- Email: [email protected]
Western Database of Lipid Variants (WDLV): A Catalogue of Genetic Variants in Monogenic Dyslipidemias
Canadian Journal of Cardiology 29 (2013) 934e939
Clinical Research
Western Database of Lipid Variants (WDLV): A Catalogue of Genetic Variants in Monogenic Dyslipidemias Jennifer Fu, Samantha Kwok, Leah Sinai, BSc, Omar Abdel-Razek, Janet Babula, Dennis Chen, Emma Farago, Nigel Fernandopulle, Sean Leith, BSc, Melissa Loyzer, Catherine Lu, Niyati Malkani, Nicole Morris, Mandi Schmidt, Randa Stringer, Heather Whitehead, BA, Matthew R. Ban, BSc, Joseph B. Dube, BSc, Adam McIntyre, BSc, Christopher T. Johansen, PhD, Henian Cao, MD, Jian Wang, MD, and Robert A. Hegele, MD, FRCPC, FACP Blackburn Cardiovascular Genetics Laboratory, Robarts Research Institute, University of Western Ontario, London, Ontario, Canada
ABSTRACT
RESUM E
Background: Next-generation sequencing (NGS) is nearing routine clinical application, especially for diagnosis of rare monogenic cardiovascular diseases. But NGS uncovers so much variation in an individual genome that filtering steps are required to streamline data management. The first step is to determine whether a potential disease-causing variant has been observed previously in affected patients. Methods: To facilitate this step for lipid disorders, we developed the Western Database of Lipid Variants (WDLV) of 2776 variants in 24 genes that cause monogenic dyslipoproteinemias, including conditions characterized primarily by either high or low low-density lipoprotein cholesterol, high or low high-density lipoprotein cholesterol, high triglyceride, and some miscellaneous disorders. We incorporated quality-control steps to maximize the likelihood that a listed mutation was disease causing. Results: The details of each mutation found in a dyslipidemia, together with a mutation map of the causative genes, are shown in graphical display items. Conclusions: WDLV will help clinicians and researchers determine the potential pathogenicity of mutations discovered by DNA sequencing of patients or research participants with lipid disorders.
quençage de nouvelle ge ne ration (SNG) tend vers Introduction : Le se l’application clinique courante, particulièrement pour le diagnostic des niques rares. Mais le SNG montre maladies cardiovasculaires monoge nome individuel que des e tapes de filtant de variations dans un ge es. La première trage sont requises pour simplifier la gestion des donne tape est de de terminer si un variant pathogène potentiel a de jà e te e chez les patients atteints. observe thodes : Pour faciliter cette e tape des troubles lipidiques, nous Me labore la Western Database of Lipid Variants (WDLV) de 2776 avons e ine mies monoge nivariants de 24 gènes qui causent les dyslipoprote rise es par un ques, incluant les affections principalement caracte rol à lipoprote ines de basse densite e leve ou faible, un chocholeste rol à lipoprote ines de haute densite e leve ou faible, des trileste rides e leve s et certains troubles divers. Nous avons inclus des glyce tapes de contrôle de la qualite pour accroître la possibilite qu’une e te pathogène. pertorie e ait e mutation re sultats : Les de tails de chaque mutation de couverte au cours d’une Re mie, ainsi qu’une carte de mutations des gènes responsables, dyslipide s dans les e le ments de la pre sentation graphique. sont montre Conclusions : La WDLV aidera les cliniciens et les chercheurs à terminer la pathoge nicite potentielle des mutations de couvertes par de quençage de l’ADN des patients ou des participants à la recherche le se ayant des troubles lipidiques.
Identifying the genetic basis of disease has become a crucial aspect of clinical diagnosis of rare single-gene disorders, also called “monogenic disorders.” Moreover, recent advances in sequencing technology have allowed for an exponential
increase in the number of novel genetic variants discovered each year (Fig. 1).1 Next-generation sequencing (NGS) methods are now used routinely in research and are on the verge of being used clinically in patients with suspected monogenic disorders. However, the amount of DNA variation that NGS can uncover in the genome of a single individual is unwieldy and cannot yet be applied in practical clinical situations. One approach to manage the vast amount of NGS information is to organize it into smaller, user-friendly diseasebased catalogues. Thus, the goal of the Western Database of Lipid Variants (WDLV) is to gather existing information on monogenic dyslipidemias and present it in a harmonized
Received for publication December 18, 2012. Accepted January 16, 2013. Corresponding author: Dr Robert A. Hegele, Blackburn Cardiovascular Genetics Laboratory, Robarts Research Institute, 4288A-100 Perth Drive, London, Ontario N6A 5K8, Canada. E-mail: [email protected] See page 938 for disclosure information.
0828-282X/$ - see front matter Ó 2013 Canadian Cardiovascular Society. Published by Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.cjca.2013.01.008
Fu et al. Western Database of Lipid Variants
Figure 1. Total human disease variants reported on Human Gene Mutation Database (HGMD) according to the year of publication.
format that is more meaningful for clinicians, researchers, patients, and the public. There are 2 main types of catalogues of genetic variants. The first type consists of comprehensive databases that include all variants and polymorphisms in the human genome, such as the Human Gene Mutation Database (HGMD)1 and the Single Nucleotide Polymorphism Database.2 These catalogues contain information regarding all genes, with no focus on any particular disease or genetic locus. In contrast, the second type of catalogue, known as a “locus-specific” or “disease-specific database,” is dedicated to providing information on only 1 or a few genes or diseases. The second type of catalogue frequently offers more detail on given variants and is potentially of greater clinical interest.3 The WDLV is a disease-specific variant database focused on monogenic dyslipidemias. Dyslipidemias are characterized by abnormal concentrations of plasma lipids, specifically, low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, or triglyceride (TG). A given dyslipidemia may involve either elevated or depressed levels of these variables in isolation or in combination. DNA variants causing monogenic dyslipidemias are generally rare large-effect mutations located within the coding sequences of causative genes.4 Such mutations contrast with the common variants that underlie multifactorial, polygenic, or complex diseases.5 Common “garden variety” dyslipidemia results from an accumulation of small-effect single nucleotide polymorphisms (SNPs), together with occasional rare large-effect mutations, and secondary factors such as poor diet, inactivity, or associated metabolic conditions.5 WDLV catalogues all variants reported to cause rare inherited monogenic dyslipidemias. While several of the same genes containing rare disease-causing mutations also contain common SNPs that affect lipids in the general population,4-6 we have endeavored to exclude common SNPs that contribute to polygenic dyslipidemias, focusing instead primarily on rare mutations that are causative for rare monogenic disorders. Most of the annotated variants lie within coding sequences rather than in regulatory or promoter regions. An example of a dedicated single dyslipidemia-specific database is the Leiden Open (source) Variation Database (LOVD) familial hypercholesterolemia (FH) mutation database established by Humphries and colleagues,3 which lists all genetic variants found to date in the LDLR, PCSK9, and LDLRAP1 genes from patients with FH, a monogenic
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disorder that is characterized by markedly elevated LDL cholesterol levels.7 WDLV expands the focus beyond FH to include 23 other monogenic dyslipidemic states. We included FH within WDLV to provide a measure of quality control for our overall procedures and processes. The FH mutations listed in Supplemental Table S1 and shown in Supplemental Figure S1 thus overlap to a large extent with the LOVD FH database. The novel aspect of the WDLV is the list and curation of the non-FH disorders listed in Supplemental Table S2 and shown in Supplemental Figures S2-S6. Beyond FH, WDLV comprehensively annotates information on additional monogenic dyslipidemias, including variants in MTTP,8 APOB,9 SAR1B,10 and PCSK911 causing hypobetalipoproteinemia (depressed LDL cholesterol); variants in CETP,12 LIPC,13 SCARB1,14 and LIPG15 causing hyperalphalipoproteinemia (increased HDL cholesterol); variants in ABCA1,16 LCAT,17 and APOA118 causing a range of disorders characterized by hypoalphalipoproteinemia (depressed HDL cholesterol); variants in LPL,19 APOC2,19 LMF1,19 GPIHBP1,19 and APOA519 causing hypertriglyceridemia; variants in APOE causing elevated levels of total cholesterol and Table 1. Accession identification numbers and nucleotide sequences used for causative genes
Gene ATP-binding cassette, subfamily A member 1 (ABCA1) ATP-binding cassette, subfamily G member 5 (ABCG5) ATP-binding cassette, subfamily G member 8 (ABCG8) Angiopoietin-like 3 protein (ANGPTL3) Apolipoprotein A-I (APOA1) Apolipoprotein A-V (APOA5) Apolipoprotein B (APOB) Apolipoprotein C-II (APOC2) Apolipoprotein C-III (APOC3) Apolipoprotein E (APOE) Cholesteryl ester transfer protein (CETP) Glycosylphosphatidylinositolanchored HDL-binding protein 1 (GPIHBP1) Lecithin cholesterol acyltransferase (LCAT) LDL receptor (LDLR) LDL receptor adaptor protein 1 (LDLRAP1, ARH) Lysosomal acid lipase A (LIPA, alias LAL) Hepatic lipase (HL, alias LIPC) Endothelial lipase (LIPG) Lipase maturation factor 1 (LMF1) Lipoprotein lipase (LPL) Microsomal triglyceride transfer protein (MTTP) Proprotein convertase subtilisin/kexin type 9 (PCSK9) SAR1 homolog B (SAR1B) Scavenger receptor class B, member 1 (SCARB1)
Nucleotide identification number
Protein identification number
NM_005502.3
NP_005493.2
NM_022436.2
NP_071881.1
NM_022437.2
NP_071882.1
NM_014495.2
NP_055310.1
NM_000039.1 NM_052968.4 NM_000384.2 NM_000483.4 NM_000040.1 NM_000041.2 NM_000078.2
NP_000030.1 NP_443200.2 NP_000375.2 NP_000474.2 NP_000031.1 NP_000032.1 NP_000069.2
NM_178172.3
NP_835466.1
NM_000229.1
NP_000220.1
NM_000527.4 NM_015627.2
NP_000518.1 NP_056442.2
NM_000235.2
NP_000226.2
NM_000236.2 NM_006033.2 NM_022773.2 NM_000237.2 NM_000253.2
NP_000227.2 NP_006024.1 NP_073610.2 NP_000228.1 NP_000244.2
NM_174936.3
NP_777596.2
NM_001033503.2 NP_001028675.1 NM_005505.4 NP_005496.4
Sequence accession numbers are found at http://www.ncbi.nlm.nih.gov/ genbank/. ATP, adenosine triphosphate; HDL, high-density lipoprotein; LDL, low-density lipoprotein.
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Canadian Journal of Cardiology Volume 29 2013
Figure 2. Nomenclature used in the Western Database of Lipid Variants (WDLV) to denote (A) missense or nonsense variants, (B) in-frame insertions or deletions, (C) frameshift mutations, and (D) intronic variants. Termination codons are indicated by an “X.”
TG due to increased lipoprotein remnant particles;20 variants in ANGPTL3 causing depressed LDL cholesterol, HDL cholesterol, and TG levels (also called “combined hypolipidemia”);21 a single variant in APOC3 causing depressed LDL cholesterol and TG levels, with increased HDL cholesterol;22 variants in ABCG5 and ABCG8 causing sitosterolemia;23 and variants in LIPA causing cholesteryl ester storage disease.24 Methods Mutation lists for each of the 24 genes were generated with Microsoft Excel spreadsheets by cross-referencing the HGMD, Online Mendelian Inheritance in Man, and Universal Protein Resource. Reference alleles and genomic coordinates were verified with the University of Santa Cruz Genome Browser.25 Both National Center for Biotechnology Information (NCBI) 36.1/hg18 and NCBI 37/hg19 coordinates are provided. Original publications were reviewed where possible for results of functional studies, as well as inheritance patterns and patient profiles. Amino acid changes as a result of insertions and deletions were determined with the Consensus Coding Sequence tool generated by the NCBI.26 Table 1 shows the specific accession identification numbers used for each gene. Mutations were annotated in accordance with Human Genome Variation Society guidelines on description of sequence variants (Fig. 2).27 When direct experimental data on altered function of a missense variant were not available, potential pathogenicity was predicted via the Sorting Intolerant From Tolerant (SIFT) Human Coding Tool (http://sift.jcvi.org/www/SIFT_chr_coords_submit.html),28 and for those indicated as being possibly or likely pathogenic, a second program, the PolyPhen-2 batch query tool (http:// genetics.bwh.harvard.edu/pph2/bgi.shtml) was used for replication.29 SIFT predictions are based on degree of conservation of amino acid residues in sequence alignments from closely
related sequences. PolyPhen-2 predicts based on the sequence, phylogenetic, and structural or physical considerations. In order to ensure accuracy and validity of the database, clinical, biochemical, genetic, and functional features of each variant were verified at least twice by 2 independent reviewers. Results A total of 2776 disease-causing mutations have been catalogued and recorded in the WDLV. Table 2 provides a summary of these. The dyslipidemia-causing variants span 24 genes, the locations of which are shown in Figure 3. Maps were constructed for each of the 24 genes, providing a visual representation of the relative locations of variants within their respective genes. The gene maps are organized into 6 general phenotypes according to lipid trait primarily affected: elevated LDL cholesterol (Supplemental Fig. S1), depressed LDL cholesterol (Supplemental Fig. S2), elevated HDL cholesterol (Supplemental Fig. S3), depressed HDL cholesterol (Supplemental Fig. S4), elevated TG (Supplemental Fig. S5), and a few miscellaneous disorders (Supplemental Fig. S6). Since variants in APOB and PCSK9 can result in either elevated or depressed levels of LDL cholesterol, these genes appear under 2 different phenotype categories. Supplemental Tables S1 and S2 provide a complete list of all dyslipidemia mutations that we curated: Supplemental Table S1 for FH and Supplemental Table S2 for all other dyslipidemias, together with their clinical and molecular characteristics and reference citations. Discussion With the explosion in DNA sequence information provided by NGS, a greater number of novel variants and mutations are being reported yearly. Efficiently annotating
Fu et al. Western Database of Lipid Variants
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Table 2. Summary of mutation types according to the primary lipid disturbance and causative gene Variant count, by type (N, %) Phenotype LDL
[
Y
HDL
[
Y TG
[
Misc.
[ IDL Y TG, LDL, [HDL Y TG, LDL, HDL Sitosterolemia CESD/Wolman
Total
Gene
All
LDLR PCSK9 APOB LDLRAP1 APOB MTTP PCSK9 SAR1B CETP LIPC LIPG SCARB1 ABCA1 LCAT APOA1 LPL APOA5 APOC2 GPIHBP1 LMF1 APOE APOC3 ANGPTL3 ABCG8 ABCG5 LIPA
1678 33 20 16 120 47 29 14 39 18 13 8 163 91 62 156 34 17 13 10 67 1 24 32 25 46 2776
Missense 745 29 18 3 21 10 23 6 14 12 9 6 100 67 26 99 16 5 11 8 46 0 16 16 10 15 1331
(44.4) (87.9) (90.0) (18.8) (17.5) (21.3) (79.3) (42.9) (35.9) (66.7) (69.2) (75.0) (61.4) (73.6) (42.0) (63.5) (47.1) (29.4) (84.6) (80.0) (68.7) (0.0) (66.7) (50.0) (40.0) (32.6) (48.0)
Nonsense 123 0 0 2 28 7 3 2 7 0 0 0 15 4 6 16 6 4 1 2 2 1 1 9 8 8 255
(7.3) (0.0) (0.0) (12.5) (23.3) (14.9) (10.3) (14.3) (18.0) (0.0) (0.0) (0.0) (9.2) (4.4) (9.7) (10.3) (17.7) (23.5) (7.7) (20.0) (3.0) (100.0) (4.2) (28.1) (32.0) (17.4) (9.2)
Small indel
Gross indel
385 2 0 5 57 12 3 4 5 1 1 1 22 17 19 25 6 4 0 0 10 0 6 4 2 14 605
272 0 0 3 2 3 0 1 0 0 0 0 6 2 6 5 1 1 1 0 3 0 0 1 3 3 313
(22.9) (6.1) (0.0) (31.3) (47.5) (25.5) (10.3) (28.6) (12.8) (5.6) (7.7) (12.5) (13.5) (18.7) (30.7) (16.0) (17.7) (23.5) (0.0) (0.0) (14.9) (0.0) (25.0) (12.5) (8.0) (30.3) (21.0)
(16.2) (0.0) (0.0) (18.8) (1.7) (6.4) (0.0) (7.1) (0.0) (0.0) (0.0) (0.0) (3.7) (2.2) (9.7) (3.2) (2.9) (5.9) (7.7) (0.0) (4.5) (0.0) (0.0) (3.1) (12.0) (6.5) (11.3)
Splicing 125 1 0 3 12 13 0 1 6 2 0 1 13 1 1 8 3 2 0 0 1 0 1 1 2 5 202
(7.5) (3.0) (0.0) (18.8) (10.0) (27.7) (0.0) (7.1) (15.3) (11.1) (0.0) (12.5) (8.0) (1.1) (1.6) (5.1) (8.8) (11.8) (0.0) (0.0) (1.4) (0.0) (4.2) (3.1) (8.0) (10.9) (7.3)
Regulatory 28 1 2 0 0 2 0 0 7 3 3 0 7 0 4 3 2 1 0 0 5 0 0 1 0 1 70
(1.7) (3.0) (10.0) (0.0) (0.0) (4.3) (0.0) (0.0) (18.0) (16.7) (23.1) (0.0) (4.3) (0.0) (6.5) (1.9) (5.9) (5.9) (0.0) (0.0) (7.5) (0.0) (0.0) (3.1) (0.0) (2.2) (2.5)
Up arrow signifies increased plasma level. Down arrow signifies decreased plasma level. CESD/Wolman, cholesterol ester storage disease and Wolman syndrome; HDL, high-density lipoprotein cholesterol; IDL, intermediate-density lipoprotein particle; indel, insertion or deletion; LDL, low-density lipoprotein cholesterol; Misc., miscellaneous; TG, triglyceride.
and cataloguing disease-causing variants for clinicians and researchers have become critical needs. The goal of the WDLV project was to compile all mutations reported to cause monogenic dyslipidemias and present them in the format of an easily accessible and user-friendly database. The WDLV offers a summary of all reported diseasecausing variants, a brief overview of the function of each gene, the phenotype associated with each variant, genomic coordinates of the variant, disease inheritance pattern, profile of the index-case patient, as well as results of any functional studies conducted on the variant. Citations of the first publication reporting each variant are provided for further follow-up and investigation. To date, we have gathered 2776 mutations located across 24 genes. Each contributes to monogenic dyslipidemias subdivided into 6 general phenotypes: elevated LDL cholesterol, depressed LDL cholesterol, elevated HDL cholesterol, depressed HDL cholesterol, elevated TG, and a miscellaneous category that contains disorders such as sitosterolemia, cholesterol ester storage disease, and Wolman disease. WDLV has many potential applications for physicians and researchers. For instance, it provides a centralized, curated resource to determine whether a mutation discovered in a patient or research subject has previously been implicated as disease causing. With this database, physicians can efficiently access previously reported mutations that have been reviewed regarding genotype-phenotype relationships and apply this information in a clinical context. A researcher can determine the potential novelty of a mutation uncovered by NGS. Other information provided by WDLV, such as patient profiles and
demographics, can be used in epidemiology, geographic distribution, and genealogy studies. Going forward, addition of newly discovered genedisease associations and variant-disease associations to the WDLV will be essential. The current plan is for yearly updating of the WDLV with the same methods described here, including automated literature searches through the National Library of Medicine and cross-referencing mutation databases such as HGMD, Online Mendelian Inheritance in Man, and Universal Protein Resource. Updates will be provided online. We also anticipate that the database might be implemented in a Web-based browser, where users can enter a query for phenotype, disease, gene name, chromosomal location of the variant, type of variant, and ethnic group. This template can be used to curate monogenic disorders involving related cardiometabolic diseases, such as monogenic forms of diabetes and hypertension. Other possible improvements include the consideration of population genetics and common SNPs in these genes, thus expanding the focus beyond monogenic dyslipidemias to polygenic disorders. Funding Sources R.A.H. is supported by the Jacob J. Wolfe Distinguished Medical Research Chair, the Edith Schulich Vinet Canada Research Chair in Human Genetics, the Martha G. Blackburn Chair in Cardiovascular Research, and operating grants from the Canadian Institutes of Health Research (MOP-13430, MOP-79523, CTP-79853), the Heart and
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Canadian Journal of Cardiology Volume 29 2013
Figure 3. Twenty-four dyslipidemia-causing genes annotated in Western Database of Lipid Variants (WDLV) and their location in the human genome.
Stroke Foundation of Ontario (NA-6059, T-6018, PRG4854), and Genome Canada through the Ontario Genomics Institute. Many of the students who worked on this project were supported by funding made available through the Edith Schulich Vinet Canada Research Chair in Human Genetics. Disclosures The authors have no conflicts of interest to disclose. References 1. Stenson PD, Ball EV, Mort M, et al. Human Gene Mutation Database (HGMD): 2003 update. Hum Mutat 2003;21:577-81.
7. Liyanage KE, Burnett JR, Hooper AJ, van Bockxmeer FM. Familial hypercholesterolemia: epidemiology, Neolithic origins and modern geographic distribution. Crit Rev Clin Lab Sci 2011;48:1-18. 8. Zamel R, Khan R, Pollex RL, Hegele RA. Abetalipoproteinemia: two case reports and literature review. Orphanet J Rare Dis 2008;3:19. 9. Burnett JR, Bell DA, Hooper AJ, Hegele RA. Clinical utility gene card for familial hypobetalipoproteinaemia (APOB). Eur J Hum Genet 2012;20:8. 10. Georges A, Bonneau J, Bonnefont-Rousselot D, et al. Molecular analysis and intestinal expression of SAR1 genes and proteins in Anderson’s disease (chylomicron retention disease). Orphanet J Rare Dis 2011;6:16. 11. Cohen JC, Boerwinkle E, Mosley TH Jr, Hobbs HH. Sequence variations in PCSK9, low LDL, and protection against coronary heart disease. N Engl J Med 2006;354:1264-72.
2. Sherry ST, Ward MH, Kholodov M, et al. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res 2001;29:308-11.
12. de Grooth GJ, Klerkx AHEM, Stroes ESG, Stalenhoef AFH, Kastelein JJP, Kuivenhoven JAA. Review of CETP and its relation to atherosclerosis. J Lipid Res 2004;45:1967-74.
3. Fokkema IF, den Dunnen JT, Taschner PE. LOVD: easy creation of a locus-specific sequence variation database using an “LSDB-in-a-box” approach. Hum Mutat 2005;26:63-8.
13. Hegele RA, Little JA, Vezina C, et al. Hepatic lipase deficiency: clinical, biochemical, and molecular genetic characteristics. Arterioscler Thromb 1993;13:720-8.
4. Farhan SM, Hegele RA. Genetics 101 for cardiologists: rare genetic variants and monogenic cardiovascular disease. Can J Cardiol 2013;29:18-22.
14. Vergeer M, Korporaal SJ, Franssen R, et al. Genetic variant of the scavenger receptor BI in humans. N Engl J Med 2011;364:136-45.
5. Dube JB, Hegele RA. Genetics 100 for cardiologists: basics of genomewide association studies. Can J Cardiol 2013;29:10-7.
15. Edmondson AC, Brown RJ, Kathiresan S, et al. Loss-of-function variants in endothelial lipase are a cause of elevated HDL cholesterol in humans. J Clin Invest 2009;119:1042-50.
6. Hegele RA. Plasma lipoproteins: genetic influences and clinical implications. Nat Rev Genet 2009;10:109-21.
16. Mott S, Yu L, Marcil M, Boucher B, Rondeau C, Genest J Jr. Decreased cellular cholesterol efflux is a common cause of familial
Fu et al. Western Database of Lipid Variants hypoalphalipoproteinemia: role of the ABCA1 gene mutations. Atherosclerosis 2000;152:457-68. 17. Recalde D, Cenarro A, Garcia-Otin A-L, Gomez-Coronado D, Civeira F, Pocovi M. Analysis of apolipoprotein A-I, lecithin: cholesterol acyltransferase and glucocerebrosidase genes in hypoalphalipoproteinemia. Atherosclerosis 2002;163:49-58. 18. Ng DS, Vezina C, Wolever TS, Kuksis A, Hegele RA, Connelly PW. Apolipoprotein A-I deficiency: biochemical and metabolic characteristics. Arterioscler Thromb Vasc Biol 1995;15:2157-64. 19. Johansen CT, Hegele RA. Genetic bases of hypertriglyceridemic phenotypes. Curr Opin Lipidol 2011;4:247-53.
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24. Pisciotta L, Fresa R, Bellocchio A, et al. Cholesteryl ester storage disease (CESD) due to novel mutations in the LIPA gene. Mol Genet Metab 2009;97:143-8. 25. Kent WJ, Sugnet CW, Furey TS, et al. The human genome browser at UCSC. Genome Res 2002;12:996-1006. 26. Pruitt KD, Harrow J, Harte RA, et al. The Consensus Coding Sequence (CCDS) project: identifying a common protein-coding gene set for the human and mouse genomes. Genome Res 2009;19:1316-23. 27. den Dunnen JT, Antonarakis SE. Mutation nomenclature extensions and suggestions to describe complex mutations: a discussion. Hum Mutat 2000;1591:7-12.
20. Walden CC, Hegele RA. Apolipoprotein E in hyperlipidemia. Ann Intern Med 1994;120:1026-36.
28. Kumar P, Henikoff S, Ng PC. Predicting the effects of coding nonsynonymous variants on protein function using the SIFT algorithm. Nat Protoc 2009;4:1073-81.
21. Musunuru K, Pirruccello JP, Do R, et al. Exome sequencing, ANGPTL3 mutations, and familial combined hypolipidemia. N Engl J Med 2010;363:2220-7.
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22. Pollin TI, Damcott CM, Shen H, et al. A null mutation in human APOC3 confers a favorable plasma lipid profile and apparent cardioprotection. Science 2008;322:1702-5. 23. Hubacek JA, Berge KE, Cohen JC, Hobbs HH. Mutations in ATPcassette binding proteins G5 (ABCG5) and G8 (ABCG8) causing sitosterolemia. Hum Mutat 2001;18:359-60.
Supplementary Material To access the supplementary material accompanying this article, visit the online version of the Canadian Journal of Cardiology at www.onlinecjc.ca and at http://dx.doi.org/10. 1016/j.cjca.2013.01.008.
Clinical Research
Western Database of Lipid Variants (WDLV): A Catalogue of Genetic Variants in Monogenic Dyslipidemias Jennifer Fu, Samantha Kwok, Leah Sinai, BSc, Omar Abdel-Razek, Janet Babula, Dennis Chen, Emma Farago, Nigel Fernandopulle, Sean Leith, BSc, Melissa Loyzer, Catherine Lu, Niyati Malkani, Nicole Morris, Mandi Schmidt, Randa Stringer, Heather Whitehead, BA, Matthew R. Ban, BSc, Joseph B. Dube, BSc, Adam McIntyre, BSc, Christopher T. Johansen, PhD, Henian Cao, MD, Jian Wang, MD, and Robert A. Hegele, MD, FRCPC, FACP Blackburn Cardiovascular Genetics Laboratory, Robarts Research Institute, University of Western Ontario, London, Ontario, Canada
ABSTRACT
RESUM E
Background: Next-generation sequencing (NGS) is nearing routine clinical application, especially for diagnosis of rare monogenic cardiovascular diseases. But NGS uncovers so much variation in an individual genome that filtering steps are required to streamline data management. The first step is to determine whether a potential disease-causing variant has been observed previously in affected patients. Methods: To facilitate this step for lipid disorders, we developed the Western Database of Lipid Variants (WDLV) of 2776 variants in 24 genes that cause monogenic dyslipoproteinemias, including conditions characterized primarily by either high or low low-density lipoprotein cholesterol, high or low high-density lipoprotein cholesterol, high triglyceride, and some miscellaneous disorders. We incorporated quality-control steps to maximize the likelihood that a listed mutation was disease causing. Results: The details of each mutation found in a dyslipidemia, together with a mutation map of the causative genes, are shown in graphical display items. Conclusions: WDLV will help clinicians and researchers determine the potential pathogenicity of mutations discovered by DNA sequencing of patients or research participants with lipid disorders.
quençage de nouvelle ge ne ration (SNG) tend vers Introduction : Le se l’application clinique courante, particulièrement pour le diagnostic des niques rares. Mais le SNG montre maladies cardiovasculaires monoge nome individuel que des e tapes de filtant de variations dans un ge es. La première trage sont requises pour simplifier la gestion des donne tape est de de terminer si un variant pathogène potentiel a de jà e te e chez les patients atteints. observe thodes : Pour faciliter cette e tape des troubles lipidiques, nous Me labore la Western Database of Lipid Variants (WDLV) de 2776 avons e ine mies monoge nivariants de 24 gènes qui causent les dyslipoprote rise es par un ques, incluant les affections principalement caracte rol à lipoprote ines de basse densite e leve ou faible, un chocholeste rol à lipoprote ines de haute densite e leve ou faible, des trileste rides e leve s et certains troubles divers. Nous avons inclus des glyce tapes de contrôle de la qualite pour accroître la possibilite qu’une e te pathogène. pertorie e ait e mutation re sultats : Les de tails de chaque mutation de couverte au cours d’une Re mie, ainsi qu’une carte de mutations des gènes responsables, dyslipide s dans les e le ments de la pre sentation graphique. sont montre Conclusions : La WDLV aidera les cliniciens et les chercheurs à terminer la pathoge nicite potentielle des mutations de couvertes par de quençage de l’ADN des patients ou des participants à la recherche le se ayant des troubles lipidiques.
Identifying the genetic basis of disease has become a crucial aspect of clinical diagnosis of rare single-gene disorders, also called “monogenic disorders.” Moreover, recent advances in sequencing technology have allowed for an exponential
increase in the number of novel genetic variants discovered each year (Fig. 1).1 Next-generation sequencing (NGS) methods are now used routinely in research and are on the verge of being used clinically in patients with suspected monogenic disorders. However, the amount of DNA variation that NGS can uncover in the genome of a single individual is unwieldy and cannot yet be applied in practical clinical situations. One approach to manage the vast amount of NGS information is to organize it into smaller, user-friendly diseasebased catalogues. Thus, the goal of the Western Database of Lipid Variants (WDLV) is to gather existing information on monogenic dyslipidemias and present it in a harmonized
Received for publication December 18, 2012. Accepted January 16, 2013. Corresponding author: Dr Robert A. Hegele, Blackburn Cardiovascular Genetics Laboratory, Robarts Research Institute, 4288A-100 Perth Drive, London, Ontario N6A 5K8, Canada. E-mail: [email protected] See page 938 for disclosure information.
0828-282X/$ - see front matter Ó 2013 Canadian Cardiovascular Society. Published by Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.cjca.2013.01.008
Fu et al. Western Database of Lipid Variants
Figure 1. Total human disease variants reported on Human Gene Mutation Database (HGMD) according to the year of publication.
format that is more meaningful for clinicians, researchers, patients, and the public. There are 2 main types of catalogues of genetic variants. The first type consists of comprehensive databases that include all variants and polymorphisms in the human genome, such as the Human Gene Mutation Database (HGMD)1 and the Single Nucleotide Polymorphism Database.2 These catalogues contain information regarding all genes, with no focus on any particular disease or genetic locus. In contrast, the second type of catalogue, known as a “locus-specific” or “disease-specific database,” is dedicated to providing information on only 1 or a few genes or diseases. The second type of catalogue frequently offers more detail on given variants and is potentially of greater clinical interest.3 The WDLV is a disease-specific variant database focused on monogenic dyslipidemias. Dyslipidemias are characterized by abnormal concentrations of plasma lipids, specifically, low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, or triglyceride (TG). A given dyslipidemia may involve either elevated or depressed levels of these variables in isolation or in combination. DNA variants causing monogenic dyslipidemias are generally rare large-effect mutations located within the coding sequences of causative genes.4 Such mutations contrast with the common variants that underlie multifactorial, polygenic, or complex diseases.5 Common “garden variety” dyslipidemia results from an accumulation of small-effect single nucleotide polymorphisms (SNPs), together with occasional rare large-effect mutations, and secondary factors such as poor diet, inactivity, or associated metabolic conditions.5 WDLV catalogues all variants reported to cause rare inherited monogenic dyslipidemias. While several of the same genes containing rare disease-causing mutations also contain common SNPs that affect lipids in the general population,4-6 we have endeavored to exclude common SNPs that contribute to polygenic dyslipidemias, focusing instead primarily on rare mutations that are causative for rare monogenic disorders. Most of the annotated variants lie within coding sequences rather than in regulatory or promoter regions. An example of a dedicated single dyslipidemia-specific database is the Leiden Open (source) Variation Database (LOVD) familial hypercholesterolemia (FH) mutation database established by Humphries and colleagues,3 which lists all genetic variants found to date in the LDLR, PCSK9, and LDLRAP1 genes from patients with FH, a monogenic
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disorder that is characterized by markedly elevated LDL cholesterol levels.7 WDLV expands the focus beyond FH to include 23 other monogenic dyslipidemic states. We included FH within WDLV to provide a measure of quality control for our overall procedures and processes. The FH mutations listed in Supplemental Table S1 and shown in Supplemental Figure S1 thus overlap to a large extent with the LOVD FH database. The novel aspect of the WDLV is the list and curation of the non-FH disorders listed in Supplemental Table S2 and shown in Supplemental Figures S2-S6. Beyond FH, WDLV comprehensively annotates information on additional monogenic dyslipidemias, including variants in MTTP,8 APOB,9 SAR1B,10 and PCSK911 causing hypobetalipoproteinemia (depressed LDL cholesterol); variants in CETP,12 LIPC,13 SCARB1,14 and LIPG15 causing hyperalphalipoproteinemia (increased HDL cholesterol); variants in ABCA1,16 LCAT,17 and APOA118 causing a range of disorders characterized by hypoalphalipoproteinemia (depressed HDL cholesterol); variants in LPL,19 APOC2,19 LMF1,19 GPIHBP1,19 and APOA519 causing hypertriglyceridemia; variants in APOE causing elevated levels of total cholesterol and Table 1. Accession identification numbers and nucleotide sequences used for causative genes
Gene ATP-binding cassette, subfamily A member 1 (ABCA1) ATP-binding cassette, subfamily G member 5 (ABCG5) ATP-binding cassette, subfamily G member 8 (ABCG8) Angiopoietin-like 3 protein (ANGPTL3) Apolipoprotein A-I (APOA1) Apolipoprotein A-V (APOA5) Apolipoprotein B (APOB) Apolipoprotein C-II (APOC2) Apolipoprotein C-III (APOC3) Apolipoprotein E (APOE) Cholesteryl ester transfer protein (CETP) Glycosylphosphatidylinositolanchored HDL-binding protein 1 (GPIHBP1) Lecithin cholesterol acyltransferase (LCAT) LDL receptor (LDLR) LDL receptor adaptor protein 1 (LDLRAP1, ARH) Lysosomal acid lipase A (LIPA, alias LAL) Hepatic lipase (HL, alias LIPC) Endothelial lipase (LIPG) Lipase maturation factor 1 (LMF1) Lipoprotein lipase (LPL) Microsomal triglyceride transfer protein (MTTP) Proprotein convertase subtilisin/kexin type 9 (PCSK9) SAR1 homolog B (SAR1B) Scavenger receptor class B, member 1 (SCARB1)
Nucleotide identification number
Protein identification number
NM_005502.3
NP_005493.2
NM_022436.2
NP_071881.1
NM_022437.2
NP_071882.1
NM_014495.2
NP_055310.1
NM_000039.1 NM_052968.4 NM_000384.2 NM_000483.4 NM_000040.1 NM_000041.2 NM_000078.2
NP_000030.1 NP_443200.2 NP_000375.2 NP_000474.2 NP_000031.1 NP_000032.1 NP_000069.2
NM_178172.3
NP_835466.1
NM_000229.1
NP_000220.1
NM_000527.4 NM_015627.2
NP_000518.1 NP_056442.2
NM_000235.2
NP_000226.2
NM_000236.2 NM_006033.2 NM_022773.2 NM_000237.2 NM_000253.2
NP_000227.2 NP_006024.1 NP_073610.2 NP_000228.1 NP_000244.2
NM_174936.3
NP_777596.2
NM_001033503.2 NP_001028675.1 NM_005505.4 NP_005496.4
Sequence accession numbers are found at http://www.ncbi.nlm.nih.gov/ genbank/. ATP, adenosine triphosphate; HDL, high-density lipoprotein; LDL, low-density lipoprotein.
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Figure 2. Nomenclature used in the Western Database of Lipid Variants (WDLV) to denote (A) missense or nonsense variants, (B) in-frame insertions or deletions, (C) frameshift mutations, and (D) intronic variants. Termination codons are indicated by an “X.”
TG due to increased lipoprotein remnant particles;20 variants in ANGPTL3 causing depressed LDL cholesterol, HDL cholesterol, and TG levels (also called “combined hypolipidemia”);21 a single variant in APOC3 causing depressed LDL cholesterol and TG levels, with increased HDL cholesterol;22 variants in ABCG5 and ABCG8 causing sitosterolemia;23 and variants in LIPA causing cholesteryl ester storage disease.24 Methods Mutation lists for each of the 24 genes were generated with Microsoft Excel spreadsheets by cross-referencing the HGMD, Online Mendelian Inheritance in Man, and Universal Protein Resource. Reference alleles and genomic coordinates were verified with the University of Santa Cruz Genome Browser.25 Both National Center for Biotechnology Information (NCBI) 36.1/hg18 and NCBI 37/hg19 coordinates are provided. Original publications were reviewed where possible for results of functional studies, as well as inheritance patterns and patient profiles. Amino acid changes as a result of insertions and deletions were determined with the Consensus Coding Sequence tool generated by the NCBI.26 Table 1 shows the specific accession identification numbers used for each gene. Mutations were annotated in accordance with Human Genome Variation Society guidelines on description of sequence variants (Fig. 2).27 When direct experimental data on altered function of a missense variant were not available, potential pathogenicity was predicted via the Sorting Intolerant From Tolerant (SIFT) Human Coding Tool (http://sift.jcvi.org/www/SIFT_chr_coords_submit.html),28 and for those indicated as being possibly or likely pathogenic, a second program, the PolyPhen-2 batch query tool (http:// genetics.bwh.harvard.edu/pph2/bgi.shtml) was used for replication.29 SIFT predictions are based on degree of conservation of amino acid residues in sequence alignments from closely
related sequences. PolyPhen-2 predicts based on the sequence, phylogenetic, and structural or physical considerations. In order to ensure accuracy and validity of the database, clinical, biochemical, genetic, and functional features of each variant were verified at least twice by 2 independent reviewers. Results A total of 2776 disease-causing mutations have been catalogued and recorded in the WDLV. Table 2 provides a summary of these. The dyslipidemia-causing variants span 24 genes, the locations of which are shown in Figure 3. Maps were constructed for each of the 24 genes, providing a visual representation of the relative locations of variants within their respective genes. The gene maps are organized into 6 general phenotypes according to lipid trait primarily affected: elevated LDL cholesterol (Supplemental Fig. S1), depressed LDL cholesterol (Supplemental Fig. S2), elevated HDL cholesterol (Supplemental Fig. S3), depressed HDL cholesterol (Supplemental Fig. S4), elevated TG (Supplemental Fig. S5), and a few miscellaneous disorders (Supplemental Fig. S6). Since variants in APOB and PCSK9 can result in either elevated or depressed levels of LDL cholesterol, these genes appear under 2 different phenotype categories. Supplemental Tables S1 and S2 provide a complete list of all dyslipidemia mutations that we curated: Supplemental Table S1 for FH and Supplemental Table S2 for all other dyslipidemias, together with their clinical and molecular characteristics and reference citations. Discussion With the explosion in DNA sequence information provided by NGS, a greater number of novel variants and mutations are being reported yearly. Efficiently annotating
Fu et al. Western Database of Lipid Variants
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Table 2. Summary of mutation types according to the primary lipid disturbance and causative gene Variant count, by type (N, %) Phenotype LDL
[
Y
HDL
[
Y TG
[
Misc.
[ IDL Y TG, LDL, [HDL Y TG, LDL, HDL Sitosterolemia CESD/Wolman
Total
Gene
All
LDLR PCSK9 APOB LDLRAP1 APOB MTTP PCSK9 SAR1B CETP LIPC LIPG SCARB1 ABCA1 LCAT APOA1 LPL APOA5 APOC2 GPIHBP1 LMF1 APOE APOC3 ANGPTL3 ABCG8 ABCG5 LIPA
1678 33 20 16 120 47 29 14 39 18 13 8 163 91 62 156 34 17 13 10 67 1 24 32 25 46 2776
Missense 745 29 18 3 21 10 23 6 14 12 9 6 100 67 26 99 16 5 11 8 46 0 16 16 10 15 1331
(44.4) (87.9) (90.0) (18.8) (17.5) (21.3) (79.3) (42.9) (35.9) (66.7) (69.2) (75.0) (61.4) (73.6) (42.0) (63.5) (47.1) (29.4) (84.6) (80.0) (68.7) (0.0) (66.7) (50.0) (40.0) (32.6) (48.0)
Nonsense 123 0 0 2 28 7 3 2 7 0 0 0 15 4 6 16 6 4 1 2 2 1 1 9 8 8 255
(7.3) (0.0) (0.0) (12.5) (23.3) (14.9) (10.3) (14.3) (18.0) (0.0) (0.0) (0.0) (9.2) (4.4) (9.7) (10.3) (17.7) (23.5) (7.7) (20.0) (3.0) (100.0) (4.2) (28.1) (32.0) (17.4) (9.2)
Small indel
Gross indel
385 2 0 5 57 12 3 4 5 1 1 1 22 17 19 25 6 4 0 0 10 0 6 4 2 14 605
272 0 0 3 2 3 0 1 0 0 0 0 6 2 6 5 1 1 1 0 3 0 0 1 3 3 313
(22.9) (6.1) (0.0) (31.3) (47.5) (25.5) (10.3) (28.6) (12.8) (5.6) (7.7) (12.5) (13.5) (18.7) (30.7) (16.0) (17.7) (23.5) (0.0) (0.0) (14.9) (0.0) (25.0) (12.5) (8.0) (30.3) (21.0)
(16.2) (0.0) (0.0) (18.8) (1.7) (6.4) (0.0) (7.1) (0.0) (0.0) (0.0) (0.0) (3.7) (2.2) (9.7) (3.2) (2.9) (5.9) (7.7) (0.0) (4.5) (0.0) (0.0) (3.1) (12.0) (6.5) (11.3)
Splicing 125 1 0 3 12 13 0 1 6 2 0 1 13 1 1 8 3 2 0 0 1 0 1 1 2 5 202
(7.5) (3.0) (0.0) (18.8) (10.0) (27.7) (0.0) (7.1) (15.3) (11.1) (0.0) (12.5) (8.0) (1.1) (1.6) (5.1) (8.8) (11.8) (0.0) (0.0) (1.4) (0.0) (4.2) (3.1) (8.0) (10.9) (7.3)
Regulatory 28 1 2 0 0 2 0 0 7 3 3 0 7 0 4 3 2 1 0 0 5 0 0 1 0 1 70
(1.7) (3.0) (10.0) (0.0) (0.0) (4.3) (0.0) (0.0) (18.0) (16.7) (23.1) (0.0) (4.3) (0.0) (6.5) (1.9) (5.9) (5.9) (0.0) (0.0) (7.5) (0.0) (0.0) (3.1) (0.0) (2.2) (2.5)
Up arrow signifies increased plasma level. Down arrow signifies decreased plasma level. CESD/Wolman, cholesterol ester storage disease and Wolman syndrome; HDL, high-density lipoprotein cholesterol; IDL, intermediate-density lipoprotein particle; indel, insertion or deletion; LDL, low-density lipoprotein cholesterol; Misc., miscellaneous; TG, triglyceride.
and cataloguing disease-causing variants for clinicians and researchers have become critical needs. The goal of the WDLV project was to compile all mutations reported to cause monogenic dyslipidemias and present them in the format of an easily accessible and user-friendly database. The WDLV offers a summary of all reported diseasecausing variants, a brief overview of the function of each gene, the phenotype associated with each variant, genomic coordinates of the variant, disease inheritance pattern, profile of the index-case patient, as well as results of any functional studies conducted on the variant. Citations of the first publication reporting each variant are provided for further follow-up and investigation. To date, we have gathered 2776 mutations located across 24 genes. Each contributes to monogenic dyslipidemias subdivided into 6 general phenotypes: elevated LDL cholesterol, depressed LDL cholesterol, elevated HDL cholesterol, depressed HDL cholesterol, elevated TG, and a miscellaneous category that contains disorders such as sitosterolemia, cholesterol ester storage disease, and Wolman disease. WDLV has many potential applications for physicians and researchers. For instance, it provides a centralized, curated resource to determine whether a mutation discovered in a patient or research subject has previously been implicated as disease causing. With this database, physicians can efficiently access previously reported mutations that have been reviewed regarding genotype-phenotype relationships and apply this information in a clinical context. A researcher can determine the potential novelty of a mutation uncovered by NGS. Other information provided by WDLV, such as patient profiles and
demographics, can be used in epidemiology, geographic distribution, and genealogy studies. Going forward, addition of newly discovered genedisease associations and variant-disease associations to the WDLV will be essential. The current plan is for yearly updating of the WDLV with the same methods described here, including automated literature searches through the National Library of Medicine and cross-referencing mutation databases such as HGMD, Online Mendelian Inheritance in Man, and Universal Protein Resource. Updates will be provided online. We also anticipate that the database might be implemented in a Web-based browser, where users can enter a query for phenotype, disease, gene name, chromosomal location of the variant, type of variant, and ethnic group. This template can be used to curate monogenic disorders involving related cardiometabolic diseases, such as monogenic forms of diabetes and hypertension. Other possible improvements include the consideration of population genetics and common SNPs in these genes, thus expanding the focus beyond monogenic dyslipidemias to polygenic disorders. Funding Sources R.A.H. is supported by the Jacob J. Wolfe Distinguished Medical Research Chair, the Edith Schulich Vinet Canada Research Chair in Human Genetics, the Martha G. Blackburn Chair in Cardiovascular Research, and operating grants from the Canadian Institutes of Health Research (MOP-13430, MOP-79523, CTP-79853), the Heart and
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Figure 3. Twenty-four dyslipidemia-causing genes annotated in Western Database of Lipid Variants (WDLV) and their location in the human genome.
Stroke Foundation of Ontario (NA-6059, T-6018, PRG4854), and Genome Canada through the Ontario Genomics Institute. Many of the students who worked on this project were supported by funding made available through the Edith Schulich Vinet Canada Research Chair in Human Genetics. Disclosures The authors have no conflicts of interest to disclose. References 1. Stenson PD, Ball EV, Mort M, et al. Human Gene Mutation Database (HGMD): 2003 update. Hum Mutat 2003;21:577-81.
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Supplementary Material To access the supplementary material accompanying this article, visit the online version of the Canadian Journal of Cardiology at www.onlinecjc.ca and at http://dx.doi.org/10. 1016/j.cjca.2013.01.008.