A transcriptome-wide association study identifies PALMD as a susceptibility gene for calcific aortic valve stenosis

Sébastien Thériault, Nathalie Gaudreault, Maxime Lamontagne, Mickael Rosa, Marie-Chloé Boulanger, David Messika-Zeitoun, Marie-Annick Clavel, Romain Capoulade, François Dagenais, Philippe Pibarot, Patrick Mathieu, Yohan Bossé, Sébastien Thériault, Nathalie Gaudreault, Maxime Lamontagne, Mickael Rosa, Marie-Chloé Boulanger, David Messika-Zeitoun, Marie-Annick Clavel, Romain Capoulade, François Dagenais, Philippe Pibarot, Patrick Mathieu, Yohan Bossé

Abstract

Calcific aortic valve stenosis (CAVS) is a common and life-threatening heart disease and the current treatment options cannot stop or delay its progression. A GWAS on 1009 cases and 1017 ethnically matched controls was combined with a large-scale eQTL mapping study of human aortic valve tissues (n = 233) to identify susceptibility genes for CAVS. Replication was performed in the UK Biobank, including 1391 cases and 352,195 controls. A transcriptome-wide association study (TWAS) reveals PALMD (palmdelphin) as significantly associated with CAVS. The CAVS risk alleles and increasing disease severity are both associated with decreased mRNA expression levels of PALMD in valve tissues. The top variant identified shows a similar effect and strong association with CAVS (P = 1.53 × 10-10) in UK Biobank. The identification of PALMD as a susceptibility gene for CAVS provides insights into the genetic nature of this disease, opens avenues to investigate its etiology and to develop much-needed therapeutic options.

Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Manhattan plots showing the GWAS in QUEBEC-CAVS and TWAS results. a Genetic associations with CAVS observed in 1009 cases and 1017 controls. The y-axis represents P value in −log10 scale. The horizontal blue line indicates a P value of 1 × 10−5. The magenta dot indicates rs6702619. b Transcriptome-wide association in valve tissue with CAVS. P values for gene expression-CAVS associations are on the y-axis in −log10 scale. The blue horizontal line represents PTWAS of 0.05. The magenta horizontal line represents the genome-wide significant threshold used in this study (PTWAS < 0.0001). Annotations for the top significant probes are indicated
Fig. 2
Fig. 2
PALMD is the candidate causal gene on the 1p21.2 CAVS susceptibility locus. a GWAS and valve eQTL results surrounding PALMD on chromosome 1p21. The upper panel shows the genetic associations with CAVS. The bottom panel shows the valve eQTL statistics for the PALMD gene. The extent of linkage disequilibrium (LD; r2 values) for all SNPs with rs6702619 is indicated by colors. The location of genes in this locus is illustrated at the bottom. bPALMD gene expression levels in aortic valves by genotyping groups for SNP rs6702619. The y-axis shows PALMD mRNA expression levels. The x-axis denotes the three genotype groups. The number of individuals is indicated in parentheses. The CAVS risk allele is illustrated in red. Boxplot boundaries represent the first and third quartiles, whiskers are the most extreme data point, which is no more than 1.5 times the interquartile range, and the center mark represents the median. The black bar and the right y-axis indicate the variance in PALMD gene expression explained by rs6702619. c Scatterplot of the 1p21.2 susceptibility locus showing SNP associations with CAVS and PALMD gene expression in aortic valve tissues. The y-axis represents variant association with CAVS (Z score). The x-axis shows association with PALMD gene expression (t statistic). Variants are colored based on the degree of LD (r2) with the top CAVS-associated variant rs6702619. The blue line is the regression slope with 95% confidence interval (red lines)
Fig. 3
Fig. 3
Mendelian randomization analysis of the association between PALMD gene expression and CAVS risk. Each circle represents 1 of 12 SNPs located within 200 kb of PALMD selected for association with PALMD gene expression (P < 0.05) using stepwise regression. The blue line is the regression slope using the Wald method. The magenta dashed lines represent 95% confidence intervals from bootstrap. a Effect on CAVS risk from QUEBEC-CAVS cohort against effect on PALMD gene expression (P = 0.0036). b Effect on CAVS risk from UK Biobank against effect on PALMD gene expression (P = 1.18 × 10−5)
Fig. 4
Fig. 4
Relationship between PALMD expression levels and CAVS severity. PALMD-normalized, age- and sex-adjusted mRNA expression levels in 239 aortic valve tissues according to CAVS disease severity assessed by a aortic valve area (P = 0.0027), b mean (P = 0.0001), and c peak transvalvular gradients (P = 8.13 × 10−5). The blue lines represent the slopes obtained by linear regression with 95% confidence interval (magenta lines)
Fig. 5
Fig. 5
Replication in UK Biobank. a Forest plot of overall effect size for rs6702619 in the discovery (QUEBEC-CAVS) and validation (UK Biobank) cohorts. The blue filled squares represent the odds ratio (OR) for each cohort with 95% confidence intervals illustrated by horizontal lines. The gray vertical line represents an OR of 1.0 and the dashed magenta line is the OR of the meta-analysis. b Manhattan plot showing the GWAS in UK Biobank comparing 1391 CAVS cases and 352,195 controls. The horizontal blue and magenta lines indicate PGWAS values of 1 × 10−5 and 5 × 10−8, respectively. Three SNPs were significant at the PGWAS threshold of 5 × 10−8: rs74617384 and rs10455872 at the LPA locus and rs7543130 (in perfect LD with rs6702619) at the PALMD locus

References

    1. Nkomo VT, et al. Burden of valvular heart diseases: a population-based study. Lancet. 2006;368:1005–1011. doi: 10.1016/S0140-6736(06)69208-8.
    1. Carabello BA, Paulus WJ. Aortic stenosis. Lancet. 2009;373:956–966. doi: 10.1016/S0140-6736(09)60211-7.
    1. Cowell SJ, et al. A randomized trial of intensive lipid-lowering therapy in calcific aortic stenosis. N. Engl. J. Med. 2005;352:2389–2397. doi: 10.1056/NEJMoa043876.
    1. Rossebo AB, et al. Intensive lipid lowering with simvastatin and ezetimibe in aortic stenosis. N. Engl. J. Med. 2008;359:1343–1356. doi: 10.1056/NEJMoa0804602.
    1. O’Brien KD. Pathogenesis of calcific aortic valve disease: a disease process comes of age (and a good deal more) Arterioscler. Thromb. Vasc. Biol. 2006;26:1721–1728. doi: 10.1161/.
    1. Freeman RV, Otto CM. Spectrum of calcific aortic valve disease: pathogenesis, disease progression, and treatment strategies. Circulation. 2005;111:3316–3326. doi: 10.1161/CIRCULATIONAHA.104.486738.
    1. Otto CM, et al. Prospective study of asymptomatic valvular aortic stenosis. Clinical, echocardiographic, and exercise predictors of outcome. Circulation. 1997;95:2262–2270. doi: 10.1161/01.CIR.95.9.2262.
    1. Sehatzadeh S, et al. Transcatheter aortic valve implantation (TAVI) for treatment of aortic valve stenosis: an evidence update. Ont. Health Technol. Assess. Ser. 2013;13:1–40.
    1. Rajamannan NM, et al. Calcific aortic valve disease: not simply a degenerative process: a review and agenda for research from the national heart and lung and blood institute aortic stenosis working group. executive summary: calcific aortic valve disease-2011 update. Circulation. 2011;124:1783–1791. doi: 10.1161/CIRCULATIONAHA.110.006767.
    1. Probst V, et al. Familial aggregation of calcific aortic valve stenosis in the western part of France. Circulation. 2006;113:856–860. doi: 10.1161/CIRCULATIONAHA.105.569467.
    1. Le Gal G, et al. Heterogeneous geographic distribution of patients with aortic valve stenosis: arguments for new aetiological hypothesis. Heart. 2005;91:247–249. doi: 10.1136/hrt.2004.037093.
    1. Bella JN, et al. Genome-wide linkage mapping for valve calcification susceptibility loci in hypertensive sibships: the hypertension genetic epidemiology network study. Hypertension. 2007;49:453–460. doi: 10.1161/01.HYP.0000256957.10242.75.
    1. Horne BD, Camp NJ, Muhlestein JB, Cannon-Albright LA. Evidence for a heritable component in death resulting from aortic and mitral valve diseases. Circulation. 2004;110:3143–3148. doi: 10.1161/01.CIR.0000147189.85636.C3.
    1. Bossé Y, Mathieu P, Pibarot P. Genomics: the next step to elucidate the etiology of calcific aortic valve stenosis. J. Am. Coll. Cardiol. 2008;51:1327–1336. doi: 10.1016/j.jacc.2007.12.031.
    1. Gaudreault N, et al. Replication of genetic association studies in aortic stenosis in adults. Am. J. Cardiol. 2011;108:1305–1310. doi: 10.1016/j.amjcard.2011.06.050.
    1. Ducharme V, et al. NOTCH1 genetic variants in patients with tricuspid calcific aortic valve stenosis. J. Heart Valve Dis. 2013;22:142–149.
    1. Thanassoulis G, et al. Genetic associations with valvular calcification and aortic stenosis. N. Engl. J. Med. 2013;368:503–512. doi: 10.1056/NEJMoa1109034.
    1. Guauque-Olarte S, et al. Calcium signaling pathway genes RUNX2 and CACNA1C are associated with calcific aortic valve disease. Circ. Cardiovasc. Genet. 2015;8:812–822. doi: 10.1161/CIRCGENETICS.115.001145.
    1. Bossé Y, et al. Refining molecular pathways leading to calcific aortic valve stenosis by studying gene expression profile of normal and calcified stenotic human aortic valves. Circ. Cardiovasc. Genet. 2009;2:489–498. doi: 10.1161/CIRCGENETICS.108.820795.
    1. Guauque-Olarte S, et al. RNA expression profile of calcified bicuspid, tricuspid and normal human aortic valves by RNA sequencing. Physiol. Genomics. 2016;48:749–761. doi: 10.1152/physiolgenomics.00041.2016.
    1. Gusev A, et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 2016;48:245–252. doi: 10.1038/ng.3506.
    1. Giambartolomei C, et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 2014;10:e1004383. doi: 10.1371/journal.pgen.1004383.
    1. Consortium GT. Human genomics. The Genotype-Tissue expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science. 2015;348:648–660. doi: 10.1126/science.1262110.
    1. Garg V, et al. Mutations in NOTCH1 cause aortic valve disease. Nature. 2005;437:270–274. doi: 10.1038/nature03940.
    1. Wild PS, et al. Large-scale genome-wide analysis identifies genetic variants associated with cardiac structure and function. J. Clin. Invest. 2017;127:1798–1812. doi: 10.1172/JCI84840.
    1. Consortium EP. An integrated encyclopedia of DNA elements in the human genome. Nature. 2012;489:57–74. doi: 10.1038/nature11247.
    1. Patel DK, et al. Racial differences in the prevalence of severe aortic stenosis. J. Am. Heart Assoc. 2014;3:e000879. doi: 10.1161/JAHA.114.000879.
    1. Sashida Y, et al. Ethnic differences in aortic valve thickness and related clinical factors. Am. Heart J. 2010;159:698–704. doi: 10.1016/j.ahj.2009.12.031.
    1. Hu B, Copeland NG, Gilbert DJ, Jenkins NA, Kilimann MW. The paralemmin protein family: identification of paralemmin-2, an isoform differentially spliced to AKAP2/AKAP-KL, and of palmdelphin, a more distant cytosolic relative. Biochem. Biophys. Res. Commun. 2001;285:1369–1376. doi: 10.1006/bbrc.2001.5329.
    1. Andreu N, et al. PALML, a novel paralemmin-related gene mapping on human chromosome 1p21. Gene. 2001;278:33–40. doi: 10.1016/S0378-1119(01)00719-3.
    1. Hu B, Petrasch-Parwez E, Laue MM, Kilimann MW. Molecular characterization and immunohistochemical localization of palmdelphin, a cytosolic isoform of the paralemmin protein family implicated in membrane dynamics. Eur. J. Cell Biol. 2005;84:853–866. doi: 10.1016/j.ejcb.2005.07.002.
    1. Nie Y, et al. Palmdelphin promotes myoblast differentiation and muscle regeneration. Sci. Rep. 2017;7:41608. doi: 10.1038/srep41608.
    1. Waldo KL, et al. Secondary heart field contributes myocardium and smooth muscle to the arterial pole of the developing heart. Dev. Biol. 2005;281:78–90. doi: 10.1016/j.ydbio.2005.02.012.
    1. Martin PS, et al. Embryonic development of the bicuspid aortic valve. J. Cardiovasc. Dev. Dis. 2015;2:248–272. doi: 10.3390/jcdd2040248.
    1. Dashzeveg N, Taira N, Lu ZG, Kimura J, Yoshida K. Palmdelphin, a novel target of p53 with Ser46 phosphorylation, controls cell death in response to DNA damage. Cell Death Dis. 2014;5:e1221. doi: 10.1038/cddis.2014.176.
    1. Nikpay M, et al. A comprehensive 1,000 Genomes-based genome-wide association meta-analysis of coronary artery disease. Nat. Genet. 2015;47:1121–1130. doi: 10.1038/ng.3396.
    1. Chang CC, et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience. 2015;4:7. doi: 10.1186/s13742-015-0047-8.
    1. Das S, et al. Next-generation genotype imputation service and methods. Nat. Genet. 2016;48:1284–1287. doi: 10.1038/ng.3656.
    1. McCarthy S, et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 2016;48:1279–1283. doi: 10.1038/ng.3643.
    1. Marchini J, Howie B, Myers S, McVean G, Donnelly P. A new multipoint method for genome-wide association studies by imputation of genotypes. Nat. Genet. 2007;39:906–913. doi: 10.1038/ng2088.
    1. Pruim RJ, et al. LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics. 2010;26:2336–2337. doi: 10.1093/bioinformatics/btq419.
    1. Warren BA, Yong JL. Calcification of the aortic valve: its progression and grading. Pathology. 1997;29:360–368. doi: 10.1080/00313029700169315.
    1. Chow ML, et al. Preprocessing and quality control strategies for illumina DASL Assay-Based brain gene expression studies with Semi-Degraded samples. Front. Genet. 2012;3:11. doi: 10.3389/fgene.2012.00011.
    1. Du P, Kibbe WA, Lin SM. Lumi: a pipeline for processing Illumina microarray. Bioinformatics. 2008;24:1547–1548. doi: 10.1093/bioinformatics/btn224.
    1. Shabalin AA. Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics. 2012;28:1353–1358. doi: 10.1093/bioinformatics/bts163.
    1. Park JH, et al. Estimation of effect size distribution from genome-wide association studies and implications for future discoveries. Nat. Genet. 2010;42:570–575. doi: 10.1038/ng.610.
    1. Swerdlow DI, et al. Selecting instruments for Mendelian randomization in the wake of genome-wide association studies. Int. J. Epidemiol. 2016;45:1600–1616. doi: 10.1093/ije/dyw088.
    1. Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int. J. Epidemiol. 2015;44:512–525. doi: 10.1093/ije/dyv080.
    1. Sudlow C, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12:e1001779. doi: 10.1371/journal.pmed.1001779.
    1. Levey AS, et al. A new equation to estimate glomerular filtration rate. Ann. Intern. Med. 2009;150:604–612. doi: 10.7326/0003-4819-150-9-200905050-00006.

Source: PubMed

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