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Background: Multi-phenotype analysis of genetically correlated phenotypes can increase the statistical power to detect loci associated with multiple traits, leading to the discovery of novel loci. This is the first study to date to comprehensively analyze the shared genetic effects within different hemostatic traits, and between these and their associated disease outcomes. Objectives: To discover novel genetic associations by combining summary data of correlated hemostatic traits and disease events. Methods: Summary statistics from genome wide-association studies (GWAS) from seven hemostatic traits (factor VII [FVII], factor VIII [FVIII], von Willebrand factor [VWF] factor XI [FXI], fibrinogen, tissue plasminogen activator [tPA], plasminogen activator inhibitor 1 [PAI-1]) and three major cardiovascular (CV) events (venous thromboembolism [VTE], coronary artery disease [CAD], ischemic stroke [IS]), were combined in 27 multi-trait combinations using metaUSAT. Genetic correlations between phenotypes were calculated using Linkage Disequilibrium Score Regression (LDSC). Newly associated loci were investigated for colocalization. We considered a significance threshold of 1.85 × 10−9 obtained after applying Bonferroni correction for the number of multi-trait combinations performed (n = 27). Results: Across the 27 multi-trait analyses, we found 4 novel pleiotropic loci (XXYLT1, KNG1, SUGP1/MAU2, TBL2/MLXIPL) that were not significant in the original individual datasets, were not described in previous GWAS for the individual traits, and that presented a common associated variant between the studied phenotypes. Conclusions: The discovery of four novel loci contributes to the understanding of the relationship between hemostasis and CV events and elucidate common genetic factors between these traits.
Temprano-Sagrera, G., Sitlani, C., Bone, W., Martin-Bornez, M., Voight, B., Morrison, A., et al. (2022). Multi-phenotype analyses of hemostatic traits with cardiovascular events reveal novel genetic associations. JOURNAL OF THROMBOSIS AND HAEMOSTASIS, 20(6), 1331-1349 [10.1111/jth.15698].
Multi-phenotype analyses of hemostatic traits with cardiovascular events reveal novel genetic associations
Temprano-Sagrera G.;Sitlani C. M.;Bone W. P.;Martin-Bornez M.;Voight B. F.;Morrison A. C.;Damrauer S. M.;de Vries P. S.;Smith N. L.;Sabater-Lleal M.;Dehghan A.;Heath A. S.;Morrison A. C.;Reiner A. P.;Johnson A.;Richmond A.;Peters A.;van Hylckama Vlieg A.;McKnight B.;Psaty B. M.;Hayward C.;Ward-Caviness C.;O'Donnell C.;Chasman D.;Strachan D. P.;Tregouet D. A.;Mook-Kanamori D.;Gill D.;Thibord F.;Asselbergs F. W.;Leebeek F. W. G.;Rosendaal F. R.;Davies G.;Homuth G.;Temprano G.;Campbell H.;Taylor H. A.;Bressler J.;Huffman J. E.;Rotter J. I.;Yao J.;Wilson J. F.;Bis J. C.;Hahn J. M.;Desch K. C.;Wiggins K. L.;Raffield L. M.;Bielak L. F.;Yanek L. R.;Kleber M. E.;Mueller M.;Kavousi M.;Mangino M.;Liu M.;Brown M. R.;Conomos M. P.;Jhun M. -A.;Chen M. -H.;de Maat M. P. M.;Pankratz N.;Smith N. L.;Peyser P. A.;Elliot P.;Wei P.;Wild P. S.;Morange P. E.;van der Harst P.;Yang Q.;Le N. -Q.;Marioni R.;Li R.;Damrauer S. M.;Cox S. R.;Trompet S.;Felix S. B.;Volker U.;Tang W.;Koenig W.;Jukema J. W.;Guo X.;Lindstrom S.;Wang L.;Smith E. N.;Gordon W.;van Hylckama Vlieg A.;de Andrade M.;Brody J. A.;Pattee J. W.;Haessler J.;Brumpton B. M.;Suchon P.;Chen M. -H.;Turman C.;Germain M.;Wiggins K. L.;MacDonald J.;Braekkan S. K.;Armasu S. M.;Pankratz N.;Jackson R. D.;Nielsen J. B.;Giulianini F.;Puurunen M. K.;Ibrahim M.;Heckbert S. R.;Bammler T. K.;Frazer K. A.;McCauley B. M.;Taylor K.;Pankow J. S.;Reiner A. P.;Gabrielsen M. E.;Deleuze J. -F.;O'Donnell C. J.;Kim J.;McKnight B.;Kraft P.;Hansen J. -B.;Rosendaal F. R.;Heit J. A.;Psaty B. M.;Tang W.;Kooperberg C.;Hveem K.;Ridker P. M.;Morange P. -E.;Johnson A. D.;Kabrhel C.;Tregouet D. -A.;Smith N. L.;Malik R.;Chauhan G.;Traylor M.;Sargurupremraj M.;Okada Y.;Mishra A.;Rutten-Jacobs L.;Giese A. -K.;van der Laan S. W.;Gretarsdottir S.;Anderson C. D.;Chong M.;Adams H. H. H.;Ago T.;Almgren P.;Amouyel P.;Ay H.;Bartz T. M.;Benavente O. R.;Bevan S.;Boncoraglio G. B.;Brown R. D.;Butterworth A. S.;Carrera C.;Carty C. L.;Chen W. -M.;Cole J. W.;Correa A.;Cotlarciuc I.;Cruchaga C.;Danesh J.;de Bakker P. I. W.;DeStefano A. L.;den Hoed M.;Duan Q.;Engelter S. T.;Falcone G. J.;Gottesman R. F.;Grewal R. P.;Gudnason V.;Gustafsson S.;Haessler J.;Harris T. B.;Hassan A.;Havulinna A. S.;Heckbert S. R.;Holliday E. G.;Howard G.;Hsu F. -C.;Hyacinth H. I.;Arfan Ikram M.;Ingelsson E.;Irvin M. R.;Jian X.;Jimenez-Conde J.;Johnson J. A.;Jukema J. W.;Kanai M.;Keene K. L.;Kissela B. M.;Kleindorfer D. O.;Kooperberg C.;Kubo M.;Lange L. A.;Langefeld C. D.;Langenberg C.;Launer L. J.;Lee J. -M.;Lemmens R.;Leys D.;Lewis C. M.;Lin W. -Y.;Lindgren A. G.;Lorentzen E.;Magnusson P. K.;Maguire J.;Manichaikul A.;McArdle P. F.;Meschia J. F.;Mitchell B. D.;Mosley T. H.;Nalls M. A.;Ninomiya T.;O'Donnell M. J.;Psaty B. M.;Pulit S. L.;Rannikmae K.;Reiner A. P.;Rexrode K. M.;Rice K.;Rich S. S.;Ridker P. M.;Rost N. S.;Rothwell P. M.;Rotter J. I.;Rundek T.;Sacco R. L.;Sakaue S.;Sale M. M.;Salomaa V.;Sapkota B. R.;Schmidt R.;Schmidt C. O.;Schminke U.;Sharma P.;Slowik A.;Sudlow C. L. M.;Tanislav C.;Tatlisumak T.;Taylor K. D.;Thijs V. N. S.;Thorleifsson G.;Thorsteinsdottir U.;Tiedt S.;Trompet S.;Tzourio C.;van Duijn C. M.;Walters M.;Wareham N. J.;Wassertheil-Smoller S.;Wilson J. G.;Wiggins K. L.;Yang Q.;Yusuf S.;Amin N.;Aparicio H. S.;Arnett D. K.;Attia J.;Beiser A. S.;Berr C.;Buring J. E.;Bustamante M.;Caso V.;Cheng Y. -C.;Hoan Choi S.;Chowhan A.;Cullell N.;Dartigues J. -F.;Delavaran H.;Delgado P.;Dorr M.;Engstrom G.;Ford I.;Gurpreet W. S.;Hamsten A.;Heitsch L.;Hozawa A.;Ibanez L.;Ilinca A.;Ingelsson M.;Iwasaki M.;Jackson R. D.;Jood K.;Jousilahti P.;Kaffashian S.;Kalra L.;Kamouchi M.;Kitazono T.;Kjartansson O.;Kloss M.;Koudstaal P. J.;Krupinski J.;Labovitz D. L.;Laurie C. C.;Levi C. R.;Li L.;Lind L.;Lindgren C. M.;Lioutas V.;Mei Liu Y.;Lopez O. L.;Makoto H.;Martinez-Majander N.;Matsuda K.;Minegishi N.;Montaner J.;Morris A. P.;Muino E.;Muller-Nurasyid M.;Norrving B.;Ogishima S.;Parati E. A.;Reddy Peddareddygari L.;Pedersen N. L.;Pera J.;Perola M.;Pezzini A.;Pileggi S.;Rabionet R.;Riba-Llena I.;Ribases M.;Romero J. R.;Roquer J.;Rudd A. G.;Sarin A. -P.;Sarju R.;Sarnowski C.;Sasaki M.;Satizabal C. L.;Satoh M.;Sattar N.;Sawada N.;Sibolt G.;Sigurdsson A.;Smith A.;Sobue K.;Soriano-Tarraga C.;Stanne T.;Colin Stine O.;Stott D. J.;Strauch K.;Takai T.;Tanaka H.;Tanno K.;Teumer A.;Tomppo L.;Torres-Aguila N. P.;Touze E.;Tsugane S.;Uitterlinden A. G.;Valdimarsson E. M.;van der Lee S. J.;Volzke H.;Wakai K.;Weir D.;Williams S. R.;Wolfe C. D. A.;Wong Q.;Xu H.;Yamaji T.;Sanghera D. K.;Melander O.;Jern C.;Strbian D.;Fernandez-Cadenas I.;Longstreth W. T.;Rolfs A.;Hata J.;Woo D.;Rosand J.;Pare G.;Hopewell J. C.;Saleheen D.;Stefansson K.;Worrall B. B.;Kittner S. J.;Seshadri S.;Fornage M.;Markus H. S.;Howson J. M. M.;Kamatani Y.;Debette S.;Dichgans M.
2022
Abstract
Background: Multi-phenotype analysis of genetically correlated phenotypes can increase the statistical power to detect loci associated with multiple traits, leading to the discovery of novel loci. This is the first study to date to comprehensively analyze the shared genetic effects within different hemostatic traits, and between these and their associated disease outcomes. Objectives: To discover novel genetic associations by combining summary data of correlated hemostatic traits and disease events. Methods: Summary statistics from genome wide-association studies (GWAS) from seven hemostatic traits (factor VII [FVII], factor VIII [FVIII], von Willebrand factor [VWF] factor XI [FXI], fibrinogen, tissue plasminogen activator [tPA], plasminogen activator inhibitor 1 [PAI-1]) and three major cardiovascular (CV) events (venous thromboembolism [VTE], coronary artery disease [CAD], ischemic stroke [IS]), were combined in 27 multi-trait combinations using metaUSAT. Genetic correlations between phenotypes were calculated using Linkage Disequilibrium Score Regression (LDSC). Newly associated loci were investigated for colocalization. We considered a significance threshold of 1.85 × 10−9 obtained after applying Bonferroni correction for the number of multi-trait combinations performed (n = 27). Results: Across the 27 multi-trait analyses, we found 4 novel pleiotropic loci (XXYLT1, KNG1, SUGP1/MAU2, TBL2/MLXIPL) that were not significant in the original individual datasets, were not described in previous GWAS for the individual traits, and that presented a common associated variant between the studied phenotypes. Conclusions: The discovery of four novel loci contributes to the understanding of the relationship between hemostasis and CV events and elucidate common genetic factors between these traits.
Temprano-Sagrera, G., Sitlani, C., Bone, W., Martin-Bornez, M., Voight, B., Morrison, A., et al. (2022). Multi-phenotype analyses of hemostatic traits with cardiovascular events reveal novel genetic associations. JOURNAL OF THROMBOSIS AND HAEMOSTASIS, 20(6), 1331-1349 [10.1111/jth.15698].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/531508
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simulazione ASN
Il report seguente simula gli indicatori relativi alla propria produzione scientifica in relazione alle soglie ASN 2023-2025 del proprio SC/SSD. Si ricorda che il superamento dei valori soglia (almeno 2 su 3) è requisito necessario ma non sufficiente al conseguimento dell'abilitazione. La simulazione si basa sui dati IRIS e sugli indicatori bibliometrici alla data indicata e non tiene conto di eventuali periodi di congedo obbligatorio, che in sede di domanda ASN danno diritto a incrementi percentuali dei valori. La simulazione può differire dall'esito di un’eventuale domanda ASN sia per errori di catalogazione e/o dati mancanti in IRIS, sia per la variabilità dei dati bibliometrici nel tempo. Si consideri che Anvur calcola i valori degli indicatori all'ultima data utile per la presentazione delle domande.
La presente simulazione è stata realizzata sulla base delle specifiche raccolte sul tavolo ER del Focus Group IRIS coordinato dall’Università di Modena e Reggio Emilia e delle regole riportate nel DM 598/2018 e allegata Tabella A. Cineca, l’Università di Modena e Reggio Emilia e il Focus Group IRIS non si assumono alcuna responsabilità in merito all’uso che il diretto interessato o terzi faranno della simulazione. Si specifica inoltre che la simulazione contiene calcoli effettuati con dati e algoritmi di pubblico dominio e deve quindi essere considerata come un mero ausilio al calcolo svolgibile manualmente o con strumenti equivalenti.
