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ArTS Archivio della ricerca di Trieste
Increased blood lipid levels are heritable risk factors of cardiovascular disease with varied prevalence worldwide owing to different dietary patterns and medication use1. Despite advances in prevention and treatment, in particular through reducing low-density lipoprotein cholesterol levels2, heart disease remains the leading cause of death worldwide3. Genome-wideassociation studies (GWAS) of blood lipid levels have led to important biological and clinical insights, as well as new drug targets, for cardiovascular disease. However, most previous GWAS4–23 have been conducted in European ancestry populations and may have missed genetic variants that contribute to lipid-level variation in other ancestry groups. These include differences in allele frequencies, effect sizes and linkage-disequilibrium patterns24. Here we conduct a multi-ancestry, genome-wide genetic discovery meta-analysis of lipid levels in approximately 1.65 million individuals, including 350,000 of non-European ancestries. We quantify the gain in studying non-European ancestries and provide evidence to support the expansion of recruitment of additional ancestries, even with relatively small sample sizes. We find that increasing diversity rather than studying additional individuals of European ancestry results in substantial improvements in fine-mapping functional variants and portability of polygenic prediction (evaluated in approximately 295,000 individuals from 7 ancestry groupings). Modest gains in the number of discovered loci and ancestry-specific variants were also achieved. As GWAS expand emphasis beyond the identification of genes and fundamental biology towards the use of genetic variants for preventive and precision medicine25, we anticipate that increased diversity of participants will lead to more accurate and equitable26 application of polygenic scores in clinical practice.
The power of genetic diversity in genome-wide association studies of lipids / Graham, S.E., Clarke, S.L., Wu, K.-H.H., Kanoni, S., Zajac, G.J.M., Ramdas, S., Surakka, I., Ntalla, I., Vedantam, S., Winkler, T.W., Locke, A.E., Marouli, E., Hwang, M.Y., Han, S., Narita, A., Choudhury, A., Bentley, A.R., Ekoru, K., Verma, A., Trivedi, B., et al.. - In: NATURE. - ISSN 0028-0836. - 600:7890(2021), pp. 675-679. [10.1038/s41586-021-04064-3]
The power of genetic diversity in genome-wide association studies of lipids
Graham S. E.;Clarke S. L.;Wu K. -H. H.;Kanoni S.;Zajac G. J. M.;Ramdas S.;Surakka I.;Ntalla I.;Vedantam S.;Winkler T. W.;Locke A. E.;Marouli E.;Hwang M. Y.;Han S.;Narita A.;Choudhury A.;Bentley A. R.;Ekoru K.;Verma A.;Trivedi B.;Martin H. C.;Hunt K. A.;Hui Q.;Klarin D.;Zhu X.;Thorleifsson G.;Helgadottir A.;Gudbjartsson D. F.;Holm H.;Olafsson I.;Akiyama M.;Sakaue S.;Terao C.;Kanai M.;Zhou W.;Brumpton B. M.;Rasheed H.;Ruotsalainen S. E.;Havulinna A. S.;Veturi Y.;Feng Q. P.;Rosenthal E. A.;Lingren T.;Pacheco J. A.;Pendergrass S. A.;Haessler J.;Giulianini F.;Bradford Y.;Miller J. E.;Campbell A.;Lin K.;Millwood I. Y.;Hindy G.;Rasheed A.;Faul J. D.;Zhao W.;Weir D. R.;Turman C.;Huang H.;Graff M.;Mahajan A.;Brown M. R.;Zhang W.;Yu K.;Schmidt E. M.;Pandit A.;Gustafsson S.;Yin X.;Luan J.;Zhao J. -H.;Matsuda F.;Jang H. -M.;Yoon K.;Medina-Gomez C.;Pitsillides A.;Hottenga J. J.;Willemsen G.;Wood A. R.;Ji Y.;Gao Z.;Haworth S.;Mitchell R. E.;Chai J. F.;Aadahl M.;Yao J.;Manichaikul A.;Warren H. R.;Ramirez J.;Bork-Jensen J.;Karhus L. L.;Goel A.;Sabater-Lleal M.;Noordam R.;Sidore C.;Fiorillo E.;McDaid A. F.;Marques-Vidal P.;Wielscher M.;Trompet S.;Sattar N.;Mollehave L. T.;Thuesen B. H.;Munz M.;Zeng L.;Huang J.;Yang B.;Poveda A.;Kurbasic A.;Lamina C.;Forer L.;Scholz M.;Galesloot T. E.;Bradfield J. P.;Daw E. W.;Zmuda J. M.;Mitchell J. S.;Fuchsberger C.;Christensen H.;Brody J. A.;Feitosa M. F.;Wojczynski M. K.;Preuss M.;Mangino M.;Christofidou P.;Verweij N.;Benjamins J. W.;Engmann J.;Kember R. L.;Slieker R. C.;Lo K. S.;Zilhao N. R.;Le P.;Kleber M. E.;Delgado G. E.;Huo S.;Ikeda D. D.;Iha H.;Yang J.;Liu J.;Leonard H. L.;Marten J.;Schmidt B.;Arendt M.;Smyth L. J.;Canadas-Garre M.;Wang C.;Nakatochi M.;Wong A.;Hutri-Kahonen N.;Sim X.;Xia R.;Huerta-Chagoya A.;Fernandez-Lopez J. C.;Lyssenko V.;Ahmed M.;Jackson A. U.;Irvin M. R.;Oldmeadow C.;Kim H. -N.;Ryu S.;Timmers P. R. H. J.;Arbeeva L.;Dorajoo R.;Lange L. A.;Chai X.;Prasad G.;Lores-Motta L.;Pauper M.;Long J.;Li X.;Theusch E.;Takeuchi F.;Spracklen C. N.;Loukola A.;Bollepalli S.;Warner S. C.;Wang Y. X.;Wei W. B.;Nutile T.;Ruggiero D.;Sung Y. J.;Hung Y. -J.;Chen S.;Liu F.;Yang J.;Kentistou K. A.;Gorski M.;Brumat M.;Meidtner K.;Bielak L. F.;Smith J. A.;Hebbar P.;Farmaki A. -E.;Hofer E.;Lin M.;Xue C.;Zhang J.;Concas M. P.;Vaccargiu S.;van der Most P. J.;Pitkanen N.;Cade B. E.;Lee J.;van der Laan S. W.;Chitrala K. N.;Weiss S.;Zimmermann M. E.;Lee J. Y.;Choi H. S.;Nethander M.;Freitag-Wolf S.;Southam L.;Rayner N. W.;Wang C. A.;Lin S. -Y.;Wang J. -S.;Couture C.;Lyytikainen L. -P.;Nikus K.;Cuellar-Partida G.;Vestergaard H.;Hildalgo B.;Giannakopoulou O.;Cai Q.;Obura M. O.;van Setten J.;Li X.;Schwander K.;Terzikhan N.;Shin J. H.;Jackson R. D.;Reiner A. P.;Martin L. W.;Chen Z.;Li L.;Highland H. M.;Young K. L.;Kawaguchi T.;Thiery J.;Bis J. C.;Nadkarni G. N.;Launer L. J.;Li H.;Nalls M. A.;Raitakari O. T.;Ichihara S.;Wild S. H.;Nelson C. P.;Campbell H.;Jager S.;Nabika T.;Al-Mulla F.;Niinikoski H.;Braund P. S.;Kolcic I.;Kovacs P.;Giardoglou T.;Katsuya T.;Bhatti K. F.;de Kleijn D.;de Borst G. J.;Kim E. K.;Adams H. H. H.;Ikram M. A.;Zhu X.;Asselbergs F. W.;Kraaijeveld A. O.;Beulens J. W. J.;Shu X. -O.;Rallidis L. S.;Pedersen O.;Hansen T.;Mitchell P.;Hewitt A. W.;Kahonen M.;Perusse L.;Bouchard C.;Tonjes A.;Chen Y. -D. I.;Pennell C. E.;Mori T. A.;Lieb W.;Franke A.;Ohlsson C.;Mellstrom D.;Cho Y. S.;Lee H.;Yuan J. -M.;Koh W. -P.;Rhee S. Y.;Woo J. -T.;Heid I. M.;Stark K. J.;Volzke H.;Homuth G.;Evans M. K.;Zonderman A. B.;Polasek O.;Pasterkamp G.;Hoefer I. E.;Redline S.;Pahkala K.;Oldehinkel A. J.;Snieder H.;Biino G.;Schmidt R.;Schmidt H.;Chen Y. E.;Bandinelli S.;Dedoussis G.;Thanaraj T. A.;Kardia S. L. R.;Kato N.;Schulze M. B.;Girotto G.;Jung B.;Boger C. A.;Joshi P. K.;Bennett D. A.;De Jager P. L.;Lu X.;Mamakou V.;Brown M.;Caulfield M. J.;Munroe P. B.;Guo X.;Ciullo M.;Jonas J. B.;Samani N. J.;Kaprio J.;Pajukanta P.;Adair L. S.;Bechayda S. A.;de Silva H. J.;Wickremasinghe A. R.;Krauss R. M.;Wu J. -Y.;Zheng W.;den Hollander A. I.;Bharadwaj D.;Correa A.;Wilson J. G.;Lind L.;Heng C. -K.;Nelson A. E.;Golightly Y. M.;Wilson J. F.;Penninx B.;Kim H. -L.;Attia J.;Scott R. J.;Rao D. C.;Arnett D. K.;Walker M.;Koistinen H. A.;Chandak G. R.;Yajnik C. S.;Mercader J. M.;Tusie-Luna T.;Aguilar-Salinas C. A.;Villalpando C. G.;Orozco L.;Fornage M.;Tai E. S.;van Dam R. M.;Lehtimaki T.;Chaturvedi N.;Yokota M.;Liu J.;Reilly D. F.;McKnight A. J.;Kee F.;Jockel K. -H.;McCarthy M. I.;Palmer C. N. A.;Vitart V.;Hayward C.;Simonsick E.;van Duijn C. M.;Lu F.;Qu J.;Hishigaki H.;Lin X.;Marz W.;Parra E. J.;Cruz M.;Gudnason V.;Tardif J. -C.;Lettre G.;'t Hart L. M.;Elders P. J. M.;Damrauer S. M.;Kumari M.;Kivimaki M.;van der Harst P.;Spector T. D.;Loos R. J. F.;Province M. A.;Psaty B. M.;Brandslund I.;Pramstaller P. P.;Christensen K.;Ripatti S.;Widen E.;Hakonarson H.;Grant S. F. A.;Kiemeney L. A. L. M.;de Graaf J.;Loeffler M.;Kronenberg F.;Gu D.;Erdmann J.;Schunkert H.;Franks P. W.;Linneberg A.;Jukema J. W.;Khera A. V.;Mannikko M.;Jarvelin M. -R.;Kutalik Z.;Cucca F.;Mook-Kanamori D. O.;van Dijk K. W.;Watkins H.;Strachan D. P.;Grarup N.;Sever P.;Poulter N.;Rotter J. I.;Dantoft T. M.;Karpe F.;Neville M. J.;Timpson N. J.;Cheng C. -Y.;Wong T. -Y.;Khor C. C.;Sabanayagam C.;Peters A.;Gieger C.;Hattersley A. T.;Pedersen N. L.;Magnusson P. K. E.;Boomsma D. I.;de Geus E. J. C.;Cupples L. A.;van Meurs J. B. J.;Ghanbari M.;Gordon-Larsen P.;Huang W.;Kim Y. J.;Tabara Y.;Wareham N. J.;Langenberg C.;Zeggini E.;Kuusisto J.;Laakso M.;Ingelsson E.;Abecasis G.;Chambers J. C.;Kooner J. S.;de Vries P. S.;Morrison A. C.;North K. E.;Daviglus M.;Kraft P.;Martin N. G.;Whitfield J. B.;Abbas S.;Saleheen D.;Walters R. G.;Holmes M. V.;Black C.;Smith B. H.;Justice A. E.;Baras A.;Buring J. E.;Ridker P. M.;Chasman D. I.;Kooperberg C.;Wei W. -Q.;Jarvik G. P.;Namjou B.;Hayes M. G.;Ritchie M. D.;Jousilahti P.;Salomaa V.;Hveem K.;Asvold B. O.;Kubo M.;Kamatani Y.;Okada Y.;Murakami Y.;Thorsteinsdottir U.;Stefansson K.;Ho Y. -L.;Lynch J. A.;Rader D. J.;Tsao P. S.;Chang K. -M.;Cho K.;O'Donnell C. J.;Gaziano J. M.;Wilson P.;Rotimi C. N.;Hazelhurst S.;Ramsay M.;Trembath R. C.;van Heel D. A.;Tamiya G.;Yamamoto M.;Kim B. -J.;Mohlke K. L.;Frayling T. M.;Hirschhorn J. N.;Kathiresan S.;Boehnke M.;Natarajan P.;Peloso G. M.;Brown C. D.;Morris A. P.;Assimes T. L.;Deloukas P.;Sun Y. V.;Willer C. J.
2021-01-01
Abstract
Increased blood lipid levels are heritable risk factors of cardiovascular disease with varied prevalence worldwide owing to different dietary patterns and medication use1. Despite advances in prevention and treatment, in particular through reducing low-density lipoprotein cholesterol levels2, heart disease remains the leading cause of death worldwide3. Genome-wideassociation studies (GWAS) of blood lipid levels have led to important biological and clinical insights, as well as new drug targets, for cardiovascular disease. However, most previous GWAS4–23 have been conducted in European ancestry populations and may have missed genetic variants that contribute to lipid-level variation in other ancestry groups. These include differences in allele frequencies, effect sizes and linkage-disequilibrium patterns24. Here we conduct a multi-ancestry, genome-wide genetic discovery meta-analysis of lipid levels in approximately 1.65 million individuals, including 350,000 of non-European ancestries. We quantify the gain in studying non-European ancestries and provide evidence to support the expansion of recruitment of additional ancestries, even with relatively small sample sizes. We find that increasing diversity rather than studying additional individuals of European ancestry results in substantial improvements in fine-mapping functional variants and portability of polygenic prediction (evaluated in approximately 295,000 individuals from 7 ancestry groupings). Modest gains in the number of discovered loci and ancestry-specific variants were also achieved. As GWAS expand emphasis beyond the identification of genes and fundamental biology towards the use of genetic variants for preventive and precision medicine25, we anticipate that increased diversity of participants will lead to more accurate and equitable26 application of polygenic scores in clinical practice.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11368/3010901
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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 589/2018 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.
