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1. Skewed X-inactivation is common in the general female population

Author: Shvetsova, Ekaterina, Sofronova, Alina, Monajemi, Ramin, Gagalova, Kristina, Draisma, Harmen H. M., White, Stefan J., Santen, Gijs W. E., Chuva de Sousa Lopes, Susana M., Heijmans, Bastiaan T., van Meurs, Joyce, Jansen, Rick, Franke, Lude, Kiełbasa, Szymon M., den Dunnen, Johan T., ‘t Hoen, Peter A. C., BIOS consortium, and GoNL consortium
Published: 2019
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2. Metabolomics reveals a link between homocysteine and lipid metabolism and leukocyte telomere length: the ENGAGE consortium

Author: van der Spek, Ashley, Broer, Linda, Draisma, Harmen H. M., Pool, René, Albrecht, Eva, Beekman, Marian, Mangino, Massimo, Raag, Mait, Nyholt, Dale R., Dharuri, Harish K., Codd, Veryan, Amin, Najaf, de Geus, Eco J. C., Deelen, Joris, Demirkan, Ayse, Yet, Idil, Fischer, Krista, Haller, Toomas, Henders, Anjali K., Isaacs, Aaron, Medland, Sarah E., Montgomery, Grant W., Mooijaart, Simon P., Strauch, Konstantin, Suchiman, H. Eka D., Vaarhorst, Anika A. M., van Heemst, Diana, Wang-Sattler, Rui, Whitfield, John B., Willemsen, Gonneke, Wright, Margaret J., Martin, Nicholas G., Samani, Nilesh J., Metspalu, Andres, Eline Slagboom, P., Spector, Tim D., Boomsma, Dorret I., van Duijn, Cornelia M., and Gieger, Christian
Published: 2019
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3. Genome Analyses of >200,000 Individuals Identify 58 Loci for Chronic Inflammation and Highlight Pathways that Link Inflammation and Complex Disorders

Author: Ligthart, Symen, Vaez, Ahmad, Võsa, Urmo, Stathopoulou, Maria G, de Vries, Paul S, Prins, Bram P, Van der Most, Peter J, Tanaka, Toshiko, Naderi, Elnaz, Rose, Lynda M, Wu, Ying, Karlsson, Robert, Barbalic, Maja, Lin, Honghuang, Pool, René, Zhu, Gu, Macé, Aurélien, Sidore, Carlo, Trompet, Stella, Mangino, Massimo, Sabater-Lleal, Maria, Kemp, John P, Abbasi, Ali, Kacprowski, Tim, Verweij, Niek, Smith, Albert V, Huang, Tao, Marzi, Carola, Feitosa, Mary F, Lohman, Kurt K, Kleber, Marcus E, Milaneschi, Yuri, Mueller, Christian, Huq, Mahmudul, Vlachopoulou, Efthymia, Lyytikäinen, Leo-Pekka, Oldmeadow, Christopher, Deelen, Joris, Perola, Markus, Zhao, Jing Hua, Feenstra, Bjarke, Amini, Marzyeh, Lahti, Jari, Schraut, Katharina E, Fornage, Myriam, Suktitipat, Bhoom, Chen, Wei-Min, Li, Xiaohui, Nutile, Teresa, Malerba, Giovanni, Luan, Jian'an, Bak, Tom, Schork, Nicholas, Del Greco M, Fabiola, Thiering, Elisabeth, Mahajan, Anubha, Marioni, Riccardo E, Mihailov, Evelin, Eriksson, Joel, Ozel, Ayse Bilge, Zhang, Weihua, Nethander, Maria, Cheng, Yu-Ching, Aslibekyan, Stella, Ang, Wei, Gandin, Ilaria, Yengo, Loïc, Portas, Laura, Kooperberg, Charles, Hofer, Edith, Rajan, Kumar B, Schurmann, Claudia, den Hollander, Wouter, Ahluwalia, Tarunveer S, Zhao, Jing, Draisma, Harmen H M, Ford, Ian, Timpson, Nicholas, Teumer, Alexander, Huang, Hongyan, Wahl, Simone, Liu, YongMei, Huang, Jie, Uh, Hae-Won, Geller, Frank, Joshi, Peter K, Yanek, Lisa R, Trabetti, Elisabetta, Lehne, Benjamin, Vozzi, Diego, Verbanck, Marie, Biino, Ginevra, Saba, Yasaman, Meulenbelt, Ingrid, O'Connell, Jeff R, Laakso, Markku, Giulianini, Franco, Magnusson, Patrik K E, Ballantyne, Christie M, Hottenga, Jouke Jan, Montgomery, Grant W, Rivadineira, Fernando, Rueedi, Rico, Steri, Maristella, Herzig, Karl-Heinz, Stott, David J, Menni, Cristina, Frånberg, Mattias, St Pourcain, Beate, Felix, Stephan B, Pers, Tune H, Bakker, Stephan J L, Kraft, Peter, Peters, Annette, Vaidya, Dhananjay, Delgado, Graciela, Smit, Johannes H, Großmann, Vera, Sinisalo, Juha, Seppälä, Ilkka, Williams, Stephen R, Holliday, Elizabeth G, Moed, Matthijs, Langenberg, Claudia, Räikkönen, Katri, Ding, Jingzhong, Campbell, Harry, Sale, Michele M, Chen, Yii-Der I, James, Alan L, Ruggiero, Daniela, Soranzo, Nicole, Hartman, Catharina A, Smith, Erin N, Berenson, Gerald S, Fuchsberger, Christian, Hernandez, Dena, Tiesler, Carla M T, Giedraitis, Vilmantas, Liewald, David, Fischer, Krista, Mellström, Dan, Larsson, Anders, Wang, Yunmei, Scott, William R, Lorentzon, Matthias, Beilby, John, Ryan, Kathleen A, Pennell, Craig E, Vuckovic, Dragana, Balkau, Beverly, Concas, Maria Pina, Schmidt, Reinhold, Mendes de Leon, Carlos F, Bottinger, Erwin P, Kloppenburg, Margreet, Paternoster, Lavinia, Boehnke, Michael, Musk, A W, Willemsen, Gonneke, Evans, David M, Madden, Pamela A F, Kähönen, Mika, Kutalik, Zoltán, Zoledziewska, Magdalena, Karhunen, Ville, Kritchevsky, Stephen B, Sattar, Naveed, Lachance, Genevieve, Clarke, Robert, Harris, Tamara B, Raitakari, Olli T, Attia, John R, van Heemst, Diana, Kajantie, Eero, Sorice, Rossella, Gambaro, Giovanni, Scott, Robert A, Hicks, Andrew A, Ferrucci, Luigi, Standl, Marie, Lindgren, Cecilia M, Starr, John M, Karlsson, Magnus, Lind, Lars, Li, Jun Z, Chambers, John C, Mori, Trevor A, de Geus, Eco J C N, Heath, Andrew C, Martin, Nicholas G, Auvinen, Juha, Buckley, Brendan M, de Craen, Anton J M, Waldenberger, Melanie, Strauch, Konstantin, Meitinger, Thomas, Scott, Rodney J, McEvoy, Mark, Beekman, Marian, Bombieri, Cristina, Ridker, Paul M, Mohlke, Karen L, Pedersen, Nancy L, Morrison, Alanna C, Boomsma, Dorret I, Whitfield, John B, Strachan, David P, Hofman, Albert, Vollenweider, Peter, Cucca, Francesco, Jarvelin, Marjo-Riitta, Jukema, J Wouter, Spector, Tim D, Hamsten, Anders, Zeller, Tanja, Uitterlinden, André G, Nauck, Matthias, Gudnason, Vilmundur, Qi, Lu, Grallert, Harald, Borecki, Ingrid B, Rotter, Jerome I, März, Winfried, Wild, Philipp S, Lokki, Marja-Liisa, Boyle, Michael, Salomaa, Veikko, Melbye, Mads, Eriksson, Johan G, Wilson, James F, Penninx, Brenda W J H, Becker, Diane M, Worrall, Bradford B, Gibson, Greg, Krauss, Ronald M, Ciullo, Marina, Zaza, Gianluigi, Wareham, Nicholas J, Oldehinkel, Albertine J, Palmer, Lyle J, Murray, Sarah S, Pramstaller, Peter P, Bandinelli, Stefania, Heinrich, Joachim, Ingelsson, Erik, Deary, Ian J, Mägi, Reedik, Vandenput, Liesbeth, van der Harst, Pim, Desch, Karl C, Kooner, Jaspal S, Ohlsson, Claes, Hayward, Caroline, Lehtimäki, Terho, Shuldiner, Alan R, Arnett, Donna K, Beilin, Lawrence J, Robino, Antonietta, Froguel, Philippe, Pirastu, Mario, Jess, Tine, Koenig, Wolfgang, Loos, Ruth J F, Evans, Denis A, Schmidt, Helena, Smith, George Davey, Slagboom, P Eline, Eiriksdottir, Gudny, Morris, Andrew P, Psaty, Bruce M, Tracy, Russell P, Nolte, Ilja M, Boerwinkle, Eric, Visvikis-Siest, Sophie, Reiner, Alex P, Gross, Myron, Bis, Joshua C, Franke, Lude, Franco, Oscar H, Benjamin, Emelia J, Chasman, Daniel I, Dupuis, Josée, Snieder, Harold, Dehghan, Abbas, Alizadeh, Behrooz Z, Ligthart, Symen, Vaez, Ahmad, Võsa, Urmo, Stathopoulou, Maria G, de Vries, Paul S, Prins, Bram P, Van der Most, Peter J, Tanaka, Toshiko, Naderi, Elnaz, Rose, Lynda M, Wu, Ying, Karlsson, Robert, Barbalic, Maja, Lin, Honghuang, Pool, René, Zhu, Gu, Macé, Aurélien, Sidore, Carlo, Trompet, Stella, Mangino, Massimo, Sabater-Lleal, Maria, Kemp, John P, Abbasi, Ali, Kacprowski, Tim, Verweij, Niek, Smith, Albert V, Huang, Tao, Marzi, Carola, Feitosa, Mary F, Lohman, Kurt K, Kleber, Marcus E, Milaneschi, Yuri, Mueller, Christian, Huq, Mahmudul, Vlachopoulou, Efthymia, Lyytikäinen, Leo-Pekka, Oldmeadow, Christopher, Deelen, Joris, Perola, Markus, Zhao, Jing Hua, Feenstra, Bjarke, Amini, Marzyeh, Lahti, Jari, Schraut, Katharina E, Fornage, Myriam, Suktitipat, Bhoom, Chen, Wei-Min, Li, Xiaohui, Nutile, Teresa, Malerba, Giovanni, Luan, Jian'an, Bak, Tom, Schork, Nicholas, Del Greco M, Fabiola, Thiering, Elisabeth, Mahajan, Anubha, Marioni, Riccardo E, Mihailov, Evelin, Eriksson, Joel, Ozel, Ayse Bilge, Zhang, Weihua, Nethander, Maria, Cheng, Yu-Ching, Aslibekyan, Stella, Ang, Wei, Gandin, Ilaria, Yengo, Loïc, Portas, Laura, Kooperberg, Charles, Hofer, Edith, Rajan, Kumar B, Schurmann, Claudia, den Hollander, Wouter, Ahluwalia, Tarunveer S, Zhao, Jing, Draisma, Harmen H M, Ford, Ian, Timpson, Nicholas, Teumer, Alexander, Huang, Hongyan, Wahl, Simone, Liu, YongMei, Huang, Jie, Uh, Hae-Won, Geller, Frank, Joshi, Peter K, Yanek, Lisa R, Trabetti, Elisabetta, Lehne, Benjamin, Vozzi, Diego, Verbanck, Marie, Biino, Ginevra, Saba, Yasaman, Meulenbelt, Ingrid, O'Connell, Jeff R, Laakso, Markku, Giulianini, Franco, Magnusson, Patrik K E, Ballantyne, Christie M, Hottenga, Jouke Jan, Montgomery, Grant W, Rivadineira, Fernando, Rueedi, Rico, Steri, Maristella, Herzig, Karl-Heinz, Stott, David J, Menni, Cristina, Frånberg, Mattias, St Pourcain, Beate, Felix, Stephan B, Pers, Tune H, Bakker, Stephan J L, Kraft, Peter, Peters, Annette, Vaidya, Dhananjay, Delgado, Graciela, Smit, Johannes H, Großmann, Vera, Sinisalo, Juha, Seppälä, Ilkka, Williams, Stephen R, Holliday, Elizabeth G, Moed, Matthijs, Langenberg, Claudia, Räikkönen, Katri, Ding, Jingzhong, Campbell, Harry, Sale, Michele M, Chen, Yii-Der I, James, Alan L, Ruggiero, Daniela, Soranzo, Nicole, Hartman, Catharina A, Smith, Erin N, Berenson, Gerald S, Fuchsberger, Christian, Hernandez, Dena, Tiesler, Carla M T, Giedraitis, Vilmantas, Liewald, David, Fischer, Krista, Mellström, Dan, Larsson, Anders, Wang, Yunmei, Scott, William R, Lorentzon, Matthias, Beilby, John, Ryan, Kathleen A, Pennell, Craig E, Vuckovic, Dragana, Balkau, Beverly, Concas, Maria Pina, Schmidt, Reinhold, Mendes de Leon, Carlos F, Bottinger, Erwin P, Kloppenburg, Margreet, Paternoster, Lavinia, Boehnke, Michael, Musk, A W, Willemsen, Gonneke, Evans, David M, Madden, Pamela A F, Kähönen, Mika, Kutalik, Zoltán, Zoledziewska, Magdalena, Karhunen, Ville, Kritchevsky, Stephen B, Sattar, Naveed, Lachance, Genevieve, Clarke, Robert, Harris, Tamara B, Raitakari, Olli T, Attia, John R, van Heemst, Diana, Kajantie, Eero, Sorice, Rossella, Gambaro, Giovanni, Scott, Robert A, Hicks, Andrew A, Ferrucci, Luigi, Standl, Marie, Lindgren, Cecilia M, Starr, John M, Karlsson, Magnus, Lind, Lars, Li, Jun Z, Chambers, John C, Mori, Trevor A, de Geus, Eco J C N, Heath, Andrew C, Martin, Nicholas G, Auvinen, Juha, Buckley, Brendan M, de Craen, Anton J M, Waldenberger, Melanie, Strauch, Konstantin, Meitinger, Thomas, Scott, Rodney J, McEvoy, Mark, Beekman, Marian, Bombieri, Cristina, Ridker, Paul M, Mohlke, Karen L, Pedersen, Nancy L, Morrison, Alanna C, Boomsma, Dorret I, Whitfield, John B, Strachan, David P, Hofman, Albert, Vollenweider, Peter, Cucca, Francesco, Jarvelin, Marjo-Riitta, Jukema, J Wouter, Spector, Tim D, Hamsten, Anders, Zeller, Tanja, Uitterlinden, André G, Nauck, Matthias, Gudnason, Vilmundur, Qi, Lu, Grallert, Harald, Borecki, Ingrid B, Rotter, Jerome I, März, Winfried, Wild, Philipp S, Lokki, Marja-Liisa, Boyle, Michael, Salomaa, Veikko, Melbye, Mads, Eriksson, Johan G, Wilson, James F, Penninx, Brenda W J H, Becker, Diane M, Worrall, Bradford B, Gibson, Greg, Krauss, Ronald M, Ciullo, Marina, Zaza, Gianluigi, Wareham, Nicholas J, Oldehinkel, Albertine J, Palmer, Lyle J, Murray, Sarah S, Pramstaller, Peter P, Bandinelli, Stefania, Heinrich, Joachim, Ingelsson, Erik, Deary, Ian J, Mägi, Reedik, Vandenput, Liesbeth, van der Harst, Pim, Desch, Karl C, Kooner, Jaspal S, Ohlsson, Claes, Hayward, Caroline, Lehtimäki, Terho, Shuldiner, Alan R, Arnett, Donna K, Beilin, Lawrence J, Robino, Antonietta, Froguel, Philippe, Pirastu, Mario, Jess, Tine, Koenig, Wolfgang, Loos, Ruth J F, Evans, Denis A, Schmidt, Helena, Smith, George Davey, Slagboom, P Eline, Eiriksdottir, Gudny, Morris, Andrew P, Psaty, Bruce M, Tracy, Russell P, Nolte, Ilja M, Boerwinkle, Eric, Visvikis-Siest, Sophie, Reiner, Alex P, Gross, Myron, Bis, Joshua C, Franke, Lude, Franco, Oscar H, Benjamin, Emelia J, Chasman, Daniel I, Dupuis, Josée, Snieder, Harold, Dehghan, Abbas, and Alizadeh, Behrooz Z
Abstract: C-reactive protein (CRP) is a sensitive biomarker of chronic low-grade inflammation and is associated with multiple complex diseases. The genetic determinants of chronic inflammation remain largely unknown, and the causal role of CRP in several clinical outcomes is debated. We performed two genome-wide association studies (GWASs), on HapMap and 1000 Genomes imputed data, of circulating amounts of CRP by using data from 88 studies comprising 204,402 European individuals. Additionally, we performed in silico functional analyses and Mendelian randomization analyses with several clinical outcomes. The GWAS meta-analyses of CRP revealed 58 distinct genetic loci (p < 5 × 10-8). After adjustment for body mass index in the regression analysis, the associations at all except three loci remained. The lead variants at the distinct loci explained up to 7.0% of the variance in circulating amounts of CRP. We identified 66 gene sets that were organized in two substantially correlated clusters, one mainly composed of immune pathways and the other characterized by metabolic pathways in the liver. Mendelian randomization analyses revealed a causal protective effect of CRP on schizophrenia and a risk-increasing effect on bipolar disorder. Our findings provide further insights into the biology of inflammation and could lead to interventions for treating inflammation and its clinical consequences.
Published: 2018
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4. Heritability estimates for 361 blood metabolites across 40 genome-wide association studies.

Author: Hagenbeek, Fiona A., Pool, René, van Dongen, Jenny, Draisma, Harmen H. M., Jan Hottenga, Jouke, Willemsen, Gonneke, Abdellaoui, Abdel, Fedko, Iryna O., den Braber, Anouk, Visser, Pieter Jelle, de Geus, Eco J. C. N., Willems van Dijk, Ko, Verhoeven, Aswin, Suchiman, H. Eka, Beekman, Marian, Slagboom, P. Eline, van Duijn, Cornelia M., BBMRI Metabolomics Consortium, Barkey Wolf, J. J. H., and Cats, D.
Subjects: HERITABILITY, SMALL molecules, ORGANIC acids, ESTIMATES, BLOOD, METABOLOMICS, METABOLITES
Abstract: Metabolomics examines the small molecules involved in cellular metabolism. Approximately 50% of total phenotypic differences in metabolite levels is due to genetic variance, but heritability estimates differ across metabolite classes. We perform a review of all genome-wide association and (exome-) sequencing studies published between November 2008 and October 2018, and identify >800 class-specific metabolite loci associated with metabolite levels. In a twin-family cohort (N = 5117), these metabolite loci are leveraged to simultaneously estimate total heritability (h2total), and the proportion of heritability captured by known metabolite loci (h2Metabolite-hits) for 309 lipids and 52 organic acids. Our study reveals significant differences in h2Metabolite-hits among different classes of lipids and organic acids. Furthermore, phosphatidylcholines with a high degree of unsaturation have higher h2Metabolite-hits estimates than phosphatidylcholines with low degrees of unsaturation. This study highlights the importance of common genetic variants for metabolite levels, and elucidates the genetic architecture of metabolite classes. Blood metabolite levels are under the influence of environmental and genetic factors. Here, Hagenbeek et al. perform heritability estimations for metabolite measures and determine the contribution of known metabolite loci to metabolite levels using data from 40 genome-wide association studies. [ABSTRACT FROM AUTHOR]
Published: 2020
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5. Comparison of HapMap and 1000 Genomes Reference Panels in a Large-Scale Genome-Wide Association Study

Author: de Vries, Paul S., Sabater-Lleal, Maria, Chasman, Daniel I., Trompet, Stella, Ahluwalia, Tarunveer S., Teumer, Alexander, Kleber, Marcus E., Chen, Ming-Huei, Wang, Jie Jin, Attia, John R., Marioni, Riccardo E., Steri, Maristella, Weng, Lu-Chen, Pool, Rene, Grossmann, Vera, Brody, Jennifer A., Venturini, Cristina, Tanaka, Toshiko, Rose, Lynda M., Oldmeadow, Christopher, Mazur, Johanna, Basu, Saonli, Frånberg, Mattias, Yang, Qiong, Ligthart, Symen, Hottenga, Jouke J., Rumley, Ann, Mulas, Antonella, de Craen, Anton J. M., Grotevendt, Anne, Taylor, Kent D., Delgado, Graciela E., Kifley, Annette, Lopez, Lorna M., Berentzen, Tina L., Mangino, Massimo, Bandinelli, Stefania, Morrison, Alanna C., Hamsten, Anders, Tofler, Geoffrey, de Maat, Moniek P. M., Draisma, Harmen H. M., Lowe, Gordon D., Zoledziewska, Magdalena, Sattar, Naveed, Lackner, Karl J., Voelker, Uwe, McKnight, Barbara, Huang, Jie, Holliday, Elizabeth G., McEvoy, Mark A., Starr, John M., Hysi, Pirro G., Hernandez, Dena G., Guan, Weihua, Rivadeneira, Fernando, McArdle, Wendy L., Slagboom, P. Eline, Zeller, Tanja, Psaty, Bruce M., Uitterlinden, Andre G., de Geus, Eco J. C., Stott, David J., Binder, Harald, Hofman, Albert, Franco, Oscar H., Rotter, Jerome I., Ferrucci, Luigi, Spector, Tim D., Deary, Ian J., Maerz, Winfried, Greinacher, Andreas, Wild, Philipp S., Cucca, Francesco, Boomsma, Dorret I., Watkins, Hugh, Tang, Weihong, Ridker, Paul M., Jukema, Jan W., Scott, Rodney J., Mitchell, Paul, Hansen, Torben, O'Donnell, Christopher J., Smith, Nicholas L., Strachan, David P., Dehghan, Abbas, de Vries, Paul S., Sabater-Lleal, Maria, Chasman, Daniel I., Trompet, Stella, Ahluwalia, Tarunveer S., Teumer, Alexander, Kleber, Marcus E., Chen, Ming-Huei, Wang, Jie Jin, Attia, John R., Marioni, Riccardo E., Steri, Maristella, Weng, Lu-Chen, Pool, Rene, Grossmann, Vera, Brody, Jennifer A., Venturini, Cristina, Tanaka, Toshiko, Rose, Lynda M., Oldmeadow, Christopher, Mazur, Johanna, Basu, Saonli, Frånberg, Mattias, Yang, Qiong, Ligthart, Symen, Hottenga, Jouke J., Rumley, Ann, Mulas, Antonella, de Craen, Anton J. M., Grotevendt, Anne, Taylor, Kent D., Delgado, Graciela E., Kifley, Annette, Lopez, Lorna M., Berentzen, Tina L., Mangino, Massimo, Bandinelli, Stefania, Morrison, Alanna C., Hamsten, Anders, Tofler, Geoffrey, de Maat, Moniek P. M., Draisma, Harmen H. M., Lowe, Gordon D., Zoledziewska, Magdalena, Sattar, Naveed, Lackner, Karl J., Voelker, Uwe, McKnight, Barbara, Huang, Jie, Holliday, Elizabeth G., McEvoy, Mark A., Starr, John M., Hysi, Pirro G., Hernandez, Dena G., Guan, Weihua, Rivadeneira, Fernando, McArdle, Wendy L., Slagboom, P. Eline, Zeller, Tanja, Psaty, Bruce M., Uitterlinden, Andre G., de Geus, Eco J. C., Stott, David J., Binder, Harald, Hofman, Albert, Franco, Oscar H., Rotter, Jerome I., Ferrucci, Luigi, Spector, Tim D., Deary, Ian J., Maerz, Winfried, Greinacher, Andreas, Wild, Philipp S., Cucca, Francesco, Boomsma, Dorret I., Watkins, Hugh, Tang, Weihong, Ridker, Paul M., Jukema, Jan W., Scott, Rodney J., Mitchell, Paul, Hansen, Torben, O'Donnell, Christopher J., Smith, Nicholas L., Strachan, David P., and Dehghan, Abbas
Abstract: An increasing number of genome-wide association (GWA) studies are now using the higher resolution 1000 Genomes Project reference panel (1000G) for imputation, with the expectation that 1000G imputation will lead to the discovery of additional associated loci when compared to HapMap imputation. In order to assess the improvement of 1000G over HapMap imputation in identifying associated loci, we compared the results of GWA studies of circulating fibrinogen based on the two reference panels. Using both HapMap and 1000G imputation we performed a meta-analysis of 22 studies comprising the same 91,953 individuals. We identified six additional signals using 1000G imputation, while 29 loci were associated using both HapMap and 1000G imputation. One locus identified using HapMap imputation was not significant using 1000G imputation. The genome-wide significance threshold of 5x10(-8) is based on the number of independent statistical tests using HapMap imputation, and 1000G imputation may lead to further independent tests that should be corrected for. When using a stricter Bonferroni correction for the 1000G GWA study (P-value < 2.5x10(-8)), the number of loci significant only using HapMap imputation increased to 4 while the number of loci significant only using 1000G decreased to 5. In conclusion, 1000G imputation enabled the identification of 20% more loci than HapMap imputation, although the advantage of 1000G imputation became less clear when a stricter Bonferroni correction was used. More generally, our results provide insights that are applicable to the implementation of other dense reference panels that are under development.
Published: 2017
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6. Comparison of HapMap and 1000 Genomes Reference Panels in a Large-Scale Genome-Wide Association Study

Author: Yao, Yong-Gang, de Vries, Paul S., Sabater-Lleal, Maria, Chasman, Daniel I., Trompet, Stella, Ahluwalia, Tarunveer S., Teumer, Alexander, Kleber, Marcus E., Chen, Ming-Huei, Wang, Jie Jin, Attia, John R., Marioni, Riccardo E., Steri, Maristella, Weng, Lu-Chen, Pool, Rene, Grossmann, Vera, Brody, Jennifer A., Venturini, Cristina, Tanaka, Toshiko, Rose, Lynda M., Oldmeadow, Christopher, Mazur, Johanna, Basu, Saonli, Frånberg, Mattias, Yang, Qiong, Ligthart, Symen, Hottenga, Jouke J., Rumley, Ann, Mulas, Antonella, de Craen, Anton J. M., Grotevendt, Anne, Taylor, Kent D., Delgado, Graciela E., Kifley, Annette, Lopez, Lorna M., Berentzen, Tina L., Mangino, Massimo, Bandinelli, Stefania, Morrison, Alanna C., Hamsten, Anders, Tofler, Geoffrey, de Maat, Moniek P. M., Draisma, Harmen H. M., Lowe, Gordon D., Zoledziewska, Magdalena, Sattar, Naveed, Lackner, Karl J., Völker, Uwe, McKnight, Barbara, Huang, Jie, Holliday, Elizabeth G., McEvoy, Mark A., Starr, John M., Hysi, Pirro G., Hernandez, Dena G., Guan, Weihua, Rivadeneira, Fernando, McArdle, Wendy L., Slagboom, P. Eline, Zeller, Tanja, Psaty, Bruce M., Uitterlinden, André G., de Geus, Eco J. C., Stott, David J., Binder, Harald, Hofman, Albert, Franco, Oscar H., Rotter, Jerome I., Ferrucci, Luigi, Spector, Tim D., Deary, Ian J., März, Winfried, Greinacher, Andreas, Wild, Philipp S., Cucca, Francesco, Boomsma, Dorret I., Watkins, Hugh, Tang, Weihong, Ridker, Paul M., Jukema, Jan W., Scott, Rodney J., Mitchell, Paul, Hansen, Torben, O'Donnell, Christopher J., Smith, Nicholas L., Strachan, David P., Dehghan, Abbas, Yao, Yong-Gang, de Vries, Paul S., Sabater-Lleal, Maria, Chasman, Daniel I., Trompet, Stella, Ahluwalia, Tarunveer S., Teumer, Alexander, Kleber, Marcus E., Chen, Ming-Huei, Wang, Jie Jin, Attia, John R., Marioni, Riccardo E., Steri, Maristella, Weng, Lu-Chen, Pool, Rene, Grossmann, Vera, Brody, Jennifer A., Venturini, Cristina, Tanaka, Toshiko, Rose, Lynda M., Oldmeadow, Christopher, Mazur, Johanna, Basu, Saonli, Frånberg, Mattias, Yang, Qiong, Ligthart, Symen, Hottenga, Jouke J., Rumley, Ann, Mulas, Antonella, de Craen, Anton J. M., Grotevendt, Anne, Taylor, Kent D., Delgado, Graciela E., Kifley, Annette, Lopez, Lorna M., Berentzen, Tina L., Mangino, Massimo, Bandinelli, Stefania, Morrison, Alanna C., Hamsten, Anders, Tofler, Geoffrey, de Maat, Moniek P. M., Draisma, Harmen H. M., Lowe, Gordon D., Zoledziewska, Magdalena, Sattar, Naveed, Lackner, Karl J., Völker, Uwe, McKnight, Barbara, Huang, Jie, Holliday, Elizabeth G., McEvoy, Mark A., Starr, John M., Hysi, Pirro G., Hernandez, Dena G., Guan, Weihua, Rivadeneira, Fernando, McArdle, Wendy L., Slagboom, P. Eline, Zeller, Tanja, Psaty, Bruce M., Uitterlinden, André G., de Geus, Eco J. C., Stott, David J., Binder, Harald, Hofman, Albert, Franco, Oscar H., Rotter, Jerome I., Ferrucci, Luigi, Spector, Tim D., Deary, Ian J., März, Winfried, Greinacher, Andreas, Wild, Philipp S., Cucca, Francesco, Boomsma, Dorret I., Watkins, Hugh, Tang, Weihong, Ridker, Paul M., Jukema, Jan W., Scott, Rodney J., Mitchell, Paul, Hansen, Torben, O'Donnell, Christopher J., Smith, Nicholas L., Strachan, David P., and Dehghan, Abbas
Abstract: An increasing number of genome-wide association (GWA) studies are now using the higher resolution 1000 Genomes Project reference panel (1000G) for imputation, with the expectation that 1000G imputation will lead to the discovery of additional associated loci when compared to HapMap imputation. In order to assess the improvement of 1000G over HapMap imputation in identifying associated loci, we compared the results of GWA studies of circulating fibrinogen based on the two reference panels. Using both HapMap and 1000G imputation we performed a meta-analysis of 22 studies comprising the same 91,953 individuals. We identified six additional signals using 1000G imputation, while 29 loci were associated using both HapMap and 1000G imputation. One locus identified using HapMap imputation was not significant using 1000G imputation. The genome-wide significance threshold of 5×10−8 is based on the number of independent statistical tests using HapMap imputation, and 1000G imputation may lead to further independent tests that should be corrected for. When using a stricter Bonferroni correction for the 1000G GWA study (P-value < 2.5×10−8), the number of loci significant only using HapMap imputation increased to 4 while the number of loci significant only using 1000G decreased to 5. In conclusion, 1000G imputation enabled the identification of 20% more loci than HapMap imputation, although the advantage of 1000G imputation became less clear when a stricter Bonferroni correction was used. More generally, our results provide insights that are applicable to the implementation of other dense reference panels that are under development.
Published: 2017

7. Comparison of HapMap and 1000 Genomes Reference Panels in a Large-Scale Genome-Wide Association Study

Author: de Vries, Paul S, Sabater-Lleal, Maria, Chasman, Daniel I, Trompet, Stella, Ahluwalia, Tarunveer S, Teumer, Alexander, Kleber, Marcus E, Chen, Ming-Huei, Wang, Jie Jin, Attia, John R, Marioni, Riccardo E, Steri, Maristella, Weng, Lu-Chen, Pool, Rene, Grossmann, Vera, Brody, Jennifer A, Venturini, Cristina, Tanaka, Toshiko, Rose, Lynda M, Oldmeadow, Christopher, Mazur, Johanna, Basu, Saonli, Frånberg, Mattias, Yang, Qiong, Ligthart, Symen, Hottenga, Jouke J, Rumley, Ann, Mulas, Antonella, de Craen, Anton J M, Grotevendt, Anne, Taylor, Kent D, Delgado, Graciela E, Kifley, Annette, Lopez, Lorna M, Berentzen, Tina L, Mangino, Massimo, Bandinelli, Stefania, Morrison, Alanna C, Hamsten, Anders, Tofler, Geoffrey, de Maat, Moniek P M, Draisma, Harmen H M, Lowe, Gordon D, Zoledziewska, Magdalena, Sattar, Naveed, Lackner, Karl J, Völker, Uwe, McKnight, Barbara, Huang, Jie, Holliday, Elizabeth G, McEvoy, Mark A, Starr, John M, Hysi, Pirro G, Hernandez, Dena G, Guan, Weihua, Rivadeneira, Fernando, McArdle, Wendy L, Slagboom, P Eline, Zeller, Tanja, Psaty, Bruce M, Uitterlinden, André G, de Geus, Eco J C, Stott, David J, Binder, Harald, Hofman, Albert, Franco, Oscar H, Rotter, Jerome I, Ferrucci, Luigi, Spector, Tim D, Deary, Ian J, März, Winfried, Greinacher, Andreas, Wild, Philipp S, Cucca, Francesco, Boomsma, Dorret I, Watkins, Hugh, Tang, Weihong, Ridker, Paul M, Jukema, Jan W, Scott, Rodney J, Mitchell, Paul, Hansen, Torben, O'Donnell, Christopher J, Smith, Nicholas L, Strachan, David P, Dehghan, Abbas, de Vries, Paul S, Sabater-Lleal, Maria, Chasman, Daniel I, Trompet, Stella, Ahluwalia, Tarunveer S, Teumer, Alexander, Kleber, Marcus E, Chen, Ming-Huei, Wang, Jie Jin, Attia, John R, Marioni, Riccardo E, Steri, Maristella, Weng, Lu-Chen, Pool, Rene, Grossmann, Vera, Brody, Jennifer A, Venturini, Cristina, Tanaka, Toshiko, Rose, Lynda M, Oldmeadow, Christopher, Mazur, Johanna, Basu, Saonli, Frånberg, Mattias, Yang, Qiong, Ligthart, Symen, Hottenga, Jouke J, Rumley, Ann, Mulas, Antonella, de Craen, Anton J M, Grotevendt, Anne, Taylor, Kent D, Delgado, Graciela E, Kifley, Annette, Lopez, Lorna M, Berentzen, Tina L, Mangino, Massimo, Bandinelli, Stefania, Morrison, Alanna C, Hamsten, Anders, Tofler, Geoffrey, de Maat, Moniek P M, Draisma, Harmen H M, Lowe, Gordon D, Zoledziewska, Magdalena, Sattar, Naveed, Lackner, Karl J, Völker, Uwe, McKnight, Barbara, Huang, Jie, Holliday, Elizabeth G, McEvoy, Mark A, Starr, John M, Hysi, Pirro G, Hernandez, Dena G, Guan, Weihua, Rivadeneira, Fernando, McArdle, Wendy L, Slagboom, P Eline, Zeller, Tanja, Psaty, Bruce M, Uitterlinden, André G, de Geus, Eco J C, Stott, David J, Binder, Harald, Hofman, Albert, Franco, Oscar H, Rotter, Jerome I, Ferrucci, Luigi, Spector, Tim D, Deary, Ian J, März, Winfried, Greinacher, Andreas, Wild, Philipp S, Cucca, Francesco, Boomsma, Dorret I, Watkins, Hugh, Tang, Weihong, Ridker, Paul M, Jukema, Jan W, Scott, Rodney J, Mitchell, Paul, Hansen, Torben, O'Donnell, Christopher J, Smith, Nicholas L, Strachan, David P, and Dehghan, Abbas
Abstract: An increasing number of genome-wide association (GWA) studies are now using the higher resolution 1000 Genomes Project reference panel (1000G) for imputation, with the expectation that 1000G imputation will lead to the discovery of additional associated loci when compared to HapMap imputation. In order to assess the improvement of 1000G over HapMap imputation in identifying associated loci, we compared the results of GWA studies of circulating fibrinogen based on the two reference panels. Using both HapMap and 1000G imputation we performed a meta-analysis of 22 studies comprising the same 91,953 individuals. We identified six additional signals using 1000G imputation, while 29 loci were associated using both HapMap and 1000G imputation. One locus identified using HapMap imputation was not significant using 1000G imputation. The genome-wide significance threshold of 5×10-8 is based on the number of independent statistical tests using HapMap imputation, and 1000G imputation may lead to further independent tests that should be corrected for. When using a stricter Bonferroni correction for the 1000G GWA study (P-value < 2.5×10-8), the number of loci significant only using HapMap imputation increased to 4 while the number of loci significant only using 1000G decreased to 5. In conclusion, 1000G imputation enabled the identification of 20% more loci than HapMap imputation, although the advantage of 1000G imputation became less clear when a stricter Bonferroni correction was used. More generally, our results provide insights that are applicable to the implementation of other dense reference panels that are under development.
Published: 2017

8. Comparison of HapMap and 1000 Genomes Reference Panels in a Large-Scale Genome-Wide Association Study

Author: de Vries, Paul S., primary, Sabater-Lleal, Maria, additional, Chasman, Daniel I., additional, Trompet, Stella, additional, Ahluwalia, Tarunveer S., additional, Teumer, Alexander, additional, Kleber, Marcus E., additional, Chen, Ming-Huei, additional, Wang, Jie Jin, additional, Attia, John R., additional, Marioni, Riccardo E., additional, Steri, Maristella, additional, Weng, Lu-Chen, additional, Pool, Rene, additional, Grossmann, Vera, additional, Brody, Jennifer A., additional, Venturini, Cristina, additional, Tanaka, Toshiko, additional, Rose, Lynda M., additional, Oldmeadow, Christopher, additional, Mazur, Johanna, additional, Basu, Saonli, additional, Frånberg, Mattias, additional, Yang, Qiong, additional, Ligthart, Symen, additional, Hottenga, Jouke J., additional, Rumley, Ann, additional, Mulas, Antonella, additional, de Craen, Anton J. M., additional, Grotevendt, Anne, additional, Taylor, Kent D., additional, Delgado, Graciela E., additional, Kifley, Annette, additional, Lopez, Lorna M., additional, Berentzen, Tina L., additional, Mangino, Massimo, additional, Bandinelli, Stefania, additional, Morrison, Alanna C., additional, Hamsten, Anders, additional, Tofler, Geoffrey, additional, de Maat, Moniek P. M., additional, Draisma, Harmen H. M., additional, Lowe, Gordon D., additional, Zoledziewska, Magdalena, additional, Sattar, Naveed, additional, Lackner, Karl J., additional, Völker, Uwe, additional, McKnight, Barbara, additional, Huang, Jie, additional, Holliday, Elizabeth G., additional, McEvoy, Mark A., additional, Starr, John M., additional, Hysi, Pirro G., additional, Hernandez, Dena G., additional, Guan, Weihua, additional, Rivadeneira, Fernando, additional, McArdle, Wendy L., additional, Slagboom, P. Eline, additional, Zeller, Tanja, additional, Psaty, Bruce M., additional, Uitterlinden, André G., additional, de Geus, Eco J. C., additional, Stott, David J., additional, Binder, Harald, additional, Hofman, Albert, additional, Franco, Oscar H., additional, Rotter, Jerome I., additional, Ferrucci, Luigi, additional, Spector, Tim D., additional, Deary, Ian J., additional, März, Winfried, additional, Greinacher, Andreas, additional, Wild, Philipp S., additional, Cucca, Francesco, additional, Boomsma, Dorret I., additional, Watkins, Hugh, additional, Tang, Weihong, additional, Ridker, Paul M., additional, Jukema, Jan W., additional, Scott, Rodney J., additional, Mitchell, Paul, additional, Hansen, Torben, additional, O'Donnell, Christopher J., additional, Smith, Nicholas L., additional, Strachan, David P., additional, and Dehghan, Abbas, additional
Published: 2017
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9. RNA-Seq in 296 phased trios provides a high-resolution map of genomic imprinting.

Author: Jadhav, Bharati, Monajemi, Ramin, Gagalova, Kristina K., Ho, Daniel, Draisma, Harmen H. M., van de Wiel, Mark A., Franke, Lude, Heijmans, Bastiaan T., van Meurs, Joyce, Jansen, Rick, 't Hoen, Peter A. C., Sharp, Andrew J., and Kiełbasa, Szymon M.
Subjects: GENOMIC imprinting, LYMPHOBLASTOID cell lines, GENETIC regulation, GENE expression
Abstract: Background: Identification of imprinted genes, demonstrating a consistent preference towards the paternal or maternal allelic expression, is important for the understanding of gene expression regulation during embryonic development and of the molecular basis of developmental disorders with a parent-of-origin effect. Combining allelic analysis of RNA-Seq data with phased genotypes in family trios provides a powerful method to detect parent-of-origin biases in gene expression. Results: We report findings in 296 family trios from two large studies: 165 lymphoblastoid cell lines from the 1000 Genomes Project and 131 blood samples from the Genome of the Netherlands (GoNL) participants. Based on parental haplotypes, we identified > 2.8 million transcribed heterozygous SNVs phased for parental origin and developed a robust statistical framework for measuring allelic expression. We identified a total of 45 imprinted genes and one imprinted unannotated transcript, including multiple imprinted transcripts showing incomplete parental expression bias that was located adjacent to strongly imprinted genes. For example, PXDC1, a gene which lies adjacent to the paternally expressed gene FAM50B, shows a 2:1 paternal expression bias. Other imprinted genes had promoter regions that coincide with sites of parentally biased DNA methylation identified in the blood from uniparental disomy (UPD) samples, thus providing independent validation of our results. Using the stranded nature of the RNA-Seq data in lymphoblastoid cell lines, we identified multiple loci with overlapping sense/antisense transcripts, of which one is expressed paternally and the other maternally. Using a sliding window approach, we searched for imprinted expression across the entire genome, identifying a novel imprinted putative lncRNA in 13q21.2. Overall, we identified 7 transcripts showing parental bias in gene expression which were not reported in 4 other recent RNA-Seq studies of imprinting. Conclusions: Our methods and data provide a robust and high-resolution map of imprinted gene expression in the human genome. [ABSTRACT FROM AUTHOR]
Published: 2019
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10. Discovery of biochemical biomarkers for aggression: A role for metabolomics in psychiatry

Author: Hagenbeek, Fiona A., primary, Kluft, Cornelis, additional, Hankemeier, Thomas, additional, Bartels, Meike, additional, Draisma, Harmen H. M., additional, Middeldorp, Christel M., additional, Berger, Ruud, additional, Noto, Antonio, additional, Lussu, Milena, additional, Pool, René, additional, Fanos, Vassilios, additional, and Boomsma, Dorret I., additional
Published: 2016
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11. Adiposity as a cause of cardiovascular disease : a Mendelian randomization study

Author: Hägg, Sara, Fall, Tove, Ploner, Alexander, Maegi, Reedik, Fischer, Krista, Draisma, Harmen H. M., Kals, Mart, de Vries, Paul S., Dehghan, Abbas, Willems, Sara M., Sarin, Antti-Pekka, Kristiansson, Kati, Nuotio, Marja-Liisa, Havulinna, Aki S., de Bruijn, Renee F. A. G., Ikram, M. Arfan, Kuningas, Maris, Stricker, Bruno H., Franco, Oscar H., Benyamin, Beben, Gieger, Christian, Hall, Alistair S., Huikari, Ville, Jula, Antti, Jarvelin, Marjo-Riitta, Kaakinen, Marika, Kaprio, Jaakko, Kobl, Michael, Mangino, Massimo, Nelson, Christopher P., Palotie, Aarno, Samani, Nilesh J., Spector, Tim D., Strachan, David P., Tobin, Martin D., Whitfield, John B., Uitterlinden, Andre G., Salomaa, Veikko, Syvänen, Ann-Christine, Kuulasmaa, Kari, Magnusson, Patrik K., Esko, Tonu, Hofman, Albert, de Geus, Eco J. C., Lind, Lars, Giedraitis, Vilmantas, Perola, Markus, Evans, Alun, Ferrieres, Jean, Virtamo, Jarmo, Kee, Frank, Tregouet, David-Alexandre, Arveiler, Dominique, Amouyel, Philippe, Gianfagna, Francesco, Brambilla, Paolo, Ripatti, Samuli, van Duijn, Cornelia M., Metspalu, Andres, Prokopenko, Inga, McCarthy, Mark I., Pedersen, Nancy L., Ingelsson, Erik, Hägg, Sara, Fall, Tove, Ploner, Alexander, Maegi, Reedik, Fischer, Krista, Draisma, Harmen H. M., Kals, Mart, de Vries, Paul S., Dehghan, Abbas, Willems, Sara M., Sarin, Antti-Pekka, Kristiansson, Kati, Nuotio, Marja-Liisa, Havulinna, Aki S., de Bruijn, Renee F. A. G., Ikram, M. Arfan, Kuningas, Maris, Stricker, Bruno H., Franco, Oscar H., Benyamin, Beben, Gieger, Christian, Hall, Alistair S., Huikari, Ville, Jula, Antti, Jarvelin, Marjo-Riitta, Kaakinen, Marika, Kaprio, Jaakko, Kobl, Michael, Mangino, Massimo, Nelson, Christopher P., Palotie, Aarno, Samani, Nilesh J., Spector, Tim D., Strachan, David P., Tobin, Martin D., Whitfield, John B., Uitterlinden, Andre G., Salomaa, Veikko, Syvänen, Ann-Christine, Kuulasmaa, Kari, Magnusson, Patrik K., Esko, Tonu, Hofman, Albert, de Geus, Eco J. C., Lind, Lars, Giedraitis, Vilmantas, Perola, Markus, Evans, Alun, Ferrieres, Jean, Virtamo, Jarmo, Kee, Frank, Tregouet, David-Alexandre, Arveiler, Dominique, Amouyel, Philippe, Gianfagna, Francesco, Brambilla, Paolo, Ripatti, Samuli, van Duijn, Cornelia M., Metspalu, Andres, Prokopenko, Inga, McCarthy, Mark I., Pedersen, Nancy L., and Ingelsson, Erik
Abstract: Background: Adiposity, as indicated by body mass index (BMI), has been associated with risk of cardiovascular diseases in epidemiological studies. We aimed to investigate if these associations are causal, using Mendelian randomization (MR) methods. Methods: The associations of BMI with cardiovascular outcomes [coronary heart disease (CHD), heart failure and ischaemic stroke], and associations of a genetic score (32 BMI single nucleotide polymorphisms) with BMI and cardiovascular outcomes were examined in up to 22 193 individuals with 3062 incident cardiovascular events from nine prospective follow-up studies within the ENGAGE consortium. We used random-effects meta-analysis in an MR framework to provide causal estimates of the effect of adiposity on cardiovascular outcomes. Results: There was a strong association between BMI and incident CHD (HR = 1.20 per SD-increase of BMI, 95% CI, 1.12-1.28, P = 1.9.10(-7)), heart failure (HR = 1.47, 95% CI, 1.35-1.60, P = 9.10(-19)) and ischaemic stroke (HR = 1.15, 95% CI, 1.06-1.24, P = 0.0008) in observational analyses. The genetic score was robustly associated with BMI (beta = 0.030 SD-increase of BMI per additional allele, 95% CI, 0.028-0.033, P = 3.10(-107)). Analyses indicated a causal effect of adiposity on development of heart failure (HR = 1.93 per SD-increase of BMI, 95% CI, 1.12-3.30, P = 0.017) and ischaemic stroke (HR = 1.83, 95% CI, 1.05-3.20, P = 0.034). Additional cross-sectional analyses using both ENGAGE and CARDIoGRAMplusC4D data showed a causal effect of adiposity on CHD. Conclusions: Using MR methods, we provide support for the hypothesis that adiposity causes CHD, heart failure and, previously not demonstrated, ischaemic stroke., De två första författarna delar förstaförfattarskapet.
Published: 2015
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12. Age- and sex-specific causal effects of adiposity on cardiovascular risk factors

Author: Fall, Tove, Hägg, Sara, Ploner, Alexander, Mägi, Reedik, Fischer, Krista, Draisma, Harmen H M, Sarin, Antti-Pekka, Benyamin, Beben, Ladenvall, Claes, Åkerlund, Mikael, Kals, Mart, Esko, Tõnu, Nelson, Christopher P, Kaakinen, Marika, Huikari, Ville, Mangino, Massimo, Meirhaeghe, Aline, Kristiansson, Kati, Nuotio, Marja-Liisa, Kobl, Michael, Grallert, Harald, Dehghan, Abbas, Kuningas, Maris, de Vries, Paul S, de Bruijn, Renée F A G, Willems, Sara M, Heikkilä, Kauko, Silventoinen, Karri, Pietiläinen, Kirsi H, Legry, Vanessa, Giedraitis, Vilmantas, Goumidi, Louisa, Syvänen, Ann-Christine, Strauch, Konstantin, Koenig, Wolfgang, Lichtner, Peter, Herder, Christian, Palotie, Aarno, Menni, Cristina, Uitterlinden, André G, Kuulasmaa, Kari, Havulinna, Aki S, Moreno, Luis A, Gonzalez-Gross, Marcela, Evans, Alun, Tregouet, David-Alexandre, Yarnell, John W G, Virtamo, Jarmo, Ferrières, Jean, Veronesi, Giovanni, Perola, Markus, Arveiler, Dominique, Brambilla, Paolo, Lind, Lars, Kaprio, Jaakko, Hofman, Albert, Stricker, Bruno H, van Duijn, Cornelia M, Ikram, M Arfan, Franco, Oscar H, Cottel, Dominique, Dallongeville, Jean, Hall, Alistair S, Jula, Antti, Tobin, Martin D, Penninx, Brenda W, Peters, Annette, Gieger, Christian, Samani, Nilesh J, Montgomery, Grant W, Whitfield, John B, Martin, Nicholas G, Groop, Leif, Spector, Tim D, Magnusson, Patrik K, Amouyel, Philippe, Boomsma, Dorret I, Nilsson, Peter M, Järvelin, Marjo-Riitta, Lyssenko, Valeriya, Metspalu, Andres, Strachan, David P, Salomaa, Veikko, Ripatti, Samuli, Pedersen, Nancy L, Prokopenko, Inga, McCarthy, Mark I, Ingelsson, Erik, Fall, Tove, Hägg, Sara, Ploner, Alexander, Mägi, Reedik, Fischer, Krista, Draisma, Harmen H M, Sarin, Antti-Pekka, Benyamin, Beben, Ladenvall, Claes, Åkerlund, Mikael, Kals, Mart, Esko, Tõnu, Nelson, Christopher P, Kaakinen, Marika, Huikari, Ville, Mangino, Massimo, Meirhaeghe, Aline, Kristiansson, Kati, Nuotio, Marja-Liisa, Kobl, Michael, Grallert, Harald, Dehghan, Abbas, Kuningas, Maris, de Vries, Paul S, de Bruijn, Renée F A G, Willems, Sara M, Heikkilä, Kauko, Silventoinen, Karri, Pietiläinen, Kirsi H, Legry, Vanessa, Giedraitis, Vilmantas, Goumidi, Louisa, Syvänen, Ann-Christine, Strauch, Konstantin, Koenig, Wolfgang, Lichtner, Peter, Herder, Christian, Palotie, Aarno, Menni, Cristina, Uitterlinden, André G, Kuulasmaa, Kari, Havulinna, Aki S, Moreno, Luis A, Gonzalez-Gross, Marcela, Evans, Alun, Tregouet, David-Alexandre, Yarnell, John W G, Virtamo, Jarmo, Ferrières, Jean, Veronesi, Giovanni, Perola, Markus, Arveiler, Dominique, Brambilla, Paolo, Lind, Lars, Kaprio, Jaakko, Hofman, Albert, Stricker, Bruno H, van Duijn, Cornelia M, Ikram, M Arfan, Franco, Oscar H, Cottel, Dominique, Dallongeville, Jean, Hall, Alistair S, Jula, Antti, Tobin, Martin D, Penninx, Brenda W, Peters, Annette, Gieger, Christian, Samani, Nilesh J, Montgomery, Grant W, Whitfield, John B, Martin, Nicholas G, Groop, Leif, Spector, Tim D, Magnusson, Patrik K, Amouyel, Philippe, Boomsma, Dorret I, Nilsson, Peter M, Järvelin, Marjo-Riitta, Lyssenko, Valeriya, Metspalu, Andres, Strachan, David P, Salomaa, Veikko, Ripatti, Samuli, Pedersen, Nancy L, Prokopenko, Inga, McCarthy, Mark I, and Ingelsson, Erik
Abstract: Observational studies have reported different effects of adiposity on cardiovascular risk factors across age and sex. Since cardiovascular risk factors are enriched in obese individuals, it has not been easy to dissect the effects of adiposity from those of other risk factors. We used a Mendelian randomization approach, applying a set of 32 genetic markers to estimate the causal effect of adiposity on blood pressure, glycemic indices, circulating lipid levels, and markers of inflammation and liver disease in up to 67,553 individuals. All analyses were stratified by age (cutoff 55 years of age) and sex. The genetic score was associated with BMI in both nonstratified analysis (P = 2.8 × 10(-107)) and stratified analyses (all P < 3.3 × 10(-30)). We found evidence of a causal effect of adiposity on blood pressure, fasting levels of insulin, C-reactive protein, interleukin-6, HDL cholesterol, and triglycerides in a nonstratified analysis and in the <55-year stratum. Further, we found evidence of a smaller causal effect on total cholesterol (P for difference = 0.015) in the ≥55-year stratum than in the <55-year stratum, a finding that could be explained by biology, survival bias, or differential medication. In conclusion, this study extends previous knowledge of the effects of adiposity by providing sex- and age-specific causal estimates on cardiovascular risk factors., T.V. and S.H. contributed equally to this work.
Published: 2015
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13. Genome-wide association study identifies novel genetic variants contributing to variation in blood metabolite levels

Author: Draisma, Harmen H. M., primary, Pool, René, additional, Kobl, Michael, additional, Jansen, Rick, additional, Petersen, Ann-Kristin, additional, Vaarhorst, Anika A. M., additional, Yet, Idil, additional, Haller, Toomas, additional, Demirkan, Ayşe, additional, Esko, Tõnu, additional, Zhu, Gu, additional, Böhringer, Stefan, additional, Beekman, Marian, additional, van Klinken, Jan Bert, additional, Römisch-Margl, Werner, additional, Prehn, Cornelia, additional, Adamski, Jerzy, additional, de Craen, Anton J. M., additional, van Leeuwen, Elisabeth M., additional, Amin, Najaf, additional, Dharuri, Harish, additional, Westra, Harm-Jan, additional, Franke, Lude, additional, de Geus, Eco J. C., additional, Hottenga, Jouke Jan, additional, Willemsen, Gonneke, additional, Henders, Anjali K., additional, Montgomery, Grant W., additional, Nyholt, Dale R., additional, Whitfield, John B., additional, Penninx, Brenda W., additional, Spector, Tim D., additional, Metspalu, Andres, additional, Eline Slagboom, P., additional, van Dijk, Ko Willems, additional, ‘t Hoen, Peter A. C., additional, Strauch, Konstantin, additional, Martin, Nicholas G., additional, van Ommen, Gert-Jan B., additional, Illig, Thomas, additional, Bell, Jordana T., additional, Mangino, Massimo, additional, Suhre, Karsten, additional, McCarthy, Mark I., additional, Gieger, Christian, additional, Isaacs, Aaron, additional, van Duijn, Cornelia M., additional, and Boomsma, Dorret I., additional
Published: 2015
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14. Identification of heart rate-associated loci and their effects on cardiac conduction and rhythm disorders

Author: den Hoed, Marcel, Eijgelsheim, Mark, Esko, Tonu, Brundel, Bianca J. J. M., Peal, David S., Evans, David M., Nolte, Ilja M., Segre, Ayellet V., Holm, Hilma, Handsaker, Robert E., Westra, Harm-Jan, Johnson, Toby, Isaacs, Aaron, Yang, Jian, Lundby, Alicia, Zhao, Jing Hua, Kim, Young Jin, Go, Min Jin, Almgren, Peter, Bochud, Murielle, Boucher, Gabrielle, Cornelis, Marilyn C., Gudbjartsson, Daniel, Hadley, David, van der Harst, Pim, Hayward, Caroline, den Heijer, Martin, Igl, Wilmar, Jackson, Anne U., Kutalik, Zoltan, Luan, Jian'an, Kemp, John P., Kristiansson, Kati, Ladenvall, Claes, Lorentzon, Mattias, Montasser, May E., Njajou, Omer T., O'Reilly, Paul F., Padmanabhan, Sandosh, Pourcain, Beate St., Rankinen, Tuomo, Salo, Perttu, Tanaka, Toshiko, Timpson, Nicholas J., Vitart, Veronique, Waite, Lindsay, Wheeler, William, Zhang, Weihua, Draisma, Harmen H. M., Feitosa, Mary F., Kerr, Kathleen F., Lind, Penelope A., Mihailov, Evelin, Onland-Moret, N. Charlotte, Song, Ci, Weedon, Michael N., Xie, Weijia, Yengo, Loic, Absher, Devin, Albert, Christine M., Alonso, Alvaro, Arking, Dan E., de Bakker, Paul I. W., Balkau, Beverley, Barlassina, Cristina, Benaglio, Paola, Bis, Joshua C., Bouatia-Naji, Nabila, Brage, Soren, Chanock, Stephen J., Chines, Peter S., Chung, Mina, Darbar, Dawood, Dina, Christian, Doerr, Marcus, Elliott, Paul, Felix, Stephan B., Fischer, Krista, Fuchsberger, Christian, de Geus, Eco J. C., Goyette, Philippe, Gudnason, Vilmundur, Harris, Tamara B., Hartikainen, Anna-Liisa, Havulinna, Aki S., Heckbert, Susan R., Hicks, Andrew A., Hofman, Albert, Holewijn, Suzanne, Hoogstra-Berends, Femke, Hottenga, Jouke-Jan, Jensen, Majken K., Johansson, Asa, Junttila, Juhani, Kaeaeb, Stefan, Kanon, Bart, Ketkar, Shamika, Khaw, Kay-Tee, Knowles, Joshua W., Kooner, Angrad S., Kors, Jan A., Kumari, Meena, Milani, Lili, Laiho, Paeivi, Lakatta, Edward G., Langenberg, Claudia, Leusink, Maarten, Liu, Yongmei, Luben, Robert N., Lunetta, Kathryn L., Lynch, Stacey N., Markus, Marcello R. P., Marques-Vidal, Pedro, Leach, Irene Mateo, McArdle, Wendy L., McCarroll, Steven A., Medland, Sarah E., Miller, Kathryn A., Montgomery, Grant W., Morrison, Alanna C., Mueller-Nurasyid, Martina, Navarro, Pau, Nelis, Mari, O'Connell, Jeffrey R., O'Donnell, Christopher J., Ong, Ken K., Newman, Anne B., Peters, Annette, Polasek, Ozren, Pouta, Anneli, Pramstaller, Peter P., Psaty, Bruce M., Rao, Dabeeru C., Ring, Susan M., Rossin, Elizabeth J., Rudan, Diana, Sanna, Serena, Scott, Robert A., Sehmi, Jaban S., Sharp, Stephen, Shin, Jordan T., Singleton, Andrew B., Smith, Albert V., Soranzo, Nicole, Spector, Tim D., Stewart, Chip, Stringham, Heather M., Tarasov, Kirill V., Uitterlinden, Andre G., Vandenput, Liesbeth, Hwang, Shih-Jen, Whitfield, John B., Wijmenga, Cisca, Wild, Sarah H., Willemsen, Gonneke, Wilson, James F., Witteman, Jacqueline C. M., Wong, Andrew, Wong, Quenna, Jamshidi, Yalda, Zitting, Paavo, Boer, Jolanda M. A., Boomsma, Dorret I., Borecki, Ingrid B., van Duijn, Cornelia M., Ekelund, Ulf, Forouhi, Nita G., Froguel, Philippe, Hingorani, Aroon, Ingelsson, Erik, Kivimaki, Mika, Kronmal, Richard A., Kuh, Diana, Lind, Lars, Martin, Nicholas G., Oostra, Ben A., Pedersen, Nancy L., Quertermous, Thomas, Rotter, Jerome I., van der Schouw, Yvonne T., Verschuren, W. M. Monique, Walker, Mark, Albanes, Demetrius, Arnar, David O., Assimes, Themistocles L., Bandinelli, Stefania, Boehnke, Michael, de Boer, Rudolf A., Bouchard, Claude, Caulfield, W. L. Mark, Chambers, John C., Curhan, Gary, Cusi, Daniele, Eriksson, Johan, Ferrucci, Luigi, van Gilst, Wiek H., Glorioso, Nicola, de Graaf, Jacqueline, Groop, Leif, Gyllensten, Ulf, Hsueh, Wen-Chi, Hu, Frank B., Huikuri, Heikki V., Hunter, David J., Iribarren, Carlos, Isomaa, Bo, Jarvelin, Marjo-Riitta, Jula, Antti, Kahonen, Mika, Kiemeney, Lambertus A., van der Klauw, Melanie M., Kooner, Jaspal S., Kraft, Peter, Iacoviello, Licia, Lehtimaki, Terho, Lokki, Marja-Liisa L., Mitchell, Braxton D., Navis, Gerjan, Nieminen, Markku S., Ohlsson, Claes, Poulter, Neil R., Qi, Lu, Raitakari, Olli T., Rimm, Eric B., Rioux, John D., Rizzi, Federica, Rudan, Igor, Salomaa, Veikko, Sever, Peter S., Shields, Denis C., Shuldiner, Alan R., Sinisalo, Juha, Stanton, Alice V., Stolk, Ronald P., Strachan, David P., Tardif, Jean-Claude, Thorsteinsdottir, Unnur, Tuomilehto, Jaako, van Veldhuisen, Dirk J., Virtamo, Jarmo, Viikari, Jorma, Vollenweider, Peter, Waeber, Gerard, Widen, Elisabeth, Cho, Yoon Shin, Olsen, Jesper V., Visscher, Peter M., Willer, Cristen, Franke, Lude, Erdmann, Jeanette, Thompson, John R., Pfeufer, Arne, Sotoodehnia, Nona, Newton-Cheh, Christopher, Ellinor, Patrick T., Stricker, Bruno H. Ch, Metspalu, Andres, Perola, Markus, Beckmann, Jacques S., Smith, George Davey, Stefansson, Kari, Wareham, Nicholas J., Munroe, Patricia B., Sibon, Ody C. M., Milan, David J., Snieder, Harold, Samani, Nilesh J., Loos, Ruth J. F., den Hoed, Marcel, Eijgelsheim, Mark, Esko, Tonu, Brundel, Bianca J. J. M., Peal, David S., Evans, David M., Nolte, Ilja M., Segre, Ayellet V., Holm, Hilma, Handsaker, Robert E., Westra, Harm-Jan, Johnson, Toby, Isaacs, Aaron, Yang, Jian, Lundby, Alicia, Zhao, Jing Hua, Kim, Young Jin, Go, Min Jin, Almgren, Peter, Bochud, Murielle, Boucher, Gabrielle, Cornelis, Marilyn C., Gudbjartsson, Daniel, Hadley, David, van der Harst, Pim, Hayward, Caroline, den Heijer, Martin, Igl, Wilmar, Jackson, Anne U., Kutalik, Zoltan, Luan, Jian'an, Kemp, John P., Kristiansson, Kati, Ladenvall, Claes, Lorentzon, Mattias, Montasser, May E., Njajou, Omer T., O'Reilly, Paul F., Padmanabhan, Sandosh, Pourcain, Beate St., Rankinen, Tuomo, Salo, Perttu, Tanaka, Toshiko, Timpson, Nicholas J., Vitart, Veronique, Waite, Lindsay, Wheeler, William, Zhang, Weihua, Draisma, Harmen H. M., Feitosa, Mary F., Kerr, Kathleen F., Lind, Penelope A., Mihailov, Evelin, Onland-Moret, N. Charlotte, Song, Ci, Weedon, Michael N., Xie, Weijia, Yengo, Loic, Absher, Devin, Albert, Christine M., Alonso, Alvaro, Arking, Dan E., de Bakker, Paul I. W., Balkau, Beverley, Barlassina, Cristina, Benaglio, Paola, Bis, Joshua C., Bouatia-Naji, Nabila, Brage, Soren, Chanock, Stephen J., Chines, Peter S., Chung, Mina, Darbar, Dawood, Dina, Christian, Doerr, Marcus, Elliott, Paul, Felix, Stephan B., Fischer, Krista, Fuchsberger, Christian, de Geus, Eco J. C., Goyette, Philippe, Gudnason, Vilmundur, Harris, Tamara B., Hartikainen, Anna-Liisa, Havulinna, Aki S., Heckbert, Susan R., Hicks, Andrew A., Hofman, Albert, Holewijn, Suzanne, Hoogstra-Berends, Femke, Hottenga, Jouke-Jan, Jensen, Majken K., Johansson, Asa, Junttila, Juhani, Kaeaeb, Stefan, Kanon, Bart, Ketkar, Shamika, Khaw, Kay-Tee, Knowles, Joshua W., Kooner, Angrad S., Kors, Jan A., Kumari, Meena, Milani, Lili, Laiho, Paeivi, Lakatta, Edward G., Langenberg, Claudia, Leusink, Maarten, Liu, Yongmei, Luben, Robert N., Lunetta, Kathryn L., Lynch, Stacey N., Markus, Marcello R. P., Marques-Vidal, Pedro, Leach, Irene Mateo, McArdle, Wendy L., McCarroll, Steven A., Medland, Sarah E., Miller, Kathryn A., Montgomery, Grant W., Morrison, Alanna C., Mueller-Nurasyid, Martina, Navarro, Pau, Nelis, Mari, O'Connell, Jeffrey R., O'Donnell, Christopher J., Ong, Ken K., Newman, Anne B., Peters, Annette, Polasek, Ozren, Pouta, Anneli, Pramstaller, Peter P., Psaty, Bruce M., Rao, Dabeeru C., Ring, Susan M., Rossin, Elizabeth J., Rudan, Diana, Sanna, Serena, Scott, Robert A., Sehmi, Jaban S., Sharp, Stephen, Shin, Jordan T., Singleton, Andrew B., Smith, Albert V., Soranzo, Nicole, Spector, Tim D., Stewart, Chip, Stringham, Heather M., Tarasov, Kirill V., Uitterlinden, Andre G., Vandenput, Liesbeth, Hwang, Shih-Jen, Whitfield, John B., Wijmenga, Cisca, Wild, Sarah H., Willemsen, Gonneke, Wilson, James F., Witteman, Jacqueline C. M., Wong, Andrew, Wong, Quenna, Jamshidi, Yalda, Zitting, Paavo, Boer, Jolanda M. A., Boomsma, Dorret I., Borecki, Ingrid B., van Duijn, Cornelia M., Ekelund, Ulf, Forouhi, Nita G., Froguel, Philippe, Hingorani, Aroon, Ingelsson, Erik, Kivimaki, Mika, Kronmal, Richard A., Kuh, Diana, Lind, Lars, Martin, Nicholas G., Oostra, Ben A., Pedersen, Nancy L., Quertermous, Thomas, Rotter, Jerome I., van der Schouw, Yvonne T., Verschuren, W. M. Monique, Walker, Mark, Albanes, Demetrius, Arnar, David O., Assimes, Themistocles L., Bandinelli, Stefania, Boehnke, Michael, de Boer, Rudolf A., Bouchard, Claude, Caulfield, W. L. Mark, Chambers, John C., Curhan, Gary, Cusi, Daniele, Eriksson, Johan, Ferrucci, Luigi, van Gilst, Wiek H., Glorioso, Nicola, de Graaf, Jacqueline, Groop, Leif, Gyllensten, Ulf, Hsueh, Wen-Chi, Hu, Frank B., Huikuri, Heikki V., Hunter, David J., Iribarren, Carlos, Isomaa, Bo, Jarvelin, Marjo-Riitta, Jula, Antti, Kahonen, Mika, Kiemeney, Lambertus A., van der Klauw, Melanie M., Kooner, Jaspal S., Kraft, Peter, Iacoviello, Licia, Lehtimaki, Terho, Lokki, Marja-Liisa L., Mitchell, Braxton D., Navis, Gerjan, Nieminen, Markku S., Ohlsson, Claes, Poulter, Neil R., Qi, Lu, Raitakari, Olli T., Rimm, Eric B., Rioux, John D., Rizzi, Federica, Rudan, Igor, Salomaa, Veikko, Sever, Peter S., Shields, Denis C., Shuldiner, Alan R., Sinisalo, Juha, Stanton, Alice V., Stolk, Ronald P., Strachan, David P., Tardif, Jean-Claude, Thorsteinsdottir, Unnur, Tuomilehto, Jaako, van Veldhuisen, Dirk J., Virtamo, Jarmo, Viikari, Jorma, Vollenweider, Peter, Waeber, Gerard, Widen, Elisabeth, Cho, Yoon Shin, Olsen, Jesper V., Visscher, Peter M., Willer, Cristen, Franke, Lude, Erdmann, Jeanette, Thompson, John R., Pfeufer, Arne, Sotoodehnia, Nona, Newton-Cheh, Christopher, Ellinor, Patrick T., Stricker, Bruno H. Ch, Metspalu, Andres, Perola, Markus, Beckmann, Jacques S., Smith, George Davey, Stefansson, Kari, Wareham, Nicholas J., Munroe, Patricia B., Sibon, Ody C. M., Milan, David J., Snieder, Harold, Samani, Nilesh J., and Loos, Ruth J. F.
Abstract: Elevated resting heart rate is associated with greater risk of cardiovascular disease and mortality. In a 2-stage meta-analysis of genome-wide association studies in up to 181,171 individuals, we identified 14 new loci associated with heart rate and confirmed associations with all 7 previously established loci. Experimental downregulation of gene expression in Drosophila melanogaster and Danio rerio identified 20 genes at 11 loci that are relevant for heart rate regulation and highlight a role for genes involved in signal transmission, embryonic cardiac development and the pathophysiology of dilated cardiomyopathy, congenital heart failure and/or sudden cardiac death. In addition, genetic susceptibility to increased heart rate is associated with altered cardiac conduction and reduced risk of sick sinus syndrome, and both heart rate-increasing and heart rate-decreasing variants associate with risk of atrial fibrillation. Our findings provide fresh insights into the mechanisms regulating heart rate and identify new therapeutic targets.
Published: 2013
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15. The Role of Adiposity in Cardiometabolic Traits : A Mendelian Randomization Analysis

Author: Fall, Tove, Hägg, Sara, Maegi, Reedik, Ploner, Alexander, Fischer, Krista, Horikoshi, Momoko, Sarin, Antti-Pekka, Thorleifsson, Gudmar, Ladenvall, Claes, Kals, Mart, Kuningas, Maris, Draisma, Harmen H. M., Ried, Janina S., van Zuydam, Natalie R., Huikari, Ville, Mangino, Massimo, Sonestedt, Emily, Benyamin, Beben, Nelson, Christopher P., Rivera, Natalia V., Kristiansson, Kati, Shen, Huei-yi, Havulinna, Aki S., Dehghan, Abbas, Donnelly, Louise A., Kaakinen, Marika, Nuotio, Marja-Liisa, Robertson, Neil, de Bruijn, Renee F. A. G., Ikram, M. Arfan, Amin, Najaf, Balmforth, Anthony J., Braund, Peter S., Doney, Alexander S. F., Doering, Angela, Elliott, Paul, Esko, Tonu, Franco, Oscar H., Gretarsdottir, Solveig, Hartikainen, Anna-Liisa, Heikkila, Kauko, Herzig, Karl-Heinz, Holm, Hilma, Hottenga, Jouke Jan, Hypponen, Elina, Illig, Thomas, Isaacs, Aaron, Isomaa, Bo, Karssen, Lennart C., Kettunen, Johannes, Koenig, Wolfgang, Kuulasmaa, Kari, Laatikainen, Tiina, Laitinen, Jaana, Lindgren, Cecilia, Lyssenko, Valeriya, Laara, Esa, Rayner, Nigel W., Mannisto, Satu, Pouta, Anneli, Rathmann, Wolfgang, Rivadeneira, Fernando, Ruokonen, Aimo, Savolainen, Markku J., Sijbrands, Eric J. G., Small, Kerrin S., Smit, Jan H., Steinthorsdottir, Valgerdur, Syvänen, Ann-Christine, Taanila, Anja, Tobin, Martin D., Uitterlinden, Andre G., Willems, Sara M., Willemsen, Gonneke, Witteman, Jacqueline, Perola, Markus, Evans, Alun, Ferrieres, Jean, Virtamo, Jarmo, Kee, Frank, Tregouet, David-Alexandre, Arveiler, Dominique, Amouyel, Philippe, Ferrario, Marco M., Brambilla, Paolo, Hall, Alistair S., Heath, AndrewC., Madden, Pamela A. F., Martin, Nicholas G., Montgomery, Grant W., Whitfield, John B., Jula, Antti, Knekt, Paul, Oostra, Ben, van Duijn, Cornelia M., Penninx, Brenda W. J. H., Smith, George Davey, Kaprio, Jaakko, Samani, Nilesh J., Gieger, Christian, Peters, Annette, Wichmann, H. -Erich, Boomsma, Dorret I., de Geus, Eco J. C., Tuomi, TiinaMaija, Power, Chris, Hammond, Christopher J., Spector, Tim D., Lind, Lars, Orho-Melander, Marju, Palmer, Colin Neil Alexander, Morris, Andrew D., Groop, Leif, Jarvelin, Marjo-Riitta, Salomaa, Veikko, Vartiainen, Erkki, Hofman, Albert, Ripatti, Samuli, Metspalu, Andres, Thorsteinsdottir, Unnur, Stefansson, Kari, Pedersen, Nancy L., McCarthy, Mark I., Ingelsson, Erik, Prokopenko, Inga, Fall, Tove, Hägg, Sara, Maegi, Reedik, Ploner, Alexander, Fischer, Krista, Horikoshi, Momoko, Sarin, Antti-Pekka, Thorleifsson, Gudmar, Ladenvall, Claes, Kals, Mart, Kuningas, Maris, Draisma, Harmen H. M., Ried, Janina S., van Zuydam, Natalie R., Huikari, Ville, Mangino, Massimo, Sonestedt, Emily, Benyamin, Beben, Nelson, Christopher P., Rivera, Natalia V., Kristiansson, Kati, Shen, Huei-yi, Havulinna, Aki S., Dehghan, Abbas, Donnelly, Louise A., Kaakinen, Marika, Nuotio, Marja-Liisa, Robertson, Neil, de Bruijn, Renee F. A. G., Ikram, M. Arfan, Amin, Najaf, Balmforth, Anthony J., Braund, Peter S., Doney, Alexander S. F., Doering, Angela, Elliott, Paul, Esko, Tonu, Franco, Oscar H., Gretarsdottir, Solveig, Hartikainen, Anna-Liisa, Heikkila, Kauko, Herzig, Karl-Heinz, Holm, Hilma, Hottenga, Jouke Jan, Hypponen, Elina, Illig, Thomas, Isaacs, Aaron, Isomaa, Bo, Karssen, Lennart C., Kettunen, Johannes, Koenig, Wolfgang, Kuulasmaa, Kari, Laatikainen, Tiina, Laitinen, Jaana, Lindgren, Cecilia, Lyssenko, Valeriya, Laara, Esa, Rayner, Nigel W., Mannisto, Satu, Pouta, Anneli, Rathmann, Wolfgang, Rivadeneira, Fernando, Ruokonen, Aimo, Savolainen, Markku J., Sijbrands, Eric J. G., Small, Kerrin S., Smit, Jan H., Steinthorsdottir, Valgerdur, Syvänen, Ann-Christine, Taanila, Anja, Tobin, Martin D., Uitterlinden, Andre G., Willems, Sara M., Willemsen, Gonneke, Witteman, Jacqueline, Perola, Markus, Evans, Alun, Ferrieres, Jean, Virtamo, Jarmo, Kee, Frank, Tregouet, David-Alexandre, Arveiler, Dominique, Amouyel, Philippe, Ferrario, Marco M., Brambilla, Paolo, Hall, Alistair S., Heath, AndrewC., Madden, Pamela A. F., Martin, Nicholas G., Montgomery, Grant W., Whitfield, John B., Jula, Antti, Knekt, Paul, Oostra, Ben, van Duijn, Cornelia M., Penninx, Brenda W. J. H., Smith, George Davey, Kaprio, Jaakko, Samani, Nilesh J., Gieger, Christian, Peters, Annette, Wichmann, H. -Erich, Boomsma, Dorret I., de Geus, Eco J. C., Tuomi, TiinaMaija, Power, Chris, Hammond, Christopher J., Spector, Tim D., Lind, Lars, Orho-Melander, Marju, Palmer, Colin Neil Alexander, Morris, Andrew D., Groop, Leif, Jarvelin, Marjo-Riitta, Salomaa, Veikko, Vartiainen, Erkki, Hofman, Albert, Ripatti, Samuli, Metspalu, Andres, Thorsteinsdottir, Unnur, Stefansson, Kari, Pedersen, Nancy L., McCarthy, Mark I., Ingelsson, Erik, and Prokopenko, Inga
Abstract: Background: The association between adiposity and cardiometabolic traits is well known from epidemiological studies. Whilst the causal relationship is clear for some of these traits, for others it is not. We aimed to determine whether adiposity is causally related to various cardiometabolic traits using the Mendelian randomization approach. Methods and Findings: We used the adiposity-associated variant rs9939609 at the FTO locus as an instrumental variable (IV) for body mass index (BMI) in a Mendelian randomization design. Thirty-six population-based studies of individuals of European descent contributed to the analyses. Age-and sex-adjusted regression models were fitted to test for association between (i) rs9939609 and BMI (n = 198,502), (ii) rs9939609 and 24 traits, and (iii) BMI and 24 traits. The causal effect of BMI on the outcome measures was quantified by IV estimators. The estimators were compared to the BMI-trait associations derived from the same individuals. In the IV analysis, we demonstrated novel evidence for a causal relationship between adiposity and incident heart failure (hazard ratio, 1.19 per BMI-unit increase; 95% CI, 1.03-1.39) and replicated earlier reports of a causal association with type 2 diabetes, metabolic syndrome, dyslipidemia, and hypertension (odds ratio for IV estimator, 1.1-1.4; all p<0.05). For quantitative traits, our results provide novel evidence for a causal effect of adiposity on the liver enzymes alanine aminotransferase and gamma-glutamyl transferase and confirm previous reports of a causal effect of adiposity on systolic and diastolic blood pressure, fasting insulin, 2-h post-load glucose from the oral glucose tolerance test, C-reactive protein, triglycerides, and high-density lipoprotein cholesterol levels (all p<0.05). The estimated causal effects were in agreement with traditional observational measures in all instances except for type 2 diabetes, where the causal estimate was larger than the observational estimat, De fyra sista författarna delar sistaförfattarskapet.
Published: 2013
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16. The Role of Adiposity in Cardiometabolic Traits: A Mendelian Randomization Analysis

Author: University of Helsinki, Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Hjelt Institute, University of Helsinki, Department of Medicine, Fall, Tove, Hagg, Sara, Maegi, Reedik, Ploner, Alexander, Fischer, Krista, Horikoshi, Momoko, Sarin, Antti-Pekka, Thorleifsson, Gudmar, Ladenvall, Claes, Kals, Mart, Kuningas, Maris, Draisma, Harmen H. M., Ried, Janina S., van Zuydam, Natalie R., Huikari, Ville, Mangino, Massimo, Sonestedt, Emily, Benyamin, Beben, Nelson, Christopher P., Rivera, Natalia V., Kristiansson, Kati, Shen, Huei-yi, Havulinna, Aki S., Dehghan, Abbas, Donnelly, Louise A., Kaakinen, Marika, Nuotio, Marja-Liisa, Robertson, Neil, de Bruijn, Renee F. A. G., Ikram, M. Arfan, Amin, Najaf, Balmforth, Anthony J., Braund, Peter S., Doney, Alexander S. F., Doering, Angela, Elliott, Paul, Esko, Tonu, Franco, Oscar H., Gretarsdottir, Solveig, Hartikainen, Anna-Liisa, Heikkilä, Kauko Veli, Herzig, Karl-Heinz, Holm, Hilma, Hottenga, Jouke Jan, Hypponen, Elina, Illig, Thomas, Isaacs, Aaron, Isomaa, Bo, Karssen, Lennart C., Kettunen, Johannes, Koenig, Wolfgang, Kuulasmaa, Kari, Laatikainen, Tiina, Laitinen, Jaana, Lindgren, Cecilia, Lyssenko, Valeriya, Laara, Esa, Rayner, Nigel W., Mannisto, Satu, Pouta, Anneli, Rathmann, Wolfgang, Rivadeneira, Fernando, Ruokonen, Aimo, Savolainen, Markku J., Sijbrands, Eric J. G., Small, Kerrin S., Smit, Jan H., Steinthorsdottir, Valgerdur, Syvanen, Ann-Christine, Taanila, Anja, Tobin, Martin D., Uitterlinden, Andre G., Willems, Sara M., Willemsen, Gonneke, Witteman, Jacqueline, Perola, Markus, Evans, Alun, Ferrieres, Jean, Virtamo, Jarmo, Kee, Frank, Tregouet, David-Alexandre, Arveiler, Dominique, Amouyel, Philippe, Ferrario, Marco M., Brambilla, Paolo, Hall, Alistair S., Heath, AndrewC., Madden, Pamela A. F., Martin, Nicholas G., Montgomery, Grant W., Whitfield, John B., Jula, Antti, Knekt, Paul, Oostra, Ben, van Duijn, Cornelia M., Penninx, Brenda W. J. H., Smith, George Davey, Kaprio, Jaakko, Samani, Nilesh J., Gieger, Christian, Peters, Annette, Wichmann, H. -Erich, Boomsma, Dorret I., de Geus, Eco J. C., Tuomi, Tiinamaija, Power, Chris, Hammond, Christopher J., Spector, Tim D., Lind, Lars, Orho-Melander, Marju, Palmer, Colin Neil Alexander, Morris, Andrew D., Groop, Leif, Jarvelin, Marjo-Riitta, Salomaa, Veikko, Vartiainen, Erkki, Hofman, Albert, Ripatti, Samuli, Metspalu, Andres, Thorsteinsdottir, Unnur, Stefansson, Kari, Pedersen, Nancy L., McCarthy, Mark I., Ingelsson, Erik, Prokopenko, Inga, ENGAGE Consortium, University of Helsinki, Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Hjelt Institute, University of Helsinki, Department of Medicine, Fall, Tove, Hagg, Sara, Maegi, Reedik, Ploner, Alexander, Fischer, Krista, Horikoshi, Momoko, Sarin, Antti-Pekka, Thorleifsson, Gudmar, Ladenvall, Claes, Kals, Mart, Kuningas, Maris, Draisma, Harmen H. M., Ried, Janina S., van Zuydam, Natalie R., Huikari, Ville, Mangino, Massimo, Sonestedt, Emily, Benyamin, Beben, Nelson, Christopher P., Rivera, Natalia V., Kristiansson, Kati, Shen, Huei-yi, Havulinna, Aki S., Dehghan, Abbas, Donnelly, Louise A., Kaakinen, Marika, Nuotio, Marja-Liisa, Robertson, Neil, de Bruijn, Renee F. A. G., Ikram, M. Arfan, Amin, Najaf, Balmforth, Anthony J., Braund, Peter S., Doney, Alexander S. F., Doering, Angela, Elliott, Paul, Esko, Tonu, Franco, Oscar H., Gretarsdottir, Solveig, Hartikainen, Anna-Liisa, Heikkilä, Kauko Veli, Herzig, Karl-Heinz, Holm, Hilma, Hottenga, Jouke Jan, Hypponen, Elina, Illig, Thomas, Isaacs, Aaron, Isomaa, Bo, Karssen, Lennart C., Kettunen, Johannes, Koenig, Wolfgang, Kuulasmaa, Kari, Laatikainen, Tiina, Laitinen, Jaana, Lindgren, Cecilia, Lyssenko, Valeriya, Laara, Esa, Rayner, Nigel W., Mannisto, Satu, Pouta, Anneli, Rathmann, Wolfgang, Rivadeneira, Fernando, Ruokonen, Aimo, Savolainen, Markku J., Sijbrands, Eric J. G., Small, Kerrin S., Smit, Jan H., Steinthorsdottir, Valgerdur, Syvanen, Ann-Christine, Taanila, Anja, Tobin, Martin D., Uitterlinden, Andre G., Willems, Sara M., Willemsen, Gonneke, Witteman, Jacqueline, Perola, Markus, Evans, Alun, Ferrieres, Jean, Virtamo, Jarmo, Kee, Frank, Tregouet, David-Alexandre, Arveiler, Dominique, Amouyel, Philippe, Ferrario, Marco M., Brambilla, Paolo, Hall, Alistair S., Heath, AndrewC., Madden, Pamela A. F., Martin, Nicholas G., Montgomery, Grant W., Whitfield, John B., Jula, Antti, Knekt, Paul, Oostra, Ben, van Duijn, Cornelia M., Penninx, Brenda W. J. H., Smith, George Davey, Kaprio, Jaakko, Samani, Nilesh J., Gieger, Christian, Peters, Annette, Wichmann, H. -Erich, Boomsma, Dorret I., de Geus, Eco J. C., Tuomi, Tiinamaija, Power, Chris, Hammond, Christopher J., Spector, Tim D., Lind, Lars, Orho-Melander, Marju, Palmer, Colin Neil Alexander, Morris, Andrew D., Groop, Leif, Jarvelin, Marjo-Riitta, Salomaa, Veikko, Vartiainen, Erkki, Hofman, Albert, Ripatti, Samuli, Metspalu, Andres, Thorsteinsdottir, Unnur, Stefansson, Kari, Pedersen, Nancy L., McCarthy, Mark I., Ingelsson, Erik, Prokopenko, Inga, and ENGAGE Consortium
Published: 2013

17. Familial Resemblance for Serum Metabolite Concentrations — Corrigendum

Author: Draisma, Harmen H. M., primary, Beekman, Marian, additional, Pool, René, additional, van Ommen, Gert-Jan B., additional, Vaarhorst, Anika A. M., additional, de Craen, Anton J. M., additional, Willemsen, Gonneke, additional, Slagboom, P. Eline, additional, and Boomsma, Dorret I., additional
Published: 2013
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18. The Adult Netherlands Twin Register: Twenty-Five Years of Survey and Biological Data Collection

Author: Willemsen, Gonneke, primary, Vink, Jacqueline M., additional, Abdellaoui, Abdel, additional, den Braber, Anouk, additional, van Beek, Jenny H. D. A., additional, Draisma, Harmen H. M., additional, van Dongen, Jenny, additional, van ‘t Ent, Dennis, additional, Geels, Lot M., additional, van Lien, Rene, additional, Ligthart, Lannie, additional, Kattenberg, Mathijs, additional, Mbarek, Hamdi, additional, de Moor, Marleen H. M., additional, Neijts, Melanie, additional, Pool, Rene, additional, Stroo, Natascha, additional, Kluft, Cornelis, additional, Suchiman, H. Eka D., additional, Slagboom, P. Eline, additional, de Geus, Eco J. C., additional, and Boomsma, Dorret I., additional
Published: 2013
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19. The continuing value of twin studies in the omics era

Author: van Dongen, Jenny, primary, Slagboom, P. Eline, additional, Draisma, Harmen H. M., additional, Martin, Nicholas G., additional, and Boomsma, Dorret I., additional
Published: 2012
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20. Equating, or Correction for Between-Block Effects with Application to Body Fluid LC−MS and NMR Metabolomics Data Sets

Author: Draisma, Harmen H. M., primary, Reijmers, Theo H., additional, van der Kloet, Frans, additional, Bobeldijk-Pastorova, Ivana, additional, Spies-Faber, Elly, additional, Vogels, Jack T. W. E., additional, Meulman, Jacqueline J., additional, Boomsma, Dorret I., additional, van der Greef, Jan, additional, and Hankemeier, Thomas, additional
Published: 2010
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21. Early changes in rat hearts with developing pulmonary arterial hypertension can be detected with three-dimensional electrocardiography

Author: Henkens, Ivo R., primary, Mouchaers, Koen T. B., additional, Vliegen, Hubert W., additional, van der Laarse, Willem J., additional, Swenne, Cees A., additional, Maan, Arie C., additional, Draisma, Harmen H. M., additional, Schalij, Ingrid, additional, van der Wall, Ernst E., additional, Schalij, Martin J., additional, and Vonk-Noordegraaf, Anton, additional
Published: 2007
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22. Effects of metformin on metabolite profiles and LDL cholesterol in patients with type 2 diabetes.

Author: Tao Xu, Brandmaier, Stefan, Messias, Ana C., Herder, Christian, Draisma, Harmen H. M., Demirkan, Ayse, Zhonghao Yu, Ried, Janina S., Haller, Toomas, Heier, Margit, Campillos, Monica, Fobo, Gisela, Stark, Renee, Holzapfel, Christina, Adam, Jonathan, Shen Chi, Rotter, Markus, Panni, Tommaso, Quante, Anne S., and Ying He
Abstract: Objective: Metformin is used as a first-line oral treatment for type 2 diabetes (T2D). However, the underlying mechanism is not fully understood. Here, we aimed to comprehensively investigate the pleiotropic effects of metformin.Research Design and Methods: We analyzed both metabolomic and genomic data of the population-based KORA cohort. To evaluate the effect of metformin treatment on metabolite concentrations, we quantified 131 metabolites in fasting serum samples and used multivariable linear regression models in three independent cross-sectional studies (n = 151 patients with T2D treated with metformin [mt-T2D]). Additionally, we used linear mixed-effect models to study the longitudinal KORA samples (n = 912) and performed mediation analyses to investigate the effects of metformin intake on blood lipid profiles. We combined genotyping data with the identified metformin-associated metabolites in KORA individuals (n = 1,809) and explored the underlying pathways.Results: We found significantly lower (P < 5.0E-06) concentrations of three metabolites (acyl-alkyl phosphatidylcholines [PCs]) when comparing mt-T2D with four control groups who were not using glucose-lowering oral medication. These findings were controlled for conventional risk factors of T2D and replicated in two independent studies. Furthermore, we observed that the levels of these metabolites decreased significantly in patients after they started metformin treatment during 7 years' follow-up. The reduction of these metabolites was also associated with a lowered blood level of LDL cholesterol (LDL-C). Variations of these three metabolites were significantly associated with 17 genes (including FADS1 and FADS2) and controlled by AMPK, a metformin target.Conclusions: Our results indicate that metformin intake activates AMPK and consequently suppresses FADS, which leads to reduced levels of the three acyl-alkyl PCs and LDL-C. Our findings suggest potential beneficial effects of metformin in the prevention of cardiovascular disease. [ABSTRACT FROM AUTHOR]
Published: 2015
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23. Equating, or Correction for Between-Block Effects with Application to Body Fluid LC-MS and NMR Metabolomics Data Sets.

Author: Draisma, Harmen H. M., Reijmers, Theo H., van der Kloet, Frans, Pastorova, Ivana BobeIdijk, Faber, Elly Spies, Vogels, Jack T. W. E., Meulman, Jacqueline J., Boomsma, Dorret I., van der Greef, Jan, and Hankemeier, Thomas
Subjects: *BODY fluids, *NUCLEAR magnetic resonance, *METABOLITES, *PEPTIDES, *MULTIVARIATE analysis, *LIQUID chromatography
Abstract: Combination of data sets from different objects (for example, from two groups of healthy volunteers from the same population) that were measured on a common set of variables (for example, metabolites or peptides) is desirable for statistical analysis in "omics" studies because it increases power. However, this type of combination is not directly possible if nonbiological systematic differences exist among the individual data sets, or "blocks". Such differences can, for example, be due to small analytical changes that are likely to accumulate over large time intervals between blocks of measurements. In this article we present a data transformation method, that we will refer to as "quantile equating", which per variable corrects for linear and nonlinear differences in distribution among blocks of semiquantitative data obtained with the same analytical method. We demonstrate the successful application of the quantile equating method to data obtained on two typical metabolomics platforms, i.e., liquid chromatography-mass spectrometry and nuclear magnetic resonance spectroscopy. We suggest uni- and multivariate methods to evaluate similarities and differences among data blocks before and after quantile equating. In conclusion, we have developed a method to correct for nonbiological systematic differences among semiquantitative data blocks and have demonstrated its successful application to metabolomics data sets. [ABSTRACT FROM AUTHOR]
Published: 2010
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24. Genome Analyses of >200,000 Individuals Identify 58 Loci for Chronic Inflammation and Highlight Pathways that Link Inflammation and Complex Disorders

Author: Ligthart, Symen, Vaez, Ahmad, Võsa, Urmo, Stathopoulou, Maria G, De Vries, Paul S, Prins, Bram P, Van Der Most, Peter J, Tanaka, Toshiko, Naderi, Elnaz, Rose, Lynda M, Wu, Ying, Karlsson, Robert, Barbalic, Maja, Lin, Honghuang, Pool, René, Zhu, Gu, Macé, Aurélien, Sidore, Carlo, Trompet, Stella, Mangino, Massimo, Sabater-Lleal, Maria, Kemp, John P, Abbasi, Ali, Kacprowski, Tim, Verweij, Niek, Smith, Albert V, Huang, Tao, Marzi, Carola, Feitosa, Mary F, Lohman, Kurt K, Kleber, Marcus E, Milaneschi, Yuri, Mueller, Christian, Huq, Mahmudul, Vlachopoulou, Efthymia, Lyytikäinen, Leo-Pekka, Oldmeadow, Christopher, Deelen, Joris, Perola, Markus, Zhao, Jing Hua, Feenstra, Bjarke, Amini, Marzyeh, Lahti, Jari, Schraut, Katharina E, Fornage, Myriam, Suktitipat, Bhoom, Chen, Wei-Min, Li, Xiaohui, Nutile, Teresa, Malerba, Giovanni, Luan, Jian'an, Bak, Tom, Schork, Nicholas, Del Greco M, Fabiola, Thiering, Elisabeth, Mahajan, Anubha, Marioni, Riccardo E, Mihailov, Evelin, Eriksson, Joel, Ozel, Ayse Bilge, Zhang, Weihua, Nethander, Maria, Cheng, Yu-Ching, Aslibekyan, Stella, Ang, Wei, Gandin, Ilaria, Yengo, Loïc, Portas, Laura, Kooperberg, Charles, Hofer, Edith, Rajan, Kumar B, Schurmann, Claudia, Den Hollander, Wouter, Ahluwalia, Tarunveer S, Zhao, Jing, Draisma, Harmen H M, Ford, Ian, Timpson, Nicholas, Teumer, Alexander, Huang, Hongyan, Wahl, Simone, Liu, YongMei, Huang, Jie, Uh, Hae-Won, Geller, Frank, Joshi, Peter K, Yanek, Lisa R, Trabetti, Elisabetta, Lehne, Benjamin, Vozzi, Diego, Verbanck, Marie, Biino, Ginevra, Saba, Yasaman, Meulenbelt, Ingrid, O'Connell, Jeff R, Laakso, Markku, Giulianini, Franco, Magnusson, Patrik K E, Ballantyne, Christie M, Hottenga, Jouke Jan, Montgomery, Grant W, Rivadineira, Fernando, Rueedi, Rico, Steri, Maristella, Herzig, Karl-Heinz, Stott, David J, Menni, Cristina, Frånberg, Mattias, St Pourcain, Beate, Felix, Stephan B, Pers, Tune H, Bakker, Stephan J L, Kraft, Peter, Peters, Annette, Vaidya, Dhananjay, Delgado, Graciela, Smit, Johannes H, Großmann, Vera, Sinisalo, Juha, Seppälä, Ilkka, Williams, Stephen R, Holliday, Elizabeth G, Moed, Matthijs, Langenberg, Claudia, Räikkönen, Katri, Ding, Jingzhong, Campbell, Harry, Sale, Michele M, Chen, Yii-Der I, James, Alan L, Ruggiero, Daniela, Soranzo, Nicole, Hartman, Catharina A, Smith, Erin N, Berenson, Gerald S, Fuchsberger, Christian, Hernandez, Dena, Tiesler, Carla M T, Giedraitis, Vilmantas, Liewald, David, Fischer, Krista, Mellström, Dan, Larsson, Anders, Wang, Yunmei, Scott, William R, Lorentzon, Matthias, Beilby, John, Ryan, Kathleen A, Pennell, Craig E, Vuckovic, Dragana, Balkau, Beverly, Concas, Maria Pina, Schmidt, Reinhold, Mendes De Leon, Carlos F, Bottinger, Erwin P, Kloppenburg, Margreet, Paternoster, Lavinia, Boehnke, Michael, Musk, A W, Willemsen, Gonneke, Evans, David M, Madden, Pamela A F, Kähönen, Mika, Kutalik, Zoltán, Zoledziewska, Magdalena, Karhunen, Ville, Kritchevsky, Stephen B, Sattar, Naveed, Lachance, Genevieve, Clarke, Robert, Harris, Tamara B, Raitakari, Olli T, Attia, John R, Van Heemst, Diana, Kajantie, Eero, Sorice, Rossella, Gambaro, Giovanni, Scott, Robert A, Hicks, Andrew A, Ferrucci, Luigi, Standl, Marie, Lindgren, Cecilia M, Starr, John M, Karlsson, Magnus, Lind, Lars, Li, Jun Z, Chambers, John C, Mori, Trevor A, De Geus, Eco J C N, Heath, Andrew C, Martin, Nicholas G, Auvinen, Juha, Buckley, Brendan M, De Craen, Anton J M, Waldenberger, Melanie, Strauch, Konstantin, Meitinger, Thomas, Scott, Rodney J, McEvoy, Mark, Beekman, Marian, Bombieri, Cristina, Ridker, Paul M, Mohlke, Karen L, Pedersen, Nancy L, Morrison, Alanna C, Boomsma, Dorret I, Whitfield, John B, Strachan, David P, Hofman, Albert, Vollenweider, Peter, Cucca, Francesco, Jarvelin, Marjo-Riitta, Jukema, J Wouter, Spector, Tim D, Hamsten, Anders, Zeller, Tanja, Uitterlinden, André G, Nauck, Matthias, Gudnason, Vilmundur, Qi, Lu, Grallert, Harald, Borecki, Ingrid B, Rotter, Jerome I, März, Winfried, Wild, Philipp S, Lokki, Marja-Liisa, Boyle, Michael, Salomaa, Veikko, Melbye, Mads, Eriksson, Johan G, Wilson, James F, Penninx, Brenda W J H, Becker, Diane M, Worrall, Bradford B, Gibson, Greg, Krauss, Ronald M, Ciullo, Marina, Zaza, Gianluigi, Wareham, Nicholas J, Oldehinkel, Albertine J, Palmer, Lyle J, Murray, Sarah S, Pramstaller, Peter P, Bandinelli, Stefania, Heinrich, Joachim, Ingelsson, Erik, Deary, Ian J, Mägi, Reedik, Vandenput, Liesbeth, Van Der Harst, Pim, Desch, Karl C, Kooner, Jaspal S, Ohlsson, Claes, Hayward, Caroline, Lehtimäki, Terho, Shuldiner, Alan R, Arnett, Donna K, Beilin, Lawrence J, Robino, Antonietta, Froguel, Philippe, Pirastu, Mario, Jess, Tine, Koenig, Wolfgang, Loos, Ruth J F, Evans, Denis A, Schmidt, Helena, Smith, George Davey, Slagboom, P Eline, Eiriksdottir, Gudny, Morris, Andrew P, Psaty, Bruce M, Tracy, Russell P, Nolte, Ilja M, Boerwinkle, Eric, Visvikis-Siest, Sophie, Reiner, Alex P, Gross, Myron, Bis, Joshua C, Franke, Lude, Franco, Oscar H, Benjamin, Emelia J, Chasman, Daniel I, Dupuis, Josée, Snieder, Harold, Dehghan, Abbas, and Alizadeh, Behrooz Z
Subjects: 2. Zero hunger, 610 Medicine & health, 360 Social problems & social services, 3. Good health
Abstract: C-reactive protein (CRP) is a sensitive biomarker of chronic low-grade inflammation and is associated with multiple complex diseases. The genetic determinants of chronic inflammation remain largely unknown, and the causal role of CRP in several clinical outcomes is debated. We performed two genome-wide association studies (GWASs), on HapMap and 1000 Genomes imputed data, of circulating amounts of CRP by using data from 88 studies comprising 204,402 European individuals. Additionally, we performed in silico functional analyses and Mendelian randomization analyses with several clinical outcomes. The GWAS meta-analyses of CRP revealed 58 distinct genetic loci (p < 5 × 10). After adjustment for body mass index in the regression analysis, the associations at all except three loci remained. The lead variants at the distinct loci explained up to 7.0% of the variance in circulating amounts of CRP. We identified 66 gene sets that were organized in two substantially correlated clusters, one mainly composed of immune pathways and the other characterized by metabolic pathways in the liver. Mendelian randomization analyses revealed a causal protective effect of CRP on schizophrenia and a risk-increasing effect on bipolar disorder. Our findings provide further insights into the biology of inflammation and could lead to interventions for treating inflammation and its clinical consequences.

25. Genome Analyses of >200,000 Individuals Identify 58 Loci for Chronic Inflammation and Highlight Pathways that Link Inflammation and Complex Disorders.

Author: Ligthart S, Vaez A, Võsa U, Stathopoulou MG, de Vries PS, Prins BP, Van der Most PJ, Tanaka T, Naderi E, Rose LM, Wu Y, Karlsson R, Barbalic M, Lin H, Pool R, Zhu G, Macé A, Sidore C, Trompet S, Mangino M, Sabater-Lleal M, Kemp JP, Abbasi A, Kacprowski T, Verweij N, Smith AV, Huang T, Marzi C, Feitosa MF, Lohman KK, Kleber ME, Milaneschi Y, Mueller C, Huq M, Vlachopoulou E, Lyytikäinen LP, Oldmeadow C, Deelen J, Perola M, Zhao JH, Feenstra B, Amini M, Lahti J, Schraut KE, Fornage M, Suktitipat B, Chen WM, Li X, Nutile T, Malerba G, Luan J, Bak T, Schork N, Del Greco M F, Thiering E, Mahajan A, Marioni RE, Mihailov E, Eriksson J, Ozel AB, Zhang W, Nethander M, Cheng YC, Aslibekyan S, Ang W, Gandin I, Yengo L, Portas L, Kooperberg C, Hofer E, Rajan KB, Schurmann C, den Hollander W, Ahluwalia TS, Zhao J, Draisma HHM, Ford I, Timpson N, Teumer A, Huang H, Wahl S, Liu Y, Huang J, Uh HW, Geller F, Joshi PK, Yanek LR, Trabetti E, Lehne B, Vozzi D, Verbanck M, Biino G, Saba Y, Meulenbelt I, O'Connell JR, Laakso M, Giulianini F, Magnusson PKE, Ballantyne CM, Hottenga JJ, Montgomery GW, Rivadineira F, Rueedi R, Steri M, Herzig KH, Stott DJ, Menni C, Frånberg M, St Pourcain B, Felix SB, Pers TH, Bakker SJL, Kraft P, Peters A, Vaidya D, Delgado G, Smit JH, Großmann V, Sinisalo J, Seppälä I, Williams SR, Holliday EG, Moed M, Langenberg C, Räikkönen K, Ding J, Campbell H, Sale MM, Chen YI, James AL, Ruggiero D, Soranzo N, Hartman CA, Smith EN, Berenson GS, Fuchsberger C, Hernandez D, Tiesler CMT, Giedraitis V, Liewald D, Fischer K, Mellström D, Larsson A, Wang Y, Scott WR, Lorentzon M, Beilby J, Ryan KA, Pennell CE, Vuckovic D, Balkau B, Concas MP, Schmidt R, Mendes de Leon CF, Bottinger EP, Kloppenburg M, Paternoster L, Boehnke M, Musk AW, Willemsen G, Evans DM, Madden PAF, Kähönen M, Kutalik Z, Zoledziewska M, Karhunen V, Kritchevsky SB, Sattar N, Lachance G, Clarke R, Harris TB, Raitakari OT, Attia JR, van Heemst D, Kajantie E, Sorice R, Gambaro G, Scott RA, Hicks AA, Ferrucci L, Standl M, Lindgren CM, Starr JM, Karlsson M, Lind L, Li JZ, Chambers JC, Mori TA, de Geus EJCN, Heath AC, Martin NG, Auvinen J, Buckley BM, de Craen AJM, Waldenberger M, Strauch K, Meitinger T, Scott RJ, McEvoy M, Beekman M, Bombieri C, Ridker PM, Mohlke KL, Pedersen NL, Morrison AC, Boomsma DI, Whitfield JB, Strachan DP, Hofman A, Vollenweider P, Cucca F, Jarvelin MR, Jukema JW, Spector TD, Hamsten A, Zeller T, Uitterlinden AG, Nauck M, Gudnason V, Qi L, Grallert H, Borecki IB, Rotter JI, März W, Wild PS, Lokki ML, Boyle M, Salomaa V, Melbye M, Eriksson JG, Wilson JF, Penninx BWJH, Becker DM, Worrall BB, Gibson G, Krauss RM, Ciullo M, Zaza G, Wareham NJ, Oldehinkel AJ, Palmer LJ, Murray SS, Pramstaller PP, Bandinelli S, Heinrich J, Ingelsson E, Deary IJ, Mägi R, Vandenput L, van der Harst P, Desch KC, Kooner JS, Ohlsson C, Hayward C, Lehtimäki T, Shuldiner AR, Arnett DK, Beilin LJ, Robino A, Froguel P, Pirastu M, Jess T, Koenig W, Loos RJF, Evans DA, Schmidt H, Smith GD, Slagboom PE, Eiriksdottir G, Morris AP, Psaty BM, Tracy RP, Nolte IM, Boerwinkle E, Visvikis-Siest S, Reiner AP, Gross M, Bis JC, Franke L, Franco OH, Benjamin EJ, Chasman DI, Dupuis J, Snieder H, Dehghan A, and Alizadeh BZ
Subjects: Adolescent, Adult, Aged, Aged, 80 and over, Biomarkers metabolism, Bipolar Disorder genetics, Bipolar Disorder metabolism, Body Mass Index, C-Reactive Protein genetics, Child, Female, Genome-Wide Association Study methods, Humans, Inflammation metabolism, Liver metabolism, Liver pathology, Male, Mendelian Randomization Analysis methods, Middle Aged, Schizophrenia genetics, Schizophrenia metabolism, Young Adult, Genetic Loci genetics, Inflammation genetics, Metabolic Networks and Pathways genetics
Abstract: C-reactive protein (CRP) is a sensitive biomarker of chronic low-grade inflammation and is associated with multiple complex diseases. The genetic determinants of chronic inflammation remain largely unknown, and the causal role of CRP in several clinical outcomes is debated. We performed two genome-wide association studies (GWASs), on HapMap and 1000 Genomes imputed data, of circulating amounts of CRP by using data from 88 studies comprising 204,402 European individuals. Additionally, we performed in silico functional analyses and Mendelian randomization analyses with several clinical outcomes. The GWAS meta-analyses of CRP revealed 58 distinct genetic loci (p < 5 × 10 -8 ). After adjustment for body mass index in the regression analysis, the associations at all except three loci remained. The lead variants at the distinct loci explained up to 7.0% of the variance in circulating amounts of CRP. We identified 66 gene sets that were organized in two substantially correlated clusters, one mainly composed of immune pathways and the other characterized by metabolic pathways in the liver. Mendelian randomization analyses revealed a causal protective effect of CRP on schizophrenia and a risk-increasing effect on bipolar disorder. Our findings provide further insights into the biology of inflammation and could lead to interventions for treating inflammation and its clinical consequences., (Copyright © 2018 American Society of Human Genetics. All rights reserved.)
Published: 2018
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26. Antisense Long Non-Coding RNAs Are Deregulated in Skin Tissue of Patients with Systemic Sclerosis.

Author: Messemaker TC, Chadli L, Cai G, Goelela VS, Boonstra M, Dorjée AL, Andersen SN, Mikkers HMM, van 't Hof P, Mei H, Distler O, Draisma HHM, Johnson ME, Orzechowski NM, Simms RW, Toes REM, Aarbiou J, Huizinga TW, Whitfield ML, DeGroot J, de Vries-Bouwstra J, and Kurreeman F
Subjects: Cells, Cultured, Humans, RNA, Long Noncoding biosynthesis, Scleroderma, Systemic metabolism, Scleroderma, Systemic pathology, Skin pathology, Transcription Factors, Transcriptional Activation, RNA, Long Noncoding genetics, Scleroderma, Systemic genetics, Skin metabolism, Up-Regulation
Abstract: Systemic sclerosis is an autoimmune disease characterized by fibrosis of skin and multiple organs of which the pathogenesis is poorly understood. We studied differentially expressed coding and non-coding genes in relation to systemic sclerosis pathogenesis with a specific focus on antisense non-coding RNAs. Skin biopsy-derived RNAs from 14 early systemic sclerosis patients and six healthy individuals were sequenced with ion-torrent and analyzed using DEseq2. Overall, 4,901 genes with a fold change >1.5 and a false discovery rate <5% were detected in patients versus controls. Upregulated genes clustered in immunologic, cell adhesion, and keratin-related processes. Interestingly, 676 deregulated non-coding genes were detected, 257 of which were classified as antisense genes. Sense genes expressed opposite of these antisense genes were also deregulated in 42% of the observed sense-antisense gene pairs. The majority of the antisense genes had a similar effect sizes in an independent North American dataset with three genes (CTBP1-AS2, OTUD6B-AS1, and AGAP2-AS1) exceeding the study-wide Bonferroni-corrected P-value (P Bonf < 0.0023, P combined = 1.1 × 10 -9 , 1.4 × 10 -8 , 1.7 × 10 -6 , respectively). In this study, we highlight that together with coding genes, (antisense) long non-coding RNAs are deregulated in skin tissue of systemic sclerosis patients suggesting a novel class of genes involved in pathogenesis of systemic sclerosis., (Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.)
Published: 2018
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27. Erratum to "Short communication: Genetic association between schizophrenia and cannabis use" [Drug Alcohol Depend. 171 (2017) 117-121].

Author: Verweij KJH, Abdellaoui A, Nivard MG, Cort AS, Ligthart L, Draisma HHM, Minică CC, Gillespie NA, Willemsen G, Hottenga JJ, Boomsma DI, and Vink JM
Published: 2017
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28. Short communication: Genetic association between schizophrenia and cannabis use.

Author: Verweij KJ, Abdellaoui A, Nivard MG, Sainz Cort A, Ligthart L, Draisma HH, Minică CC, Gillespie, Willemsen G, Hottenga JJ, Boomsma, and Vink JM
Subjects: Adolescent, Adult, Aged, Aged, 80 and over, Diseases in Twins epidemiology, Diseases in Twins genetics, Female, Genetic Predisposition to Disease epidemiology, Genetic Predisposition to Disease genetics, Genome-Wide Association Study methods, Humans, Male, Middle Aged, Netherlands epidemiology, Phenotype, Registries, Young Adult, Marijuana Smoking epidemiology, Marijuana Smoking genetics, Multifactorial Inheritance genetics, Schizophrenia epidemiology, Schizophrenia genetics
Abstract: Background and Aim: Previous studies have shown a relationship between schizophrenia and cannabis use. As both traits are substantially heritable, a shared genetic liability could explain the association. We use two recently developed genomics methods to investigate the genetic overlap between schizophrenia and cannabis use., Methods: Firstly, polygenic risk scores for schizophrenia were created based on summary statistics from the largest schizophrenia genome-wide association (GWA) meta-analysis to date. We analysed the association between these schizophrenia polygenic scores and multiple cannabis use phenotypes (lifetime use, regular use, age at initiation, and quantity and frequency of use) in a sample of 6,931 individuals. Secondly, we applied LD-score regression to the GWA summary statistics of schizophrenia and lifetime cannabis use to calculate the genome-wide genetic correlation., Results: Polygenic risk scores for schizophrenia were significantly (α<0.05) associated with five of the eight cannabis use phenotypes, including lifetime use, regular use, and quantity of use, with risk scores explaining up to 0.5% of the variance. Associations were not significant for age at initiation of use and two measures of frequency of use analyzed in lifetime users only, potentially because of reduced power due to a smaller sample size. The LD-score regression revealed a significant genetic correlation of r g =0.22 (SE=0.07, p=0.003) between schizophrenia and lifetime cannabis use., Conclusions: Common genetic variants underlying schizophrenia and lifetime cannabis use are partly overlapping. Individuals with a stronger genetic predisposition to schizophrenia are more likely to initiate cannabis use, use cannabis more regularly, and consume more cannabis over their lifetime., (Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.)
Published: 2017
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29. Genome-wide study for circulating metabolites identifies 62 loci and reveals novel systemic effects of LPA.

Author: Kettunen J, Demirkan A, Würtz P, Draisma HH, Haller T, Rawal R, Vaarhorst A, Kangas AJ, Lyytikäinen LP, Pirinen M, Pool R, Sarin AP, Soininen P, Tukiainen T, Wang Q, Tiainen M, Tynkkynen T, Amin N, Zeller T, Beekman M, Deelen J, van Dijk KW, Esko T, Hottenga JJ, van Leeuwen EM, Lehtimäki T, Mihailov E, Rose RJ, de Craen AJ, Gieger C, Kähönen M, Perola M, Blankenberg S, Savolainen MJ, Verhoeven A, Viikari J, Willemsen G, Boomsma DI, van Duijn CM, Eriksson J, Jula A, Järvelin MR, Kaprio J, Metspalu A, Raitakari O, Salomaa V, Slagboom PE, Waldenberger M, Ripatti S, and Ala-Korpela M
Subjects: Adult, Aged, Cardiovascular Diseases metabolism, Chromosome Mapping, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Lipoproteins, VLDL metabolism, Magnetic Resonance Spectroscopy, Male, Mendelian Randomization Analysis, Middle Aged, Triglycerides metabolism, Young Adult, Cardiovascular Diseases genetics, Lipoprotein(a) genetics, Metabolomics methods
Abstract: Genome-wide association studies have identified numerous loci linked with complex diseases, for which the molecular mechanisms remain largely unclear. Comprehensive molecular profiling of circulating metabolites captures highly heritable traits, which can help to uncover metabolic pathophysiology underlying established disease variants. We conduct an extended genome-wide association study of genetic influences on 123 circulating metabolic traits quantified by nuclear magnetic resonance metabolomics from up to 24,925 individuals and identify eight novel loci for amino acids, pyruvate and fatty acids. The LPA locus link with cardiovascular risk exemplifies how detailed metabolic profiling may inform underlying aetiology via extensive associations with very-low-density lipoprotein and triglyceride metabolism. Genetic fine mapping and Mendelian randomization uncover wide-spread causal effects of lipoprotein(a) on overall lipoprotein metabolism and we assess potential pleiotropic consequences of genetically elevated lipoprotein(a) on diverse morbidities via electronic health-care records. Our findings strengthen the argument for safe LPA-targeted intervention to reduce cardiovascular risk.
Published: 2016
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30. Effects of metformin on metabolite profiles and LDL cholesterol in patients with type 2 diabetes.

Author: Xu T, Brandmaier S, Messias AC, Herder C, Draisma HH, Demirkan A, Yu Z, Ried JS, Haller T, Heier M, Campillos M, Fobo G, Stark R, Holzapfel C, Adam J, Chi S, Rotter M, Panni T, Quante AS, He Y, Prehn C, Roemisch-Margl W, Kastenmüller G, Willemsen G, Pool R, Kasa K, van Dijk KW, Hankemeier T, Meisinger C, Thorand B, Ruepp A, Hrabé de Angelis M, Li Y, Wichmann HE, Stratmann B, Strauch K, Metspalu A, Gieger C, Suhre K, Adamski J, Illig T, Rathmann W, Roden M, Peters A, van Duijn CM, Boomsma DI, Meitinger T, and Wang-Sattler R
Subjects: Aged, Cross-Sectional Studies, Delta-5 Fatty Acid Desaturase, Diabetes Mellitus, Type 2 blood, Diabetes Mellitus, Type 2 prevention & control, Diabetic Angiopathies prevention & control, Fasting blood, Fatty Acid Desaturases metabolism, Female, Genomics, Genotype, Humans, Lipid Metabolism drug effects, Male, Metabolomics, Middle Aged, Risk Factors, Cholesterol, LDL metabolism, Diabetes Mellitus, Type 2 drug therapy, Hypoglycemic Agents therapeutic use, Metformin therapeutic use
Abstract: Objective: Metformin is used as a first-line oral treatment for type 2 diabetes (T2D). However, the underlying mechanism is not fully understood. Here, we aimed to comprehensively investigate the pleiotropic effects of metformin., Research Design and Methods: We analyzed both metabolomic and genomic data of the population-based KORA cohort. To evaluate the effect of metformin treatment on metabolite concentrations, we quantified 131 metabolites in fasting serum samples and used multivariable linear regression models in three independent cross-sectional studies (n = 151 patients with T2D treated with metformin [mt-T2D]). Additionally, we used linear mixed-effect models to study the longitudinal KORA samples (n = 912) and performed mediation analyses to investigate the effects of metformin intake on blood lipid profiles. We combined genotyping data with the identified metformin-associated metabolites in KORA individuals (n = 1,809) and explored the underlying pathways., Results: We found significantly lower (P < 5.0E-06) concentrations of three metabolites (acyl-alkyl phosphatidylcholines [PCs]) when comparing mt-T2D with four control groups who were not using glucose-lowering oral medication. These findings were controlled for conventional risk factors of T2D and replicated in two independent studies. Furthermore, we observed that the levels of these metabolites decreased significantly in patients after they started metformin treatment during 7 years' follow-up. The reduction of these metabolites was also associated with a lowered blood level of LDL cholesterol (LDL-C). Variations of these three metabolites were significantly associated with 17 genes (including FADS1 and FADS2) and controlled by AMPK, a metformin target., Conclusions: Our results indicate that metformin intake activates AMPK and consequently suppresses FADS, which leads to reduced levels of the three acyl-alkyl PCs and LDL-C. Our findings suggest potential beneficial effects of metformin in the prevention of cardiovascular disease., (© 2015 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered.)
Published: 2015
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31. Age- and sex-specific causal effects of adiposity on cardiovascular risk factors.

Author: Fall T, Hägg S, Ploner A, Mägi R, Fischer K, Draisma HH, Sarin AP, Benyamin B, Ladenvall C, Åkerlund M, Kals M, Esko T, Nelson CP, Kaakinen M, Huikari V, Mangino M, Meirhaeghe A, Kristiansson K, Nuotio ML, Kobl M, Grallert H, Dehghan A, Kuningas M, de Vries PS, de Bruijn RF, Willems SM, Heikkilä K, Silventoinen K, Pietiläinen KH, Legry V, Giedraitis V, Goumidi L, Syvänen AC, Strauch K, Koenig W, Lichtner P, Herder C, Palotie A, Menni C, Uitterlinden AG, Kuulasmaa K, Havulinna AS, Moreno LA, Gonzalez-Gross M, Evans A, Tregouet DA, Yarnell JW, Virtamo J, Ferrières J, Veronesi G, Perola M, Arveiler D, Brambilla P, Lind L, Kaprio J, Hofman A, Stricker BH, van Duijn CM, Ikram MA, Franco OH, Cottel D, Dallongeville J, Hall AS, Jula A, Tobin MD, Penninx BW, Peters A, Gieger C, Samani NJ, Montgomery GW, Whitfield JB, Martin NG, Groop L, Spector TD, Magnusson PK, Amouyel P, Boomsma DI, Nilsson PM, Järvelin MR, Lyssenko V, Metspalu A, Strachan DP, Salomaa V, Ripatti S, Pedersen NL, Prokopenko I, McCarthy MI, and Ingelsson E
Subjects: Blood Pressure, C-Reactive Protein metabolism, Cholesterol, HDL blood, Cholesterol, HDL metabolism, Female, Humans, Insulin blood, Interleukin-6, Male, Middle Aged, Sex Factors, Triglycerides blood, Triglycerides metabolism, Adiposity physiology, Aging physiology, Cardiovascular Diseases metabolism
Abstract: Observational studies have reported different effects of adiposity on cardiovascular risk factors across age and sex. Since cardiovascular risk factors are enriched in obese individuals, it has not been easy to dissect the effects of adiposity from those of other risk factors. We used a Mendelian randomization approach, applying a set of 32 genetic markers to estimate the causal effect of adiposity on blood pressure, glycemic indices, circulating lipid levels, and markers of inflammation and liver disease in up to 67,553 individuals. All analyses were stratified by age (cutoff 55 years of age) and sex. The genetic score was associated with BMI in both nonstratified analysis (P = 2.8 × 10(-107)) and stratified analyses (all P < 3.3 × 10(-30)). We found evidence of a causal effect of adiposity on blood pressure, fasting levels of insulin, C-reactive protein, interleukin-6, HDL cholesterol, and triglycerides in a nonstratified analysis and in the <55-year stratum. Further, we found evidence of a smaller causal effect on total cholesterol (P for difference = 0.015) in the ≥55-year stratum than in the <55-year stratum, a finding that could be explained by biology, survival bias, or differential medication. In conclusion, this study extends previous knowledge of the effects of adiposity by providing sex- and age-specific causal estimates on cardiovascular risk factors., (© 2015 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered.)
Published: 2015
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32. Adiposity as a cause of cardiovascular disease: a Mendelian randomization study.

Author: Hägg S, Fall T, Ploner A, Mägi R, Fischer K, Draisma HH, Kals M, de Vries PS, Dehghan A, Willems SM, Sarin AP, Kristiansson K, Nuotio ML, Havulinna AS, de Bruijn RF, Ikram MA, Kuningas M, Stricker BH, Franco OH, Benyamin B, Gieger C, Hall AS, Huikari V, Jula A, Järvelin MR, Kaakinen M, Kaprio J, Kobl M, Mangino M, Nelson CP, Palotie A, Samani NJ, Spector TD, Strachan DP, Tobin MD, Whitfield JB, Uitterlinden AG, Salomaa V, Syvänen AC, Kuulasmaa K, Magnusson PK, Esko T, Hofman A, de Geus EJ, Lind L, Giedraitis V, Perola M, Evans A, Ferrières J, Virtamo J, Kee F, Tregouet DA, Arveiler D, Amouyel P, Gianfagna F, Brambilla P, Ripatti S, van Duijn CM, Metspalu A, Prokopenko I, McCarthy MI, Pedersen NL, and Ingelsson E
Subjects: Body Mass Index, Cross-Sectional Studies, Female, Humans, Male, Mendelian Randomization Analysis, Middle Aged, Prospective Studies, Adiposity genetics, Cardiovascular Diseases genetics, Polymorphism, Single Nucleotide genetics
Abstract: Background: Adiposity, as indicated by body mass index (BMI), has been associated with risk of cardiovascular diseases in epidemiological studies. We aimed to investigate if these associations are causal, using Mendelian randomization (MR) methods., Methods: The associations of BMI with cardiovascular outcomes [coronary heart disease (CHD), heart failure and ischaemic stroke], and associations of a genetic score (32 BMI single nucleotide polymorphisms) with BMI and cardiovascular outcomes were examined in up to 22,193 individuals with 3062 incident cardiovascular events from nine prospective follow-up studies within the ENGAGE consortium. We used random-effects meta-analysis in an MR framework to provide causal estimates of the effect of adiposity on cardiovascular outcomes., Results: There was a strong association between BMI and incident CHD (HR = 1.20 per SD-increase of BMI, 95% CI, 1.12-1.28, P = 1.9.10(-7)), heart failure (HR = 1.47, 95% CI, 1.35-1.60, P = 9.10(-19)) and ischaemic stroke (HR = 1.15, 95% CI, 1.06-1.24, P = 0.0008) in observational analyses. The genetic score was robustly associated with BMI (β = 0.030 SD-increase of BMI per additional allele, 95% CI, 0.028-0.033, P = 3.10(-107)). Analyses indicated a causal effect of adiposity on development of heart failure (HR = 1.93 per SD-increase of BMI, 95% CI, 1.12-3.30, P = 0.017) and ischaemic stroke (HR = 1.83, 95% CI, 1.05-3.20, P = 0.034). Additional cross-sectional analyses using both ENGAGE and CARDIoGRAMplusC4D data showed a causal effect of adiposity on CHD., Conclusions: Using MR methods, we provide support for the hypothesis that adiposity causes CHD, heart failure and, previously not demonstrated, ischaemic stroke., (© The Author 2015; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.)
Published: 2015
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33. Identification of heart rate-associated loci and their effects on cardiac conduction and rhythm disorders.

Author: den Hoed M, Eijgelsheim M, Esko T, Brundel BJ, Peal DS, Evans DM, Nolte IM, Segrè AV, Holm H, Handsaker RE, Westra HJ, Johnson T, Isaacs A, Yang J, Lundby A, Zhao JH, Kim YJ, Go MJ, Almgren P, Bochud M, Boucher G, Cornelis MC, Gudbjartsson D, Hadley D, van der Harst P, Hayward C, den Heijer M, Igl W, Jackson AU, Kutalik Z, Luan J, Kemp JP, Kristiansson K, Ladenvall C, Lorentzon M, Montasser ME, Njajou OT, O'Reilly PF, Padmanabhan S, St Pourcain B, Rankinen T, Salo P, Tanaka T, Timpson NJ, Vitart V, Waite L, Wheeler W, Zhang W, Draisma HH, Feitosa MF, Kerr KF, Lind PA, Mihailov E, Onland-Moret NC, Song C, Weedon MN, Xie W, Yengo L, Absher D, Albert CM, Alonso A, Arking DE, de Bakker PI, Balkau B, Barlassina C, Benaglio P, Bis JC, Bouatia-Naji N, Brage S, Chanock SJ, Chines PS, Chung M, Darbar D, Dina C, Dörr M, Elliott P, Felix SB, Fischer K, Fuchsberger C, de Geus EJ, Goyette P, Gudnason V, Harris TB, Hartikainen AL, Havulinna AS, Heckbert SR, Hicks AA, Hofman A, Holewijn S, Hoogstra-Berends F, Hottenga JJ, Jensen MK, Johansson A, Junttila J, Kääb S, Kanon B, Ketkar S, Khaw KT, Knowles JW, Kooner AS, Kors JA, Kumari M, Milani L, Laiho P, Lakatta EG, Langenberg C, Leusink M, Liu Y, Luben RN, Lunetta KL, Lynch SN, Markus MR, Marques-Vidal P, Mateo Leach I, McArdle WL, McCarroll SA, Medland SE, Miller KA, Montgomery GW, Morrison AC, Müller-Nurasyid M, Navarro P, Nelis M, O'Connell JR, O'Donnell CJ, Ong KK, Newman AB, Peters A, Polasek O, Pouta A, Pramstaller PP, Psaty BM, Rao DC, Ring SM, Rossin EJ, Rudan D, Sanna S, Scott RA, Sehmi JS, Sharp S, Shin JT, Singleton AB, Smith AV, Soranzo N, Spector TD, Stewart C, Stringham HM, Tarasov KV, Uitterlinden AG, Vandenput L, Hwang SJ, Whitfield JB, Wijmenga C, Wild SH, Willemsen G, Wilson JF, Witteman JC, Wong A, Wong Q, Jamshidi Y, Zitting P, Boer JM, Boomsma DI, Borecki IB, van Duijn CM, Ekelund U, Forouhi NG, Froguel P, Hingorani A, Ingelsson E, Kivimaki M, Kronmal RA, Kuh D, Lind L, Martin NG, Oostra BA, Pedersen NL, Quertermous T, Rotter JI, van der Schouw YT, Verschuren WM, Walker M, Albanes D, Arnar DO, Assimes TL, Bandinelli S, Boehnke M, de Boer RA, Bouchard C, Caulfield WL, Chambers JC, Curhan G, Cusi D, Eriksson J, Ferrucci L, van Gilst WH, Glorioso N, de Graaf J, Groop L, Gyllensten U, Hsueh WC, Hu FB, Huikuri HV, Hunter DJ, Iribarren C, Isomaa B, Jarvelin MR, Jula A, Kähönen M, Kiemeney LA, van der Klauw MM, Kooner JS, Kraft P, Iacoviello L, Lehtimäki T, Lokki ML, Mitchell BD, Navis G, Nieminen MS, Ohlsson C, Poulter NR, Qi L, Raitakari OT, Rimm EB, Rioux JD, Rizzi F, Rudan I, Salomaa V, Sever PS, Shields DC, Shuldiner AR, Sinisalo J, Stanton AV, Stolk RP, Strachan DP, Tardif JC, Thorsteinsdottir U, Tuomilehto J, van Veldhuisen DJ, Virtamo J, Viikari J, Vollenweider P, Waeber G, Widen E, Cho YS, Olsen JV, Visscher PM, Willer C, Franke L, Erdmann J, Thompson JR, Pfeufer A, Sotoodehnia N, Newton-Cheh C, Ellinor PT, Stricker BH, Metspalu A, Perola M, Beckmann JS, Smith GD, Stefansson K, Wareham NJ, Munroe PB, Sibon OC, Milan DJ, Snieder H, Samani NJ, and Loos RJ
Subjects: Animals, Arrhythmias, Cardiac physiopathology, Gene Frequency, Genetic Loci, Genome-Wide Association Study, Heart Conduction System physiopathology, Humans, Metabolic Networks and Pathways, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Arrhythmias, Cardiac genetics, Heart Rate genetics
Abstract: Elevated resting heart rate is associated with greater risk of cardiovascular disease and mortality. In a 2-stage meta-analysis of genome-wide association studies in up to 181,171 individuals, we identified 14 new loci associated with heart rate and confirmed associations with all 7 previously established loci. Experimental downregulation of gene expression in Drosophila melanogaster and Danio rerio identified 20 genes at 11 loci that are relevant for heart rate regulation and highlight a role for genes involved in signal transmission, embryonic cardiac development and the pathophysiology of dilated cardiomyopathy, congenital heart failure and/or sudden cardiac death. In addition, genetic susceptibility to increased heart rate is associated with altered cardiac conduction and reduced risk of sick sinus syndrome, and both heart rate-increasing and heart rate-decreasing variants associate with risk of atrial fibrillation. Our findings provide fresh insights into the mechanisms regulating heart rate and identify new therapeutic targets.
Published: 2013
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34. The role of adiposity in cardiometabolic traits: a Mendelian randomization analysis.

Author: Fall T, Hägg S, Mägi R, Ploner A, Fischer K, Horikoshi M, Sarin AP, Thorleifsson G, Ladenvall C, Kals M, Kuningas M, Draisma HH, Ried JS, van Zuydam NR, Huikari V, Mangino M, Sonestedt E, Benyamin B, Nelson CP, Rivera NV, Kristiansson K, Shen HY, Havulinna AS, Dehghan A, Donnelly LA, Kaakinen M, Nuotio ML, Robertson N, de Bruijn RF, Ikram MA, Amin N, Balmforth AJ, Braund PS, Doney AS, Döring A, Elliott P, Esko T, Franco OH, Gretarsdottir S, Hartikainen AL, Heikkilä K, Herzig KH, Holm H, Hottenga JJ, Hyppönen E, Illig T, Isaacs A, Isomaa B, Karssen LC, Kettunen J, Koenig W, Kuulasmaa K, Laatikainen T, Laitinen J, Lindgren C, Lyssenko V, Läärä E, Rayner NW, Männistö S, Pouta A, Rathmann W, Rivadeneira F, Ruokonen A, Savolainen MJ, Sijbrands EJ, Small KS, Smit JH, Steinthorsdottir V, Syvänen AC, Taanila A, Tobin MD, Uitterlinden AG, Willems SM, Willemsen G, Witteman J, Perola M, Evans A, Ferrières J, Virtamo J, Kee F, Tregouet DA, Arveiler D, Amouyel P, Ferrario MM, Brambilla P, Hall AS, Heath AC, Madden PA, Martin NG, Montgomery GW, Whitfield JB, Jula A, Knekt P, Oostra B, van Duijn CM, Penninx BW, Smith GD, Kaprio J, Samani NJ, Gieger C, Peters A, Wichmann HE, Boomsma DI, de Geus EJ, Tuomi T, Power C, Hammond CJ, Spector TD, Lind L, Orho-Melander M, Palmer CN, Morris AD, Groop L, Järvelin MR, Salomaa V, Vartiainen E, Hofman A, Ripatti S, Metspalu A, Thorsteinsdottir U, Stefansson K, Pedersen NL, McCarthy MI, Ingelsson E, and Prokopenko I
Subjects: Alpha-Ketoglutarate-Dependent Dioxygenase FTO, Body Mass Index, Case-Control Studies, Confounding Factors, Epidemiologic, Genetic Association Studies, Humans, Meta-Analysis as Topic, Polymorphism, Single Nucleotide genetics, Proteins genetics, Adiposity genetics, Cardiovascular Diseases genetics, Cardiovascular Diseases metabolism, Mendelian Randomization Analysis, Quantitative Trait, Heritable
Abstract: Background: The association between adiposity and cardiometabolic traits is well known from epidemiological studies. Whilst the causal relationship is clear for some of these traits, for others it is not. We aimed to determine whether adiposity is causally related to various cardiometabolic traits using the Mendelian randomization approach., Methods and Findings: We used the adiposity-associated variant rs9939609 at the FTO locus as an instrumental variable (IV) for body mass index (BMI) in a Mendelian randomization design. Thirty-six population-based studies of individuals of European descent contributed to the analyses. Age- and sex-adjusted regression models were fitted to test for association between (i) rs9939609 and BMI (n = 198,502), (ii) rs9939609 and 24 traits, and (iii) BMI and 24 traits. The causal effect of BMI on the outcome measures was quantified by IV estimators. The estimators were compared to the BMI-trait associations derived from the same individuals. In the IV analysis, we demonstrated novel evidence for a causal relationship between adiposity and incident heart failure (hazard ratio, 1.19 per BMI-unit increase; 95% CI, 1.03-1.39) and replicated earlier reports of a causal association with type 2 diabetes, metabolic syndrome, dyslipidemia, and hypertension (odds ratio for IV estimator, 1.1-1.4; all p < 0.05). For quantitative traits, our results provide novel evidence for a causal effect of adiposity on the liver enzymes alanine aminotransferase and gamma-glutamyl transferase and confirm previous reports of a causal effect of adiposity on systolic and diastolic blood pressure, fasting insulin, 2-h post-load glucose from the oral glucose tolerance test, C-reactive protein, triglycerides, and high-density lipoprotein cholesterol levels (all p < 0.05). The estimated causal effects were in agreement with traditional observational measures in all instances except for type 2 diabetes, where the causal estimate was larger than the observational estimate (p = 0.001)., Conclusions: We provide novel evidence for a causal relationship between adiposity and heart failure as well as between adiposity and increased liver enzymes.
Published: 2013
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35. Hierarchical clustering analysis of blood plasma lipidomics profiles from mono- and dizygotic twin families.

Author: Draisma HH, Reijmers TH, Meulman JJ, van der Greef J, Hankemeier T, and Boomsma DI
Subjects: Adolescent, C-Reactive Protein genetics, C-Reactive Protein metabolism, Female, Gene-Environment Interaction, Humans, Male, Models, Genetic, Netherlands, Pedigree, Cluster Analysis, Lipids blood, Lipids genetics, Twins, Dizygotic genetics, Twins, Monozygotic genetics
Abstract: Twin and family studies are typically used to elucidate the relative contribution of genetic and environmental variation to phenotypic variation. Here, we apply a quantitative genetic method based on hierarchical clustering, to blood plasma lipidomics data obtained in a healthy cohort consisting of 37 monozygotic and 28 dizygotic twin pairs, and 52 of their biological nontwin siblings. Such data are informative of the concentrations of a wide range of lipids in the studied blood samples. An important advantage of hierarchical clustering is that it can be applied to a high-dimensional 'omics' type data, whereas the use of many other quantitative genetic methods for analysis of such data is hampered by the large number of correlated variables. For this study we combined two lipidomics data sets, originating from two different measurement blocks, which we corrected for block effects by 'quantile equating'. In the analysis of the combined data, average similarities of lipidomics profiles were highest between monozygotic (MZ) cotwins, and became progressively lower between dizygotic (DZ) cotwins, among sex-matched nontwin siblings and among sex-matched unrelated participants, respectively. Our results suggest that (1) shared genetic background, shared environment, and similar age contribute to similarities in blood plasma lipidomics profiles among individuals; and (2) that the power of quantitative genetic analyses is enhanced by quantile equating and combination of data sets obtained in different measurement blocks.
Published: 2013
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36. Normal limits of the spatial QRS-T angle and ventricular gradient in 12-lead electrocardiograms of young adults: dependence on sex and heart rate.

Author: Scherptong RW, Henkens IR, Man SC, Le Cessie S, Vliegen HW, Draisma HH, Maan AC, Schalij MJ, and Swenne CA
Subjects: Adult, Female, Humans, Male, Netherlands epidemiology, Reference Values, Reproducibility of Results, Sensitivity and Specificity, Sex Factors, Young Adult, Diagnosis, Computer-Assisted methods, Diagnosis, Computer-Assisted statistics & numerical data, Electrocardiography methods, Electrocardiography statistics & numerical data, Heart Rate physiology
Abstract: Background and Purpose: Normal limits of the spatial QRS-T angle and spatial ventricular gradient (SVG) are only available from Frank vectorcardiograms (VCGs) of male subjects. We determined normal limits for these variables derived from standard 12-lead electrocardiograms (ECGs) of 660 male and female students aged 18 to 29 years., Methods: A computer algorithm was used that constructed approximated VCG leads by inverse Dower matrix transformation of the 12-lead ECG and subsequently calculated the spatial QRS-T angle, SVG magnitude, and orientation., Results: In female subjects, the QRS-T angle was more acute (females, 66 degrees +/- 23 degrees; normal, 20 degrees-116 degrees; males, 80 degrees +/- 24 degrees; normal, 30 degrees-130 degrees; P < .001), and the SVG magnitude was smaller (females, 81 +/- 23 mV x ms; normal, 39-143 mV x ms; males, 110 +/- 29 mV x ms; normal, 59-187 mV x ms; P < .001) than in male subjects. The male SVG magnitude in our study was larger than that computed in Frank VCGs (79 +/- 28 mV.ms; P < .001)., Conclusions: The spatial QRS-T angle and SVG depend strongly on sex. Furthermore, normal limits of SVG derived from Frank VCGs differ markedly from those derived from VCGs synthesized from the standard ECG. As nowadays, VCGs are usually synthesized from the 12-lead ECG; normal limits derived from the standard ECG should preferably be used.
Published: 2008
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37. Reconstruction of standard 12-lead electrocardiograms from 12-lead electrocardiograms recorded with the Mason-Likar electrode configuration.

Author: Man SC, Maan AC, Kim E, Draisma HH, Schalij MJ, van der Wall EE, and Swenne CA
Subjects: Adult, Aged, Body Surface Potential Mapping instrumentation, Body Surface Potential Mapping standards, Electrocardiography instrumentation, Electrocardiography standards, Electrodes standards, Female, Humans, Male, Middle Aged, Reproducibility of Results, Sensitivity and Specificity, Algorithms, Body Surface Potential Mapping methods, Diagnosis, Computer-Assisted methods, Electrocardiography methods
Abstract: Electrocardiograms (ECGs) made with Mason-Likar electrode configuration (ML-ECGs) show well-known differences from standard 12-lead ECGs (Std-ECGs). We recorded, simultaneously, Std-ECGs and ML-ECGs in 180 subjects. Using these ECGs, 8 x 8 individual and general conversion matrices were created by linear regression, and standard ECGs were reconstructed from ML-ECGs using these matrices. The performance of the matrices was assessed by the root mean square differences between the original Std-ECGs and the reconstructed standard ECGs, by the differences in major ECG parameters, and by comparison of computer-generated diagnostic statements. As a result, we conclude that, based on the root mean square differences, reconstructions with 8 x 8 individual matrices perform significantly better than reconstructions with the group matrix and perform equally well with respect to the calculation of major electrocardiographic parameters, which gives an improved reliability of the QRS frontal axis and the maximal QRS and T amplitudes. Both types of matrices were able to reverse the underdiagnosis of inferior myocardial infarctions and the erroneous statements about the QRS frontal axis that arose in the ECGs that were made by using the Mason-Likar electrode positions.
Published: 2008
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38. Pulmonary valve replacement in tetralogy of Fallot improves the repolarization.

Author: Hooft van Huysduynen B, Henkens IR, Swenne CA, Oosterhof T, Draisma HH, Maan AC, Hazekamp MG, de Roos A, Schalij MJ, van der Wall EE, and Vliegen HW
Subjects: Adult, Electrocardiography, Female, Humans, Magnetic Resonance Imaging, Male, Pulmonary Valve Insufficiency diagnosis, Pulmonary Valve Insufficiency physiopathology, Pulmonary Valve Insufficiency surgery, Tachycardia, Ventricular prevention & control, Tetralogy of Fallot diagnosis, Tetralogy of Fallot physiopathology, Treatment Outcome, Heart Conduction System physiopathology, Heart Valve Prosthesis Implantation methods, Pulmonary Valve surgery, Tetralogy of Fallot surgery
Abstract: Objective: To assess the effect of pulmonary valve replacement (PVR) on the repolarization of patients with tetralogy of Fallot., Background: Pulmonary valve regurgitation may cause right ventricular failure in adult patients with Fallot's tetralogy. In these patients, prolonged depolarization and disturbed repolarization are associated with ventricular arrhythmias and sudden cardiac death., Methods: Thirty Fallot patients (age 32+/-9 years, 19 male) eligible for PVR were studied with cardiac magnetic resonance imaging (CMR) before and 6 months after PVR. Electrocardiograms obtained during initial and follow-up CMR were analyzed and occurrence of ventricular arrhythmias was studied., Results: Right ventricular end-diastolic volume (RV EDV) decreased from 322+/-87 to 215+/-57 ml after PVR (P<0.0001). The spatial QRS-T angle normalized from 117+/-34 to 100+/-35 degrees , P=0.0004 (normal angle <105 degrees). QT dispersion and T-wave complexity did not change significantly. T-wave amplitude decreased from 376+/-121 to 329+/-100 microV (P=0.01). T-wave area decreased from 43+/-15 to 38+/-13 microV s (P=0.02). Decreases in T-wave amplitude and area were most prominent in the right precordial leads overlying the RV. Three patients had sustained ventricular arrhythmias and one patient died suddenly. These patients had a QRS duration >160 ms. No severe ventricular arrhythmias were found in patients with a RV EDV <220 ml, QRS-T angle <100 degrees , QT dispersion <60 ms or T-wave complexity <0.30., Conclusion: Normal repolarization indices may be associated with the absence of severe ventricular arrhythmias. PVR in Fallot patients with dilated right ventricles has a beneficial effect on electrocardiographic indices of repolarization heterogeneity.
Published: 2008
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39. Similarities and differences in lipidomics profiles among healthy monozygotic twin pairs.

Author: Draisma HH, Reijmers TH, Bobeldijk-Pastorova I, Meulman JJ, Estourgie-Van Burk GF, Bartels M, Ramaker R, van der Greef J, Boomsma DI, and Hankemeier T
Subjects: Adolescent, C-Reactive Protein metabolism, Chromatography, Liquid, Female, Humans, Male, Spectrometry, Mass, Electrospray Ionization, Lipids blood, Twins, Monozygotic blood, Twins, Monozygotic genetics
Abstract: Differences in genetic background and/or environmental exposure among individuals are expected to give rise to differences in measurable characteristics, or phenotypes. Consequently, genetic resemblance and similarities in environment should manifest as similarities in phenotypes. The metabolome reflects many of the system properties, and is therefore an important part of the phenotype. Nevertheless, it has not yet been examined to what extent individuals sharing part of their genome and/or environment indeed have similar metabolomes. Here we present the results of hierarchical clustering of blood plasma lipid profile data obtained by liquid chromatography-mass spectrometry from 23 healthy, 18-year-old twin pairs, of which 21 pairs were monozygotic, and 8 of their siblings. For 13 monozygotic twin pairs, within-pair similarities in relative concentrations of the detected lipids were indeed larger than the similarities with any other study participant. We demonstrate such high coclustering to be unexpected on basis of chance. The similarities between dizygotic twins and between nontwin siblings, as well as between nonfamilial participants, were less pronounced. In a number of twin pairs, within-pair dissimilarity of lipid profiles positively correlated with increased blood plasma concentrations of C-reactive protein in one twin. In conclusion, this study demonstrates that in healthy individuals, the individual genetic background contributes to the blood plasma lipid profile. Furthermore, lipid profiling may prove useful in monitoring health status, for example, in the context of personalized medicine.
Published: 2008
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40. Early changes in rat hearts with developing pulmonary arterial hypertension can be detected with three-dimensional electrocardiography.

Author: Henkens IR, Mouchaers KT, Vliegen HW, van der Laarse WJ, Swenne CA, Maan AC, Draisma HH, Schalij I, van der Wall EE, Schalij MJ, and Vonk-Noordegraaf A
Subjects: Action Potentials, Animals, Disease Models, Animal, Hypertension, Pulmonary complications, Hypertension, Pulmonary diagnostic imaging, Hypertrophy, Right Ventricular diagnostic imaging, Hypertrophy, Right Ventricular physiopathology, Male, Monocrotaline, Myocardial Contraction, Rats, Rats, Wistar, Severity of Illness Index, Systole, Time Factors, Ultrasonography, Ventricular Pressure, Body Surface Potential Mapping methods, Hypertension, Pulmonary physiopathology, Hypertrophy, Right Ventricular etiology, Ventricular Function, Right
Abstract: The study aim was to assess three-dimensional electrocardiogram (ECG) changes during development of pulmonary arterial hypertension (PAH). PAH was induced in male Wistar rats (n = 23) using monocrotaline (MCT; 40 mg/kg sc). Untreated healthy rats served as controls (n = 5). ECGs were recorded with an orthogonal three-lead system on days 0, 14, and 25 and analyzed with dedicated computer software. In addition, left ventricular (LV)-to-right ventricular (RV) fractional shortening ratio was determined using echocardiography. Invasively measured RV systolic pressure was 49 (SD 10) mmHg on day 14 and 64 (SD 10) mmHg on day 25 vs. 25 (SD 2) mmHg in controls (both P < 0.001). Baseline ECGs of controls and MCT rats were similar, and ECGs of controls did not change over time. In MCT rats, ECG changes were already present on day 14 but more explicit on day 25: increased RV electromotive forces decreased mean QRS-vector magnitude and changed QRS-axis orientation. Important changes in action potential duration distribution and repolarization sequence were reflected by a decreased spatial ventricular gradient magnitude and increased QRS-T spatial angle. On day 25, LV-to-RV fractional shortening ratio was increased, and RV hypertrophy was found, but not on day 14. In conclusion, developing PAH is characterized by early ECG changes preceding RV hypertrophy, whereas severe PAH is marked by profound ECG changes associated with anatomical and functional changes in the RV. Three-dimensional ECG analysis appears to be very sensitive to early changes in RV afterload.
Published: 2007
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41. Biventricular pacing and transmural dispersion of the repolarization.

Author: Swenne CA, van Huysduynen BH, Bax JJ, Bleeker GB, Draisma HH, van Erven L, Molhoek SG, van de Vooren H, van der Wall EE, and Schalij MJ
Subjects: Arrhythmias, Cardiac etiology, Arrhythmias, Cardiac physiopathology, Cardiac Output, Low physiopathology, Cardiac Output, Low therapy, Electrocardiography, Electrodes, Heart Conduction System physiopathology, Humans, Action Potentials physiology, Heart Ventricles physiopathology, Pacemaker, Artificial adverse effects
Published: 2007
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42. Elucidation of the spatial ventricular gradient and its link with dispersion of repolarization.

Author: Draisma HH, Schalij MJ, van der Wall EE, and Swenne CA
Subjects: Heart Conduction System physiology, History, 20th Century, Humans, Ventricular Function, Electrocardiography history
Abstract: The ventricular gradient, a notion conceived by Wilson et al during the 1930s, has contributed considerably to a better understanding of the ECG manifestations of the cardiac repolarization process. The power of the ventricular gradient is its ability to assess the primary factors that contribute to the T wave (i.e., heterogeneity of action potential morphology throughout the ventricles) in the presence of secondary factors contributing to the T wave (i.e., heterogeneity in ventricular depolarization instants). Where T-wave morphology is an ECG expression of heterogeneity of the repolarization, the ventricular gradient discriminates between primary or secondary causes of such heterogeneity. Besides the spatial ventricular gradient (Burger's three-dimensional elaboration of Wilson's two-dimensional concept), body surface mapping of local components of the ventricular gradient has emerged as a technique for assessing local ventricular action potential duration heterogeneity. The latter is believed to contribute to localization of arrhythmogenic areas in the heart. The spatial ventricular gradient, which can be computed on the basis of a regular routine ECG and does not require body surface mapping, aims to assess the overall heterogeneity of ventricular action potential morphology. This review addresses the nature and diagnostic potential of the spatial ventricular gradient. The main focus is the role of the spatial ventricular gradient in ECG assessment of dispersion of repolarization, a key factor in arrhythmogeneity.
Published: 2006
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43. Dispersion of repolarization in cardiac resynchronization therapy.

Author: van Huysduynen BH, Swenne CA, Bax JJ, Bleeker GB, Draisma HH, van Erven L, Molhoek SG, van de Vooren H, van der Wall EE, and Schalij MJ
Subjects: Aged, Body Surface Potential Mapping, Computer Simulation, Electrocardiography, Female, Humans, Male, Models, Cardiovascular, Pacemaker, Artificial, Cardiac Pacing, Artificial, Heart Failure therapy, Ventricular Dysfunction therapy
Abstract: Background: Proarrhythmic effects of cardiac resynchronization therapy (CRT) as a result of increased transmural dispersion of repolarization (TDR) induced by left ventricular (LV) epicardial pacing in a subset of vulnerable patients have been reported. The possibility of identifying these patients by ECG repolarization indices has been suggested., Objectives: The purpose of this study was to test whether repolarization indices on the ECG can be used to measure dispersion of repolarization during pacing., Methods: CRT devices of 28 heart failure patients were switched among biventricular, LV, and right ventricular (RV) pacing. ECG indices proposed to measure dispersion of repolarization were calculated. The effects of CRT on repolarization were simulated in ECGSIM, a mathematical model of electrocardiogram genesis. TDR was calculated as the difference in repolarization time between the epicardial and endocardial nodes of the heart model., Patients: The interval from the apex to the end of the T wave was shorter during biventricular pacing (102 +/- 18 ms) and LV pacing (106 +/- 21 ms) than during RV pacing (117 +/- 22 ms, P < or =.005). T-wave amplitude and area were low during biventricular pacing (287 +/- 125 microV and 56 +/- 22 microV.s, respectively, P = .0006 vs RV pacing). T-wave complexity was high during biventricular pacing (0.42 +/- 0.26, P = .004 vs RV pacing). Simulations: Repolarization patterns were highly similar to the preceding depolarization patterns. The repolarization patterns of different pacing modes explained the observed magnitudes of the ECG repolarization indices. Average and local TDR were not different between pacing modes., Conclusion: In patients treated with CRT, ECG repolarization indices are related to pacing-induced activation sequences rather than transmural dispersion. TDR during biventricular and LV pacing is not larger than TDR during conventional RV endocardial pacing.
Published: 2005
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44. Validation of ECG indices of ventricular repolarization heterogeneity: a computer simulation study.

Author: Van Huysduynen BH, Swenne CA, Draisma HH, Antoni ML, Van De Vooren H, Van Der Wall EE, and Schalij MJ
Subjects: Computer Simulation, Heart Conduction System physiopathology, Humans, Long QT Syndrome physiopathology, Models, Cardiovascular, Electrocardiography, Heart Conduction System physiology, Ventricular Function
Abstract: Introduction: Repolarization heterogeneity (RH) is functionally linked to dispersion in refractoriness and to arrhythmogenicity. In the current study, we validate several proposed electrocardiogram (ECG) indices for RH: T-wave amplitude, -area, -complexity, and -symmetry ratio, QT dispersion, and the Tapex-end interval (the latter being an index of transmural dispersion of the repolarization (TDR))., Methods and Results: We used ECGSIM, a mathematical simulation model of ECG genesis in a human thorax, and varied global RH by increasing the standard deviation (SD) of the repolarization instants from 20 (default) to 70 msec in steps of 10 msec. T-wave amplitude, -area, -symmetry, and Tapex-end depended linearly on SD. T-wave amplitude increased from 275 +/- 173 to 881 +/- 456 muV, T-wave area from 34 x 10(3)+/- 21 x 10(3) to 141 x 10(3)+/- 58 x 10(3)muV msec, T-wave symmetry decreased from 1.55 +/- 0.11 to 1.06 +/- 0.23, and Tapex-end increased from 84 +/- 17 to 171 +/- 52 msec. T-wave complexity increased initially but saturated at SD = 50 msec. QT dispersion increased modestly until SD = 40 msec and more rapidly for higher values of SD. TDR increased linearly with SD. Tapex-end increased linearly with TDR, but overestimated it., Conclusion: T-wave complexity did not discriminate between differences in larger RH values. QT dispersion had low sensitivity in the transitional zone between normal and abnormal RH. In conclusion, T-wave amplitude, -area, -symmetry, and, with some limitations, Tapex-end and T-wave complexity reliably reflect changes in RH.
Published: 2005
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