Identification of COL3A1 variants associated with sporadic thoracic aortic dissection: a case--control study

Yanghui Chen, Yang Sun, Zongzhe Li, Chenze Li, Lei Xiao, Jiaqi Dai, Shiyang Li, Hao Liu, Dong Hu, Dongyang Wu, Senlin Hu, Bo Yu, Peng Chen, Ping Xu, Wei Kong, Dao Wen Wang

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Front. Med. ›› 2021, Vol. 15 ›› Issue (3) : 438-447. DOI: 10.1007/s11684-020-0826-1
RESEARCH ARTICLE

Identification of COL3A1 variants associated with sporadic thoracic aortic dissection: a case--control study

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Abstract

Thoracic aortic dissection (TAD) without familial clustering or syndromic features is known as sporadic TAD (STAD). So far, the genetic basis of STAD remains unknown. Whole exome sequencing was performed in 223 STAD patients and 414 healthy controls from the Chinese Han population (N = 637). After population structure and genetic relationship and ancestry analyses, we used the optimal sequence kernel association test to identify the candidate genes or variants of STAD. We found that COL3A1 was significantly relevant to STAD (P = 7.35 × 10−6) after 10 000 times permutation test (P = 2.49 × 10−3). Moreover, another independent cohort, including 423 cases and 734 non-STAD subjects (N = 1157), replicated our results (P = 0.021). Further bioinformatics analysis showed that COL3A1 was highly expressed in dissected aortic tissues, and its expression was related to the extracellular matrix (ECM) pathway. Our study identified a profile of known heritable TAD genes in the Chinese STAD population and found that COL3A1 could increase the risk of STAD through the ECM pathway. We wanted to expand the knowledge of the genetic basis and pathology of STAD, which may further help in providing better genetic counseling to the patients.

Keywords

sporadic thoracic aortic dissection / exome sequencing / gene COL3A1 / case–control study / extracellular matrix

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Yanghui Chen, Yang Sun, Zongzhe Li, Chenze Li, Lei Xiao, Jiaqi Dai, Shiyang Li, Hao Liu, Dong Hu, Dongyang Wu, Senlin Hu, Bo Yu, Peng Chen, Ping Xu, Wei Kong, Dao Wen Wang. Identification of COL3A1 variants associated with sporadic thoracic aortic dissection: a case--control study. Front. Med., 2021, 15(3): 438‒447 https://doi.org/10.1007/s11684-020-0826-1

References

[1]
Howard DP, Banerjee A, Fairhead JF, Perkins J, Silver LE, Rothwell PM; the Oxford Vascular Study. Population-based study of incidence and outcome of acute aortic dissection and premorbid risk factor control: 10-year results from the Oxford Vascular Study. Circulation 2013; 127(20): 2031–2037
CrossRef Pubmed Google scholar
[2]
Pinard A, Jones GT, Milewicz DM. Genetics of thoracic and abdominal aortic diseases. Circ Res 2019; 124(4): 588–606
CrossRef Pubmed Google scholar
[3]
Wu D, Shen YH, Russell L, Coselli JS, LeMaire SA. Molecular mechanisms of thoracic aortic dissection. J Surg Res 2013; 184(2): 907–924
CrossRef Pubmed Google scholar
[4]
Renard M, Francis C, Ghosh R, Scott AF, Witmer PD, Adès LC, Andelfinger GU, Arnaud P, Boileau C, Callewaert BL, Guo D, Hanna N, Lindsay ME, Morisaki H, Morisaki T, Pachter N, Robert L, Van Laer L, Dietz HC, Loeys BL, Milewicz DM, De Backer J. Clinical validity of genes for heritable thoracic aortic aneurysm and dissection. J Am Coll Cardiol 2018; 72(6): 605–615
CrossRef Pubmed Google scholar
[5]
Shen YH, LeMaire SA. Molecular pathogenesis of genetic and sporadic aortic aneurysms and dissections. Curr Probl Surg 2017; 54(3): 95–155
CrossRef Pubmed Google scholar
[6]
Campens L, Callewaert B, Muiño Mosquera L, Renard M, Symoens S, De Paepe A, Coucke P, De Backer J. Gene panel sequencing in heritable thoracic aortic disorders and related entities— results of comprehensive testing in a cohort of 264 patients. Orphanet J Rare Dis 2015; 10(1): 9
CrossRef Pubmed Google scholar
[7]
Weerakkody R, Ross D, Parry DA, Ziganshin B, Vandrovcova J, Gampawar P, Abdullah A, Biggs J, Dumfarth J, Ibrahim Y, Yale Aortic Institute Data and Repository Team, Bicknell C, Field M, Elefteriades J, Cheshire N, Aitman TJ. Targeted genetic analysis in a large cohort of familial and sporadic cases of aneurysm or dissection of the thoracic aorta. Genet Med 2018; 20(11): 1414–1422
CrossRef Pubmed Google scholar
[8]
Arnaud P, Hanna N, Benarroch L, Aubart M, Bal L, Bouvagnet P, Busa T, Dulac Y, Dupuis-Girod S, Edouard T, Faivre L, Gouya L, Lacombe D, Langeois M, Leheup B, Milleron O, Naudion S, Odent S, Tchitchinadze M, Ropers J, Jondeau G, Boileau C. Genetic diversity and pathogenic variants as possible predictors of severity in a French sample of nonsyndromic heritable thoracic aortic aneurysms and dissections (nshTAAD). Genet Med 2019; 21(9): 2015–2024
CrossRef Pubmed Google scholar
[9]
Li Z, Zhou C, Tan L, Chen P, Cao Y, Li X, Yan J, Zeng H, Wang DW, Wang DW. A targeted sequencing approach to find novel pathogenic genes associated with sporadic aortic dissection. Sci China Life Sci 2018; 61(12): 1545–1553
CrossRef Pubmed Google scholar
[10]
Lee S, Emond MJ, Bamshad MJ, Barnes KC, Rieder MJ, Nickerson DA, NHLBI GO Exome Sequencing Project—ESP Lung Project Team; Christiani DC, Wurfel MM, Lin X. Optimal unified approach for rare-variant association testing with application to small-sample case-control whole-exome sequencing studies. Am J Hum Genet 2012; 91(2): 224–237
CrossRef Pubmed Google scholar
[11]
Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 2014; 30(15): 2114–2120
CrossRef Pubmed Google scholar
[12]
Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 2009; 25(14): 1754–1760
CrossRef Pubmed Google scholar
[13]
McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M, DePristo MA. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res 2010; 20(9): 1297–1303
CrossRef Pubmed Google scholar
[14]
Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, Maller J, Sklar P, de Bakker PI, Daly MJ, Sham PC. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 2007; 81(3): 559–575
CrossRef Pubmed Google scholar
[15]
Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet 2011; 88(1): 76–82
CrossRef Pubmed Google scholar
[16]
Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 2006; 38(8):904–909PMID: 16862161
CrossRef Google scholar
[17]
Li Z, Huang J, Zhao J, Chen C, Wang H, Ding H, Wang DW, Wang DW. Rapid molecular genetic diagnosis of hypertrophic cardiomyopathy by semiconductor sequencing. J Transl Med 2014; 12(1): 173
CrossRef Pubmed Google scholar
[18]
Mayakonda A, Lin DC, Assenov Y, Plass C, Koeffler HP. Maftools: efficient and comprehensive analysis of somatic variants in cancer. Genome Res 2018; 28(11): 1747–1756
CrossRef Pubmed Google scholar
[19]
Ng PC, Henikoff S. SIFT: predicting amino acid changes that affect protein function. Nucleic Acids Res 2003; 31(13): 3812–3814
CrossRef Pubmed Google scholar
[20]
Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, Kondrashov AS, Sunyaev SR. A method and server for predicting damaging missense mutations. Nat Methods 2010; 7(4): 248–249
CrossRef Pubmed Google scholar
[21]
Schwarz JM, Cooper DN, Schuelke M, Seelow D. MutationTaster2: mutation prediction for the deep-sequencing age. Nat Methods 2014; 11(4): 361–362
CrossRef Pubmed Google scholar
[22]
Carter H, Douville C, Stenson PD, Cooper DN, Karchin R. Identifying Mendelian disease genes with the variant effect scoring tool. BMC Genomics 2013; 14(Suppl 3): S3
CrossRef Pubmed Google scholar
[23]
Ioannidis NM, Rothstein JH, Pejaver V, Middha S, McDonnell SK, Baheti S, Musolf A, Li Q, Holzinger E, Karyadi D, Cannon-Albright LA, Teerlink CC, Stanford JL, Isaacs WB, Xu J, Cooney KA, Lange EM, Schleutker J, Carpten JD, Powell IJ, Cussenot O, Cancel-Tassin G, Giles GG, MacInnis RJ, Maier C, Hsieh CL, Wiklund F, Catalona WJ, Foulkes WD, Mandal D, Eeles RA, Kote-Jarai Z, Bustamante CD, Schaid DJ, Hastie T, Ostrander EA, Bailey-Wilson JE, Radivojac P, Thibodeau SN, Whittemore AS, Sieh W. REVEL: an ensemble method for predicting the pathogenicity of rare missense variants. Am J Hum Genet 2016; 99(4): 877–885
CrossRef Pubmed Google scholar
[24]
Dong C, Wei P, Jian X, Gibbs R, Boerwinkle E, Wang K, Liu X. Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies. Hum Mol Genet 2015; 24(8): 2125–2137
CrossRef Pubmed Google scholar
[25]
Rentzsch P, Witten D, Cooper GM, Shendure J, Kircher M. CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Res 2019; 47(D1): D886–D894
CrossRef Pubmed Google scholar
[26]
Davydov EV, Goode DL, Sirota M, Cooper GM, Sidow A, Batzoglou S. Identifying a high fraction of the human genome to be under selective constraint using GERP++. PLOS Comput Biol 2010; 6(12): e1001025
CrossRef Pubmed Google scholar
[27]
Maag JLV. gganatogram: an R package for modular visualisation of anatograms and tissues based on ggplot2. F1000 Res 2018; 7: 1576
CrossRef Pubmed Google scholar
[28]
Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 2015; 43(7): e47
CrossRef Pubmed Google scholar
[29]
Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser A Stat Soc 1995; 57(1): 289–300
[30]
Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 2012; 16(5): 284–287
CrossRef Pubmed Google scholar
[31]
Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA 2005; 102(43): 15545–15550
CrossRef Pubmed Google scholar
[32]
Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, Grody WW, Hegde M, Lyon E, Spector E, Voelkerding K, Rehm HL; the ACMG Laboratory Quality Assurance Committee. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med 2015; 17(5): 405–423
CrossRef Pubmed Google scholar
[33]
Hagan PG, Nienaber CA, Isselbacher EM, Bruckman D, Karavite DJ, Russman PL, Evangelista A, Fattori R, Suzuki T, Oh JK, Moore AG, Malouf JF, Pape LA, Gaca C, Sechtem U, Lenferink S, Deutsch HJ, Diedrichs H, Marcos y Robles J, Llovet A, Gilon D, Das SK, Armstrong WF, Deeb GM, Eagle KA. The International Registry of Acute Aortic Dissection (IRAD): new insights into an old disease. JAMA 2000; 283(7): 897–903
CrossRef Pubmed Google scholar
[34]
Wang W, Duan W, Xue Y, Wang L, Liu J, Yu S, Yi D; the Registry of Aortic Dissection in China (Sino-RAD) Investigators. Clinical features of acute aortic dissection from the Registry of Aortic Dissection in China. J Thorac Cardiovasc Surg 2014; 148(6): 2995–3000
CrossRef Pubmed Google scholar
[35]
Zheng J, Guo J, Huang L, Wu Q, Yin K, Wang L, Zhang T, Quan L, Zhao Q, Cheng J. Genetic diagnosis of acute aortic dissection in South China Han population using next-generation sequencing. Int J Legal Med 2018; 132(5): 1273–1280
CrossRef Pubmed Google scholar
[36]
LeMaire SA, McDonald ML, Guo DC, Russell L, Miller CC 3rd, Johnson RJ, Bekheirnia MR, Franco LM, Nguyen M, Pyeritz RE, Bavaria JE, Devereux R, Maslen C, Holmes KW, Eagle K, Body SC, Seidman C, Seidman JG, Isselbacher EM, Bray M, Coselli JS, Estrera AL, Safi HJ, Belmont JW, Leal SM, Milewicz DM. Genome-wide association study identifies a susceptibility locus for thoracic aortic aneurysms and aortic dissections spanning FBN1 at 15q21.1. Nat Genet 2011; 43(10): 996–1000
CrossRef Pubmed Google scholar
[37]
Tam V, Patel N, Turcotte M, Bossé Y, Paré G, Meyre D. Benefits and limitations of genome-wide association studies. Nat Rev Genet 2019; 20(8): 467–484
CrossRef Pubmed Google scholar
[38]
Kuivaniemi H, Tromp G. Type III collagen (COL3A1): gene and protein structure, tissue distribution, and associated diseases. Gene 2019; 707: 151–171
CrossRef Pubmed Google scholar
[39]
Pepin M, Schwarze U, Superti-Furga A, Byers PH. Clinical and genetic features of Ehlers-Danlos syndrome type IV, the vascular type. N Engl J Med 2000; 342(10): 673–680
CrossRef Pubmed Google scholar
[40]
Lin CJ, Lin CY, Stitziel NO. Genetics of the extracellular matrix in aortic aneurysmal diseases. Matrix Biol 2018; 71-72: 128–143
CrossRef Pubmed Google scholar
[41]
D'Hondt S, Guillemyn B, Syx D, Symoens S, De Rycke R, Vanhoutte L, Toussaint W, Lambrecht BN, De Paepe A, Keene DR, Ishikawa Y, Bächinger HP, Janssens S, Bertrand MJM, Malfait F. Type III collagen affects dermal and vascular collagen fibrillogenesis and tissue integrity in a mutant Col3a1 transgenic mouse model. Matrix Biol 2018; 70: 72–83PMID: 29551664
CrossRef Google scholar

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 91839302, 91439203, and 81700413), the National Key R&D Program of China (No. 2017YFC0909400), and the Municipal Science and Technology Major Project (No. 2017SHZDZX01).

Compliance with ethics guidelines

Yanghui Chen, Yang Sun, Zongzhe Li, Chenze Li, Lei Xiao, Jiaqi Dai, Shiyang Li, Hao Liu, Dong Hu, Dongyang Wu, Senlin Hu, Bo Yu, Peng Chen, Ping Xu, Wei Kong, and Dao Wen Wang declare that they have no conflicts of interest. All procedures were performed in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000 (5). Informed consent was obtained from all patients for being included in the study.

Electronic Supplementary Material

Supplementary material is available in the online version of this article at https://doi.org/ 10.1007/s11684-020-0826-1 and is accessible for authorized users.

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