Common non-synonymous variants in PCSK1 confer

risk of obesity in Europeans.

Michael Benzinou1,2

, John W.M. Creemers3, Helene Choquet

2, Stephane Lobbens

2, Christian

Dina2, Emmanuelle Durand

2, Audrey Guerardel

2, Philippe Boutin

2, Beatrice Jouret

4, Barbara

Heude5, Beverley Balkau

5, Jean Tichet

6, Michel Marre

7,8,9, Natascha Potoczna

6, Fritz Horber

10,

Catherine Le Stunff11

, Sebastien Czernichow12

, Annelli Sandbaek13

, Torsten Lauritzen13

, Knut

Borch-Johnsen14,15

, Gitte Andersen14

, Wieland Kiess16

, Antje Körner16

, Peter Kovacs17

, Peter

Jacobson18

, Lena MS Carlsson18

, Andrew J Walley1, Torben Jørgensen

19, Torben Hansen

14, Oluf

Pedersen14,15

, David Meyre2 and Philippe Froguel

1,2.

1 Genomic Medicine, Imperial College London, Hammersmith Hospital, United Kingdom

2 CNRS 8090-Institute of Biology, Pasteur Institute, Lille France

3 Department of Human Genetics, University of Leuven, Belgium

4 INSERM U563, Children's Hospital, Toulouse, France

5 INSERM, U780-IFR69, Villejuif; Univ Paris Sud, Faculty of Medicine, Le Kremlin-Bicêtre, France

6 the Regional Institut for Health, Tours, France

7 INSERM U695, Paris, France

8 Université Paris Diderot - Paris 7, Paris, France

9 Department of Endocrinology-Diabetology and Nutrition, Bichat Claude Bernard Hospital, Paris, France

10 Klinik Lindberg, Winterthur, Switzerland

11 Department of Pediatric Endocrinology and INSERM U561, Saint Vincent de Paul Hospital, René Descartes

University, Paris, France 12

INSERM U557, INRA U1125, CNAM EA3200, Université Paris 13, CRNH IdF, & Hôpital Avicenne (AP-HP),

Bobigny, F-93017 France 13

Department of General Practice, University of Aarhus, Aarhus, Denmark

14 Steno Diabetes Center, DK-2820 Gentofte, Copenhagen, Denmark

15 Faculty of Health Science, University of Aarhus, Aarhus, Denmark

16 University Hospital for Children & Adolescents, University of Leipzig, Germany

17 Department of Internal Medicine III, Interdisciplinary Centre for Clinical Research, University of Leipzig,

Germany 18

Department of Molecular and Clinical Medicine Institute of Medicine. The Sahlgrenska Academy, Göteborg

University, Göteborg, Sweden 19

Research Centre for Prevention and Health, Glostrup University Hospital, Glostrup, Denmark.

Supplementary Methods

Subjects

The study protocols were approved by all local ethics committees and informed consent was

obtained from each subject before participation in the study.

Sequencing:

We selected 48 obese patients in families that contributed to the linkage with obesity: 23 subjects

from the Hager et al. genome wide scan and 25 subjects from the Bell et al. genome wide scan

(mean BMI = 45.8 ±6.95 kg/m2)

1,2.

Initial French case-control and French familial association study:

Obese subjects in the initial case-control were all French of European descent recruited through a

multimedia campaign run by the Centre National de la Recherche Scientifique (CNRS) or by the

Hotel Dieu Hospital. The initial case-control association study included 1,045 adults obese cases

(BMI>30 kg/m2) and 1,265 unrelated non-obese individuals (Supplementary Table 4). The

control subjects were unrelated adult non-obese French of European descents pooled from three

separate studies:’ 1) 266 individuals (mean BMI = 23.1 ±2.16 kg/m²; mean age = 42.5 ±4.5

years; men/women 105/161), 2) 263 controls (mean BMI = 22.6 ±2.6 kg/m²; mean age =

59.4 ±11.1 years; men/women 119/144), 3) 736 individuals (mean BMI = 23.8±1.8 kg/m²; mean

age = 53.5± 5.6 years; men/women 293/443) recruited respectively, at the CNRS Lille, the

“Fleurbaix-Laventie Ville Santé” study 3 and the Epidemiologic Data on the Insulin Resistance

Syndrome (D.E.S.I.R.) Study 4.

Replication studies: (Supplementary Table 4)

The Danish case-control consists of 3,074 unrelated obese adults recruited via the Danish

ADDITION study sampled through Department of General Practice at University of Aarhus 5.

The case group has been compared to 2,790 non-obese, non-diabetic, Danish being part of the

population-based Inter99 sample of middle-aged people sampled at Research Centre for

Prevention and Health 6.

The Swiss case-control consist of 542 subjects representative of a Swiss general population

ascertained from anonymous healthy blood donors from CHUV of Lausanne and 551 unrelated

class III obese Swiss adults from Zurich 7. These consecutive subjects were referred to the clinic

for refractory obesity between January 1999 and December 2000. A physician specialized in

obesity obtained phenotypic data, including gender, age, weight, height, body-mass index, and

parents, siblings, and children) was routinely obtained.

The French childhood obesity cohort 1 consists of 580 obese children (BMI above the 97th

percentile) recruited by the CNRS UMR8090 for familial obesity (at least one first-degree

relative displaying obesity) 8. They have been compared to 1,010 unrelated non-obese (BMI < 27

kg/m2) and non-diabetic French adults of European descents issued from the “Supplémentation

en Vitamines et Minéraux Antioxydants” (SU.VI.MAX) cohort 9.

The French childhood obesity cohort 2 consist of 505 French obese children 10

(BMI > 97th

percentile) recruited in the Saint Vincent de Paul Hospital (Paris) that has been compared to 532

unrelated young adult non-obese, non-diabetic French of European descents selected from the

Haguenau study 11

(BMI < 25 kg/m²).

The German Leipzig Schoolchildren cohort consist of 715 lean children (BMI between 16th

and

85th

percentile) and 283 obese children (BMI >90th

percentile) both of German origin 12

. Schools

were chosen to cover representative local areas within Leipzig and suburbs and different levels

of schools to establish a representative cohort of German children 12

.

The 154 Swedish families discordant for class III obesity have been described elsewhere 13

.

Sequencing and Genotyping

Sequencing was performed using an Applied Biosystems (ABI) 3730XL DNA sequencer in

combination with the Big Dye Terminator Cycle Sequencing Ready Reaction Kit (ABI).

Purification of the sequencing reaction was carried out using MultiScreen® SEQ384 filter plates

(Millipore).

Initial case-control genotyping was done using the ABIPlex™ assay, which is based on the

Oligonucleotide Ligation Assay (OLA) combined with multiplex PCR target amplification

(http://www.appliedbiosystems.com). The chemistry of the assay relies on a set of universal core

reagent kits and a set of SNP-specific ligation probes allowing a multiplex genotyping of 48

SNPs simultaneously in a unique sample. A quality control measure was included by using

specific internal controls for each step of the assay (according to the manufacturer’s

instructions). Allelic discrimination was performed by capillary electrophoresis analysis using an

ABI 3730XL DNA Analyzer and GeneMapper3.7 software. Duplicate samples were assayed

with a concordance rate of 100%.

High-throughput genotyping for the variants rs6232 and rs6235 in replication samples was

performed using the TaqMan®

SNP Genotyping Assays (Applied Biosystems, Foster City, Calif.

USA). The PCR primers and TaqMan probes were designed by Primer Express and optimized

according to the manufacturer’s protocol.

As a standard laboratory quality control measure, a random 10% of DNA samples were

systematically re-genotyped to ensure minimal genotyping error. Concordance rate was

comprised between 99% and 100%.

Statistical analysis

Allele frequencies between cases and controls were compared using the χ2

test integrated in

Cocaphase 14

. Odds ratios and P-values are from logistic regression, adjusted for age and gender,

in the adult obesity case-control studies. For the childhood obesity case-controls, the tests were

only adjusted for gender.

For the childhood obesity case controls, the test was only adjusted for gender. The significance

of the associated SNPs with obesity was not altered after the logistic regression analysis. We

tested independence of association using the software THESIAS 15

.

Association testing of both SNPs in the family based cohort was performed using the TDT test

which compares the number of transmissions of the at-risk allele, from heterozygous parent to

affected offspring, to its expectation. A McNemar test assessed the significance.

To produce an overall significance of increased allelic frequency in obese individuals in Europe,

we combined the P-values of our different studies using the Mantel-Haenszel method. All SNPs

were in Hardy-W[einberg equilibrium (P >0.05).

Multiple testing issues in the case-control comparisons were addressed using permutation

analysis that takes into consideration the inter-SNPs correlation. For the study of 9 SNPs, we

performed 1000 permutations of case-labels for each case-control analysis, obtaining an

empirical P-value (eP-value) for each SNP.

We used linear mixed models to assess the significancy of in vitro enzymatic assays (R

software).

To test the effect of N221D on one side and of S690T-Q665E on the other side, we used a four

haplotypes genetic model using the program THESIAS. This method estimates likelihood of

specified models for haplotype analyses under the GLM framework. This allowed testing of

constrained models. The center information was included in the logistic regression

(Supplementary Table 6).

In silico analysis of associated SNPs

Variants were aligned against the « most conservation » track from UCSC (Vertebrate Multi

Alignment & Conservation) 16

and the « 5-way regulatory potential » track. The « most

conservation » track showed the predictions of conserved elements.

In vitro studies

HEK-293T cells were transfected with empty vector (-), wild type, Q665E-S690T, N221D,

N222D or G593R mutated PC1/3 using FuGENE (Roche Molecular Biochemicals) for both

enzymatic and processing studies. Catalytic activity of the variant PC1/3 proteins was analyzed

using immunopurified enzyme, as described before 17

. Activities were normalized for expression

levels using Kodak Digital Science (Kodak Imager with 1D Image Analysis Software, version

3.0). Activities were determined in the linear phase (45-180 min). Western blot analyses were

performed as previously described 18

using anti-FLAG M2 (Sigma-Aldrich, Bornem, Belgium).

Acknowledgement

This work was in part supported by Conseil Regional Nord-Pas de Calais/FEDER. Work on the

Danish dataset had received support from the Lundbeck Foundation Centre of Applied Medical

Genomics in Personalized Disease Prediction, Prevention and Care, the FOOD Study Group/the

Danish Ministry of Food, Agriculture and Fisheries and Ministry of Family and Consumer

Affairs, grant no. 2101-05-0044. Work on the German replication data set was supported by

grants from the German Research Council DFG (KFO 152: ‘‘Atherobesity’’, project KO

3512/1–1 (TP 1) to A.K. and 1264/10–1 (TP5) to W.K.), from the EC (‘‘PIONEER’’ integrated

project grant to W.K.), and from the German Hypertension Association to A.K. The Leipzig

Schoolchildren project was supported by unrestricted grants from Pfizer Pharma and Novo

Nordisk (W.K.). J.C is supported by GOA2008/16. We would also like to thank Cecile Lecoeur

for her statistical assistance.

Supplementary Figure 1 – Linkage disequilibrium map (D’) of PCSK1 haplotype blocks

(extending 103.7 kb: between rs271913-rs4869134) and its flanking regions produced using

Haploview. Shading is used to indicate LD between pairs of SNPs: darker shades indicate

stronger LD.

Ell2

PCSK1

CAST

rs271913

rs4869134

103.7

kb

Ell2

PCSK1

CAST

rs271913

rs4869134

103.7

kb

Supplementary Table 1 - Names and genomic sequence location of

genes found in a 5.6 Mb interval on chromosome 5q between

D5S644 and D5S1463 microsatellite markers.

Markers and Genes Genomic sequence location

D5S1463 chr5: 90,241,952-90,242,277

KIAA0686; GPR98 chr5: 89,890,373-90,495,789

AK091866 chr5: 90,642,594-90,645,975

BC043415 chr5: 90,712,010-90,752,288

ARRDC3 chr5: 90,700,297-90,714,877

AK056485 chr5: 91,531,110-91,768,107

BX647510 chr5: 92,771,283-92,924,925

AK124699 chr5: 92,771,283-92,932,255

BC048999 chr5: 92,771,283-92,932,281

BC042879; BC018089 chr5: 92,771,283-92,942,756

AK095719; CR605542 chr5: 92,903,334-92,942,756

NR2F1 chr5: 92,944,799-92,955,542

C5orf21 chr5: 92,979,531-93,436,230

FLJ25680 chr5: 93,101,771-93,103,065

DKFZp564D172 chr5: 93,242,773-93,472,995

AK130941 chr5: 93,514,427-93,757,737

KIAA0825 chr5: 93,774,447-93,838,645

C5orf36 chr5: 93,880,919-93,980,040

CR590796 chr5: 93,931,013-93,932,110

DKFZp564C0469 chr5: 93,980,141-94,057,329

ANKRD32 chr5: 94,040,269-94,057,329

MCTP1 chr5: 94,068,957-94,646,035

CR614705 chr5: 94,150,250-94,155,060

FAM81B chr5: 94,752,804-94,811,900

KIAA0372 chr5: 94,825,878-94,916,438

UNQ630 chr5: 94,916,581-94,918,513

ARSK chr5: 94,916,581-94,966,562

GPR150 chr5: 94,981,736-94,983,040

RFESD chr5: 95,008,342-95,018,600

SPATA9 chr5: 95,013,641-95,044,470

KIAA0878 chr5: 95,079,093-95,117,553

RHOBTB3 chr5: 95,092,606-95,157,827

GLRX chr5: 95,175,431-95,184,137

FIS chr5: 95,213,900-95,221,590

ELL2 chr5: 95,248,853-95,323,531

PCSK1 chr5: 95,751,875-95,794,708

D5S644 chr5: 95,838,450-95,838,765

Supplementary Table 2 - List of polymorphisms identified through sequencing in

PCSK1 coding regions, plus 3.5 Kb extending both 5’ and 3’.

SNP description dbSNP Chromosome

position

Gene

position

SNP

change

Amino-

Acid change

MAF

-2459T>C rs155982 95,797,051 5' t>c - 0.14

-2116G>A rs3762986 95,796,618 5' g>a - 0.43

-1980T>G rs3762985 95,796,483 5' t>g - 0.16

-1152C>G rs155979 95,795,654 5' c>g - 0.14

-101T>C rs6230 95,794,603 5' t>c - 0.16

-96C>T rs35753085 95,794,598 5' c>t - < 0.05

IVS1+37A>G rs725522 95,794,286 intron 1 a>g - < 0.05

IVS3-449G>T rs459608 95,785,368 intron 3 g>t - 0.39

IVS3-260C>T rs456709 95,785,179 intron 3 c>t - 0.33

T175M - 95,784,824 exon 4 c>t T/M < 0.05

N204N rs6231 95,783,348 exon 5 c>t N/N < 0.05

N221D rs6232 95,777,541 exon 6 a>g N/D 0.06

IVS6-43C>T - 95,774,008 intron 6 c>t - 0.39

S325N - 95,772,361 exon 8 g>a S/N < 0.05

IVS8+676G>A rs156021 95,771,558 intron 8 g>a - 0.33

IVS9-1273A>C rs271922 95,762,919 intron 9 a>c - 0.39

IVS9-1224G>A rs271921 95,762,870 intron 9 g>a - 0.33

N550N rs6233 95,758,868 exon 12 c>t N/N 0.39

Q665E rs6234 95,754,730 exon 14 c>g Q/E 0.28

S690T rs6235 95,754,654 exon 14 c>g S/T 0.28

+40317T>C rs3811942 95,754,196 3' t>c - 0.33

+41093A>T rs17085675 95,753,420 3' a>t - 0.28

+41582T>C rs2882298 95,752,931 3' t>c - 0.28

+43144G>A rs271939 95,751,369 3' g>a - 0.33

Supplementary Table 3 – Linkage disequilibrium (r2 and D’) between the 19 frequent

polymorphisms of PCSK1 identified in 48 obese subjects.

Supplementary Table 4 - Description of Study Populations.

Population Status n Age (years)

[SD] BMI [SD]

BMI Zscore

[SD]

% of

Males

French Adult

Obesity

Controls 1,265 51 [10] 22.8 [2] - 45

Obese adults 1,045 46.7 [12.7] 42.8 [6.1] - 68

Danish Adult

obesity

Controls 2,790 55.5 [10.1] 22.7 [1.7] - 50

Obese adults 3,074 57.3 [7.9] 33.8 [2.6] - 49

Swiss Adult obesity General population 551 - - - -

Class III obese adults 542 42.9 [10.5] 46.8 [6.2] - 20

French Childhood

Obesity 1

Controls 1,010 49 [5.5] 22.3 [2.3] - 29

Obese children 580 10.64 [3.5] - 4.35 [1.3] 45

French Childhood

obesity 2

Controls 532 21.95 [3.7] 21.11 [2] -0.41 [0.5] 54

Obese children 505 11.75 [3.1] 29.75 [5.8] 4.02 [1] 39

German Childhood

obesity

Controls 715 11.7 [2.7] 18.1 [2.1] -0.02 [0.02] 46

Obese children 283 11.5 [3.7] 30.4 [6.2] 2.70 [0.03] 45

Supplementary Table 5 - Genotypic distribution of PCSK1 SNPs in French Caucasian

obese adults compared to unrelated adult non-obese, non-diabetic, French Caucasians.

Cohorts Genotypes n (Frequency) MAF

Genotyping

success

rate (%)

Allelic

P-value Odds ratio [CI]

rs155982 SNP-2459T>C TT TC CC

Controls 939 (76.1) 272 (22) 23 (1.9) 26.5 97.5 0.096 1.15 [0.97-1.37]

Obese adults 756 (73.5) 244 (23.7) 28 (2.7) 27.3 98.4

rs3762986 SNP-2116G>A GG GA AA

Controls 388 (31.6) 602 (49.1) 236 (19.2) 43.8 96.9 0.0041 1.19 [1.06-1.34]

Obese adults 258 (27) 474 (49.6) 223 (23.4) 48 91.4

rs3762985 SNP-1980T>G TT TG GG

Controls 905 (72.5) 311 (24.9) 32 (2.6) 27.1 98.7 0.16 1.12 [0.96-1.32]

Obese adults 717 (69.8) 281 (27.4) 29 (2.8) 27.4 98.3

rs6232 N221D AA AG GG

Controls 1159 (93) 83 (6.7) 4 (0.3) 24.9 98.5 0.0042 1.51 [1.14-2.00]

Obese adults 930 (89.8) 100 (9.7) 6 (0.6) 25.2 99.1

rs271922 IVS9-1273A>C AA AC CC

Controls 438 (35.3) 613 (49.5) 189 (15.2) 39.8 97.9 0.066 0.89 [0.79-1.01]

Obese adults 411 (39.7) 477 (46) 148 (14.3) 38.9 99.1

rs6234 Q665E CC CG GG

Controls 689 (56.5) 471 (38.6) 59 (4.8) 29.4 96.4 0.00007 1.31 [1.15-1.50]

Obese adults 490 (49.3) 422 (42.5) 82 (8.2) 32.8 95.1

rs6235 S690T GG GC CC

Controls 707 (56.2) 483 (38.4) 68 (5.4) 30 99.4 0.00004 1.32 [1.15-1.50]

Obese adults 503 (48.6) 439 (42.6) 90 (8.7) 33.3 98.7

rs3811942 SNP+40317T>C TT TC CC

Controls 677 (54.4) 495 (39.8) 73 (5.9) 25.7 98.5 0.045 0.82 [0.76-0.99]

Obese adults 621 (60) 347 (33.5) 67 (6.5) 23.2 99.1

rs271939 SNP+43144G>A GG GA AA

Controls 555 (43.6) 588 (46.2) 130 (10.2) 34.8 97.5 0.0046 0.83 [0.73-0.94]

Obese adults 518 (50.8) 403 (39.5) 98 (9.6) 34.2 97.5

Odds ratios and P-values are from logistic regression, adjusted for age and gender.

Supplementary Table 6 – Independency effect of N221D and S690T-Q665E

N221D S690T F unaff

(%)

F aff

(%) OR P-value V1 V2 V3

1 1 74.2 70.4 1 1 1

1 2 21 22.9 1.15 [1.1-1.2] 0.000003 OR12 OR12 =1 OR12=OR22 2 1 0.3 0.1 0.55 [0.3-1.1] 0.088 OR21 OR21=OR22 OR21=1 2 2 4.5 6.4 1.46 [1.3-1.6] 1.29.10

-11 OR22 OR22=OR21 OR22=OR12

-8307.83 -8312.41 -8318 .82

a1.4x10

-13 b0.002 c

2.7x10-6

F unaff = frequency in controls

F aff = frequency in cases

OR : haplotype OddsRatio

ORxx = means that under the column model, estimation of ORxx is estimated without constraint.

ORxx=ORyy : means that under the column model, ORxx is estimated but constrained to be equal to ORyy (and vice-

versa).

V1 : is the global two-variant effect.

V2 : under this model, SNP N221D has no effect.

V3 : under this model, SNP S690T –Q665E has no effect.

a: Test V1 compared to a model in which there is no genetic effect.

b: Likelihood comparison of model V2 and V1. absence of N221D effect.

c: Likelihood comparison of model V3 and V1. absence of S690T-Q665E effect.

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