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1.  Correction: The Influence of Age and Sex on Genetic Associations with Adult Body Size and Shape: A Large-Scale Genome-Wide Interaction Study 
Winkler, Thomas W. | Justice, Anne E. | Graff, Mariaelisa | Barata, Llilda | Feitosa, Mary F. | Chu, Su | Czajkowski, Jacek | Esko, Tõnu | Fall, Tove | Kilpeläinen, Tuomas O. | Lu, Yingchang | Mägi, Reedik | Mihailov, Evelin | Pers, Tune H. | Rüeger, Sina | Teumer, Alexander | Ehret, Georg B. | Ferreira, Teresa | Heard-Costa, Nancy L. | Karjalainen, Juha | Lagou, Vasiliki | Mahajan, Anubha | Neinast, Michael D. | Prokopenko, Inga | Simino, Jeannette | Teslovich, Tanya M. | Jansen, Rick | Westra, Harm-Jan | White, Charles C. | Absher, Devin | Ahluwalia, Tarunveer S. | Ahmad, Shafqat | Albrecht, Eva | Alves, Alexessander Couto | Bragg-Gresham, Jennifer L. | de Craen, Anton J. M. | Bis, Joshua C. | Bonnefond, Amélie | Boucher, Gabrielle | Cadby, Gemma | Cheng, Yu-Ching | Chiang, Charleston W. K. | Delgado, Graciela | Demirkan, Ayse | Dueker, Nicole | Eklund, Niina | Eiriksdottir, Gudny | Eriksson, Joel | Feenstra, Bjarke | Fischer, Krista | Frau, Francesca | Galesloot, Tessel E. | Geller, Frank | Goel, Anuj | Gorski, Mathias | Grammer, Tanja B. | Gustafsson, Stefan | Haitjema, Saskia | Hottenga, Jouke-Jan | Huffman, Jennifer E. | Jackson, Anne U. | Jacobs, Kevin B. | Johansson, Åsa | Kaakinen, Marika | Kleber, Marcus E. | Lahti, Jari | Mateo Leach, Irene | Lehne, Benjamin | Liu, Youfang | Lo, Ken Sin | Lorentzon, Mattias | Luan, Jian'an | Madden, Pamela A. F. | Mangino, Massimo | McKnight, Barbara | Medina-Gomez, Carolina | Monda, Keri L. | Montasser, May E. | Müller, Gabriele | Müller-Nurasyid, Martina | Nolte, Ilja M. | Panoutsopoulou, Kalliope | Pascoe, Laura | Paternoster, Lavinia | Rayner, Nigel W. | Renström, Frida | Rizzi, Federica | Rose, Lynda M. | Ryan, Kathy A. | Salo, Perttu | Sanna, Serena | Scharnagl, Hubert | Shi, Jianxin | Smith, Albert Vernon | Southam, Lorraine | Stančáková, Alena | Steinthorsdottir, Valgerdur | Strawbridge, Rona J. | Sung, Yun Ju | Tachmazidou, Ioanna | Tanaka, Toshiko | Thorleifsson, Gudmar | Trompet, Stella | Pervjakova, Natalia | Tyrer, Jonathan P. | Vandenput, Liesbeth | van der Laan, Sander W | van der Velde, Nathalie | van Setten, Jessica | van Vliet-Ostaptchouk, Jana V. | Verweij, Niek | Vlachopoulou, Efthymia | Waite, Lindsay L. | Wang, Sophie R. | Wang, Zhaoming | Wild, Sarah H. | Willenborg, Christina | Wilson, James F. | Wong, Andrew | Yang, Jian | Yengo, Loïc | Yerges-Armstrong, Laura M. | Yu, Lei | Zhang, Weihua | Zhao, Jing Hua | Andersson, Ehm A. | Bakker, Stephan J. L. | Baldassarre, Damiano | Banasik, Karina | Barcella, Matteo | Barlassina, Cristina | Bellis, Claire | Benaglio, Paola | Blangero, John | Blüher, Matthias | Bonnet, Fabrice | Bonnycastle, Lori L. | Boyd, Heather A. | Bruinenberg, Marcel | Buchman, Aron S | Campbell, Harry | Chen, Yii-Der Ida | Chines, Peter S. | Claudi-Boehm, Simone | Cole, John | Collins, Francis S. | de Geus, Eco J. C. | de Groot, Lisette C. P. G. M. | Dimitriou, Maria | Duan, Jubao | Enroth, Stefan | Eury, Elodie | Farmaki, Aliki-Eleni | Forouhi, Nita G. | Friedrich, Nele | Gejman, Pablo V. | Gigante, Bruna | Glorioso, Nicola | Go, Alan S. | Gottesman, Omri | Gräßler, Jürgen | Grallert, Harald | Grarup, Niels | Gu, Yu-Mei | Broer, Linda | Ham, Annelies C. | Hansen, Torben | Harris, Tamara B. | Hartman, Catharina A. | Hassinen, Maija | Hastie, Nicholas | Hattersley, Andrew T. | Heath, Andrew C. | Henders, Anjali K. | Hernandez, Dena | Hillege, Hans | Holmen, Oddgeir | Hovingh, Kees G | Hui, Jennie | Husemoen, Lise L. | Hutri-Kähönen, Nina | Hysi, Pirro G. | Illig, Thomas | De Jager, Philip L. | Jalilzadeh, Shapour | Jørgensen, Torben | Jukema, J. Wouter | Juonala, Markus | Kanoni, Stavroula | Karaleftheri, Maria | Khaw, Kay Tee | Kinnunen, Leena | Kittner, Steven J. | Koenig, Wolfgang | Kolcic, Ivana | Kovacs, Peter | Krarup, Nikolaj T. | Kratzer, Wolfgang | Krüger, Janine | Kuh, Diana | Kumari, Meena | Kyriakou, Theodosios | Langenberg, Claudia | Lannfelt, Lars | Lanzani, Chiara | Lotay, Vaneet | Launer, Lenore J. | Leander, Karin | Lindström, Jaana | Linneberg, Allan | Liu, Yan-Ping | Lobbens, Stéphane | Luben, Robert | Lyssenko, Valeriya | Männistö, Satu | Magnusson, Patrik K. | McArdle, Wendy L. | Menni, Cristina | Merger, Sigrun | Milani, Lili | Montgomery, Grant W. | Morris, Andrew P. | Narisu, Narisu | Nelis, Mari | Ong, Ken K. | Palotie, Aarno | Pérusse, Louis | Pichler, Irene | Pilia, Maria G. | Pouta, Anneli | Rheinberger, Myriam | Ribel-Madsen, Rasmus | Richards, Marcus | Rice, Kenneth M. | Rice, Treva K. | Rivolta, Carlo | Salomaa, Veikko | Sanders, Alan R. | Sarzynski, Mark A. | Scholtens, Salome | Scott, Robert A. | Scott, William R. | Sebert, Sylvain | Sengupta, Sebanti | Sennblad, Bengt | Seufferlein, Thomas | Silveira, Angela | Slagboom, P. Eline | Smit, Jan H. | Sparsø, Thomas H. | Stirrups, Kathleen | Stolk, Ronald P. | Stringham, Heather M. | Swertz, Morris A | Swift, Amy J. | Syvänen, Ann-Christine | Tan, Sian-Tsung | Thorand, Barbara | Tönjes, Anke | Tremblay, Angelo | Tsafantakis, Emmanouil | van der Most, Peter J. | Völker, Uwe | Vohl, Marie-Claude | Vonk, Judith M. | Waldenberger, Melanie | Walker, Ryan W. | Wennauer, Roman | Widén, Elisabeth | Willemsen, Gonneke | Wilsgaard, Tom | Wright, Alan F. | Zillikens, M. Carola | van Dijk, Suzanne C. | van Schoor, Natasja M. | Asselbergs, Folkert W. | de Bakker, Paul I. W. | Beckmann, Jacques S. | Beilby, John | Bennett, David A. | Bergman, Richard N. | Bergmann, Sven | Böger, Carsten A. | Boehm, Bernhard O. | Boerwinkle, Eric | Boomsma, Dorret I. | Bornstein, Stefan R. | Bottinger, Erwin P. | Bouchard, Claude | Chambers, John C. | Chanock, Stephen J. | Chasman, Daniel I. | Cucca, Francesco | Cusi, Daniele | Dedoussis, George | Erdmann, Jeanette | Eriksson, Johan G. | Evans, Denis A. | de Faire, Ulf | Farrall, Martin | Ferrucci, Luigi | Ford, Ian | Franke, Lude | Franks, Paul W. | Froguel, Philippe | Gansevoort, Ron T. | Gieger, Christian | Grönberg, Henrik | Gudnason, Vilmundur | Gyllensten, Ulf | Hall, Per | Hamsten, Anders | van der Harst, Pim | Hayward, Caroline | Heliövaara, Markku | Hengstenberg, Christian | Hicks, Andrew A | Hingorani, Aroon | Hofman, Albert | Hu, Frank | Huikuri, Heikki V. | Hveem, Kristian | James, Alan L. | Jordan, Joanne M. | Jula, Antti | Kähönen, Mika | Kajantie, Eero | Kathiresan, Sekar | Kiemeney, Lambertus A. L. M. | Kivimaki, Mika | Knekt, Paul B. | Koistinen, Heikki A. | Kooner, Jaspal S. | Koskinen, Seppo | Kuusisto, Johanna | Maerz, Winfried | Martin, Nicholas G | Laakso, Markku | Lakka, Timo A. | Lehtimäki, Terho | Lettre, Guillaume | Levinson, Douglas F. | Lind, Lars | Lokki, Marja-Liisa | Mäntyselkä, Pekka | Melbye, Mads | Metspalu, Andres | Mitchell, Braxton D. | Moll, Frans L. | Murray, Jeffrey C. | Musk, Arthur W. | Nieminen, Markku S. | Njølstad, Inger | Ohlsson, Claes | Oldehinkel, Albertine J. | Oostra, Ben A. | Palmer, Lyle J | Pankow, James S. | Pasterkamp, Gerard | Pedersen, Nancy L. | Pedersen, Oluf | Penninx, Brenda W. | Perola, Markus | Peters, Annette | Polašek, Ozren | Pramstaller, Peter P. | Psaty, Bruce M. | Qi, Lu | Quertermous, Thomas | Raitakari, Olli T. | Rankinen, Tuomo | Rauramaa, Rainer | Ridker, Paul M. | Rioux, John D. | Rivadeneira, Fernando | Rotter, Jerome I. | Rudan, Igor | den Ruijter, Hester M. | Saltevo, Juha | Sattar, Naveed | Schunkert, Heribert | Schwarz, Peter E. H. | Shuldiner, Alan R. | Sinisalo, Juha | Snieder, Harold | Sørensen, Thorkild I. A. | Spector, Tim D. | Staessen, Jan A. | Stefania, Bandinelli | Thorsteinsdottir, Unnur | Stumvoll, Michael | Tardif, Jean-Claude | Tremoli, Elena | Tuomilehto, Jaakko | Uitterlinden, André G. | Uusitupa, Matti | Verbeek, André L. M. | Vermeulen, Sita H. | Viikari, Jorma S. | Vitart, Veronique | Völzke, Henry | Vollenweider, Peter | Waeber, Gérard | Walker, Mark | Wallaschofski, Henri | Wareham, Nicholas J. | Watkins, Hugh | Zeggini, Eleftheria | Chakravarti, Aravinda | Clegg, Deborah J. | Cupples, L. Adrienne | Gordon-Larsen, Penny | Jaquish, Cashell E. | Rao, D. C. | Abecasis, Goncalo R. | Assimes, Themistocles L. | Barroso, Inês | Berndt, Sonja I. | Boehnke, Michael | Deloukas, Panos | Fox, Caroline S. | Groop, Leif C. | Hunter, David J. | Ingelsson, Erik | Kaplan, Robert C. | McCarthy, Mark I. | Mohlke, Karen L. | O'Connell, Jeffrey R. | Schlessinger, David | Strachan, David P. | Stefansson, Kari | van Duijn, Cornelia M. | Hirschhorn, Joel N. | Lindgren, Cecilia M. | Heid, Iris M. | North, Kari E. | Borecki, Ingrid B. | Kutalik, Zoltán | Loos, Ruth J. F.
PLoS Genetics  2016;12(6):e1006166.
doi:10.1371/journal.pgen.1006166
PMCID: PMC4927064  PMID: 27355579
3.  Transcript Expression Data from Human Islets Links Regulatory Signals from Genome-Wide Association Studies for Type 2 Diabetes and Glycemic Traits to Their Downstream Effectors 
PLoS Genetics  2015;11(12):e1005694.
The intersection of genome-wide association analyses with physiological and functional data indicates that variants regulating islet gene transcription influence type 2 diabetes (T2D) predisposition and glucose homeostasis. However, the specific genes through which these regulatory variants act remain poorly characterized. We generated expression quantitative trait locus (eQTL) data in 118 human islet samples using RNA-sequencing and high-density genotyping. We identified fourteen loci at which cis-exon-eQTL signals overlapped active islet chromatin signatures and were coincident with established T2D and/or glycemic trait associations. ‎At some, these data provide an experimental link between GWAS signals and biological candidates, such as DGKB and ADCY5. At others, the cis-signals implicate genes with no prior connection to islet biology, including WARS and ZMIZ1. At the ZMIZ1 locus, we show that perturbation of ZMIZ1 expression in human islets and beta-cells influences exocytosis and insulin secretion, highlighting a novel role for ZMIZ1 in the maintenance of glucose homeostasis. Together, these findings provide a significant advance in the mechanistic insights of T2D and glycemic trait association loci.
Author Summary
Genetic studies have uncovered many different parts of the genome playing a role in the risk of developing diabetes, or affecting blood sugar levels in the normal population. However, it has so far been difficult to tie these parts of the genome to genes that are responsible for the observed changes in risk and/or blood sugar levels (“effector transcripts”). It is clear from the genetic data that one of the key tissues in these phenotypes is the human pancreatic islet of Langerhans, but the limited availability of this tissue has been a major hurdle in translating the genetics into biology. Here, we present a study linking genetic variation to gene expression changes in 118 islet preparations. Using these cis-eQTLs, we provide candidate effector transcripts at 14 regions of the genome previously associated with glucose phenotypes. Many of the genes implicated through this approach have no known role in the islet. By experimentally changing the expression levels of one of these novel genes, ZMIZ1, in human islets and beta-cells, we uncovered a novel role for ZMIZ1 in exocytosis and insulin secretion. These findings therefore significantly improve the discovery of biology underlying type 2 diabetes and glucose trait association.
doi:10.1371/journal.pgen.1005694
PMCID: PMC4666611  PMID: 26624892
4.  The Influence of Age and Sex on Genetic Associations with Adult Body Size and Shape: A Large-Scale Genome-Wide Interaction Study 
Winkler, Thomas W. | Justice, Anne E. | Graff, Mariaelisa | Barata, Llilda | Feitosa, Mary F. | Chu, Su | Czajkowski, Jacek | Esko, Tõnu | Fall, Tove | Kilpeläinen, Tuomas O. | Lu, Yingchang | Mägi, Reedik | Mihailov, Evelin | Pers, Tune H. | Rüeger, Sina | Teumer, Alexander | Ehret, Georg B. | Ferreira, Teresa | Heard-Costa, Nancy L. | Karjalainen, Juha | Lagou, Vasiliki | Mahajan, Anubha | Neinast, Michael D. | Prokopenko, Inga | Simino, Jeannette | Teslovich, Tanya M. | Jansen, Rick | Westra, Harm-Jan | White, Charles C. | Absher, Devin | Ahluwalia, Tarunveer S. | Ahmad, Shafqat | Albrecht, Eva | Alves, Alexessander Couto | Bragg-Gresham, Jennifer L. | de Craen, Anton J. M. | Bis, Joshua C. | Bonnefond, Amélie | Boucher, Gabrielle | Cadby, Gemma | Cheng, Yu-Ching | Chiang, Charleston W. K. | Delgado, Graciela | Demirkan, Ayse | Dueker, Nicole | Eklund, Niina | Eiriksdottir, Gudny | Eriksson, Joel | Feenstra, Bjarke | Fischer, Krista | Frau, Francesca | Galesloot, Tessel E. | Geller, Frank | Goel, Anuj | Gorski, Mathias | Grammer, Tanja B. | Gustafsson, Stefan | Haitjema, Saskia | Hottenga, Jouke-Jan | Huffman, Jennifer E. | Jackson, Anne U. | Jacobs, Kevin B. | Johansson, Åsa | Kaakinen, Marika | Kleber, Marcus E. | Lahti, Jari | Leach, Irene Mateo | Lehne, Benjamin | Liu, Youfang | Lo, Ken Sin | Lorentzon, Mattias | Luan, Jian'an | Madden, Pamela A. F. | Mangino, Massimo | McKnight, Barbara | Medina-Gomez, Carolina | Monda, Keri L. | Montasser, May E. | Müller, Gabriele | Müller-Nurasyid, Martina | Nolte, Ilja M. | Panoutsopoulou, Kalliope | Pascoe, Laura | Paternoster, Lavinia | Rayner, Nigel W. | Renström, Frida | Rizzi, Federica | Rose, Lynda M. | Ryan, Kathy A. | Salo, Perttu | Sanna, Serena | Scharnagl, Hubert | Shi, Jianxin | Smith, Albert Vernon | Southam, Lorraine | Stančáková, Alena | Steinthorsdottir, Valgerdur | Strawbridge, Rona J. | Sung, Yun Ju | Tachmazidou, Ioanna | Tanaka, Toshiko | Thorleifsson, Gudmar | Trompet, Stella | Pervjakova, Natalia | Tyrer, Jonathan P. | Vandenput, Liesbeth | van der Laan, Sander W | van der Velde, Nathalie | van Setten, Jessica | van Vliet-Ostaptchouk, Jana V. | Verweij, Niek | Vlachopoulou, Efthymia | Waite, Lindsay L. | Wang, Sophie R. | Wang, Zhaoming | Wild, Sarah H. | Willenborg, Christina | Wilson, James F. | Wong, Andrew | Yang, Jian | Yengo, Loïc | Yerges-Armstrong, Laura M. | Yu, Lei | Zhang, Weihua | Zhao, Jing Hua | Andersson, Ehm A. | Bakker, Stephan J. L. | Baldassarre, Damiano | Banasik, Karina | Barcella, Matteo | Barlassina, Cristina | Bellis, Claire | Benaglio, Paola | Blangero, John | Blüher, Matthias | Bonnet, Fabrice | Bonnycastle, Lori L. | Boyd, Heather A. | Bruinenberg, Marcel | Buchman, Aron S | Campbell, Harry | Chen, Yii-Der Ida | Chines, Peter S. | Claudi-Boehm, Simone | Cole, John | Collins, Francis S. | de Geus, Eco J. C. | de Groot, Lisette C. P. G. M. | Dimitriou, Maria | Duan, Jubao | Enroth, Stefan | Eury, Elodie | Farmaki, Aliki-Eleni | Forouhi, Nita G. | Friedrich, Nele | Gejman, Pablo V. | Gigante, Bruna | Glorioso, Nicola | Go, Alan S. | Gottesman, Omri | Gräßler, Jürgen | Grallert, Harald | Grarup, Niels | Gu, Yu-Mei | Broer, Linda | Ham, Annelies C. | Hansen, Torben | Harris, Tamara B. | Hartman, Catharina A. | Hassinen, Maija | Hastie, Nicholas | Hattersley, Andrew T. | Heath, Andrew C. | Henders, Anjali K. | Hernandez, Dena | Hillege, Hans | Holmen, Oddgeir | Hovingh, Kees G | Hui, Jennie | Husemoen, Lise L. | Hutri-Kähönen, Nina | Hysi, Pirro G. | Illig, Thomas | De Jager, Philip L. | Jalilzadeh, Shapour | Jørgensen, Torben | Jukema, J. Wouter | Juonala, Markus | Kanoni, Stavroula | Karaleftheri, Maria | Khaw, Kay Tee | Kinnunen, Leena | Kittner, Steven J. | Koenig, Wolfgang | Kolcic, Ivana | Kovacs, Peter | Krarup, Nikolaj T. | Kratzer, Wolfgang | Krüger, Janine | Kuh, Diana | Kumari, Meena | Kyriakou, Theodosios | Langenberg, Claudia | Lannfelt, Lars | Lanzani, Chiara | Lotay, Vaneet | Launer, Lenore J. | Leander, Karin | Lindström, Jaana | Linneberg, Allan | Liu, Yan-Ping | Lobbens, Stéphane | Luben, Robert | Lyssenko, Valeriya | Männistö, Satu | Magnusson, Patrik K. | McArdle, Wendy L. | Menni, Cristina | Merger, Sigrun | Milani, Lili | Montgomery, Grant W. | Morris, Andrew P. | Narisu, Narisu | Nelis, Mari | Ong, Ken K. | Palotie, Aarno | Pérusse, Louis | Pichler, Irene | Pilia, Maria G. | Pouta, Anneli | Rheinberger, Myriam | Ribel-Madsen, Rasmus | Richards, Marcus | Rice, Kenneth M. | Rice, Treva K. | Rivolta, Carlo | Salomaa, Veikko | Sanders, Alan R. | Sarzynski, Mark A. | Scholtens, Salome | Scott, Robert A. | Scott, William R. | Sebert, Sylvain | Sengupta, Sebanti | Sennblad, Bengt | Seufferlein, Thomas | Silveira, Angela | Slagboom, P. Eline | Smit, Jan H. | Sparsø, Thomas H. | Stirrups, Kathleen | Stolk, Ronald P. | Stringham, Heather M. | Swertz, Morris A | Swift, Amy J. | Syvänen, Ann-Christine | Tan, Sian-Tsung | Thorand, Barbara | Tönjes, Anke | Tremblay, Angelo | Tsafantakis, Emmanouil | van der Most, Peter J. | Völker, Uwe | Vohl, Marie-Claude | Vonk, Judith M. | Waldenberger, Melanie | Walker, Ryan W. | Wennauer, Roman | Widén, Elisabeth | Willemsen, Gonneke | Wilsgaard, Tom | Wright, Alan F. | Zillikens, M. Carola | van Dijk, Suzanne C. | van Schoor, Natasja M. | Asselbergs, Folkert W. | de Bakker, Paul I. W. | Beckmann, Jacques S. | Beilby, John | Bennett, David A. | Bergman, Richard N. | Bergmann, Sven | Böger, Carsten A. | Boehm, Bernhard O. | Boerwinkle, Eric | Boomsma, Dorret I. | Bornstein, Stefan R. | Bottinger, Erwin P. | Bouchard, Claude | Chambers, John C. | Chanock, Stephen J. | Chasman, Daniel I. | Cucca, Francesco | Cusi, Daniele | Dedoussis, George | Erdmann, Jeanette | Eriksson, Johan G. | Evans, Denis A. | de Faire, Ulf | Farrall, Martin | Ferrucci, Luigi | Ford, Ian | Franke, Lude | Franks, Paul W. | Froguel, Philippe | Gansevoort, Ron T. | Gieger, Christian | Grönberg, Henrik | Gudnason, Vilmundur | Gyllensten, Ulf | Hall, Per | Hamsten, Anders | van der Harst, Pim | Hayward, Caroline | Heliövaara, Markku | Hengstenberg, Christian | Hicks, Andrew A | Hingorani, Aroon | Hofman, Albert | Hu, Frank | Huikuri, Heikki V. | Hveem, Kristian | James, Alan L. | Jordan, Joanne M. | Jula, Antti | Kähönen, Mika | Kajantie, Eero | Kathiresan, Sekar | Kiemeney, Lambertus A. L. M. | Kivimaki, Mika | Knekt, Paul B. | Koistinen, Heikki A. | Kooner, Jaspal S. | Koskinen, Seppo | Kuusisto, Johanna | Maerz, Winfried | Martin, Nicholas G | Laakso, Markku | Lakka, Timo A. | Lehtimäki, Terho | Lettre, Guillaume | Levinson, Douglas F. | Lind, Lars | Lokki, Marja-Liisa | Mäntyselkä, Pekka | Melbye, Mads | Metspalu, Andres | Mitchell, Braxton D. | Moll, Frans L. | Murray, Jeffrey C. | Musk, Arthur W. | Nieminen, Markku S. | Njølstad, Inger | Ohlsson, Claes | Oldehinkel, Albertine J. | Oostra, Ben A. | Palmer, Lyle J | Pankow, James S. | Pasterkamp, Gerard | Pedersen, Nancy L. | Pedersen, Oluf | Penninx, Brenda W. | Perola, Markus | Peters, Annette | Polašek, Ozren | Pramstaller, Peter P. | Psaty, Bruce M. | Qi, Lu | Quertermous, Thomas | Raitakari, Olli T. | Rankinen, Tuomo | Rauramaa, Rainer | Ridker, Paul M. | Rioux, John D. | Rivadeneira, Fernando | Rotter, Jerome I. | Rudan, Igor | den Ruijter, Hester M. | Saltevo, Juha | Sattar, Naveed | Schunkert, Heribert | Schwarz, Peter E. H. | Shuldiner, Alan R. | Sinisalo, Juha | Snieder, Harold | Sørensen, Thorkild I. A. | Spector, Tim D. | Staessen, Jan A. | Stefania, Bandinelli | Thorsteinsdottir, Unnur | Stumvoll, Michael | Tardif, Jean-Claude | Tremoli, Elena | Tuomilehto, Jaakko | Uitterlinden, André G. | Uusitupa, Matti | Verbeek, André L. M. | Vermeulen, Sita H. | Viikari, Jorma S. | Vitart, Veronique | Völzke, Henry | Vollenweider, Peter | Waeber, Gérard | Walker, Mark | Wallaschofski, Henri | Wareham, Nicholas J. | Watkins, Hugh | Zeggini, Eleftheria | Chakravarti, Aravinda | Clegg, Deborah J. | Cupples, L. Adrienne | Gordon-Larsen, Penny | Jaquish, Cashell E. | Rao, D. C. | Abecasis, Goncalo R. | Assimes, Themistocles L. | Barroso, Inês | Berndt, Sonja I. | Boehnke, Michael | Deloukas, Panos | Fox, Caroline S. | Groop, Leif C. | Hunter, David J. | Ingelsson, Erik | Kaplan, Robert C. | McCarthy, Mark I. | Mohlke, Karen L. | O'Connell, Jeffrey R. | Schlessinger, David | Strachan, David P. | Stefansson, Kari | van Duijn, Cornelia M. | Hirschhorn, Joel N. | Lindgren, Cecilia M. | Heid, Iris M. | North, Kari E. | Borecki, Ingrid B. | Kutalik, Zoltán | Loos, Ruth J. F.
PLoS Genetics  2015;11(10):e1005378.
Genome-wide association studies (GWAS) have identified more than 100 genetic variants contributing to BMI, a measure of body size, or waist-to-hip ratio (adjusted for BMI, WHRadjBMI), a measure of body shape. Body size and shape change as people grow older and these changes differ substantially between men and women. To systematically screen for age- and/or sex-specific effects of genetic variants on BMI and WHRadjBMI, we performed meta-analyses of 114 studies (up to 320,485 individuals of European descent) with genome-wide chip and/or Metabochip data by the Genetic Investigation of Anthropometric Traits (GIANT) Consortium. Each study tested the association of up to ~2.8M SNPs with BMI and WHRadjBMI in four strata (men ≤50y, men >50y, women ≤50y, women >50y) and summary statistics were combined in stratum-specific meta-analyses. We then screened for variants that showed age-specific effects (G x AGE), sex-specific effects (G x SEX) or age-specific effects that differed between men and women (G x AGE x SEX). For BMI, we identified 15 loci (11 previously established for main effects, four novel) that showed significant (FDR<5%) age-specific effects, of which 11 had larger effects in younger (<50y) than in older adults (≥50y). No sex-dependent effects were identified for BMI. For WHRadjBMI, we identified 44 loci (27 previously established for main effects, 17 novel) with sex-specific effects, of which 28 showed larger effects in women than in men, five showed larger effects in men than in women, and 11 showed opposite effects between sexes. No age-dependent effects were identified for WHRadjBMI. This is the first genome-wide interaction meta-analysis to report convincing evidence of age-dependent genetic effects on BMI. In addition, we confirm the sex-specificity of genetic effects on WHRadjBMI. These results may provide further insights into the biology that underlies weight change with age or the sexually dimorphism of body shape.
Author Summary
Adult body size and body shape differ substantially between men and women and change over time. More than 100 genetic variants that influence body mass index (measure of body size) or waist-to-hip ratio (measure of body shape) have been identified. While there is evidence that some genetic loci affect body shape differently in men than in women, little is known about whether genetic effects differ in older compared to younger adults, and whether such changes differ between men and women. Therefore, we conducted a systematic genome-wide search, including 114 studies (>320,000 individuals), to specifically identify genetic loci with age- and or sex-dependent effects on body size and shape. We identified 15 loci of which the effect on BMI was different in older compared to younger adults, whereas we found no evidence for loci with different effects in men compared to women. The opposite was seen for body shape as we identified 44 loci of which the effect on waist-to-hip ratio differed between men and women, but no difference between younger and older adults were observed. Our observations may provide new insights into the biology that underlies weight change with age or the sexual dimorphism of body shape.
doi:10.1371/journal.pgen.1005378
PMCID: PMC4591371  PMID: 26426971
5.  Evaluating the Performance of Fine-Mapping Strategies at Common Variant GWAS Loci 
PLoS Genetics  2015;11(9):e1005535.
The growing availability of high-quality genomic annotation has increased the potential for mechanistic insights when the specific variants driving common genome-wide association signals are accurately localized. A range of fine-mapping strategies have been advocated, and specific successes reported, but the overall performance of such approaches, in the face of the extensive linkage disequilibrium that characterizes the human genome, is not well understood. Using simulations based on sequence data from the 1000 Genomes Project, we quantify the extent to which fine-mapping, here conducted using an approximate Bayesian approach, can be expected to lead to useful improvements in causal variant localization. We show that resolution is highly variable between loci, and that performance is severely degraded as the statistical power to detect association is reduced. We confirm that, where causal variants are shared between ancestry groups, further improvements in performance can be obtained in a trans-ethnic fine-mapping design. Finally, using empirical data from a recently published genome-wide association study for ankylosing spondylitis, we provide empirical confirmation of the behaviour of the approximate Bayesian approach and demonstrate that seven of twenty-six loci can be fine-mapped to fewer than ten variants.
Author Summary
Over the last few years, several approaches for fine-mapping genome-wide association studies (GWAS) loci have been proposed and used to localize potential causal variants. However, the performance of these types of tests is often poorly characterized. In this study, we used extensive simulations to show that statistical fine-mapping can indeed accurately reduce the number of likely causal variants at common GWAS loci. These approaches can be further improved by changes in study design, such as the inclusion of multiple ethnic groups in the study population. Finally, we demonstrate the utility of this type of approach on a recently published genome-wide association study for ankylosing spondylitis, where we could fine-map seven of the twenty-six loci to a number of variants (n = 10) which is tractable for follow-up in a laboratory setting.
doi:10.1371/journal.pgen.1005535
PMCID: PMC4583479  PMID: 26406328
6.  Discovery and Fine-Mapping of Glycaemic and Obesity-Related Trait Loci Using High-Density Imputation 
Horikoshi, Momoko | Mӓgi, Reedik | van de Bunt, Martijn | Surakka, Ida | Sarin, Antti-Pekka | Mahajan, Anubha | Marullo, Letizia | Thorleifsson, Gudmar | Hӓgg, Sara | Hottenga, Jouke-Jan | Ladenvall, Claes | Ried, Janina S. | Winkler, Thomas W. | Willems, Sara M. | Pervjakova, Natalia | Esko, Tõnu | Beekman, Marian | Nelson, Christopher P. | Willenborg, Christina | Wiltshire, Steven | Ferreira, Teresa | Fernandez, Juan | Gaulton, Kyle J. | Steinthorsdottir, Valgerdur | Hamsten, Anders | Magnusson, Patrik K. E. | Willemsen, Gonneke | Milaneschi, Yuri | Robertson, Neil R. | Groves, Christopher J. | Bennett, Amanda J. | Lehtimӓki, Terho | Viikari, Jorma S. | Rung, Johan | Lyssenko, Valeriya | Perola, Markus | Heid, Iris M. | Herder, Christian | Grallert, Harald | Müller-Nurasyid, Martina | Roden, Michael | Hypponen, Elina | Isaacs, Aaron | van Leeuwen, Elisabeth M. | Karssen, Lennart C. | Mihailov, Evelin | Houwing-Duistermaat, Jeanine J. | de Craen, Anton J. M. | Deelen, Joris | Havulinna, Aki S. | Blades, Matthew | Hengstenberg, Christian | Erdmann, Jeanette | Schunkert, Heribert | Kaprio, Jaakko | Tobin, Martin D. | Samani, Nilesh J. | Lind, Lars | Salomaa, Veikko | Lindgren, Cecilia M. | Slagboom, P. Eline | Metspalu, Andres | van Duijn, Cornelia M. | Eriksson, Johan G. | Peters, Annette | Gieger, Christian | Jula, Antti | Groop, Leif | Raitakari, Olli T. | Power, Chris | Penninx, Brenda W. J. H. | de Geus, Eco | Smit, Johannes H. | Boomsma, Dorret I. | Pedersen, Nancy L. | Ingelsson, Erik | Thorsteinsdottir, Unnur | Stefansson, Kari | Ripatti, Samuli | Prokopenko, Inga | McCarthy, Mark I. | Morris, Andrew P.
PLoS Genetics  2015;11(7):e1005230.
Reference panels from the 1000 Genomes (1000G) Project Consortium provide near complete coverage of common and low-frequency genetic variation with minor allele frequency ≥0.5% across European ancestry populations. Within the European Network for Genetic and Genomic Epidemiology (ENGAGE) Consortium, we have undertaken the first large-scale meta-analysis of genome-wide association studies (GWAS), supplemented by 1000G imputation, for four quantitative glycaemic and obesity-related traits, in up to 87,048 individuals of European ancestry. We identified two loci for body mass index (BMI) at genome-wide significance, and two for fasting glucose (FG), none of which has been previously reported in larger meta-analysis efforts to combine GWAS of European ancestry. Through conditional analysis, we also detected multiple distinct signals of association mapping to established loci for waist-hip ratio adjusted for BMI (RSPO3) and FG (GCK and G6PC2). The index variant for one association signal at the G6PC2 locus is a low-frequency coding allele, H177Y, which has recently been demonstrated to have a functional role in glucose regulation. Fine-mapping analyses revealed that the non-coding variants most likely to drive association signals at established and novel loci were enriched for overlap with enhancer elements, which for FG mapped to promoter and transcription factor binding sites in pancreatic islets, in particular. Our study demonstrates that 1000G imputation and genetic fine-mapping of common and low-frequency variant association signals at GWAS loci, integrated with genomic annotation in relevant tissues, can provide insight into the functional and regulatory mechanisms through which their effects on glycaemic and obesity-related traits are mediated.
Author Summary
Human genetic studies have demonstrated that quantitative human anthropometric and metabolic traits, including body mass index, waist-hip ratio, and plasma concentrations of glucose and insulin, are highly heritable, and are established risk factors for type 2 diabetes and cardiovascular diseases. Although many regions of the genome have been associated with these traits, the specific genes responsible have not yet been identified. By making use of advanced statistical “imputation” techniques applied to more than 87,000 individuals of European ancestry, and publicly available “reference panels” of more than 37 million genetic variants, we have been able to identify novel regions of the genome associated with these glycaemic and obesity-related traits and localise genes within these regions that are most likely to be causal. This improved understanding of the biological mechanisms underlying glycaemic and obesity-related traits is extremely important because it may advance drug development for downstream disease endpoints, ultimately leading to public health benefits.
doi:10.1371/journal.pgen.1005230
PMCID: PMC4488845  PMID: 26132169
7.  The Power of Gene-Based Rare Variant Methods to Detect Disease-Associated Variation and Test Hypotheses About Complex Disease 
PLoS Genetics  2015;11(4):e1005165.
Genome and exome sequencing in large cohorts enables characterization of the role of rare variation in complex diseases. Success in this endeavor, however, requires investigators to test a diverse array of genetic hypotheses which differ in the number, frequency and effect sizes of underlying causal variants. In this study, we evaluated the power of gene-based association methods to interrogate such hypotheses, and examined the implications for study design. We developed a flexible simulation approach, using 1000 Genomes data, to (a) generate sequence variation at human genes in up to 10K case-control samples, and (b) quantify the statistical power of a panel of widely used gene-based association tests under a variety of allelic architectures, locus effect sizes, and significance thresholds. For loci explaining ~1% of phenotypic variance underlying a common dichotomous trait, we find that all methods have low absolute power to achieve exome-wide significance (~5-20% power at α=2.5×10-6) in 3K individuals; even in 10K samples, power is modest (~60%). The combined application of multiple methods increases sensitivity, but does so at the expense of a higher false positive rate. MiST, SKAT-O, and KBAC have the highest individual mean power across simulated datasets, but we observe wide architecture-dependent variability in the individual loci detected by each test, suggesting that inferences about disease architecture from analysis of sequencing studies can differ depending on which methods are used. Our results imply that tens of thousands of individuals, extensive functional annotation, or highly targeted hypothesis testing will be required to confidently detect or exclude rare variant signals at complex disease loci.
Author Summary
Re-sequencing technologies allow for a more complete interrogation of the role of human variation in complex disease. The inadequate power of single variant methods to assess the role of less common variation has led to the development of numerous statistical methods for testing aggregate groups of variants for association with disease. Such endeavors pose substantial analytical challenges, however, due to the diverse array of genetic hypotheses that need to be considered. In this work, we systematically quantify and compare the performance of a panel of commonly used gene-based association methods under a range of allelic architectures, significance thresholds, locus effect sizes, sample sizes, and filters for neutral variation. We find that MiST, SKAT-O, and KBAC have the highest mean power across simulated datasets. Across all methods, however, the power to detect even loci of relatively large effect is very low at exome-wide significance thresholds for sample sizes comparable with those of ongoing sequencing studies; as such, the absence of signal in studies of a few thousand individuals does not exclude a role for rare variation in complex traits. Finally, we directly compare the results reported by different gene-based methods in order to identify their comparative advantages and disadvantages under distinct locus architectures. Our findings have implications for meaningful interpretation of both positive and negative findings in ongoing and future sequencing studies.
doi:10.1371/journal.pgen.1005165
PMCID: PMC4407972  PMID: 25906071
8.  Identification and Functional Characterization of G6PC2 Coding Variants Influencing Glycemic Traits Define an Effector Transcript at the G6PC2-ABCB11 Locus 
Mahajan, Anubha | Sim, Xueling | Ng, Hui Jin | Manning, Alisa | Rivas, Manuel A. | Highland, Heather M. | Locke, Adam E. | Grarup, Niels | Im, Hae Kyung | Cingolani, Pablo | Flannick, Jason | Fontanillas, Pierre | Fuchsberger, Christian | Gaulton, Kyle J. | Teslovich, Tanya M. | Rayner, N. William | Robertson, Neil R. | Beer, Nicola L. | Rundle, Jana K. | Bork-Jensen, Jette | Ladenvall, Claes | Blancher, Christine | Buck, David | Buck, Gemma | Burtt, Noël P. | Gabriel, Stacey | Gjesing, Anette P. | Groves, Christopher J. | Hollensted, Mette | Huyghe, Jeroen R. | Jackson, Anne U. | Jun, Goo | Justesen, Johanne Marie | Mangino, Massimo | Murphy, Jacquelyn | Neville, Matt | Onofrio, Robert | Small, Kerrin S. | Stringham, Heather M. | Syvänen, Ann-Christine | Trakalo, Joseph | Abecasis, Goncalo | Bell, Graeme I. | Blangero, John | Cox, Nancy J. | Duggirala, Ravindranath | Hanis, Craig L. | Seielstad, Mark | Wilson, James G. | Christensen, Cramer | Brandslund, Ivan | Rauramaa, Rainer | Surdulescu, Gabriela L. | Doney, Alex S. F. | Lannfelt, Lars | Linneberg, Allan | Isomaa, Bo | Tuomi, Tiinamaija | Jørgensen, Marit E. | Jørgensen, Torben | Kuusisto, Johanna | Uusitupa, Matti | Salomaa, Veikko | Spector, Timothy D. | Morris, Andrew D. | Palmer, Colin N. A. | Collins, Francis S. | Mohlke, Karen L. | Bergman, Richard N. | Ingelsson, Erik | Lind, Lars | Tuomilehto, Jaakko | Hansen, Torben | Watanabe, Richard M. | Prokopenko, Inga | Dupuis, Josee | Karpe, Fredrik | Groop, Leif | Laakso, Markku | Pedersen, Oluf | Florez, Jose C. | Morris, Andrew P. | Altshuler, David | Meigs, James B. | Boehnke, Michael | McCarthy, Mark I. | Lindgren, Cecilia M. | Gloyn, Anna L.
PLoS Genetics  2015;11(1):e1004876.
Genome wide association studies (GWAS) for fasting glucose (FG) and insulin (FI) have identified common variant signals which explain 4.8% and 1.2% of trait variance, respectively. It is hypothesized that low-frequency and rare variants could contribute substantially to unexplained genetic variance. To test this, we analyzed exome-array data from up to 33,231 non-diabetic individuals of European ancestry. We found exome-wide significant (P<5×10-7) evidence for two loci not previously highlighted by common variant GWAS: GLP1R (p.Ala316Thr, minor allele frequency (MAF)=1.5%) influencing FG levels, and URB2 (p.Glu594Val, MAF = 0.1%) influencing FI levels. Coding variant associations can highlight potential effector genes at (non-coding) GWAS signals. At the G6PC2/ABCB11 locus, we identified multiple coding variants in G6PC2 (p.Val219Leu, p.His177Tyr, and p.Tyr207Ser) influencing FG levels, conditionally independent of each other and the non-coding GWAS signal. In vitro assays demonstrate that these associated coding alleles result in reduced protein abundance via proteasomal degradation, establishing G6PC2 as an effector gene at this locus. Reconciliation of single-variant associations and functional effects was only possible when haplotype phase was considered. In contrast to earlier reports suggesting that, paradoxically, glucose-raising alleles at this locus are protective against type 2 diabetes (T2D), the p.Val219Leu G6PC2 variant displayed a modest but directionally consistent association with T2D risk. Coding variant associations for glycemic traits in GWAS signals highlight PCSK1, RREB1, and ZHX3 as likely effector transcripts. These coding variant association signals do not have a major impact on the trait variance explained, but they do provide valuable biological insights.
Author Summary
Understanding how FI and FG levels are regulated is important because their derangement is a feature of T2D. Despite recent success from GWAS in identifying regions of the genome influencing glycemic traits, collectively these loci explain only a small proportion of trait variance. Unlocking the biological mechanisms driving these associations has been challenging because the vast majority of variants map to non-coding sequence, and the genes through which they exert their impact are largely unknown. In the current study, we sought to increase our understanding of the physiological pathways influencing both traits using exome-array genotyping in up to 33,231 non-diabetic individuals to identify coding variants and consequently genes associated with either FG or FI levels. We identified novel association signals for both traits including the receptor for GLP-1 agonists which are a widely used therapy for T2D. Furthermore, we identified coding variants at several GWAS loci which point to the genes underlying these association signals. Importantly, we found that multiple coding variants in G6PC2 result in a loss of protein function and lower fasting glucose levels.
doi:10.1371/journal.pgen.1004876
PMCID: PMC4307976  PMID: 25625282
9.  Distribution and Medical Impact of Loss-of-Function Variants in the Finnish Founder Population 
PLoS Genetics  2014;10(7):e1004494.
Exome sequencing studies in complex diseases are challenged by the allelic heterogeneity, large number and modest effect sizes of associated variants on disease risk and the presence of large numbers of neutral variants, even in phenotypically relevant genes. Isolated populations with recent bottlenecks offer advantages for studying rare variants in complex diseases as they have deleterious variants that are present at higher frequencies as well as a substantial reduction in rare neutral variation. To explore the potential of the Finnish founder population for studying low-frequency (0.5–5%) variants in complex diseases, we compared exome sequence data on 3,000 Finns to the same number of non-Finnish Europeans and discovered that, despite having fewer variable sites overall, the average Finn has more low-frequency loss-of-function variants and complete gene knockouts. We then used several well-characterized Finnish population cohorts to study the phenotypic effects of 83 enriched loss-of-function variants across 60 phenotypes in 36,262 Finns. Using a deep set of quantitative traits collected on these cohorts, we show 5 associations (p<5×10−8) including splice variants in LPA that lowered plasma lipoprotein(a) levels (P = 1.5×10−117). Through accessing the national medical records of these participants, we evaluate the LPA finding via Mendelian randomization and confirm that these splice variants confer protection from cardiovascular disease (OR = 0.84, P = 3×10−4), demonstrating for the first time the correlation between very low levels of LPA in humans with potential therapeutic implications for cardiovascular diseases. More generally, this study articulates substantial advantages for studying the role of rare variation in complex phenotypes in founder populations like the Finns and by combining a unique population genetic history with data from large population cohorts and centralized research access to National Health Registers.
Author Summary
We explored the coding regions of 3,000 Finnish individuals with 3,000 non-Finnish Europeans (NFEs) using whole-exome sequence data, in order to understand how an individual from a bottlenecked population might differ from an individual from an out-bred population. We provide empirical evidence that there are more rare and low-frequency deleterious alleles in Finns compared to NFEs, such that an average Finn has almost twice as many low-frequency complete knockouts of a gene. As such, we hypothesized that some of these low-frequency loss-of-function variants might have important medical consequences in humans and genotyped 83 of these variants in 36,000 Finns. In doing so, we discovered that completely knocking out the TSFM gene might result in inviability or a very severe phenotype in humans and that knocking out the LPA gene might confer protection against coronary heart diseases, suggesting that LPA is likely to be a good potential therapeutic target.
doi:10.1371/journal.pgen.1004494
PMCID: PMC4117444  PMID: 25078778
10.  A Central Role for GRB10 in Regulation of Islet Function in Man 
PLoS Genetics  2014;10(4):e1004235.
Variants in the growth factor receptor-bound protein 10 (GRB10) gene were in a GWAS meta-analysis associated with reduced glucose-stimulated insulin secretion and increased risk of type 2 diabetes (T2D) if inherited from the father, but inexplicably reduced fasting glucose when inherited from the mother. GRB10 is a negative regulator of insulin signaling and imprinted in a parent-of-origin fashion in different tissues. GRB10 knock-down in human pancreatic islets showed reduced insulin and glucagon secretion, which together with changes in insulin sensitivity may explain the paradoxical reduction of glucose despite a decrease in insulin secretion. Together, these findings suggest that tissue-specific methylation and possibly imprinting of GRB10 can influence glucose metabolism and contribute to T2D pathogenesis. The data also emphasize the need in genetic studies to consider whether risk alleles are inherited from the mother or the father.
Author Summary
In this paper, we report the first large genome-wide association study in man for glucose-stimulated insulin secretion (GSIS) indices during an oral glucose tolerance test. We identify seven genetic loci and provide effects on GSIS for all previously reported glycemic traits and obesity genetic loci in a large-scale sample. We observe paradoxical effects of genetic variants in the growth factor receptor-bound protein 10 (GRB10) gene yielding both reduced GSIS and reduced fasting plasma glucose concentrations, specifically showing a parent-of-origin effect of GRB10 on lower fasting plasma glucose and enhanced insulin sensitivity for maternal and elevated glucose and decreased insulin sensitivity for paternal transmissions of the risk allele. We also observe tissue-specific differences in DNA methylation and allelic imbalance in expression of GRB10 in human pancreatic islets. We further disrupt GRB10 by shRNA in human islets, showing reduction of both insulin and glucagon expression and secretion. In conclusion, we provide evidence for complex regulation of GRB10 in human islets. Our data suggest that tissue-specific methylation and imprinting of GRB10 can influence glucose metabolism and contribute to T2D pathogenesis. The data also emphasize the need in genetic studies to consider whether risk alleles are inherited from the mother or the father.
doi:10.1371/journal.pgen.1004235
PMCID: PMC3974640  PMID: 24699409
11.  Re-sequencing Expands Our Understanding of the Phenotypic Impact of Variants at GWAS Loci 
PLoS Genetics  2014;10(1):e1004147.
Genome-wide association studies (GWAS) have identified >500 common variants associated with quantitative metabolic traits, but in aggregate such variants explain at most 20–30% of the heritable component of population variation in these traits. To further investigate the impact of genotypic variation on metabolic traits, we conducted re-sequencing studies in >6,000 members of a Finnish population cohort (The Northern Finland Birth Cohort of 1966 [NFBC]) and a type 2 diabetes case-control sample (The Finland-United States Investigation of NIDDM Genetics [FUSION] study). By sequencing the coding sequence and 5′ and 3′ untranslated regions of 78 genes at 17 GWAS loci associated with one or more of six metabolic traits (serum levels of fasting HDL-C, LDL-C, total cholesterol, triglycerides, plasma glucose, and insulin), and conducting both single-variant and gene-level association tests, we obtained a more complete understanding of phenotype-genotype associations at eight of these loci. At all eight of these loci, the identification of new associations provides significant evidence for multiple genetic signals to one or more phenotypes, and at two loci, in the genes ABCA1 and CETP, we found significant gene-level evidence of association to non-synonymous variants with MAF<1%. Additionally, two potentially deleterious variants that demonstrated significant associations (rs138726309, a missense variant in G6PC2, and rs28933094, a missense variant in LIPC) were considerably more common in these Finnish samples than in European reference populations, supporting our prior hypothesis that deleterious variants could attain high frequencies in this isolated population, likely due to the effects of population bottlenecks. Our results highlight the value of large, well-phenotyped samples for rare-variant association analysis, and the challenge of evaluating the phenotypic impact of such variants.
Author Summary
Abnormal serum levels of various metabolites, including measures relevant to cholesterol, other fats, and sugars, are known to be risk factors for cardiovascular disease and type 2 diabetes. Identification of the genes that play a role in generating such abnormalities could advance the development of new treatment and prevention strategies for these disorders. Investigations of common genetic variants carried out in large sets of research subjects have successfully pinpointed such genes within many regions of the human genome. However, these studies often have not led to the identification of the specific genetic variations affecting metabolic traits. To attempt to detect such causal variations, we sequenced genes in 17 genomic regions implicated in metabolic traits in >6,000 people from Finland. By conducting statistical analyses relating specific variations (individually and grouped by gene) to the measures for these metabolic traits observed in the study subjects, we added to our understanding of how genotypes affect these traits. Our findings support a long-held hypothesis that the unique history of the Finnish population provides important advantages for analyzing the relationship between genetic variations and biomedically important traits.
doi:10.1371/journal.pgen.1004147
PMCID: PMC3907339  PMID: 24497850
12.  Sex-stratified Genome-wide Association Studies Including 270,000 Individuals Show Sexual Dimorphism in Genetic Loci for Anthropometric Traits 
Randall, Joshua C. | Winkler, Thomas W. | Kutalik, Zoltán | Berndt, Sonja I. | Jackson, Anne U. | Monda, Keri L. | Kilpeläinen, Tuomas O. | Esko, Tõnu | Mägi, Reedik | Li, Shengxu | Workalemahu, Tsegaselassie | Feitosa, Mary F. | Croteau-Chonka, Damien C. | Day, Felix R. | Fall, Tove | Ferreira, Teresa | Gustafsson, Stefan | Locke, Adam E. | Mathieson, Iain | Scherag, Andre | Vedantam, Sailaja | Wood, Andrew R. | Liang, Liming | Steinthorsdottir, Valgerdur | Thorleifsson, Gudmar | Dermitzakis, Emmanouil T. | Dimas, Antigone S. | Karpe, Fredrik | Min, Josine L. | Nicholson, George | Clegg, Deborah J. | Person, Thomas | Krohn, Jon P. | Bauer, Sabrina | Buechler, Christa | Eisinger, Kristina | Bonnefond, Amélie | Froguel, Philippe | Hottenga, Jouke-Jan | Prokopenko, Inga | Waite, Lindsay L. | Harris, Tamara B. | Smith, Albert Vernon | Shuldiner, Alan R. | McArdle, Wendy L. | Caulfield, Mark J. | Munroe, Patricia B. | Grönberg, Henrik | Chen, Yii-Der Ida | Li, Guo | Beckmann, Jacques S. | Johnson, Toby | Thorsteinsdottir, Unnur | Teder-Laving, Maris | Khaw, Kay-Tee | Wareham, Nicholas J. | Zhao, Jing Hua | Amin, Najaf | Oostra, Ben A. | Kraja, Aldi T. | Province, Michael A. | Cupples, L. Adrienne | Heard-Costa, Nancy L. | Kaprio, Jaakko | Ripatti, Samuli | Surakka, Ida | Collins, Francis S. | Saramies, Jouko | Tuomilehto, Jaakko | Jula, Antti | Salomaa, Veikko | Erdmann, Jeanette | Hengstenberg, Christian | Loley, Christina | Schunkert, Heribert | Lamina, Claudia | Wichmann, H. Erich | Albrecht, Eva | Gieger, Christian | Hicks, Andrew A. | Johansson, Åsa | Pramstaller, Peter P. | Kathiresan, Sekar | Speliotes, Elizabeth K. | Penninx, Brenda | Hartikainen, Anna-Liisa | Jarvelin, Marjo-Riitta | Gyllensten, Ulf | Boomsma, Dorret I. | Campbell, Harry | Wilson, James F. | Chanock, Stephen J. | Farrall, Martin | Goel, Anuj | Medina-Gomez, Carolina | Rivadeneira, Fernando | Estrada, Karol | Uitterlinden, André G. | Hofman, Albert | Zillikens, M. Carola | den Heijer, Martin | Kiemeney, Lambertus A. | Maschio, Andrea | Hall, Per | Tyrer, Jonathan | Teumer, Alexander | Völzke, Henry | Kovacs, Peter | Tönjes, Anke | Mangino, Massimo | Spector, Tim D. | Hayward, Caroline | Rudan, Igor | Hall, Alistair S. | Samani, Nilesh J. | Attwood, Antony Paul | Sambrook, Jennifer G. | Hung, Joseph | Palmer, Lyle J. | Lokki, Marja-Liisa | Sinisalo, Juha | Boucher, Gabrielle | Huikuri, Heikki | Lorentzon, Mattias | Ohlsson, Claes | Eklund, Niina | Eriksson, Johan G. | Barlassina, Cristina | Rivolta, Carlo | Nolte, Ilja M. | Snieder, Harold | Van der Klauw, Melanie M. | Van Vliet-Ostaptchouk, Jana V. | Gejman, Pablo V. | Shi, Jianxin | Jacobs, Kevin B. | Wang, Zhaoming | Bakker, Stephan J. L. | Mateo Leach, Irene | Navis, Gerjan | van der Harst, Pim | Martin, Nicholas G. | Medland, Sarah E. | Montgomery, Grant W. | Yang, Jian | Chasman, Daniel I. | Ridker, Paul M. | Rose, Lynda M. | Lehtimäki, Terho | Raitakari, Olli | Absher, Devin | Iribarren, Carlos | Basart, Hanneke | Hovingh, Kees G. | Hyppönen, Elina | Power, Chris | Anderson, Denise | Beilby, John P. | Hui, Jennie | Jolley, Jennifer | Sager, Hendrik | Bornstein, Stefan R. | Schwarz, Peter E. H. | Kristiansson, Kati | Perola, Markus | Lindström, Jaana | Swift, Amy J. | Uusitupa, Matti | Atalay, Mustafa | Lakka, Timo A. | Rauramaa, Rainer | Bolton, Jennifer L. | Fowkes, Gerry | Fraser, Ross M. | Price, Jackie F. | Fischer, Krista | KrjutÅ¡kov, Kaarel | Metspalu, Andres | Mihailov, Evelin | Langenberg, Claudia | Luan, Jian'an | Ong, Ken K. | Chines, Peter S. | Keinanen-Kiukaanniemi, Sirkka M. | Saaristo, Timo E. | Edkins, Sarah | Franks, Paul W. | Hallmans, Göran | Shungin, Dmitry | Morris, Andrew David | Palmer, Colin N. A. | Erbel, Raimund | Moebus, Susanne | Nöthen, Markus M. | Pechlivanis, Sonali | Hveem, Kristian | Narisu, Narisu | Hamsten, Anders | Humphries, Steve E. | Strawbridge, Rona J. | Tremoli, Elena | Grallert, Harald | Thorand, Barbara | Illig, Thomas | Koenig, Wolfgang | Müller-Nurasyid, Martina | Peters, Annette | Boehm, Bernhard O. | Kleber, Marcus E. | März, Winfried | Winkelmann, Bernhard R. | Kuusisto, Johanna | Laakso, Markku | Arveiler, Dominique | Cesana, Giancarlo | Kuulasmaa, Kari | Virtamo, Jarmo | Yarnell, John W. G. | Kuh, Diana | Wong, Andrew | Lind, Lars | de Faire, Ulf | Gigante, Bruna | Magnusson, Patrik K. E. | Pedersen, Nancy L. | Dedoussis, George | Dimitriou, Maria | Kolovou, Genovefa | Kanoni, Stavroula | Stirrups, Kathleen | Bonnycastle, Lori L. | Njølstad, Inger | Wilsgaard, Tom | Ganna, Andrea | Rehnberg, Emil | Hingorani, Aroon | Kivimaki, Mika | Kumari, Meena | Assimes, Themistocles L. | Barroso, Inês | Boehnke, Michael | Borecki, Ingrid B. | Deloukas, Panos | Fox, Caroline S. | Frayling, Timothy | Groop, Leif C. | Haritunians, Talin | Hunter, David | Ingelsson, Erik | Kaplan, Robert | Mohlke, Karen L. | O'Connell, Jeffrey R. | Schlessinger, David | Strachan, David P. | Stefansson, Kari | van Duijn, Cornelia M. | Abecasis, Gonçalo R. | McCarthy, Mark I. | Hirschhorn, Joel N. | Qi, Lu | Loos, Ruth J. F. | Lindgren, Cecilia M. | North, Kari E. | Heid, Iris M.
PLoS Genetics  2013;9(6):e1003500.
Given the anthropometric differences between men and women and previous evidence of sex-difference in genetic effects, we conducted a genome-wide search for sexually dimorphic associations with height, weight, body mass index, waist circumference, hip circumference, and waist-to-hip-ratio (133,723 individuals) and took forward 348 SNPs into follow-up (additional 137,052 individuals) in a total of 94 studies. Seven loci displayed significant sex-difference (FDR<5%), including four previously established (near GRB14/COBLL1, LYPLAL1/SLC30A10, VEGFA, ADAMTS9) and three novel anthropometric trait loci (near MAP3K1, HSD17B4, PPARG), all of which were genome-wide significant in women (P<5×10−8), but not in men. Sex-differences were apparent only for waist phenotypes, not for height, weight, BMI, or hip circumference. Moreover, we found no evidence for genetic effects with opposite directions in men versus women. The PPARG locus is of specific interest due to its role in diabetes genetics and therapy. Our results demonstrate the value of sex-specific GWAS to unravel the sexually dimorphic genetic underpinning of complex traits.
Author Summary
Men and women differ substantially regarding height, weight, and body fat. Interestingly, previous work detecting genetic effects for waist-to-hip ratio, to assess body fat distribution, has found that many of these showed sex-differences. However, systematic searches for sex-differences in genetic effects have not yet been conducted. Therefore, we undertook a genome-wide search for sexually dimorphic genetic effects for anthropometric traits including 133,723 individuals in a large meta-analysis and followed promising variants in further 137,052 individuals, including a total of 94 studies. We identified seven loci with significant sex-difference including four previously established (near GRB14/COBLL1, LYPLAL1/SLC30A10, VEGFA, ADAMTS9) and three novel anthropometric trait loci (near MAP3K1, HSD17B4, PPARG), all of which were significant in women, but not in men. Of interest is that sex-difference was only observed for waist phenotypes, but not for height or body-mass-index. We found no evidence for sex-differences with opposite effect direction for men and women. The PPARG locus is of specific interest due to its link to diabetes genetics and therapy. Our findings demonstrate the importance of investigating sex differences, which may lead to a better understanding of disease mechanisms with a potential relevance to treatment options.
doi:10.1371/journal.pgen.1003500
PMCID: PMC3674993  PMID: 23754948
13.  The Metabochip, a Custom Genotyping Array for Genetic Studies of Metabolic, Cardiovascular, and Anthropometric Traits 
PLoS Genetics  2012;8(8):e1002793.
Genome-wide association studies have identified hundreds of loci for type 2 diabetes, coronary artery disease and myocardial infarction, as well as for related traits such as body mass index, glucose and insulin levels, lipid levels, and blood pressure. These studies also have pointed to thousands of loci with promising but not yet compelling association evidence. To establish association at additional loci and to characterize the genome-wide significant loci by fine-mapping, we designed the “Metabochip,” a custom genotyping array that assays nearly 200,000 SNP markers. Here, we describe the Metabochip and its component SNP sets, evaluate its performance in capturing variation across the allele-frequency spectrum, describe solutions to methodological challenges commonly encountered in its analysis, and evaluate its performance as a platform for genotype imputation. The metabochip achieves dramatic cost efficiencies compared to designing single-trait follow-up reagents, and provides the opportunity to compare results across a range of related traits. The metabochip and similar custom genotyping arrays offer a powerful and cost-effective approach to follow-up large-scale genotyping and sequencing studies and advance our understanding of the genetic basis of complex human diseases and traits.
Author Summary
Recent genetic studies have identified hundreds of regions of the human genome that contribute to risk for type 2 diabetes, coronary artery disease and myocardial infarction, and to related quantitative traits such as body mass index, glucose and insulin levels, blood lipid levels, and blood pressure. These results motivate two central questions: (1) can further genetic investigation identify additional associated regions?; and (2) can more detailed genetic investigation help us identify the causal variants (or variants more strongly correlated with the causal variants) in the regions identified so far? Addressing these questions requires assaying many genetic variants in DNA samples from thousands of individuals, which is expensive and timeconsuming when done a few SNPs at a time. To facilitate these investigations, we designed the “Metabochip,” a custom genotyping array that assays variation in nearly 200,000 sites in the human genome. Here we describe the Metabochip, evaluate its performance in assaying human genetic variation, and describe solutions to methodological challenges commonly encountered in its analysis.
doi:10.1371/journal.pgen.1002793
PMCID: PMC3410907  PMID: 22876189
14.  Evidence of Inbreeding Depression on Human Height 
McQuillan, Ruth | Eklund, Niina | Pirastu, Nicola | Kuningas, Maris | McEvoy, Brian P. | Esko, Tõnu | Corre, Tanguy | Davies, Gail | Kaakinen, Marika | Lyytikäinen, Leo-Pekka | Kristiansson, Kati | Havulinna, Aki S. | Gögele, Martin | Vitart, Veronique | Tenesa, Albert | Aulchenko, Yurii | Hayward, Caroline | Johansson, Åsa | Boban, Mladen | Ulivi, Sheila | Robino, Antonietta | Boraska, Vesna | Igl, Wilmar | Wild, Sarah H. | Zgaga, Lina | Amin, Najaf | Theodoratou, Evropi | Polašek, Ozren | Girotto, Giorgia | Lopez, Lorna M. | Sala, Cinzia | Lahti, Jari | Laatikainen, Tiina | Prokopenko, Inga | Kals, Mart | Viikari, Jorma | Yang, Jian | Pouta, Anneli | Estrada, Karol | Hofman, Albert | Freimer, Nelson | Martin, Nicholas G. | Kähönen, Mika | Milani, Lili | Heliövaara, Markku | Vartiainen, Erkki | Räikkönen, Katri | Masciullo, Corrado | Starr, John M. | Hicks, Andrew A. | Esposito, Laura | Kolčić, Ivana | Farrington, Susan M. | Oostra, Ben | Zemunik, Tatijana | Campbell, Harry | Kirin, Mirna | Pehlic, Marina | Faletra, Flavio | Porteous, David | Pistis, Giorgio | Widén, Elisabeth | Salomaa, Veikko | Koskinen, Seppo | Fischer, Krista | Lehtimäki, Terho | Heath, Andrew | McCarthy, Mark I. | Rivadeneira, Fernando | Montgomery, Grant W. | Tiemeier, Henning | Hartikainen, Anna-Liisa | Madden, Pamela A. F. | d'Adamo, Pio | Hastie, Nicholas D. | Gyllensten, Ulf | Wright, Alan F. | van Duijn, Cornelia M. | Dunlop, Malcolm | Rudan, Igor | Gasparini, Paolo | Pramstaller, Peter P. | Deary, Ian J. | Toniolo, Daniela | Eriksson, Johan G. | Jula, Antti | Raitakari, Olli T. | Metspalu, Andres | Perola, Markus | Järvelin, Marjo-Riitta | Uitterlinden, André | Visscher, Peter M. | Wilson, James F.
PLoS Genetics  2012;8(7):e1002655.
Stature is a classical and highly heritable complex trait, with 80%–90% of variation explained by genetic factors. In recent years, genome-wide association studies (GWAS) have successfully identified many common additive variants influencing human height; however, little attention has been given to the potential role of recessive genetic effects. Here, we investigated genome-wide recessive effects by an analysis of inbreeding depression on adult height in over 35,000 people from 21 different population samples. We found a highly significant inverse association between height and genome-wide homozygosity, equivalent to a height reduction of up to 3 cm in the offspring of first cousins compared with the offspring of unrelated individuals, an effect which remained after controlling for the effects of socio-economic status, an important confounder (χ2 = 83.89, df = 1; p = 5.2×10−20). There was, however, a high degree of heterogeneity among populations: whereas the direction of the effect was consistent across most population samples, the effect size differed significantly among populations. It is likely that this reflects true biological heterogeneity: whether or not an effect can be observed will depend on both the variance in homozygosity in the population and the chance inheritance of individual recessive genotypes. These results predict that multiple, rare, recessive variants influence human height. Although this exploratory work focuses on height alone, the methodology developed is generally applicable to heritable quantitative traits (QT), paving the way for an investigation into inbreeding effects, and therefore genetic architecture, on a range of QT of biomedical importance.
Author Summary
Studies investigating the extent to which genetics influences human characteristics such as height have concentrated mainly on common variants of genes, where having one or two copies of a given variant influences the trait or risk of disease. This study explores whether a different type of genetic variant might also be important. We investigate the role of recessive genetic variants, where two identical copies of a variant are required to have an effect. By measuring genome-wide homozygosity—the phenomenon of inheriting two identical copies at a given point of the genome—in 35,000 individuals from 21 European populations, and by comparing this to individual height, we found that the more homozygous the genome, the shorter the individual. The offspring of first cousins (who have increased homozygosity) were predicted to be up to 3 cm shorter on average than the offspring of unrelated parents. Height is influenced by the combined effect of many recessive variants dispersed across the genome. This may also be true for other human characteristics and diseases, opening up a new way to understand how genetic variation influences our health.
doi:10.1371/journal.pgen.1002655
PMCID: PMC3400549  PMID: 22829771
15.  A Genome-Wide Association Meta-Analysis of Circulating Sex Hormone–Binding Globulin Reveals Multiple Loci Implicated in Sex Steroid Hormone Regulation 
Coviello, Andrea D. | Haring, Robin | Wellons, Melissa | Vaidya, Dhananjay | Lehtimäki, Terho | Keildson, Sarah | Lunetta, Kathryn L. | He, Chunyan | Fornage, Myriam | Lagou, Vasiliki | Mangino, Massimo | Onland-Moret, N. Charlotte | Chen, Brian | Eriksson, Joel | Garcia, Melissa | Liu, Yong Mei | Koster, Annemarie | Lohman, Kurt | Lyytikäinen, Leo-Pekka | Petersen, Ann-Kristin | Prescott, Jennifer | Stolk, Lisette | Vandenput, Liesbeth | Wood, Andrew R. | Zhuang, Wei Vivian | Ruokonen, Aimo | Hartikainen, Anna-Liisa | Pouta, Anneli | Bandinelli, Stefania | Biffar, Reiner | Brabant, Georg | Cox, David G. | Chen, Yuhui | Cummings, Steven | Ferrucci, Luigi | Gunter, Marc J. | Hankinson, Susan E. | Martikainen, Hannu | Hofman, Albert | Homuth, Georg | Illig, Thomas | Jansson, John-Olov | Johnson, Andrew D. | Karasik, David | Karlsson, Magnus | Kettunen, Johannes | Kiel, Douglas P. | Kraft, Peter | Liu, Jingmin | Ljunggren, Östen | Lorentzon, Mattias | Maggio, Marcello | Markus, Marcello R. P. | Mellström, Dan | Miljkovic, Iva | Mirel, Daniel | Nelson, Sarah | Morin Papunen, Laure | Peeters, Petra H. M. | Prokopenko, Inga | Raffel, Leslie | Reincke, Martin | Reiner, Alex P. | Rexrode, Kathryn | Rivadeneira, Fernando | Schwartz, Stephen M. | Siscovick, David | Soranzo, Nicole | Stöckl, Doris | Tworoger, Shelley | Uitterlinden, André G. | van Gils, Carla H. | Vasan, Ramachandran S. | Wichmann, H.-Erich | Zhai, Guangju | Bhasin, Shalender | Bidlingmaier, Martin | Chanock, Stephen J. | De Vivo, Immaculata | Harris, Tamara B. | Hunter, David J. | Kähönen, Mika | Liu, Simin | Ouyang, Pamela | Spector, Tim D. | van der Schouw, Yvonne T. | Viikari, Jorma | Wallaschofski, Henri | McCarthy, Mark I. | Frayling, Timothy M. | Murray, Anna | Franks, Steve | Järvelin, Marjo-Riitta | de Jong, Frank H. | Raitakari, Olli | Teumer, Alexander | Ohlsson, Claes | Murabito, Joanne M. | Perry, John R. B.
PLoS Genetics  2012;8(7):e1002805.
Sex hormone-binding globulin (SHBG) is a glycoprotein responsible for the transport and biologic availability of sex steroid hormones, primarily testosterone and estradiol. SHBG has been associated with chronic diseases including type 2 diabetes (T2D) and with hormone-sensitive cancers such as breast and prostate cancer. We performed a genome-wide association study (GWAS) meta-analysis of 21,791 individuals from 10 epidemiologic studies and validated these findings in 7,046 individuals in an additional six studies. We identified twelve genomic regions (SNPs) associated with circulating SHBG concentrations. Loci near the identified SNPs included SHBG (rs12150660, 17p13.1, p = 1.8×10−106), PRMT6 (rs17496332, 1p13.3, p = 1.4×10−11), GCKR (rs780093, 2p23.3, p = 2.2×10−16), ZBTB10 (rs440837, 8q21.13, p = 3.4×10−09), JMJD1C (rs7910927, 10q21.3, p = 6.1×10−35), SLCO1B1 (rs4149056, 12p12.1, p = 1.9×10−08), NR2F2 (rs8023580, 15q26.2, p = 8.3×10−12), ZNF652 (rs2411984, 17q21.32, p = 3.5×10−14), TDGF3 (rs1573036, Xq22.3, p = 4.1×10−14), LHCGR (rs10454142, 2p16.3, p = 1.3×10−07), BAIAP2L1 (rs3779195, 7q21.3, p = 2.7×10−08), and UGT2B15 (rs293428, 4q13.2, p = 5.5×10−06). These genes encompass multiple biologic pathways, including hepatic function, lipid metabolism, carbohydrate metabolism and T2D, androgen and estrogen receptor function, epigenetic effects, and the biology of sex steroid hormone-responsive cancers including breast and prostate cancer. We found evidence of sex-differentiated genetic influences on SHBG. In a sex-specific GWAS, the loci 4q13.2-UGT2B15 was significant in men only (men p = 2.5×10−08, women p = 0.66, heterogeneity p = 0.003). Additionally, three loci showed strong sex-differentiated effects: 17p13.1-SHBG and Xq22.3-TDGF3 were stronger in men, whereas 8q21.12-ZBTB10 was stronger in women. Conditional analyses identified additional signals at the SHBG gene that together almost double the proportion of variance explained at the locus. Using an independent study of 1,129 individuals, all SNPs identified in the overall or sex-differentiated or conditional analyses explained ∼15.6% and ∼8.4% of the genetic variation of SHBG concentrations in men and women, respectively. The evidence for sex-differentiated effects and allelic heterogeneity highlight the importance of considering these features when estimating complex trait variance.
Author Summary
Sex hormone-binding globulin (SHBG) is the key protein responsible for binding and transporting the sex steroid hormones, testosterone and estradiol, in the circulatory system. SHBG regulates their bioavailability and therefore their effects in the body. SHBG has been linked to chronic diseases including type 2 diabetes and to hormone-sensitive cancers such as breast and prostate cancer. SHBG concentrations are approximately 50% heritable in family studies, suggesting SHBG concentrations are under significant genetic control; yet, little is known about the specific genes that influence SHBG. We conducted a large study of the association of SHBG concentrations with markers in the human genome in ∼22,000 white men and women to determine which loci influence SHBG concentrations. Genes near the identified genomic markers in addition to the SHBG protein coding gene included PRMT6, GCKR, ZBTB10, JMJD1C, SLCO1B1, NR2F2, ZNF652, TDGF3, LHCGR, BAIAP2L1, and UGT2B15. These genes represent a wide range of biologic pathways that may relate to SHBG function and sex steroid hormone biology, including liver function, lipid metabolism, carbohydrate metabolism and type 2 diabetes, and the development and progression of sex steroid hormone-responsive cancers.
doi:10.1371/journal.pgen.1002805
PMCID: PMC3400553  PMID: 22829776
16.  Stratifying Type 2 Diabetes Cases by BMI Identifies Genetic Risk Variants in LAMA1 and Enrichment for Risk Variants in Lean Compared to Obese Cases 
Perry, John R. B. | Voight, Benjamin F. | Yengo, Loïc | Amin, Najaf | Dupuis, Josée | Ganser, Martha | Grallert, Harald | Navarro, Pau | Li, Man | Qi, Lu | Steinthorsdottir, Valgerdur | Scott, Robert A. | Almgren, Peter | Arking, Dan E. | Aulchenko, Yurii | Balkau, Beverley | Benediktsson, Rafn | Bergman, Richard N. | Boerwinkle, Eric | Bonnycastle, Lori | Burtt, Noël P. | Campbell, Harry | Charpentier, Guillaume | Collins, Francis S. | Gieger, Christian | Green, Todd | Hadjadj, Samy | Hattersley, Andrew T. | Herder, Christian | Hofman, Albert | Johnson, Andrew D. | Kottgen, Anna | Kraft, Peter | Labrune, Yann | Langenberg, Claudia | Manning, Alisa K. | Mohlke, Karen L. | Morris, Andrew P. | Oostra, Ben | Pankow, James | Petersen, Ann-Kristin | Pramstaller, Peter P. | Prokopenko, Inga | Rathmann, Wolfgang | Rayner, William | Roden, Michael | Rudan, Igor | Rybin, Denis | Scott, Laura J. | Sigurdsson, Gunnar | Sladek, Rob | Thorleifsson, Gudmar | Thorsteinsdottir, Unnur | Tuomilehto, Jaakko | Uitterlinden, Andre G. | Vivequin, Sidonie | Weedon, Michael N. | Wright, Alan F. | Hu, Frank B. | Illig, Thomas | Kao, Linda | Meigs, James B. | Wilson, James F. | Stefansson, Kari | van Duijn, Cornelia | Altschuler, David | Morris, Andrew D. | Boehnke, Michael | McCarthy, Mark I. | Froguel, Philippe | Palmer, Colin N. A. | Wareham, Nicholas J. | Groop, Leif | Frayling, Timothy M. | Cauchi, Stéphane
PLoS Genetics  2012;8(5):e1002741.
Common diseases such as type 2 diabetes are phenotypically heterogeneous. Obesity is a major risk factor for type 2 diabetes, but patients vary appreciably in body mass index. We hypothesized that the genetic predisposition to the disease may be different in lean (BMI<25 Kg/m2) compared to obese cases (BMI≥30 Kg/m2). We performed two case-control genome-wide studies using two accepted cut-offs for defining individuals as overweight or obese. We used 2,112 lean type 2 diabetes cases (BMI<25 kg/m2) or 4,123 obese cases (BMI≥30 kg/m2), and 54,412 un-stratified controls. Replication was performed in 2,881 lean cases or 8,702 obese cases, and 18,957 un-stratified controls. To assess the effects of known signals, we tested the individual and combined effects of SNPs representing 36 type 2 diabetes loci. After combining data from discovery and replication datasets, we identified two signals not previously reported in Europeans. A variant (rs8090011) in the LAMA1 gene was associated with type 2 diabetes in lean cases (P = 8.4×10−9, OR = 1.13 [95% CI 1.09–1.18]), and this association was stronger than that in obese cases (P = 0.04, OR = 1.03 [95% CI 1.00–1.06]). A variant in HMG20A—previously identified in South Asians but not Europeans—was associated with type 2 diabetes in obese cases (P = 1.3×10−8, OR = 1.11 [95% CI 1.07–1.15]), although this association was not significantly stronger than that in lean cases (P = 0.02, OR = 1.09 [95% CI 1.02–1.17]). For 36 known type 2 diabetes loci, 29 had a larger odds ratio in the lean compared to obese (binomial P = 0.0002). In the lean analysis, we observed a weighted per-risk allele OR = 1.13 [95% CI 1.10–1.17], P = 3.2×10−14. This was larger than the same model fitted in the obese analysis where the OR = 1.06 [95% CI 1.05–1.08], P = 2.2×10−16. This study provides evidence that stratification of type 2 diabetes cases by BMI may help identify additional risk variants and that lean cases may have a stronger genetic predisposition to type 2 diabetes.
Author Summary
Individuals with Type 2 diabetes (T2D) can present with variable clinical characteristics. It is well known that obesity is a major risk factor for type 2 diabetes, yet patients can vary considerably—there are many lean diabetes patients and many overweight people without diabetes. We hypothesized that the genetic predisposition to the disease may be different in lean (BMI<25 Kg/m2) compared to obese cases (BMI≥30 Kg/m2). Specifically, as lean T2D patients had lower risk than obese patients, they must have been more genetically susceptible. Using genetic data from multiple genome-wide association studies, we tested genetic markers across the genome in 2,112 lean type 2 diabetes cases (BMI<25 kg/m2), 4,123 obese cases (BMI≥30 kg/m2), and 54,412 healthy controls. We confirmed our results in an additional 2,881 lean cases, 8,702 obese cases, and 18,957 healthy controls. Using these data we found differences in genetic enrichment between lean and obese cases, supporting our original hypothesis. We also searched for genetic variants that may be risk factors only in lean or obese patients and found two novel gene regions not previously reported in European individuals. These findings may influence future study design for type 2 diabetes and provide further insight into the biology of the disease.
doi:10.1371/journal.pgen.1002741
PMCID: PMC3364960  PMID: 22693455
17.  Extent, Causes, and Consequences of Small RNA Expression Variation in Human Adipose Tissue 
PLoS Genetics  2012;8(5):e1002704.
Small RNAs are functional molecules that modulate mRNA transcripts and have been implicated in the aetiology of several common diseases. However, little is known about the extent of their variability within the human population. Here, we characterise the extent, causes, and effects of naturally occurring variation in expression and sequence of small RNAs from adipose tissue in relation to genotype, gene expression, and metabolic traits in the MuTHER reference cohort. We profiled the expression of 15 to 30 base pair RNA molecules in subcutaneous adipose tissue from 131 individuals using high-throughput sequencing, and quantified levels of 591 microRNAs and small nucleolar RNAs. We identified three genetic variants and three RNA editing events. Highly expressed small RNAs are more conserved within mammals than average, as are those with highly variable expression. We identified 14 genetic loci significantly associated with nearby small RNA expression levels, seven of which also regulate an mRNA transcript level in the same region. In addition, these loci are enriched for variants significant in genome-wide association studies for body mass index. Contrary to expectation, we found no evidence for negative correlation between expression level of a microRNA and its target mRNAs. Trunk fat mass, body mass index, and fasting insulin were associated with more than twenty small RNA expression levels each, while fasting glucose had no significant associations. This study highlights the similar genetic complexity and shared genetic control of small RNA and mRNA transcripts, and gives a quantitative picture of small RNA expression variation in the human population.
Author Summary
Genetic information is transmitted to the cell only through RNA molecules. A special class of RNAs is comprised of the small (up to 30 nucleotide) ones, known to be potent regulators of various cellular processes. At the same time, they have not been as widely studied as messenger RNAs—we do not know how much variation in their sequence and expression level occurs naturally in human populations or how this variability influences other traits. We measured small RNA levels and genetic variability in fat tissue from 131 individuals by high-throughput sequencing. We could associate the expression levels with genetic background of the individuals, as well as changes in metabolic traits. Surprisingly, we found no large scale influence of small RNA variation on mRNA levels, their main regulatory target. Overall, our study is the first to give a quantitative picture of the naturally occurring variation in these important regulatory molecules in human fat tissue.
doi:10.1371/journal.pgen.1002704
PMCID: PMC3349731  PMID: 22589741
18.  Epigenome-Wide Scans Identify Differentially Methylated Regions for Age and Age-Related Phenotypes in a Healthy Ageing Population 
PLoS Genetics  2012;8(4):e1002629.
Age-related changes in DNA methylation have been implicated in cellular senescence and longevity, yet the causes and functional consequences of these variants remain unclear. To elucidate the role of age-related epigenetic changes in healthy ageing and potential longevity, we tested for association between whole-blood DNA methylation patterns in 172 female twins aged 32 to 80 with age and age-related phenotypes. Twin-based DNA methylation levels at 26,690 CpG-sites showed evidence for mean genome-wide heritability of 18%, which was supported by the identification of 1,537 CpG-sites with methylation QTLs in cis at FDR 5%. We performed genome-wide analyses to discover differentially methylated regions (DMRs) for sixteen age-related phenotypes (ap-DMRs) and chronological age (a-DMRs). Epigenome-wide association scans (EWAS) identified age-related phenotype DMRs (ap-DMRs) associated with LDL (STAT5A), lung function (WT1), and maternal longevity (ARL4A, TBX20). In contrast, EWAS for chronological age identified hundreds of predominantly hyper-methylated age DMRs (490 a-DMRs at FDR 5%), of which only one (TBX20) was also associated with an age-related phenotype. Therefore, the majority of age-related changes in DNA methylation are not associated with phenotypic measures of healthy ageing in later life. We replicated a large proportion of a-DMRs in a sample of 44 younger adult MZ twins aged 20 to 61, suggesting that a-DMRs may initiate at an earlier age. We next explored potential genetic and environmental mechanisms underlying a-DMRs and ap-DMRs. Genome-wide overlap across cis-meQTLs, genotype-phenotype associations, and EWAS ap-DMRs identified CpG-sites that had cis-meQTLs with evidence for genotype–phenotype association, where the CpG-site was also an ap-DMR for the same phenotype. Monozygotic twin methylation difference analyses identified one potential environmentally-mediated ap-DMR associated with total cholesterol and LDL (CSMD1). Our results suggest that in a small set of genes DNA methylation may be a candidate mechanism of mediating not only environmental, but also genetic effects on age-related phenotypes.
Author Summary
Epigenetic patterns vary during healthy ageing and development. Age-related DNA methylation changes have been implicated in cellular senescence and longevity, yet the causes and functional consequences of these variants remain unclear. To understand the biological mechanisms involved in potential longevity and rate of healthy ageing, we performed genome-wide association of epigenetic and genetic variation with both chronological age and age-related phenotypes. We identified hundreds of DNA methylation variants significantly associated with age and replicated these in an independent sample of young adult twins. Only a small proportion of these variants were also associated with age-related phenotypes. Therefore, the majority of age-related epigenetic changes do not contribute to rate of healthy ageing at later stages in life. Our results suggest that age-related changes in methylation occur throughout an individual's lifespan and that a proportion of these may be initiated from an early age. Intriguingly, a fraction of the age differentially methylated regions also associated with genetic variants in our sample, suggesting that DNA methylation may be a candidate mechanism of mediating not only environmental but also genetic effects on age-related phenotypes.
doi:10.1371/journal.pgen.1002629
PMCID: PMC3330116  PMID: 22532803
19.  The Human Pancreatic Islet Transcriptome: Expression of Candidate Genes for Type 1 Diabetes and the Impact of Pro-Inflammatory Cytokines 
PLoS Genetics  2012;8(3):e1002552.
Type 1 diabetes (T1D) is an autoimmune disease in which pancreatic beta cells are killed by infiltrating immune cells and by cytokines released by these cells. Signaling events occurring in the pancreatic beta cells are decisive for their survival or death in diabetes. We have used RNA sequencing (RNA–seq) to identify transcripts, including splice variants, expressed in human islets of Langerhans under control conditions or following exposure to the pro-inflammatory cytokines interleukin-1β (IL-1β) and interferon-γ (IFN-γ). Based on this unique dataset, we examined whether putative candidate genes for T1D, previously identified by GWAS, are expressed in human islets. A total of 29,776 transcripts were identified as expressed in human islets. Expression of around 20% of these transcripts was modified by pro-inflammatory cytokines, including apoptosis- and inflammation-related genes. Chemokines were among the transcripts most modified by cytokines, a finding confirmed at the protein level by ELISA. Interestingly, 35% of the genes expressed in human islets undergo alternative splicing as annotated in RefSeq, and cytokines caused substantial changes in spliced transcripts. Nova1, previously considered a brain-specific regulator of mRNA splicing, is expressed in islets and its knockdown modified splicing. 25/41 of the candidate genes for T1D are expressed in islets, and cytokines modified expression of several of these transcripts. The present study doubles the number of known genes expressed in human islets and shows that cytokines modify alternative splicing in human islet cells. Importantly, it indicates that more than half of the known T1D candidate genes are expressed in human islets. This, and the production of a large number of chemokines and cytokines by cytokine-exposed islets, reinforces the concept of a dialog between pancreatic islets and the immune system in T1D. This dialog is modulated by candidate genes for the disease at both the immune system and beta cell level.
Author Summary
Pancreatic beta cells are destroyed by the immune system in type 1 diabetes mellitus, causing insulin dependence for life. Candidate genes for diabetes contribute to this process by acting both at the immune system and, as we suggest here, at the pancreatic beta cell level. We have utilized a novel technology, RNA sequencing, to define all transcripts expressed in human pancreatic islets under basal conditions and following exposure to cytokines, pro-inflammatory mediators that contribute to trigger diabetes. Our observations double the number of known genes present in human islets and indicate that >60% of the candidate genes for type 1 diabetes are expressed in beta cells. The data also show that pro-inflammatory cytokines modify alternative splicing in human islets, a process that may generate novel RNAs and proteins recognizable by the immune system. This, taken together with the findings that pancreatic beta cells themselves express and release many cytokines and chemokines (proteins that attract immune cells), indicates that early type 1 diabetes is characterized by a dialog between beta cells and the immune system. We suggest that candidate genes for diabetes function at least in part as “writers” for the beta cell words in this dialog.
doi:10.1371/journal.pgen.1002552
PMCID: PMC3297576  PMID: 22412385
20.  Coexpression Network Analysis in Abdominal and Gluteal Adipose Tissue Reveals Regulatory Genetic Loci for Metabolic Syndrome and Related Phenotypes 
PLoS Genetics  2012;8(2):e1002505.
Metabolic Syndrome (MetS) is highly prevalent and has considerable public health impact, but its underlying genetic factors remain elusive. To identify gene networks involved in MetS, we conducted whole-genome expression and genotype profiling on abdominal (ABD) and gluteal (GLU) adipose tissue, and whole blood (WB), from 29 MetS cases and 44 controls. Co-expression network analysis for each tissue independently identified nine, six, and zero MetS–associated modules of coexpressed genes in ABD, GLU, and WB, respectively. Of 8,992 probesets expressed in ABD or GLU, 685 (7.6%) were expressed in ABD and 51 (0.6%) in GLU only. Differential eigengene network analysis of 8,256 shared probesets detected 22 shared modules with high preservation across adipose depots (DABD-GLU = 0.89), seven of which were associated with MetS (FDR P<0.01). The strongest associated module, significantly enriched for immune response–related processes, contained 94/620 (15%) genes with inter-depot differences. In an independent cohort of 145/141 twins with ABD and WB longitudinal expression data, median variability in ABD due to familiality was greater for MetS–associated versus un-associated modules (ABD: 0.48 versus 0.18, P = 0.08; GLU: 0.54 versus 0.20, P = 7.8×10−4). Cis-eQTL analysis of probesets associated with MetS (FDR P<0.01) and/or inter-depot differences (FDR P<0.01) provided evidence for 32 eQTLs. Corresponding eSNPs were tested for association with MetS–related phenotypes in two GWAS of >100,000 individuals; rs10282458, affecting expression of RARRES2 (encoding chemerin), was associated with body mass index (BMI) (P = 6.0×10−4); and rs2395185, affecting inter-depot differences of HLA-DRB1 expression, was associated with high-density lipoprotein (P = 8.7×10−4) and BMI–adjusted waist-to-hip ratio (P = 2.4×10−4). Since many genes and their interactions influence complex traits such as MetS, integrated analysis of genotypes and coexpression networks across multiple tissues relevant to clinical traits is an efficient strategy to identify novel associations.
Author Summary
Metabolic Syndrome (MetS) is a highly prevalent disorder with considerable public health concern, but its underlying genetic factors remain elusive. Given that most cellular components exert their functions through interactions with other cellular components, even the largest of genome-wide association (GWA) studies may often not detect their effects, nor necessarily provide insight into the complex molecular mechanisms of the disease. Rather than focusing on individual genes, the analysis of coexpression networks can be used for finding clusters (modules) of correlated expression levels across samples. In this study, we used a gene network–based approach for integrating clinical MetS, genotypic, and gene expression data from abdominal and gluteal adipose tissue and whole blood. We identified modules of genes related to MetS significantly enriched for immune response and oxidative phosphorylation pathways. We tested SNPs for association with MetS–associated expression (eSNPs), and tested prioritised eSNPs for association with MetS–related phenotypes in two large-scale GWA datasets. We identified two loci, neither of which had reached genome-wide significance levels in GWAs, associated with expression levels of RARRES2 and HLA-DRB1 and with MetS–related phenotypes, demonstrating that the integrated analysis of genotype and expression data from relevant multiple tissues can identify novel associations with complex traits such as MetS.
doi:10.1371/journal.pgen.1002505
PMCID: PMC3285582  PMID: 22383892
21.  A Genome-Wide Screen for Interactions Reveals a New Locus on 4p15 Modifying the Effect of Waist-to-Hip Ratio on Total Cholesterol 
Surakka, Ida | Isaacs, Aaron | Karssen, Lennart C. | Laurila, Pirkka-Pekka P. | Middelberg, Rita P. S. | Tikkanen, Emmi | Ried, Janina S. | Lamina, Claudia | Mangino, Massimo | Igl, Wilmar | Hottenga, Jouke-Jan | Lagou, Vasiliki | van der Harst, Pim | Mateo Leach, Irene | Esko, Tõnu | Kutalik, Zoltán | Wainwright, Nicholas W. | Struchalin, Maksim V. | Sarin, Antti-Pekka | Kangas, Antti J. | Viikari, Jorma S. | Perola, Markus | Rantanen, Taina | Petersen, Ann-Kristin | Soininen, Pasi | Johansson, Åsa | Soranzo, Nicole | Heath, Andrew C. | Papamarkou, Theodore | Prokopenko, Inga | Tönjes, Anke | Kronenberg, Florian | Döring, Angela | Rivadeneira, Fernando | Montgomery, Grant W. | Whitfield, John B. | Kähönen, Mika | Lehtimäki, Terho | Freimer, Nelson B. | Willemsen, Gonneke | de Geus, Eco J. C. | Palotie, Aarno | Sandhu, Manj S. | Waterworth, Dawn M. | Metspalu, Andres | Stumvoll, Michael | Uitterlinden, André G. | Jula, Antti | Navis, Gerjan | Wijmenga, Cisca | Wolffenbuttel, Bruce H. R. | Taskinen, Marja-Riitta | Ala-Korpela, Mika | Kaprio, Jaakko | Kyvik, Kirsten O. | Boomsma, Dorret I. | Pedersen, Nancy L. | Gyllensten, Ulf | Wilson, James F. | Rudan, Igor | Campbell, Harry | Pramstaller, Peter P. | Spector, Tim D. | Witteman, Jacqueline C. M. | Eriksson, Johan G. | Salomaa, Veikko | Oostra, Ben A. | Raitakari, Olli T. | Wichmann, H.-Erich | Gieger, Christian | Järvelin, Marjo-Riitta | Martin, Nicholas G. | Hofman, Albert | McCarthy, Mark I. | Peltonen, Leena | van Duijn, Cornelia M. | Aulchenko, Yurii S. | Ripatti, Samuli
PLoS Genetics  2011;7(10):e1002333.
Recent genome-wide association (GWA) studies described 95 loci controlling serum lipid levels. These common variants explain ∼25% of the heritability of the phenotypes. To date, no unbiased screen for gene–environment interactions for circulating lipids has been reported. We screened for variants that modify the relationship between known epidemiological risk factors and circulating lipid levels in a meta-analysis of genome-wide association (GWA) data from 18 population-based cohorts with European ancestry (maximum N = 32,225). We collected 8 further cohorts (N = 17,102) for replication, and rs6448771 on 4p15 demonstrated genome-wide significant interaction with waist-to-hip-ratio (WHR) on total cholesterol (TC) with a combined P-value of 4.79×10−9. There were two potential candidate genes in the region, PCDH7 and CCKAR, with differential expression levels for rs6448771 genotypes in adipose tissue. The effect of WHR on TC was strongest for individuals carrying two copies of G allele, for whom a one standard deviation (sd) difference in WHR corresponds to 0.19 sd difference in TC concentration, while for A allele homozygous the difference was 0.12 sd. Our findings may open up possibilities for targeted intervention strategies for people characterized by specific genomic profiles. However, more refined measures of both body-fat distribution and metabolic measures are needed to understand how their joint dynamics are modified by the newly found locus.
Author Summary
Circulating serum lipids contribute greatly to the global health by affecting the risk for cardiovascular diseases. Serum lipid levels are partly inherited, and already 95 loci affecting high- and low-density lipoprotein cholesterol, total cholesterol, and triglycerides have been found. Serum lipids are also known to be affected by multiple epidemiological risk factors like body composition, lifestyle, and sex. It has been hypothesized that there are loci modifying the effects between risk factors and serum lipids, but to date only candidate gene studies for interactions have been reported. We conducted a genome-wide screen with meta-analysis approach to identify loci having interactions with epidemiological risk factors on serum lipids with over 30,000 population-based samples. When combining results from our initial datasets and 8 additional replication cohorts (maximum N = 17,102), we found a genome-wide significant locus in chromosome 4p15 with a joint P-value of 4.79×10−9 modifying the effect of waist-to-hip ratio on total cholesterol. In the area surrounding this genetic variant, there were two genes having association between the genotypes and the gene expression in adipose tissue, and we also found enrichment of association in genes belonging to lipid metabolism related functions.
doi:10.1371/journal.pgen.1002333
PMCID: PMC3197672  PMID: 22028671
22.  A Genome-Wide Metabolic QTL Analysis in Europeans Implicates Two Loci Shaped by Recent Positive Selection 
PLoS Genetics  2011;7(9):e1002270.
We have performed a metabolite quantitative trait locus (mQTL) study of the 1H nuclear magnetic resonance spectroscopy (1H NMR) metabolome in humans, building on recent targeted knowledge of genetic drivers of metabolic regulation. Urine and plasma samples were collected from two cohorts of individuals of European descent, with one cohort comprised of female twins donating samples longitudinally. Sample metabolite concentrations were quantified by 1H NMR and tested for association with genome-wide single-nucleotide polymorphisms (SNPs). Four metabolites' concentrations exhibited significant, replicable association with SNP variation (8.6×10−11
Author Summary
Physiological concentrations of metabolites—small molecules involved in biochemical processes in living systems—can be measured and used to diagnose and predict disease states. A common goal is to detect and clinically exploit statistical differences in metabolite concentrations between diseased and healthy individuals. As a basis for the design and interpretation of case-control studies, it is useful to have a characterization of metabolic diversity amongst healthy individuals, some of which stems from inter-individual genetic variation. When a single genetic locus has a sufficiently strong effect on metabolism, its genomic position can be determined by collecting metabolite concentration data and genome-wide genotype data on a set of individuals and searching for associations between the two data sets—a so-called metabolite quantitative trait locus (mQTL) study. By so tracing mQTLs, we can identify the genetic drivers of metabolism, characterize how the nature or quantity of the corresponding expressed protein(s) feeds forward to influence metabolite levels, and specify disease-predictive models that incorporate mutual dependence amongst genetics, environment, and metabolism.
doi:10.1371/journal.pgen.1002270
PMCID: PMC3169529  PMID: 21931564
PLoS Genetics  2011;7(2):e1001307.
An age-dependent association between variation at the FTO locus and BMI in children has been suggested. We meta-analyzed associations between the FTO locus (rs9939609) and BMI in samples, aged from early infancy to 13 years, from 8 cohorts of European ancestry. We found a positive association between additional minor (A) alleles and BMI from 5.5 years onwards, but an inverse association below age 2.5 years. Modelling median BMI curves for each genotype using the LMS method, we found that carriers of minor alleles showed lower BMI in infancy, earlier adiposity rebound (AR), and higher BMI later in childhood. Differences by allele were consistent with two independent processes: earlier AR equivalent to accelerating developmental age by 2.37% (95% CI 1.87, 2.87, p = 10−20) per A allele and a positive age by genotype interaction such that BMI increased faster with age (p = 10−23). We also fitted a linear mixed effects model to relate genotype to the BMI curve inflection points adiposity peak (AP) in infancy and AR. Carriage of two minor alleles at rs9939609 was associated with lower BMI at AP (−0.40% (95% CI: −0.74, −0.06), p = 0.02), higher BMI at AR (0.93% (95% CI: 0.22, 1.64), p = 0.01), and earlier AR (−4.72% (−5.81, −3.63), p = 10−17), supporting cross-sectional results. Overall, we confirm the expected association between variation at rs9939609 and BMI in childhood, but only after an inverse association between the same variant and BMI in infancy. Patterns are consistent with a shift on the developmental scale, which is reflected in association with the timing of AR rather than just a global increase in BMI. Results provide important information about longitudinal gene effects and about the role of FTO in adiposity. The associated shifts in developmental timing have clinical importance with respect to known relationships between AR and both later-life BMI and metabolic disease risk.
Author Summary
Variation at the FTO locus is reliably associated with BMI and adiposity-related traits, but little is still known about the effects of variation at this gene, particularly in children. We have examined a large collection of samples for which both genotypes at rs9939609 and multiple measurements of BMI are available. We observe a positive association between the minor allele (A) at rs9939609 and BMI emerging in childhood that has the characteristics of a shift in the age scale leading simultaneously to lower BMI during infancy and higher BMI in childhood. Assessed in cross section and longitudinally, we find evidence of variation at rs9939609 being associated with the timing of AR and the concert of events expected with such a change to the BMI curve. Importantly, the apparently negative association between the minor allele (A) and BMI in early life, which is then followed by an earlier AR and greater BMI in childhood, is a pattern known to be associated with both the risk of adult BMI and metabolic disorders such as type 2 diabetes (T2D). These findings are important in our understanding of the contribution of FTO to adiposity, but also in light of efforts to appreciate genetic effects in a lifecourse context.
doi:10.1371/journal.pgen.1001307
PMCID: PMC3040655  PMID: 21379325
PLoS Genetics  2011;7(2):e1002003.
While there have been studies exploring regulatory variation in one or more tissues, the complexity of tissue-specificity in multiple primary tissues is not yet well understood. We explore in depth the role of cis-regulatory variation in three human tissues: lymphoblastoid cell lines (LCL), skin, and fat. The samples (156 LCL, 160 skin, 166 fat) were derived simultaneously from a subset of well-phenotyped healthy female twins of the MuTHER resource. We discover an abundance of cis-eQTLs in each tissue similar to previous estimates (858 or 4.7% of genes). In addition, we apply factor analysis (FA) to remove effects of latent variables, thus more than doubling the number of our discoveries (1,822 eQTL genes). The unique study design (Matched Co-Twin Analysis—MCTA) permits immediate replication of eQTLs using co-twins (93%–98%) and validation of the considerable gain in eQTL discovery after FA correction. We highlight the challenges of comparing eQTLs between tissues. After verifying previous significance threshold-based estimates of tissue-specificity, we show their limitations given their dependency on statistical power. We propose that continuous estimates of the proportion of tissue-shared signals and direct comparison of the magnitude of effect on the fold change in expression are essential properties that jointly provide a biologically realistic view of tissue-specificity. Under this framework we demonstrate that 30% of eQTLs are shared among the three tissues studied, while another 29% appear exclusively tissue-specific. However, even among the shared eQTLs, a substantial proportion (10%–20%) have significant differences in the magnitude of fold change between genotypic classes across tissues. Our results underline the need to account for the complexity of eQTL tissue-specificity in an effort to assess consequences of such variants for complex traits.
Author Summary
Regulation of gene expression is a fundamental cellular process determining a large proportion of the phenotypic variance. Previous studies have identified genetic loci influencing gene expression levels (eQTLs), but the complexity of their tissue-specific properties has not yet been well-characterized. In this study, we perform cis-eQTL analysis in a unique matched co-twin design for three human tissues derived simultaneously from the same set of individuals. The study design allows validation of the substantial discoveries we make in each tissue. We explore in depth the tissue-dependent features of regulatory variants and estimate the proportions of shared and specific effects. We use continuous measures of eQTL sharing to circumvent the statistical power limitations of comparing direct overlap of eQTLs in multiple tissues. In this framework, we demonstrate that 30% of eQTLs are shared among tissues, while 29% are exclusively tissue-specific. Furthermore, we show that the fold change in expression between eQTL genotypic classes differs between tissues. Even among shared eQTLs, we report a substantial proportion (10%–20%) of significant tissue differences in magnitude of these effects. The complexities we highlight here are essential for understanding the impact of regulatory variants on complex traits.
doi:10.1371/journal.pgen.1002003
PMCID: PMC3033383  PMID: 21304890
PLoS Genetics  2010;6(4):e1000916.
Meta-analyses of population-based genome-wide association studies (GWAS) in adults have recently led to the detection of new genetic loci for obesity. Here we aimed to discover additional obesity loci in extremely obese children and adolescents. We also investigated if these results generalize by estimating the effects of these obesity loci in adults and in population-based samples including both children and adults. We jointly analysed two GWAS of 2,258 individuals and followed-up the best, according to lowest p-values, 44 single nucleotide polymorphisms (SNP) from 21 genomic regions in 3,141 individuals. After this DISCOVERY step, we explored if the findings derived from the extremely obese children and adolescents (10 SNPs from 5 genomic regions) generalized to (i) the population level and (ii) to adults by genotyping another 31,182 individuals (GENERALIZATION step). Apart from previously identified FTO, MC4R, and TMEM18, we detected two new loci for obesity: one in SDCCAG8 (serologically defined colon cancer antigen 8 gene; p = 1.85×10−8 in the DISCOVERY step) and one between TNKS (tankyrase, TRF1-interacting ankyrin-related ADP-ribose polymerase gene) and MSRA (methionine sulfoxide reductase A gene; p = 4.84×10−7), the latter finding being limited to children and adolescents as demonstrated in the GENERALIZATION step. The odds ratios for early-onset obesity were estimated at ∼1.10 per risk allele for both loci. Interestingly, the TNKS/MSRA locus has recently been found to be associated with adult waist circumference. In summary, we have completed a meta-analysis of two GWAS which both focus on extremely obese children and adolescents and replicated our findings in a large followed-up data set. We observed that genetic variants in or near FTO, MC4R, TMEM18, SDCCAG8, and TNKS/MSRA were robustly associated with early-onset obesity. We conclude that the currently known major common variants related to obesity overlap to a substantial degree between children and adults.
Author Summary
Genome-wide association studies (GWAS) have successfully contributed to the detection of genetic variants involved in body-weight regulation. We jointly analysed two GWAS for early-onset extreme obesity in 2,258 individuals of European origin and followed-up the findings in 3,141 individuals. Evidence for association of markers in two new genetic loci was shown (SDCCAG8 on chromosome 1q43–q44 and between TNKS/MSRA on chromosome 8p23.1). We also re-identified variants in or near FTO, MC4R, and TMEM18 to be associated with extreme obesity. In addition, we assessed the effect of the markers in 31,182 obese, lean, normal weight, and unselected individuals from population-based samples and showed that the variants near FTO, MC4R, TMEM18, and SDCCAG8 were consistently associated with obesity. For variants of TNKS/MSRA, the obesity association was limited to children and adolescents. In summary, we detected two new obesity loci and confirmed that the currently known major common variants related to obesity overlap to a substantial degree between children and adults.
doi:10.1371/journal.pgen.1000916
PMCID: PMC2858696  PMID: 20421936

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