordestgaard, Bruce M. Psaty, Caroline Hayward, Charles L. Kooperberg, Charlotta Pisinger,

Christian Gieger, Christian M. Shaffer, Christie M. Ballantyne, Claudia Langenberg, Colin N. A.

Palmer, Cramer Christensen, Cristen J. Willer, Dajiang J. Liu, Dan M. Roden, Daniel I.

Chasman, Danish Saleheen, David C. M. Liewald, Dewar Alam, Dominique Arveiler, Dorota

Pasko, Eirini Marouli, Eleftheria Zeggini, Ellen M. Schmidt, Emanuele di Angelantonio, EPIC-

CVD Consortium, Eric Boerwinkle, Erwin P. Bottinger, Francesco Cucca, Franco Giulianini,

Frank Kee, Fredrik Karpe, Frida Renström, Gail Davies, George Dedoussis, Georgio Pistis, Gina

M. Peloso, Goncalo Abecasis, Gorm B. Jensen, Gudny Eiriksdottir, Hanieh Yaghootkar, Harald

Grallert, Hayato Tada, He Zhang, Heather M. Stringham, Helen R. Warren, Hua Tang, Ian Ford,

Ian J. Deary, Ivan Brandslund, J. Wouter Jukema, Jaakko Tuomilehto, James G. Wilson, Jarmo

Virtamo, Jaspal S. Kooner, Jean Ferrieres, Jean-Claude Tardif, Jennifer E. Huffman, Jerome I.

Rotter, Jette Bork-Jensen, Joanna M. M. Howson, Johanna Jakobsdottir, Johanna Kuusisto,

Johanne M. Justesen, John C. Chambers, John Danesh, John M. Connell, John M. Starr, Jonathan

Martin, Jose M. Ordovas, Joshua C. Bis, Joshua C. Denny, Kari Kuulasmaa, Kathleen E.

Stirrups, Kent D. Taylor, Kerrin S. Small, Konstantin Strauch, Kristian Hveem, L. Adrienne

Cupples, Lenore J. Launer, Li An Lin, Lia E. Bang, Lorraine Southam, Marco Ferrario,

Marianne Benn, Marie-Pierre Dubé, Marit E. Jørgensen, Marjo-Riitta Jarvelin, Mark Caulfield,

Mark I. McCarthy, Mark J. Caulfield, Markku Laakso, Markus Perola, Martina Müller-Nurasyid,

Mary F. Feitosa, Matt J. Neville, Megan L. Grove, Melanie Waldenberger, Melissa E. Garcia,

Michael Boehnke, Ming Xu, Morris Brown, Myriam Fornage, Natalie R. van Zuydam, Naveed

Sattar, Neil Poulter, Neil R. Robertson, Nicholas G. D. Masca, Nick J. Wareham, Niels Grarup,

Nilesh J. Samani, Oddgeir L. Holmen, Oluf Pedersen, Panos Deloukas, Patricia B. Munroe, Paul

L. Auer, Paul M. Ridker, Paul W. Franks, Pekka Mäntyselkä, Peter E. Weeke, Peter Sever,

Philippe Amouyel, Philippe Frossard, Pia R. Kamstrup, Praveen Surendran, Rainer Raumaraa

Rajiv Chowdhury, Robert A. Scott, Robin Young, Ruth Frikke-Schmidt, Ruth J.F. Loos,

Sandosh Padmanabhan, Santhi K. Ganesh, Sehrish Jabeen, Sekar Kathiresan, Stavroula Kanoni,

Stella Trompet, Stephen S. Rich, Sune F. Nielsen, Suthesh Sivapalaratnam, Tamara B. Harris,

Tapani Ebeling, The EPIC-InterAct Consortium, Tibor V. Varga, Timo Lakka, Timothy D.

Spector, Timothy M. Frayling, Tõnu Esko, Torben Hansen, Torsten Lauritzen, Veikko Salomaa,

Vilmundur Gudnason, Wei Gao, Wei Zhou, Weihua Zhang, Xiangfeng Lu, Xueling Sim, Y.

Eugene Chen, Yan Zhang, Yanhua Zhou, Yii-Der Ida Chen, Yingchang Lu, Yong Huo

______________________________________________________________________________

CARDIoGRAM Exome Consortium Nathan O. Stitziel, Kathleen E. Stirrups, Nicholas G. D.

Masca, Jeanette Erdmann, Paola G. Ferrario, Inke R. König, Peter E. Weeke, Thomas R. Webb,

Paul L. Auer, Ursula M. Schick, Yingchang Lu, He Zhang, Marie-Pierre Dubé, Anuj Goel,

Martin Farrall, Gina M. Peloso, Hong-Hee Won, Ron Do, Erik van Iperen, Stavroula Kanoni,

Jochen Kruppa, Anubha Mahajan, Robert A. Scott, Christina Willenborg, Peter S. Braund, Julian

C. van Capelleveen, Alex S. F. Doney, Louise A. Donnell, Rosanna Asselta, Piera A. Merlini,

Stefano Duga, Nicola Marziliano, Joshua C. Denny, Christian M. Shaffer, Nour Eddine El-

Mokhtari, Andre Franke, Omri Gottesman, Stefanie Heilmann, Christian Hengstenberg, Per

Hoffmann, Oddgeir L. Holmen, Kristian Hveem, Jan-Håkan Jansson, Karl-Heinz Jöckel,

Thorsten Kessler, Jennifer Kriebel, Karl L. Laugwitz, Eirini Marouli, Nicola Martinelli, Mark I.

McCarthy, Natalie R. Van Zuydam, Christa Meisinger, Tõnu Esko, Evelin Mihailov, Stefan A.

Escher, Maris Alver, Susanne Moebus, Andrew D. Morris, Martina Müller-Nurasyid, Majid

Nikpay, Oliviero Olivieri, Louis-Philippe Lemieux Perreault, Alaa AlQarawi, Neil R. Robertson,

Karen O. Akinsanya, Dermot F. Reilly, Thomas F. Vogt, Wu Yin, Folkert W. Asselbergs,

Charles Kooperberg, Rebecca D. Jackson, Eli Stahl, Konstantin Strauch, Tibor V. Varga,

Melanie Waldenberger, Lingyao Zeng, Aldi T. Kraja, Chunyu Liu, Georg B. Ehret, Christopher

Newton-Cheh, Daniel I. Chasman, Rajiv Chowdhury, Marco Ferrario, Ian Ford, J. Wouter

Jukema, Frank Kee, Kari Kuulasmaa, Børge G. Nordestgaard, Markus Perola, Danish Saleheen,

Naveed Sattar, Praveen Surendran, David Tregouet, Robin Young, Joanna M. M. Howson,

Adam S. Butterworth, John Danesh, Diego Ardissino, Erwin P. Bottinger, Raimund Erbel, Paul

W. Franks, Domenico Girelli, Alistair S. Hall, G. Kees Hovingh, Adnan Kastrati, Wolfgang

Lieb, Thomas Meitinger, William E. Kraus, Svati H. Shah, Ruth McPherson, Marju Orho-

Melander, Olle Melander, Andres Metspalu, Colin N. A. Palmer, Annette Peters, Daniel J.

Rader, Muredach P. Reilly, Ruth J. F. Loos, Alex P. Reiner, Dan M. Roden, Jean-Claude Tardif,

John R. Thompson, Nicholas J. Wareham, Hugh Watkins, Cristen J. Willer, Panos Deloukas,

Nilesh J. Samani, Heribert Schunkert, Sekar Kathiresan

AUTHORS AND AFFILIATIONS BY GROUP CHD Exome+ Consortium

Primary Investigators

Adam S. Butterworth

Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University

of Cambridge, Cambridge, UK.

John Danesh


of Cambridge, Cambridge, UK. Wellcome Trust Sanger Institute, Genome Campus, Hinxton

CB10 1HH, UK.

Joanna M. M. Howson



Danish Saleheen


of Cambridge, Cambridge, UK. Department of Biostatistics and Epidemiology, Perelman School

of Medicine, University of Pennsylvania, Philadelphia, PA, USA. Centre for Non-

Communicable Diseases, Karachi, Pakistan.

Praveen Surendran


of Cambridge, Cambridge, UK

Contributing groups to CHD Exome+ Consortium and Primary Investigators from these groups:

Copenhagen City Heart Study/CIHDS/CGPS

Sune F. Nielsen

Department of Clinical Biochemistry Herlev Hospital, Copenhagen University Hospital, Herlev,

Denmark.

Børge G. Nordestgaard

Department of Clinical Biochemistry Herlev Hospital, Copenhagen University Hospital, Herlev,

Denmark.

Ruth Frikke-Schmidt

Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital,

Copenhagen, Denmark.

Anne Tybjærg-Hansen

Copenhagen University Hospital, University of Copenhagen, Copenhagen, Denmark.

EPIC-CVD

Adam S. Butterworth



John Danesh


of Cambridge, Cambridge, UK. Wellcome Trust Sanger Institute, Genome Campus, Hinxton

CB10 1HH, UK.

Sarah Watson



MORGAM

Philippe Amouyel

Department of Epidemiology and Public Health, Institut Pasteur de Lille, Lille, France.

Dominique Arveiler

Department of Epidemiology and Public Health, EA 3430, University of Strasbourg, Strasbourg,

F- 67085, France.

Stefan Blankenberg

Department of General and Interventional Cardiology, University Heart Center Hamburg,

Germany. University Medical Center Hamburg-Eppendorf, Hamburg, Germany.

Marco Ferrario

Research Centre in Epidemiology and Preventive Medicine – EPIMED, Department of Clinical

and Experimental Medicine, University of Insubria, Via O Rossi 9, 21100 Varese, Italy.

Jean Ferrieres

Department of Epidemiology, UMR 1027- INSERM, Toulouse University-CHU Toulouse,

Toulouse, France.

Frank Kee

UKCRC Centre of Excellence for Public Health, Queens University, Belfast, Northern Ireland.

Kari Kuulasmaa

Department of Health, National Institute for Health and Welfare, Helsinki, Finland.

Satu Männistö

Department of Health, National Institute for Health and Welfare, Helsinki, Finland.

Martina Müller-Nurasyid

Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for

Environmental Health, Neuherberg, Germany

Markus Perola

Department of Health, National Institute for Health and Welfare, Helsinki, Finland. Institute of

Molecular Medicine FIMM, University of Helsinki, Finland.

Veikko Salomaa

Department of Health, National Institute for Health and Welfare, FI-00271, Helsinki, Finland.


Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for

Environmental Health, Neuherberg, Germany. Department of Medicine I, Ludwig-Maximilians-

Universität, Munich, Germany. DZHK (German Centre for Cardiovascular Research), partner

site Munich Heart Alliance, Munich, Germany.

PROSPER

Anton De Craen

Leiden University Medical Center, Leiden, The Netherlands

Stella Trompet


J. Wouter Jukema


WOSCOPS

Ian Ford

University of Glasgow, Glasgow, Scotland, UK.

Christopher J. Packard


Naveed Sattar


BRAVE

Abdulla al Shafi Majumder

National Institute of Cardiovascular Diseases, Sher-e-Bangla Nagar, Dhaka, Bangladesh.

Dewan S. Alam

ICDDR, B; Mohakhali, Dhaka 1212, Bangladesh.

Emanuele Di Angelantonio, Rajiv Chowdhury, John Danesh



PROMIS

Danish Saleheen


of Cambridge, Cambridge, UK. Department of Biostatistics and Epidemiology, Perelman School

of Medicine, University of Pennsylvania, Philadelphia, PA, USA. Centre for Non-

Communicable Diseases, Karachi, Pakistan.

John Danesh

Department of Public Health and Primary Care, Strangeways Research Laboratory, University of

Cambridge, Cambridge CB1 8RN, UK.

Global Lipids Genetics Consortium

Adam Butterworth



The National Institute for Health Research Blood and Transplant Unit (NIHR BTRU) in Donor

Health and Genomics at the University of Cambridge, UK.

Alanna C. Morrison

Human Genetics Center, School of Public Health, The University of Texas School Health

Science Center at Houston, Houston, TX 77030, USA.

Albert V. Smith

The Icelandic Heart Association, Kopavogur, Iceland. The University of Iceland, Reykjavik,

Iceland.

Alex P. Reiner

Department of Epidemiology, University of Washington, Seattle WA 98195, USA. Division of

Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle WA 98195, USA.

Alexessander Couto Alves

Faculty of Medicine, School of Public Health, Imperial College London

Aliki-Eleni Farmaki

Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio

University, Athens, Greece. Harokopio University of Athens, Kallithea, Athens, Greece.

Alisa Manning

Broad Institute of the Massachusetts Institute of Technology and Harvard University,

Cambridge, MA 02142, USA

Allan Linneberg

Research Centre for Prevention and Health, Capital Region of Denmark, Copenhagen, Denmark.

Department of Clinical Experimental Research, Glostrup University Hospital, Glostrup,

Denmark. Department of Clinical Medicine, Faculty of Health and Medical Sciences, University

of Copenhagen, Copenhagen, Denmark.

Andrew P. Morris

Department of Biostatistics, University of Liverpool, Liverpool, UK. Wellcome Trust Centre for

Human Genetics, University of Oxford, Oxford, UK.

Anette Varbo

Department of Clinical Biochemistry and The Copenhagen General Population Study, Herlev

and Gentofte Hospital, Copenhagen University Hospital, Denmark, and Faculty of Health and

Medical Sciences, University of Denmark, Denmark. Department of Clinical Biochemistry,

54M1, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark.

Ani Manichaikul

Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22903, USA

Aniruddh P. Patel

Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA 02114, USA.

Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA.

Department of Medicine, Harvard Medical School, Boston, MA 02114, USA. Program in

Medical and Population Genetics, Broad Institute, 7 Cambridge Center, Cambridge, MA 02142,

USA.

Anna Dominiczak

Institute of Cardiovascular and Medical Sciences, College of Medical, Veterinary and Life

Sciences, University of Glasgow, Glasgow G12 8TA,Scotland, UK.

Anne Langsted



Medical Sciences, University of Denmark, Denmark.

Anne Tybjaerg-Hansen

Department of Clinical Biochemistry, Rigshospitalet, Copenhagen, Denmark and Faculty of

Health and Medical Sciences, University of Copenhagen, Denmark.

Anne U. Jackson

Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann

Arbor, MI 48109, USA.

Annette Peters

Institute for Epidemiology II, Helmholtz Zentrum München, Neuherberg, Germany.

German Center for Cardiovascular Disease Research, partner-site Munich, Munich, Germany.

German Center for Diabetes Research, Neuherberg, Germany.

Anubha Mahajan

Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of

Oxford, Oxford, UK.

Asif Rasheed

Center for Non-Communicable Diseases, Karachi, Pakistan.

Audrey Y. Chu

Division of Preventive Medicine, Brigham and Women’s Hospital, Boston, MA 02215, USA.

NHLBI Framingham Heart Study, Framingham, MA 01702, USA.





Bruce M. Psaty

Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health

Services, University of Washington, Seattle, WA 98101, USA. Group Health Research Institute,

Group Health Cooperative, Seattle, WA 98101, USA.

Caroline Hayward

Generation Scotland, Centre for Genomic and Experimental Medicine, Institute of Genetics and

Molecular Medicine, University of Edinburgh, Edinburgh, UK. Medical Research Council

Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh,

Edinburgh, UK.

Charles L. Kooperberg

Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle WA

98195, USA.

Charlotta Pisinger

Research Centre for Prevention and Health, Capital Region of Denmark, Copenhagen, Denmark.

Christian Gieger

Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research

Center for Environmental Health, Neuherberg, Germany. Institute of Epidemiologie II,

Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg,

Germany. German Center for Diabetes Research (DZD e.V.), Neuherberg, Germany.

Christian M. Shaffer

Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA.

Christie M. Ballantyne

Department of Medicine, Baylor College of Medicine, Houston, TX 77030 , USA.

Claudia Langenberg

MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of

Clinical Medicine, Cambridge, UK.

Colin N. A. Palmer

Medical Research Institute, University of Dundee, Ninewells Hospital and Medical School,

Dundee, UK.

Cramer Christensen

Medical department, Lillebaelt Hospital, Vejle, Denmark.

Cristen J. Willer

Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan,

Ann Arbor, Michigan 48109, USA. Department of Computational Medicine and Bioinformatics,

University of Michigan, Ann Arbor, Michigan 48109, USA. Department of Human Genetics,

University of Michigan, Ann Arbor, Michigan 48109, USA.

Dajiang J. Liu

Department of Public Health Sciences and Institute of Personalized Medicine, College of

Medicine, Penn State College of Medicine, Hershey, PA 17033, USA.

Dan M. Roden


Daniel I. Chasman

Division of Preventive Medicine, Department of Medicine, Brigham and Women’s Hospital and

Harvard Medical School, Boston, MA 02215, USA.

Danish Saleheen

Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of

Pennsylvania, Philadelphia, PA 19104, USA. Centre for Non-Communicable Diseases, Karachi,

Pakistan. Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care,

University of Cambridge, Cambridge, UK.

David C. M. Liewald

Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh,

UK. Department of Psychology, University of Edinburgh, Edinburgh, UK.

Dewan S. Alam

ICDDR, B; Mohakhali, Dhaka 1212, Bangladesh.

Dominique Arveiler

Department of Epidemiology and Public Health, EA 3430, University of Strasbourg, Strasbourg,

F- 67085, France.

Dorota Pasko

Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter,

EX2 5DW, UK.

Eirini Marouli

William Harvey Research Institute, Barts and The London School of Medicine and Dentistry,

Queen Mary University of London, London, UK.

Eleftheria Zeggini

Wellcome Trust Sanger Institute, Hinxton, CB10 1SA, UK.

Ellen M. Schmidt

Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor,

MI 48109, USA.

Emanuele di Angelantonio


of Cambridge, Cambridge, UK. The National Institute for Health Research Blood and Transplant

Unit (NIHR BTRU) in Donor Health and Genomics at the University of Cambridge, UK.

EPIC-CVD Consortium

Eric Boerwinkle


Science Center at Houston, Houston, TX 77225, USA.

Erwin P. Bottinger

The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount

Sinai, New York, NY 10029, USA.

Francesco Cucca

Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche, c/o

Cittadella Universitaria di Monserrato, Cagliari, Italy. Department of Biomedical Sciences,

Azienda Ospedaliero-Universitaria di Sassari, Sassari, Italy.

Franco Giulianini



Frank Kee

Director, UKCRC Centre of Excellence for Public Health, Queens University, Belfast, Northern

Ireland.

Fredrik Karpe

Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine,

University of Oxford, Oxford, UK. Oxford NIHR Biomedical Research Centre, Oxford

University Hospitals Trust, Oxford, UK.

Frida Renström

Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University,

Malmö, Sweden. Department of Biobank Research, Umeå University, Umeå, Sweden.

Gail Davies



George Dedoussis

Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio

University, Athens, 17671, Greece.

Georgio Pistis

Istituto di Ricerca Genetica e. Biomedica, Consiglio Nazionale delle Ricerche (CNR),

Monserrato, Cagliari, Italy.

Gina M. Peloso





USA. Department of Biostatistics, Boston University School of Public Health, Boston, MA

02118, USA.

Goncalo Abecasis

Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor,

MI 48109, USA.

Gorm B. Jensen

The Copenhagen City Heart Study, Frederiksberg Hospital, Denmark.

Gudny Eiriksdottir

The Icelandic Heart Association, Kopavogur, Iceland.

Hanieh Yaghootkar

Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter

EX2 5DW, UK.

Harald Grallert

Research Unit of Molecular Epidemiology, Helmholtz Zentrum Muenchen, German Research

Center for Environmental Health, Neuherberg, Germany. Institute of Epidemiologie II,

Helmholtz Zentrum Muenchen, German Research Center for Environmental Health, Neuherberg,

Germany. German Center for Diabetes Research (DZD e.V.), Neuherberg, Germany.

Hayato Tada

Division of Cardiovascular Medicine, Kanazawa University Graduate School of Medicine,

Kanazawa, Japan.

He Zhang


Ann Arbor, MI 48109, USA.

Heather M. Stringham



Helen R. Warren

Clinical Pharmacology, William Harvey Research Institute, Barts and The London, Queen Mary

University of London, Charterhouse Square, London, EC1M 6BQ, UK.

Hua Tang

Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.

Ian Ford

University of Glasgow, Glasgow, UK.

Ian J. Deary



Ivan Brandslund

Department of Clinical Biochemistry, Lillebaelt Hospital, Vejle, Denmark. Institute of Regional

Health Research, University of Southern Denmark, Odense, Denmark.

J. Wouter Jukema

Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands.

Interuniversity Cardiology Institute of the Netherlands, Utrecht, The Netherlands.

Jaakko Tuomilehto

Chronic Disease Prevention Unit, National Institute for Health and Welfare, 00271 Helsinki,

Finland. Dasman Diabetes Institute, Dasman 15462, Kuwait. Centre for Vascular Prevention,

Danube-University Krems, 3500 Krems, Austria. Saudi Diabetes Research Group, King

Abdulaziz University, Fahd Medical Research Center, Jeddah 21589, Saudi Arabia.

James G. Wilson

Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson,

MS 39216, USA.

Jarmo Virtamo


Jaspal S. Kooner

National Heart and Lung Institute, Imperial College London, Hammersmith Hospital Campus,

London, UK. Department of Cardiology, Ealing Hospital NHS Trust, Uxbridge Road, Southall,

Middlesex UB1 3HW, UK. Imperial College Healthcare NHS Trust, London, UK.

Jean Ferrieres

Department of Epidemiology, UMR 1027- INSERM, Toulouse University-CHU Toulouse,

Toulouse, France.

Jean-Claude Tardif

Montreal Heart Institute, Montreal, Quebec, Canada. Université de Montréal, Montreal, Quebec,

Canada.

Jennifer E. Huffman

Framingham Heart Study, Population Sciences Branch, Division of Intramural Research National

Heart Lung and Blood Institute, National Institutes of Health, Framingham, MA 02118, USA.

Jerome I. Rotter

The Institute for Translational Genomics and Population Sciences, Departments of Pediatrics and

Medicine, LABioMed at Harbor-UCLA Medical Center, Torrance, CA 90502, USA.

Jette Bork-Jensen

The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and

Medical Sciences, University of Copenhagen, Copenhagen, Denmark.

Joanna M. M. Howson



Johanna Jakobsdottir

The Icelandic Heart Association, Kopavogur, Iceland. The University of Iceland, Reykjavik,

Iceland.

Johanna Kuusisto

Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio

University Hospital, 70210 Kuopio, Finland.

Johanne M. Justesen



John C. Chambers

Department of Epidemiology and Biostatistics, School of Public Health, Imperial College

London, Norfolk Place, London W2 1PG, UK. Department of Cardiology, Ealing Hospital NHS

Trust, Uxbridge Road, Southall, Middlesex UB1 3HW, UK. Imperial College Healthcare NHS

Trust, London, UK.

John Danesh




Wellcome Trust Sanger Institute, Genome Campus, Hinxton CB10 1HH, UK.

John M. Connell


Dundee, UK.

John M. Starr


UK. Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, UK.

Jonathan Martin

Generation Scotland, Centre for Genomic and Experimental Medicine, Institute of Genetics and

Molecular Medicine, University of Edinburgh, Edinburgh, UK.

Jose M. Ordovas

Department of Cardiovascular Epidemiology and Population Genetics, National Center for

Cardiovascular Investigation, Madrid 28049, Spain.

Nutrition and Genomics Laboratory, Jean Mayer-USDA Human Nutrition Research Center on

Aging at Tufts University, Boston, MA 02111, USA.

Joshua C. Bis

Cardiovascular Health Research Unit, Department of Medicine, University of Washington,

Seattle, WA 98102, USA

Joshua C. Denny

Departments of Medicine and Biomedical Informatics, Vanderbilt University Medical Center,

Nashville, TN 37203, USA

Kari Kuulasmaa


Kathleen E. Stirrups

Department of Haematology, University of Cambridge, Cambridge, UK. William Harvey

Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary

University of London, London, UK.

Kent D. Taylor

Los Angeles Biomedical Research Institute at Harbor, UCLA, Los Angeles, CA 90048, USA.

Kerrin S. Small

Department of Twin Research and Genetic Epidemiology, King's College London, London, UK.

Konstantin Strauch


Environmental Health, Neuherberg, Germany.

Institute of Medical Informatics, Biometry and Epidemiology, Chair of Genetic Epidemiology,

Ludwig-Maximilians-Universität, Munich, Germany.

Kristian Hveem

Department of Public Health and General Practice, HUNT Research Centre, Norwegian

University of Science and Technology.

L. Adrienne Cupples

Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118,

USA.

Lenore J. Launer

Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Bethesda,

MD 20892, USA.

Li An Lin

Institute of Molecular Medicine; the University of Texas Health Science Center at Houston,

Houston, TX 77030, USA.

Lia E. Bang

Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen,

Denmark.

Lorraine Southam

Wellcome Trust Sanger Institute, Hinxton, CB10 1SA, UK. Wellcome Trust Centre for Human

Genetics, University of Oxford, Oxford,OX3 7BN, UK. Wellcome Trust Sanger Institute, The

Morgan Building, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1HH, UK.

Marco Ferrario



Marianne Benn




Marie-Pierre Dubé

Montreal Heart Institute, Montreal, Quebec, Canada; 2) Université de Montréal, Montreal,

Quebec, Canada.

Université de Montréal Beaulieu-Saucier Pharmacogenomics Center, Montreal, Quebec, Canada.

Marit E. Jørgensen

Steno Diabetes Center, Gentofte, Denmark; National Institute of Public Health, Southern

Denmark University, Denmark.

Marjo-Riitta Jarvelin


London.

Mark Caulfield

Clinical Pharmacology, William Harvey Research Institute, Queen Mary University of London,

London, EC1M 6BQ, UK. NIHR Barts Cardiovascular Biomedical Research Unit, Queen Mary

University of London, London, EC1M 6BQ, UK.

Mark I. McCarthy


University of Oxford, Oxford, UK. Wellcome Trust Centre for Human Genetics, Nuffield

Department of Medicine, University of Oxford, Oxford, UK. Oxford NIHR Biomedical Research

Centre, Oxford University Hospitals Trust, Oxford, UK.

Mark J. Caulfield

The Barts Heart Centre, William Harvey Research Institute, Queen Mary University of London,

Charterhouse Square, London EC1M 6BQ. Clinical Pharmacology, William Harvey Research

Institute, Barts and The London, Queen Mary University of London, Charterhouse Square,

London, EC1M 6BQ, UK.

Markku Laakso

Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio

University Hospital, 70210 Kuopio, Finland.

Markus Perola


Institute of Molecular Medicine FIMM, University of Helsinki, Finland.






Mary F. Feitosa

Division of Statistical Genomics, Department of Genetics, Washington University School of

Medicine, St. Louis, MO 63108, USA.

Matt J. Neville


University of Oxford, Oxford, UK.

Megan L. Grove


Science Center at Houston, Houston, TX 77030. USA.

Melanie Waldenberger

Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for

Environmental Health, Neuherberg, 85764, Germany. Research Unit of Molecular

Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental

Health, Neuherberg, 85764, Germany.

Melissa E. Garcia


MD 20892, USA.

Michael Boehnke



Ming Xu

Department of Cardiology, Institute of Vascular Medicine, Peking University Third Hospital,

Key Laboratory of Molecular Cardiovascular Sciences, Ministry of Education, Beijing 100191,

China.

Morris Brown

Clinical Pharmacology Unit, University of Cambridge, Addenbrookes Hospital, Hills Road,

Cambridge CB2 2QQ, UK.

Myriam Fornage

Institute of Molecular Medicine; the University of Texas Health Science Center at Houston,

Houston, TX 77030, USA.

Natalie R. van Zuydam

Medical Research Institute, University of Dundee, UK. WTCHG, Oxford University, Oxford,

Oxfordshire, UK.

Naveed Sattar


Neil Poulter

International Centre for Circulatory Health, Imperial College London, W2 1PG, UK.

Neil R. Robertson


Oxford, Oxford, UK. Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe

Department of Medicine, University of Oxford, Oxford, UK.

Nicholas G. D. Masca

Department of Cardiovascular Sciences, University of Leicester, UK; NIHR Leicester

Cardiovascular Biomedical Research Unit, Glenfield Hospital, Leicester, UK.

Nick J. Wareham



Niels Grarup



Nilesh J. Samani

Department of Cardiovascular Sciences, University of Leicester, UK; NIHR Leicester

Cardiovascular Biomedical Research Unit, Glenfield Hospital, UK. BHF Cardiovascular

Research Centre, Glenfield Hospital, Leicester, LE3 9QP, UK.

Oddgeir L. Holmen

Department of Public Health and General Practice, HUNT Research Centre, Norwegian

University of Science and Technology, 7600 Levanger, Norway. St Olav Hospital, Trondheim

University Hospital, 7030 Trondheim, Norway.

Oluf Pedersen



Panos Deloukas


Queen Mary University of London, London, UK. Princess Al-Jawhara Al-Brahim Centre of

Excellence in Research of Hereditary Disorders (PACER-HD), King Abdulaziz University,

Jeddah 21589, Saudi Arabia.

Patricia B. Munroe

Clinical Pharmacology, William Harvey Research Institute, Queen Mary University of London,

London, EC1M 6BQ, UK. NIHR Barts Cardiovascular Biomedical Research Unit, Queen Mary

University of London, London, EC1M 6BQ, UK.

Paul L. Auer

Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee WI 53201,

USA.

Paul M. Ridker

Division of Preventive Medicine, Brigham and Women’s Hospital, Boston, MA 02215, USA.

Paul W. Franks


Malmö, Sweden. Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston,

MA 02115, USA. Department of Public Health & Clinical Medicine, Umeå University, Umeå,

Sweden.

Pekka Mäntyselkä

Department of General Medicine, University of Eastern Finland, Kuopio, Finland.

Peter E. Weeke

Department of Medicine, Vanderbilt, University Medical Center, Nashville, TN 37232, USA.

The Heart Centre, Department of Cardiology, Copenhagen University Hospital, Rigshospitalet,


Peter Sever


Philippe Amouyel

Department of Epidemiology and Public Health, Institut Pasteur de Lille, Lille, France.

Philippe Frossard


Pia R. Kamstrup




Praveen Surendran



Rainer Raumaraa

Kuopio Research Institute of Exercise Medicine, Kuopio, Finland and Department of Clinical

Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland.

Rajiv Chowdhury



Robert A. Scott



Robin Young



Ruth Frikke-Schmidt

Department of Clinical Biochemistry, Rigshospitalet, Copenhagen, Denmark and Faculty of

Health and Medical Sciences, University of Copenhagen, Denmark.

Ruth J. F. Loos



The Mindich Child Health and Development Institute, Ichan School of Medicine at Mount Sinai,

New York, NY 10029, USA.

Sandosh Padmanabhan

Institute of Cardiovascular and Medical Sciences, School of Medicine, University of Glasgow,

Glasgow, UK.

Santhi K. Ganesh


Ann Arbor, MI 48109, USA.

Department of Human Genetics, University of Michigan, Ann Arbor, Michigan 48109, USA.

Sehrish Jabeen


Sekar Kathiresan





USA.

Stavroula Kanoni



Stella Trompet

Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands.

Department of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the

Netherlands.

Stephen S. Rich

Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22903, USA.

Sune F. Nielsen




Suthesh Sivapalaratnam

Department of Vascular Medicine, Academic Medical Center, University of Amsterdam,

Amsterdam, NL.

Tamara B. Harris


MD 20892, USA.

Tapani Ebeling

Department of Medicine, Oulu University Hospital, Oulu, Finland.

The EPIC-InterAct consortium

Tibor V. Varga


Malmö, Sweden.

Timo Lakka

Department of Physiology, Institute of Biomedicine, University of Eastern Finland, Kuopio

Campus, Kuopio, Finland and Kuopio Research Institute of Exercise Medicine, Kuopio, Finland.

Timothy D. Spector

Department of Twin Research and Genetic Epidemiology, King's College London, London, UK.

Timothy M. Frayling

Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter

EX2 5DW, UK.

Tõnu Esko

Broad Institute of the Massachusetts Institute of Technology and Harvard University,

Cambridge, MA 02142, USA. Estonian Genome Center, University of Tartu, Tartu, Estonia.

Division of Endocrinology, Genetics and Basic and Translational Obesity Research, Children’s

Hospital Boston, Boston, MA 02115, USA.

Torben Hansen


Medical Sciences, University of Copenhagen, Copenhagen, Denmark. Faculty of Health

Sciences, University of Southern Denmark, Odense, Denmark.

Torsten Lauritzen

Department of Public Health, Section of General Practice, University of Aarhus, Aarhus,

Denmark.

Veikko Salomaa


Vilmundur Gudnason

The Icelandic Heart Association, Kopavogur, Iceland; The University of Iceland, Reykjavik,

Iceland [email protected] Holtasmari 1, 201 Kopavogur, Iceland.

Wei Gao

Department of Cardiology, Peking University Third Hospital, Key Laboratory of Cardiovascular

Molecular Biology and Regulatory Peptides, Ministry of Health, Beijing 100191, China.

Wei Zhou


Michigan 48109, USA.

Weihua Zhang


London, Norfolk Place, London W2 1PG, UK.

Wouter Jukema

Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands; The


Xiangfeng Lu


Ann Arbor, MI 48109, USA. State Key Laboratory of Cardiovascular Disease, Fuwai Hospital,

National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking

Union Medical College, Beijing, China.

Xueling Sim



Saw Swee Hock School of Public Health, National University of Singapore, Singapore, 117549,

Singapore.

Y. Eugene Chen


Ann Arbor, Michigan 48109, USA.

Yan Zhang

Department of Cardiology, Peking University First Hospital, Beijing 100034, China.

Yanhua Zhou

Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118,

USA.

Yii-Der Ida Chen

Institute for Translational Genomics and Population Sciences, Los Angeles BioMedical Research

Institute at Harbor-UCLA Medical Center, Torrance, CA 90502, USA.

Yingchang Lu

The Charles Bronfman Institute for Personalized Medicine, Icachn School of Medicine at Mount

Sinai, New York, NY 10029.

Yong Huo

Department of Cardiology, Peking University First Hospital, Beijing 100034, China.

______________________________________________________________________________

CARDIoGRAM Exome Consortium

Nathan O. Stitziel

Cardiovascular Division, Departments of Medicine and Genetics, and the McDonnell Genome

Institute, Washington University School of Medicine, St. Louis, MO 63110, USA.

Aldi T. Kraja

Division of Statistical Genomics, Department of Genetics and Center for Genome Sciences and

Systems Biology, Washington University School of Medicine, St. Louis, MO 63108, USA.

Kathleen E. Stirrups

William Harvey Research Institute, Barts and the London School of Medicine and Dentistry,

Queen Mary University of London, London, UK. Department of Haematology, University of

Cambridge, Cambridge, UK.

Nicholas G. D. Masca

Departments of Cardiovascular Sciences, University of Leicester, Leicester, UK. NIHR Leicester


Jeanette Erdmann

Institute for Integrative and Experimental Genomics, University of Lübeck, Lübeck, Germany.

DZHK (German Center for Cardiovascular Research), partner site Hamburg/Lübeck/Kiel,

Lübeck, Germany.

Paola G. Ferrario


Lübeck, Germany. Institut für Med-izinische Biometrie und Statistik, Universität zu Lübeck,

Lübeck, Germany. Dr. rer. nat., Inke R. König, Dr. rer. biol. hum. DZHK (German Center for

Cardiovascular Research), partner site Hamburg/Lübeck/Kiel, Lübeck, Germany. Institut für

Med-izinische Biometrie und Statistik, Universität zu Lübeck (P.G.F., I.R.K., J. Kruppa),

Lübeck, Germany.

Peter E. Weeke

Department of Medicine, Vanderbilt, University Medical Center, Nashville, TN 37232, USA.

The Heart Centre, Department of Cardiology, Copenhagen University Hospital, Rigshospitalet,


Thomas R. Webb



Paul L. Auer

School of Public Heath, University of Wisconsin–Milwaukee, Milwaukee, WI 53201, USA.

Ursula M. Schick

Fred Hutchinson Cancer Research Center, University of Washington, Seattle, WA 98109, USA.

Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai,


Yingchang Lu

Genetics of Obesity and Related Metabolic Traits Program and the Charles Bronfman Institute

for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029,

USA.

He Zhang

Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, Michigan 48109,

USA.

Marie-Pierre Dubé

Département de medicine, and the Montreal Heart Institute, Montreal, Canada.

Anuj Goel

Division of Cardiovascular Medicine, Radcliffe Department of Medicine, and the Wellcome

Trust Centre for Human Genetics, University of Oxford, Oxford, UK.

Martin Farrall



Gina M. Peloso





USA. Department of Biostatistics, Boston University School of Public Health, Boston, MA

02118, USA.

Hong-Hee Won





USA. Samsung Advanced Institute for Health Sciences and Tech-nology (SAIHST),

Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea.

Ron Do

Charles Bronfman Institute for Personalized Medicine, Center for Statistical Genetics and

Institute for Genomics and Multiscale Biology, Department of Genetics and Genomic Sciences,

Zena and Michael A. Weiner Cardiovascular Institute, Icahn School of Medicine at Mount Sinai,


Erik van Iperen

Department of Biostatistics, Academic Medical Center, Amsterdam, the Netherlands.

Stavroula Kanoni



Jochen Kruppa

Institut für Med-izinische Biometrie und Statistik, Universität zu Lübeck, Lübeck, Germany.

Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.

Anubha Mahajan


Oxford, Oxford, UK.

Robert A. Scott,

University of Cambridge, MRC Epidemiology Unit, Institute of Metabolic Science,

Addenbrooke’s Hospital, Cambridge, UK.

Christina Willenborg

Institute for Integrative and Experimental Genomics, University of Lübeck, Lübeck, Germany.


Lübeck, Germany.

Peter S. Braund



Julian C. van Capelleveen

Department of Vascular Medicine, Academic Medical Center, Amsterdam, the Netherlands.

Alex S.F. Doney


Dundee, Scotland, UK.

Louise A. Donnelly


Dundee, Scotland, UK.

Rosanna Asselta

Department of Biomedical Sciences, Humanitas University, and Humanitas Clinical and

Research Center, Milan, Italy.

Piera A. Merlini

Division of Cardiology, Niguarda Hospital, Milan, Italy.

Stefano Duga

Department of Biomedical Sciences, Humanitas University, and Humanitas Clinical and

Research Center, Milan, Italy.

Nicola Marziliano

Division of Cardiology, Azienda Ospedaliero–Uni-versitaria di Parma, Parma, Italy.

Associazione per lo Studio della Trombosi in Cardiologia, Pavia, Italy.

Josh C. Denny

Departments of Medicine and Biomedical Informatics, Vanderbilt University Medical Center,

Nashville, TN 37232, USA.

Christian M. Shaffer


Nour Eddine El-Mokhtari

Klinik für Innere Medizin, Kreiskrankenhaus Rendsburg, Rendsburg, Germany.

Andre Franke

Institute of Clinical Molecular Biology, Christian-Albrechts-University Kiel, Kiel, Germany.

Omri Gottesman

Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai,


Stefanie Heilmann

Institute of Human Genetics, and Department of Genomics, Life and Brain Center, University of

Bonn, Bonn, Germany.

Christian Hengstenberg

Deutsches Herzzentrum München, Technische Universität München, Germany. DZHK partner

site Munich Heart Alliance, Germany.

Per Hoffmann

Institute of Human Genetics and Department of Genomics, Life and Brain Center, University of

Bonn, Bonn, Germany.

Oddgeir L. Holmen

HUNT Research Center, Department of Public Health and Gen-eral Practice, Norwegian

University of Science and Technology, Levanger, Norway. St. Olav Hospital, Trondheim

University Hospital, Trondheim, Norway.

Kristian Hveem

HUNT Research Center, Department of Public Health and General Practice, Norwegian

University of Science and Technology, and Department of Medicine, Levanger Hospital, Nord-

Trøndelag Health Trust Norway, Levanger, Norway.

Jan-Håkan Jansson

Department of Public Health and Clinical Medicine, Research Unit Skellefteå, Umeå University,

Umeå, Sweden.

Karl-Heinz Jöckel

Institute for Medical Informatics, Biometry and Epidemiology, University Hospital Essen, Essen,

Germany.

Thorsten Kessler

Deutsches Herzzentrum München, Technische Universität München, Germany.

Jennifer Kriebel

Research Unit of Molecular Epidemiology, Institutes of Epidemiology II, and German Center for

Diabetes Research, Neuherberg, Germany.

Karl L. Laugwitz

DZHK partner site Munich Heart Alliance, I. Medizinische Klinik und Poliklinik, Klinikum

rechts der Isar der Technischen Universität München, Ludwig-Maximilians-Universität, Munich,

Germany.

Eirini Marouli



Nicola Martinelli

Department of Medicine, Section of Internal Medicine, University of Verona, Verona, Italy.

Mark I. McCarthy

Wellcome Trust Centre for Human Genetics, and Oxford Centre for Diabetes, Endocrinology

and Metabolism, University of Oxford, and Oxford National Institute for Health Research

Biomedical Research Centre, Churchill Hospital, Oxford, UK.

Natalie R. Van Zuydam

Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK.

Christa Meisinger

Institutes of Epidemiology II, Helmholtz Zentrum München–German Research Center for


Tõnu Esko

Department of Genetics, Harvard Medical School, Boston, MA 02114, USA. Division of

Endocrinology, Boston Children’s Hospital, Boston, MA 02115, USA. Broad Institute of the

Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA. Estonian

Genome Center, University of Tartu, Tartu, Estonia.

Evelin Mihailov

Estonian Genome Center, University of Tartu, Tartu, Estonia.

Stefan A. Escher

Department of Clinical Sciences, Department of Clinical Sciences in Malmo, Lund University

Diabetes Center, Lund University, Lund, Sweden.

Maris Alver

Estonian Genome Center, University of Tartu, Tartu, Estonia. Institute of Molecular and Cell

Biology, Tartu, Estonia.

Susanne Moebus


Germany.

Andrew D. Morris

School of Molecular, Genetic and Population Health Sciences, University of Edinburgh Medical

School, Edinburgh, Scotland, UK.






Majid Nikpay

Ruddy Canadian Cardiovascular Genetics Centre, University of Ottawa Heart Institute, Ottawa,

Canada.

Oliviero Olivieri


Louis-Philippe Lemieux Perreault

Montreal Heart Institute, Montreal, Canada.

Alaa AlQarawi

King Abdulaziz University, Jeddah, Saudi Arabia.

Neil R. Robertson

Wellcome Trust Centre for Human Genetics, and Oxford Centre for Diabetes, Endocrinology

and Metabolism, University of Oxford, Oxford, UK.

Karen O. Akinsanya

Merck Sharp & Dohme, Rahway, NJ 07065, USA.

Dermot F. Reilly


Thomas F. Vogt


Wu Yin


Folkert W. Asselbergs

Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College

London, London, UK.

ICIN-Netherlands Heart Institute, Utrecht, the Netherlands.

Charles Kooperberg

Fred Hutchinson Cancer Research Center, University of Washington, Seattle, WA 98109, USA.

Rebecca D. Jackson

Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, Ohio State

University, Columbus, OH 43210, USA.

Eli Stahl

Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029,

USA.

Konstantin Strauch

Institute for Medical Informatics, Biometry and Epidemiology, Chair of Genetic Epidemiology,

Ludwig-Maximilians-Universität, Munich, Germany. Institute for Genetic Epidemiology (M.M.-

N., K.S.), Helmholtz Zentrum München–German Research Center for Environmental Health,

Neuherberg, Germany.

Tibor V. Varga

Department of Clinical Sciences, Lund University Diabetes Center, Lund University, Lund,

Sweden.

Melanie Waldenberger

Research Unit of Molecular Epidemiology and Institute of Epidemiology II, Helmholtz Zentrum

München–German Research Center for Environmental Health, and German Center for Diabetes

Research, Neuherberg, Germany.

Lingyao Zeng


Chunyu Liu

Framingham Heart Study, Framingham, MA 01702, USA.

Population Sciences Branch, National Heart, Lung, and Blood Institute, Bethesda, MD 20892,

USA.

Georg B. Ehret

Cardiology Division, Department of Medicine, Geneva University Hospital, Geneva,

Switzerland.

Center for Complex Disease Genomics, McKusick–Nathans Institute of Genetic Medicine, Johns

Hopkins University School of Medicine, Baltimore, MD 21205, USA.

Christopher Newton-Cheh





USA.

Daniel I. Chasman



Rajiv Chowdhury



Marco Ferrario



Ian Ford

Robertson Centre for Biostatistics, University of Glasgow, Glasgow, UK.

J. Wouter Jukema

Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands; The


Frank Kee

Director, UKCRC Centre of Excellence for Public Health, Queens University, Belfast, Northern

Ireland.

Kari Kuulasmaa






Markus Perola


Institute of Molecular Medicine FIMM, University of Helsinki, Finland.

Danish Saleheen

Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of

Pennsylvania, Philadelphia, PA 19104, USA. Centre for Non-Communicable Diseases, Karachi,

Pakistan. Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care,

University of Cambridge, Cambridge, UK.

Naveed Sattar


Neil Poulter


Praveen Surendran



David Tregouet

Sorbonne Université, UPMC Univ Paris 06, ICAN Insti-tute for Cardiometabolism and

Nutrition, Paris, France.

Robin Young



Joanna M.M. Howson



Adam S. Butterworth




John Danesh




Wellcome Trust Sanger Institute, Genome Campus, Hinxton CB10 1HH, UK.

Diego Ardissino

Division of Cardiology, Azienda Ospedaliero–Uni-versitaria di Parma, Parma, Italy.

Associazione per lo Studio della Trombosi in Cardiologia, Pavia, Italy.

Erwin P. Bottinger



Raimund Erbel


Germany.

Paul W. Franks


Malmö, Sweden. Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston,

MA 02115, USA. Department of Public Health & Clinical Medicine, Umeå University, Umeå,

Sweden.

Domenico Girelli


Alistair S. Hall

Leeds Institute of Genetics, Health and Therapeutics, University of Leeds, Leeds, UK.

G. Kees Hovingh

Department of Vascular Medicine, Academic Medical Center, Amsterdam, the Netherlands.

Adnan Kastrati


Wolfgang Lieb

Institute of Epidemiology and Biobank popgen (W.L.), Christian-Albrechts-University Kiel,

Kiel, Germany.

Thomas Meitinger

Institute of Human Genetics (T.M.), Technische Universität München, Germany. DZHK partner

site Munich Heart Alliance (C.H., K.L.L., M.M.-N., T.M., A.P., H.S.), I. Medizinische Klinik

und Poliklinik, Klinikum rechts der Isar der Technischen Universität München, Germany.

Institute of Human Genetics, Helmholtz Zentrum München–German Research Center for


William E. Kraus

Duke Molecular Physiology Institute and Division of Cardiology, Department of Medicine,

Duke University, Durham, NC 27710, USA.

Svati H. Shah

Duke Molecular Physiology Institute and Division of Cardiology, Department of Medicine,

Duke University, Durham, NC 27710, USA.

Ruth McPherson

Ruddy Canadian Cardiovascular Genetics Centre, University of Ottawa Heart Institute, Ottawa,

Canada.

Marju Orho-Melander

Department of Clinical Sciences in Malmo, Lund University Diabetes Center, Lund University,

Clinical Research Center, Skåne University Hospital, Malmo, Sweden.

Olle Melander

Department of Clinical Sciences in Malmo, Lund University Diabetes Center, Lund University,

Clinical Research Center, Skåne University Hospital, Malmo, Sweden.

Andres Metspalu

Estonian Genome Center, University of Tartu, and Institute of Molecular and Cell Biology,

Tartu, Estonia.

Colin N.A. Palmer


Dundee, UK.

Annette Peters

Institute for Epidemiology II, Helmholtz Zentrum München, Neuherberg, Germany.

German Center for Cardiovascular Disease Research, partner-site Munich, Munich, Germany.

German Center for Diabetes Research, Neuherberg, Germany.

Daniel J. Rader

Departments of Medicine and Genetics and the Cardiovascular Institute, Perelman School of

Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.

Muredach P. Reilly

Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania,

Philadelphia, PA 19104, USA.

Ruth J.F. Loos, Ph.D.,



The Mindich Child Health and Development Institute, Ichan School of Medicine at Mount Sinai,


Alex P. Reiner

Department of Epidemiology, University of Washington, Seattle WA 98195, USA. Division of

Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle WA 98195, USA.

Dan M. Roden


Jean-Claude Tardif

Montreal Heart Institute, Montreal, Quebec, Canada. Université de Montréal, Montreal, Quebec,

Canada.

John R. Thompson

Departments of Cardiovascular Sciences and Health Sciences, University of Leicester, and NIHR

Leicester Cardiovascular Biomedical Research Unit, Glenfield Hospital Leicester, UK.

Nicholas J. Wareham

University of Cambridge, MRC Epidemiology Unit, Institute of Metabolic Science,

Addenbrooke’s Hospital, Cambridge, UK.

Hugh Watkins



Cristen J. Willer


Ann Arbor, Michigan 48109, USA.


Michigan 48109, USA. Department of Human Genetics, University of Michigan, Ann Arbor,

Michigan 48109, USA.

Panos Deloukas


Queen Mary University of London, London, UK. Princess Al-Jawhara Al-Brahim Centre of

Excellence in Research of Hereditary Disorders (PACER-HD), King Abdulaziz University,

Jeddah 21589, Saudi Arabia.

Nilesh J. Samani

Department of Cardiovascular Sciences, University of Leicester, UK. NIHR Leicester

Cardiovascular Biomedical Research Unit, Glenfield Hospital, UK. BHF Cardiovascular

Research Centre, Glenfield Hospital, Leicester, LE3 9QP, UK.

Heribert Schunkert

Deutsches Herzzentrum München, Technische Universität München, Germany. DZHK partner

site Munich Heart Alliance, Ludwig-Maximilians-Universität, Munich, Germany.

Sekar Kathiresan





USA.

Materials and Methods

Subject Identification and Ascertainment

Individuals with high HDL-C levels above the 95th percentile for age and sex were

systematically recruited. For this study 341 individuals of European ancestry with HDL-C > 95th

percentile were selected for targeted sequencing, as were 407 individuals of European ancestry

with low HDL-C levels below the 25th percentile for age and sex. Briefly, using customized

hybrid capture (Agilent) baits, we targeted and enriched for sequencing the exons of the ~990

genes located within 300 kb of each of the 95 loci with significant associations (P < 5 × 10–8)

with plasma lipid levels identified by the Global Lipids Genetics Consortium as of 2010 (22).

We performed next-generation sequencing using Illumina Genome Analyzers at the Broad

Institute as described previously (40). Base pairs were called and sequencing reads were aligned

to the human genome reference GRCh37 (hg19), and sequencing metrics were processed using

the Picard pipeline with an output of Binary Alignment Map (BAM) files.

The Genome Analysis Toolkit Unified Genotyper was used to genotype all variant sites,

calculate initial quality control metrics, and filter based on these values, and variants were

annotated using SnpEFF (41). Variants were indexed per sample in Variant Call Format (VCF)

files. Genotypes for each individual were computed at each site and tabulated.

Additionally, SCARB1 mutation carriers were identified by genotyping subjects with high

vs. low HDL-C using the Exome Chip (HumanExome BeadChip v1.0, Illumina, Inc., San Diego,

CA). The Exome Chip contains >240,000 coding SNPs derived from all mutations found >2

times across >1 data set among 23 separate data sets comprising a total of >12,000 individual

exome and whole genome sequences. The P376L variant is included. In total, 353 high HDL-C

males (HDL-C ≥ 70 mg/dL), 747 high HDL-C females (HDL-C ≥80 mg/dL), 1106 low HDL-C

males (HDL-C ≤40 mg/dL), and 573 low HDL-C females (HDL-C ≤50 mg/dL) were genotyped

using the Exome Chip. Subject samples were drawn from previous research studies conducted in

our laboratory. After removing subjects from analysis for which demographic covariates were

not available, there remained 805 subjects with high HDL-C and 989 subjects with low HDL-C

analyzed for association of SCARB1 P376L allele frequency with HDL-C.

To confirm the presence of the P376L mutation in carriers, a nearby region of 995 bp of

SCARB1 was amplified by PCR using the following primers: forward:

TGGTTTGGTTGGTCAGTGGCG, reverse: AGGGCTGCCTCCAGCTCACAT; and the

following PCR conditions: 95ºC, 2 min; (95ºC, 30 s; 62.6ºC, 30 s; 72ºC; 100.0 s) repeat 29

times; 72ºC, 5 min; 4ºC forever. PCR products were purified using a QIAquick PCR Purification

Kit (Qiagen, Germantown, MD, USA) and sequenced by Sanger sequencing (42) by Genewiz

Inc (South Plainfield, NJ, USA). Sequences were compared with SCARB1 NCBI reference

sequence NG_028199.1 using Sequencher (Gene Codes, Ann Arbor, MI, USA).

In silico analysis

To predict the effect of the P376L mutation on SR-BI protein structure and function we

used the publicly available algorithms Condel and Raptor X (43-46). Condel generated a

“consensus deleteriousness” score, obtained by combining 3 prediction algorithms (PolyPhen-2,

MutationAssessor, and SIFT). Raptor X was used to predict effects on local secondary structure

of the SR-BI due to the P376L variant. Protein alignment data were generated by MacVector

(MacVector, Inc., Cary, NC) using a Gonnet similarity matrix with open gap penalty = 10 and

extended gap penalty = 0.1.

DNA cloning and adeno-associated virus generation

The coding sequence of SR-BI (CCDS 9259.1) was obtained from Thermo Scientific

(cDNA clone MGC:120767 IMAGE:7939577) in a pCR4-TOPO cloning vector (Invitrogen).

The P376L and the P297S mutations were inserted in the sequence by site directed mutagenesis

using the Quickchange II site directed mutagenesis kit (Agilent technologies) and following

primers:

P376L-F 5'-cctggacatccacctggtcacgggaatcc-3'

P376L-R 5'-ggattcccgtgaccaggtggatgtccagg-3'

P297S-F 5'-ggtgtttgaaggcatctccacctatcgcttcgt-3'

P297S-R 5'-acgaagcgataggtggagatgccttcaaacacc-3'

The wild type and the mutated coding sequences were then amplified by PCR and subcloned into

a pcDNA3.1/V5-His TOPO expression plasmid (Invitrogen) according to manufacturer`s

instructions. For in vivo studies, SCARB1 WT and P376L cDNA sequences were amplified by

PCR using the following primers containing KpnI and NotI sites, and then subcloned by

KpnI/NotI double digestion followed by ligation into adeno-associated virus serotype 8 vector

(AAV8) containing the liver-specific thyroxine-binding globulin (TBG) promoter provided by

the University of Pennsylvania Vector Core (47-52). Virus production, purification and

quantification were carried out by the core facility.

Radioactive labeling of HDL

Total human HDL (1.063<d<1.21 g/ml) and HDL3 (1.125<d<1.21 g/ml) were obtained

by sequential ultracentrifugation as described before (53). HDL was labeled with 125I by a

modification of the iodine monochloride method for the binding experiment at 4°C and they

were labeled with 125I-tyramine-cellobiose (125I-TCB) for all the other experiments (54). To

further label 125I-TCB-HDL with 3H-cholesteryl-ether (3H-CE), 500 μCi of 3H-CE resuspended

in 50 μl of ethanol were added to a solution containing heat-inactivated human lipoprotein

deficient serum (200 mg protein) and iodinated HDL (6 mg protein). The solution was incubated

overnight at 37°C with gentle shaking, followed by reisolation of the dual-labeled HDL by

sequential ultracentrifugation.

Cell culture and in vitro assays

COS7 cells were cultured in Dulbecco modified Eagle medium (DMEM) supplemented

with 10% fetal bovine serum at 37°C in a humidified 5% CO2 incubator and were passaged using

trypsin. For selective cholesterol uptake COS7 cells were plated at a density of 3 × 104 cells/cm2

in 6 well plates. One day following plating (Day 1), cells were transfected using Lipofectamine

2000 (4 μg DNA/well, 3:1 Lipofectamine:DNA ratio) with a pcDNA3.1/V5-His-TOPO plasmid

encoding either SCARB1 WT, SCARB1 P376L, or SCARB1 P297S cDNAs. One group received a

mock transfection with plasmid containing no cDNA. Each experimental group was tested in

quintuplicate. On Day 2, culture medium was changed to DMEM 0.5% BSA containing 20

μg/ml of 3H-cholesteryl hexadecyl ether/125I-TCB-labeled HDL3 and incubated for 3 hours at

37°C. In one well per plate a 40-fold excess of nonradiolabeled (“cold”) HDL was added to a

final concentration of 800 μg/ml. After incubation cells were washed with PBS and lipids were

extracted in two consecutive steps using a 3:2 hexane-isopropanol mixture. After drying, proteins

were solubilized using 2 ml of 0.1N NaOH. The hexane/isopropanol fraction was dried under

nitrogen, resuspended in 600 μl of toluene, and counted separately in scintillation and gamma

counters. The NaOH fraction was counted in the gamma counter and measured for protein

concentration using a Lowry protein assay (55). Counts from wells with 40-fold excess of cold

HDL (non receptor-specific counts) were subtracted from counts from other wells to calculate

the receptor-specific activity internalized. Data analysis was performed as previously described

(56), with the following modification: 125I activity (counts per minute) in the NaOH fraction

were considered as cell-surface associated lipoproteins (it has been shown previously that when

performing this experiment under similar conditions, the fraction of cell associated counts that is

trichloroacetic acid-soluble is approximately 5%) (57). A negligible amount of 125I counts were

found in the hexane-isopropanol fraction. SR-BI protein expression was determined by western

blotting. 10 μg of protein from cellular lysates determined by Lowry assay from each sample was

loaded in NuPAGE gels (Life Technologies) using denaturing and reducing conditions for one-

dimensional SDS-PAGE using MOPS running buffer. Proteins were separated by electrophoresis

for approx. 1 hour, transferred to nitrocellulose membranes, blocked for 2 hours at room

temperature using 5% fat-free milk in PBS (0.05% tween 20). Membranes were then incubated

with anti-SR-BI antibody (NB400-131, Novus, 1:500 dilution in 5% milk-PBS-tween) at room

temperature for 1 hour, washed three times with PBS-tween, and then with anti-goat IgG-HRP

conjugate (700-035-147, Jackson Immunoresearch, 1:2500 dilution in 5% milk-PBS-tween) at

room temperature for 1 hour. Proteins were visualized after washing again after secondary

antibody incubation using Luminata Crescendo chemiluminscent agent (Millipore). Films were

incubated with membranes after chemiluminescent reagent in the dark typically for 30 s, 1 min, 2

min, and 5 min exposures for each membrane. Human β-actin was used as a loading control and

was visualized after incubation with mouse anti-actin primary antibody (sc-81178, Santa Cruz,

1:20 dilution in 5% milk-PBS-tween) and then goat anti-mouse IgG HRP (sc-2302, Santa Cruz,

1:1000 in 5% milk-PBS-tween).

The binding of 125I-HDL3 at 4°C to COS7 cells was measured using a modification of the

method from Nieland et al (58). Briefly, COS7 cells were seeded at a concentration of 3 × 104

cells/cm2 in 12 well plates. The next day, cells were transfected using Lipofectamine 2000 (1.6

μg DNA/well, 3:1 Lipofectamine:DNA ratio) with a pcDNA3.1/V5-His-TOPO plasmid

encoding either SCARB1 WT, SCARB1 P376L, or SCARB1 P297S cDNAs. An additional plate

of cells was transfected with a plasmid encoding GFP (pAAV-CB-EGFP), which was used as a

control. On the following day, cell plates were precooled for 30 min on ice, washed with cold

DMEM and then exposed for 2 hours to the assay medium (DMEM, 1% P/S, 0.5%BSA +

lipoproteins) at 4°C. After the incubation, cells were washed twice with ice-cold PBS containing

2 mg/ml of bovine serum albumin (BSA) and a third time with PBS without BSA. Cells were

then lysed in 0.1N NaOH. Lysates were used for 125I counting and for measuring protein content

using a Lowry assay. In parallel to this, cells were seeded in 10 cm plates, transfected as

described above and lysed after 24 hours in RIPA buffer to check SR-BI protein expression by

immunoblotting for SR-BI as described above using 10 μg of protein from cellular lysates.

Cell surface biotinylation assay

COS7 cells were seeded and transfected as described above in 175 cm2 flasks. Twenty-

four hours after transfection, cell-surface-associated proteins were isolated using the Pierce Cell

Surface Protein Isolation Kit (Pierce Biotechnology Inc., Rockford, IL) according to the

manufacturer`s instructions. After lysis, protein concentration was determined by bicinchoninic

acid (BCA) assay and the same amount of proteins was loaded on NeutrAvidin Agarose beads to

isolate biotinylated proteins. After multiple washing steps, proteins were eluted from the beads

and loaded on a 10% Bis-Tris polyacrylamide gels for western blotting. Blots for β-actin and

Na+/K+-ATPase were used as intracellular and surface-associated controls, respectively.

We generated induced pluripotent stem cells (iPSC) from peripheral blood mononuclear cells

(PBMCs) and differentiated them into hepatocyte-like cells as described elsewhere (24-26, 59,

60) After complete differentiation of iPSCs to hepatocyte-like cells (approx. 20–22 days from

initiation of differentiation), we performed selective cholesterol uptake as described above.

For Endoglycosidase H sensitivity assays, 100 μg of liver homogenates from mice

transduced with human SCARB1 or Null AAV8 vectors were treated with Endoglicosidase H

(New England Biolabs, Ipswich, MA), Sialidase A, or a combination of Sialidase A and PNGase

F (Prozyme, Hayward, CA) according to manufacturer’s instructions. 30 μg of digestion products

were then loaded on a 10% Bis-Tris polyacrylamide gel for western blotting as described above.

Non-treated homogenates were used as controls. Endoglycosidase H sensitivity assays were also

performed using lysates from transfected COS7 cells (10 μg each lysate) and from iPSC-derived

hepatocyte-like cells (50 μg each lysate). In these experiments, cell lysate in RIPA buffer plus

Complete Protease Inhibitor Cocktail (Roche) was used.

AAV-mediated overexpression of SCARB1 WT and P376L in Scarb1 KO mice

AAV serotype 8 vectors expressing SCARB1 WT or P376L were expressed in Scarb1 KO

mice, which were subsequently studied for effects on lipoprotein metabolism. Male Scarb1 KO

mice (6 per group) were weighed, bled through retro-orbital bleeding and injected with 1012

genome copies (GC) of AAV-SCARB1-WT, AAV-SCARB1-P376L, or Null vector via

intraperitoneal injection. Mice were fasted for 5 hours, weighed, and bled again at 12 days after

injection. Plasma lipid levels were determined as described above. At 2 weeks after injection,

HDL kinetics using radiolabeled human HDL was measured (61). Briefly, mice were injected via

tail-vein injection with a mixture of 125I-tyramine-cellobiose-HDL (3 × 106 cpm/mouse) and 3H-

cholesteryl-ether (CE)-HDL (3 × 106 cpm/mouse) and bled at 2 min post-injection and 1, 3, 6, 9,

and 24 hours. Plasma 3H and 125I activity at each time point were determined by using

scintillation counting and gamma counting, respectively. At 24 hours post-injection of

radiolabeled HDLs, mice were weighed, anesthetized, terminally bled, and sacrificed. Livers

were lysed in phosphate buffered saline (PBS), and 3H and 125I were counted in the lysates to

determine liver-associated counts. Liver-associated counts were then expressed as micrograms of

HDL component/mg of liver to allow a direct comparison between liver associated 3H and 125I

counts. Selective cholesterol uptake was calculated as the difference between liver-associated 3H

counts and liver associated 125I counts expressed as micrograms of HDL component/mg of liver.

SR-BI protein expression levels in liver homogenates were determined by western blot as

described above using 30 μg of protein from tissue lysates followed by densitometric analysis

performed with ImageJ (U.S. National Institutes of Health, Bethesda, MD) (62).

Subject selection and study visit

Carriers of the P376L variant identified through next-generation sequencing were

recruited through a comprehensive recall protocol approved by the institutional review board of

the Perelman School of Medicine at the University of Pennsylvania, Philadelphia. Additionally,

control subjects were chosen from a database of previous participants in studies conducted in our

laboratory. All subjects recruited gave informed consent. Two groups of controls were selected:

those having HDL-C levels between the 25th and the 75th percentile for age and sex (normal

HDL-C control group) and those with HDL-C levels greater than the 75th percentile for age and

sex but confirmed to lack the P376L variant (high HDL-C control group). The two control

groups were selected to match on aggregate the age, gender and race of the carrier group. Study

visits were performed in the Clinical and Translational Research Center (CTRC) facility at the

Perelman School of Medicine at the University of Pennsylvania. Venous blood after overnight

fasting was drawn from each subject to measure a comprehensive metabolic panel, complete

blood count, standard urinalysis, reticulocyte count, T- and B-cell counts, antinuclear antibody

screen, anti double-stranded DNA antibody screen, and anti-neutrophil cytoplasmic antibody

screen, which were measured by the William Pepper Laboratory of the Hospital of the University

of Pennsylvania. A comprehensive lipid panel {total cholesterol [TC], VLDL cholesterol

[VLDL-C], LDL cholesterol [LDL-C], HDL-C, triglycerides [TG], lipoprotein(a) [Lp(a)], and

apolipoproteins A-I, A-II, C-II, C-III, and E [ApoA-1, ApoA-II, ApoC-II, ApoC-III, ApoE]}was

also performed by the Lipid Research Laboratory of the Hospital of the University of

Pennsylvania per standard protocols as described previously (63). HDL-C and LDL-C levels

were measured both directly and after precipitation with phosphotungstic acid (PTA).

Lipoproteins were separated by fast protein liquid chromatography on a Superose 6 column as

described previously (64).

Blood was also collected in BD Vacutainer CPT Cell Preparation tubes (BD, Franklin

Lakes, NJ, USA) for PBMC isolation and storage and in sodium citrate tubes for platelet

isolation and testing. Platelet aggregation studies (PAS) were performed at the Translational

Core Laboratory (TCL) using a photometric aggregometer (Biodata Corp, Horsham, PA).

To induce platelet aggregation the following stimuli were used at the concentrations

reported in brackets: arachidonic acid (200, 250, 300, 400, 500 mg/ml), collagen (0.04, 0.08,

0.12, 0.16, 0.2 mg/ml), ADP (0.625, 1.25, 2.5, 5, 10 mM), TRAP (1, 2, 3, 4, 5 μM). Arachidonic

acid, collagen, and ADP were obtained from Biodata Corp. (Horsham, PA); TRAP was obtained

from Sigma Chemical Corp (St. Louis, MO). To correct for possibly unreported intake of drugs

with antiaggregant action, subjects whose platelets failed to reach 95% aggregation with 500

mg/ml of arachidonic acid were excluded from the analysis. Additional blood was also drawn

and frozen for later batch measurement of adrenocorticotropic hormone (ACTH), cortisol,

estradiol, progesterone, luteinizing hormone (LH), follicle-stimulating hormone (FSH), and

testosterone also by the TCL using standard radioimmunoassay methods (“Coat-A-Count”) from

SIEMENS Healthcare Diagnostics. Subjects taking exogenous steroids were excluded from the

analysis. Study subjects were also given the option of performing a 24-hour urine collection

according to standard methods the day before their visit or on a later date. Collected urine was

frozen in single-use aliquots for batch measurement of cortisol by the Translational Core

Laboratory as per standard protocols. The acquisition of carotid intima-media thickness (IMT)

ultrasound images was performed according to a standardized protocol, adopted from the

Atherosclerosis Risk in Communities (ARIC) study (65) and as per American Society of

Echocardiography and Society for Vascular Medicine Guidelines for IMT analysis (66). The

scanner used was a Siemens Sequoia (Mountain View, CA, USA) with a 9 Linear probe and a

custom designed carotid IMT preset. One heterozygous subject who was unable to travel to the

study site had blood drawn locally. These samples were shipped to the study site for the lipid

panel and autoimmune tests; the subject also sent the results of a recent comprehensive metabolic

panel and complete blood count. Data from clinical phenotyping studies were collected and

managed using REDCap (Research Electronic Data Capture) electronic data capture tools hosted

at the Perelman School of Medicine at the University of Pennsylvania (67).

Platelet separation and cholesterol measurement

Approximately 4.5 ml of blood was drawn from each subject into tubes containing 25 g

sodium citrate, 8 g citric acid and 500 ml H2O2. Tubes were centrifuged using a tabletop

centrifuge at 200xg for 15 min. at room temperature. Platelet-rich plasma (PRP) was collected as

supernatant from this spin and was centrifuged at 900g for 5 min. The pellet from this spin was

resuspended in 8 ml 1xPBS and centrifuged again at 900g for 5 min. Platelet pellets were

collected and resuspended in 200 μl 1 × PBS and stored at –20°C. For measurement of

cholesterol, platelet extracts were dried using a centrifugal evaporator (Genevac). D7-cholesterol

(Cat. No. 700041; Avanti Polar Lipids, Inc.) was added to each sample as an internal standard.

Lipids were extracted by addition of 1 ml chloroform:methanol (2:1) at 4°C for 2 hours. The

nonpolar phase from the platelet extracts was collected and dried through centrifugal evaporation

along with cholesterol standards (1.0–100.0 nmol) containing equivalent amounts of D7-

cholesterol internal standard. All samples were derivativized using pentaflurobenzoyl chloride

and extracted with petroleum ether before measurement by gas chromatography-mass

spectrometry using negative chemical ionization. Peak areas for cholesterol and D7-cholesterol

from platelet extracts were compared to that from the standard curve generated from cholesterol

standards to yield cholesterol content in each sample. For platelet protein measurement, aqueous

extracts from chloroform:methanol extraction of platelet samples were spun to remove excess

methanol and dried using centrifugal evaporation and then resuspended in 200 μl RIPA buffer

containing 0.1 N NaOH and heated at 55°C for approximately 24 hours with periodic vortexing.

Protein concentrations from these extracts were measured by a Lowry assay (55) (Pierce).

Cholesterol measurements for each sample were then normalized to the corresponding protein

concentrations.

Fractionation of plasma lipoproteins from study subjects

VLDL, LDL, and HDL subfractions were isolated from EDTA plasma by single step,

isopycnic non-denaturing density gradient ultracentrifugation based on a modification of the

method developed by Chapman et al. as previously described(68, 69). Using this procedure, five

subfractions of LDL (LDL1, d 1.019–1.023 g/ml; LDL2, d 1.023–1.029 g/ml; LDL3, d 1.029–

1.039 g/ml; LDL4, d 1.039–1.050 g/ml; and LDL5, d 1.050–1.063 g/ml) were isolated, followed

by two subfractions of large, light HDL2 (HDL2b, d 1.063–1.087 g/ml and HDL2a, d 1.088–1.110

g/ml), and three subfractions of small, dense HDL3 (HDL3a, d 1.110–1.129 g/ml; HDL3b, d

1.129–1.154 g/ml; and HDL3c, d 1.154–1.170 g/ml). Total HDL was prepared as a mixture of

HDL2b, 2a, 3a, 3b and 3c subfractions at their equivalent plasma concentrations.

Protein quantification and chemical composition of HDL

Overall chemical composition of HDL subfractions was assessed using commercially

available enzymatic kits (total protein: Thermo Scientific, Villebon-sur-Yvette, France; total

cholesterol, free cholesterol, phospholipids: DiaSys, Holzheim, Germany; triglycerides:

Biomérieux, Marcy l’Etoile, France). Cholesteryl ester (CE) concentration was calculated by

multiplying the difference between total and free cholesterol concentrations by 1.67. Total

lipoprotein mass was calculated as the sum of total protein, CE, FC, PL and TG and expressed as

plasma concentrations (mg/dl). HDL apoplipoproteins (ApoA-I, ApoA-II, ApoC-III) were

quantified using automated enzymatic methods (Konelab, Thermo Scientific, Waltham, MA,

USA).

Cellular cholesterol efflux capacity of HDL

The cholesterol efflux capacity of total HDL was assessed in a human THP-1 monocytic

cell line (ATCC, Manassas, VA, USA) as previously reported(70). THP-1 monocytes were

cultured at 37°C and 5% CO2 in RPMI 1640 media with 10% FBS, 2 mM glutamine, 100 µg/ml

penicillin, and 100 µg/ml streptomycin (complete media) using standard cell culture procedures

and differentiated into macrophage-like cells using 50 ng/ml phorbol 12-myristate 13-acetate

(PMA). Cells were loaded for 24 h with [3H] cholesterol-labeled acetylated LDL (acLDL, 1

µCi/ml) in serum-free RPMI 1640 culture medium supplemented with 50 mM glucose, 2 mM

glutamine, 0.2% BSA, 100 µg/ml penicillin and 100 µg/ml streptomycin (serum-free media).

After equilibration in serum-free media, cells were incubated for 4 h with HDL from subjects.

Efflux capacity was normalized to HDL-phospholipid content because PL has been shown to

represent the key component in determining cholesterol efflux capacity of HDL (15 µg/ml HDL-

PL in serum-free media, total volume 250 µL) (27). Cholesterol efflux capacity was measured as

the percent of radioactivity counts in the media over counts in the cell lysate, after adjustment for

non-specific diffusion to non-HDL containing media.

Statistical analysis

Data analysis was conducted using Excel (Microsoft Corp.) and GraphPad Prism 6.0

(GraphPad Software Inc.). In vitro and in vivo data were compared by Student`s unpaired T-test.

A p-value of less than 0.05 was considered statistically significant. The same test was employed

to compare HDL subclasses and composition between controls and heterozygotes. For data

regarding clinical samples, Chi-square testing (for categorical variables) and one-way ANOVA

(for continuous variables) were used to determine the effect of SCARB1 P376L carrier status on a

number of variables. A P value less than 0.05 was considered statistically significant. Tukey's

multiple comparison test (α=0.05) was used following ANOVA where appropriate to determine

which groups were responsible for statistically significant differences among groups. Data are

reported as mean ± SD. The association of P376L and CHD was tested among CHD cases and

controls of European ancestry assembled from the CARDIoGRAM Exome Consortium and the

CHD Exome+ Consortium. The summary OR of CHD for P376L carriers was calculated using a

Mantel-Haenszel fixed-effects meta-analysis without continuity correction, a method that is well

suited to low (and even zero) counts and resultant ORs. The association of the P376L variant

with HDL-C, LDL-C and TG in the Global Lipids Genetics Consortium Exome Chip

Genotyping study was assessed through meta-analysis of single variant score statistics in up to

301,025 individuals (71).

Supplemental Text

Conservation of SR-BI Proline 376 Across Species and Paralogues

The proline at position 376 of SR-BI is highly conserved down through Drosophila and

Anopheles, as well as in the closely related human genes CD36 and LIMP2 and is in a sequence

of highly invariant residues (Fig. S1). The “consensus deleteriousness” score according to

Condel was 0.906 (on a scale from 0 to 1, with 1 indicating maximum deleteriousness). Raptor

X, a secondary structure prediction program, predicts that substitution of proline 376 with

leucine increases the probability of this region to be in a beta-sheet confirmation from 36% to

61%. This finding is in agreement with the suggestion from the recently reported structure of

another scavenger receptor LIMP-2 (72) that the region containing P376 in SR-BI is a disordered

linker sequence joining beta strands 15 and 16 on the extracellular loop proximal to the C-

terminal transmembrane domain.

Impact of SCARB1 P376L on SR-BI processing and cholesterol ester uptake in transfected

COS7 cells

We tested the functional impact of the P376L variant on HDL-CE selective uptake in

transfected COS7 cells. Studies with transfected COS7 cells revealed that the P376L variant was

defective in promoting selective CE uptake (Fig. S3A) despite similar protein expression in total

cell lysates (Fig. S3B). Notably, the P376L variant had less activity in this assay than one

previously reported P297S variant (32) (Fig. S3A). In studies of 125I-labeled HDL3 binding at 4

ºC, the P376L variant abrogated cell-surface binding (Fig. S3C), despite equal SR-BI protein

levels in total cell lysates among groups (Fig. S3D).

We hypothesized that the reduced CE uptake and cell-surface HDL-binding by the SR-BI

P376L variant could be due to abnormal processing of the mutant protein, preventing its cell-

surface localization. To study this, we isolated cell-surface proteins from COS7 cells transfected

with SCARB1 WT, P376L and P297S using biotinylation and streptavidin pull-down. Western

blotting of whole cell lysates compared with purified biotinylated samples (as tested by probing

for β-actin and Na/K-ATPase) indicated that cell-surface expression of SR-BI P376L was

markedly reduced compared to both WT and P297S (Fig. S3E). This suggested that the P376L

mutation causes SR-BI to be retained inside the cell after translation. We next sought to

determine the molecular defect underlying loss of cell-surface localization and CE uptake

function by SR-BI P376L observed in the cell and mouse models. Given that SR-BI undergoes

N-glycosylation in the endoplasmic reticulum concomitant with proper folding and localization,

we hypothesized that altered post-translational modification may underlie its reduced cell-surface

localization (27-29). We measured the molecular weights of SR-BI forms after endoglycosidase-

H (Endo-H) treatment of transfected COS7 expressing WT or mutant SR-BI (Fig. S3F). In COS7

cell lysates in the absence of Endo-H, whole cell lysates from cells transfected with SR-BI WT,

P376L, or the P297S variant displayed a single band of approximately 65 kDa, representing fully

glycosylated mature SR-BI. For all three constructs, Endo-H treatment resulted in a major band

of approximately 45 kDa, representing the fully-deglycosylated (fully Endo-H sensitive) SR-BI

protein. However, WT and P297S displayed additional larger bands consistent with a pool of

partially Endo-H sensitive SR-BI possessing complex N-linked glycans, whereas P376L

displayed only the single, unmodified, fully-sensitive band. Because of high levels of cDNA

overexpression in the COS7 transfection, we believe the majority of the SR-BI across all groups

in this experiment is not fully processed, thus resulting the relatively higher proportion of the

total SR-BI exhibiting complete Endo-H sensitivity (Fig. S3F) relative to that observed in

analogous experiments in iPSC-derived hepatocyte-like cell and murine hepatocyte lysates after

Endo-H treatment (Fig. 2F-G of main text).

Phenotyping studies of P376L homozygote and heterozygotes

The P376L homozygote and eight P376L heterozygotes were recruited for additional

deep phenotyping. Two age, sex, and race matched control groups were used, one with normal

HDL-C levels (25th–75th percentile for age, race and sex) and a second with high HDL-C levels

(>95th percentile for age, race and sex) in which SCARB1 mutations were excluded. All of the

P376L study participants were of European ancestry, almost exclusively of Ashkenazi Jewish

descent. Two P376L heterozygotes, 4 normal HDL-C controls and 4 high HDL-C controls

reported an alcohol intake of more than 1 drink per day, but there was no self-reported alcohol

abuse. Smokers and subjects with diabetes were excluded. The P376L homozygote reported

having a seizure disorder that was not treated pharmacologically at the time of participation.

Previous in vitro, mouse and human genetics studies have suggested that SR-BI in

platelets is necessary for proper platelet activity and thrombosis (6, 32, 73-75). To test the effects

of the P376L variant on platelet activity, we isolated platelets from carriers and controls and

performed light transmission aggregometry (LTA) after stimulation with known platelet

activators arachidonic acid, collagen, ADP and TRAP over a range of doses. We found only a

slight decrease in ADP-induced maximal aggregation in platelets isolated from the P376L

homozygote relative to heterozygotes and control subjects at a dose of 5 mmol (Fig. S5A). No

differences in platelet activity in response to other stimulants were observed between the groups.

We also extracted lipids from platelets and measured platelet cholesterol content among groups.

We observed that platelet cholesterol increased in a genotype dose-dependent manner from

controls (mean 122 nmol/mg protein) to heterozygotes (mean 139 nmol/mg protein) to the

homozygote subject (244 nmol/mg protein) (Fig. S5B). However, the difference between normal

HDL-C controls and heterozygotes was not significant, and these differences were reduced when

the values normalized to plasma total cholesterol levels, suggesting that elevated platelet

cholesterol in carriers reflects increased plasma HDL-C levels rather than a platelet SR-BI

specific function (Fig. S5C). There was also no difference in total circulating platelet levels

among groups (data not shown).

SR-BI also takes up HDL-cholesteryl esters in adrenal glands and reproductive tissues for

steroid hormone production in mice and humans (6, 18, 76), so we evaluated the impact of

SCARB1 loss-of-function on steroid hormones in our recruited participants. We found no

difference in morning serum cortisol, ACTH and 24-hr urinary cortisol-to-creatinine (Fig. S6)

across participants, moderately higher testosterone in male P376L heterozygotes relative to

normal HDL-C controls, but no differences across groups in FSH and LH.

Fig. S1

Fig. S1. SR-BI Protein Sequence Alignment Across Species. Amino acid sequence

alignment of SR-BI across 12 species and human SR-BI paralogues CD36 and LIMP-2. Shown

is the ~60 residue sequence alignment adjacent human SR-BI residue Pro376 (indicated by red

box). Dark grey shading indicates full conservation of a given residue across indicated species.

Light grey shading indicates a different but conservative amino acid for the given species

compared to the others listed.

Fig. S2

Fig. S2. Gene expression in control and SCARB1 P376L iPSC-derived hepatocyte-like cells

(HLCs). A. ALB Gene expression by quantitative RT-PCR of mRNA from control and P376L

mutant iPSC-derived HLCs. Cells were differentiated 21–25 days before experiments and RNA

isolation for gene expression analysis. B. AFP gene expression in iPSC-derived HLCs. C.

SCARB1 gene expression in iPSC-derived HLCs.

ALB

M15-4 M15-10 M14-5 M14-11 Human Liver012345

90100110120130140 Control HLCs

Pro376Leu HLCs

Rel

ativ

e E

xpre

ssio

n(N

orm

aliz

ed t

o

-act

in)

AFP

M15-4 M15-10 M14-5 M14-11 Human Liver0

20406080

100200000.0

400000.0

600000.0

800000.0 Control HLCs

Pro376Leu HLCs

Rel

ativ

e E

xpre

ssio

n(N

orm

aliz

ed t

o

-act

in)

SCARB1

M15-4 M15-10 M14-5 M14-11 Human Liver0

20

40

60

80

100

120

140 Control HLCs

Pro376Leu HLCs

Rel

ativ

e E

xpre

ssio

n(N

orm

aliz

ed t

o

-act

in)

A.

C.

B.

Fig. S3

Fig S3. SCARB1 P376L abrogates SR-BI function in transfected COS7 cells. A. Selective

cholesterol uptake in COS7 cells expressing SR-BI WT vs. P376L. Cells were transfected with

plasmids expressing WT, P376L or P297S forms of SR-BI and incubated at 37°C for 3 hours

with 125I/3H-labeled HDL3 to determine HDL cholesterol ester (CE) specific uptake. Data

represent the mean of a quintuplicate determination after subtraction of the determinations done

with the addition of a 40-fold excess of cold HDL. B. Western blot showing SR-BI expression

levels in whole cell lysates from COS7 cells transfected for selective cholesterol uptake

experiment in (A). C. Binding of HDL to SR-BI at 4°C in transfected COS7 cells. Transfected

cells were exposed to 125I-labeled HDL3 for 2 hours at 4°C to measure HDL binding. Radioactive

counts from cells were then quantified to determine the amount of cell-associated 125I-HDL3.

Data points represent the mean +/- S.D. of a triplicate determination after subtraction of the

A. B. B.

D.

NTC WT P376L P297S

GFP WT P376L P297S

β-actin

SR-BI

β-actin

SR-BI

P297S P376L WT

125I HDL concentration (µg protein/ml medium)�

Bo

un

d H

DL

(n

g H

DL

pro

tein

/mg

ce

ll p

rote

in) �

C.

P297S

P376L

WT

β-actin

SR-BI

Na+/K+ ATPase

GFP WT P376L P297S GFP WT P376L P297S

Whole Cell Lysate Cell Surface E.

WT P376L A.

GFP P297S WT P376L GFP P297S

- - + + EndoH:

SR-BI

- +

45

52

65 Mature

Partially EndoH sensitive

Fully EndoH sensitive

kDa - +

F.

determinations done with the addition of a 40-fold excess of unlabeled HDL3. ** P<0.01, one-

way ANOVA. D. Western blot showing SR-BI expression levels in whole cell lysates from

COS7 cells transfected in panel (C). E. Immunoblot of SR-BI after cell-surface biotinylation in

transfected COS7 cells. Cells were transfected with GFP, SR-BI WT, P376L or P297S plasmids

and biotinylated before collection of whole cell lysate (left) or incubation with NuetrAvidin

beads and elution of cell-surface localized proteins (right). Whole cell lysates (lanes 1–4) and

cell-surface proteins (lanes 5–8) were separated by SDS-PAGE and immunoblotted for human

SR-BI. Actin and Na/K-ATPase were used respectively as intracellular and surface-associated

controls. F. Endo-H sensitivity of SR-BI from transfected COS7 cells. Cells were transfected

with plasmids encoding GFP or different forms of SR-BI (WT, P376L, P297S) and cell lysates

were treated with Endo-H to release complex N-linked glycans and molecular forms of SR-BI

were monitored by immunoblotting. For A & C, data represent mean Error bars indicate mean

values ± S.D. ** P<0.01, Student’s unpaired T-test.

Fig. S4

Fig. S4. Hepatic SCARB1 expression and impact on selective cholesterol uptake from HDL

in mice. A. Human SCARB1 transcript expression levels measured by quantitative RT-PCR from

livers of mice expressing Null or SR-BI AAV vectors. Gene expression was measured from total

hepatic RNA after reverse transcription and normalized to expression levels of actin. B. SR-BI

immunoblot (right) from livers of Scarb1 KO mice expressing Null, SR-BI WT, and SR-BI

P376L 2 week s after AAV administration. C. Liver 3H CE uptake from dual-labeled HDL

administration in mice expressing Null or SR-BI AAVs. D. Hepatic selective cholesterol uptake

measured by relative difference of hepatic 3H CE and 125I TC uptake in livers of mice expressing

Null or SR-BI AAVs after dual-labeled HDL administration. All data represent mean values ±

S.D. for each of the 3 groups. *P<0.05, ** P<0.01, ***P<0.001, Unpaired T-test.

SR-BI

AAV-Null AAV-SRBI-WT AAV-SRBI-P376L

kDa

64

ID#

β-actin

77 81 84 87 90 93 78 82 85 88 91 94 79 83 86 89 92 95

42

Null WT P376L0

200

400

600

800

1000

Rel

ativ

e L

iver

SCARB1

mR

NA

(n

orm

aliz

ed t

o

-act

in)

A. B.

Null WT P376L0

2

4

6

8

Hep

atic

3H

CE

Up

take

(n

g H

DL

/mg

cel

l pro

tein

)

N.S.

******

F.

Null Wild type P376L0

1

2

3

4

5

Hep

atic

Sel

ecti

ve C

ho

lest

ero

l Up

take

(n

g H

DL

/mg

cel

l pro

tein

)

N.S.

******

G.C. D.

Fig. S5

Fig. S5. Platelet aggregation and cholesterol content. A. Platelet aggregation measured by

light transmission aggregometry after stimulation with increasing doses of ADP. Data represent

the percentage maximal aggregation. B. Platelet cholesterol content measured by LC/MS. C.

Platelet cholesterol content after normalization for plasma total cholesterol levels. Bars represent

mean values ± S.D.. * P<0.05, one-way ANOVA followed by Tukey's multiple comparisons test.

A. B. C.

Fig. S6

Fig. S6. Impact of SCARB1 P376L on adrenal and gonadic steroidogenesis. A. Morning

serum cortisol in carriers vs. controls. B. Morning plasma ACTH. C. Cortisol / creatinine ratio in

24-hour urine. D. Serum testosterone in males. E. Serum LH in males. F. Serum FSH in males.

Bars represent mean values ± S.D.. * P<0.05, one-way ANOVA followed by Tukey's multiple

comparisons test.

A. B. C.

D. E. F.

Fig. S7

Fig. S7. Carotid intima media thickness (cIMT) in SCARB1 P376L carriers vs. controls. A.

cIMT for all subjects (male and female combined). B & C. cIMT results for males and females,

respectively. Dotted lines represent the 25th and the 75th percentile values from the ARIC study.

Bars represent mean values ± S.D.. All data shows the average left / right cIMT.

A. B. C.

Table S1. Association of SCARB1 P376L with plasma lipid traits in global lipids genetics

consortium exome array genotyping. The relationship between the frequency of P376L carriers

and plasma lipid traits was measured in the Global Lipids Genetics Consortium cohort by

genotyping of the variant on the Exome Array and using the Score test.

Trait Number of

subject included

Minor allele

frequency Beta (SE) in SD

P value (score

test)

HDL-C 301025 0.00033 +0.57 (0.071) 1.41 × 10–15

LDL-C 280551 0.00033 +0.065 (0.074) 0.381

TG 290277 0.00034 –0.052 (0.072) 0.474

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