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144 results on '"Viñuela, Ana"'

1. Role of human plasma metabolites in prediabetes and type 2 diabetes from the IMI-DIRECT study

Author

Sharma, Sapna, Dong, Qiuling, Haid, Mark, Adam, Jonathan, Bizzotto, Roberto, Fernandez-Tajes, Juan J., Jones, Angus G., Tura, Andrea, Artati, Anna, Prehn, Cornelia, Kastenmüller, Gabi, Koivula, Robert W., Franks, Paul W., Walker, Mark, Forgie, Ian M., Giordano, Giuseppe, Pavo, Imre, Ruetten, Hartmut, Dermitzakis, Manolis, McCarthy, Mark I., Pedersen, Oluf, Schwenk, Jochen M., Tsirigos, Konstantinos D., De Masi, Federico, Brunak, Soren, Viñuela, Ana, Mari, Andrea, McDonald, Timothy J., Kokkola, Tarja, Adamski, Jerzy, Pearson, Ewan R., and Grallert, Harald

Published
2024
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2. Genetic analysis of blood molecular phenotypes reveals common properties in the regulatory networks affecting complex traits

Author

Brown, Andrew A., Fernandez-Tajes, Juan J., Hong, Mun-gwan, Brorsson, Caroline A., Koivula, Robert W., Davtian, David, Dupuis, Théo, Sartori, Ambra, Michalettou, Theodora-Dafni, Forgie, Ian M., Adam, Jonathan, Allin, Kristine H., Caiazzo, Robert, Cederberg, Henna, De Masi, Federico, Elders, Petra J. M., Giordano, Giuseppe N., Haid, Mark, Hansen, Torben, Hansen, Tue H., Hattersley, Andrew T., Heggie, Alison J., Howald, Cédric, Jones, Angus G., Kokkola, Tarja, Laakso, Markku, Mahajan, Anubha, Mari, Andrea, McDonald, Timothy J., McEvoy, Donna, Mourby, Miranda, Musholt, Petra B., Nilsson, Birgitte, Pattou, Francois, Penet, Deborah, Raverdy, Violeta, Ridderstråle, Martin, Romano, Luciana, Rutters, Femke, Sharma, Sapna, Teare, Harriet, ‘t Hart, Leen, Tsirigos, Konstantinos D., Vangipurapu, Jagadish, Vestergaard, Henrik, Brunak, Søren, Franks, Paul W., Frost, Gary, Grallert, Harald, Jablonka, Bernd, McCarthy, Mark I., Pavo, Imre, Pedersen, Oluf, Ruetten, Hartmut, Walker, Mark, Adamski, Jerzy, Schwenk, Jochen M., Pearson, Ewan R., Dermitzakis, Emmanouil T., and Viñuela, Ana

Published
2023
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3. Proteomic analysis of 92 circulating proteins and their effects in cardiometabolic diseases

Author

Carland, Corinne, Png, Grace, Malarstig, Anders, Kho, Pik Fang, Gustafsson, Stefan, Michaelsson, Karl, Lind, Lars, Tsafantakis, Emmanouil, Karaleftheri, Maria, Dedoussis, George, Ramisch, Anna, Macdonald-Dunlop, Erin, Klaric, Lucija, Joshi, Peter K., Chen, Yan, Björck, Hanna M., Eriksson, Per, Carrasco-Zanini, Julia, Wheeler, Eleanor, Suhre, Karsten, Gilly, Arthur, Zeggini, Eleftheria, Viñuela, Ana, Dermitzakis, Emmanouil T., Wilson, James F., Langenberg, Claudia, Thareja, Gaurav, Halama, Anna, Schmidt, Frank, Zanetti, Daniela, and Assimes, Themistocles

Published
2023
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4. The Association of Cardiometabolic, Diet and Lifestyle Parameters With Plasma Glucagon-like Peptide-1: An IMI DIRECT Study.

Author

Eriksen, Rebeca, White, Margaret C, Dawed, Adem Y, Perez, Isabel Garcia, Posma, Joram M, Haid, Mark, Sharma, Sapna, Prehn, Cornelia, Thomas, E Louise, Koivula, Robert W, Bizzotto, Roberto, Mari, Andrea, Giordano, Giuseppe N, Pavo, Imre, Schwenk, Jochen M, Masi, Federico De, Tsirigos, Konstantinos D, Brunak, Søren, Viñuela, Ana, and Mahajan, Anubha

Subjects
TYPE 2 diabetes, PEOPLE with diabetes, MULTIPLE regression analysis, INSULIN resistance, FOOD consumption
Abstract

Context The role of glucagon-like peptide-1 (GLP-1) in type 2 diabetes (T2D) and obesity is not fully understood. Objective We investigate the association of cardiometabolic, diet, and lifestyle parameters on fasting and postprandial GLP-1 in people at risk of, or living with, T2D. Methods We analyzed cross-sectional data from the two Innovative Medicines Initiative (IMI) Diabetes Research on Patient Stratification (DIRECT) cohorts, cohort 1 (n = 2127) individuals at risk of diabetes; cohort 2 (n = 789) individuals with new-onset T2D. Results Our multiple regression analysis reveals that fasting total GLP-1 is associated with an insulin-resistant phenotype and observe a strong independent relationship with male sex, increased adiposity, and liver fat, particularly in the prediabetes population. In contrast, we showed that incremental GLP-1 decreases with worsening glycemia, higher adiposity, liver fat, male sex, and reduced insulin sensitivity in the prediabetes cohort. Higher fasting total GLP-1 was associated with a low intake of wholegrain, fruit, and vegetables in people with prediabetes, and with a high intake of red meat and alcohol in people with diabetes. Conclusion These studies provide novel insights into the association between fasting and incremental GLP-1, metabolic traits of diabetes and obesity, and dietary intake, and raise intriguing questions regarding the relevance of fasting GLP-1 in the pathophysiology T2D. [ABSTRACT FROM AUTHOR]

Published
2024
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5. Four groups of type 2 diabetes contribute to the etiological and clinical heterogeneity in newly diagnosed individuals: An IMI DIRECT study

Author

Wesolowska-Andersen, Agata, Brorsson, Caroline A., Bizzotto, Roberto, Mari, Andrea, Tura, Andrea, Koivula, Robert, Mahajan, Anubha, Vinuela, Ana, Tajes, Juan Fernandez, Sharma, Sapna, Haid, Mark, Prehn, Cornelia, Artati, Anna, Hong, Mun-Gwan, Musholt, Petra B., Kurbasic, Azra, De Masi, Federico, Tsirigos, Kostas, Pedersen, Helle Krogh, Gudmundsdottir, Valborg, Thomas, Cecilia Engel, Banasik, Karina, Jennison, Chrisopher, Jones, Angus, Kennedy, Gwen, Bell, Jimmy, Thomas, Louise, Frost, Gary, Thomsen, Henrik, Allin, Kristine, Hansen, Tue Haldor, Vestergaard, Henrik, Hansen, Torben, Rutters, Femke, Elders, Petra, t’Hart, Leen, Bonnefond, Amelie, Canouil, Mickaël, Brage, Soren, Kokkola, Tarja, Heggie, Alison, McEvoy, Donna, Hattersley, Andrew, McDonald, Timothy, Teare, Harriet, Ridderstrale, Martin, Walker, Mark, Forgie, Ian, Giordano, Giuseppe N., Froguel, Philippe, Pavo, Imre, Ruetten, Hartmut, Pedersen, Oluf, Dermitzakis, Emmanouil, Franks, Paul W., Schwenk, Jochen M., Adamski, Jerzy, Pearson, Ewan, McCarthy, Mark I., and Brunak, Søren

Published
2022
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9. Genetic studies of abdominal MRI data identify genes regulating hepcidin as major determinants of liver iron concentration

Author

Jennison, Christopher, Ehrhardt, Beate, Baum, Patrick, Schoelsch, Corinna, Freijer, Jan, Grempler, Rolf, Graefe-Mody, Ulrike, Hennige, Anita, Dings, Christiane, Lehr, Thorsten, Scherer, Nina, Sihinecich, Iryna, Pattou, Francois, Raverdi, Violeta, Caiazzo, Robert, Torres, Fanelly, Verkindt, Helene, Mari, Andrea, Tura, Andrea, Giorgino, Toni, Bizzotto, Roberto, Froguel, Philippe, Bonneford, Amelie, Canouil, Mickael, Dhennin, Veronique, Brorsson, Caroline, Brunak, Soren, De Masi, Federico, Gudmundsdóttir, Valborg, Pedersen, Helle, Banasik, Karina, Thomas, Cecilia, Sackett, Peter, Staerfeldt, Hans-Henrik, Lundgaard, Agnete, Nilsson, Birgitte, Nielsen, Agnes, Mazzoni, Gianluca, Karaderi, Tugce, Rasmussen, Simon, Johansen, Joachim, Allesøe, Rosa, Fritsche, Andreas, Thorand, Barbara, Adamski, Jurek, Grallert, Harald, Haid, Mark, Sharma, Sapna, Troll, Martina, Adam, Jonathan, Ferrer, Jorge, Eriksen, Heather, Frost, Gary, Haussler, Ragna, Hong, Mun-gwan, Schwenk, Jochen, Uhlen, Mathias, Nicolay, Claudia, Pavo, Imre, Steckel-Hamann, Birgit, Thomas, Melissa, Adragni, Kofi, Wu, Han, Hart, Leen't, Roderick, Slieker, van Leeuwen, Nienke, Dekkers, Koen, Frau, Francesca, Gassenhuber, Johann, Jablonka, Bernd, Musholt, Petra, Ruetten, Hartmut, Tillner, Joachim, Baltauss, Tania, Bernard Poenaru, Oana, de Preville, Nathalie, Rodriquez, Marianne, Arumugam, Manimozhiyan, Allin, Kristine, Engelbrechtsen, Line, Hansen, Torben, Hansen, Tue, Forman, Annemette, Jonsson, Anna, Pedersen, Oluf, Dutta, Avirup, Vogt, Josef, Vestergaard, Henrik, Laakso, Markku, Kokkola, Tarja, Kuulasmaa, Teemu, Franks, Paul, Giordano, Nick, Pomares-Millan, Hugo, Fitipaldi, Hugo, Mutie, Pascal, Klintenberg, Maria, Bergstrom, Margit, Groop, Leif, Ridderstrale, Martin, Atabaki Pasdar, Naeimeh, Deshmukh, Harshal, Heggie, Alison, Wake, Dianne, McEvoy, Donna, McVittie, Ian, Walker, Mark, Hattersley, Andrew, Hill, Anita, Jones, Angus, McDonald, Timothy, Perry, Mandy, Nice, Rachel, Hudson, Michelle, Thorne, Claire, Dermitzakis, Emmanouil, Viñuela, Ana, Cabrelli, Louise, Loftus, Heather, Dawed, Adem, Donnelly, Louise, Forgie, Ian, Pearson, Ewan, Palmer, Colin, Brown, Andrew, Koivula, Robert, Wesolowska-Andersen, Agata, Abdalla, Moustafa, McRobert, Nicky, Fernandez, Juan, Jiao, Yunlong, Robertson, Neil, Gough, Stephen, Kaye, Jane, Mourby, Miranda, Mahajan, Anubha, McCarthy, Mark, Shah, Nisha, Teare, Harriet, Holl, Reinhard, Koopman, Anitra, Rutters, Femke, Beulens, Joline, Groeneveld, Lenka, Bell, Jimmy, Thomas, Louise, Whitcher, Brandon, Wilman, Henry R., Parisinos, Constantinos A., Atabaki-Pasdar, Naeimeh, Kelly, Matt, Thomas, E. Louise, Neubauer, Stefan, Hingorani, Aroon D., Patel, Riyaz S., Hemingway, Harry, Franks, Paul W., Bell, Jimmy D., Banerjee, Rajarshi, and Yaghootkar, Hanieh

Published
2019
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10. Discovery of biomarkers for glycaemic deterioration before and after the onset of type 2 diabetes: descriptive characteristics of the epidemiological studies within the IMI DIRECT Consortium

Author

Koivula, Robert W., Forgie, Ian M., Kurbasic, Azra, Viñuela, Ana, Heggie, Alison, Giordano, Giuseppe N., Hansen, Tue H., Hudson, Michelle, Koopman, Anitra D. M., Rutters, Femke, Siloaho, Maritta, Allin, Kristine H., Brage, Søren, Brorsson, Caroline A., Dawed, Adem Y., De Masi, Federico, Groves, Christopher J., Kokkola, Tarja, Mahajan, Anubha, Perry, Mandy H., Rauh, Simone P., Ridderstråle, Martin, Teare, Harriet J. A., Thomas, E. Louise, Tura, Andrea, Vestergaard, Henrik, White, Tom, Adamski, Jerzy, Bell, Jimmy D., Beulens, Joline W., Brunak, Søren, Dermitzakis, Emmanouil T., Froguel, Philippe, Frost, Gary, Gupta, Ramneek, Hansen, Torben, Hattersley, Andrew, Jablonka, Bernd, Kaye, Jane, Laakso, Markku, McDonald, Timothy J., Pedersen, Oluf, Schwenk, Jochen M., Pavo, Imre, Mari, Andrea, McCarthy, Mark I., Ruetten, Hartmut, Walker, Mark, Pearson, Ewan, Franks, Paul W., and for the IMI DIRECT Consortium

Published
2019
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11. Whole blood co-expression modules associate with metabolic traits and type 2 diabetes: an IMI-DIRECT study

Author

Gudmundsdottir, Valborg, Pedersen, Helle Krogh, Mazzoni, Gianluca, Allin, Kristine H., Artati, Anna, Beulens, Joline W., Banasik, Karina, Brorsson, Caroline, Cederberg, Henna, Chabanova, Elizaveta, De Masi, Federico, Elders, Petra J., Forgie, Ian, Giordano, Giuseppe N., Grallert, Harald, Gupta, Ramneek, Haid, Mark, Hansen, Torben, Hansen, Tue H., Hattersley, Andrew T., Heggie, Alison, Hong, Mun-Gwan, Jones, Angus G., Koivula, Robert, Kokkola, Tarja, Laakso, Markku, Løngreen, Peter, Mahajan, Anubha, Mari, Andrea, McDonald, Timothy J., McEvoy, Donna, Musholt, Petra B., Pavo, Imre, Prehn, Cornelia, Ruetten, Hartmut, Ridderstråle, Martin, Rutters, Femke, Sharma, Sapna, Slieker, Roderick C., Syed, Ali, Tajes, Juan Fernandez, Thomas, Cecilia Engel, Thomsen, Henrik S., Vangipurapu, Jagadish, Vestergaard, Henrik, Viñuela, Ana, Wesolowska-Andersen, Agata, Walker, Mark, Adamski, Jerzy, Schwenk, Jochen M., McCarthy, Mark I., Pearson, Ewan, Dermitzakis, Emmanouil, Franks, Paul W., Pedersen, Oluf, and Brunak, Søren

Published
2020
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12. Genome-Wide Association Shows that Pigmentation Genes Play a Role in Skin Aging

Author

Law, Matthew H., Medland, Sarah E., Zhu, Gu, Yazar, Seyhan, Viñuela, Ana, Wallace, Leanne, Shekar, Sri Niranjan, Duffy, David L., Bataille, Veronique, Glass, Dan, Spector, Tim D., Wood, Diane, Gordon, Scott D., Barbour, Julie M., Henders, Anjali K., Hewitt, Alex W., Montgomery, Grant W., Sturm, Richard A., Mackey, David A., Green, Adèle C., Martin, Nicholas G., and MacGregor, Stuart

Published
2017
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13. Genetic variant effects on gene expression in human pancreatic islets and their implications for T2D

Author

Viñuela, Ana, Varshney, Arushi, van de Bunt, Martijn, Prasad, Rashmi B., Asplund, Olof, Bennett, Amanda, Boehnke, Michael, Brown, Andrew A., Erdos, Michael R., Fadista, João, Hansson, Ola, Hatem, Gad, Howald, Cédric, Iyengar, Apoorva K., Johnson, Paul, Krus, Ulrika, MacDonald, Patrick E., Mahajan, Anubha, Manning Fox, Jocelyn E., Narisu, Narisu, Nylander, Vibe, Orchard, Peter, Oskolkov, Nikolay, Panousis, Nikolaos I., Payne, Anthony, Stitzel, Michael L., Vadlamudi, Swarooparani, Welch, Ryan, Collins, Francis S., Mohlke, Karen L., Gloyn, Anna L., Scott, Laura J., Dermitzakis, Emmanouil T., Groop, Leif, Parker, Stephen C. J., and McCarthy, Mark I.

Published
2020
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15. Regulatory variants at KLF14 influence type 2 diabetes risk via a female-specific effect on adipocyte size and body composition

Author

Small, Kerrin S., Todorčević, Marijana, Civelek, Mete, El-Sayed Moustafa, Julia S., Wang, Xiao, Simon, Michelle M., Fernandez-Tajes, Juan, Mahajan, Anubha, Horikoshi, Momoko, Hugill, Alison, Glastonbury, Craig A., Quaye, Lydia, Neville, Matt J., Sethi, Siddharth, Yon, Marianne, Pan, Calvin, Che, Nam, Viñuela, Ana, Tsai, Pei-Chien, Nag, Abhishek, Buil, Alfonso, Thorleifsson, Gudmar, Raghavan, Avanthi, Ding, Qiurong, Morris, Andrew P., Bell, Jordana T., Thorsteinsdottir, Unnur, Stefansson, Kari, Laakso, Markku, Dahlman, Ingrid, Arner, Peter, Gloyn, Anna L., Musunuru, Kiran, Lusis, Aldons J., Cox, Roger D., Karpe, Fredrik, and McCarthy, Mark I.

Published
2018
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17. Smoking induces coordinated DNA methylation and gene expression changes in adipose tissue with consequences for metabolic health

Author

Tsai, Pei-Chien, Glastonbury, Craig A., Eliot, Melissa N., Bollepalli, Sailalitha, Yet, Idil, Castillo-Fernandez, Juan E., Carnero-Montoro, Elena, Hardiman, Thomas, Martin, Tiphaine C., Vickers, Alice, Mangino, Massimo, Ward, Kirsten, Pietiläinen, Kirsi H., Deloukas, Panos, Spector, Tim D., Viñuela, Ana, Loucks, Eric B., Ollikainen, Miina, Kelsey, Karl T., Small, Kerrin S., and Bell, Jordana T.

Published
2018
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19. Multiallelic Copy Number Variation in ORM1 is Associated with Plasma Cell-Free DNA Levels as an Intermediate Phenotype for Venous Thromboembolism.

Author

Martin-Fernandez, Laura, Garcia-Martínez, Iris, Lopez, Sonia, Martinez-Perez, Angel, Vilalta, Noelia, Plaza, Melania, Moret, Carla, Viñuela, Ana, Brown, Andrew A., Panousis, Nikolaos I., Buil, Alfonso, Dermitzakis, Emmanouil T., Corrales, Irene, Souto, Juan Carlos, Vidal, Francisco, and Soria, Jose Manuel

Published
2023
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21. Genome-wide association analysis identifies six new loci associated with forced vital capacity

Author

Loth, Daan W, Artigas, María Soler, Gharib, Sina A, Wain, Louise V, Franceschini, Nora, Koch, Beate, Pottinger, Tess D, Smith, Albert Vernon, Duan, Qing, Oldmeadow, Chris, Lee, Mi Kyeong, Strachan, David P, James, Alan L, Huffman, Jennifer E, Vitart, Veronique, Ramasamy, Adaikalavan, Wareham, Nicholas J, Kaprio, Jaakko, Wang, Xin-Qun, Trochet, Holly, Kähönen, Mika, Flexeder, Claudia, Albrecht, Eva, Lopez, Lorna M, de Jong, Kim, Thyagarajan, Bharat, Alves, Alexessander Couto, Enroth, Stefan, Omenaas, Ernst, Joshi, Peter K, Fall, Tove, Viñuela, Ana, Launer, Lenore J, Loehr, Laura R, Fornage, Myriam, Li, Guo, Wilk, Jemma B, Tang, Wenbo, Manichaikul, Ani, Lahousse, Lies, Harris, Tamara B, North, Kari E, Rudnicka, Alicja R, Hui, Jennie, Gu, Xiangjun, Lumley, Thomas, Wright, Alan F, Hastie, Nicholas D, Campbell, Susan, Kumar, Rajesh, Pin, Isabelle, Scott, Robert A, Pietiläinen, Kirsi H, Surakka, Ida, Liu, Yongmei, Holliday, Elizabeth G, Schulz, Holger, Heinrich, Joachim, Davies, Gail, Vonk, Judith M, Wojczynski, Mary, Pouta, Anneli, Johansson, Åsa, Wild, Sarah H, Ingelsson, Erik, Rivadeneira, Fernando, Völzke, Henry, Hysi, Pirro G, Eiriksdottir, Gudny, Morrison, Alanna C, Rotter, Jerome I, Gao, Wei, Postma, Dirkje S, White, Wendy B, Rich, Stephen S, Hofman, Albert, Aspelund, Thor, Couper, David, Smith, Lewis J, Psaty, Bruce M, Lohman, Kurt, Burchard, Esteban G, Uitterlinden, André G, Garcia, Melissa, Joubert, Bonnie R, McArdle, Wendy L, Musk, A Bill, Hansel, Nadia, Heckbert, Susan R, Zgaga, Lina, van Meurs, Joyce B J, Navarro, Pau, Rudan, Igor, Oh, Yeon-Mok, Redline, Susan, Jarvis, Deborah L, Zhao, Jing Hua, Rantanen, Taina, O'Connor, George T, Ripatti, Samuli, Scott, Rodney J, Karrasch, Stefan, Grallert, Harald, Gaddis, Nathan C, Starr, John M, Wijmenga, Cisca, Minster, Ryan L, Lederer, David J, Pekkanen, Juha, Gyllensten, Ulf, Campbell, Harry, Morris, Andrew P, Gläser, Sven, Hammond, Christopher J, Burkart, Kristin M, Beilby, John, Kritchevsky, Stephen B, Gudnason, Vilmundur, Hancock, Dana B, Williams, O Dale, Polasek, Ozren, Zemunik, Tatijana, Kolcic, Ivana, Petrini, Marcy F, Wjst, Matthias, Kim, Woo Jin, Porteous, David J, Scotland, Generation, Smith, Blair H, Viljanen, Anne, Heliövaara, Markku, Attia, John R, Sayers, Ian, Hampel, Regina, Gieger, Christian, Deary, Ian J, Boezen, H Marike, Newman, Anne, Jarvelin, Marjo-Riitta, Wilson, James F, Lind, Lars, Stricker, Bruno H, Teumer, Alexander, Spector, Timothy D, Melén, Erik, Peters, Marjolein J, Lange, Leslie A, Barr, R Graham, Bracke, Ken R, Verhamme, Fien M, Sung, Joohon, Hiemstra, Pieter S, Cassano, Patricia A, Sood, Akshay, Hayward, Caroline, Dupuis, Josée, Hall, Ian P, Brusselle, Guy G, Tobin, Martin D, and London, Stephanie J

Published
2014
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23. Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans

Author

Amin Al Olama, Ali, Dadaev, Tokhir, Hazelett, Dennis J., Li, Qiuyan, Leongamornlert, Daniel, Saunders, Edward J., Stephens, Sarah, Cieza-Borrella, Clara, Whitmore, Ian, Benlloch Garcia, Sara, Giles, Graham G., Southey, Melissa C., Fitzgerald, Liesel, Gronberg, Henrik, Wiklund, Fredrik, Aly, Markus, Henderson, Brian E., Schumacher, Fredrick, Haiman, Christopher A., Schleutker, Johanna, Wahlfors, Tiina, Tammela, Teuvo L., Nordestgaard, Børge G., Key, Tim J., Travis, Ruth C., Neal, David E., Donovan, Jenny L., Hamdy, Freddie C., Pharoah, Paul, Pashayan, Nora, Khaw, Kay-Tee, Stanford, Janet L., Thibodeau, Stephen N., Mcdonnell, Shannon K., Schaid, Daniel J., Maier, Christiane, Vogel, Walther, Luedeke, Manuel, Herkommer, Kathleen, Kibel, Adam S., Cybulski, Cezary, Wokołorczyk, Dominika, Kluzniak, Wojciech, Cannon-Albright, Lisa, Brenner, Hermann, Butterbach, Katja, Arndt, Volker, Park, Jong Y., Sellers, Thomas, Lin, Hui-Yi, Slavov, Chavdar, Kaneva, Radka, Mitev, Vanio, Batra, Jyotsna, Clements, Judith A., Spurdle, Amanda, Teixeira, Manuel R., Paulo, Paula, Maia, Sofia, Pandha, Hardev, Michael, Agnieszka, Kierzek, Andrzej, Govindasami, Koveela, Guy, Michelle, Lophatonanon, Artitaya, Muir, Kenneth, Viñuela, Ana, Brown, Andrew A., Freedman, Mathew, Conti, David V., Easton, Douglas, Coetzee, Gerhard A., Eeles, Rosalind A., Kote-Jarai, Zsofia, Easton, Douglas F., Pharoah, Paul, Michailidou, Kyriaki, Bolla, Manjeet K., Wang, Qin, Berchuck, Andrew, Eeles, Rosalind A., Easton, Douglas F., Kote-Jarai, Zsofia, Al Olama, Ali Amin, Benlloch, Sara, Chenevix-Trench, Georgia, Antoniou, Antonis, McGuffog, Lesley, Couch, Fergus, Offit, Ken, Dennis, Joe, Dunning, Alison M., Lee, Andrew, Dicks, Ed, Luccarini, Craig, Benitez, Javier, Gonzalez-Neira, Anna, Simard, Jacques, Tessier, Daniel C., Bacot, Francois, Vincent, Daniel, LaBoissière, Sylvie, Robidoux, Frederic, Bojesen, Stig E., Nielsen, Sune F., Nordestgaard, Borge G., Cunningham, Julie M., Windebank, Sharon A., Hilker, Christopher A., and Meyer, Jeffrey

Published
2015
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24. Circulating Proteomic Signatures of Chronological Age

Author

Menni, Cristina, Kiddle, Steven J., Mangino, Massimo, Viñuela, Ana, Psatha, Maria, Steves, Claire, Sattlecker, Martina, Buil, Alfonso, Newhouse, Stephen, Nelson, Sally, Williams, Stephen, Voyle, Nicola, Soininen, Hilkka, Kloszewska, Iwona, Mecocci, Patrizia, Tsolaki, Magda, Vellas, Bruno, Lovestone, Simon, Spector, Tim D., Dobson, Richard, and Valdes, Ana M.

Published
2015
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27. Analysis pipeline for 'The impact of sex on gene expression across human tissues'

Author

Oliva, Meritxell, Muñoz-Aguirre, Manuel, Kim-Hellmuth, Sarah, Wucher, Valentin, Gewirtz, Ariel D.H., Cotter, Daniel J., Parsana, Princy, Kasela, Silva, Balliu, Brunilda, Viñuela, Ana, Castel, Stephane E., Mohammadi, Pejman, Aguet, François, Zou, Yuxin, Khramtsova, Ekaterina A., Skol, Andrew D., Garrido-Martín, Diego, Reverter, Ferran, Brown, Andrew, Evans, Patrick, Gamazon, Eric R., Payne, Anthony, Bonazzola, Rodrigo, Barbeira, Alvaro N., Hamel, Andrew R., Martinez-Perez, Angel, Soria, José Manuel, GTEx Consortium, Pierce, Brandon L., Stephens, Matthew, Eskin, Eleazar, Dermitzakis, Emmanouil T., Segrè, Ayellet V., Im, Hae Kyung, Engelhardt, Barbara E., Ardlie, Kristin G., Montgomery, Stephen B., Battle, Alexis J., Lappalainen, Tuuli, Guigó, Roderic, and Stranger, Barbara E.

Subjects
gene expression
Abstract

Sex-biased gene expression analysis pipeline for the research publication "The impact of sex on gene expressionacross human tissues". Details on how to set up and runthe pipeline are available on the README.md file.

Published
2020
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28. Predicting and elucidating the etiology of fatty liver disease:A machine learning modeling and validation study in the IMI DIRECT cohorts

Author

Atabaki-Pasdar, Naeimeh, Ohlsson, Mattias, Viñuela, Ana, Frau, Francesca, Pomares-Millan, Hugo, Haid, Mark, Jones, Angus G, Thomas, E Louise, Koivula, Robert W, Kurbasic, Azra, Mutie, Pascal M, Fitipaldi, Hugo, Fernandez, Juan, Dawed, Adem Y, Giordano, Giuseppe N, Forgie, Ian M, McDonald, Timothy J, Rutters, Femke, Cederberg, Henna, Chabanova, Elizaveta, Dale, Matilda, Masi, Federico De, Thomas, Cecilia Engel, Allin, Kristine H., Hansen, Tue H, Heggie, Alison, Hong, Mun-Gwan, Elders, Petra J M, Kennedy, Gwen, Kokkola, Tarja, Pedersen, Helle Krogh, Mahajan, Anubha, McEvoy, Donna, Pattou, Francois, Raverdy, Violeta, Häussler, Ragna S, Sharma, Sapna, Thomsen, Henrik S, Vangipurapu, Jagadish, Vestergaard, Henrik, Adamski, Jerzy, Musholt, Petra B, Brage, Søren, Brunak, Søren, Dermitzakis, Emmanouil, Frost, Gary, Hansen, Torben, Laakso, Markku, and Pedersen, Oluf

Abstract

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is highly prevalent and causes serious health complications in individuals with and without type 2 diabetes (T2D). Early diagnosis of NAFLD is important, as this can help prevent irreversible damage to the liver and, ultimately, hepatocellular carcinomas. We sought to expand etiological understanding and develop a diagnostic tool for NAFLD using machine learning.METHODS AND FINDINGS: We utilized the baseline data from IMI DIRECT, a multicenter prospective cohort study of 3,029 European-ancestry adults recently diagnosed with T2D (n = 795) or at high risk of developing the disease (n = 2,234). Multi-omics (genetic, transcriptomic, proteomic, and metabolomic) and clinical (liver enzymes and other serological biomarkers, anthropometry, measures of beta-cell function, insulin sensitivity, and lifestyle) data comprised the key input variables. The models were trained on MRI-image-derived liver fat content (CONCLUSIONS: In this study, we developed several models with different combinations of clinical and omics data and identified biological features that appear to be associated with liver fat accumulation. In general, the clinical variables showed better prediction ability than the complex omics variables. However, the combination of omics and clinical variables yielded the highest accuracy. We have incorporated the developed clinical models into a web interface (see: https://www.predictliverfat.org/) and made it available to the community.TRIAL REGISTRATION: ClinicalTrials.gov NCT03814915.

Published
2020

29. Genome-wide meta-analysis points to CTC1 and ZNF676 as genes regulating telomere homeostasis in humans

Author

Mangino, Massimo, Hwang, Shih-Jen, Spector, Timothy D., Hunt, Steven C., Kimura, Masayuki, Fitzpatrick, Annette L., Christiansen, Lene, Petersen, Inge, Elbers, Clara C., Harris, Tamara, Chen, Wei, Srinivasan, Sathanur R., Kark, Jeremy D., Benetos, Athanase, El Shamieh, Said, Visvikis-Siest, Sophie, Christensen, Kaare, Berenson, Gerald S., Valdes, Ana M., Viñuela, Ana, Garcia, Melissa, Arnett, Donna K., Broeckel, Ulrich, Province, Michael A., Pankow, James S., Kammerer, Candace, Liu, Yongmei, Nalls, Michael, Tishkoff, Sarah, Thomas, Fridtjof, Ziv, Elad, Psaty, Bruce M., Bis, Joshua C., Rotter, Jerome I., Taylor, Kent D., Smith, Erin, Schork, Nicholas J., Levy, Daniel, and Aviv, Abraham

Published
2012
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30. Profiles of Glucose Metabolism in Different Prediabetes Phenotypes, Classified by Fasting Glycemia, 2-Hour OGTT, Glycated Hemoglobin, and 1-Hour OGTT: An IMI DIRECT Study.

Author

Tura, Andrea, Grespan, Eleonora, Göbl, Christian S., Koivula, Robert W., Franks, Paul W., Pearson, Ewan R., Walker, Mark, Forgie, Ian M., Giordano, Giuseppe N., Pavo, Imre, Ruetten, Hartmut, Dermitzakis, Emmanouil T., McCarthy, Mark I., Pedersen, Oluf, Schwenk, Jochen M., Adamski, Jerzy, De Masi, Federico, Tsirigos, Konstantinos D., Brunak, Søren, and Viñuela, Ana

Subjects
GLYCOSYLATED hemoglobin, GLUCOSE metabolism, INSULIN sensitivity, PREDIABETIC state, TYPE 2 diabetes
Abstract

Differences in glucose metabolism among categories of prediabetes have not been systematically investigated. In this longitudinal study, participants (N = 2,111) underwent a 2-h 75-g oral glucose tolerance test (OGTT) at baseline and 48 months. HbA1c was also measured. We classified participants as having isolated prediabetes defect (impaired fasting glucose [IFG], impaired glucose tolerance [IGT], or HbA1c indicative of prediabetes [IA1c]), two defects (IFG+IGT, IFG+IA1c, or IGT+IA1c), or all defects (IFG+IGT+IA1c). β-Cell function (BCF) and insulin sensitivity were assessed from OGTT. At baseline, in pooling of participants with isolated defects, they showed impairment in both BCF and insulin sensitivity compared with healthy control subjects. Pooled groups with two or three defects showed progressive further deterioration. Among groups with isolated defect, those with IGT showed lower insulin sensitivity, insulin secretion at reference glucose (ISRr), and insulin secretion potentiation (P < 0.002). Conversely, those with IA1c showed higher insulin sensitivity and ISRr (P < 0.0001). Among groups with two defects, we similarly found differences in both BCF and insulin sensitivity. At 48 months, we found higher type 2 diabetes incidence for progressively increasing number of prediabetes defects (odds ratio >2, P < 0.008). In conclusion, the prediabetes groups showed differences in type/degree of glucometabolic impairment. Compared with the pooled group with isolated defects, those with double or triple defect showed progressive differences in diabetes incidence. [ABSTRACT FROM AUTHOR]

Published
2021
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31. Genome-Wide Association Analysis of Pancreatic Beta-Cell Glucose Sensitivity.

Author

Deshmukh, Harshal A, Madsen, Anne Lundager, Viñuela, Ana, Have, Christian Theil, Grarup, Niels, Tura, Andrea, Mahajan, Anubha, Heggie, Alison J, Koivula, Robert W, De Masi, Federico, Tsirigos, Konstantinos K, Linneberg, Allan, Drivsholm, Thomas, Pedersen, Oluf, Sørensen, Thorkild I A, Astrup, Arne, Gjesing, Anette A P, Pavo, Imre, Wood, Andrew R, and Ruetten, Hartmut

Subjects
PANCREATIC beta cells, GLUCOSE tolerance tests, TYPE 2 diabetes, GLUCOSE intolerance, RESEARCH, SEQUENCE analysis, META-analysis, RESEARCH methodology, GENETIC polymorphisms, CASE-control method, EVALUATION research, COMPARATIVE studies, DIAGNOSIS, DIGESTIVE organs, DISEASE susceptibility, RESEARCH funding, GLUCOSE, PREDIABETIC state, LONGITUDINAL method
Abstract

Context: Pancreatic beta-cell glucose sensitivity is the slope of the plasma glucose-insulin secretion relationship and is a key predictor of deteriorating glucose tolerance and development of type 2 diabetes. However, there are no large-scale studies looking at the genetic determinants of beta-cell glucose sensitivity.Objective: To understand the genetic determinants of pancreatic beta-cell glucose sensitivity using genome-wide meta-analysis and candidate gene studies.Design: We performed a genome-wide meta-analysis for beta-cell glucose sensitivity in subjects with type 2 diabetes and nondiabetic subjects from 6 independent cohorts (n = 5706). Beta-cell glucose sensitivity was calculated from mixed meal and oral glucose tolerance tests, and its associations between known glycemia-related single nucleotide polymorphisms (SNPs) and genome-wide association study (GWAS) SNPs were estimated using linear regression models.Results: Beta-cell glucose sensitivity was moderately heritable (h2 ranged from 34% to 55%) using SNP and family-based analyses. GWAS meta-analysis identified multiple correlated SNPs in the CDKAL1 gene and GIPR-QPCTL gene loci that reached genome-wide significance, with SNP rs2238691 in GIPR-QPCTL (P value = 2.64 × 10-9) and rs9368219 in the CDKAL1 (P value = 3.15 × 10-9) showing the strongest association with beta-cell glucose sensitivity. These loci surpassed genome-wide significance when the GWAS meta-analysis was repeated after exclusion of the diabetic subjects. After correction for multiple testing, glycemia-associated SNPs in or near the HHEX and IGF2B2 loci were also associated with beta-cell glucose sensitivity.Conclusion: We show that, variation at the GIPR-QPCTL and CDKAL1 loci are key determinants of pancreatic beta-cell glucose sensitivity. [ABSTRACT FROM AUTHOR]

Published
2021
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33. Genome-Wide Association Reveals Pigmentation Genes Play a Role in Skin Aging

Author

Law, Matthew H., Medland, Sarah E., Zhu, Gu, Yazar, Seyhan, Viñuela, Ana, Wallace, Leanne, Shekar, Sri Niranjan, Duffy, David L., Bataille, Veronique, Glass, Dan, Spector, Tim D., Wood, Diane, Gordon, Scott D., Barbour, Julie M., Henders, Anjali K., Hewitt, Alex W., Montgomery, Grant W., Sturm, Richard A., Mackey, David A., Green, Adèle C., Martin, Nicholas G., and MacGregor, Stuart

Subjects
Photoaging, Skin aging, integumentary system, Pigmentation, GWAS, Gene
Abstract

Loss of fine skin patterning is a sign of both aging and photoaging. Studies investigating the genetic contribution to skin patterning offer an opportunity to better understand a trait that influences both physical appearance and risk of keratinocyte skin cancer. We undertook a meta-analysis of genome-wide association studies (GWAS) of a measure of skin pattern (microtopography score) damage in 1,671 twin pairs and 1,745 singletons (N = 5,087) drawn from three independent cohorts. We identified that rs185146 near SLC45A2 is associated with a skin aging trait (p = 4.1 × 10-9); to our knowledge this is previously unreported. We also confirm previously identified loci, rs12203592 near IRF4 (p = 8.8 × 10-13), and rs4268748 near MC1R (p = 1.2 × 10-15). At all three loci we highlight putative functionally relevant SNPs. There are a number of red hair/low pigmentation alleles of MC1R; we found that together these MC1R alleles explained 4.1% of variance in skin pattern damage. We also show that skin aging and reported experience of sunburns was proportional to the degree of penetrance for red hair of alleles of MC1R. Our work has uncovered genetic contributions to skin aging and confirmed previous findings, showing that pigmentation is a critical determinate of skin aging.

Published
2017

34. Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans

Author

Amin Al Olama, Ali, Dadaev, Tokhir, Hazelett, Dennis J., Li, Qiuyan, Leongamornlert, Daniel, Saunders, Edward J., Stephens, Sarah, Cieza-Borrella, Clara, Whitmore, Ian, Benlloch Garcia, Sara, Giles, Graham G., Southey, Melissa C., Fitzgerald, Liesel, Gronberg, Henrik, Wiklund, Fredrik, Aly, Markus, Henderson, Brian E., Schumacher, Fredrick, Haiman, Christopher A., Schleutker, Johanna, Wahlfors, Tiina, Tammela, Teuvo L., Nordestgaard, Børge G., Key, Tim J., Travis, Ruth C., Neal, David E., Donovan, Jenny L., Hamdy, Freddie C., Pharoah, Paul, Pashayan, Nora, Khaw, Kay-Tee, Stanford, Janet L., Thibodeau, Stephen N., Mcdonnell, Shannon K., Schaid, Daniel J., Maier, Christiane, Vogel, Walther, Luedeke, Manuel, Herkommer, Kathleen, Kibel, Adam S., Cybulski, Cezary, Wokołorczyk, Dominika, Kluzniak, Wojciech, Cannon-Albright, Lisa, Brenner, Hermann, Butterbach, Katja, Arndt, Volker, Park, Jong Y., Sellers, Thomas, Lin, Hui-Yi, Slavov, Chavdar, Kaneva, Radka, Mitev, Vanio, Batra, Jyotsna, Clements, Judith A., Spurdle, Amanda, Teixeira, Manuel R., Paulo, Paula, Maia, Sofia, Pandha, Hardev, Michael, Agnieszka, Kierzek, Andrzej, Govindasami, Koveela, Guy, Michelle, Lophatonanon, Artitaya, Muir, Kenneth, Viñuela, Ana, Brown, Andrew A., Freedman, Mathew, Conti, David V., Easton, Douglas, Coetzee, Gerhard A., Eeles, Rosalind A., Kote-Jarai, Zsofia, Easton, Douglas F., Michailidou, Kyriaki, Bolla, Manjeet K., Wang, Qin, Berchuck, Andrew, Al Olama, Ali Amin, Benlloch, Sara, Chenevix-Trench, Georgia, Antoniou, Antonis, McGuffog, Lesley, Couch, Fergus, Offit, Ken, Dennis, Joe, Dunning, Alison M., Lee, Andrew, Dicks, Ed, Luccarini, Craig, Benitez, Javier, Gonzalez-Neira, Anna, Simard, Jacques, Tessier, Daniel C., Bacot, Francois, Vincent, Daniel, LaBoissière, Sylvie, Robidoux, Frederic, Bojesen, Stig E., Nielsen, Sune F., Nordestgaard, Borge G., Cunningham, Julie M., Windebank, Sharon A., Hilker, Christopher A., and Meyer, Jeffrey

Abstract

Genome-wide association studies (GWAS) have identified numerous common prostate cancer (PrCa) susceptibility loci. We have fine-mapped 64 GWAS regions known at the conclusion of the iCOGS study using large-scale genotyping and imputation in 25 723 PrCa cases and 26 274 controls of European ancestry. We detected evidence for multiple independent signals at 16 regions, 12 of which contained additional newly identified significant associations. A single signal comprising a spectrum of correlated variation was observed at 39 regions; 35 of which are now described by a novel more significantly associated lead SNP, while the originally reported variant remained as the lead SNP only in 4 regions. We also confirmed two association signals in Europeans that had been previously reported only in East-Asian GWAS. Based on statistical evidence and linkage disequilibrium (LD) structure, we have curated and narrowed down the list of the most likely candidate causal variants for each region. Functional annotation using data from ENCODE filtered for PrCa cell lines and eQTL analysis demonstrated significant enrichment for overlap with bio-features within this set. By incorporating the novel risk variants identified here alongside the refined data for existing association signals, we estimate that these loci now explain ∼38.9% of the familial relative risk of PrCa, an 8.9% improvement over the previously reported GWAS tag SNPs. This suggests that a significant fraction of the heritability of PrCa may have been hidden during the discovery phase of GWAS, in particular due to the presence of multiple independent signals within the same region

Published
2017

36. Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts.

Author

Atabaki-Pasdar, Naeimeh, Ohlsson, Mattias, Viñuela, Ana, Frau, Francesca, Pomares-Millan, Hugo, Haid, Mark, Jones, Angus G., Thomas, E. Louise, Koivula, Robert W., Kurbasic, Azra, Mutie, Pascal M., Fitipaldi, Hugo, Fernandez, Juan, Dawed, Adem Y., Giordano, Giuseppe N., Forgie, Ian M., McDonald, Timothy J., Rutters, Femke, Cederberg, Henna, and Chabanova, Elizaveta

Subjects
FATTY liver, LIVER disease etiology, MACHINE learning, LIVER disease diagnosis, MODEL validation, RECEIVER operating characteristic curves
Abstract

Background: Non-alcoholic fatty liver disease (NAFLD) is highly prevalent and causes serious health complications in individuals with and without type 2 diabetes (T2D). Early diagnosis of NAFLD is important, as this can help prevent irreversible damage to the liver and, ultimately, hepatocellular carcinomas. We sought to expand etiological understanding and develop a diagnostic tool for NAFLD using machine learning. Methods and findings: We utilized the baseline data from IMI DIRECT, a multicenter prospective cohort study of 3,029 European-ancestry adults recently diagnosed with T2D (n = 795) or at high risk of developing the disease (n = 2,234). Multi-omics (genetic, transcriptomic, proteomic, and metabolomic) and clinical (liver enzymes and other serological biomarkers, anthropometry, measures of beta-cell function, insulin sensitivity, and lifestyle) data comprised the key input variables. The models were trained on MRI-image-derived liver fat content (<5% or ≥5%) available for 1,514 participants. We applied LASSO (least absolute shrinkage and selection operator) to select features from the different layers of omics data and random forest analysis to develop the models. The prediction models included clinical and omics variables separately or in combination. A model including all omics and clinical variables yielded a cross-validated receiver operating characteristic area under the curve (ROCAUC) of 0.84 (95% CI 0.82, 0.86; p < 0.001), which compared with a ROCAUC of 0.82 (95% CI 0.81, 0.83; p < 0.001) for a model including 9 clinically accessible variables. The IMI DIRECT prediction models outperformed existing noninvasive NAFLD prediction tools. One limitation is that these analyses were performed in adults of European ancestry residing in northern Europe, and it is unknown how well these findings will translate to people of other ancestries and exposed to environmental risk factors that differ from those of the present cohort. Another key limitation of this study is that the prediction was done on a binary outcome of liver fat quantity (<5% or ≥5%) rather than a continuous one. Conclusions: In this study, we developed several models with different combinations of clinical and omics data and identified biological features that appear to be associated with liver fat accumulation. In general, the clinical variables showed better prediction ability than the complex omics variables. However, the combination of omics and clinical variables yielded the highest accuracy. We have incorporated the developed clinical models into a web interface (see: https://www.predictliverfat.org/) and made it available to the community. Trial registration: ClinicalTrials.gov NCT03814915. In a modelling study, Naeimeh Atabaki-Pasdar and colleagues apply machine learning techniques to develop models to predict non-alcoholic fatty liver disease diagnosis using multi-omic and clinical data from individuals with and without type 2 diabetes in the IMI DIRECT cohorts. Author summary: Why was this study done?: Globally, about 1 in 4 adults have non-alcoholic fatty liver disease (NAFLD), which adversely affects energy homeostasis (in particular blood glucose concentrations), blood detoxification, drug metabolism, and food digestion. Although numerous noninvasive tests to detect NAFLD exist, these typically include inaccurate blood-marker tests or expensive imaging methods. The purpose of this work was to develop accurate noninvasive methods to aid in the clinical prediction of NAFLD. What did the researchers do and find?: The analyses applied machine learning methods to data from the deep-phenotyped IMI DIRECT cohorts (n = 1,514) to identify sets of highly informative variables for the prediction of NAFLD. The criterion measure was liver fat quantified from MRI. We developed a total of 18 prediction models that ranged from very inexpensive models of modest accuracy to more expensive biochemistry- and/or omics-based models with high accuracy. We found that models using measures commonly collected in either clinical settings or research studies proved adequate for the prediction of NAFLD. The addition of detailed omics data significantly improved the predictive utility of these models. We also found that of all omics markers, proteomic markers yielded the highest predictive accuracy when appropriately combined. What do these findings mean?: We envisage that these new approaches to predicting fatty liver may be of clinical value when screening at-risk populations for NAFLD. The identification of specific molecular features that underlie the development of NAFLD provides novel insights into the disease's etiology, which may lead to the development of new treatments. [ABSTRACT FROM AUTHOR]

Published
2020
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37. Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans

Author

Al Olama, Ali Amin, Dadaev, Tokhir, Hazelett, Dennis J., Li, Qiuyan, Leongamornlert, Daniel A., Saunders, Edward J., Stephens, Sarah, Cieza-Borrella, Clara, Whitmore, Ian, Benlloch Garcia, Sara, Giles, Graham G., Southey, Melissa C., FitzGerald, Liesel M., Gronberg, Henrik, Wiklund, Fredrik, Aly, Markus, Henderson, Brian E., Schumacher, Fredrick, Haiman, Christopher A., Schleutker, Johanna, Wahlfors, Tiina, Tammela, Teuvo L. J., Nordestgaard, Børge G., Key, Tim J., Travis, Ruth C., Neal, David E, Donovan, Jenny L., Hamdy, Freddie C., Pharoah, Paul, Pashayan, Nora, Khaw, Kay-Tee, Stanford, Janet L., Thibodeau, Stephen N., McDonnell, Shannon K., Schaid, Daniel J., Maier, Christiane, Vogel, Walther, Luedeke, Manuel, Herkommer, Kathleen, Kibel, Adam S., Cybulski, Cezary, Wokozorczyk, Dominika, Kluźniak, Wojciech, Cannon-Albright, Lisa, Brenner, Hermann, Butterbach, Katja, Arndt, Volker, Park, Jong Y., Sellers, Thomas A., Lim, Hui-Yi, Slavov, Chavdar, Kaneva, Radka, Mitev, Vanio, Batra, Jyotsna, Clements, Judith A., Spurdle, Amanda, Teixeira, Manuel R., Paulo, Paula, Maia, Sofia, Pandha, Hardev, Michael, Agnieszka, Kierzek, Andrzej M., Govindasami, Koveela, Guy, Michelle, Muir, Kenneth, Viñuela, Ana, Brown, Andrew A., Freedman, Mathew, Conti, David V., Easton, Douglas F., Coetzee, Gerhard A., Eeles, Rosalind A., and Kote-Jarai, Zsofia

Subjects
RC0254
Abstract

Genome-wide association studies (GWAS) have identified numerous common prostate cancer (PrCa) susceptibility loci. We have fine-mapped 64 GWAS regions known at the conclusion of the iCOGS study using large scale genotyping and imputation in 25,723 PrCa cases and 26,274 controls of European ancestry.We detected evidence for multiple independent signals at 16 regions, 12 of which contained additional newly identified significant associations. A single signal comprising a spectrum of correlated variation was observed at 39 regions; 35 of which are now described by a novel more significantly associated lead SNP, whilst the originally reported variant remained as the lead SNP only in 4 regions. We also confirmed 2 association signals in Europeans that had been previously reported only in East-Asian GWAS.Based on statistical evidence and LD structure we have curated and narrowed down the list of the most likely candidate causal variants for each region. Functional annotation using data from ENCODE filtered for PrCa cell lines and eQTL analysis demonstrated significant enrichment for overlap with bio-features within this set. By incorporating the novel risk variants identified here alongside the refined data for existing association signals, we estimate that these loci now explain approximately 38.9% of the familial relative risk of PrCa, an 8.9% improvement over the previously reported GWAS tag SNPs. This suggests that a significant fraction of the heritability of PrCa may have been hidden during the discovery phase of GWAS, in particular due to the presence of multiple independent signals within the same region.

Published
2015

38. Genome-wide association analysis identifies six new loci associated with forced vital capacity

Author

Hayward, Caroline, Verhamme, Fien M, London, Stephanie J, Tobin, Martin D, Viñuela, Ana, Lopez, Lorna M, Hammond, Christopher J, Enroth, Stefan, Peters, Marjolein J, Heinrich, Joachim, Barr, R Graham, Lange, Leslie A, Teumer, Alexander, Johansson, Åsa, Brusselle, Guy G, Spector, Timothy D, Dupuis, Josée, Sood, Akshay, Hall, Ian P, Hiemstra, Pieter S, Melén, Erik, Bracke, Ken R, Sung, Joohon, Cassano, Patricia A, and Rudan, Igor

Subjects
respiratory system, respiratory tract diseases
Abstract

Forced vital capacity (FVC), a spirometric measure of pulmonary function, reflects lung volume and is used to diagnose and monitor lung diseases. We performed genome-wide association study meta-analysis of FVC in 52,253 individuals from 26 studies and followed up the top associations in 32,917 additional individuals of European ancestry. We found six new regions associated at genome-wide significance (P < 5 × 10−8) with FVC in or near EFEMP1, BMP6, MIR-129-2/HSD17B12, PRDM11, WWOX, and KCNJ2. Two (GSTCD and PTCH1) loci previously associated with spirometric measures were related to FVC. Newly implicated regions were followed-up in samples of African American, Korean, Chinese, and Hispanic individuals. We detected transcripts for all six newly implicated genes in human lung tissue. The new loci may inform mechanisms involved in lung development and pathogenesis of restrictive lung disease.

Published
2014
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39. Ageing Places in an Ageing Country: The Local Dynamics of the Elderly Population in Spain.

Author

Gutiérrez Posada, Diana, Rubiera Morollón, Fernando, and Viñuela, Ana

Subjects
OLDER people, DEMOGRAPHY, DECENTRALIZATION in government, SOCIOECONOMICS, ECONOMIC convergence
Abstract

Abstract: Spain is an ageing country, and the present demographic burden is not homogeneously distributed across space. Will the aged population be evenly distributed in the future, or will disparities broaden over time? Identifying the spatial patterns of the aged population concentration and the existence of a demographic burden convergence/divergence process is a pertinent question in Spain after the regional devolution the country has undergone in the last decades and the coexistence of different regimes. In this paper, we use a non‐parametric approach (geographically weighted regressions) to identify the determinants of the ageing dynamics, checking for the existence of a convergence/divergence ageing process after controlling for the socio‐economic characteristics of the Spanish municipalities. Although global estimations support the convergence hypotheses posed, GWR results show a significant variability of the effects depending on the area considered, which calls for a careful treatment of the results both for analysis and policy purposes. [ABSTRACT FROM AUTHOR]

Published
2018
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41. A Low-Frequency Inactivating Variant Enriched in the Finnish Population Is Associated With Fasting Insulin Levels and Type 2 Diabetes Risk.

Author

Manning, Alisa, Highland, Heather M., Gasser, Jessica, Sim, Xueling, Tukiainen, Taru, Fontanillas, Pierre, Grarup, Niels, Rivas, Manuel A., Mahajan, Anubha, Locke, Adam E., Cingolani, Pablo, Pers, Tune H., Viñuela, Ana, Brown, Andrew A., Ying Wu, Flannick, Jason, Fuchsberger, Christian, Gamazon, Eric R., Gaulton, Kyle J., and Hae Kyung Im

Subjects
INSULIN, TYPE 2 diabetes risk factors, DIABETES risk factors, EXOMES, GENOMES, ALLELES, ASIANS, BLACK people, DISEASE susceptibility, FASTING, GENES, HISPANIC Americans, INSULIN resistance, TYPE 2 diabetes, RESEARCH funding, TRANSFERASES, WHITE people, CASE-control method, ODDS ratio, GENOTYPES
Abstract

To identify novel coding association signals and facilitate characterization of mechanisms influencing glycemic traits and type 2 diabetes risk, we analyzed 109,215 variants derived from exome array genotyping together with an additional 390,225 variants from exome sequence in up to 39,339 normoglycemic individuals from five ancestry groups. We identified a novel association between the coding variant (p.Pro50Thr) in AKT2 and fasting plasma insulin (FI), a gene in which rare fully penetrant mutations are causal for monogenic glycemic disorders. The low-frequency allele is associated with a 12% increase in FI levels. This variant is present at 1.1% frequency in Finns but virtually absent in individuals from other ancestries. Carriers of the FI-increasing allele had increased 2-h insulin values, decreased insulin sensitivity, and increased risk of type 2 diabetes (odds ratio 1.05). In cellular studies, the AKT2-Thr50 protein exhibited a partial loss of function. We extend the allelic spectrum for coding variants in AKT2 associated with disorders of glucose homeostasis and demonstrate bidirectional effects of variants within the pleckstrin homology domain of AKT2. [ABSTRACT FROM AUTHOR]

Published
2017
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42. Heterogeneity in the Determinants of Population Growth at the Local Level.

Author

Gutiérrez-Posada, Diana, Rubiera-Morollon, Fernando, and Viñuela, Ana

Subjects
POPULATION, ECOLOGICAL heterogeneity, REGRESSION analysis, LEAST squares
Abstract

Formal modeling of local population growth has usually tended to focus on identifying patterns that are presumed to hold universally. However, as Glaeser, Ponzetto, and Tobio highlighted, these laws are reliable for long-term dynamics; but in some moments or for some places, the balance between the different factors may change, giving rise to different specific behaviors. In this article, we study local population growth in Spain with no intention of searching for universal patterns. Rather, we are interested in identifying how relevant the temporal and spatial heterogeneity may be, that is, to assess the even and uneven effects that population growth determinants can exert across time and space. The geographically weighted regression (GWR) approach applied in this article for two different decades, 1991–2001 and 2001–2011, captures the spatial heterogeneity. Results on the spatially differentiated population growth factors are compared with the global ordinary least squares (OLS) estimators for both decades. Essential factors in urban and regional economics such as size (initial population) or distance (either to the big cities or to the coast) can have different effects on population growth across both space and time, corresponding to the global estimated effects for some areas but diverging from these in others. Using GWR estimation procedures, we can identify changes in the sign or in the intensity of a factor’s effect across space, such that some factors could enhance population growth in one place but reduce it in another. Only after all spatially differentiated local effects have been analyzed and taken into consideration can appropriate national or regional policies be designed à la carte to promote, retain, or deter population growth. [ABSTRACT FROM AUTHOR]

Published
2017
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43. Adiposity-Dependent Regulatory Effects on Multi-tissue Transcriptomes.

Author

Glastonbury, Craig A., Viñuela, Ana, Buil, Alfonso, Halldorsson, Gisli H., Thorleifsson, Gudmar, Helgason, Hannes, Thorsteinsdottir, Unnur, Stefansson, Kari, Dermitzakis, Emmanouil T., Spector, Tim D., and Small, Kerrin S.

Subjects
*OBESITY genetics, *GENOTYPES, *GENETIC regulation, *BODY mass index, *ADIPOSE tissues
Abstract

Obesity is a global epidemic that is causally associated with a range of diseases, including type 2 diabetes and cardiovascular disease, at the population-level. However, there is marked heterogeneity in obesity-related outcomes among individuals. This might reflect genotype-dependent responses to adiposity. Given that adiposity, measured by BMI, is associated with widespread changes in gene expression and regulatory variants mediate the majority of known complex trait loci, we sought to identify gene-by-BMI (G × BMI) interactions on the regulation of gene expression in a multi-tissue RNA-sequencing (RNA-seq) dataset from the TwinsUK cohort (n = 856). At a false discovery rate of 5%, we identified 16 cis G × BMI interactions (top cis interaction: CHURC1 , rs7143432, p = 2.0 × 10 −12 ) and one variant regulating 53 genes in trans (top trans interaction: ZNF423 , rs3851570, p = 8.2 × 10 −13 ), all in adipose tissue. The interactions were adipose-specific and enriched for variants overlapping adipocyte enhancers, and regulated genes were enriched for metabolic and inflammatory processes. We replicated a subset of the interactions in an independent adipose RNA-seq dataset (deCODE genetics, n = 754). We also confirmed the interactions with an alternate measure of obesity, dual-energy X-ray absorptiometry (DXA)-derived visceral-fat-volume measurements, in a subset of TwinsUK individuals (n = 682). The identified G × BMI regulatory effects demonstrate the dynamic nature of gene regulation and reveal a functional mechanism underlying the heterogeneous response to obesity. Additionally, we have provided a web browser allowing interactive exploration of the dataset, including of association between expression, BMI, and G × BMI regulatory effects in four tissues. [ABSTRACT FROM AUTHOR]

Published
2016
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44. Gene-gene and gene-environment interactions detected by transcriptome sequence analysis in twins.

Author

Buil, Alfonso, Dermitzakis, Emmanouil T, Brown, Andrew Anand, Lappalainen, Tuuli, Viñuela, Ana, Davies, Matthew N, Glass, Daniel, Small, Kerrin S, Spector, Timothy D, Zheng, Hou-Feng, Richards, J Brent, and Durbin, Richard

Subjects
GENE expression in mammals, GENETIC recombination, TWINS, ALLELES, RNA sequencing, PHYSIOLOGY, MAMMALS
Abstract

Understanding the genetic architecture of gene expression is an intermediate step in understanding the genetic architecture of complex diseases. RNA sequencing technologies have improved the quantification of gene expression and allow measurement of allele-specific expression (ASE). ASE is hypothesized to result from the direct effect of cis regulatory variants, but a proper estimation of the causes of ASE has not been performed thus far. In this study, we take advantage of a sample of twins to measure the relative contributions of genetic and environmental effects to ASE, and we find substantial effects from gene × gene (G×G) and gene × environment (G×E) interactions. We propose a model where ASE requires genetic variability in cis, a difference in the sequence of both alleles, but where the magnitude of the ASE effect depends on trans genetic and environmental factors that interact with the cis genetic variants. [ABSTRACT FROM AUTHOR]

Published
2015
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45. Cigarette smoking reduces DNA methylation levels at multiple genomic loci but the effect is partially reversible upon cessation.

Author

Tsaprouni, Loukia G, Yang, Tsun-Po, Bell, Jordana, Dick, Katherine J, Kanoni, Stavroula, Nisbet, James, Viñuela, Ana, Grundberg, Elin, Nelson, Christopher P, Meduri, Eshwar, Buil, Alfonso, Cambien, Francois, Hengstenberg, Christian, Erdmann, Jeanette, Schunkert, Heribert, Goodall, Alison H, Ouwehand, Willem H, Dermitzakis, Emmanouil, Spector, Tim D, and Samani, Nilesh J

Published
2014
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46. Gene expression changes with age in skin, adipose tissue, blood and brain.

Author

Glass, Daniel, Viñuela, Ana, Davies, Matthew N, Ramasamy, Adaikalavan, Parts, Leopold, Knowles, David, Brown, Andrew A, Hedman, Åsa K, Small, Kerrin S, Buil, Alfonso, Grundberg, Elin, Nica, Alexandra C, Nestle, Frank O, Ryten, Mina, the UK Brain Expression consortium, the MuTHER consortium, Durbin, Richard, McCarthy, Mark I, Deloukas, Panagiotis, and Dermitzakis, Emmanouil T

Published
2013
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47. A probabilistic model of biological ageing of the lungs for analysing the effects of smoking, asthma and COPD.

Author

Chiappa, Silvia, Winn, John, Viñuela, Ana, Tipney, Hannah, and Spector, Timothy David

Subjects
LUNG aging, PULMONARY function tests, HEALTH, SMOKING, ASTHMA, OBSTRUCTIVE lung diseases
Abstract

Background: Although a large body of literature is available that describes the effects of smoking, asthma and COPD on lung function, most studies are restricted to a small age range and to one factor. As a consequence, available results are incomplete and often difficult to compare, also due to the ways the effects are expressed. Furthermore, current approaches consider one type of measurement only or several types separately. Methods: We propose a probabilistic model that expresses the effects as number of years added to chronological age or, in other words, that estimates the biological age of the lungs. Using biological age as a measure of the effects has the advantage of facilitating the understanding of their severity and comparison of results. In our model, chronological age and other factors affecting the health status of the lungs generate biological age, which in turn generates lung function measurements. This structure enables the use of multiple types of measurement to obtain a more precise estimate of the effects and parameter sharing for characterization over large age ranges and of co-occurrence of factors with little data. We treat the parameters that model smoking habits and lung diseases as random variables to obtain uncertainty in the estimated effects. Results: We use the model to investigate the effects of smoking, asthma and COPD on the TwinsUK Registry. Our results suggest that the combination of smoking with lung disease(s) has higher effect than smoking or lung disease(s) alone, and that, in smokers, co-occurrence of asthma and COPD is more detrimental than asthma or COPD alone. Conclusions: The proposed model or other models based on a similar approach could be of help in improving the understanding of factors affecting lung function by enabling characterizations over large age ranges and of co-occurrence of factors with little data and the use of multiple types of measurement. The software implementing the model can be downloaded at the first author's webpage. [ABSTRACT FROM AUTHOR]

Published
2013
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48. Gene Expression Modifications by Temperature- Toxicants Interactions in Caenorhabditis elegans.

Author

Viñuela, Ana, Snoek, L. Basten, Riksen, Joost A. G., and Kammenga, Jan E.

Subjects
*CAENORHABDITIS elegans, *GENE expression, *ORGANOPHOSPHORUS pesticides, *CHLORPYRIFOS, *DIAZINON, *GENETIC transcription
Abstract

Although organophosphorus pesticides (OP) share a common mode of action, there is increased awareness that they elicit a diverse range of gene expression responses. As yet however, there is no clear understanding of these responses and how they interact with ambient environmental conditions. In the present study, we investigated genome-wide gene expression profiles in the nematode Caenorhabditis elegans exposed to two OP, chlorpyrifos and diazinon, in single and combined treatments at different temperatures. Our results show that chlorpyrifos and diazinon induced expression of different genes and that temperature affected the response of detoxification genes to the pesticides. The analysis of transcriptional responses to a combination of chlorpyrifos and diazinon shows interactions between toxicants that affect gene expression. Furthermore, our combined analysis of the transcriptional responses to OP at different temperatures suggests that the combination of OP and high temperatures affect detoxification genes and modified the toxic levels of the pesticides. [ABSTRACT FROM AUTHOR]

Published
2011
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49. Genome-Wide Gene Expression Analysis in Response to Organophosphorus Pesticide Chlorpyrifos and Diazinon in C. elegans.

Author

Viñuela, Ana, Snoek, L. Basten, Riksen, Joost A. G., and Kammenga, Jan E.

Subjects
*CAENORHABDITIS elegans genetics, *GENE expression, *PHYSIOLOGICAL effects of pesticides, *CHLORPYRIFOS, *DIAZINON, *ACETYLCHOLINESTERASE, *ACETYLCHOLINE, *GENETIC toxicology, *DNA microarrays, *METABOLIC detoxification, *LIPID metabolism
Abstract

Organophosphorus pesticides (OPs) were originally designed to affect the nervous system by inhibiting the enzyme acetylcholinesterase, an important regulator of the neurotransmitter acetylcholine. Over the past years evidence is mounting that these compounds affect many other processes. Little is known, however, about gene expression responses against OPs in the nematode Caenorhabditis elegans. This is surprising because C. elegans is extensively used as a model species in toxicity studies. To address this question we performed a microarray study in C. elegans which was exposed for 72 hrs to two widely used Ops, chlorpyrifos and diazinon, and a low dose mixture of these two compounds. Our analysis revealed transcriptional responses related to detoxification, stress, innate immunity, and transport and metabolism of lipids in all treatments. We found that for both compounds as well as in the mixture, these processes were regulated by different gene transcripts. Our results illustrate intense, and unexpected crosstalk between gene pathways in response to chlorpyrifos and diazinon in C. elegans. [ABSTRACT FROM AUTHOR]

Published
2010
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50. Adiposity-Dependent Regulatory Effects on Multi-tissue Transcriptomes

Author

Glastonbury, Craig A., Viñuela, Ana, Buil, Alfonso, Halldorsson, Gisli H., Thorleifsson, Gudmar, Helgason, Hannes, Thorsteinsdottir, Unnur, Stefansson, Kari, Dermitzakis, Emmanouil T., Spector, Tim D., and Small, Kerrin S.

Subjects
Male, Sequence Analysis, RNA, Twins, Datasets as Topic, Proteins, Intra-Abdominal Fat, Middle Aged, Polymorphism, Single Nucleotide, Article, United Kingdom, Body Mass Index, Cohort Studies, DNA-Binding Proteins, Absorptiometry, Photon, Organ Specificity, Genetics, Humans, Female, Genetics(clinical), ddc:576.5, Obesity, Transcriptome, Adiposity
Abstract

Obesity is a global epidemic that is causally associated with a range of diseases, including type 2 diabetes and cardiovascular disease, at the population-level. However, there is marked heterogeneity in obesity-related outcomes among individuals. This might reflect genotype-dependent responses to adiposity. Given that adiposity, measured by BMI, is associated with widespread changes in gene expression and regulatory variants mediate the majority of known complex trait loci, we sought to identify gene-by-BMI (G × BMI) interactions on the regulation of gene expression in a multi-tissue RNA-sequencing (RNA-seq) dataset from the TwinsUK cohort (n = 856). At a false discovery rate of 5%, we identified 16 cis G × BMI interactions (top cis interaction: CHURC1, rs7143432, p = 2.0 × 10(-12)) and one variant regulating 53 genes in trans (top trans interaction: ZNF423, rs3851570, p = 8.2 × 10(-13)), all in adipose tissue. The interactions were adipose-specific and enriched for variants overlapping adipocyte enhancers, and regulated genes were enriched for metabolic and inflammatory processes. We replicated a subset of the interactions in an independent adipose RNA-seq dataset (deCODE genetics, n = 754). We also confirmed the interactions with an alternate measure of obesity, dual-energy X-ray absorptiometry (DXA)-derived visceral-fat-volume measurements, in a subset of TwinsUK individuals (n = 682). The identified G × BMI regulatory effects demonstrate the dynamic nature of gene regulation and reveal a functional mechanism underlying the heterogeneous response to obesity. Additionally, we have provided a web browser allowing interactive exploration of the dataset, including of association between expression, BMI, and G × BMI regulatory effects in four tissues.

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