Role of the Proprotein Convertase Subtilisin/Kexin 5 Gene in High-density Lipoprotein Metabolism: Potential Implications for Atherosclerotic Cardiovascular Disease Development

By

Iulia Iatan Department of Biochemistry McGill University, Montreal August 2008

A thesis submitted to McGill University in partial fulfillment of the requirements of the degree of Master of Science in Biochemistry

© Iulia Iatan, 2008

ABSTRACT

Low plasma high-density lipoprotein cholesterol (HDL-C) is a well- established risk factor for coronary artery disease (CAD). The proprotein convertase subtilisin/kexin 5 (PC5/PCSK5) is known to inactivate endothelial lipase, enzyme critical in modulating HDL-C levels. In this study, we investigated the role of human PCSK5 genetic variants on HDL-C. We examined haplotypes at the PCSK5 locus in 9 multigenerational families with HDL-C<10th percentile and discovered segregation with low HDL-C in one family. We genotyped novel single nucleotide polymorphisms (SNPs) found through sequencing and tagSNPs from the HapMap Project (n=182 total) in 457 individuals with CAD and identified 9 SNPs associated with HDL-C (P<0.05), the strongest being rs11144782 (minor allele frequency 0.164, p=0.002). This SNP decreased HDL-C by 0.076 mmol/L in a gene dosage-effect and was also associated with very low- density lipoprotein (P=0.039), triglycerides (P=0.049) and apolipoprotein B (P=0.022) levels. We conclude that variability at PCSK5 influences HDL-C levels and consequently, CAD risk.

2 RÉSUMÉ

Le rôle protecteur des lipoprotéines de haute densité (HDL) envers les maladies cardiovasculaires est documenté par un grand nombre d‟études épidémiologiques. Il est bien établi qu‟un niveau bas de HDL-cholestérol (HDL- C) représente un facteur de risque des maladies coronariennes. Des études récentes ont démontré que la proprotéine convertase subtilisin/kexin 5 (PC5/PCSK5) est impliquée dans l‟inactivation de la lipase endothéliale, enzyme clé dans la modulation des niveaux plasmatiques de HDL-C. Dans cette étude, nous avons examiné la relation entre les variantes génétiques de PCSK5 et les niveaux du HDL-C chez l‟humain. L‟analyse des haplotypes PCSK5 dans 9 familles multigénérationnelles caractérisées par un HDL-C<10e centile a montré une ségrégation avec des niveaux faibles de HDL-C chez une famille. D‟autre part, l‟analyse génotypique des polymorphismes de nucléotide simple (SNP) trouvés par séquençage et des tagSNPs du projet HapMap (n=182 en total) dans 457 personnes coronariennes, a permis l‟identification de 9 variantes dans PCSK5 associées à une déficience en HDL-C (P<0.05), la plus forte représentant le rs11144782 (fréquence d‟allèle mineure 0.164, P=0.002) et causant une diminution de HDL-C par 0.076 mmol/L dans une relation gène dose-effet. Celui- ci à également été associé à des niveaux faibles de lipoprotéines de très basse densité (P=0.039), des triglycérides (P=0.049) et de l'apolipoproteine B (P=0.022). En conclusion, nos travaux indiquent que la variabilité du gène PCSK5 influence les niveaux du HDL-C et conséquemment, le risque des maladies coronariennes.

3 ACKNOWLEDGEMENTS

I would like to acknowledge the contributions of the many people who helped bring the completion of this study to fruition. First and foremost, I would like to give my sincerest thanks to my supervisor, Dr. Jacques Genest, for his constant guidance, experimental insight and for his endless enthusiasm for research. His direction and understanding were fundamental factors to the success of this work, and his contributions to my future endeavors will continue beyond its completion. Thank you for providing me with numerous research opportunities, for your incredible devotion to teach, and most importantly, for giving me the motivation and inspiration in continuing on in research! I would also like to especially thank my good friend Zari Dastani for her support in every step of this study, from the very beginning until the very end. I am extremely grateful for your friendship, ideas and help over the past two years. Likewise, I am very thankful for the genetic expertise and tremendous advice received from Dr. Engert, throughout all stages of the project. Thank you for always pushing me to go further in my learning! As well, a very sincere thank you goes to Michel Marcil for his remarkable patience and numerous explanations, particularly at the beginning of this study, and for his immense friendship. I am also extremely appreciative of the help received from Ron, for his continuous clarifications and patience in helping me with the analyses and completion of this work. Finally, but not least, I would like to give my heartfelt thanks to my family and friends for their never-ending support and encouragement. A special thanks goes to my mom for her constant help, encouragement, and faith in me; I couldn‟t have done it without you!

4 TABLE OF CONTENTS

ABSTRACT ...... 2 RÉSUMÉ ...... 3 ACKNOWLEDGEMENTS ...... 4 TABLE OF CONTENTS ...... 5 LIST OF TABLES ...... 6 LIST OF FIGURES ...... 7 LIST OF ABBREVIATIONS ...... 8 1. INTRODUCTION ...... 9 1.1 Cardiovascular disease and lipoprotein metabolism ...... 9 1.1.1 Risk factors associated with coronary heart disease ...... 9 1.1.2 Lipoprotein metabolism and transport ...... 10 1.2 Protein Convertases ...... 15 1.2.1 Introduction to the protein convertases superfamily ...... 15 1.2.2 Lipidemic effects of PCs ...... 18 1.2.3 PCSK5 and PC5 ...... 18 1.3 Involvement of PC5 in HDL metabolism ...... 21 1.3.1 Study rationale ...... 21 1.3.2 Hypothesis ...... 23 1.3.3 Goal and objectives ...... 23 2. METHODS ...... 24 2.1 Study population ...... 24 2.1.1 Family subjects ...... 24 2.1.2 Unrelated individuals ...... 24 2.2 Biochemical measurements ...... 25 2.3 Haplotyping...... 26 2.4 Sequencing ...... 26 2.5 Single nucleotide polymorphisms selection...... 26 2.6 Genotyping ...... 27 2.7 Statistical analyses ...... 27 3. RESULTS ...... 28 3.1 Familial segregation analyses (haplotypes-building) ...... 28 3.2 Association studies...... 43 3.2.1 PCSK5 sequencing reveals novel polymorphisms ...... 43 3.2.2 TagSNPs selection ...... 49 3.2.3 Genotyping ...... 51 3.2.4 Quantitative and case-control analyses ...... 51 4. DISCUSSION ...... 59 4.1 Conclusions ...... 59 4.2 Future studies ...... 64 APPENDICES ...... 75

5 LIST OF TABLES

Table 1. Plasma lipoprotein composition………………………………………....14

Table 2. Characteristics of the 9 French Canadian low HDL-C families .………29

Table 3. Phenotypic characteristics of 9 French Canadian Families with low HDL- C shown separately as affected individuals, unaffected individuals, and probands ………...………………………………...……………………………...... 30

Table 4. PCSK5 microsatellite genetic markers………………………………....31

Table 5. Oligonucleotide primers for PCSK5 gene sequencing ……………...... 44

Table 6. Phenotypic characteristics of the 12 French Canadian probands chosen for DNA sequencing …………………………………………………...... 44

Table 7. PCSK5 polymorphisms identified by sequencing …………………...... 46

Table 8. Quantitative trait analysis …………………………………………...... 53

Table 9. Traits associated with rs11144782………………………….………...... 54

Table 10. Case-control analysis…………………………………….…………....55

Table 11. Genotypic means of the 4 independent signals………….…………….57

6 LIST OF FIGURES

Figure 1. Schematic diagram of the lipid transport system …………………...... 13

Figure 2. Schematic primary structures of the nine PCs……………….……...... 17

Figure 3. Subcellular localization of PCs and their trafficking……….……...... 17

Figure 4. PCSK5 gene locus …………………………………………………….20

Figure 5. Isoforms PC5A and PC5B and their processing………………...... 20

Figure 6. Inactivation of EL and LPL by PC5, PACE4, and furin...... 22 Figure 7. PCSK5 genetic markers...... 32

Figure 8. (A-I). Familial segregation analyses in 9 kindred with familial low HDL-C...... 34-42

Figure 9. Novel variants identified at the PCSK5 locus (A-G)...... 47-49

Figure 10. Linkage disequilibrium (LD) map of SNPs investigated in the PCSK5 gene...... 50

Figure 11. LD Plot of Significant SNPs in the QTL Analysis...... 53

Figure 12. Significant independent signals found at the PCSK5 locus...... 56

Figure 13. Effect of rs11144782 on HDL-C levels...... 58

Figure 14. Conceptual Mechanism of Action of PC5 in Relation to HDL Metabolism...... 62

7 LIST OF ABBREVIATIONS

ABCA1: ATP-binding cassette A1 PCR: polymerase chain reaction

ADAM: a disintegrin and metalloproteinase sPLA1: phospholipase A2 Apo: apolipoprotein PLTP: phospholipid transfer protein ApoA1: apolipoprotein A1 RCT: reverse cholesterol transport ApoB: apolipoprotein B SMase: sphingomyelinase BMI: body mass index SNP: single nucleotide polymorphism CAD: coronary artery disease SR-BI: scavenger receptor BI CETP: cholesteryl ester transfer protein T. Chol: total cholesterol CHD: coronary heart disease TGN: trans-Golgi network CRD: cys-rich domain TIMPs: tissue inhibitors of metalloproteases E: embryonic day Tg: triglycerides EL: endothelial lipase VLDL: very low density lipoprotein FFA: free fatty acids HL: hepatic lipase HDL: high density lipoprotein HDL-C: high density lipoprotein cholesterol HLGAG: heparin-like glycosaminoglycans HSPG: heparin sulfate proteoglycans IDL: intermediate-density lipoprotein KO: knock-out LCAT: lecithin:cholesterol acyl transferase LDL: low density lipoprotein LDL-C: low density lipoprotein cholesterol LDLR: LDL receptor LPL: lipoprotein lipase MAF: minor allele frequency PC: protein convertase PCSK: PC subtilisin/kexin

8 1. INTRODUCTION

1.1 Cardiovascular disease and lipoprotein metabolism Atherosclerosis and its major sequelae, coronary artery disease (CAD) are the leading causes of mortality and morbidity in the developed world [1-3]. They constitute a major public health burden and are the most costly diseases affecting Canadians, costing an estimated $20 billion annually, and 12% of total health expenditure of all illness in Canada. Despite decades of progress in the field of coronary heart disease (CHD) prevention and the benefits gained from years of advice on lifestyle, public health recommendations and selected pharmacologic therapies, the incidence of fatal and non-fatal acute myocardial infarctions is expected to increase dramatically in the next two decades. Additionally, the aging of the population, coupled with the current epidemic of obesity, is expected to partly reverse gains made since the 1970s [1;2]. Thus, our best weapon to tackle this growing burden is to gain a greater understanding of the disease; that is, to learn more about its mechanisms of actions and its genetic regulations.

1.1.1 Risk factors associated with coronary heart disease CAD is a multifactorial disease for which the cardiovascular risk factors include hypertension, diabetes mellitus, smoking, obesity, age, psychological stress, male gender, and dyslipidemia, a disruption in the amount of lipids in the blood [4]. The latter is considered to be one of the most potent atherosclerosis-causing factors, resulting from elevated levels of low-density lipoprotein cholesterol (LDL-C) (hyperlipidemia) or reduced high-density lipoprotein cholesterol (HDL-C) (hypolipidemia). For the past three decades, an inverse relationship has been observed between low serum HDL-C concentrations and increased risk of CHD [2;5]. However, there is still controversy surrounding the causal role of low HDL-C in atherosclerosis. Experimental evidence shows that the atheroprotective effects of HDL are pleiotropic and extend far beyond removing cholesterol from lipid-laden macrophages in the atherosclerotic plaque. HDL particles are known to have anti- inflammatory effects, inhibit LDL oxidation, possess antithrombotic properties,

9 modulate vasomotor tone, and possibly improve endothelial cell survival (by preventing apoptosis), migration, and proliferation [2;6;7]. Data from epidemiological studies suggest that each 1 mg/dl (1%) increase in HDL-C, reduces CAD risk by 2%–3% [8;9]. Nonetheless, the major cardioprotective effect of HDL has been attributed to its key role in reverse cholesterol transport (RCT), a process in which cholesterol from peripheral tissues, such as foam cells, is selectively returned to the liver for excretion in the bile [2;6]. Mutations in any of the proteins regulating this complex metabolic pathway may potentially decrease HDL-C levels and, thus, accelerate CHD.

1.1.2 Lipoprotein metabolism and transport The formation of HDL particles, referred to as HDL biogenesis, comprises many steps taking place in a carefully orchestrated series of events in both intracellular and interstitial fluid or plasma compartments. Lipoproteins shuttle hydrophobic molecules between organs in the aqueous environment of plasma. They have an envelope of phospholipids and free (unesterified) cholesterol, and a core of triglycerides (Tg) and cholesteryl esters. Their protein components, known as apolipoproteins (apo), are arranged circumferentially and provide specific binding to receptors. Lipoproteins vary in size, density, lipid and apolipoprotein content (Table 1). Tg-rich lipoproteins (chylomicrons and very-low density lipoproteins (VLDL)), LDLs and HDLs are the major lipoprotein classes [6;10]. Lipoproteins are synthesized in the liver and intestine, mainly as VLDL and chylomicrons (Figure 1). Chylomicrons contain a single apoB48 and transport dietary Tg (~ 85% of their lipid content) that are hydrolyzed by lipoprotein lipase (LPL) and delivered primarily to adipose tissue and muscle for energy storage or production. VLDL are Tg-rich particles containing a single apoB100 (containing a domain recognized by the LDL receptor (LDLR)) as their main lipoprotein in human, and either apoB100 or apoB48 in mouse. They follow the same catabolic pathway through LPL as chylomicrons, and, during hydrolysis of Tg-rich lipoproteins, an exchange takes place: VLDL particles, as well as chylomicrons

10 acquire apoCs and apoE in part from HDL particles. VLDLs also exchange Tg for cholesteryl esters from HDL (mediated by cholesteryl ester transfer protein (CETP)). The VLDL remnant is now called an intermediate-density lipoprotein, which is taken by the liver via its apoE moiety or further delipidated by hepatic lipase (HL) to form an LDL particle (Figure 1). While LDL transport cholesterol to peripheral tissues, HDL, synthesized by the liver (80%) and intestine (20%) [11], incorporates cholesterol from peripheral tissues and recruits it back to the liver. Like other lipoproteins, HDL is extensively modified in plasma by a variety of lipases and lipid transfer proteins. HDL originates as apoA1, the main protein of HDL, as a lipid-free or lipid-poor protein. ApoA1 acquires phospholipids and cholesterol from the hydrolysis of Tg-rich lipoproteins and from cell-mediated phospholipid and cholesterol efflux through the ATP-binding cassette A1 (ABCA1) to form a nascent HDL particle in hepatic and peripheral cells, including macrophages [12]. The hepatic expression of the ABCA1 transporter is necessary and essential to generate nascent HDL and for the formation of mature forms of HDLs [13]. Once in the circulation, HDL particles continue to remove cellular cholesterol. The free cholesterol in the discoidal HDL is then acylated via lecithin:cholesterol acyltransferase (LCAT) to form cholesteryl esters which, once formed, move to a more thermodynamically stable position in the core of the HDL particle, assuming a spherical configuration (HDL3). With further cholesterol etherification, the particle increases in size to become the more buoyant HDL2 which is further modified in the plasma by the combined actions of CETP and phospholipid transfer protein (PLTP) [14;15]. These proteins mediate the exchange of HDL cholesteryl esters for Tg in VLDL. The Tg-enriched HDL2b (larger HDL particle) is hydrolyzed by endothelial lipase (EL), and HL, while phospholipids are altered by the secretion of phospholipase A2 (sPLA1) and sphingomyelinase (SMase). Noteworthy to mention, in the context of our study, that EL is also a phospholipase, hydrolyzing phospholipids within these particles (Figure 1). The metabolism of HDL is complex and incompletely understood. This complexity arises because HDL particles acquire their components from several

11 sources while these components also are metabolized at different sites. Thus, defects in any of the proteins involved in this intricate pathway, may lead to low HDL and consequently, CHD. In fact, all the genes coding for the proteins mentioned above have been implicated in disorders of HDL either in humans or in animal models. ApoA1, LCAT, LPL, and sphingomyelin phosphodiesterase 1 are among many HDL candidate genes that cause low HDL-C levels in a Mendelian fashion. However, on a population basis, the contribution of these genes to both serum levels of HDL-C and clinical end points is, at most, quite weak. In contrast, functional defects of ABCA1 can induce a decrease in HDL-C levels, implicating the ABCA1 gene as a potential candidate for CHD [2;16-18]. About half of the variation in HDL-C levels is under genetic control. As with most common phenotypes, the search for the genetic components of HDL levels has been difficult. Several studies have reported genetic data to explain variations in HDL-C in humans. These studies are based on different approaches, including the heritability of HDL-C levels in families, familial segregation analyses, candidate genes involved in HDL metabolism and linkage analyses from genome-wide scans. The identification of new chromosomal loci and association studies are promising for the discovery of new genes responsible for disorders of HDL.

12 FFA HL Liver LPL Exogenous Chylomicron Chylo (Intestinal) Remnant Pathway

Peripheral ApoA-I, A-II Free Cells Intestine ApoC-I, C-II, C-III Cholesterol Phospholipids Steroidogenic Free cholesterol HL,EL Cells

LCAT Nascent HDL HDL LDL HDL 3 2 Liver Tg ApoA-I, A-II ApoC-I, C-II, C-III CETP Phospholipids PLTP Endogenous Free cholesterol (Hepatic) Pathway CE Liver 3 LPL HL

IDL VLDL

FF A Figure 1. Schematic diagram of the lipid transport system. Apo: apolipoprotein; CETP: cholesteryl ester transfer protein; EL: endothelial lipase; FFA: free fatty acids; HL: hepatic lipase; HDL: high-density lipoprotein; IDL: intermediate-density lipoprotein; LCAT: lecithin cholesterol acyltransferase; LDL: low-density lipoprotein; LPL: lipoprotein lipase; PLTP: phospholipid transfer protein; VLDL: very-low density lipoprotein.

13 Table 1. Plasma lipoprotein composition [Chol] in plasma [Tg] in plasma Origin Density (g/mL) Size (nm) % Protein Major Apo Other Apo (mmol/L) (mmol/L) Chylomicrons Intestine <0.95 100-1000 1-2 0.0 0 B48 AI, C's VLDL Liver <1.006 40-50 10 0.1-0.4 0.2-1.2 B100 AI, C's IDL VLDL 1.006-1.019 25-30 18 0.1-0.3 0.1-0.3 B100, E LDL IDL 1.019-1.063 20-25 25 1.5-3.5 0.2-0.4 B100 HDL Tissues 1.063-1.210 6-10 40-55 0.9-1.6 0.1-0.2 AI AII, AIV Lp(a) Liver 1.051-1.082 25 30-50 B100, (a)

Apo: apolipoproteins; HDL: high-density lipoprotein; IDL: intermediate-density; LDL: low density lipoprotein; VLDL: very-low density lipoprotein; Chol: cholesterol; Tg: triglycerides.

14 1.2 Protein Convertases

1.2.1 Introduction to the protein convertases superfamily Cardiovascular homeostasis is intimately dependant on protease activities [19]. Every protein undergoes proteolysis during synthesis and/or clearance. All known vasoactive proteins and peptides result from protease processing and activation events. These enzymes are implicated in various functions regulating cell homeostasis and numerous pathologies including atherosclerosis, inflammation, cancer, neurodegenerative diseases, and viral infections. Analysis of the mammalian genome database predicts the presence of 550-700 protease genes (~1.7% of genes) [20] covering all potential enzymatic cleavages of a given species at all developmental stages. Of these, the serine proteases represent about one third of all 5 protease classes [20]. However, proteases do not operate alone, but form cascades, regulatory circuits and networks that dynamically interconnect to form a “protease web” [21]. Moreover, proteases interact with and are dynamically regulated by a myriad of other proteins, cofactors, receptors, substrates and cleavage products necessitating new genomic and proteomic approaches to elucidate their in vivo roles. Proprotein convertases (PCs) are implicated in the limited cellular proteolysis of secretory precursor proteins resulting in a diversity of bioactive products, such as zymogen activation, the generation of active proteins and peptides, and in some cases, inactivation of key proteins [22]. Mammalian PCs, a family of 9 bacterial subtilisin and yeast kexin-like serine proteinases, distinct from the trypsin/chymotrypsin subfamily, are critically involved in these activation/inactivation events. They are encoded by genes numbered from PCSK1 to PCSK9 (PC subtilisin/kexin). The nine known PCs (Figure 2) are: PC1/3, PC2, Furin, PC4, PC5/6, PACE4, PC7, SKI-1/S1P and PCSK9/NARC-1 [23-25]. The first seven are basic amino acid-specific PCs cleaving precursor proteins at single or paired basic residues. These PCs are phylogenetically more closely related to each other and to yeast kexin than to SKI-1 or PCSK9 that belong to the pyrolysin [26] and proteinase K [25] subfamilies, respectively. All PCs contain a signal peptide, a

15 prosegment and a catalytic domain. The C-terminal domain of each convertase contains unique sequences regulating their cellular localization and trafficking. Analysis of PCs‟ subcellular distribution classified them into at least three distinct groups (Figure 3). First, the membrane-bound convertases furin, the isoform PC5/6B [27], PC7 and SKI-1 that cycle from the cell surface to the trans- Golgi network (TGN) through the endosomal pathway and regulated by signals in their cytosolic tail. All of them with the exception of PC7 are shed to produce soluble secreted enzymes. Secondly, the secretory convertases PC5/6A and PACE4 contain a specific Cys-rich domain (CRD) (Figure 2) that binds to the C- terminal segment of tissue inhibitors of metalloproteases (TIMPs). This complex is retained at the cell surface by heparin sulfate proteoglycans (HSPG; Figure 3), possibly a site at which processing of specific HSPG bound precursors takes place [28]. Finally, the regulated convertases PC1/3 and PC2, and sometimes PC5/6A, are sorted to dense core secretory granules and process most polypeptide hormones that are secreted following specific stimuli [26].

16

Figure 2. Schematic primary structures of the nine PCs [29]. The basic amino acid-specific PCs together with ykexin, SKI-1, and PCSK9 are individually boxed to emphasize their distinct subclasses. PC5 exists as two alternatively spliced isoforms, soluble PC5A and membrane-bound PC5B. The catalytic triad residues Asp, His, and Ser and the oxyanion hole Asn are indicated. h, human; r, rat; m, mouse; and y, yeast.

Figure 3. Subcellular localization of PCs and their trafficking [22]. Subcellular localization of the color-coded convertases in the Golgi, trans-Golgi network (TGN), endosomes (Endo), cell surface and dense core secretory granules (SG). The scheme also shows that the drug brefeldin (BFA) can cause the reversible retrieval of both SKI-1 and the isoform PC5B into the endoplasmic reticulum (ER). The inhibitors of the basic-aa PCs are also shown.

17 1.2.2 Lipidemic effects of PCs PCs are known to be implicated in numerous pathologies, including cancer malignancies, tissue regenerations, atherosclerosis, restenosis and viral infections. Recently however, they have been found to play critical roles in regulating lipids and/or sterols [22], either by enhancing the degradation of LDLR by PCSK9 [30- 33], the activation of specific membrane-bound transcription factors (sterol regulatory element binding protein-1 and -2) by SKI-1 or through the cleavage/inactivation of EL and possibly LPL by PC5A, and furin [34;35], critically involving them in HDL, VLDL and chylomicron metabolism. Indeed, it is now believed that PCSK9, SKI-1, PC5/6 and furin are the clinically relevant PC targets in the future management of cardiovascular disorders, such as dyslipidemias and atherosclerosis. Familial autosomal dominant hypercholesterolemia is characterized by high levels of plasma cholesterol, xanthomas, and premature CAD. In vivo functions of PCSK9 have been shown in humans exhibiting gain- or loss-of-function mutations associated with hypercholesterolemia [36-42] or hypocholesterolemia [43-46] and in mouse knockout (KO) models [47]. Proof of in vivo functions has also been recently obtained for SKI-1 in mouse [48], but although there are strong ex vivo indications [34;49-51], the proof of in vivo function of furin and PC5 in dyslipidemia and cardiovascular pathologies has yet to be established.

1.2.3 PCSK5 and PC5 PC5, also called PC6 or PC5/6 [52] was first identified and cloned by Lusson et al [53;54]. The PC5 gene, PCSK5, spanning 302 714 bp (Figure 4), is localized on human chromosome 9q21.13 and mouse chromosome 19. Human PCSK5, similarly to mouse Pcsk5, encodes two alternatively spliced isoforms, soluble PC5A (915 aa) and membrane bound PC5B (1870 aa) [53] (Figure 5). They share their first 20 exons, which encode the signal peptide, prosegment, catalytic domain, and N-terminal region of the CRD. A specific additional exon (21A) encodes the last 38 residues of PC5A, while exons 21B to 38 encode the last 1,000 residues of PC5B. Both zymogens undergo a first autocatalytic cleavage

18 in the ER, and a second in the TGN [55;56] or possibly at the cell surface. Although devoid of a transmembrane domain, PC5A can exert its proteolytic action at the cell surface, as it is retained at the plasma membrane as a complex with tissue inhibitors of TIMPs and HSPG [28]. To investigate physiological roles of PC5, Essalmani et al. have generated a

Pcsk5-deficient allele missing exon 4 that encodes the catalytic Asp173. While heterozygote 4/+ mice were healthy and fertile, homozygote 4/4 embryos were found to die at embryonic day (E)4.5−E7.5 [57]. Thus, the absence of catalytically functional PC5 leads to early embryonic death, indicating that Pcsk5 is essential. In addition, in the same paper, and through in situ hybridization and quantitative Real Time-Polymerase Chain Reaction (PCR), the authors have documented tissue distribution patterns of PC5 during development and adulthood, and in various cell lines [57]. PC5 was found richest in the adrenal cortex, small intestine, kidney, ovary, uterus, lung, aorta, and brain cortex. Data revealed that, except in the liver where both isoforms are equally expressed, PC5A is the major isoform in most tissues (87 to 100%), and only the intestine and kidney show a predominance of PC5B (74 to 92%). The two isoforms (Figure 5) were shown to have distinct properties: (i) they do not show similar efficiencies toward various substrates in cell lines [34;58;59], and (ii) the transmembrane PC5B is restricted to the constitutive secretory pathway [55] and cycles between the Golgi and the cell surface [60], while the soluble PC5A is sorted to both regulated and constitutive secretory pathways [55]. However, as previously mentioned, PC5A can also be associated with the plasma membrane via a ternary complex involving its CRD, TIMPs, and HSPGs [28].

19

Figure 4. PCSK5 gene locus. PCSK5 spans 302 714 bp and it is situated on chr 9q21.13 (77.8 Mb position)

Figure 5. Isoforms PC5A and PC5B and their processing [57]. Exons 1 to 20 of the mouse Pcsk5 gene encode the signal peptide (SP; amino acids [aa] 1 to 34), prosegment (aa 35 to 116), catalytic domain (aa 117 to 458), P domain (aa 459 to 600), and N-terminal part of the Cys-rich domain (aa 638 to 877). Exon 21A, specific to PC5A, encodes its last 38 residues, while the 18 additional exons of PC5B (21B to 38) extend the Cys-rich domain up to a transmembrane domain (TMD; aa 1769 to 1789) followed by an 88-aa-long cytosolic tail (CT). The residues of the catalytic triad comprise Asp173 (boxed D), encoded by exon 4; His214 (H); and Ser388 (S). The position of the oxyanion hole Asn315 (N) is also shown. Arrows in the prosegment indicate autocatalytic cleavage sites, and the arrowhead points to the intracellular cleavage site, generating an ~68-kDa PC5A-ΔC form. White ellipses emphasize the putative N-glycosylation sites.

20 1.3 Involvement of PC5 in HDL metabolism

1.3.1 Study rationale Many substrates are reported to be efficiently processed ex vivo by PC5: matrix metalloproteases and a disintegrin and metalloproteinases (ADAM) family enzymes [61;62], growth factors such as platelet-derived growth factor-A [63], platelet-derived growth factor-B [64] and vascular endothelial growth factor-C [65], receptors such as insulin-like growth factor 1 receptor [66], integrins [58;59], renin [54] and lipases [34]. There is strong biological plausibility for the involvement of PC5 in lipoprotein metabolism [22]. First, EL is one of the several lipases involved in HDL metabolism and remodeling and is processed by PC5. EL, first discovered by Rader‟s group [67] is predominantly a phospholipid-specific phospholipase. Other lipases, including LPL, and HL, both triglyceride-specific lipases, contribute to HDL metabolism. Thus, PCs that activate these lipases may play a role in lipoprotein metabolism in general and in HDL remodeling in particular. Seidah and others [34;51] showed that PC5A (soluble isoform) and furin were the most efficient PCs to inactivate ex vivo EL and LPL. Both lipases are present at the vascular endothelial surface. EL is synthesized by endothelial cells and acts on HDL mainly as a phospholipase, whicle LPL is synthesized by adipocytes and muscle cells but is active at the vascular endothelial surface where it is retained via HLGAGs (Figure 6). LPL essentially acts as a Tg hydrolase mainly on chylomicrons and VLDL. The cleavage of EL and LPL occurs at typical sites,

RNKR330 and RAKR324, respectively, and is expected to inactivate the enzymes. For EL however, the efficiency of cleavage is PC5A > Furin, whereas for LPL, the efficiency of cleavage is Furin > PC5A. Thus, overall, the cleavage of LPL is less efficient than that of EL. In light of this, the most compelling evidence that PC5 is involved in cardiovascular tissue remodeling is through its inactivation by cleavage of EL, which may subsequently down regulate HDL-C levels and thus modulate plasma levels of HDL-C. Secondly, it has been demonstrated that in atherosclerotic plaques and during arterial restenosis, PC5 expression is highly upregulated

21 [49;61;68-71]. High expression of PC5 in enterocytes suggests a possible role in processing protein substrates that could regulate food and/or sterol/lipid absorption [53;72]. Additionally, it was recently reported that a genetic locus close to the PCSK5 gene on chromosome 9 is implicated in lipid regulation in humans [73] but its link to HDL deficiency remains to be explored. Finally, at least two genome- wide scans in humans showed association between chromosome 9q21 and HDL-C levels (near PCSK5 locus) [73;74]. These data therefore suggest that PC5 may be good candidate proteinase in the control of circulating HDL-C levels.

Figure 6. Inactivation of EL and LPL by PC5, PACE4, and furin (adapted from [29] and [75]). EL and LPL bind heparan-sulfate proteoglycans (HSPGs) and heparin-like glycosaminoglycans (HLGAGs), respectively, and are cleaved by PCs internally at the C-terminus of Arg in the motif RxKR↓. This cleavage hampers the phospholipase role of HSPG-bound EL on HDL and the triglycerides hydrolase function of HLGAG-bound LPL on chylomicrons and VLDL .

22 1.3.2 Hypothesis As exhaustively described above, mounting evidence converges towards a pivotal implication of PCSK5 and its protein, PC5, in HDL-C metabolism. Given this rationale, we thus hypothesized that genetic variations within the PCSK5 gene may contribute to the variability of HDL-C levels and the predisposition to develop atherosclerotic cardiovascular disease. Ultimately, we believe that the convertase PC5 is implicated in the regulation of the sterol and/or lipid levels of mammals and that inhibitors, modulators or partners of this PC will be future lead compounds to diagnose and treat cardiovascular disorders caused by dyslipidemias.

1.3.3 Goal and objectives Our goal was to investigate whether genetic variability at the PCSK5 gene locus contributes to HDL-C levels in families with HDL deficiency and unrelated subjects of French Canadian descent. Thus, the purpose of the present study was to examine any possible associations between PCSK5 mutations and HDL-C dyslipidemia in humans. Two main objectives have been set forth towards achieving this target: 1. Investigate the co-segregation between the PCSK5 gene locus and the low HDL-C trait in 9 HDL-deficient families. 2. Verify the association of PCSK5 SNPs‟ with HDL-C levels in 457 unrelated subjects of French Canadian descent through two types of analyses:  Quantitative trait analysis  Case-control binary analysis We thus hope, as an ultimate and future outcome of this human study, to better understand HDL metabolic pathways (metabolomics), to improve molecular diagnosis of patients with severe hypoalphalipoproteinemia (low HDL-C) (clinical genetics), expand the panel of determinants for genetic epidemiologic studies (population genomics) and improve the ability to predict response to lipid-lowering agents according to an individual‟s genotype, as well as to potentially adjust therapy for specific patients (pharmacogenomics).

23 2. METHODS

2.1 Study population

2.1.1 Family subjects A total of 9 multigenerational French Canadian families consisting of 175 genotyped family members were examined and sampled in the Preventive Cardiology/Lipid Clinic of the McGill University Health Centre, Royal Victoria Hospital, Montreal, Canada. The selection criterion for probands was an HDL-C level < 5th percentile (age and gender-matched), based on the Lipid Research Clinics Population Studies Data Book [76]. Exclusion criteria for the probands were as follows: severe hypertriglyceridemia (>10 mmol/L), cellular cholesterol efflux or phospholipid efflux defect in skin fibroblasts, or previously known mutations in the genes associated with HDL-C deficiency, such as ABCA1 mutations. All available living relatives were invited to participate in the study. Family members were sampled under strict conditions (after a 12-hour fast and discontinuation of lipid-modifying medications for 4 weeks). Extensive demographic and clinical information, medications, blood pressure, serum glucose, weight, body mass index (BMI) and lipoprotein profiles were determined on all subjects. All subjects provided separate informed consent forms for plasma and DNA sampling, isolation, and storage. The research protocol was reviewed and approved by the Research Ethics Board of the McGill University Health Centre (please refer to Appendices section).

2.1.2 Unrelated individuals A second group of subjects used for both the quantitative and binary analysis consisted of 457 unrelated patients of French Canadian origin ranging in HDL-C values < 5th percentile to HDL-C values > 95th percentile. Individuals were selected from the Cardiology Clinic of the Clinical Research Institute of Montreal on the basis of being less than 60 years of age at the time of blood sampling and had angiographically documented CAD (grater than 50% stenosis of a major epicardial coronary artery) or a documented myocardial infarction by enzymatic and electrocardiographic critera. For the case-control association study,

24 cases were chosen as individuals with HDL-C levels < 5th percentile or < 10th percentile (age and gender-matched), based on the Lipid Research Clinics Population Studies Data Book [76]. The control group was of the same origin and chosen based on HDL-C levels > 5th percentile or > 10th percentile respectively, matched for age and gender. Subjects with low HDL-C had no known cause of HDL deficiency (severe hypertriglyceridemia defined as plasma triglycerides > 10 mmol/L, cellular phospholipid or cholesterol efflux defect or previously known mutations in genes associated with HDL deficiency). Demographic and clinical information, medications, blood pressure, and lipoprotein profiles were determined on all participating subjects. Consent was obtained for the plasma sampling and DNA isolation. Phenotypes included in this study were HDL-C, LDL-C, Tg, VLDL and ApoB total. The research protocol was reviewed and approved by the Research Ethics Board of the McGill University Health Centre (please refer to the Appendices section).

2.2 Biochemical measurements The lipid lowering agents were withdrawn in all study subjects for at least four weeks before measurement of the lipid profile. The study was conducted between 1990 and 1999 and stored plasma samples were used [77]. At the time of blood sampling, standard care and good medical practices were followed, therefore baseline characteristics with regard to metabolic factors were obtained in all patients prior to pharmacotherapy. Routine statin prescription in CAD patients were not initiated until the release of the 1st National Cholesterol Education Program in 2001, therefore at the time the study was initiated, there were no required consent forms. Insulin and oral hypoglycemic agents were maintained in diabetic patients. Plasma was isolated in all study subjects, after a 12-hour fast, in EDTA-containing tubes for lipid, lipoprotein cholesterol, apoA-I, and triglyceride analyses. The buffy coat was kept for DNA isolation. Lipids and lipoproteins were measured using standardized techniques and the LDL-cholesterol was calculated according to the Friedewald formula (LDL-C = total cholesterol − triglycerides/2.2 − HDL-C, all concentrations in mmol/L), unless

25 triglyceride levels were > 4.5 mmol/L [16;78]. In this case, ultracentrifugation of plasma was used and the lipoprotein cholesterol concentration was measured directly.

2.3 Haplotyping Genotypes (deCODE Genetics, Reykjavik, Iceland) at markers D9S1777, D91876, D9S175, D9S1843 spanning 11.3 Mb and flanking the PCSK5 gene on chromosome 9q21.13, were used to construct haplotypes to examine the segregation of the PCSK5 locus with the low HDL-C trait in 9 French Canadian families. Haplotypes were built with Cyrillic version 2.1.3 (Cherwell Scientific Publishing Ltd).

2.4 Sequencing Sequencing of the PCSK5 gene (21 exons and exon-intron boundaries) was performed in 12 unrelated individuals. Exon-specific oligonucleotides (Table 4) were synthesized (Integrated DNA Technologies) and designed using the Primer3 (http://frodo.wi.mit.edu/, Steve Rozen and Helen Skaletsky, Whitehead Institute and Howard Hughes Medical Institute) [79] in such a way that each intro-exon boundary included at least 22 bp on intronic sequence. PCR-amplified fragments were purified using the Millipore purification plate (Multiscreen PCR) and directly sequenced (McGill University and Genome Québec Innovation Centre Sequencing Platform, Montreal, Qc, Canada). Sequencing was performed using the Applied Biosystems 3730-xl DNA Analyzer system. The collected data were analysed by Sequencing Analysis version 5.2. The Mutation Surveyor version 2.41 computer software (SoftGenetics, USA) was used for sequence alignment and analysis.

2.5 Single nucleotide polymorphisms selection 182 single nucleotide polymorphisms (SNPs) (see Appendices section) were selected in the PCSK5 gene (including 2 kb upstream of the gene) for

26 genotyping. These SNPs were chosen based on novel variants identified by sequencing, SNPs selected from different databases (NCBI, SeattleSNPs, Broad), as well as PCSK5 SNPs chosen from the HapMap Project database for the Caucasian European population. Haploview version 4.0 (Whitehead Institute for Biomedical Research Cambridge, MA 02142, USA) [80] was used in the latter stage of SNP selection in choosing tagSNPs with a minimum minor allele frequency (MAF) of 0.05, r2 threshold > 0.80, 0 Mendel Errors and a Hardy- Weinberg P-value cutoff of 0.0010. 163 tag SNPs were found by pairwise tagging capturing 363 with mean r2 of 0.963.

2.6 Genotyping Genotyping was performed using the Sequenom iPLEX Gold Assay (Sequenom, Cambridge, MA). Locus-specific PCR primers and allele-specific detection primers were designed using MassARRAY Assay Design 3.1 software. DNA was amplified in a multiplex PCR and labeled using a locus-specific single base extension reaction. The products were desalted and transferred to a 384- element SpectroCHIP array. Allele detection was performed using Matrix- Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (compact MALDI-TOF MS). Mass spectrograms and clusters were analyzed by the TYPER 3.4 software package. Ehrich et al. [81] have previously provided details of the procedure.

2.7 Statistical analyses Statistical analyses were performed with the following softwares: Plink version 1.03 (http://pngu.mgh.harvard.edu/purcell/plink/, Shaun Purcell, Center for Human Genetic Research, Massachusetts General Hospital and the Broad Institute of Harvard & MIT) [82]. SAS package version 9.1.3 (SAS Institute Inc., NC, USA) and Haploview version 4.0 (Whitehead Institute for Biomedical Research Cambridge, MA 02142, USA) [80]. Association tests for SNPs were performed using linear regression, after adjustments for age and sex. Conditional

27 analyses were performed using manual step-wise linear regression modeling. This involved including the SNP with the strongest result into the model and then testing the other SNPs for association. This was performed until the remaining SNPS were no longer statistically significant. The nominal level of significance for all statistical analyses was P <0.05.

3. RESULTS

3.1 Familial segregation analyses (haplotypes-building) We first examined subjects with low HDL-C, defined as an HDL-C level <10th percentile for age- and gender-matched individuals, based on the Lipid Research Clinic Database [76] and their families for genetic variability and linkage association at the PCSK5 locus. A total of 9 unrelated multigenerational French Canadian families consisting of 175 individuals were collected in the Preventive Cardiology/Lipid Clinic of the McGill University Health Centre. The selection criterion for the 9 probands was HDL-C levels < 5th percentile but for the purposes of this study, affected individuals from the overall 175 were defined as having HDL-C levels <10th percentile (age and sex-specific). This condition was chosen, rather than the more stringent 5th percentile, because using the 10th percentile increases our sample size and hence, the power of our analysis. We performed a familial segregation study in these French Canadian families, by examining haplotypes at the PCSK5 gene constructed with microsatellite genetic markers. The 9 families included 60 affected subjects, resulting in an average of 7 affected individuals per family. All of the families had ≥ 3 generations of which DNA was available and each exhibited affected individuals in consecutive generations (Table 2 and Figure 8). Importantly, families with a defect in cellular cholesterol efflux or phospholipid efflux in skin fibroblasts or any previously known mutations in genes associated with HDL-C deficiency (i.e. ABCA1 or ApoA1), as well as exhibiting severe hypertriglyceridemia (>10 mmol/L) were excluded. This was done to minimize confounding factors and to increase chances of identifying PCSK5 as a gene

28 implicated in HDL-C metabolism. We also anticipated that extended families with an average of 7 HDL-C-affected subjects per family could provide a powerful approach to investigate whether variants in the PCSK5 gene segregate with the complex HDL-C trait. The demographic, clinical and phenotypic characteristics of the subjects are shown in Table 2 and 3. There were 71 men and 104 women, of which 29 and 31 were affected (HDL-C ≤ 10th percentile), respectively. Mean age for males was 49.83±15.73 years and mean BMI was 28.2±4 kg/m2, while women has an age average of 47.87±19.44 years and a BMI 24.3±4.4 kg/m2. The BMI was within normal limits for the affected females, but slightly elevated for men. Regarding lipid profiles, as seen in Table 2, affected men had average HDL-C levels of 0.64 mmol/L in comparison to 1.12 mmol/L for their unaffected counterparts. Similarly, comparing affected versus unaffected females, HDL-C levels of 0.87 mmol/L and 1.39 mmol/L were observed, correspondingly. Fasting triglyceride levels were significantly elevated in both affected sexes. Importantly, all 9 probands (7 males and 2 females) had an HDL-C < 5th percentile, age and gender- matched, as demonstrated by their low HDL-C levels (0.67±0.04 mmol/L and 0.57±0.13 mmol/L respectively), especially in women (Table 3).

Table 2. Characteristics of the 9 French Canadian low HDL-C families

Number of individuals with available DNA; M/F 175; 71 M, 104 F

Probands; M/F 9; 7 M, 2 F

Families in each group 3 generations 2 4 generations 6 5 generations 1

Affected subjects ; M/F HDL-C ≤ 10th percentile 60; 29 M, 31 F

M indicates males; F, females

29 Table 3. Phenotypic characteristics of 9 French Canadian families with low HDL-C shown separately as affected individuals, unaffected individuals, and probands French Canadian Affected* Unaffected Probands HDL-C Families Males Females Males Females Males Females

Individuals, M/F 29 31 42 73 7 2

Age, years 49.83±15.73 47.87±19.44 40.88±0.22 42.62±19.87 49±15.56 44±5.66

BMI, kg/m2 28.2±4.0 24.3±4.4 25.3±4.6 23.5±4.6 29.12±10.76 25.33±5.43

TG, mmol/L 4.23±4.04 2.52±2.57 1.58±0.92 1.36±0.64 2.84±1.4 3.73±0.08

HDL-C, mmol/L 0.64±0.08 0.87±0.15 1.12±0.22 1.39±0.26 0.67±0.04 0.57±0.13

*The affection status is determined as HDL-C < 10th age-sex specific population percentile. M indicates males; F, females; BMI, body mass index; and TG, triglycerides

Given our families‟ characteristics and the purpose of our study, we were thus able to perform a familial segregation linkage analysis in 9 multigenerational French Canadian families, by examining haplotypes at the PCSK5 gene constructed with microsatellite genetic markers. We were therefore looking for a rare variant displaying a strong effect segregating within the families. Building allele-specific haplotypes was one of the methods used in our analysis. It is well established that a haplotype is a genomic locus identified by genetic markers (microsatellites or SNPs) determined to characterize alleles in individuals from the same family in order to examine the transmission of this locus and its potential association with a specific phenotype. The latter, in our case, being low HDL-C. Our choice in the repeat-containing microstatellite markers defining the PCSK5 gene was influenced by the fact that it is well known that during the early stages of cell division in meiosis, recombination will rarely separate loci that lie very close together on the same chromosome because of no cross-over located precisely in the small space between the loci. Thus, markers situated on the same chromosome and right next to each other will segregate together during meiosis. The further apart two loci are on the same chromosome, the more likely it is that a recombination event a meiosis will break up the co- segregation. Given this, we were interested in selecting the closest markers to our possibly disease-causing gene of interest in order to see if the same microsatellites

30 co-segregated with the low HDL-C trait in affected families. From the deCODE Genetics genotype data, 21 markers were found on chromosome 9, but in order to minimize the risk of recombination, only the 4 closest, upstream and downstream to the PCSK5 locus, were taken (Table 4 and Figure 7). These were respectively D9S1777, D9S1876, D9S175 and D9S1843.

Table 4. PCSK5 microsatellite genetic markers

Marker cM (Haldane) Start of Marker End of Marker Mb Band

D9S1779 0.001 506800 507069 0.5 9p24.3 D9S1686 11.931 4634418 4634759 4.6 9p24.1 D9S286 18.529 8043378 8043709 8 9p24.1 D9S168 24.283 10578156 10578494 10.6 9p23 D9S1808 28.239 13412992 13413314 13.4 9p23 D9S235 32.645 14797255 14797988 14.8 9p22.3 D9S171 47.163 24524209 24524427 24.5 9p21.3 D9S1777 67.339 70377473 70377811 70.4 9q13 D9S1876 70.422 74422514 74422781 74.4 9q21.13 D9S175 72.803 77137375 77137705 77.1 9q21.13 D9S1843 79.874 81728949 81729283 81.7 9q21.31 D9S307 89.23 88811430 88811598 88.8 9q21.33 D9S283 95.899824 91604131 91604448 91.6 9q22.2 D9S287 101.542 97505911 97506235 97.5 9q22.32 D9S261 114.649 109874962 109875291 109.9 9q31.2 D9S1828 117.673 112372526 112372714 112.4 9q31.3 D9S934 127.719 120135477 120135997 120.1 9q33.1 D9S1682 132.086 124033005 124033322 124 9q33.2 D9S1825 136.348 126927953 126928102 126.9 9q33.3 D9S1830 149.357 134705487 134705694 134.7 9q34.13 D9S1793 150.424 135387982 135388340 135.4 9q34.2

31 Indeed, these microsatellite genetic markers span 11.3 Mb, flanking the 9q21.13 region (starting at 77 695 441bp and ending at 77998155 bp) and comprising the PCSK5 gene (Figure 7).

Figure 7. PCSK5 genetic markers. Repeat-containing microsatellite genetic markers (D9S1777, D91876, D9S175, D9S1843) spanning 11.3 Mb were used to build familial haplotypes at the PCSK5 locus and to examine the segregation of the PCSK5 gene in 9 available families from 9 probands.

Knowing that one of the advantages of using microsatellites as useful markers is that they are locus specific, the 4 repeat-markers were used to build familial haplotypes at the PCSK5 gene to examine the segregation of this locus with low HDL in 9 HDL-deficient families. Pedigrees for kindreds 1434384, 1430581, 1200242, 27413, 1384255, 1435859, 25242, 1440369, 24292 are depicted in Figure 8 (A-I). To facilitate visual progression of the 4 loci across generations, colors and patterns have been attributed to the alleles describing a specific haplotype. In order for the low HDL-C trait to segregate with the PCSK5 locus, one must keep in mind that the same haplotype must be coinherited from generation to generation in all affected family members. This is referred to as complete segregation and can be observed in the 1434384 pedigree (Figure 8 (A)). Although the latter is only comprised of 7 members, one can fully appreciate that the haplotype represented by the 4 microsatellites is shared by all affected subjects and is thus transmitted in a simple Mendelian inheritance pattern. In contrast, kindred 1430581 (B), 27413 (D), 1384255 (E), 1435859 (F), 1440369 (H) and 24292 (I) exerted no segregation at the PCSK5 locus with the complex

32 HDL-C trait. The remaining 2 families, 1200242 (C) and 25242 (G), show evidence of partial or incomplete segregation, where the 4 loci describing the region of interest is observed in almost all affected subjects, but is also found in some unaffected individuals. Furthermore, these kindred also possess in the second and third or in the third and fourth generation, respectively, subjects where recombinations have occurred between markers. Cumulatively, from the 9 families investigated, only one was observed to display a complete segregation with the PCSK5 gene and the low HDL-C trait. Nevertheless, this finding was not significantly sufficient to conclude that PCSK5 may contribute to the variability of HDL-C levels. Therefore, we did not find an unambiguous segregation of this locus with the low HDL-C trait using a dominant model of inheritance, suggesting that PCSK5 does not exert a Mendelian monogenic effect on HDL-C.

Figure 8. Familial segregation analyses in 9 kindred with familial low HDL-C (A-I). Pedigrees for kindreds 1434384, 1430581, 1200242, 27413, 1384255, 1435859, 25242, 1440369, 24292 are shown below. Squares = men, circles = women, filled = affected individual, half-filled = possibly affected individual, clear = unaffected individual, crossed symbols = refusal to participate, diagonally crossed symbols = deceased subject. Genotypes in brackets indicate they have been inferred from other members in the same or different generation, while an interrogation point (?) corresponds to an unknown genotype. By convention, all probands are identified by a thick arrow and by the suffix 301, their spouses by 302, siblings by 303 and up, parents by 200 and up, and children by 400 and up. Individual IDs, total cholesterol (T. Chol), triglycerides (TG), HDL-C and LDL-C in mmol/L are indicated for all family members. The HDL-C percentile level for age- and gender-matched subjects is shown in parentheses. The haplotypes defining a segregation are shown by the colored or patterned vertical bars corresponding to a specific allele.

33 (A)

Family 1434384

Affected individual

ID 201 202 T. Chol 4.39 TG 1.44 HDL-C (%HDL) 0.59 (<5) LDL-C 3.15

D9S1777 2 6 (?) (0) D9S1876 4 -10 (?) (2) D9S175 16 4 (?) (0) D9S1843 4 4 (?) (4)

303 ID 304 301 302 T. Chol 3.50 3.98 4.76 TG 1.31 3.67 1.32 HDL-C (%HDL) 0.70 (<5) 0.47 (<5) 0.93 (25) LDL-C 2.21 1.86 3.24

D9S1777 2 0 2 0 2 0 D9S1876 4 2 (4) (2) 0 -10 D9S175 16 0 16 0 6 6 D9S1843 4 4 4 4 6 0

ID 401 T. Chol 3.82 TG 1.34 HDL-C (%HDL) 0.46 (<5) LDL-C 2.76

D9S1777 2 0 D9S1876 4 -10 D9S175 16 6 D9S1843 4 0

34 Family 1430581

Affected individuals (B)

Possibly affected individuals

201 202 D9S1777 (0) (0) (2) (0) D9S1876 (-8) (2) (8) (6) D9S175 (6) (20) (0) (10) D9S1843 (0) (2) (12) (0)

ID 302 303 311 306 312 307 313 308 309 315 301 310 T. Chol 5.71 4.13 3.22 4.31 4.17 3.99 6.75 6.56 5.92 6.22 4.97 TG 5.31 2.68 2.06 0.77 4.66 0.66 1.72 3.12 1.81 3.78 1.06 HDL-C (%HDL) 0.67 (<5) 0.61 (<5) 0.57 (<5) 1.40 (32) 0.70 (<5) 1.75 (66) 1.52 (43) 1.04 (10) 1.01 (28) 0.66 (<5) 0.90 (21) LDL-C 2.65 2.31 1.72 2.56 1.37 1.94 4.46 4.12 4.10 3.86 3.59 D9S1777 0 2 (0) (2) (0) (2) (0) (2) 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 8 D9S1876 2 8 -8 8 (6) (0) 2 8 -8 8 2 6 6 6 -8 6 -8 6 2 4 2 6 2 0 D9S175 20 0 6 0 (18) (2) 20 0 6 0 20 10 6 6 6 10 6 10 0 6 20 10 6 8 D9S1843 (2) (12) 2 12 (0) (4) 2 12 2 10 0 0 12 8 0 12 2 0 0 4 2 0 0 6

416 ID 403 404 405 406 407 408 409 412 414 401 402 T. Chol 5.03 4.63 4.55 4.23 4.16 3.68 4.01 3.95 4.58 3.43 3.87 TG 1.19 1.35 0.53 2.36 1.01 2.43 1.64 1.56 0.60 1.17 1.61 HDL-C (%HDL) 0.99 (9) 0.96 (28) 1.57 (69) 0.45 (<5) 1.15 (20) 0.61 (<5) 0.93 (<5) 1.01 (10) 1.41 (82) 0.59 (<5) 1.00 (12) LDL-C 3.50 3.06 2.74 2.72 2.56 1.98 2.34 2.24 2.90 2.31 2.15 D9S1777 2 2 0 0 0 2 2 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 D9S1876 8 0 -8 6 6 0 8 -8 2 -8 6 6 6 6 2 6 2 -8 2 6 2 2 D9S175 0 2 6 18 6 2 0 6 20 6 10 6 10 6 0 6 0 6 6 10 6 20 D9S1843 12 4 2 0 10 4 12 2 2 2 0 8 0 12 0 2 0 2 0 0 0 2

ID 504 T. Chol 4.99 TG 1.53 HDL-C (%HDL) 0.95 (<5) LDL-C 3.35

D9S1777 0 2 D9S1876 6 8 D9S175 6 0 D9S1843 12 12

35 (C) Family 1200242

Affected individuals

101 102

D9S1777 (0) (?) (2) (0) D9S1876 (-8) (?) (0) (-8) D9S175 (8) (?) (12) (16) D9S1843 (0) (?) (10) (10)

ID 203 204 205 206 201 202 T. Chol 4.96 5.95 3.91 TG 1.38 0.79 2.93 HDL-C (%HDL) 1.30 (29) 1.70 (64) 0.59 (<5) LDL-C 3.03 3.89 1.92

D9S1777 0 2 0 0 0 0 (0) (0) D9S1876 -8 0 -8 -8 (-8) (0) (-8) (8) D9S175 8 12 8 16 8 12 (14) (14) D9S1843 0 10 0 10 0 10 (10) (0)

310 303 311 304 305 306 313 307 308 312 ID 302 301 T. Chol 6.20 4.91 TG 1.41 3.64 HDL-C (%HDL) 1.41 (33) 0.57 (<5) LDL-C 4.15 2.47 D9S1777 0 0 0 0 D9S1876 -8 -8 0 8 D9S175 8 14 12 14 D9S1843 0 10 0 10

403 404 406 407 408 409 410

36 (D)

Family 27413

Affected 102 101 Possibly affected D9S1777 (0) (0) (0) (6) D9S1876 (4) (0) (4) (0) D9S175 (2) (20) (6) (14) Refusal D9S1843 (14) (6) (4) (0)

ID 201 202 203 204 205 206 207 209 211 212 213 215 216 217 219 220 T. Chol 3.07 2.93 3.83 5.17 TG 1.98 2.18 2.23 1.14 HDL-C (%HDL) 0.83 [<5] 0.74 (<5) 1.19 (21) 1.12 (35) LDL-C 1.33 1.20 1.63 3.53 (0) (0) (0) (0) D9S1777 (6) (0) 0 0 (0) (4) (-8) (2) 0 6 (6) (0) (0) (0) D9S1876 (2) (-8) 4 4 (20) (6) (0) (14) 4 0 (0) (4) 0 4 D9S175 (0) (6) 2 6 (6) (4) (8) (0) 2 14 (6) (6) 20 6 D9S1843 (10) (0) 14 4 14 0 (0) (0) 6 4

300 301 302 303 305 306 307 308 309 310 311 312 313 314 317 318 361 319 320 350 329 330 337 338 352 339 340 353 341 342 354 343 344 355 345 356 346 357 358 359 360 347 348 349 5.26 4.68 3.69 4.84 4.74 4.92 2.93 5.45 5.92 4.11 5.10 3.58 4.84 5.78 4.95 6.34 5.36 4.90 4.03 3.30 6.24 4.87 4.42 4.49 4.29 4.46 5.07 3.85 3.91 4.40 4.31 5.30 1.61 2.76 0.82 1.35 4.15 1.91 4.23 0.69 11.83 0.72 1.74 0.79 1.36 4.06 1.30 14.47 1.51 1.65 1.17 0.81 1.84 1.85 1.58 0.81 0.94 2.42 2.44 1.08 0.64 1.27 0.78 1.15 1.29 (24) 0.65 (<5) 1.60 (51) 1.58 (49)0.76 (<5) 1.98 (81)1.34 (36)1.78 (>95)1.25 (68) 1.42 (46)1.54 (64) 1.01 (9) 0.91 (<5) 1.33 (66) 1.17 (55) 0.93 (<5) 1.01 (34)1.32 (33) 1.63(94) 1.29 (68) 1.33 (34) 1.19 (54) 1.24 (71) 1.61 (94) 1.22 (24) 0.97 (30) 0.96 (8) 1.56 (63) 1.27 (70)1.31 (38) 2.21 (>95) 1.69 (69) 3.23 2.76 1.17 2.64 2.08 2.06 1.55 3.35 - 2.36 2.76 2.21 3.31 2.60 3.19 - 3.66 2.83 1.87 1.64 4.07 2.84 2.46 2.51 2.64 2.39 2.77 1.80 2.35 2.51 1.75 3.09

D9S1777 0 0 6 0 2 2 6 0 6 0 0 6 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 6 6 6 0 6 6 6 (0) (?) 0 0 6 0 6 0 D9S1876 -8 2 2 4 0 2 2 4 2 4 -8 6 -8 4 -8 4 2 4 -8 4 4 2 4 2 -8 6 0 -8 4 -8 4 4 4 0 4 0 0 0 4 0 0 0 (6) (?) (4) (4) 0 4 0 0 D9S175 0 0 0 2 (?) (4) 0 6 0 6 20 8 6 6 6 6 0 2 6 6 6 14 6 14 0 12 20 0 6 0 2 6 2 6 2 6 14 6 2 6 14 6 (2) (?) (2) (6) 14 6 14 6 D9S1843 0 10 10 14 0 10 10 4 10 4 0 6 0 14 0 4 10 14 0 14 4 0 4 0 10 6 6 8 4 8 14 0 14 0 14 0 0 0 14 0 0 0 (0) (?) 14 0 0 0 0 0

401 402 403 411 412 421 422 431 471 470 472 450 451 442 440 441 404 405 406 407 408 409 410 413 414 415 4.75 2.85 4.39 3.76 5.16 4.91 3.57 4.83 3.78 5.42 4.32 3.48 1.57 0.89 2.97 0.70 1.82 0.68 0.82 1.84 0.84 2.32 0.81 1.07 1.47 (65)1.02 (29)1.03 [11] 1.51 (68) 1.30 (76) 1.28 (46) 1.09 (35) 1.23 (27) 1.32 (37) 1.07 (19)1.24 (41) 1.52 (70) 2.56 1.42 2.00 1.93 2.64 3.32 2.11 2.76 2.08 3.30 2.71 1.47 D9S1777 0 6 6 2 6 2 0 0 0 6 0 0 6 0 D9S1876 -8 2 2 2 2 2 4 -8 4 6 -8 0 0 6 D9S175 0 0 0 4 0 4 6 20 6 8 0 20 6 2 D9S1843 0 1010 10 10 0 4 0 4 6 10 8 0 0

37 Family 1384255

Affected individual (E)

Possibly affected individual

201 202

Refusal D9S1777 (0) (2) (2) (?) D9S1876 (-10) (8) -10 0 D9S175 (6) (6) 0 6 D9S1843 (10) (0) 6 2

ID 302 306 303 307 301 305 304 308 T. Chol 6.94 4.38 5.02 4.27 4.83 5.77 4.31 TG 1.52 2.53 1.26 4.22 1.86 1.66 0.78 HDL-C (%HDL) 1.56(46) 0.99(23) 1.00(11) 0.51 (<5) 1.04 (5) 0.91 (18) 1.19 (14) LDL-C 4.70 2.25 3.45 1.86 2.95 4.11 2.77

D9S1777 0 2 2 8 2 2 (0) (0) 2 2 0 0 (2) (?) D9S1876 -10 -10 -10 6 8 -10 (2) (6) -10 -10 6 6 8 0 D9S175 6 0 0 4 6 0 (?) (0) 6 0 0 0 6 6 D9S1843 10 6 0 12 0 2 (0) (2) 10 6 0 10 0 2

ID 403 404 405 406 407 408 401 402 410 412 411 T. Chol 4.18 4.66 2.32 3.15 3.86 5.58 6.69 5.70 TG 2.48 1.38 0.35 0.45 1.44 2.11 1.32 1.27 HDL-C (%HDL) 0.77 (<5) 0.97 (8) 0.88 (5) 0.78(<5) 0.60 (<5) 0.65 (<5) 1.01 (35) 1.00 (27) LDL-C 2.29 3.07 1.28 2.17 2.61 3.98 5.09 4.13

D9S1777 2 2 8 0 0 2 0 2 2 0 2 0 D9S1876 -10 -10 6 -10 2 8 6 8 -10 6 -10 6 D9S175 0 0 4 6 ? 6 0 6 6 0 6 0 D9S1843 0 6 12 10 0 0 2 0 10 10 10 0

38 Family 1435859

Affected individual (F)

Possibly affected individual

003 004 001 002

Refusal

101 102 107 ID 103 104 105 T. Chol 4.66 3.57 6.32 TG 1.27 2.09 2.50 HDL-C (%HDL) 0.81 (6) 0.86 (5) 0.93 (8) LDL-C 3.27 1.77 4.27 D9S1777 0 0 0 2 0 2 D9S1876 -8 6 0 -8 0 -8 D9S175 0 6 0 14 0 14 D9S1843 0 0 0 12 0 12

ID 203 204 201 202 205 209 206 210 207 211 208 212 T. Chol 5.98 6.51 5.84 4.16 5.22 4.47 5.22 TG 1.10 2.46 2.35 0.79 1.95 2.07 0.81 HDL-C (%HDL) 1.54 (90) 0.91 (22) 0.87 (<5) 1.31 (32) 0.98 (6) 1.09 (38) 1.26 (67) LDL-C 3.95 4.49 3.91 2.49 3.35 2.45 3.60

D9S1777 2 0 0 0 (0) (0) 0 0 0 2 0 2 D9S1876 6 2 8 2 -8 0 -8 0 -8 -8 6 -8 D9S175 12 16 2 16 0 0 0 0 0 14 6 14 D9S1843 10 10 6 10 0 0 0 0 0 12 0 12

306 ID 302 301 303 304 305 307 308 309 310 311 312 313 T. Chol 4.96 3.08 4.98 4.57 4.16 4.55 TG 0.85 0.57 0.63 0.78 0.94 0.91 HDL-C (%HDL) 1.22 (26) 0.79 (<5) 1.06 (18) 1.03 (11) 1.23 (33) 1.29 (45) LDL-C 3.36 2.03 3.64 3.19 2.51 2.85 D9S1777 0 0 0 0 0 0 (0) (0) 0 0 2 0 D9S1876 8 -8 2 -8 8 0 2 -8 8 0 (0) (?) D9S175 2 0 16 0 2 0 16 0 16 0 2 0 D9S1843 6 0 10 0 6 0 10 0 10 0 10 0

401 402

39 Family 25242

Affected individual (G)

Possibly affected individual

103 101 102

D9S1777 (?) (0) (0) (0) (0) (0) D9S1876 (?) (-8) (6) (4) (-8) (8) D9S175 (?) (0) (0) (2) (18) (6) D9S1843 (?) (10) (10) (10) (0) (12)

ID 222 221 220 202 201 T. Chol 6.19 5.10 4.69 6.68 / 7.28 TG 1.89 5.04 2.90 2.36 / 2.00 HDL-C [%HDL] 0.87 [<5] 0.67 [<5] 1.24 [25] 1.26 [26] / 1.39 [36] LDL-C 4.45 - 2.12 4.34 / 4.97 D9S1777 0 0 0 0 0 0 0 0 (2) (8) D9S1876 -8 6 6 -8 4 8 6 -8 (4) (8) D9S175 0 0 0 18 2 6 0 18 (10) (10) D9S1843 10 10 10 0 10 12 10 0 (0) (10)

ID 301 302 303 304 305 307 309 310 311 312 313 314 315 316 T. Chol 7.38 4.13 5.70 4.57 6.72 7.76 8.54 7.3 5.10 5.85 5.06 5.59 0.96 6.21 TG 12.45 0.90 5.21 0.75 2.34 2.66 20.31 1.26 3.85 1.58 4.69 1.37 6.90 1.44 HDL-C (%HDL) 0.62 [<5] 1.53 [54] 0.83 [12] 1.84 [72] 1.37 [29] 1.28 [25] 0.48 [<5] 2.13 [85] 0.72 [<5] 1.60 [52] 0.66 [<5] 1.69 [59] 1.91 [80] 1.36 [79] LDL-C 1.66 2.19 2.90 2.39 4.28 5.26 - 4.59 2.61 3.58 - - 4.55 4.19

D9S1777 0 2 0 0 0 2 2 8 0 8 0 8 (0) (8) 0 0 0 2 0 6 0 8 0 2 0 8 0 0 D9S1876 6 4 8 4 6 4 6 -10 -8 8 6 8 6 8 -8 -8 6 4 0 6 -8 4 8 -10 -8 8 -8 4 D9S175 0 10 0 0 0 10 6 6 18 10 0 10 0 10 2 12 0 10 2 16 18 10 8 14 18 10 0 -2 D9S1843 10 0 0 0 10 0 0 10 10 10 10 10 10 10 10 4 10 0 10 14 0 0 0 12 0 10 4 0

401 402 411 412 413 421 422 423 431 432 433 441 442 451 452 453 3.55 2.72 4.96 4.28 5.34 5.54 5.13 6.16 4.38 5.48 3.97 4.39 1.77 4.44 4.67 4.37 1.04 0.35 2.75 1.03 1.64 5.12 1.62 1.79 1.66 0.52 0.97 0.90 5.02 1.31 1.66 1.39 1.06 [18] 1.06 [18] 1.19 [24] 1.55 [65] 1.02 [32] 0.74 [<5] 1.41 [54] 1.05 [34] 0.99 [26] 1.66 [75] 1.27 [72] 1.18 [23] 0.87 [18] 1.36 [53] 0.99 [26] 1.27 [45] 2.01 1.50 2.51 2.26 3.57 2.47 2.98 4.29 2.63 3.58 2.26 2.80 3.34 2.48 2.92 2.46

D9S1777 0 0 2 0 2 2 0 2 2 2 8 0 (8) (0) 8 0 0 0 0 0 (0) (6) 8 2 0 0 0 0 0 0 8 0 D9S1876 6 8 4 4 4 6 6 -10 4 6 8 -8 8 -8 8 -8 (6) (0) 6 0 (6) (6) (4) (-10) -8 8 -8 4 -8 4 8 4 D9S175 0 0 10 0 10 6 0 6 10 6 10 2 10 12 10 2 0 2 0 2 0 16 10 14 18 8 18 -2 10 -2 10 -2 D9S1843 10 0 0 0 0 0 10 10 0 0 10 10 10 4 10 4 10 14 0 10 10 14 0 12 0 0 0 0 10 0 10 0

40 Family 1440369

Affected (H) Possibly affected Refusal

203 202 201 D9S1777 (?) (2) (0) (0) (0) (0) D9S1876 (?) (4) (8) (-10) (4) (-8) D9S175 (?) (2) (6) (0) (24) (6) D9S1843 (?) (4) (14) (0) (6) (0)

ID 302 311 312 303 319 304 313 305 314 306 315 301 310 320 307 316 308 317 309 318 T. Chol 4.14 5.66 4.07 5.90 4.36 4.08 4.62 4.69 4.06 3.62 5.54 5.64 TG 1.93 3.07 2.15 2.19 1.79 4.87 3.83 0.85 2.35 1.50 2.98 1.42 HDL-C (%HDL) 0.84 (<5) 0.89 (13) 1.19 (14) 1.15 (11) 1.98 (77) 0.96 (16) 0.64 (<5) 2.31 (>95) 0.93 (20) 0.91 (<5) 0.64 (<5) 1.68 (68) LDL-C 2.43 3.39 1.91 3.76 1.57 1.32 2.26 2.00 2.07 2.04 3.56 3.32 (6) (?) D9S1777 2 0 (0) (0) 2 0 (0) (?) 0 0 (?) (0) 0 0 0 0 0 0 (?) (2) 0 0 (?) (0) 0 0 (0) (0) 0 2 D9S1876 4 -10 (6) (-10) 4 -10 (0) (?) -10 -8 (?) (4) 8 -8 -10 -8 8 4 (?) (8) -10 4 (?) (2) -10 -8 8 -8 -10 2 D9S175 2 0 (0) (10) 2 0 (6) (10) 0 6 (?) (20) 6 6 6 6 6 24 (?) (0) 0 24 (?) (2) 0 6 6 6 16 16 D9S1843 4 14 (4) (14) 4 0 0 0 (?) (10) 14 0 14 0 14 6 (?) (12) 0 6 (?) (4) 0 0 14 0 6 6

ID 425 403 404 424 405 406 426 407 408 427 409 410 411 412 414 415 416 417 401 402 418 419 420 421 422 423 T. Chol 4.29 5.30 4.78 4.26 3.86 4.07 4.80 3.56 4.63 4.62 3.75 3.11 3.99 3.73 3.10 4.47 7.23 3.40 3.44 TG 0.86 1.62 1.22 0.80 0.99 0.65 1.12 1.79 1.49 2.19 1.94 1.04 1.35 0.54 0.58 1.39 2.06 0.60 0.68 HDL-C (%HDL) 1.28 (29) 0.94 (26) 1.33 (35) 1.23 (65) 1.02(13) 1.74 (95) 1.42 (88) 0.97 (10) 1.08 (46) 1.16 (19)0.89 (20)1.07 (18) 1.17 (21) 1.44 (52) 1.25 (29) 1.31 (36) 1.41 (87) 1.29 (45) 1.13 (42) LDL-C 2.62 3.63 2.90 2.67 2.39 2.04 2.88 1.78 2.88 2.47 1.99 1.57 2.21 2.05 1.59 2.53 4.89 1.84 2.00 D9S1777 (2) (?) 2 0 2 0 (?) (6) 0 0 6 0 6 0 0 0 6 0 0 0 0 2 0 2 0 0 0 0 0 2 D9S1876 (0) (?) 4 -10 4 6 (?) (4) 2 6 0 -10 0 -10 -10 4 0 -8 -8 4 4 8 8 8 4 2 8 -10 -8 2 D9S175 (6) (?) 2 10 2 0 (?) (10) 2 2 0 0 0 0 16 20 0 6 6 20 24 0 6 0 24 2 6 16 6 16 D9S1843 (0) (?) 4 14 4 4 (?) (0) 4 4 10 0 6 0 12 6 6 0 0 10 6 12 14 12 6 4 0 6 0 6

ID 506 501 T. Chol 4.18 4.42 TG 0.80 1.11 HDL-C (%HDL) 1.19 (51) 0.89 (12) 502 503 504 505 507 508 LDL-C 2.63 3.03 3.92 4.00 4.62 1.50 0.75 1.01 D9S1777 2 2 0 6 1.32 (50) 1.31 (49) 1.98 (>95) D9S1876 0 4 6 4 1.93 2.35 2.19 D9S175 6 2 0 10 D9S1843 0 4 4 0 0 0 0 0 0 0 2 -10 -10 -10 -10 4 2 0 0 16 0 20 4 0 0 12 0 6

41 Family 24292 (I)

Affected individuals

103 104

Possibly affected, linked

Refusal

201 ID 202 214 215 T. Chol 4.77 4.43 TG 2.57 1.50 HDL-C [%HDL] 0.68 [<5] 1.03 [12] LDL-C 2.91 2.71

D9S1777 (0) (2) (0) (2) 2 0 (2) (0) D9S1876 2 -10 0 -8 -8 0 (6) (2) D9S175 8 2 6 18 18 0 (16) (4) D9S1843 0 10 0 0 0 0 (12) (0)

ID 304 307 303 308 305 309 306 310 301 302 331 341 332 333 338 334 399 335 336 T. Chol 4.32 4.20 5.03 3.63 5.44 5.27 4.72 4.91 5.60 10.04 5.63 6.01 5.04 4.34 5.54 TG 2.43 1.04 0.66 1.39 5.83 1.59 1.85 1.65 2.53 6.71 2.30 0.89 1.29 0.66 1.88 HDL-C (%HDL) 0.67 [<5] 1.36 1.00 [33] 1.34 [47] 0.70 [<5] 1.55 (46) 0.70 [<5] 1.55 [67] 0.68 [<5] 0.93 [7] 1.10 [14] 1.23 [25] 1.04 [10] 1.32 [45] 0.72 [<5] LDL-C 2.57 2.36 3.73 1.65 2.09 3.00 3.29 2.60 3.76 3.47 4.37 3.41 2.72 3.96

D9S1777 0 0 2 2 D9S1876 2 0 -10 -8 (0) (2) 0 0 (2) (0) 0 6 ? 0 2 8 (0) (2) 2 2 (2) (0) (0) (0) (0) (0) D9S175 8 6 2 18 2 -8 0 6 -10 0 -8 4 ? 2 -8 0 0 6 -8 6 -8 2 0 2 0 2 D9S1843 0 0 10 0 8 18 0 6 2 6 0 0 ? 4 0 0 0 16 18 16 18 4 0 4 0 4 0 0 0 10 10 0 10 14 ? 0 6 6 0 12 0 12 0 0 0 0 0 0

406 404 405 407 ID 408 409 410 401 411 402 403 431 432 420 421 424 T. Chol 4.67 4.25 4.04 5.66 TG 1.58 0.94 1.61 1.42 HDL-C (%HDL) 1.05 [33] 1.13 [26] 1.10 (17) 0.79 [<5] LDL-C 2.90 2.69 2.20 4.22

D9S1777 0 0 0 0 0 6 0 8 D9S1876 2 0 2 6 0 4 2 0 D9S175 8 0 8 6 6 0 4 0 D9S1843 0 0 0 10 0 14 0 6

42 3.2 Association studies Having hypothesized that genetic variation within the PCSK5 gene might be associated with a change in HDL-C levels, it was necessary to investigate all variants spanning the PCSK5 gene region. This analysis was essential, especially given the inconclusive results obtained from our haplotype studies. We were thus interested in examining the impact PCSK5 polymorphisms would have on HDL-C metabolism and ultimately, CAD risk, by means of quantitative and qualitative association analyses.

3.2.1 PCSK5 sequencing reveals novel polymorphisms Consequently, for the second part of our study, and in concordance with our hypothesis, we first sought to sequence the entire PCSK5 gene for novel variants. Taking in account that the gene is 302 714 bp long, sequencing was performed on all 21 exons and exon-intron boundaries using 22 pairs of primers for a total of 7455 bps (Table 5). The 5‟UTR, as well as the 3‟UTR were included in the analysis, and exon-specific oligonucleotides were designed in such a way that each intron-exon boundary included intronic regions ranging from 22 bp to 186 bp situated between primers and exons of interest. Sequencing for SNPs at the PCSK5 locus was performed in 12 unrelated individuals with low HDL-C levels (<5th percentile). Table 6 shows the lipid and lipoprotein lipid levels of these HDL-C deficient subjects. The latter were selected from the Cardiology Clinic of the Clinical Research Institute of Montreal on the basis of being less than 60 years of age at the time of blood sampling and having angiographically documented CAD (grater than 50% stenosis of a major epicardial coronary artery) or a documented myocardial infarction by enzymatic and electrocardiographic critera.

43 Table 5. Oligonucleotide primers for PCSK5 gene sequencing Product Size Exon Forward (5’ to 3’) Reverse (5’ to 3’) (bp)* PCSK5-5'UTR ggaagtcatttatgcagagc acagcaggtccaaacgtc 594 PCSK5-X1 gccggagaagttagttgtg tagagcacatctggaagagg 370 PCSK5-X2 caccaaggttcaggtaacat tcagcccctagtgtgttatt 344 PCSK5-X3 cctgggtttctaaggaatct gaaacaatccctggtcagta 395 PCSK5-X4 ccaggagtttgaggttacag tgaaagggttatctcatgct 478 PCSK5-X5 cgtgaggtgagcgtattagt atgagcagaaaacaaagctc 271 PCSK5-X6 aagttccagaaacaccacag cagtcagccaaagagctaac 267 PCSK5-X7 ggggaagatatgctcactta atcacccatccacacagtat 346 PCSK5-X8 tcagtggattctgacctctt tgtttatggttctccctttg 360 PCSK5-X9 acattggcctcagaaatcta catgctaggggtcctataca 233 PCSK5-X10 atttgcttgtgactctctgc aatgggggaaaaataaaaat 234 PCSK5-X11 gtgtctggggtctctactga agcttttagcttctgtgtgc 300 PCSK5-X12 tggtaatcctttcagtgacc cgctgtgactactacactgg 396 PCSK5-X13 ctagaccctcattggatttg ggccaaaatatactccatca 258 PCSK5-X14 ctctgcgtttgatgtagtga catgagctgcctctttttag 332 PCSK5-X15 ccaaattcaaggtaaagctg tgggagaatagtggaagatg 289 PCSK5-X16 aaaagtgatttggaagctga tcactcaaatgaaaacacca 363 PCSK5-X17 aggccataggtactgtagca taacccttctttagccaaca 397 PCSK5-X18 agtttggggagtcattacg gaaagaaaagcaaaaccaga 224 PCSK5-X19 ttaaagaagaagcccatgac ctgaggcaaaggaataaaaa 387 PCSK5-X20 gtgcatgtgtgaatggatta agggcataagttcaagatga 317 PCSK5-X21 + 3'UTR aaccttcttccatgtgtgta gaaagagagaggtggcttg 300 * Product size includes exon-intron boundaries, including intronic regions (ranging from 22 bp to 186bp) situated between primers and exons of interest.

Table 6. Phenotypic characteristics of the 12 French Canadian probands chosen for DNA sequencing Family Individual Age Sex BMI GLU T. Chol TG HDL %HDL LDL ApoBtot ApoE ID ID y mmol/L mmol/L mmol/L mmol/L mmol/L g/L 27587 301 56 0 24.3 2.58 6.36 0.36 <5 0.47 3/2 25694 301 45 0 27.7 5.8 4.51 1.49 0.53 <5 3.43 1408018 301 51 0 28.7 4.83 1.15 0.54 <5 3.77 24039 301 58 1 25.8 6.7 4.50 2.24 0.56 <5 2.91 1.33 3/3 1067248 327 63 0 25.9 5.9 3.17 1.87 0.58 <5 1.75 28139 301 47 0 30.3 5.88 2.05 0.58 <5 1340981 301 67 0 31.2 5.7 4.95 2.14 0.58 <5 25623 301 59 0 33.0 5.1 3.74 1.36 0.60 <5 1222541 301 58 0 27.3 6.3 3.65 1.29 0.63 <5 2.44 0.84 23611 301 51 0 24.4 4.9 4.22 2.08 0.64 <5 2.25 1.06 1420372 301 72 0 25.3 5.2 4.63 1.50 0.65 <5 3.31 1.00

44 Direct sequencing from genomic DNA revealed 19 SNPs, 7 of which were novel non-coding genetic variants (Figure 9). Table 7 depicts these polymorphisms classified by functional classes: exonic, intronic or UTR SNPs. The variants obtained from our patients were compared to previously published polymorphisms for the PCSK5 gene available in different genetic databases. Hence, variants that were discovered through sequencing but were already established in literature were categorized as „validated‟ SNPs, while new polymorphisms were classified as „novel‟. Additionally, well-known coding SNPs from the National Center for Biotechnology Information (NCBI) database were taken as reference in our analysis should we uncover non-synonymous or synonymous variants. Surprisingly, our findings confirmed only 2 synonymous SNPs, as well as 12 intronic PCSK5 variants (Table 7). Accordingly, our 7 newly-detected polymorphisms were distributed as follows (Figure 9): 3 were intronic, respectively found in intron 4 (IVS4-3016T>A), 19 (IVS19-71insTAAAA ) and 20 (IVS20-50delTACTTTCAGGACTAAT ), the latter two characterized as insetions/deletions; the remaining 4 were located in the 5‟UTR region (c.125C>A, c.72C>T, c.385insGAGCTGCGGCGGCCCGGGGCTGC) and 3‟UTR region (c.323G>A). Although none of these 7 identified variants represent a missense, frameshift or non-sense polymorphism with obvious functional consequences, the possibility of their regulatory role cannot be excluded without actual functional analyses.

45 Table 7. PCSK5 Polymorphisms Identified by Sequencing Validated Reference - NCBI Functional Class Location Novel SNPs SNPs Selected SNPs Exon 12 rs34579561 Non- ― synonymous Exon 18 rs41304222 Exon 1 rs34813806 Exon 1 rs7040769 rs7040769 Exon 1 rs7020560 rs7020560 Exonic Exon 4 rs34417623 Synonymous Exon 9 ― rs7861246 Exon 9 rs41310061 Exon 12 rs2297342 rs2297342 Exon 14 rs10124541 Exon 18 rs10521468 rs10521468 Int 4 IVS4-3016T>A Int 7 rs2297344 Int 8 rs1416547 Int 11 rs3824474 Intronic Non-reported Int 16 rs2270570 Int 17 rs1537183 Int 19 rs3830384 IVS19-71insTAAAA Int 20 rs10869726 IVS20-50delTACTTTCAGGACTAAT rs12005073 c.125C>A rs12005073 5'UTR c.72C>T UTR c.385insGAGCTGCGGCGGCCCGGGGCTGC 3'UTR c.323G>A rs3077117

46

A) c.385insGAGCTGCGGCGGCCCGGGGCTGC

B) c.125C>A

C) Figure 1: c.72C>T

Figure 9. Novel variants identified at the PCSK5 locus. (A-C).

47

D) IVS4-3016T>A

E) IVS19-71insTAAAA

F) c.323G>A Figure 9. Novel variants identified at the PCSK5 locus. (D-F).

48

G) IVS20-50delTACTTTCAGGACTAAT Figure 9. Novel variants identified at the PCSK5 locus. (G).

3.2.2 TagSNPs selection In light of these new findings and in line with our hypothesis, we reasoned that it would be necessary to capture all variants found at the PCSK5 locus in addition to the novel ones discovered by sequencing, in order to test for association with the HDL-C trait. We thus choose to select tagSNPs from the HapMap project for the PCSK5 gene that are correlated with, and therefore can serve as a proxy for, much of the known remaining common variation at that locus. Moreover, we also decided to include 2 kb upstream of the PCSK5 region in the tagging process in order to capture putative regulatory variants within the promoter region that might have an impact on gene transcription or regulation, and thus ultimately on protein function. Consequently, using the Haploview version 4.0 software (Whitehead Institute for Biomedical Research Cambridge, MA 02142, USA) [76], tagSNPs spanning a total of 304 714 bp were selected from the Caucasian European population from the HapMap project database. Our criteria for common SNP selection was a minor allele frequency (MAF) > 0.05, r2 threshold > 0.80. In total, 163 tag SNPs captured by pairwise tagging 363 SNPs with a mean r2 of 0.965. Figure 10 displays these 163 polymorphisms in an LD map.

49 (A)

(B)

Figure 10. Linkage disequilibrium (LD) map of SNPs investigated in the PCSK5 gene. (A-B). A) The LD map was generated using Haploview (Barrett 2005). B) Close-up of A. Numbers and white to black shading indicate r2 values (black = high, white = low).

50 Additionally, with the intention of capturing all already-documented PCSK5 variants, public genetic databases such as NCBI, SeattleSNP, Broad, Entrez-Gene were consulted, and any complementary SNPs meeting our selection requirements were added to the exhaustive SNP list describing the PCSK5 locus (see Appendices section). It is important to mention however that variants covered in these databases that did not meet our criterion in terms of MAF > 5% or that did not possess any frequency data in terms of population diversity and that were common in other ethnic groups, such as the African American population, were not included in our analyses. For example, while there exists 9 synonymous SNPs in the PCSK5 gene, only 4 were selected for our association study.

3.2.3 Genotyping The next step in our investigation was to genotype all SNPs identified by sequencing (7), pairwise tagging (163) and publicly available genetic databases (12) in 457 unrelated individuals of French Canadian descent with documented CAD, for association with HDL-C levels. Overall, 182 PCSK5 SNPs (including 2 kb upstream of the gene) were investigated and sent for genotyping at the McGill University and Genome Québec Innovation Centre. From these, 2 were non- synonymous, 4 silent, 4 situated in the PCSK5 locus region, 4 covering the 5‟ and 3‟ UTR PCSK5 region, and the remaining were intronic and tagSNPs. Please see (see Appendices section) for a detailed classification of these polymorphisms.

3.2.4 Quantitative and case-control analyses Subsequently, we assessed the association of the 182 genotyped SNPs with the HDL-C parameter, both as a quantitative and binary analysis, performed in 457 unrelated French Canadian individuals with angiographically documented CAD. Subjects had known HDL-C levels ranging from the < 5th percentile to >95th percentile (age and gender-matched), based on the Lipid Research Clinics Population Studies Data Book [76].

51 Statistical analysis were performed using the Plink, SAS and Haploview softwares with significance set at P<0.05 for all tests. First, prior to association analysis, we performed a quality control-check by assessing the integrity of our genotypic data. We eliminated any SNPs that failed genotyping and any monomorphic SNPs. We also assessed for SNPs with non-calls in 20 or more individuals through the missingness test implemented in Plink, eliminating SNPs with failure rates >10%. Similarly, deviation from Hardy-Weinberg equilibrium was evaluated using the exact test implemented in Plink. Likewise, given our cohort, we selected for SNPs with an allele frequency > 1%. Next, with the purpose of analyzing HDL-C as a continuous trait using linear regression, we examined the HDL-C phenotypic data for normal distribution, mandatory assumption needed for such analysis. Should the data follow a non-normal distribution, one would have had to use non-parametric statistics, such as natural log or inverse normal scores, i.e. Z-scores, to be able to effectively analyze it. However, given that our genotypic data met the above criteria, and since there were no potential outliers (defined as any value greater than 3 standard deviations from the mean), we conducted our statistical analysis using an additive linear regression model implemented in Plink and, adjusted for age and sex as covariates. Interestingly, we identified 9 SNPs significantly associated with HDL (P<0.05), with the strongest result being the rs11144782 SNP (MAF 0.164, P=0.00202) (Table 8). The latter was observed to have an effect size of -0.07623, or a decrease in HDL-C levels of 0.076 mmol/L caused by the rare allele of rs11144782. Likewise, 4 other SNPs were detected to decrease plasma HDL-C levels (rs11144766, rs11144688, rs11144690, rs1338746), while others had a positive effect on HDL (rs1339246, rs1331384, rs4745522, rs2050833). Closer examination of these 9 SNPs however, points towards non-coding SNPs and no LD between one another (Figure 11), yet considering their effect size, they exhibit a profound impact on HDL metabolism. Particularly, our most significant SNP, rs11144782, lowers plasma HDL-C levels by 0.076 mmol/L, corresponding to a

52 ~8% decrease in HDL-C levels in comparison to general normal laboratory values of clinical importance (1.0 mmol/L).

Table 8. Quantitative Trait Analysis Chromosome SNP Trait Beta P-Value MAF 9 rs11144782 HDL -0.07623 0.00202 0.16437 9 rs11144766 HDL -0.06287 0.00506 0.19740 9 rs1339246 HDL 0.05575 0.01767 0.17620 9 rs1331384 HDL 0.03717 0.03825 0.48499 9 rs11144688 HDL -0.05339 0.03921 0.13678 9 rs11144690 HDL -0.09334 0.04026 0.04051 9 rs1338746 HDL -0.03619 0.04393 0.42414 9 rs4745522 HDL 0.05101 0.04487 0.14302 9 rs2050833 HDL 0.04527 0.04515 0.19907

Figure 11. LD Plot of Significant SNPs in the QTL Analysis. LD map generated using Haploview depicting no significant linkage between SNPs. * Please note that in this plot there are 10 SNPs, rather than 9, due to an additional SNP, rs474552, that will be later elucidated in the case-control analyses.

53 To extend our analysis, we further investigated the effect of this variant on other lipoprotein levels, specifically on LDL-C, Tg, VLDL and ApoB total. All phenotypes were adjusted for age and sex and were examined under the same previous statistical model. Remarkably, we found a significant positive correlation with Tg (P=0.04878), VLDL (P=0.03858), and ApoB (P=0.02234) levels (Table 9) suggesting that this PCSK5 SNP plays an important role in lipid metabolism.

Table 9. Traits associated with rs11144782 Chromosome SNP Trait Beta P-Value 9 rs11144782 HDL -0.07623 0.00202 9 rs11144782 TG 0.50190 0.04878 9 rs11144782 VLDL 0.16860 0.03858 9 rs11144782 APOBtot 10.7200 0.02234

Subsequently, we sought to perform a binary analysis on the same French Canadian cohort, testing for HDL-C as a dichotomous variable. The selection criterion was an HDL-C < 5th or < 10th percentile (112 and 178 individuals respectively, age- and gender-matched) for cases, and an HDL-C > 5th or > 10th percentile (339 and 273 individuals respectively) for controls. In comparison to our segregation analysis, where we looked for a rare variant segregating within the families, in our association analysis, we used cutoffs of 5th percentile, as well as the less stringent 10th percentile to identify more common variants with weaker effects in unrelated individuals. As a result, in an additive logistic regression test that adjusted for age and gender, we discovered significant associations (P<0.05) in the affected participants: 11 SNPs when HDL < 5% and 3 polymorphisms when HDL < 10%. Intriguingly, we were unable to replicate our most significant finding, rs11144782, in this case-control study. Nevertheless, when taking HDL-C as a discrete trait, 3 of the previously identified SNPs were also associated with low HDL-C at either the 5th or the 10th percentile (gray-shaded SNPs in Table 8 and 10). All variants detected were positively correlated with HDL-C plasma levels (Table 10).

54 Table 10. Case-control analysis Chromosome SNP Trait Beta P-Value MAF 9 rs4313218 HDL < 5% 1.596 0.00332 0.358 9 rs4745522 HDL < 5% 0.6573 0.00963 0.143 9 rs4644325 HDL < 5% 0.5713 0.01315 0.242 9 rs10781319 HDL < 5% 0.6728 0.0142 0.226 9 rs4745488 HDL < 5% 2.071 0.01482 0.296 9 rs2789608 HDL < 5% 0.5828 0.01987 0.337 9 rs11144692 HDL < 5% 0.7146 0.02929 0.288 9 rs17719860 HDL < 5% 0.5679 0.03247 0.135 9 rs11144690 HDL < 5% 0.7116 0.03906 0.040 9 rs7847011 HDL < 5% 0.7232 0.04389 0.267 9 rs2994426 HDL < 5% 0.6436 0.0479 0.084 9 rs11144688 HDL <10% 1.647 0.009588 0.137 9 rs10781319 HDL <10% 0.7325 0.02773 0.226 9 rs1475615 HDL <10% 1.979 0.03798 0.348

Conversely, much interested in rs11144782, but intrigued as to why it did not surface in our binary analysis, we decided to further investigate its effect upon the other 8 SNPs found in the QTL study. We thus conducted a stepwise conditional regression analysis, adjusting not only for age and sex as parameters, but also for this strongly significant polymorphism. Testing for association with HDL-C, we identified rs11144766 as our new strongest signal (P<0.00125) among other 6 significant SNPs. We assessed for further association with HDL-C by taking age, gender, 11144782 and rs11144766 as our new covariates and surprisingly found 2 significant SNPs, the strongest being a previously discovered variant, rs2050833 (P<0.03594). Performing the same procedure, but this time, adding rs2050833 to the other covariates, and testing for association, we discovered yet another signal, rs4745488 (P<0.03811), already-established as a significantly associated variant in our binary analysis. Remarkably, these results provide further proof of the implication of PCSK5 in HDL-C metabolism. All the 3 additional SNPs contribute strongly to HDL-C levels (P<0.05), independently of the rs11144782 effect. Indeed, they represent 4 independent signals (bolded SNPs in Table 8 and 10; Figure 12) at the PCSK5 locus, supporting previous findings depicted in Figure 11. The LD map

55 clearly illustrated no linkage disequilibrium between the 9 QTL significant SNPs (rs4745488, being an independent signal, was also added to that LD plot).

Figure 12. Significant independent signals found at the PCSK5 locus.

We next assessed these 4 SNPs‟ genotypic means (Table 11) and, corresponding to the rare alleles effect sizes previously found (Table 8), one could fully appreciate the gene dosage effect of the 4 variants in individuals possessing the respective rare allele. In fact, we discovered that in the case of rs11144782, the rare G allele found in 10 homozygous CAD patients had a more detrimental effect than in the rs11144782 heterozygotes. This effect on HDL-C levels is also well-portrayed in the boxplot diagram of Figure 13, where HDL-C levels decrease according to number of G alleles in one‟s genotype. Thus, the rare allele of rs111447782 lowers plasma HDL-C by 0.076 mmol/L in a gene dosage- dependent fashion.

56 Table 11. Genotypic Means of the 4 Independent Signals Chromosome SNP Value Genotype 22 Genotype 12 Genotype 11 9 rs11144782 Genotype C/C G/C G/G 9 rs11144782 Counts 301 123 10 9 rs11144782 Frequency 0.6935 0.2834 0.02304 9 rs11144782 Mean 0.9757 0.8872 0.8 9 rs11144782 Standard Deviation 0.2884 0.2412 0.2015

9 rs11144766 Genotype G/G A/G A/A 9 rs11144766 Counts 275 128 19 9 rs11144766 Frequency 0.6517 0.3033 0.04502 9 rs11144766 Mean 0.9685 0.912 0.8458 9 rs11144766 Standard Deviation 0.2965 0.2331 0.1769

9 rs2050833 Genotype T/T C/T C/C 9 rs2050833 Counts 276 138 17 9 rs2050833 Frequency 0.6404 0.3202 0.03944 9 rs2050833 Mean 0.928 0.9708 0.9994 9 rs2050833 Standard Deviation 0.2706 0.2824 0.317

9 rs4745488 Genotype G/G C/G C/C 9 rs4745488 Counts 224 166 46 9 rs4745488 Frequency 0.5138 0.3807 0.1055 9 rs4745488 Mean 0.9555 0.9623 0.8391 9 rs4745488 Standard Deviation 0.2786 0.2945 0.1647

57 Figure 13. Effect of rs11144782 on HDL-C levels. Boxplot of effect of rs11144782 on HDL-C according to number of G alleles.

58 4. DISCUSSION

4.1 Conclusions One of the principal cardiovascular research interests over the last 15 years has been the investigation of molecular genetics and the pathophysiology of HDL deficiency, which represents one of the major risks of atherosclerotic cardiovascular disease. Despite a large body of information identifying HDL as a potent physiological protector against CAD, the fundamental mechanisms underlying the regulation of the HDL metabolic pathway remain complex and poorly understood. A growing body of evidence indicates that the endothelium has a major effect on lipoprotein remodeling and function. Indeed, several lipases, including EL, LPL, S-PLA2, and S-SMase, are bound to the endothelial cell matrix and have the ability to hydrolyze lipoprotein Tg and phospholipids. Importantly, recent studies have shown that EL is a negative regulator of plasma HDL levels and is cleaved by members of the PCs family including furin, PACE4 and PC5, resulting in its inactivation. This metabolic effect of PCs is critically dependent on expression of EL that directly hydrolyzes HDL phospholipids and promotes its catabolism. Thus, factors operating in the extracellular space, including the PCs family may play a pivotal role in the regulation of HDL concentration, composition and subpopulation distribution and could be potential targets for therapies designed to inhibit the development of CAD. In the present study, we have demonstrated that genetic variability at the PCSK5 gene modules and contributes to HDL-C levels. We first examined haplotypes constructed at the PCSK5 locus in 9 multigenerational families with HDL-C levels <10th percentile and discovered that one family displayed segregation with the low HDL-C trait under a dominant model of inheritance. Given that this segregation was not observed in all of the families studied, suggested that PCSK5 does not exert a Mendelian monogenic effect on HDL-C. By sequencing the gene however, we identified 7 novel non-coding genetic variants in patients with HDL-C deficiency. In a second analysis, we investigated

59 the association of PCSK5 SNPs‟ with HDL-C levels in 457 unrelated subjects of French Canadian descent, both as a quantitative and qualitative analyses. Through genotyping, we discovered 9 significant SNPs (P<0.05) in the study of HDL as a continuous trait, 4 associated with a decrease in HDL-C levels and 5 with an increase. While these results strongly indicate a role of PCSK5 in HDL remodeling either as a positive or negative modulator of HDL levels, our strongest finding, rs11144782 (MAF 0.164, P=0.002), points towards a gene dosage-effect decrease in HDL-C levels. The variant was also associated with VLDL (P=0.039), Tg (P=0.049) and total ApoB (P=0.022) levels. In an analysis of HDL-C as a dichotomous trait, 3 of the 9 SNPs were also associated with low HDL-C at either the 5th or the 10th percentile (P<0.05). Furthermore, in condition regression analysis, we identified 3 of those already-identified SNPs to contribute to HDL-C, as autonomous signals, independent of the effect of rs11144782. The discovery that the rare rs11144782 G allele contributes to a decrease of 0.07623 mmol/L in plasma HDL-C levels (Table 8), indicates that homozygous patients for this allele have significantly lower serum levels of HDL-C and may be more predisposed to CAD. Specifically, from our French Canadian cohort documented with CAD, after assessing genotypic data integrity, we found that 10 patients were homozygous for this rare allele, 123 were heterozygous and 301 were homozygous for the common C allele. Accordingly, we propose that in future studies, it would be particularly interesting to assess EL activity in those three subject groups in the hopes of elucidating the in vivo mechanism of the PC5- EL system and further confirming our hypothesis. Knowing the role of our most significant SNP in HDL-C metabolism, and its effect in elevating Tg by 0.5 mmol/L, VLDL by 0.1686 mmol/L and ApoBtotal by 10.72 g/L, we propose the following in vivo PC5 conceptual mechanism of action (Figure 14). A homozygous patient for rs11144782 would have a loss of function mutation of the PC5 protein which would consequently prevent the internal cleavage of the HSPG-bound EL at the C-terminus of Arg in the RxKR↓ motif. As a result, EL will be overexpressed and fully active, exerting its phospholipase activity on HDL. The levels of this lipoprotein will subsequently

60 decrease and small HDL particles with reduced phospholipid content would be produced. Similarly, a loss of function mutation in PC5 would also allow the activation of the HLGAG-bound LPL and its triglyceride hydrolase function on chylomicrons and VLDL. These lipoproteins would then slightly increase, in concordance with our findings. Also, plasma elevations of ApoB and VLDL are risk factors for CAD and sometimes, severe elevations of plasma Tg may lead to acute pancreatitis. It is important to point out though that due to the before- mentioned fact that PC5 does not act to the same extent on LPL as it does on the EL, its role is more pronounced in HDL metabolism. Although in 2001, Cao et al. [83] identified 2 silent SNPs in PCSK5 and found that they varied in frequency among ethnic groups, no other studies until now have analyzed the genetic variability in the PCSK5 gene and its contribution to HDL-C levels. Thus, this report is a first examination of such genetic variation, implicating PCSK5 as an important and influential modulator of HDL-C serum levels in humans. Moreover, although all of the significant SNPs in our analysis are intronic and thus non functional, the possibility that they exert a regulatory role cannot be excluded. Indeed, they can play significant roles in regulating gene expression, such as affecting the binding sites of key enhancers or repressors, which may lead to altered expression patterns. As well, while these genotyped SNPs are intronic, it may be possible that these variants are in LD with other ungenotyped SNPs and thus have an effect on PCSK5 expression. In conclusion, the multiple statistical analyses and the compelling overall significance of associations with HDL-C found for PCSK5 variants in a total of 457 participants of French Canadian ancestry, as well as the significant catalytic effect found for the rs11144782 SNP, support the contribution of PCSK5 to CAD risk. Although additional functional analysis and replication in other cohorts will be needed, these findings can firmly place PCSK5 on the list of genes associated with CAD and emphasize the need to investigate PC5 and its related substrates for identification of specific therapeutic targets for treatment of cardiovascular

61 diseases.

Lipid-free apoAI

HDL2

Phospholipase activity FFA, Lyso-PC EL

PC5/6

HSPG Loss of function mutation

Endothelial cells Smooth muscle cells Macrophages in human atherosclerotic lesions

Figure 14. Conceptual Mechanism of Action of PC5 in Relation to HDL Metabolism

On the other hand, our study had also some shortfalls. First, the case- control binary analysis might have had an ascertainment bias in regards to the control population which was taken from the same group as the cases, given that it was composed of subjects which came to the clinic for CAD treatment and were therefore not randomly sampled. Secondly, due to the relatively small sample size of our CAD cohort used in our association studies and the high number of SNPs tested in our analysis, we encountered problems of multiple testing and lack of statistical power. These could, in theory, increase our P value, as well as chances of discovering false positive results, and thus, the likelihood that our SNP findings were attributed to chance. In statistics, the Bonferroni correction is sometimes used as a safeguard against multiple tests of statistical significance. We decided to test it in our analysis. This statistical correction states that if an experimenter is testing n dependent or independent hypotheses on a set of data, then the statistical significance level that should be used for each hypothesis separately is 1/n times what it would be if only one hypothesis were tested. Hence, instead of using a P threshold of significance of 0.05, according to the Bonferroni equation, we could have used a much stricter threshold of 0.05/182=0.000274. However, as

62 previously stated, given our elevated number of tests and a somewhat small sample size, our results did not pass this Bonferroni threshold. Nevertheless, one should take into consideration that this correction is extremely stringent and conservative, and should not be taken as primary measure of statistical significance, reason for which we did not use it in our results. In contrast, less rigorous corrections for multiple testing could be used, such as the false discovery rate, permutation-based analyses or methods using correlated SNPs [84]. Most importantly however, we believe that the possible shortcoming of not meeting the Bonferroni correction was surpassed given that our rs11144782 SNP was not only found to be associated with HDL-C, but also with other lipid parameters, implicating it as a strong and veritable signal in our association. Nevertheless, in order to limit multiple testing and most importantly, with the purpose of reconfirming our discovery, one should replicate these findings in a larger French Canadian cohort, as well as in others of different ethnicities. Likewise, for a stronger statistical analysis, a dominant statistical test model should be used in addition to the additive model utilized here, both in the quantitative and in the discrete studies. Likewise, power calculations should be initially carried out before deciding on the sample parameters of an association study [85]. Post-hoc power calculations were performed in our binary analysis (cases HDL-C <5th percentile, controls HDL-C >5th percentile) as an example to calculate the sample size required to detect a change in HDL-C of 10% in both the control and the low HDL-C groups. Given a mean HDL-C of 1.04 mmol/L with a standard deviation of 0.25 mmol/L in the entire French Canadian cohort, in the control group, a sample size of 269 is required (α = 0.001 and β = 0.90). For the low HDL-C group, a sample size of 90 is required to detect a 10% change in HDL-C levels. Given that this study was based off of 112 cases with HDL-C <5th percentile and 339 controls with HDL-C >5th percentile, which are well-above the threshold limits, signifies that the analysis did not lack power. While it is essential to replicate our results in other studies, these findings can nevertheless be applicable to non-French Canadian populations as well, given

63 that the tagSNPs chosen for our association analysis were selected from the Caucasian HapMap population of European ancestry. Since the latter is comprised of several other ethnicities aside from French Canadians, the tagSNPs and thus the association study, can pertain to non-French Canadians as well. This holds true nonetheless when one considers French Canadians genetically comparable to Europeans Caucasians, given similar LD patterns and allele frequencies. Therefore, these findings, and the LD pattern obtained, can also be replicated in other non-French Canadian populations of the same European ancestry. In comparison, limitations of our results to non-French Canadians are apparent when one cannot replicate the findings in other populations.

4.2 Future studies Given the significance of our results, we believe that a clearer understanding of the molecular interactions between the PC5-EL system and HDL, as well as the direct impact of HDL remodeling by PC5-EL on the RCT process and endothelial function, should be the main focus in the design of future scientific studies. It would be essential to first confirm through genome-wide association studies a concrete association between genetic variability at the PCSK5 locus and risk of CAD, in addition to the one found here with HDL-C levels. Secondly, it would be interesting to determine the effect of the PC5-EL system on HDL subspecies in an in vitro cell system by manipulating the expression of both proteins. Identifying the in vivo roles of the PC5 in lipid homeostasis through hepatocyte- and endothelial cell-specific KOs, could also help define better its effect on the inactivation of the EL and LPL, with the ultimate goal of screening patients with low HDL for mutations at the PCSK5 locus. Next, one should characterize the EL-PC5-modified HDL and determine its ability to promote lipid efflux through different RCT pathways, including ABCA1, SR-BI and macrophages RCT, as well as the impact on vascular endothelial function biomarkers. Lastly, investigating whether manipulation of the expression of PC5

64 in an vitro cell system affects other enzymes involved in the hydrolysis of HDL phospholipids, such as S-SMase and S-PLA2, could be of interest as well. The results of these studies could consequently shed new light on the importance of the PCSK5 gene and the PC5 protein in the modulation of plasma HDL levels in humans. Additionally, the unraveling of the in vivo and in vitro effects of the PC5-EL system could refine our comprehension of the complex HDL metabolic pathway and could provide novel insights into the human atheroprotective system in health and disease. This will also form a basis for studies designed to augment HDL-mediated cholesterol efflux in vivo, with the ultimate goal of deriving therapies for increasing plasma HDL concentrations and RCT to protect against cardiovascular diseases.

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55. De B, I, Marcinkiewicz M, Malide D, Lazure C, Nakayama K, Bendayan M, Seidah NG: The isoforms of proprotein convertase PC5 are sorted to different subcellular compartments. J.Cell Biol. 1996, 135:1261- 1275.

56. Nour N, Basak A, Chretien M, Seidah NG: Structure-function analysis of the prosegment of the proprotein convertase PC5A. J.Biol.Chem. 2003, 278:2886-2895.

57. Essalmani R, Hamelin J, Marcinkiewicz J, Chamberland A, Mbikay M, Chretien M, Seidah NG, Prat A: Deletion of the gene encoding proprotein convertase 5/6 causes early embryonic lethality in the mouse. Mol.Cell Biol. 2006, 26:354-361.

58. Bergeron E, Basak A, Decroly E, Seidah NG: Processing of alpha4 integrin by the proprotein convertases: histidine at position P6 regulates cleavage. Biochem.J. 2003, 373:475-484.

59. Lissitzky JC, Luis J, Munzer JS, Benjannet S, Parat F, Chretien M, Marvaldi J, Seidah NG: Endoproteolytic processing of integrin pro- alpha subunits involves the redundant function of furin and proprotein convertase (PC) 5A, but not paired basic amino acid converting enzyme (PACE) 4, PC5B or PC7. Biochem.J. 2000, 346 Pt 1:133-138.

60. Xiang Y, Molloy SS, Thomas L, Thomas G: The PC6B cytoplasmic domain contains two acidic clusters that direct sorting to distinct trans-Golgi network/endosomal compartments. Mol.Biol.Cell 2000, 11:1257-1273.

61. Stawowy P, Margeta C, Kallisch H, Seidah NG, Chretien M, Fleck E, Graf K: Regulation of matrix metalloproteinase MT1-MMP/MMP-2 in cardiac fibroblasts by TGF-beta1 involves furin-convertase. Cardiovasc.Res. 2004, 63:87-97.

62. Srour N, Lebel A, McMahon S, Fournier I, Fugere M, Day R, Dubois CM: TACE/ADAM-17 maturation and activation of sheddase activity require proprotein convertase activity. FEBS Lett. 2003, 554:275-283.

63. Siegfried G, Khatib AM, Benjannet S, Chretien M, Seidah NG: The proteolytic processing of pro-platelet-derived growth factor-A at

71 RRKR(86) by members of the proprotein convertase family is functionally correlated to platelet-derived growth factor-A-induced functions and tumorigenicity. Cancer Res. 2003, 63:1458-1463.

64. Siegfried G, Basak A, Prichett-Pejic W, Scamuffa N, Ma L, Benjannet S, Veinot JP, Calvo F, Seidah N, Khatib AM: Regulation of the stepwise proteolytic cleavage and secretion of PDGF-B by the proprotein convertases. Oncogene 2005, 24:6925-6935.

65. Siegfried G, Basak A, Cromlish JA, Benjannet S, Marcinkiewicz J, Chretien M, Seidah NG, Khatib AM: The secretory proprotein convertases furin, PC5, and PC7 activate VEGF-C to induce tumorigenesis. J.Clin.Invest 2003, 111:1723-1732.

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72 72. Seidah NG, Chretien M, Day R: The family of subtilisin/kexin like pro- protein and pro-hormone convertases: divergent or shared functions. Biochimie 1994, 76:197-209.

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74. Wang X, Paigen B: Genetics of variation in HDL cholesterol in humans and mice. Circ.Res. 2005, 96:27-42.

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77. Weber M, McNicoll S, Marcil M, Connelly P, Lussier-Cacan S, Davignon J, Latour Y, Genest J, Jr.: Metabolic factors clustering, lipoprotein cholesterol, apolipoprotein B, lipoprotein (a) and apolipoprotein E phenotypes in premature coronary artery disease in French Canadians. Can.J.Cardiol. 1997, 13:253-260.

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APPENDICES

Memorandum KEEP THIS FOR YOUR RECORDS Research Grants Office James Administration Building, Room 429, Local 5211, Fax 4626 ______

To: Dr. Jacques Genest From: France Larivière, Local 5211 MEDICINE Awards Assistant france.lariviere@mcgill .ca Subject: Renewal of Fund 204595 Sponsor: CANADIAN INSTITUTES OF HEALTH RESEARCH (CIHR) Project Title: Genetics of high density lipoproteins (HDL) Agency #: MOP 62834 Amounts: Periods: $163,374.00 April 1, 2008 to March 31, 2009

Date: Wednesday, April 23, 2008 ______Please be informed that on April 23, 2008 the Research Grants Office (RGO) sent an authorization to the Research and Restricted Funds Office (R&RFO) for them to renew your fund 204595. The current approval is for the period(s) April 1, 2008 to March 31, 2009. Subsequent amounts (if any) will be approved in the corresponding fiscal period. If you have any questions relating to the financial aspects of your fund, please contact your fund administrator or send an email to the following address: [email protected].

NB: In order to ensure a prompt renewal of your fund for next year, if applicable, please provide RGO with all required documents at least two (2) weeks prior to the end date of the current grant period as indicated above.

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79 McGill University Health Centre Hôpital Royal Victoria Cardiology Research Unit S4.85 687, Pine Avenue West Montréal, Québec H3A 1A1

Genetic Determinants of High Density Lipoproteins (HDL)

Consent Form

Principal Investigators : Jacques Genest MD, FRCP (C), Professor and Director Michel Marcil MSc, PhD, Assistant Professor Division of Cardiology, Department of Medicine, McGill University Health Centre - Royal Victoria Hospital.

Funding for this study: Canadian Institutes of Health Research Heart & Stroke Foundation of Quebec Fonds de la recherche en Santé du Québec

Introduction Cholesterol disorders are very common in patients with - or at risk of developing - coronary artery disease (blockage of the arteries leading to the heart). Particularly common, is a low blood level of high-density lipoproteins cholesterol (HDL cholesterol) also known as the “good cholesterol”. Approximately one third to one half of patients with early onset heart disease have a low level of HDL cholesterol in their blood. Conversely, patients with an elevated level of HDL cholesterol appear to be resistant to develop heart disease. We are currently studying the genes associated with cardiovascular disease. Following your visit (or visit from a member of your family) at the McGill University Health Centre, we have identified an abnormal level of HDL cholesterol. For this reason, you are being invited to participate in a research project on the genetics of HDL. This study is supported by peer-reviewed funding agencies: the Canadian Institutes of Health Research (CIHR), the Heart & Stroke Foundation of Canada, and the Fonds de la Recherche en Santé du Québec (FRSQ).

80 McGill University Health Centre Hôpital Royal Victoria Cardiology Research Unit S4.85 687, Pine Avenue West Montréal, Québec H3A 1A1

Genetic Determinants of High Density Lipoproteins (HDL)

Purpose of this Study The main purpose of this study is to identify and study the genes that control blood levels of HDL cholesterol. These discoveries would allow us to have a better understanding of how the human body handles cholesterol and produces HDL cholesterol. Identification of new genes which control this important cholesterol pathway may help to develop new therapies aiming to reduce the risk of developing heart disease.

Procedure In order to identify the genetic basis of abnormal (low or high) HDL cholesterol, several steps will be carried out. 1. A sample of your blood will be drawn and used to perform specific laboratory tests in order to determine your lipid and lipoprotein profile, including the level of HDL cholesterol. Additional biochemical analyses will also be performed to rule out secondary causes that could lead to an abnormal level of HDL cholesterol. These analyses will be performed at the laboratories of the McGill University Health Centre (MUHC), except for some specialized tests including other proteins, lipids, enzymes, and metabolites which might be performed in outside laboratories because the highly sophisticated nature of those tests. Your genetic material (DNA) will be isolated and stored with your blood plasma for further biochemical analyses. A small biopsy of your skin might be required in order to study cellular cholesterol metabolism. We will also ask your permission to perform a family study in order to understand the genetics of this cholesterol anomaly. 2. If a skin biopsy is required, the biopsy will be performed on the forearm skin. After careful disinfection of the skin, local anaesthesia is performed with 2% xylocaine and a small 3 mm biopsy is performed using a disposal bioptome. The bioptome is sterile and is used only one time. Once the biopsy is taken, the wound is closed by steri-strip in a small bandage. The biopsy is then taken to the laboratory for cell culture. 3. Using your DNA, we will proceed with an analysis called “genome-wide scan”, to compare it with DNA from other people (in your family and others) in order to identify the chromosome (where the genes are found) responsible for the HDL cholesterol disorder in your family. Your DNA will be also analyzed to verify defects in genes already known to cause HDL cholesterol anomalies. In a separate consent form, we will ask your permission to take a sample of your DNA for these analyses.

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McGill University Health Centre Hôpital Royal Victoria Cardiology Research Unit S4.85 687, Pine Avenue West Montréal, Québec H3A 1A1

Genetic Determinants of High Density Lipoproteins (HDL)

Side Effects Blood sampling may cause discomfort when the needle is inserted in the vein and soreness or bruising may result for a few days thereafter. There is a very remote risk of infection. In less than 1% of cases, patient may faint during the blood drawing procedure. If a skin biopsy is performed, side effects are very infrequent. The biopsy may cause a slight pain at the site (less than 5%) and bleeding may also occur (less than 1%) or infection (less than 1%). Should the area around the biopsy site become red, painful, or bleeds, you should seek medical help at a local clinic or at the MUHC.

Storage of Blood Plasma and Cells and Laboratory Analyses Your blood sample will be coded and kept confidential. Small amounts of your blood plasma will be kept in tubes at very low temperature. Highly specialized analyses will be performed on biochemical markers, specific enzymes, lipids or proteins linked to HDL metabolism. In case of a skin biopsy, cells from your skin, called fibroblasts, will be cultured in laboratory to study the metabolism of cellular cholesterol. Cell samples are also coded and kept at an extremely low temperature between experiments. The storage period of samples may be up to 15 years following the end of the study, after which the samples will be destroyed.

Study Results It is likely that the results of this research project on your specific family will not be known for several months. The principal investigator will communicate to you any results that are pertinent to your health, including the results of your blood tests for diabetes, blood cholesterol and a complete lipoprotein profile. Most of the data generated from these studies is of an experimental nature and will not be part of your medical chart. None of the results may be transmitted to any third party without your written consent.

Confidentiality All information collected during this project will be kept strictly confidential. To ensure confidentiality, you will be assigned a coded number, the identity of which is known only to the principal investigators. All samples including blood plasma, cells, and DNA will then be anonymized (given an identification number that only the principal investigators can trace). Should your blood samples be sent to an

82 outside laboratory, a second coded number would be assigned to your sample (a technique known as double encryption) so that anonimity is fully respected. Only authorized personnel will be permitted to access the samples and the results but only the principal investigators and the research nurse will be able to trace these results back to the patient.

Compensation No financial compensation is granted for your participation in this study. However, travel expenses will be reimbursed.

83 McGill University Health Centre Hôpital Royal Victoria Cardiology Research Unit S4.85 687, Pine Avenue West Montréal, Québec H3A 1A1 Genetic Determinants of High Density Lipoproteins (HDL)

Who to Contact for Information You will be given a signed copy of this informed consent document. If you have any questions or need additional information at any time during the study, please do not hesitate to contact us at the MUHC, phone number (514) 934-1934; for the Investigators: Dr. Jacques Genest MD, ext. 34642; Dr. Michel Marcil PhD, ext. 35030; for the Research Nurse: Mrs. Colette Rondeau, ext. 36578. Should you have any questions regarding your rights as patient participating in a research project, you may contact the Royal Victoria Hospital’s Ombudsman at ext. 35655.

Consent

Your participation at this study is voluntarily. You have been given all the time required to answer your questions and have received appropriate answers.

 I ______voluntarily consent to participate in this project and I accept that a sample of my blood be taken and kept in the Cardiovascular Genetics Laboratory at McGill University Health Centre.

 I consent to have a skin biopsy for this project:

YES NO If required during the study

 I would like to be informed of the results of these analyses:

YES NO

______Patient‟s Name Signature of Patient Date

______Investigator‟s Name Signature of Investigator Date

84 DNA Sample Purpose The purpose of this form is to ask your permission to take your DNA (your genetic material) and to conserve it in the Cardiovascular Genetics Laboratory at the McGill University Health Centre (MUHC) for genetic analysis. This research project is part of a national effort to detect genes associated with cardiovascular disease in general and in HDL anomalies specifically. The identification of genes involved in human diseases relies on techniques entitled “genome-wide scanning”, “candidate gene sequencing” and “genotyping”, which will be used in this study. The candidate gene sequencing and genotyping techniques will be carried out in our research laboratory. The genome-wide scanning technique is very expensive and laborious and requires highly sophisticated equipment in specialized laboratory currently available in biotechnological industries. Part of our genetic analyses will be performed in industry. We are also collaborating with internationally known experts in the fields of genetics and bioinformatics (the science of combined mathematics and genetics) for the data analysis.

Storage of DNA Sample and Laboratory Analyses Your DNA sample will be coded and kept in a fridge of our laboratory where only authorized personnel have access. Only the principal investigators will be able to link your name with your sample. A portion of your DNA sample may be sent to a biopharmaceutical company to perform sophisticated genetic analyses. The storage period of your DNA sample may be up to 15 years following the end of the study, after which your sample will be destroyed. However, all DNA samples becoming degraded or unusable will be destroyed before the end of this period.

Confidentiality The information gathered from our genetic analyses will be kept confidential and only the principal investigators will be able to trace the results back to the patient. No personnal information will be given to a non-authorized third party without your written consent. Your participation in this project is voluntarily and you can refuse to submit your blood to the genetic analyses. Whatever your decision may be, it will not affect the quality of care which you are entitled within the MUHC.

85 McGill University Health Centre Hôpital Royal Victoria Cardiology Research Unit S4.85 687, Pine Avenue West Montréal, Québec H3A 1A1

Genetic Determinants of High Density Lipoproteins (HDL)

Consent Form

You have the right to revoke your consent at any time during the study and this will not prejudice or change your future care. With your written request, your sample of DNA held in our possession may be destroyed anytime.

 I consent to the use of my DNA sample for this research study on genes involved in cardiovascular disease and HDL cholesterol anomalies:

YES NO

 I would like to be informed of the results of the genetic test:

YES NO

 I consent to the preservation of my DNA at the Cardiovascular Genetics Laboratory of the McGill University Health Centre:

YES NO

______Patient‟s Name Signature of Patient Date

______Investigator‟s Name Signature of Investigator Date

86 Project: PCSK5 Gene Genotyping Iulia Iatan Principal Investigator: Dr. Jacques Genest

Number Priority SNP Name rs # Species Chromosome Polymorphism

1 1 PCSK5 Non-synonymous rs34579561 Human 9 2 1 PCSK5 Non-synonymous rs41304222 Human 9 3 1 PCSK5 Synonymous rs7040769 Human 9 4 1 PCSK5 Synonymous rs7020560 Human 9 PCSK5 Synonymous - tagSNP 5 1 HapMap rs2297342 Human 9 PCSK5 Synonymous - tagSNP 6 1 HapMap rs10521468 Human 9 7 1 PCSK5 Broad - tagSNP HapMap rs7847011 Human 9 8 1 PCSK5 Broad rs12379097 Human 9 9 1 PCSK5-1788 3'UTR - Human 9 G/A 10 1 PCSK5-723 5'UTR - Human 9 C/A 11 1 PCSK5-1719 5'UTR - Human 9 C/T 12 1 PCSK5-insertion intron 19 - Human 9 -/TAAAA - 13 1 PCSK5-deletion intron 20 - Human 9 /TACTTTCAGGACTAAT 14 1 PCSK5-1788 intron 4 - Human 9 T/A 15 1 PCSK5 tagSNP HapMap rs10781339 Human 9 16 1 PCSK5 tagSNP HapMap rs2789610 Human 9 17 1 PCSK5 tagSNP HapMap rs7042701 Human 9 18 1 PCSK5 tagSNP HapMap rs17720049 Human 9 19 1 PCSK5 tagSNP HapMap rs11144724 Human 9 20 1 PCSK5 tagSNP HapMap rs2377524 Human 9 21 1 PCSK5 tagSNP HapMap rs4745464 Human 9 22 1 PCSK5 tagSNP HapMap rs914256 Human 9 23 1 PCSK5 tagSNP HapMap rs10781317 Human 9 24 1 PCSK5 tagSNP HapMap rs12000040 Human 9 25 1 PCSK5 tagSNP HapMap rs10869705 Human 9 26 1 PCSK5 tagSNP HapMap rs10512052 Human 9 27 1 PCSK5 tagSNP HapMap rs914368 Human 9 28 1 PCSK5 tagSNP HapMap rs4644325 Human 9 29 1 PCSK5 tagSNP HapMap rs1417287 Human 9 30 1 PCSK5 tagSNP HapMap rs4552980 Human 9 31 1 PCSK5 tagSNP HapMap rs10781328 Human 9 32 1 PCSK5 tagSNP HapMap rs11144742 Human 9 33 1 PCSK5 tagSNP HapMap rs1411956 Human 9 34 1 PCSK5 tagSNP HapMap rs7033416 Human 9 35 1 PCSK5 tagSNP HapMap rs11144766 Human 9 36 1 PCSK5 tagSNP HapMap rs13285921 Human 9 37 1 PCSK5 tagSNP HapMap rs9314838 Human 9 38 1 PCSK5 tagSNP HapMap rs7037905 Human 9 39 1 PCSK5 tagSNP HapMap rs12684735 Human 9 40 1 PCSK5 tagSNP HapMap rs4313218 Human 9 41 1 PCSK5 tagSNP HapMap rs6560500 Human 9 42 1 PCSK5 tagSNP HapMap rs6560496 Human 9 43 1 PCSK5 tagSNP HapMap rs4504704 Human 9 44 1 PCSK5 tagSNP HapMap rs6560488 Human 9 45 1 PCSK5 tagSNP HapMap rs4745495 Human 9 46 1 PCSK5 tagSNP HapMap rs7860650 Human 9 47 1 PCSK5 tagSNP HapMap rs11144764 Human 9

87 48 1 PCSK5 tagSNP HapMap rs2066185 Human 9 49 1 PCSK5 tagSNP HapMap rs17062158 Human 9 50 1 PCSK5 tagSNP HapMap rs1016192 Human 9 51 1 PCSK5 tagSNP HapMap rs1331384 Human 9 52 1 PCSK5 tagSNP HapMap rs1571793 Human 9 53 1 PCSK5 tagSNP HapMap rs11144672 Human 9 54 1 PCSK5 tagSNP HapMap rs7870046 Human 9 55 1 PCSK5 tagSNP HapMap rs1029035 Human 9 56 1 PCSK5 tagSNP HapMap rs10512054 Human 9 57 1 PCSK5 tagSNP HapMap rs12115357 Human 9 58 1 PCSK5 tagSNP HapMap rs7858468 Human 9 59 1 PCSK5 tagSNP HapMap rs6560476 Human 9 60 1 PCSK5 tagSNP HapMap rs17061846 Human 9 61 1 PCSK5 tagSNP HapMap rs4744776 Human 9 62 1 PCSK5 tagSNP HapMap rs7037635 Human 9 63 1 PCSK5 tagSNP HapMap rs11144732 Human 9 64 1 PCSK5 tagSNP HapMap rs7047865 Human 9 65 1 PCSK5 tagSNP HapMap rs1416552 Human 9 66 1 PCSK5 tagSNP HapMap rs1029039 Human 9 67 1 PCSK5 tagSNP HapMap rs1340503 Human 9 68 1 PCSK5 tagSNP HapMap rs1888706 Human 9 69 1 PCSK5 tagSNP HapMap rs6560489 Human 9 70 1 PCSK5 tagSNP HapMap rs11144751 Human 9 71 1 PCSK5 tagSNP HapMap rs1416553 Human 9 72 1 PCSK5 tagSNP HapMap rs10781320 Human 9 73 1 PCSK5 tagSNP HapMap rs2777020 Human 9 74 1 PCSK5 tagSNP HapMap rs12000319 Human 9 75 1 PCSK5 tagSNP HapMap rs7034828 Human 9 76 1 PCSK5 tagSNP HapMap rs17062197 Human 9 77 1 PCSK5 tagSNP HapMap rs11144692 Human 9 78 1 PCSK5 tagSNP HapMap rs1258097 Human 9 79 1 PCSK5 tagSNP HapMap rs1467773 Human 9 80 1 PCSK5 tagSNP HapMap rs7871177 Human 9 81 1 PCSK5 tagSNP HapMap rs7046850 Human 9 82 1 PCSK5 tagSNP HapMap rs928355 Human 9 83 1 PCSK5 tagSNP HapMap rs914367 Human 9 84 1 PCSK5 tagSNP HapMap rs1339230 Human 9 85 1 PCSK5 tagSNP HapMap rs10869665 Human 9 86 1 PCSK5 tagSNP HapMap rs7874700 Human 9 87 1 PCSK5 tagSNP HapMap rs17062041 Human 9 88 1 PCSK5 tagSNP HapMap rs2994426 Human 9 89 1 PCSK5 tagSNP HapMap rs12342299 Human 9 90 1 PCSK5 tagSNP HapMap rs10746994 Human 9 91 1 PCSK5 tagSNP HapMap rs11998749 Human 9 92 1 PCSK5 tagSNP HapMap rs4745522 Human 9 93 1 PCSK5 tagSNP HapMap rs13283154 Human 9 94 1 PCSK5 tagSNP HapMap rs11144782 Human 9 95 1 PCSK5 tagSNP HapMap rs12555061 Human 9 96 1 PCSK5 tagSNP HapMap rs10869695 Human 9 97 1 PCSK5 tagSNP HapMap rs10869720 Human 9 98 1 PCSK5 tagSNP HapMap rs11144773 Human 9 99 1 PCSK5 tagSNP HapMap rs914365 Human 9 100 1 PCSK5 tagSNP HapMap rs2270571 Human 9 101 1 PCSK5 tagSNP HapMap rs17061835 Human 9 102 1 PCSK5 tagSNP HapMap rs7031971 Human 9

88 103 1 PCSK5 tagSNP HapMap rs10465170 Human 9 104 1 PCSK5 tagSNP HapMap rs13295517 Human 9 105 1 PCSK5 tagSNP HapMap rs10781344 Human 9 106 1 PCSK5 tagSNP HapMap rs4271035 Human 9 107 1 PCSK5 tagSNP HapMap rs10869713 Human 9 108 1 PCSK5 tagSNP HapMap rs10869684 Human 9 109 1 PCSK5 tagSNP HapMap rs1258095 Human 9 110 1 PCSK5 tagSNP HapMap rs10781325 Human 9 111 1 PCSK5 tagSNP HapMap rs1888705 Human 9 112 1 PCSK5 tagSNP HapMap rs12344217 Human 9 113 1 PCSK5 tagSNP HapMap rs2050833 Human 9 114 1 PCSK5 tagSNP HapMap rs2377426 Human 9 115 1 PCSK5 tagSNP HapMap rs10124834 Human 9 116 1 PCSK5 tagSNP HapMap rs17061934 Human 9 117 1 PCSK5 tagSNP HapMap rs10869673 Human 9 118 1 PCSK5 tagSNP HapMap rs7045212 Human 9 119 1 PCSK5 tagSNP HapMap rs4744780 Human 9 120 1 PCSK5 tagSNP HapMap rs13291278 Human 9 121 1 PCSK5 tagSNP HapMap rs7021825 Human 9 122 1 PCSK5 tagSNP HapMap rs11793311 Human 9 123 1 PCSK5 tagSNP HapMap rs10781324 Human 9 124 1 PCSK5 tagSNP HapMap rs1272208 Human 9 125 1 PCSK5 tagSNP HapMap rs10781319 Human 9 126 1 PCSK5 tagSNP HapMap rs1023181 Human 9 127 1 PCSK5 tagSNP HapMap rs4584211 Human 9 128 1 PCSK5 tagSNP HapMap rs10869668 Human 9 129 1 PCSK5 tagSNP HapMap rs4745488 Human 9 130 1 PCSK5 tagSNP HapMap rs11144746 Human 9 131 1 PCSK5 tagSNP HapMap rs10521467 Human 9 132 1 PCSK5 tagSNP HapMap rs17668141 Human 9 133 1 PCSK5 tagSNP HapMap rs2185230 Human 9 134 1 PCSK5 tagSNP HapMap rs1571790 Human 9 135 1 PCSK5 tagSNP HapMap rs10869666 Human 9 136 1 PCSK5 tagSNP HapMap rs1338746 Human 9 137 1 PCSK5 tagSNP HapMap rs13297074 Human 9 138 1 PCSK5 tagSNP HapMap rs7850358 Human 9 139 1 PCSK5 tagSNP HapMap rs7035578 Human 9 140 1 PCSK5 tagSNP HapMap rs17719860 Human 9 141 1 PCSK5 tagSNP HapMap rs2777019 Human 9 142 1 PCSK5 tagSNP HapMap rs1258094 Human 9 143 1 PCSK5 tagSNP HapMap rs12376855 Human 9 144 1 PCSK5 tagSNP HapMap rs2377527 Human 9 145 1 PCSK5 tagSNP HapMap rs10869717 Human 9 146 1 PCSK5 tagSNP HapMap rs10869696 Human 9 147 1 PCSK5 tagSNP HapMap rs10869722 Human 9 148 1 PCSK5 tagSNP HapMap rs958225 Human 9 149 1 PCSK5 tagSNP HapMap rs2297343 Human 9 150 1 PCSK5 tagSNP HapMap rs12686149 Human 9 151 1 PCSK5 tagSNP HapMap rs11144689 Human 9 152 1 PCSK5 tagSNP HapMap rs1832525 Human 9 153 1 PCSK5 tagSNP HapMap rs6560478 Human 9 154 1 PCSK5 tagSNP HapMap rs10869698 Human 9 155 1 PCSK5 tagSNP HapMap rs2789608 Human 9 156 1 PCSK5 tagSNP HapMap rs4745507 Human 9 157 1 PCSK5 tagSNP HapMap rs2153226 Human 9

89 158 1 PCSK5 tagSNP HapMap rs10869675 Human 9 159 1 PCSK5 tagSNP HapMap rs10869725 Human 9 160 1 PCSK5 tagSNP HapMap rs6560494 Human 9 161 1 PCSK5 tagSNP HapMap rs11144688 Human 9 162 1 PCSK5 tagSNP HapMap rs1339246 Human 9 163 1 PCSK5 tagSNP HapMap rs7027130 Human 9 164 1 PCSK5 tagSNP HapMap rs2792222 Human 9 165 1 PCSK5 tagSNP HapMap rs11144690 Human 9 166 1 PCSK5 tagSNP HapMap rs6560506 Human 9 167 1 PCSK5 tagSNP HapMap rs1340510 Human 9 168 1 PCSK5 tagSNP HapMap rs10869710 Human 9 169 1 PCSK5 tagSNP HapMap rs1538505 Human 9 170 1 PCSK5 tagSNP HapMap rs7872680 Human 9 171 1 PCSK5-5'UTR rs12005073 Human 9 172 1 PCSK5-ins intron 19 rs3830384 Human 9 173 1 PCSK5 Intronic rs1416547 Human 9 174 1 PCSK5 Intronic rs3824474 Human 9 175 1 PCSK5 Intronic rs1537183 Human 9 176 1 PCSK5 Intronic - tagSNP HapMap rs2297344 Human 9 177 1 PCSK5 Intronic - tagSNP HapMap rs2270570 Human 9 178 1 PCSK5 Intronic - tagSNP HapMap rs10869726 Human 9 PCSK9 - R46L + HapMap but not 179 1 tagSNP + n-s rs11591147 Human 1 PCSK9 - G670E + HapMap but 180 1 not tagSNP + n-s rs505151 Human 1 PCSK9 - Promoter + tagSNP 181 1 HapMap rs11206510 Human 1 PCSK9 - V474I + tagSNP 182 1 HapMap + n-s rs562556 Human 1 PCSK9 - A53V + tagSNP 183 1 HapMap + n-s rs11583680 Human 1 PCSK9 - tagSNP HapMap + 184 1 synonymous rs540796 Human 1 PCSK9 - tagSNP HapMap + 185 1 3'UTR rs662145 Human 1 186 1 PCSK9 - tagSNP HapMap rs7525649 Human 1 187 1 PCSK9 - tagSNP HapMap rs2094470 Human 1 188 1 PCSK9 - tagSNP HapMap rs568052 Human 1 189 1 PCSK9 - tagSNP HapMap rs2495497 Human 1 190 1 PCSK9 - tagSNP HapMap rs11206514 Human 1 191 1 PCSK9 - tagSNP HapMap rs535471 Human 1 192 1 PCSK9 - tagSNP HapMap rs12739979 Human 1 193 1 PCSK9 - tagSNP HapMap rs2495495 Human 1 194 1 PCSK9 - tagSNP HapMap rs676297 Human 1 195 1 PCSK9 - tagSNP HapMap rs12067569 Human 1 196 1 PCSK9 - tagSNP HapMap rs2479409 Human 1 197 1 PCSK9 - tagSNP HapMap rs557435 Human 1 198 1 PCSK9 - tagSNP HapMap rs4927193 Human 1 199 1 PCSK9 - tagSNP HapMap rs10888896 Human 1 200 1 PCSK9 - tagSNP HapMap rs2479408 Human 1 201 1 PCSK9 - tagSNP HapMap rs2479415 Human 1 202 1 PCSK9 - tagSNP HapMap rs17111495 Human 1 203 1 PCSK9 - tagSNP HapMap rs572512 Human 1 204 1 PCSK9 - tagSNP HapMap rs17111490 Human 1 205 1 PCSK9 - tagSNP HapMap rs7552841 Human 1 206 1 PCSK9 - tagSNP HapMap rs10465832 Human 1 207 1 PCSK9 - tagSNP HapMap rs2479417 Human 1 208 1 PCSK9 - 5'UTR rs45448095 Human 1

90 209 1 PCSK9 - 3'UTR rs17111557 Human 1 210 1 PCSK9 - 3'UTR rs17111555 Human 1 211 1 PCSK9 - 3'UTR rs13376071 Human 1 PCSK9 - SeattleSNP 002335 In- 212 1 Frame Deletion - Human 1 -/CTG PCSK9 - SeattleSNP 002227 5' 213 1 UTR - Human 1 C/T 214 1 PCSK9 - Synonymous rs45454392 Human 1 PCSK9 - Litterature - Intron 1 C(- 215 1 161)T rs2495480 Human 1 216 2 VNN1 rs4897612 Human 6 217 2 VNN1 rs2272996 Human 6 218 2 VNN1 rs2294757 Human 6 219 2 VNN1 rs45562238 Human 6 220 2 PCSK3-Broad rs16944918 Human 9 221 2 PCSK5 Locus region rs10869729 Human 9 222 2 PCSK5 Locus region rs7853608 Human 9 PCSK5 Locus region - tagSNP 223 2 HapMap rs6560474 Human 9 PCSK5 Locus region - tagSNP 224 2 HapMap rs3814115 Human 9 225 3 Zari rs282988 Human 16 226 3 Zari rs7203041 Human 16 227 3 Zari rs9937892 Human 16 228 3 Zari rs4337315 Human 16 229 3 Zari rs4888543 Human 16 230 3 Zari rs8057477 Human 16 231 3 Zari rs4888535 Human 16 232 3 Zari rs6564359 Human 16 233 3 Zari rs7404386 Human 16 234 3 Zari rs3924497 Human 16 235 3 Zari rs8049365 Human 16 236 4 UBIAD1 rs2295080 Human 1 237 4 UBIAD1 rs1074078 Human 1 238 4 UBIAD1 rs7513418 Human 1 239 4 UBIAD1 rs6685758 Human 1 240 4 UBIAD1 rs2295079 Human 1 241 4 UBIAD1 rs727974 Human 1 242 4 UBIAD1 - S75F - Human 1 C/T 243 5 NUDT7 exon 4 rs16946429 Human 16 244 6 Ron HDL hits rs4846914 Human 1 245 6 Ron HDL hits rs1998067 Human 1 246 6 Ron HDL hits rs935262 Human 2 247 6 Ron HDL hits rs16857512 Human 1 248 6 Ron HDL hits rs4757456 Human 11 249 6 Ron HDL hits rs1399708 Human 3 250 6 Ron HDL hits rs17151505 Human 8 251 6 Ron HDL hits rs2282557 Human 18 252 6 Ron HDL hits rs17083563 Human 13 253 6 Ron HDL hits rs1357182 Human 2 254 6 Ron HDL hits rs4888544 Human 16

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