19 results on '"Moore, Kristjan H. S."'
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2. Start codon variant in LAG3 is associated with decreased LAG-3 expression and increased risk of autoimmune thyroid disease
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Saevarsdottir, Saedis, Bjarnadottir, Kristbjörg, Markusson, Thorsteinn, Berglund, Jonas, Olafsdottir, Thorunn A., Halldorsson, Gisli H., Rutsdottir, Gudrun, Gunnarsdottir, Kristbjorg, Arnthorsson, Asgeir Orn, Lund, Sigrun H., Stefansdottir, Lilja, Gudmundsson, Julius, Johannesson, Ari J., Sturluson, Arni, Oddsson, Asmundur, Halldorsson, Bjarni, Ludviksson, Björn R., Ferkingstad, Egil, Ivarsdottir, Erna V., Sveinbjornsson, Gardar, Grondal, Gerdur, Masson, Gisli, Eldjarn, Grimur Hjorleifsson, Thorisson, Gudmundur A., Kristjansdottir, Katla, Knowlton, Kirk U., Moore, Kristjan H. S., Gudjonsson, Sigurjon A., Rognvaldsson, Solvi, Knight, Stacey, Nadauld, Lincoln D., Holm, Hilma, Magnusson, Olafur T., Sulem, Patrick, Gudbjartsson, Daniel F., Rafnar, Thorunn, Thorleifsson, Gudmar, Melsted, Pall, Norddahl, Gudmundur L., Jonsdottir, Ingileif, and Stefansson, Kari
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- 2024
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3. Rare variants with large effects provide functional insights into the pathology of migraine subtypes, with and without aura
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Bjornsdottir, Gyda, Chalmer, Mona A., Stefansdottir, Lilja, Skuladottir, Astros Th., Einarsson, Gudmundur, Andresdottir, Margret, Beyter, Doruk, Ferkingstad, Egil, Gretarsdottir, Solveig, Halldorsson, Bjarni V., Halldorsson, Gisli H., Helgadottir, Anna, Helgason, Hannes, Hjorleifsson Eldjarn, Grimur, Jonasdottir, Adalbjorg, Jonasdottir, Aslaug, Jonsdottir, Ingileif, Knowlton, Kirk U., Nadauld, Lincoln D., Lund, Sigrun H., Magnusson, Olafur Th., Melsted, Pall, Moore, Kristjan H. S., Oddsson, Asmundur, Olason, Pall I., Sigurdsson, Asgeir, Stefansson, Olafur A., Saemundsdottir, Jona, Sveinbjornsson, Gardar, Tragante, Vinicius, Unnsteinsdottir, Unnur, Walters, G. Bragi, Zink, Florian, Rødevand, Linn, Andreassen, Ole A., Igland, Jannicke, Lie, Rolv T., Haavik, Jan, Banasik, Karina, Brunak, Søren, Didriksen, Maria, T. Bruun, Mie, Erikstrup, Christian, Kogelman, Lisette J. A., Nielsen, Kaspar R., Sørensen, Erik, Pedersen, Ole B., Ullum, Henrik, Masson, Gisli, Thorsteinsdottir, Unnur, Olesen, Jes, Ludvigsson, Petur, Thorarensen, Olafur, Bjornsdottir, Anna, Sigurdardottir, Gudrun R., Sveinsson, Olafur A., Ostrowski, Sisse R., Holm, Hilma, Gudbjartsson, Daniel F., Thorleifsson, Gudmar, Sulem, Patrick, Stefansson, Hreinn, Thorgeirsson, Thorgeir E., Hansen, Thomas F., and Stefansson, Kari
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- 2023
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4. Publisher Correction: Deficit of homozygosity among 1.52 million individuals and genetic causes of recessive lethality
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Oddsson, Asmundur, Sulem, Patrick, Sveinbjornsson, Gardar, Arnadottir, Gudny A., Steinthorsdottir, Valgerdur, Halldorsson, Gisli H., Atlason, Bjarni A., Oskarsson, Gudjon R., Helgason, Hannes, Nielsen, Henriette Svarre, Westergaard, David, Karjalainen, Juha M., Katrinardottir, Hildigunnur, Fridriksdottir, Run, Jensson, Brynjar O., Tragante, Vinicius, Ferkingstad, Egil, Jonsson, Hakon, Gudjonsson, Sigurjon A., Beyter, Doruk, Moore, Kristjan H. S., Thordardottir, Helga B., Kristmundsdottir, Snaedis, Stefansson, Olafur A., Rantapää-Dahlqvist, Solbritt, Sonderby, Ida Elken, Didriksen, Maria, Stridh, Pernilla, Haavik, Jan, Tryggvadottir, Laufey, Frei, Oleksandr, Walters, G. Bragi, Kockum, Ingrid, Hjalgrim, Henrik, Olafsdottir, Thorunn A., Selbaek, Geir, Nyegaard, Mette, Erikstrup, Christian, Brodersen, Thorsten, Saevarsdottir, Saedis, Olsson, Tomas, Nielsen, Kaspar Rene, Haraldsson, Asgeir, Bruun, Mie Topholm, Hansen, Thomas Folkmann, Steingrimsdottir, Thora, Jacobsen, Rikke Louise, Lie, Rolv T., Djurovic, Srdjan, Alfredsson, Lars, Lopez de Lapuente Portilla, Aitzkoa, Brunak, Soren, Melsted, Pall, Halldorsson, Bjarni V., Saemundsdottir, Jona, Magnusson, Olafur Th., Padyukov, Leonid, Banasik, Karina, Rafnar, Thorunn, Askling, Johan, Klareskog, Lars, Pedersen, Ole Birger, Masson, Gisli, Havdahl, Alexandra, Nilsson, Bjorn, Andreassen, Ole A., Daly, Mark, Ostrowski, Sisse Rye, Jonsdottir, Ingileif, Stefansson, Hreinn, Holm, Hilma, Helgason, Agnar, Thorsteinsdottir, Unnur, Stefansson, Kari, and Gudbjartsson, Daniel F.
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- 2023
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5. Deficit of homozygosity among 1.52 million individuals and genetic causes of recessive lethality
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Oddsson, Asmundur, Sulem, Patrick, Sveinbjornsson, Gardar, Arnadottir, Gudny A., Steinthorsdottir, Valgerdur, Halldorsson, Gisli H., Atlason, Bjarni A., Oskarsson, Gudjon R., Helgason, Hannes, Nielsen, Henriette Svarre, Westergaard, David, Karjalainen, Juha M., Katrinardottir, Hildigunnur, Fridriksdottir, Run, Jensson, Brynjar O., Tragante, Vinicius, Ferkingstad, Egil, Jonsson, Hakon, Gudjonsson, Sigurjon A., Beyter, Doruk, Moore, Kristjan H. S., Thordardottir, Helga B., Kristmundsdottir, Snaedis, Stefansson, Olafur A., Rantapää-Dahlqvist, Solbritt, Sonderby, Ida Elken, Didriksen, Maria, Stridh, Pernilla, Haavik, Jan, Tryggvadottir, Laufey, Frei, Oleksandr, Walters, G. Bragi, Kockum, Ingrid, Hjalgrim, Henrik, Olafsdottir, Thorunn A., Selbaek, Geir, Nyegaard, Mette, Erikstrup, Christian, Brodersen, Thorsten, Saevarsdottir, Saedis, Olsson, Tomas, Nielsen, Kaspar Rene, Haraldsson, Asgeir, Bruun, Mie Topholm, Hansen, Thomas Folkmann, Steingrimsdottir, Thora, Jacobsen, Rikke Louise, Lie, Rolv T., Djurovic, Srdjan, Alfredsson, Lars, Lopez de Lapuente Portilla, Aitzkoa, Brunak, Soren, Melsted, Pall, Halldorsson, Bjarni V., Saemundsdottir, Jona, Magnusson, Olafur Th., Padyukov, Leonid, Banasik, Karina, Rafnar, Thorunn, Askling, Johan, Klareskog, Lars, Pedersen, Ole Birger, Masson, Gisli, Havdahl, Alexandra, Nilsson, Bjorn, Andreassen, Ole A., Daly, Mark, Ostrowski, Sisse Rye, Jonsdottir, Ingileif, Stefansson, Hreinn, Holm, Hilma, Helgason, Agnar, Thorsteinsdottir, Unnur, Stefansson, Kari, and Gudbjartsson, Daniel F.
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- 2023
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6. The Anglo-Saxon migration and the formation of the early English gene pool
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Gretzinger, Joscha, Sayer, Duncan, Justeau, Pierre, Altena, Eveline, Pala, Maria, Dulias, Katharina, Edwards, Ceiridwen J., Jodoin, Susanne, Lacher, Laura, Sabin, Susanna, Vågene, Åshild J., Haak, Wolfgang, Ebenesersdóttir, S. Sunna, Moore, Kristjan H. S., Radzeviciute, Rita, Schmidt, Kara, Brace, Selina, Bager, Martina Abenhus, Patterson, Nick, Papac, Luka, Broomandkhoshbacht, Nasreen, Callan, Kimberly, Harney, Éadaoin, Iliev, Lora, Lawson, Ann Marie, Michel, Megan, Stewardson, Kristin, Zalzala, Fatma, Rohland, Nadin, Kappelhoff-Beckmann, Stefanie, Both, Frank, Winger, Daniel, Neumann, Daniel, Saalow, Lars, Krabath, Stefan, Beckett, Sophie, Van Twest, Melanie, Faulkner, Neil, Read, Chris, Barton, Tabatha, Caruth, Joanna, Hines, John, Krause-Kyora, Ben, Warnke, Ursula, Schuenemann, Verena J., Barnes, Ian, Dahlström, Hanna, Clausen, Jane Jark, Richardson, Andrew, Popescu, Elizabeth, Dodwell, Natasha, Ladd, Stuart, Phillips, Tom, Mortimer, Richard, Sayer, Faye, Swales, Diana, Stewart, Allison, Powlesland, Dominic, Kenyon, Robert, Ladle, Lilian, Peek, Christina, Grefen-Peters, Silke, Ponce, Paola, Daniels, Robin, Spall, Cecily, Woolcock, Jennifer, Jones, Andy M., Roberts, Amy V., Symmons, Robert, Rawden, Anooshka C., Cooper, Alan, Bos, Kirsten I., Booth, Tom, Schroeder, Hannes, Thomas, Mark G., Helgason, Agnar, Richards, Martin B., Reich, David, Krause, Johannes, and Schiffels, Stephan
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- 2022
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7. The sequences of 150,119 genomes in the UK Biobank
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Halldorsson, Bjarni V., Eggertsson, Hannes P., Moore, Kristjan H. S., Hauswedell, Hannes, Eiriksson, Ogmundur, Ulfarsson, Magnus O., Palsson, Gunnar, Hardarson, Marteinn T., Oddsson, Asmundur, Jensson, Brynjar O., Kristmundsdottir, Snaedis, Sigurpalsdottir, Brynja D., Stefansson, Olafur A., Beyter, Doruk, Holley, Guillaume, Tragante, Vinicius, Gylfason, Arnaldur, Olason, Pall I., Zink, Florian, Asgeirsdottir, Margret, Sverrisson, Sverrir T., Sigurdsson, Brynjar, Gudjonsson, Sigurjon A., Sigurdsson, Gunnar T., Halldorsson, Gisli H., Sveinbjornsson, Gardar, Norland, Kristjan, Styrkarsdottir, Unnur, Magnusdottir, Droplaug N., Snorradottir, Steinunn, Kristinsson, Kari, Sobech, Emilia, Jonsson, Helgi, Geirsson, Arni J., Olafsson, Isleifur, Jonsson, Palmi, Pedersen, Ole Birger, Erikstrup, Christian, Brunak, Søren, Ostrowski, Sisse Rye, Thorleifsson, Gudmar, Jonsson, Frosti, Melsted, Pall, Jonsdottir, Ingileif, Rafnar, Thorunn, Holm, Hilma, Stefansson, Hreinn, Saemundsdottir, Jona, Gudbjartsson, Daniel F., Magnusson, Olafur T., Masson, Gisli, Thorsteinsdottir, Unnur, Helgason, Agnar, Jonsson, Hakon, Sulem, Patrick, and Stefansson, Kari
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- 2022
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8. The genetic structure of Norway
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Mattingsdal, Morten, Ebenesersdóttir, S. Sunna, Moore, Kristjan H. S., Andreassen, Ole A., Hansen, Thomas F., Werge, Thomas, Kockum, Ingrid, Olsson, Tomas, Alfredsson, Lars, Helgason, Agnar, Stefánsson, Kári, and Hovig, Eivind
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- 2021
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9. Author Correction: The Anglo-Saxon migration and the formation of the early English gene pool
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Gretzinger, Joscha, Sayer, Duncan, Justeau, Pierre, Altena, Eveline, Pala, Maria, Dulias, Katharina, Edwards, Ceiridwen J., Jodoin, Susanne, Lacher, Laura, Sabin, Susanna, Vågene, Åshild J., Haak, Wolfgang, Ebenesersdóttir, S. Sunna, Moore, Kristjan H. S., Radzeviciute, Rita, Schmidt, Kara, Brace, Selina, Bager, Martina Abenhus, Patterson, Nick, Papac, Luka, Broomandkhoshbacht, Nasreen, Callan, Kimberly, Harney, Éadaoin, Iliev, Lora, Lawson, Ann Marie, Michel, Megan, Stewardson, Kristin, Zalzala, Fatma, Rohland, Nadin, Kappelhoff-Beckmann, Stefanie, Both, Frank, Winger, Daniel, Neumann, Daniel, Saalow, Lars, Krabath, Stefan, Beckett, Sophie, Van Twest, Melanie, Faulkner, Neil, Read, Chris, Barton, Tabatha, Caruth, Joanna, Hines, John, Krause-Kyora, Ben, Warnke, Ursula, Schuenemann, Verena J., Barnes, Ian, Dahlström, Hanna, Clausen, Jane Jark, Richardson, Andrew, Popescu, Elizabeth, Dodwell, Natasha, Ladd, Stuart, Phillips, Tom, Mortimer, Richard, Sayer, Faye, Swales, Diana, Stewart, Allison, Powlesland, Dominic, Kenyon, Robert, Ladle, Lilian, Peek, Christina, Grefen-Peters, Silke, Ponce, Paola, Daniels, Robin, Spall, Cecily, Woolcock, Jennifer, Jones, Andy M., Roberts, Amy V., Symmons, Robert, Rawden, Anooshka C., Cooper, Alan, Bos, Kirsten I., Booth, Tom, Schroeder, Hannes, Thomas, Mark G., Helgason, Agnar, Richards, Martin B., Reich, David, Krause, Johannes, and Schiffels, Stephan
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- 2022
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10. Ancient genomes from Iceland reveal the making of a human population
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Ebenesersdóttir, S. Sunna, Sandoval-Velasco, Marcela, Gunnarsdóttir, Ellen D., Jagadeesan, Anuradha, Guðmundsdóttir, Valdís B., Thordardóttir, Elísabet L., Einarsdóttir, Margrét S., Moore, Kristjan H. S., Sigurðsson, Ásgeir, Magnúsdóttir, Droplaug N., Jónsson, Hákon, Snorradóttir, Steinunn, Hovig, Eivind, Møller, Pål, Kockum, Ingrid, Olsson, Tomas, Alfredsson, Lars, Hansen, Thomas F., Werge, Thomas, Cavalleri, Gianpiero L., Gilbert, Edmund, Lalueza-Fox, Carles, W.Walser, Joe, Kristjánsdóttir, Steinunn, Gopalakrishnan, Shyam, Árnadóttir, Lilja, Magnússon, Ólafur Þ., Gilbert, M. Thomas P., Stefánsson, Kári, and Helgason, Agnar
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- 2018
11. Associations of autozygosity with a broad range of human phenotypes
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Clark, David W, Okada, Yukinori, Moore, Kristjan H S, Mason, Dan, Pirastu, Nicola, Gandin, Ilaria, Mattsson, Hannele, Barnes, Catriona L K, Lin, Kuang, Zhao, Jing Hua, Deelen, Patrick, Rohde, Rebecca, Schurmann, Claudia, Guo, Xiuqing, Giulianini, Franco, Zhang, Weihua, Medina-Gomez, Carolina, Karlsson, Robert, Bao, Yanchun, Bartz, Traci M, Baumbach, Clemens, Biino, Ginevra, Bixley, Matthew J, Brumat, Marco, Chai, Jin-Fang, Corre, Tanguy, Cousminer, Diana L, Dekker, Annelot M, Eccles, David A, van Eijk, Kristel R, Fuchsberger, Christian, Gao, He, Germain, Marine, Gordon, Scott D, de Haan, Hugoline G, Harris, Sarah E, Hofer, Edith, Huerta-Chagoya, Alicia, Igartua, Catherine, Jansen, Iris E, Jia, Yucheng, Kacprowski, Tim, Karlsson, Torgny, Kleber, Marcus E, Li, Shengchao Alfred, Li-Gao, Ruifang, Mahajan, Anubha, Matsuda, Koichi, Meidtner, Karina, Meng, Weihua, Montasser, May E, van der Most, Peter J, Munz, Matthias, Nutile, Teresa, Palviainen, Teemu, Prasad, Gauri, Prasad, Rashmi B, Priyanka, Tallapragada Divya Sri, Rizzi, Federica, Salvi, Erika, Sapkota, Bishwa R, Shriner, Daniel, Skotte, Line, Smart, Melissa C, Smith, Albert Vernon, van der Spek, Ashley, Spracklen, Cassandra N, Strawbridge, Rona J, Tajuddin, Salman M, Trompet, Stella, Turman, Constance, Verweij, Niek, Viberti, Clara, Wang, Lihua, Warren, Helen R, Wootton, Robyn E, Yanek, Lisa R, Yao, Jie, Yousri, Noha A, Zhao, Wei, Adeyemo, Adebowale A, Afaq, Saima, Aguilar-Salinas, Carlos Alberto, Akiyama, Masato, Albert, Matthew L, Allison, Matthew A, Alver, Maris, Aung, Tin, Azizi, Fereidoun, Bentley, Amy R, Boeing, Heiner, Boerwinkle, Eric, Borja, Judith B, de Borst, Gert J, Bottinger, Erwin P, Broer, Linda, Campbell, Harry, Chanock, Stephen, Chee, Miao-Li, Chen, Guanjie, Chen, Yii-Der I, Chen, Zhengming, Chiu, Yen-Feng, Cocca, Massimiliano, Collins, Francis S, Concas, Maria Pina, Corley, Janie, Cugliari, Giovanni, van Dam, Rob M, Damulina, Anna, Daneshpour, Maryam S, Day, Felix R, Delgado, Graciela E, Dhana, Klodian, Doney, Alexander S F, Dörr, Marcus, Doumatey, Ayo P, Dzimiri, Nduna, Ebenesersdóttir, S Sunna, Elliott, Joshua, Elliott, Paul, Ewert, Ralf, Felix, Janine F, Fischer, Krista, Freedman, Barry I, Girotto, Giorgia, Goel, Anuj, Gögele, Martin, Goodarzi, Mark O, Graff, Mariaelisa, Granot-Hershkovitz, Einat, Grodstein, Francine, Guarrera, Simonetta, Gudbjartsson, Daniel F, Guity, Kamran, Gunnarsson, Bjarni, Guo, Yu, Hagenaars, Saskia P, Haiman, Christopher A, Halevy, Avner, Harris, Tamara B, Hedayati, Mehdi, van Heel, David A, Hirata, Makoto, Höfer, Imo, Hsiung, Chao Agnes, Huang, Jinyan, Hung, Yi-Jen, Ikram, M Arfan, Jagadeesan, Anuradha, Jousilahti, Pekka, Kamatani, Yoichiro, Kanai, Masahiro, Kerrison, Nicola D, Kessler, Thorsten, Khaw, Kay-Tee, Khor, Chiea Chuen, de Kleijn, Dominique P V, Koh, Woon-Puay, Kolcic, Ivana, Kraft, Peter, Krämer, Bernhard K, Kutalik, Zoltán, Kuusisto, Johanna, Langenberg, Claudia, Launer, Lenore J, Lawlor, Deborah A, Lee, I-Te, Lee, Wen-Jane, Lerch, Markus M, Li, Liming, Liu, Jianjun, Loh, Marie, London, Stephanie J, Loomis, Stephanie, Lu, Yingchang, Luan, Jian’an, Mägi, Reedik, Manichaikul, Ani W, Manunta, Paolo, Másson, Gísli, Matoba, Nana, Mei, Xue W, Meisinger, Christa, Meitinger, Thomas, Mezzavilla, Massimo, Milani, Lili, Millwood, Iona Y, Momozawa, Yukihide, Moore, Amy, Morange, Pierre-Emmanuel, Moreno-Macías, Hortensia, Mori, Trevor A, Morrison, Alanna C, Muka, Taulant, Murakami, Yoshinori, Murray, Alison D, de Mutsert, Renée, Mychaleckyj, Josyf C, Nalls, Mike A, Nauck, Matthias, Neville, Matt J, Nolte, Ilja M, Ong, Ken K, Orozco, Lorena, Padmanabhan, Sandosh, Pálsson, Gunnar, Pankow, James S, Pattaro, Cristian, Pattie, Alison, Polasek, Ozren, Poulter, Neil, Pramstaller, Peter P, Quintana-Murci, Lluis, Räikkönen, Katri, Ralhan, Sarju, Rao, Dabeeru C, van Rheenen, Wouter, Rich, Stephen S, Ridker, Paul M, Rietveld, Cornelius A, Robino, Antonietta, van Rooij, Frank J A, Ruggiero, Daniela, Saba, Yasaman, Sabanayagam, Charumathi, Sabater-Lleal, Maria, Sala, Cinzia Felicita, Salomaa, Veikko, Sandow, Kevin, Schmidt, Helena, Scott, Laura J, Scott, William R, Sedaghati-Khayat, Bahareh, Sennblad, Bengt, van Setten, Jessica, Sever, Peter J, Sheu, Wayne H-H, Shi, Yuan, Shrestha, Smeeta, Shukla, Sharvari Rahul, Sigurdsson, Jon K, Sikka, Timo Tonis, Singh, Jai Rup, Smith, Blair H, Stančáková, Alena, Stanton, Alice, Starr, John M, Stefansdottir, Lilja, Straker, Leon, Sulem, Patrick, Sveinbjornsson, Gardar, Swertz, Morris A, Taylor, Adele M, Taylor, Kent D, Terzikhan, Natalie, Tham, Yih-Chung, Thorleifsson, Gudmar, Thorsteinsdottir, Unnur, Tillander, Annika, Tracy, Russell P, Tusié-Luna, Teresa, Tzoulaki, Ioanna, Vaccargiu, Simona, Vangipurapu, Jagadish, Veldink, Jan H, Vitart, Veronique, Völker, Uwe, Vuoksimaa, Eero, Wakil, Salma M, Waldenberger, Melanie, Wander, Gurpreet S, Wang, Ya Xing, Wareham, Nicholas J, Wild, Sarah, Yajnik, Chittaranjan S, Yuan, Jian-Min, Zeng, Lingyao, Zhang, Liang, Zhou, Jie, Amin, Najaf, Asselbergs, Folkert W, Bakker, Stephan J L, Becker, Diane M, Lehne, Benjamin, Bennett, David A, van den Berg, Leonard H, Berndt, Sonja I, Bharadwaj, Dwaipayan, Bielak, Lawrence F, Bochud, Murielle, Boehnke, Mike, Bouchard, Claude, Bradfield, Jonathan P, Brody, Jennifer A, Campbell, Archie, Carmi, Shai, Caulfield, Mark J, Cesarini, David, Chambers, John C, Chandak, Giriraj Ratan, Cheng, Ching-Yu, Ciullo, Marina, Cornelis, Marilyn, Cusi, Daniele, Smith, George Davey, Deary, Ian J, Dorajoo, Rajkumar, van Duijn, Cornelia M, Ellinghaus, David, Erdmann, Jeanette, Eriksson, Johan G, Evangelou, Evangelos, Evans, Michele K, Faul, Jessica D, Feenstra, Bjarke, Feitosa, Mary, Foisy, Sylvain, Franke, Andre, Friedlander, Yechiel, Gasparini, Paolo, Gieger, Christian, Gonzalez, Clicerio, Goyette, Philippe, Grant, Struan F A, Griffiths, Lyn R, Groop, Leif, Gudnason, Vilmundur, Gyllensten, Ulf, Hakonarson, Hakon, Hamsten, Anders, van der Harst, Pim, Heng, Chew-Kiat, Hicks, Andrew A, Hochner, Hagit, Huikuri, Heikki, Hunt, Steven C, Jaddoe, Vincent W V, De Jager, Philip L, Johannesson, Magnus, Johansson, Åsa, Jonas, Jost B, Jukema, J Wouter, Junttila, Juhani, Kaprio, Jaakko, Kardia, Sharon L. R., Karpe, Fredrik, Kumari, Meena, Laakso, Markku, van der Laan, Sander W, Lahti, Jari, Laudes, Matthias, Lea, Rodney A, Lieb, Wolfgang, Lumley, Thomas, Martin, Nicholas G, März, Winfried, Matullo, Giuseppe, McCarthy, Mark I, Medland, Sarah E, Merriman, Tony R, Metspalu, Andres, Meyer, Brian F, Mohlke, Karen L, Montgomery, Grant W, Mook-Kanamori, Dennis, Munroe, Patricia B, North, Kari E, Nyholt, Dale R, O’connell, Jeffery R, Ober, Carole, Oldehinkel, Albertine J, Palmas, Walter, Palmer, Colin, Pasterkamp, Gerard G, Patin, Etienne, Pennell, Craig E, Perusse, Louis, Peyser, Patricia A, Pirastu, Mario, Polderman, Tinca J. C., Porteous, David J, Posthuma, Danielle, Psaty, Bruce M, Rioux, John D, Rivadeneira, Fernando, Rotimi, Charles, Rotter, Jerome I, Rudan, Igor, Den Ruijter, Hester M, Sanghera, Dharambir K, Sattar, Naveed, Schmidt, Reinhold, Schulze, Matthias B, Schunkert, Heribert, Scott, Robert A, Shuldiner, Alan R, Sim, Xueling, Small, Neil, Smith, Jennifer A, Sotoodehnia, Nona, Tai, E-Shyong, Teumer, Alexander, Timpson, Nicholas J, Toniolo, Daniela, Tregouet, David-Alexandre, Tuomi, Tiinamaija, Vollenweider, Peter, Wang, Carol A, Weir, David R, Whitfield, John B, Wijmenga, Cisca, Wong, Tien-Yin, Wright, John, Yang, Jingyun, Yu, Lei, Zemel, Babette S, Zonderman, Alan B, Perola, Markus, Magnusson, Patrik K. E., Uitterlinden, André G, Kooner, Jaspal S, Chasman, Daniel I, Loos, Ruth J. F., Franceschini, Nora, Franke, Lude, Haley, Chris S, Hayward, Caroline, Walters, Robin G, Perry, John R. B., Esko, Tōnu, Helgason, Agnar, Stefansson, Kari, Joshi, Peter K, Kubo, Michiaki, and Wilson, James F
- Published
- 2019
- Full Text
- View/download PDF
12. Evaluation of Large-Scale Proteomics for Prediction of Cardiovascular Events.
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Helgason, Hannes, Eiriksdottir, Thjodbjorg, Ulfarsson, Magnus O., Choudhary, Abhishek, Lund, Sigrun H., Ivarsdottir, Erna V., Hjorleifsson Eldjarn, Grimur, Einarsson, Gudmundur, Ferkingstad, Egil, Moore, Kristjan H. S., Honarpour, Narimon, Liu, Thomas, Wang, Huei, Hucko, Thomas, Sabatine, Marc S., Morrow, David A., Giugliano, Robert P., Ostrowski, Sisse Rye, Pedersen, Ole Birger, and Bundgaard, Henning
- Subjects
DYSLIPIDEMIA ,DISEASE risk factors ,CARDIOVASCULAR diseases ,MAJOR adverse cardiovascular events ,MONOGENIC & polygenic inheritance (Genetics) ,PROTEOMICS ,BLOOD proteins - Abstract
Key Points: Question: How does risk prediction based on proteomics data compare with clinical risk factors and polygenic risk scores? Findings: In a retrospective analysis using measurements of thousands of plasma proteins in primary- and secondary-event populations, a protein risk score, developed using more than 4900 plasma protein levels to predict major atherosclerotic cardiovascular disease events, stratified risk as well as a risk factor score and was slightly better than polygenic risk scores for coronary artery disease and stroke. The protein score modestly improved discriminative accuracy (measured by C index) and risk classification (measured by category-free net reclassification improvement and integrated discrimination improvement) when added to a model using clinical risk factors. Meaning: A protein risk score, derived from large-scale proteomics, yielded a modest improvement in discrimination when added to models based on clinical risk factors. Importance: Whether protein risk scores derived from a single plasma sample could be useful for risk assessment for atherosclerotic cardiovascular disease (ASCVD), in conjunction with clinical risk factors and polygenic risk scores, is uncertain. Objective: To develop protein risk scores for ASCVD risk prediction and compare them to clinical risk factors and polygenic risk scores in primary and secondary event populations. Design, Setting, and Participants: The primary analysis was a retrospective study of primary events among 13 540 individuals in Iceland (aged 40-75 years) with proteomics data and no history of major ASCVD events at recruitment (study duration, August 23, 2000 until October 26, 2006; follow-up through 2018). We also analyzed a secondary event population from a randomized, double-blind lipid-lowering clinical trial (2013-2016), consisting of individuals with stable ASCVD receiving statin therapy and for whom proteomic data were available for 6791 individuals. Exposures: Protein risk scores (based on 4963 plasma protein levels and developed in a training set in the primary event population); polygenic risk scores for coronary artery disease and stroke; and clinical risk factors that included age, sex, statin use, hypertension treatment, type 2 diabetes, body mass index, and smoking status at the time of plasma sampling. Main Outcomes and Measures: Outcomes were composites of myocardial infarction, stroke, and coronary heart disease death or cardiovascular death. Performance was evaluated using Cox survival models and measures of discrimination and reclassification that accounted for the competing risk of non-ASCVD death. Results: In the primary event population test set (4018 individuals [59.0% women]; 465 events; median follow-up, 15.8 years), the protein risk score had a hazard ratio (HR) of 1.93 per SD (95% CI, 1.75 to 2.13). Addition of protein risk score and polygenic risk scores significantly increased the C index when added to a clinical risk factor model (C index change, 0.022 [95% CI, 0.007 to 0.038]). Addition of the protein risk score alone to a clinical risk factor model also led to a significantly increased C index (difference, 0.014 [95% CI, 0.002 to 0.028]). Among White individuals in the secondary event population (6307 participants; 432 events; median follow-up, 2.2 years), the protein risk score had an HR of 1.62 per SD (95% CI, 1.48 to 1.79) and significantly increased C index when added to a clinical risk factor model (C index change, 0.026 [95% CI, 0.011 to 0.042]). The protein risk score was significantly associated with major adverse cardiovascular events among individuals of African and Asian ancestries in the secondary event population. Conclusions and Relevance: A protein risk score was significantly associated with ASCVD events in primary and secondary event populations. When added to clinical risk factors, the protein risk score and polygenic risk score both provided statistically significant but modest improvement in discrimination. This study of 13 540 individuals in Iceland (aged 40-75 years) evaluated the utility of protein risk scores for prediction of atherosclerotic cardiovascular disease events compared to risk prediction using polygenic risk scores in addition to risk scores based on clinical risk factors [ABSTRACT FROM AUTHOR]
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- 2023
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13. HaploGrouper: a generalized approach to haplogroup classification.
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Jagadeesan, Anuradha, Ebenesersdóttir, S Sunna, Guðmundsdóttir, Valdis B, Thordardottir, Elisabet Linda, Moore, Kristjan H S, and Helgason, Agnar
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Y chromosome ,MITOCHONDRIAL DNA ,HUMAN DNA ,HAPLOTYPES ,INTERNET servers ,CLASSIFICATION ,HAPLOGROUPS - Abstract
Motivation We introduce HaploGrouper, a versatile software to classify haplotypes into haplogroups on the basis of a known phylogenetic tree. A typical use case for this software is the assignment of haplogroups to human mitochondrial DNA (mtDNA) or Y-chromosome haplotypes. Existing state-of-the-art haplogroup-calling software is typically hard-wired to work only with either mtDNA or Y-chromosome haplotypes from humans. Results HaploGrouper exhibits comparable accuracy in these instances and has the advantage of being able to assign haplogroups to any kind of haplotypes from any species—given an extant annotated phylogenetic tree defined by sequence variants. Availability and implementation The software is available at the following URL https://gitlab.com/bio%5fanth%5fdecode/haploGrouper. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]
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- 2021
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14. Ancient genomes from Iceland reveal the making of a human population.
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Sunna Ebenesersdóttir, S., Sandoval-Velasco, Marcela, Gunnarsdóttir, Ellen D., Jagadeesan, Anuradha, Guðmundsdóttir, Valdís B., Thordardóttir, Elísabet L., Einarsdóttir, Margrét S., Moore, Kristjan H. S., Sigurðsson, Ásgeir, Magnúsdóttir, Droplaug N., Jónsson, Hákon, Snorradóttir, Steinunn, Hovig, Eivind, Møller, Pål, Kockum, Ingrid, Olsson, Tomas, Alfredsson, Lars, Hansen, Thomas F., Werge, Thomas, and Cavalleri, Gianpiero L.
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- 2018
15. Corroborating written history with ancient DNA: The case of the Well-man described in an Old Norse saga .
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Ellegaard MR, Ebenesersdóttir SS, Moore KHS, Petersén A, Vågene ÅJ, Bieker VC, Denham SD, Cavalleri GL, Gilbert E, Werge T, Hansen TF, Kockum I, Alfredsson L, Olsson T, Hovig E, Gilbert MTP, Stefánsson K, Stenøien HK, Helgason A, and Martin MD
- Abstract
The potential of ancient DNA analyses to provide independent sources of information about events in the historical record remains to be demonstrated. Here we apply palaeogenomic analysis to human remains excavated from a medieval well at the ruins of Sverresborg Castle in central Norway. In Sverris Saga , the Old Norse saga of King Sverre Sigurdsson, one passage details a 1197-CE raid on the castle and mentions a dead man thrown into the well. Radiocarbon dating supports that these are that individual's remains. We sequenced the Well-man's nuclear genome to 3.4× and compared it to Scandinavian populations, revealing he was closely related to inhabitants of southern Norway. This was surprising because King Sverre's defeated army was assumed to be recruited from parts of central Norway, whereas the raiders were from the south. The findings also indicate that the unique genetic drift seen in present-day southern Norwegians already existed 800 years ago., Competing Interests: The authors declare no competing interests., (© 2024 The Author(s).)
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- 2024
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16. Actionable Genotypes and Their Association with Life Span in Iceland.
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Jensson BO, Arnadottir GA, Katrinardottir H, Fridriksdottir R, Helgason H, Oddsson A, Sveinbjornsson G, Eggertsson HP, Halldorsson GH, Atlason BA, Jonsson H, Oskarsson GR, Sturluson A, Gudjonsson SA, Thorisson GA, Zink F, Moore KHS, Palsson G, Sigurdsson A, Jonasdottir A, Jonasdottir A, Magnusson MK, Helgadottir A, Steinthorsdottir V, Gudmundsson J, Stacey SN, Hilmarsson R, Olafsson I, Johannsson OT, Arnar DO, Saemundsdottir J, Magnusson OT, Masson G, Halldorsson BV, Helgason A, Stefansson H, Jonsdottir I, Holm H, Rafnar T, Thorsteinsdottir U, Gudbjartsson DF, Stefansson K, and Sulem P
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- Humans, Alleles, Genetic Testing, Genetic Variation, Genotype, Iceland epidemiology, Cardiovascular Diseases genetics, Neoplasms genetics, Genomics, Longevity genetics, Disease genetics
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Background: In 2021, the American College of Medical Genetics and Genomics (ACMG) recommended reporting actionable genotypes in 73 genes associated with diseases for which preventive or therapeutic measures are available. Evaluations of the association of actionable genotypes in these genes with life span are currently lacking., Methods: We assessed the prevalence of coding and splice variants in genes on the ACMG Secondary Findings, version 3.0 (ACMG SF v3.0), list in the genomes of 57,933 Icelanders. We assigned pathogenicity to all reviewed variants using reported evidence in the ClinVar database, the frequency of variants, and their associations with disease to create a manually curated set of actionable genotypes (variants). We assessed the relationship between these genotypes and life span and further examined the specific causes of death among carriers., Results: Through manual curation of 4405 sequence variants in the ACMG SF v3.0 genes, we identified 235 actionable genotypes in 53 genes. Of the 57,933 participants, 2306 (4.0%) carried at least one actionable genotype. We found shorter median survival among persons carrying actionable genotypes than among noncarriers. Specifically, we found that carrying an actionable genotype in a cancer gene was associated with survival that was 3 years shorter than that among noncarriers, with causes of death among carriers attributed primarily to cancer-related conditions. Furthermore, we found evidence of association between carrying an actionable genotype in certain genes in the cardiovascular disease group and a reduced life span., Conclusions: On the basis of the ACMG SF v3.0 guidelines, we found that approximately 1 in 25 Icelanders carried an actionable genotype and that carrying such a genotype was associated with a reduced life span. (Funded by deCODE Genetics-Amgen.)., (Copyright © 2023 Massachusetts Medical Society.)
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- 2023
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17. The genetic history of Scandinavia from the Roman Iron Age to the present.
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Rodríguez-Varela R, Moore KHS, Ebenesersdóttir SS, Kilinc GM, Kjellström A, Papmehl-Dufay L, Alfsdotter C, Berglund B, Alrawi L, Kashuba N, Sobrado V, Lagerholm VK, Gilbert E, Cavalleri GL, Hovig E, Kockum I, Olsson T, Alfredsson L, Hansen TF, Werge T, Munters AR, Bernhardsson C, Skar B, Christophersen A, Turner-Walker G, Gopalakrishnan S, Daskalaki E, Omrak A, Pérez-Ramallo P, Skoglund P, Girdland-Flink L, Gunnarsson F, Hedenstierna-Jonson C, Gilbert MTP, Lidén K, Jakobsson M, Einarsson L, Victor H, Krzewińska M, Zachrisson T, Storå J, Stefánsson K, Helgason A, and Götherström A
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- Humans, Europe, Genetic Variation, Scandinavian and Nordic Countries, United Kingdom, White People genetics, White People history, Human Migration, Genome, Human
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We investigate a 2,000-year genetic transect through Scandinavia spanning the Iron Age to the present, based on 48 new and 249 published ancient genomes and genotypes from 16,638 modern individuals. We find regional variation in the timing and magnitude of gene flow from three sources: the eastern Baltic, the British-Irish Isles, and southern Europe. British-Irish ancestry was widespread in Scandinavia from the Viking period, whereas eastern Baltic ancestry is more localized to Gotland and central Sweden. In some regions, a drop in current levels of external ancestry suggests that ancient immigrants contributed proportionately less to the modern Scandinavian gene pool than indicated by the ancestry of genomes from the Viking and Medieval periods. Finally, we show that a north-south genetic cline that characterizes modern Scandinavians is mainly due to the differential levels of Uralic ancestry and that this cline existed in the Viking Age and possibly earlier., Competing Interests: Declaration of interests A.H., K.H.S.M., K.S., and S.S.E. are employees of deCODE genetics., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)
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- 2023
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18. The population genomic legacy of the second plague pandemic.
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Gopalakrishnan S, Ebenesersdóttir SS, Lundstrøm IKC, Turner-Walker G, Moore KHS, Luisi P, Margaryan A, Martin MD, Ellegaard MR, Magnússon ÓÞ, Sigurðsson Á, Snorradóttir S, Magnúsdóttir DN, Laffoon JE, van Dorp L, Liu X, Moltke I, Ávila-Arcos MC, Schraiber JG, Rasmussen S, Juan D, Gelabert P, de-Dios T, Fotakis AK, Iraeta-Orbegozo M, Vågene ÅJ, Denham SD, Christophersen A, Stenøien HK, Vieira FG, Liu S, Günther T, Kivisild T, Moseng OG, Skar B, Cheung C, Sandoval-Velasco M, Wales N, Schroeder H, Campos PF, Guðmundsdóttir VB, Sicheritz-Ponten T, Petersen B, Halgunset J, Gilbert E, Cavalleri GL, Hovig E, Kockum I, Olsson T, Alfredsson L, Hansen TF, Werge T, Willerslev E, Balloux F, Marques-Bonet T, Lalueza-Fox C, Nielsen R, Stefánsson K, Helgason A, and Gilbert MTP
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- Humans, Pandemics history, Metagenomics, Genome, Bacterial, Phylogeny, Plague epidemiology, Plague genetics
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Human populations have been shaped by catastrophes that may have left long-lasting signatures in their genomes. One notable example is the second plague pandemic that entered Europe in ca. 1,347 CE and repeatedly returned for over 300 years, with typical village and town mortality estimated at 10%-40%.
1 It is assumed that this high mortality affected the gene pools of these populations. First, local population crashes reduced genetic diversity. Second, a change in frequency is expected for sequence variants that may have affected survival or susceptibility to the etiologic agent (Yersinia pestis).2 Third, mass mortality might alter the local gene pools through its impact on subsequent migration patterns. We explored these factors using the Norwegian city of Trondheim as a model, by sequencing 54 genomes spanning three time periods: (1) prior to the plague striking Trondheim in 1,349 CE, (2) the 17th -19th century, and (3) the present. We find that the pandemic period shaped the gene pool by reducing long distance immigration, in particular from the British Isles, and inducing a bottleneck that reduced genetic diversity. Although we also observe an excess of large FST values at multiple loci in the genome, these are shaped by reference biases introduced by mapping our relatively low genome coverage degraded DNA to the reference genome. This implies that attempts to detect selection using ancient DNA (aDNA) datasets that vary by read length and depth of sequencing coverage may be particularly challenging until methods have been developed to account for the impact of differential reference bias on test statistics., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)- Published
- 2022
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19. Multiomics analysis of rheumatoid arthritis yields sequence variants that have large effects on risk of the seropositive subset.
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Saevarsdottir S, Stefansdottir L, Sulem P, Thorleifsson G, Ferkingstad E, Rutsdottir G, Glintborg B, Westerlind H, Grondal G, Loft IC, Sorensen SB, Lie BA, Brink M, Ärlestig L, Arnthorsson AO, Baecklund E, Banasik K, Bank S, Bjorkman LI, Ellingsen T, Erikstrup C, Frei O, Gjertsson I, Gudbjartsson DF, Gudjonsson SA, Halldorsson GH, Hendricks O, Hillert J, Hogdall E, Jacobsen S, Jensen DV, Jonsson H, Kastbom A, Kockum I, Kristensen S, Kristjansdottir H, Larsen MH, Linauskas A, Hauge EM, Loft AG, Ludviksson BR, Lund SH, Markusson T, Masson G, Melsted P, Moore KHS, Munk H, Nielsen KR, Norddahl GL, Oddsson A, Olafsdottir TA, Olason PI, Olsson T, Ostrowski SR, Hørslev-Petersen K, Rognvaldsson S, Sanner H, Silberberg GN, Stefansson H, Sørensen E, Sørensen IJ, Turesson C, Bergman T, Alfredsson L, Kvien TK, Brunak S, Steinsson K, Andersen V, Andreassen OA, Rantapää-Dahlqvist S, Hetland ML, Klareskog L, Askling J, Padyukov L, Pedersen OB, Thorsteinsdottir U, Jonsdottir I, and Stefansson K
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- Genetic Predisposition to Disease genetics, Humans, Interferon-alpha, Janus Kinases genetics, Protein Tyrosine Phosphatase, Non-Receptor Type 22 genetics, Proteomics, STAT Transcription Factors genetics, Signal Transduction genetics, Arthritis, Rheumatoid genetics, Genome-Wide Association Study
- Abstract
Objectives: To find causal genes for rheumatoid arthritis (RA) and its seropositive (RF and/or ACPA positive) and seronegative subsets., Methods: We performed a genome-wide association study (GWAS) of 31 313 RA cases (68% seropositive) and ~1 million controls from Northwestern Europe. We searched for causal genes outside the HLA-locus through effect on coding, mRNA expression in several tissues and/or levels of plasma proteins (SomaScan) and did network analysis (Qiagen)., Results: We found 25 sequence variants for RA overall, 33 for seropositive and 2 for seronegative RA, altogether 37 sequence variants at 34 non-HLA loci, of which 15 are novel. Genomic, transcriptomic and proteomic analysis of these yielded 25 causal genes in seropositive RA and additional two overall. Most encode proteins in the network of interferon-alpha/beta and IL-12/23 that signal through the JAK/STAT-pathway. Highlighting those with largest effect on seropositive RA, a rare missense variant in STAT4 (rs140675301-A) that is independent of reported non-coding STAT4 -variants, increases the risk of seropositive RA 2.27-fold (p=2.1×10
-9 ), more than the rs2476601-A missense variant in PTPN22 (OR=1.59, p=1.3×10-160 ). STAT4 rs140675301-A replaces hydrophilic glutamic acid with hydrophobic valine (Glu128Val) in a conserved, surface-exposed loop. A stop-mutation (rs76428106-C) in FLT3 increases seropositive RA risk (OR=1.35, p=6.6×10-11 ). Independent missense variants in TYK2 (rs34536443-C, rs12720356-C, rs35018800-A, latter two novel) associate with decreased risk of seropositive RA (ORs=0.63-0.87, p=10-9 -10-27 ) and decreased plasma levels of interferon-alpha/beta receptor 1 that signals through TYK2/JAK1/STAT4., Conclusion: Sequence variants pointing to causal genes in the JAK/STAT pathway have largest effect on seropositive RA, while associations with seronegative RA remain scarce., Competing Interests: Competing interests: Authors affiliated with deCODE Genetics/Amgen declare competing financial interests as employees. OAA is a consultant to HealthLytix. The following coauthors report the following but unrelated to the current report: Karolinska Institutet, with JA as principal investigator, has entered into agreements with the following entities, mainly but not exclusively for safety monitoring of rheumatology immunomodulators: Abbvie, BMS, Eli Lilly, Janssen, MSD, Pfizer, Roche, Samsung Bioepis and Sanofi, unrelated to the present study. SB has ownerships in Intomics A/S, Hoba Therapeutics Aps, Novo Nordisk A/S, Lundbeck A/S and managing board memberships in Proscion A/S and Intomics A/S. BG has received research grants from AbbVie, Bristol Myers-Squibb and Pfizer; OH has received research grants from AbbVie, Novartis and Pfizer, DVJ has received speaker and consultation fees from AbbVie, Janssen, Lilly, MSD, Novartis, Pfizer, Roche and UCB, AGL has received speaking and/or consulting fees from AbbVie, Janssen, Lilly, MSD, Novartis, Pfizer, Roche and UCB; and CT has received consulting fees from Roche, speaker fees from Abbvie, Bristol Myers-Squibb, Nordic Drugs, Pfizer and Roche, and an unrestricted grant from Bristol Myers-Squibb., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)- Published
- 2022
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