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1. Whole-genome sequencing in 333,100 individuals reveals rare non-coding single variant and aggregate associations with height

Author

Gareth Hawkes, Robin N. Beaumont, Zilin Li, Ravi Mandla, Xihao Li, Christine M. Albert, Donna K. Arnett, Allison E. Ashley-Koch, Aneel A. Ashrani, Kathleen C. Barnes, Eric Boerwinkle, Jennifer A. Brody, April P. Carson, Nathalie Chami, Yii-Der Ida Chen, Mina K. Chung, Joanne E. Curran, Dawood Darbar, Patrick T. Ellinor, Myrian Fornage, Victor R. Gordeuk, Xiuqing Guo, Jiang He, Chii-Min Hwu, Rita R. Kalyani, Robert Kaplan, Sharon L. R. Kardia, Charles Kooperberg, Ruth J. F. Loos, Steven A. Lubitz, Ryan L. Minster, Take Naseri, Satupa’itea Viali, Braxton D. Mitchell, Joanne M. Murabito, Nicholette D. Palmer, Bruce M. Psaty, Susan Redline, M. Benjamin Shoemaker, Edwin K. Silverman, Marilyn J. Telen, Scott T. Weiss, Lisa R. Yanek, Hufeng Zhou, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, Ching-Ti Liu, Kari E. North, Anne E. Justice, Jonathan M. Locke, Nick Owens, Anna Murray, Kashyap Patel, Timothy M. Frayling, Caroline F. Wright, Andrew R. Wood, Xihong Lin, Alisa Manning, and Michael N. Weedon

Subjects
Science
Abstract

Abstract The role of rare non-coding variation in complex human phenotypes is still largely unknown. To elucidate the impact of rare variants in regulatory elements, we performed a whole-genome sequencing association analysis for height using 333,100 individuals from three datasets: UK Biobank (N = 200,003), TOPMed (N = 87,652) and All of Us (N = 45,445). We performed rare (

Published
2024
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2. Bioinformatics pipeline for the systematic mining genomic and proteomic variation linked to rare diseases: The example of monogenic diabetes.

Author

Ksenia G Kuznetsova, Jakub Vašíček, Dafni Skiadopoulou, Janne Molnes, Miriam Udler, Stefan Johansson, Pål Rasmus Njølstad, Alisa Manning, and Marc Vaudel

Subjects
Medicine, Science
Abstract

Monogenic diabetes is characterized as a group of diseases caused by rare variants in single genes. Like for other rare diseases, multiple genes have been linked to monogenic diabetes with different measures of pathogenicity, but the information on the genes and variants is not unified among different resources, making it challenging to process them informatically. We have developed an automated pipeline for collecting and harmonizing data on genetic variants linked to monogenic diabetes. Furthermore, we have translated variant genetic sequences into protein sequences accounting for all protein isoforms and their variants. This allows researchers to consolidate information on variant genes and proteins linked to monogenic diabetes and facilitates their study using proteomics or structural biology. Our open and flexible implementation using Jupyter notebooks enables tailoring and modifying the pipeline and its application to other rare diseases.

Published
2024
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3. Building a collaborative cloud platform to accelerate heart, lung, blood, and sleep research.

Author

Stan Ahalt, Paul Avillach, Rebecca R. Boyles, Kira Bradford, Steven Cox 0001, Brandi Davis-Dusenbery, Robert L. Grossman, Ashok K. Krishnamurthy 0001, Alisa Manning, Benedict Paten, Anthony Philippakis, Ingrid Borecki, Shu Hui Chen, Jon Kaltman, Sweta Ladwa, Chip Schwartz, Alastair Thomson, Sarah Davis, Alison Leaf, Jessica Lyons, Elizabeth Sheets, Joshua C. Bis, Matthew P. Conomos, Alessandro Culotti, Thomas N. Desain, Jack DiGiovanna, Milan Domazet, Stephanie M. Gogarten, Alba Gutiérrez-Sacristán, Tim Harris 0003, Benjamin D. Heavner, Deepti Jain, Brian O'Connor, Kevin Osborn, Danielle Pillion, Jacob Pleiness, Ken Rice, Garrett Rupp, Arnaud Serret-Larmande, Albert Smith, Jason P. Stedman, Adrienne Stilp, Teresa Barsanti, John B. Cheadle, Christopher Erdmann, Brandy Farlow, Allie Gartland-Gray, Julie Hayes, Hannah Hiles, Paul Kerr, Chris Lenhardt 0001, Tom Madden, Joanna O. Mieczkowska, Amanda Miller, Patrick Patton, Marcie Rathbun, Stephanie Suber, and Joe Asare

Published
2023
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4. Epigenome-wide DNA methylation association study of circulating IgE levels identifies novel targets for asthmaResearch in context

Author

Kathryn Recto, Priyadarshini Kachroo, Tianxiao Huan, David Van Den Berg, Gha Young Lee, Helena Bui, Dong Heon Lee, Jessica Gereige, Chen Yao, Shih-Jen Hwang, Roby Joehanes, Scott T. Weiss, George T. O’Connor, Daniel Levy, Dawn L. DeMeo, Namiko Abe, Gonçalo Abecasis, Francois Aguet, Christine Albert, Laura Almasy, Alvaro Alonso, Seth Ament, Peter Anderson, Pramod Anugu, Deborah Applebaum-Bowden, Kristin Ardlie, Dan Arking, Donna K. Arnett, Allison Ashley-Koch, Stella Aslibekyan, Tim Assimes, Paul Auer, Dimitrios Avramopoulos, Najib Ayas, Adithya Balasubramanian, John Barnard, Kathleen Barnes, R. Graham Barr, Emily Barron-Casella, Lucas Barwick, Terri Beaty, Gerald Beck, Diane Becker, Lewis Becker, Rebecca Beer, Amber Beitelshees, Emelia Benjamin, Takis Benos, Marcos Bezerra, Larry Bielak, Joshua Bis, Thomas Blackwell, John Blangero, Nathan Blue, Eric Boerwinkle, Donald W. Bowden, Russell Bowler, Jennifer Brody, Ulrich Broeckel, Jai Broome, Deborah Brown, Karen Bunting, Esteban Burchard, Carlos Bustamante, Erin Buth, Brian Cade, Jonathan Cardwell, Vincent Carey, Julie Carrier, April P. Carson, Cara Carty, Richard Casaburi, Juan P. Casas Romero, James Casella, Peter Castaldi, Mark Chaffin, Christy Chang, Yi-Cheng Chang, Daniel Chasman, Sameer Chavan, Bo-Juen Chen, Wei-Min Chen, Yii-Der Ida Chen, Michael Cho, Seung Hoan Choi, Lee-Ming Chuang, Mina Chung, Ren-Hua Chung, Clary Clish, Suzy Comhair, Matthew Conomos, Elaine Cornell, Adolfo Correa, Carolyn Crandall, James Crapo, L. Adrienne Cupples, Joanne Curran, Jeffrey Curtis, Brian Custer, Coleen Damcott, Dawood Darbar, Sean David, Colleen Davis, Michelle Daya, Mariza de Andrade, Lisa de las Fuentes, Paul de Vries, Michael DeBaun, Ranjan Deka, Dawn DeMeo, Scott Devine, Huyen Dinh, Harsha Doddapaneni, Qing Duan, Shannon Dugan-Perez, Ravi Duggirala, Jon Peter Durda, Susan K. Dutcher, Charles Eaton, Lynette Ekunwe, Adel El Boueiz, Patrick Ellinor, Leslie Emery, Serpil Erzurum, Charles Farber, Jesse Farek, Tasha Fingerlin, Matthew Flickinger, Myriam Fornage, Nora Franceschini, Chris Frazar, Mao Fu, Stephanie M. Fullerton, Lucinda Fulton, Stacey Gabriel, Weiniu Gan, Shanshan Gao, Yan Gao, Margery Gass, Heather Geiger, Bruce Gelb, Mark Geraci, Soren Germer, Robert Gerszten, Auyon Ghosh, Richard Gibbs, Chris Gignoux, Mark Gladwin, David Glahn, Stephanie Gogarten, Da-Wei Gong, Harald Goring, Sharon Graw, Kathryn J. Gray, Daniel Grine, Colin Gross, C. Charles Gu, Yue Guan, Xiuqing Guo, Namrata Gupta, Jeff Haessler, Michael Hall, Yi Han, Patrick Hanly, Daniel Harris, Nicola L. Hawley, Jiang He, Ben Heavner, Susan Heckbert, Ryan Hernandez, David Herrington, Craig Hersh, Bertha Hidalgo, James Hixson, Brian Hobbs, John Hokanson, Elliott Hong, Karin Hoth, Chao (Agnes) Hsiung, Jianhong Hu, Yi-Jen Hung, Haley Huston, Chii Min Hwu, Marguerite Ryan Irvin, Rebecca Jackson, Deepti Jain, Cashell Jaquish, Jill Johnsen, Andrew Johnson, Craig Johnson, Rich Johnston, Kimberly Jones, Hyun Min Kang, Robert Kaplan, Sharon Kardia, Shannon Kelly, Eimear Kenny, Michael Kessler, Alyna Khan, Ziad Khan, Wonji Kim, John Kimoff, Greg Kinney, Barbara Konkle, Charles Kooperberg, Holly Kramer, Christoph Lange, Ethan Lange, Leslie Lange, Cathy Laurie, Cecelia Laurie, Meryl LeBoff, Jiwon Lee, Sandra Lee, Wen-Jane Lee, Jonathon LeFaive, David Levine, Joshua Lewis, Xiaohui Li, Yun Li, Henry Lin, Honghuang Lin, Xihong Lin, Simin Liu, Yongmei Liu, Yu Liu, Ruth J.F. Loos, Steven Lubitz, Kathryn Lunetta, James Luo, Ulysses Magalang, Michael Mahaney, Barry Make, Ani Manichaikul, Alisa Manning, JoAnn Manson, Lisa Martin, Melissa Marton, Susan Mathai, Rasika Mathias, Susanne May, Patrick McArdle, Merry-Lynn McDonald, Sean McFarland, Stephen McGarvey, Daniel McGoldrick, Caitlin McHugh, Becky McNeil, Hao Mei, James Meigs, Vipin Menon, Luisa Mestroni, Ginger Metcalf, Deborah A. Meyers, Emmanuel Mignot, Julie Mikulla, Nancy Min, Mollie Minear, Ryan L. Minster, Braxton D. Mitchell, Matt Moll, Zeineen Momin, May E. Montasser, Courtney Montgomery, Donna Muzny, Josyf C. Mychaleckyj, Girish Nadkarni, Rakhi Naik, Take Naseri, Pradeep Natarajan, Sergei Nekhai, Sarah C. Nelson, Bonnie Neltner, Caitlin Nessner, Deborah Nickerson, Osuji Nkechinyere, Kari North, Jeff O'Connell, Tim O'Connor, Heather Ochs-Balcom, Geoffrey Okwuonu, Allan Pack, David T. Paik, Nicholette Palmer, James Pankow, George Papanicolaou, Cora Parker, Gina Peloso, Juan Manuel Peralta, Marco Perez, James Perry, Ulrike Peters, Patricia Peyser, Lawrence S. Phillips, Jacob Pleiness, Toni Pollin, Wendy Post, Julia Powers Becker, Meher Preethi Boorgula, Michael Preuss, Bruce Psaty, Pankaj Qasba, Dandi Qiao, Zhaohui Qin, Nicholas Rafaels, Laura Raffield, Mahitha Rajendran, Vasan S. Ramachandran, D.C. Rao, Laura Rasmussen-Torvik, Aakrosh Ratan, Susan Redline, Robert Reed, Catherine Reeves, Elizabeth Regan, Alex Reiner, Muagututi‘a Sefuiva Reupena, Ken Rice, Stephen Rich, Rebecca Robillard, Nicolas Robine, Dan Roden, Carolina Roselli, Jerome Rotter, Ingo Ruczinski, Alexi Runnels, Pamela Russell, Sarah Ruuska, Kathleen Ryan, Ester Cerdeira Sabino, Danish Saleheen, Shabnam Salimi, Sejal Salvi, Steven Salzberg, Kevin Sandow, Vijay G. Sankaran, Jireh Santibanez, Karen Schwander, David Schwartz, Frank Sciurba, Christine Seidman, Jonathan Seidman, Frédéric Sériès, Vivien Sheehan, Stephanie L. Sherman, Amol Shetty, Aniket Shetty, Wayne Hui-Heng Sheu, M. Benjamin Shoemaker, Brian Silver, Edwin Silverman, Robert Skomro, Albert Vernon Smith, Jennifer Smith, Josh Smith, Nicholas Smith, Tanja Smith, Sylvia Smoller, Beverly Snively, Michael Snyder, Tamar Sofer, Nona Sotoodehnia, Adrienne M. Stilp, Garrett Storm, Elizabeth Streeten, Jessica Lasky Su, Yun Ju Sung, Jody Sylvia, Adam Szpiro, Daniel Taliun, Hua Tang, Margaret Taub, Kent Taylor, Matthew Taylor, Simeon Taylor, Marilyn Telen, Timothy A. Thornton, Machiko Threlkeld, Lesley Tinker, David Tirschwell, Sarah Tishkoff, Hemant Tiwari, Catherine Tong, Russell Tracy, Michael Tsai, Dhananjay Vaidya, Peter VandeHaar, Scott Vrieze, Tarik Walker, Robert Wallace, Avram Walts, Fei Fei Wang, Heming Wang, Jiongming Wang, Karol Watson, Jennifer Watt, Daniel E. Weeks, Joshua Weinstock, Bruce Weir, Lu-Chen Weng, Jennifer Wessel, Cristen Willer, Kayleen Williams, L. Keoki Williams, Scott Williams, Carla Wilson, James Wilson, Lara Winterkorn, Quenna Wong, Baojun Wu, Joseph Wu, Huichun Xu, Lisa Yanek, Ivana Yang, Ketian Yu, Seyedeh Maryam Zekavat, Yingze Zhang, Snow Xueyan Zhao, Wei Zhao, Xiaofeng Zhu, Elad Ziv, Michael Zody, and Sebastian Zoellner

Subjects
EWAS, DNA methylation, IgE, Asthma, RNA-Sequencing, Mendelian randomization, Medicine, Medicine (General), R5-920
Abstract

Summary: Background: Identifying novel epigenetic signatures associated with serum immunoglobulin E (IgE) may improve our understanding of molecular mechanisms underlying asthma and IgE-mediated diseases. Methods: We performed an epigenome-wide association study using whole blood from Framingham Heart Study (FHS; n = 3,471, 46% females) participants and validated results using the Childhood Asthma Management Program (CAMP; n = 674, 39% females) and the Genetic Epidemiology of Asthma in Costa Rica Study (CRA; n = 787, 41% females). Using the closest gene to each IgE-associated CpG, we highlighted biologically plausible pathways underlying IgE regulation and analyzed the transcription patterns linked to IgE-associated CpGs (expression quantitative trait methylation loci; eQTMs). Using prior UK Biobank summary data from genome-wide association studies of asthma and allergy, we performed Mendelian randomization (MR) for causal inference testing using the IgE-associated CpGs from FHS with methylation quantitative trait loci (mQTLs) as instrumental variables. Findings: We identified 490 statistically significant differentially methylated CpGs associated with IgE in FHS, of which 193 (39.3%) replicated in CAMP and CRA (FDR < 0.05). Gene ontology analysis revealed enrichment in pathways related to transcription factor binding, asthma, and other immunological processes. eQTM analysis identified 124 cis-eQTMs for 106 expressed genes (FDR < 0.05). MR in combination with drug-target analysis revealed CTSB and USP20 as putatively causal regulators of IgE levels (Bonferroni adjusted P

Published
2023
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6. Deriving stratified effects from joint models investigating gene-environment interactions

Author

Vincent Laville, Timothy Majarian, Paul S. de Vries, Amy R. Bentley, Mary F. Feitosa, Yun J. Sung, D. C. Rao, Alisa Manning, Hugues Aschard, and on behalf of the CHARGE Gene-Lifestyle Interactions Working Group

Subjects
Gene-environment interaction, Binary exposure, Stratified analysis, Summary statistics, Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5
Abstract

Abstract Background Models including an interaction term and performing a joint test of SNP and/or interaction effect are often used to discover Gene-Environment (GxE) interactions. When the environmental exposure is a binary variable, analyses from exposure-stratified models which consist of estimating genetic effect in unexposed and exposed individuals separately can be of interest. In large-scale consortia focusing on GxE interactions in which only the joint test has been performed, it may be challenging to get summary statistics from both exposure-stratified and marginal (i.e not accounting for interaction) models. Results In this work, we developed a simple framework to estimate summary statistics in each stratum of a binary exposure and in the marginal model using summary statistics from the “joint” model. We performed simulation studies to assess our estimators’ accuracy and examined potential sources of bias, such as correlation between genotype and exposure and differing phenotypic variances within exposure strata. Results from these simulations highlight the high theoretical accuracy of our estimators and yield insights into the impact of potential sources of bias. We then applied our methods to real data and demonstrate our estimators’ retained accuracy after filtering SNPs by sample size to mitigate potential bias. Conclusions These analyses demonstrated the accuracy of our method in estimating both stratified and marginal summary statistics from a joint model of gene-environment interaction. In addition to facilitating the interpretation of GxE screenings, this work could be used to guide further functional analyses. We provide a user-friendly Python script to apply this strategy to real datasets. The Python script and documentation are available at https://gitlab.pasteur.fr/statistical-genetics/j2s .

Published
2020
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8. Multi-ancestry sleep-by-SNP interaction analysis in 126,926 individuals reveals lipid loci stratified by sleep duration

Author

Raymond Noordam, Maxime M. Bos, Heming Wang, Thomas W. Winkler, Amy R. Bentley, Tuomas O. Kilpeläinen, Paul S. de Vries, Yun Ju Sung, Karen Schwander, Brian E. Cade, Alisa Manning, Hugues Aschard, Michael R. Brown, Han Chen, Nora Franceschini, Solomon K. Musani, Melissa Richard, Dina Vojinovic, Stella Aslibekyan, Traci M. Bartz, Lisa de las Fuentes, Mary Feitosa, Andrea R. Horimoto, Marjan Ilkov, Minjung Kho, Aldi Kraja, Changwei Li, Elise Lim, Yongmei Liu, Dennis O. Mook-Kanamori, Tuomo Rankinen, Salman M. Tajuddin, Ashley van der Spek, Zhe Wang, Jonathan Marten, Vincent Laville, Maris Alver, Evangelos Evangelou, Maria E. Graff, Meian He, Brigitte Kühnel, Leo-Pekka Lyytikäinen, Pedro Marques-Vidal, Ilja M. Nolte, Nicholette D. Palmer, Rainer Rauramaa, Xiao-Ou Shu, Harold Snieder, Stefan Weiss, Wanqing Wen, Lisa R. Yanek, Correa Adolfo, Christie Ballantyne, Larry Bielak, Nienke R. Biermasz, Eric Boerwinkle, Niki Dimou, Gudny Eiriksdottir, Chuan Gao, Sina A. Gharib, Daniel J. Gottlieb, José Haba-Rubio, Tamara B. Harris, Sami Heikkinen, Raphaël Heinzer, James E. Hixson, Georg Homuth, M. Arfan Ikram, Pirjo Komulainen, Jose E. Krieger, Jiwon Lee, Jingmin Liu, Kurt K. Lohman, Annemarie I. Luik, Reedik Mägi, Lisa W. Martin, Thomas Meitinger, Andres Metspalu, Yuri Milaneschi, Mike A. Nalls, Jeff O’Connell, Annette Peters, Patricia Peyser, Olli T. Raitakari, Alex P. Reiner, Patrick C. N. Rensen, Treva K. Rice, Stephen S. Rich, Till Roenneberg, Jerome I. Rotter, Pamela J. Schreiner, James Shikany, Stephen S. Sidney, Mario Sims, Colleen M. Sitlani, Tamar Sofer, Konstantin Strauch, Morris A. Swertz, Kent D. Taylor, André G. Uitterlinden, Cornelia M. van Duijn, Henry Völzke, Melanie Waldenberger, Robert B. Wallance, Ko Willems van Dijk, Caizheng Yu, Alan B. Zonderman, Diane M. Becker, Paul Elliott, Tõnu Esko, Christian Gieger, Hans J. Grabe, Timo A. Lakka, Terho Lehtimäki, Kari E. North, Brenda W. J. H. Penninx, Peter Vollenweider, Lynne E. Wagenknecht, Tangchun Wu, Yong-Bing Xiang, Wei Zheng, Donna K. Arnett, Claude Bouchard, Michele K. Evans, Vilmundur Gudnason, Sharon Kardia, Tanika N. Kelly, Stephen B. Kritchevsky, Ruth J. F. Loos, Alexandre C. Pereira, Mike Province, Bruce M. Psaty, Charles Rotimi, Xiaofeng Zhu, Najaf Amin, L. Adrienne Cupples, Myriam Fornage, Ervin F. Fox, Xiuqing Guo, W. James Gauderman, Kenneth Rice, Charles Kooperberg, Patricia B. Munroe, Ching-Ti Liu, Alanna C. Morrison, Dabeeru C. Rao, Diana van Heemst, and Susan Redline

Subjects
Science
Abstract

Sleep duration is associated with an adverse lipid profile. Here, the authors perform genome-wide gene-by-sleep interaction analysis and find 49 previously unreported lipid loci when considering short or long total sleep time.

Published
2019
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9. Multi-ancestry study of blood lipid levels identifies four loci interacting with physical activity

Author

Tuomas O. Kilpeläinen, Amy R. Bentley, Raymond Noordam, Yun Ju Sung, Karen Schwander, Thomas W. Winkler, Hermina Jakupović, Daniel I. Chasman, Alisa Manning, Ioanna Ntalla, Hugues Aschard, Michael R. Brown, Lisa de las Fuentes, Nora Franceschini, Xiuqing Guo, Dina Vojinovic, Stella Aslibekyan, Mary F. Feitosa, Minjung Kho, Solomon K. Musani, Melissa Richard, Heming Wang, Zhe Wang, Traci M. Bartz, Lawrence F. Bielak, Archie Campbell, Rajkumar Dorajoo, Virginia Fisher, Fernando P. Hartwig, Andrea R. V. R. Horimoto, Changwei Li, Kurt K. Lohman, Jonathan Marten, Xueling Sim, Albert V. Smith, Salman M. Tajuddin, Maris Alver, Marzyeh Amini, Mathilde Boissel, Jin Fang Chai, Xu Chen, Jasmin Divers, Evangelos Evangelou, Chuan Gao, Mariaelisa Graff, Sarah E. Harris, Meian He, Fang-Chi Hsu, Anne U. Jackson, Jing Hua Zhao, Aldi T. Kraja, Brigitte Kühnel, Federica Laguzzi, Leo-Pekka Lyytikäinen, Ilja M. Nolte, Rainer Rauramaa, Muhammad Riaz, Antonietta Robino, Rico Rueedi, Heather M. Stringham, Fumihiko Takeuchi, Peter J. van der Most, Tibor V. Varga, Niek Verweij, Erin B. Ware, Wanqing Wen, Xiaoyin Li, Lisa R. Yanek, Najaf Amin, Donna K. Arnett, Eric Boerwinkle, Marco Brumat, Brian Cade, Mickaël Canouil, Yii-Der Ida Chen, Maria Pina Concas, John Connell, Renée de Mutsert, H. Janaka de Silva, Paul S. de Vries, Ayşe Demirkan, Jingzhong Ding, Charles B. Eaton, Jessica D. Faul, Yechiel Friedlander, Kelley P. Gabriel, Mohsen Ghanbari, Franco Giulianini, Chi Charles Gu, Dongfeng Gu, Tamara B. Harris, Jiang He, Sami Heikkinen, Chew-Kiat Heng, Steven C. Hunt, M. Arfan Ikram, Jost B. Jonas, Woon-Puay Koh, Pirjo Komulainen, Jose E. Krieger, Stephen B. Kritchevsky, Zoltán Kutalik, Johanna Kuusisto, Carl D. Langefeld, Claudia Langenberg, Lenore J. Launer, Karin Leander, Rozenn N. Lemaitre, Cora E. Lewis, Jingjing Liang, Lifelines Cohort Study, Jianjun Liu, Reedik Mägi, Ani Manichaikul, Thomas Meitinger, Andres Metspalu, Yuri Milaneschi, Karen L. Mohlke, Thomas H. Mosley, Alison D. Murray, Mike A. Nalls, Ei-Ei Khaing Nang, Christopher P. Nelson, Sotoodehnia Nona, Jill M. Norris, Chiamaka Vivian Nwuba, Jeff O’Connell, Nicholette D. Palmer, George J. Papanicolau, Raha Pazoki, Nancy L. Pedersen, Annette Peters, Patricia A. Peyser, Ozren Polasek, David J. Porteous, Alaitz Poveda, Olli T. Raitakari, Stephen S. Rich, Neil Risch, Jennifer G. Robinson, Lynda M. Rose, Igor Rudan, Pamela J. Schreiner, Robert A. Scott, Stephen S. Sidney, Mario Sims, Jennifer A. Smith, Harold Snieder, Tamar Sofer, John M. Starr, Barbara Sternfeld, Konstantin Strauch, Hua Tang, Kent D. Taylor, Michael Y. Tsai, Jaakko Tuomilehto, André G. Uitterlinden, M. Yldau van der Ende, Diana van Heemst, Trudy Voortman, Melanie Waldenberger, Patrik Wennberg, Gregory Wilson, Yong-Bing Xiang, Jie Yao, Caizheng Yu, Jian-Min Yuan, Wei Zhao, Alan B. Zonderman, Diane M. Becker, Michael Boehnke, Donald W. Bowden, Ulf de Faire, Ian J. Deary, Paul Elliott, Tõnu Esko, Barry I. Freedman, Philippe Froguel, Paolo Gasparini, Christian Gieger, Norihiro Kato, Markku Laakso, Timo A. Lakka, Terho Lehtimäki, Patrik K. E. Magnusson, Albertine J. Oldehinkel, Brenda W. J. H. Penninx, Nilesh J. Samani, Xiao-Ou Shu, Pim van der Harst, Jana V. Van Vliet-Ostaptchouk, Peter Vollenweider, Lynne E. Wagenknecht, Ya X. Wang, Nicholas J. Wareham, David R. Weir, Tangchun Wu, Wei Zheng, Xiaofeng Zhu, Michele K. Evans, Paul W. Franks, Vilmundur Gudnason, Caroline Hayward, Bernardo L. Horta, Tanika N. Kelly, Yongmei Liu, Kari E. North, Alexandre C. Pereira, Paul M. Ridker, E. Shyong Tai, Rob M. van Dam, Ervin R. Fox, Sharon L. R. Kardia, Ching-Ti Liu, Dennis O. Mook-Kanamori, Michael A. Province, Susan Redline, Cornelia M. van Duijn, Jerome I. Rotter, Charles B. Kooperberg, W. James Gauderman, Bruce M. Psaty, Kenneth Rice, Patricia B. Munroe, Myriam Fornage, L. Adrienne Cupples, Charles N. Rotimi, Alanna C. Morrison, Dabeeru C. Rao, and Ruth J. F. Loos

Subjects
Science
Abstract

GWAS have identified more than 500 genetic loci associated with blood lipid levels. Here, the authors report a genome-wide analysis of interactions between genetic markers and physical activity, and find that physical activity modifies the effects of four genetic loci on HDL or LDL cholesterol.

Published
2019
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10. Identification and functional characterization of G6PC2 coding variants influencing glycemic traits define an effector transcript at the G6PC2-ABCB11 locus.

Author

Anubha Mahajan, Xueling Sim, Hui Jin Ng, Alisa Manning, Manuel A Rivas, Heather M Highland, Adam E Locke, Niels Grarup, Hae Kyung Im, Pablo Cingolani, Jason Flannick, Pierre Fontanillas, Christian Fuchsberger, Kyle J Gaulton, Tanya M Teslovich, N William Rayner, Neil R Robertson, Nicola L Beer, Jana K Rundle, Jette Bork-Jensen, Claes Ladenvall, Christine Blancher, David Buck, Gemma Buck, Noël P Burtt, Stacey Gabriel, Anette P Gjesing, Christopher J Groves, Mette Hollensted, Jeroen R Huyghe, Anne U Jackson, Goo Jun, Johanne Marie Justesen, Massimo Mangino, Jacquelyn Murphy, Matt Neville, Robert Onofrio, Kerrin S Small, Heather M Stringham, Ann-Christine Syvänen, Joseph Trakalo, Goncalo Abecasis, Graeme I Bell, John Blangero, Nancy J Cox, Ravindranath Duggirala, Craig L Hanis, Mark Seielstad, James G Wilson, Cramer Christensen, Ivan Brandslund, Rainer Rauramaa, Gabriela L Surdulescu, Alex S F Doney, Lars Lannfelt, Allan Linneberg, Bo Isomaa, Tiinamaija Tuomi, Marit E Jørgensen, Torben Jørgensen, Johanna Kuusisto, Matti Uusitupa, Veikko Salomaa, Timothy D Spector, Andrew D Morris, Colin N A Palmer, Francis S Collins, Karen L Mohlke, Richard N Bergman, Erik Ingelsson, Lars Lind, Jaakko Tuomilehto, Torben Hansen, Richard M Watanabe, Inga Prokopenko, Josee Dupuis, Fredrik Karpe, Leif Groop, Markku Laakso, Oluf Pedersen, Jose C Florez, Andrew P Morris, David Altshuler, James B Meigs, Michael Boehnke, Mark I McCarthy, Cecilia M Lindgren, Anna L Gloyn, and T2D-GENES consortium and GoT2D consortium

Subjects
Genetics, QH426-470
Abstract

Genome wide association studies (GWAS) for fasting glucose (FG) and insulin (FI) have identified common variant signals which explain 4.8% and 1.2% of trait variance, respectively. It is hypothesized that low-frequency and rare variants could contribute substantially to unexplained genetic variance. To test this, we analyzed exome-array data from up to 33,231 non-diabetic individuals of European ancestry. We found exome-wide significant (P

Published
2015
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11. Distribution and medical impact of loss-of-function variants in the Finnish founder population.

Author

Elaine T Lim, Peter Würtz, Aki S Havulinna, Priit Palta, Taru Tukiainen, Karola Rehnström, Tõnu Esko, Reedik Mägi, Michael Inouye, Tuuli Lappalainen, Yingleong Chan, Rany M Salem, Monkol Lek, Jason Flannick, Xueling Sim, Alisa Manning, Claes Ladenvall, Suzannah Bumpstead, Eija Hämäläinen, Kristiina Aalto, Mikael Maksimow, Marko Salmi, Stefan Blankenberg, Diego Ardissino, Svati Shah, Benjamin Horne, Ruth McPherson, Gerald K Hovingh, Muredach P Reilly, Hugh Watkins, Anuj Goel, Martin Farrall, Domenico Girelli, Alex P Reiner, Nathan O Stitziel, Sekar Kathiresan, Stacey Gabriel, Jeffrey C Barrett, Terho Lehtimäki, Markku Laakso, Leif Groop, Jaakko Kaprio, Markus Perola, Mark I McCarthy, Michael Boehnke, David M Altshuler, Cecilia M Lindgren, Joel N Hirschhorn, Andres Metspalu, Nelson B Freimer, Tanja Zeller, Sirpa Jalkanen, Seppo Koskinen, Olli Raitakari, Richard Durbin, Daniel G MacArthur, Veikko Salomaa, Samuli Ripatti, Mark J Daly, Aarno Palotie, and Sequencing Initiative Suomi (SISu) Project

Subjects
Genetics, QH426-470
Abstract

Exome sequencing studies in complex diseases are challenged by the allelic heterogeneity, large number and modest effect sizes of associated variants on disease risk and the presence of large numbers of neutral variants, even in phenotypically relevant genes. Isolated populations with recent bottlenecks offer advantages for studying rare variants in complex diseases as they have deleterious variants that are present at higher frequencies as well as a substantial reduction in rare neutral variation. To explore the potential of the Finnish founder population for studying low-frequency (0.5-5%) variants in complex diseases, we compared exome sequence data on 3,000 Finns to the same number of non-Finnish Europeans and discovered that, despite having fewer variable sites overall, the average Finn has more low-frequency loss-of-function variants and complete gene knockouts. We then used several well-characterized Finnish population cohorts to study the phenotypic effects of 83 enriched loss-of-function variants across 60 phenotypes in 36,262 Finns. Using a deep set of quantitative traits collected on these cohorts, we show 5 associations (p

Published
2014
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12. The Type 2 Deiodinase (DIO2) A/G Polymorphism Is Not Associated with Glycemic Traits: The Framingham Heart Study.

Author

Ana Luiza Maia, Josée Dupuis, Alisa Manning, Chunyu Liu, James B. Meigs, L. Adrienne Cupples, P. Reed Larsen, and Caroline S. Fox

Subjects
GENETIC polymorphisms, GENETIC research, ENDOCRINE diseases, DIABETIC acidosis
Abstract

Background:Type 2 deiodinase plays a critical role in thyroid hormone homeostasis. A single nucleotide polymorphism in DIO2gene (A/G) in humans has been associated with a ∼20% lower glucose disposal rate and greater insulin resistance in type 2 diabetes (DM2) patients. Objective:This study was designed to test whether homozygosity for the DIO2A/G polymorphism would be associated with risk of DM2 or elevated levels of diabetes intermediate traits. Design and setting:Community-based, longitudinal study. Participants were withdrawn from a subset of unrelated individuals from the Offspring Cohort of the Framingham Heart Study who had DNA collected between 1995 and 1998. Methods:DNA samples from 1633 participants (mean age, 62 years) underwent genotyping of the DIO2A/G polymorphism. Incident DM2 and diabetes-related traits (fasting plasma glucose, mean fasting plasma glucose, 2-hour glucose, hemoglobin A1c, fasting insulin, insulin resistance) were measured. Results:The minor allele (G) frequency was 0.37. Using multivariable regression for intermediate traits and Cox proportional hazards regression for DM2, pvalues were calculated for two models: Model 1: age, age-squared, sex, and smoking; Model 2: Model 1 body mass index. There were no significant associations (all p> 0.20) for any trait examined. For DM2 risk, the hazard ratios associated with A/G or G/G relative to the A/A genotype were 1.0 (95% confidence interval CI 0.7–1.3) and 1.2 (95% CI 0.7–1.9), respectively. Conclusions:Our results indicate that in this community-based sample, there is no association of the DIO2A/G polymorphism with diabetes intermediate trait levels or DM2 risk. [ABSTRACT FROM AUTHOR]

Published
2007
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