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1. Predicting molecular mechanisms of hereditary diseases by using their tissue‐selective manifestation

Author

Eyal Simonovsky, Moran Sharon, Maya Ziv, Omry Mauer, Idan Hekselman, Juman Jubran, Ekaterina Vinogradov, Chanan M Argov, Omer Basha, Lior Kerber, Yuval Yogev, Ayellet V Segrè, Hae Kyung Im, GTEx Consortium, Ohad Birk, Lior Rokach, and Esti Yeger‐Lotem

Subjects
data integration, genomic medicine, machine learning, omics, tissue selectivity, Biology (General), QH301-705.5, Medicine (General), R5-920
Abstract

Abstract How do aberrations in widely expressed genes lead to tissue‐selective hereditary diseases? Previous attempts to answer this question were limited to testing a few candidate mechanisms. To answer this question at a larger scale, we developed “Tissue Risk Assessment of Causality by Expression” (TRACE), a machine learning approach to predict genes that underlie tissue‐selective diseases and selectivity‐related features. TRACE utilized 4,744 biologically interpretable tissue‐specific gene features that were inferred from heterogeneous omics datasets. Application of TRACE to 1,031 disease genes uncovered known and novel selectivity‐related features, the most common of which was previously overlooked. Next, we created a catalog of tissue‐associated risks for 18,927 protein‐coding genes (https://netbio.bgu.ac.il/trace/). As proof‐of‐concept, we prioritized candidate disease genes identified in 48 rare‐disease patients. TRACE ranked the verified disease gene among the patient's candidate genes significantly better than gene prioritization methods that rank by gene constraint or tissue expression. Thus, tissue selectivity combined with machine learning enhances genetic and clinical understanding of hereditary diseases.

Published
2023
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2. Interaction of background genetic risk, psychotropic medications, and primary angle closure glaucoma in the UK Biobank.

Author

Sayuri Sekimitsu, Jiali Wang, Tobias Elze, Ayellet V Segrè, Janey L Wiggs, and Nazlee Zebardast

Subjects
Medicine, Science
Abstract

Background/aimsPsychotropic medications have been reported as a risk factor for angle closure disease. However, the interaction between background genetic risk for primary angle closure glaucoma (PACG) and susceptibility to angle closure disease among psychotropic medication users has not been investigated. Here we demonstrate the utility of a genome-wide polygenic risk score (PRS) in identifying and risk-stratifying subjects with PACG and investigate the association between PACG genetic burden and exposure to psychotropic medications on prevalent angle closure.MethodsThis analysis used the UK Biobank dataset, a prospective cohort study of 502,506 UK residents. We constructed a PACG PRS for participants using genome-wide association study summary statistics from a multiethnic meta-analysis using the Lassosum method.ResultsAmong the 441,054 participants, 959 (0.22%) were identified as PACG cases. Individuals with PACG had higher PRS compared to those without PACG (0.24±1.03 SD vs. 0.00±1.00 SD, pConclusionWe demonstrate the utility of a PRS for identifying individuals at higher risk of PACG. Additionally, we demonstrate an important relationship where the association between psychotropic medications use and PACG diagnosis varies across the polygenic risk spectrum.

Published
2022
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3. Integrative genomics reveals novel molecular pathways and gene networks for coronary artery disease.

Author

Ville-Petteri Mäkinen, Mete Civelek, Qingying Meng, Bin Zhang, Jun Zhu, Candace Levian, Tianxiao Huan, Ayellet V Segrè, Sujoy Ghosh, Juan Vivar, Majid Nikpay, Alexandre F R Stewart, Christopher P Nelson, Christina Willenborg, Jeanette Erdmann, Stefan Blakenberg, Christopher J O'Donnell, Winfried März, Reijo Laaksonen, Stephen E Epstein, Sekar Kathiresan, Svati H Shah, Stanley L Hazen, Muredach P Reilly, Coronary ARtery DIsease Genome-Wide Replication And Meta-Analysis (CARDIoGRAM) Consortium, Aldons J Lusis, Nilesh J Samani, Heribert Schunkert, Thomas Quertermous, Ruth McPherson, Xia Yang, and Themistocles L Assimes

Subjects
Genetics, QH426-470
Abstract

The majority of the heritability of coronary artery disease (CAD) remains unexplained, despite recent successes of genome-wide association studies (GWAS) in identifying novel susceptibility loci. Integrating functional genomic data from a variety of sources with a large-scale meta-analysis of CAD GWAS may facilitate the identification of novel biological processes and genes involved in CAD, as well as clarify the causal relationships of established processes. Towards this end, we integrated 14 GWAS from the CARDIoGRAM Consortium and two additional GWAS from the Ottawa Heart Institute (25,491 cases and 66,819 controls) with 1) genetics of gene expression studies of CAD-relevant tissues in humans, 2) metabolic and signaling pathways from public databases, and 3) data-driven, tissue-specific gene networks from a multitude of human and mouse experiments. We not only detected CAD-associated gene networks of lipid metabolism, coagulation, immunity, and additional networks with no clear functional annotation, but also revealed key driver genes for each CAD network based on the topology of the gene regulatory networks. In particular, we found a gene network involved in antigen processing to be strongly associated with CAD. The key driver genes of this network included glyoxalase I (GLO1) and peptidylprolyl isomerase I (PPIL1), which we verified as regulatory by siRNA experiments in human aortic endothelial cells. Our results suggest genetic influences on a diverse set of both known and novel biological processes that contribute to CAD risk. The key driver genes for these networks highlight potential novel targets for further mechanistic studies and therapeutic interventions.

Published
2014
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4. Novel loci for adiponectin levels and their influence on type 2 diabetes and metabolic traits: a multi-ethnic meta-analysis of 45,891 individuals.

Author

Zari Dastani, Marie-France Hivert, Nicholas Timpson, John R B Perry, Xin Yuan, Robert A Scott, Peter Henneman, Iris M Heid, Jorge R Kizer, Leo-Pekka Lyytikäinen, Christian Fuchsberger, Toshiko Tanaka, Andrew P Morris, Kerrin Small, Aaron Isaacs, Marian Beekman, Stefan Coassin, Kurt Lohman, Lu Qi, Stavroula Kanoni, James S Pankow, Hae-Won Uh, Ying Wu, Aurelian Bidulescu, Laura J Rasmussen-Torvik, Celia M T Greenwood, Martin Ladouceur, Jonna Grimsby, Alisa K Manning, Ching-Ti Liu, Jaspal Kooner, Vincent E Mooser, Peter Vollenweider, Karen A Kapur, John Chambers, Nicholas J Wareham, Claudia Langenberg, Rune Frants, Ko Willems-Vandijk, Ben A Oostra, Sara M Willems, Claudia Lamina, Thomas W Winkler, Bruce M Psaty, Russell P Tracy, Jennifer Brody, Ida Chen, Jorma Viikari, Mika Kähönen, Peter P Pramstaller, David M Evans, Beate St Pourcain, Naveed Sattar, Andrew R Wood, Stefania Bandinelli, Olga D Carlson, Josephine M Egan, Stefan Böhringer, Diana van Heemst, Lyudmyla Kedenko, Kati Kristiansson, Marja-Liisa Nuotio, Britt-Marie Loo, Tamara Harris, Melissa Garcia, Alka Kanaya, Margot Haun, Norman Klopp, H-Erich Wichmann, Panos Deloukas, Efi Katsareli, David J Couper, Bruce B Duncan, Margreet Kloppenburg, Linda S Adair, Judith B Borja, DIAGRAM+ Consortium, MAGIC Consortium, GLGC Investigators, MuTHER Consortium, James G Wilson, Solomon Musani, Xiuqing Guo, Toby Johnson, Robert Semple, Tanya M Teslovich, Matthew A Allison, Susan Redline, Sarah G Buxbaum, Karen L Mohlke, Ingrid Meulenbelt, Christie M Ballantyne, George V Dedoussis, Frank B Hu, Yongmei Liu, Bernhard Paulweber, Timothy D Spector, P Eline Slagboom, Luigi Ferrucci, Antti Jula, Markus Perola, Olli Raitakari, Jose C Florez, Veikko Salomaa, Johan G Eriksson, Timothy M Frayling, Andrew A Hicks, Terho Lehtimäki, George Davey Smith, David S Siscovick, Florian Kronenberg, Cornelia van Duijn, Ruth J F Loos, Dawn M Waterworth, James B Meigs, Josee Dupuis, J Brent Richards, Benjamin F Voight, Laura J Scott, Valgerdur Steinthorsdottir, Christian Dina, Ryan P Welch, Eleftheria Zeggini, Cornelia Huth, Yurii S Aulchenko, Gudmar Thorleifsson, Laura J McCulloch, Teresa Ferreira, Harald Grallert, Najaf Amin, Guanming Wu, Cristen J Willer, Soumya Raychaudhuri, Steve A McCarroll, Oliver M Hofmann, Ayellet V Segrè, Mandy van Hoek, Pau Navarro, Kristin Ardlie, Beverley Balkau, Rafn Benediktsson, Amanda J Bennett, Roza Blagieva, Eric Boerwinkle, Lori L Bonnycastle, Kristina Bengtsson Boström, Bert Bravenboer, Suzannah Bumpstead, Noël P Burtt, Guillaume Charpentier, Peter S Chines, Marilyn Cornelis, Gabe Crawford, Alex S F Doney, Katherine S Elliott, Amanda L Elliott, Michael R Erdos, Caroline S Fox, Christopher S Franklin, Martha Ganser, Christian Gieger, Niels Grarup, Todd Green, Simon Griffin, Christopher J Groves, Candace Guiducci, Samy Hadjadj, Neelam Hassanali, Christian Herder, Bo Isomaa, Anne U Jackson, Paul R V Johnson, Torben Jørgensen, Wen H L Kao, Augustine Kong, Peter Kraft, Johanna Kuusisto, Torsten Lauritzen, Man Li, Aloysius Lieverse, Cecilia M Lindgren, Valeriya Lyssenko, Michel Marre, Thomas Meitinger, Kristian Midthjell, Mario A Morken, Narisu Narisu, Peter Nilsson, Katharine R Owen, Felicity Payne, Ann-Kristin Petersen, Carl Platou, Christine Proença, Inga Prokopenko, Wolfgang Rathmann, N William Rayner, Neil R Robertson, Ghislain Rocheleau, Michael Roden, Michael J Sampson, Richa Saxena, Beverley M Shields, Peter Shrader, Gunnar Sigurdsson, Thomas Sparsø, Klaus Strassburger, Heather M Stringham, Qi Sun, Amy J Swift, Barbara Thorand, Jean Tichet, Tiinamaija Tuomi, Rob M van Dam, Timon W van Haeften, Thijs van Herpt, Jana V van Vliet-Ostaptchouk, G Bragi Walters, Michael N Weedon, Cisca Wijmenga, Jacqueline Witteman, Richard N Bergman, Stephane Cauchi, Francis S Collins, Anna L Gloyn, Ulf Gyllensten, Torben Hansen, Winston A Hide, Graham A Hitman, Albert Hofman, David J Hunter, Kristian Hveem, Markku Laakso, Andrew D Morris, Colin N A Palmer, Igor Rudan, Eric Sijbrands, Lincoln D Stein, Jaakko Tuomilehto, Andre Uitterlinden, Mark Walker, Richard M Watanabe, Goncalo R Abecasis, Bernhard O Boehm, Harry Campbell, Mark J Daly, Andrew T Hattersley, Oluf Pedersen, Inês Barroso, Leif Groop, Rob Sladek, Unnur Thorsteinsdottir, James F Wilson, Thomas Illig, Philippe Froguel, Cornelia M van Duijn, Kari Stefansson, David Altshuler, Michael Boehnke, Mark I McCarthy, Nicole Soranzo, Eleanor Wheeler, Nicole L Glazer, Nabila Bouatia-Naji, Reedik Mägi, Joshua Randall, Paul Elliott, Denis Rybin, Abbas Dehghan, Jouke Jan Hottenga, Kijoung Song, Anuj Goel, Taina Lajunen, Alex Doney, Christine Cavalcanti-Proença, Meena Kumari, Nicholas J Timpson, Carina Zabena, Erik Ingelsson, Ping An, Jeffrey O'Connell, Jian'an Luan, Amanda Elliott, Steven A McCarroll, Rosa Maria Roccasecca, François Pattou, Praveen Sethupathy, Yavuz Ariyurek, Philip Barter, John P Beilby, Yoav Ben-Shlomo, Sven Bergmann, Murielle Bochud, Amélie Bonnefond, Knut Borch-Johnsen, Yvonne Böttcher, Eric Brunner, Suzannah J Bumpstead, Yii-Der Ida Chen, Peter Chines, Robert Clarke, Lachlan J M Coin, Matthew N Cooper, Laura Crisponi, Ian N M Day, Eco J C de Geus, Jerome Delplanque, Annette C Fedson, Antje Fischer-Rosinsky, Nita G Forouhi, Maria Grazia Franzosi, Pilar Galan, Mark O Goodarzi, Jürgen Graessler, Scott Grundy, Rhian Gwilliam, Göran Hallmans, Naomi Hammond, Xijing Han, Anna-Liisa Hartikainen, Caroline Hayward, Simon C Heath, Serge Hercberg, David R Hillman, Aroon D Hingorani, Jennie Hui, Joe Hung, Marika Kaakinen, Jaakko Kaprio, Y Antero Kesaniemi, Mika Kivimaki, Beatrice Knight, Seppo Koskinen, Peter Kovacs, Kirsten Ohm Kyvik, G Mark Lathrop, Debbie A Lawlor, Olivier Le Bacquer, Cécile Lecoeur, Yun Li, Robert Mahley, Massimo Mangino, María Teresa Martínez-Larrad, Jarred B McAteer, Ruth McPherson, Christa Meisinger, David Melzer, David Meyre, Braxton D Mitchell, Sutapa Mukherjee, Silvia Naitza, Matthew J Neville, Marco Orrù, Ruth Pakyz, Giuseppe Paolisso, Cristian Pattaro, Daniel Pearson, John F Peden, Nancy L Pedersen, Andreas F H Pfeiffer, Irene Pichler, Ozren Polasek, Danielle Posthuma, Simon C Potter, Anneli Pouta, Michael A Province, Nigel W Rayner, Kenneth Rice, Samuli Ripatti, Fernando Rivadeneira, Olov Rolandsson, Annelli Sandbaek, Manjinder Sandhu, Serena Sanna, Avan Aihie Sayer, Paul Scheet, Udo Seedorf, Stephen J Sharp, Beverley Shields, Gunnar Sigurðsson, Eric J G Sijbrands, Angela Silveira, Laila Simpson, Andrew Singleton, Nicholas L Smith, Ulla Sovio, Amy Swift, Holly Syddall, Ann-Christine Syvänen, Anke Tönjes, André G Uitterlinden, Ko Willems van Dijk, Dhiraj Varma, Sophie Visvikis-Siest, Veronique Vitart, Nicole Vogelzangs, Gérard Waeber, Peter J Wagner, Andrew Walley, Kim L Ward, Hugh Watkins, Sarah H Wild, Gonneke Willemsen, Jaqueline C M Witteman, John W G Yarnell, Diana Zelenika, Björn Zethelius, Guangju Zhai, Jing Hua Zhao, M Carola Zillikens, DIAGRAM Consortium, GIANT Consortium, Global B Pgen Consortium, Ingrid B Borecki, Pierre Meneton, Patrik K E Magnusson, David M Nathan, Gordon H Williams, Kaisa Silander, Stefan R Bornstein, Peter Schwarz, Joachim Spranger, Fredrik Karpe, Alan R Shuldiner, Cyrus Cooper, Manuel Serrano-Ríos, Lars Lind, Lyle J Palmer, Paul W Franks, Shah Ebrahim, Michael Marmot, W H Linda Kao, Peter Paul Pramstaller, Alan F Wright, Michael Stumvoll, Anders Hamsten, Procardis Consortium, Thomas A Buchanan, Timo T Valle, Jerome I Rotter, Brenda W J H Penninx, Dorret I Boomsma, Antonio Cao, Angelo Scuteri, David Schlessinger, Manuela Uda, Aimo Ruokonen, Marjo-Riitta Jarvelin, Leena Peltonen, Vincent Mooser, Robert Sladek, MAGIC investigators, GLGC Consortium, Kiran Musunuru, Albert V Smith, Andrew C Edmondson, Ioannis M Stylianou, Masahiro Koseki, James P Pirruccello, Daniel I Chasman, Christopher T Johansen, Sigrid W Fouchier, Gina M Peloso, Maja Barbalic, Sally L Ricketts, Joshua C Bis, Mary F Feitosa, Marju Orho-Melander, Olle Melander, Xiaohui Li, Mingyao Li, Yoon Shin Cho, Min Jin Go, Young Jin Kim, Jong-Young Lee, Taesung Park, Kyunga Kim, Xueling Sim, Rick Twee-Hee Ong, Damien C Croteau-Chonka, Leslie A Lange, Joshua D Smith, Andreas Ziegler, Weihua Zhang, Robert Y L Zee, John B Whitfield, John R Thompson, Ida Surakka, Tim D Spector, Johannes H Smit, Juha Sinisalo, James Scott, Juha Saharinen, Chiara Sabatti, Lynda M Rose, Robert Roberts, Mark Rieder, Alex N Parker, Guillaume Pare, Christopher J O'Donnell, Markku S Nieminen, Deborah A Nickerson, Grant W Montgomery, Wendy McArdle, David Masson, Nicholas G Martin, Fabio Marroni, Gavin Lucas, Robert Luben, Marja-Liisa Lokki, Guillaume Lettre, Lenore J Launer, Edward G Lakatta, Reijo Laaksonen, Kirsten O Kyvik, Inke R König, Kay-Tee Khaw, Lee M Kaplan, Åsa Johansson, A Cecile J W Janssens, Wilmar Igl, G Kees Hovingh, Christian Hengstenberg, Aki S Havulinna, Nicholas D Hastie, Tamara B Harris, Talin Haritunians, Alistair S Hall, Leif C Groop, Elena Gonzalez, Nelson B Freimer, Jeanette Erdmann, Kenechi G Ejebe, Angela Döring, Anna F Dominiczak, Serkalem Demissie, Panagiotis Deloukas, Ulf de Faire, Gabriel Crawford, Yii-der I Chen, Mark J Caulfield, S Matthijs Boekholdt, Themistocles L Assimes, Thomas Quertermous, Mark Seielstad, Tien Y Wong, E-Shyong Tai, Alan B Feranil, Christopher W Kuzawa, Herman A Taylor, Stacey B Gabriel, Hilma Holm, Vilmundur Gudnason, Ronald M Krauss, Jose M Ordovas, Patricia B Munroe, Jaspal S Kooner, Alan R Tall, Robert A Hegele, John J P Kastelein, Eric E Schadt, David P Strachan, Muredach P Reilly, Nilesh J Samani, Heribert Schunkert, L Adrienne Cupples, Manjinder S Sandhu, Paul M Ridker, Daniel J Rader, and Sekar Kathiresan

Subjects
Genetics, QH426-470
Abstract

Circulating levels of adiponectin, a hormone produced predominantly by adipocytes, are highly heritable and are inversely associated with type 2 diabetes mellitus (T2D) and other metabolic traits. We conducted a meta-analysis of genome-wide association studies in 39,883 individuals of European ancestry to identify genes associated with metabolic disease. We identified 8 novel loci associated with adiponectin levels and confirmed 2 previously reported loci (P = 4.5×10(-8)-1.2×10(-43)). Using a novel method to combine data across ethnicities (N = 4,232 African Americans, N = 1,776 Asians, and N = 29,347 Europeans), we identified two additional novel loci. Expression analyses of 436 human adipocyte samples revealed that mRNA levels of 18 genes at candidate regions were associated with adiponectin concentrations after accounting for multiple testing (p

Published
2012
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5. Identification of novel type 2 diabetes candidate genes involved in the crosstalk between the mitochondrial and the insulin signaling systems.

Author

Josep M Mercader, Montserrat Puiggros, Ayellet V Segrè, Evarist Planet, Eleonora Sorianello, David Sebastian, Sergio Rodriguez-Cuenca, Vicent Ribas, Sílvia Bonàs-Guarch, Sorin Draghici, Chenjing Yang, Sílvia Mora, Antoni Vidal-Puig, Josée Dupuis, DIAGRAM Consortium, Jose C Florez, MITIN Consortium, Antonio Zorzano, and David Torrents

Subjects
Genetics, QH426-470
Abstract

Type 2 Diabetes (T2D) is a highly prevalent chronic metabolic disease with strong co-morbidity with obesity and cardiovascular diseases. There is growing evidence supporting the notion that a crosstalk between mitochondria and the insulin signaling cascade could be involved in the etiology of T2D and insulin resistance. In this study we investigated the molecular basis of this crosstalk by using systems biology approaches. We combined, filtered, and interrogated different types of functional interaction data, such as direct protein-protein interactions, co-expression analyses, and metabolic and signaling dependencies. As a result, we constructed the mitochondria-insulin (MITIN) network, which highlights 286 genes as candidate functional linkers between these two systems. The results of internal gene expression analysis of three independent experimental models of mitochondria and insulin signaling perturbations further support the connecting roles of these genes. In addition, we further assessed whether these genes are involved in the etiology of T2D using the genome-wide association study meta-analysis from the DIAGRAM consortium, involving 8,130 T2D cases and 38,987 controls. We found modest enrichment of genes associated with T2D amongst our linker genes (p = 0.0549), including three already validated T2D SNPs and 15 additional SNPs, which, when combined, were collectively associated to increased fasting glucose levels according to MAGIC genome wide meta-analysis (p = 8.12×10(-5)). This study highlights the potential of combining systems biology, experimental, and genome-wide association data mining for identifying novel genes and related variants that increase vulnerability to complex diseases.

Published
2012
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6. A genome-wide association search for type 2 diabetes genes in African Americans.

Author

Nicholette D Palmer, Caitrin W McDonough, Pamela J Hicks, Bong H Roh, Maria R Wing, S Sandy An, Jessica M Hester, Jessica N Cooke, Meredith A Bostrom, Megan E Rudock, Matthew E Talbert, Joshua P Lewis, DIAGRAM Consortium, MAGIC Investigators, Assiamira Ferrara, Lingyi Lu, Julie T Ziegler, Michele M Sale, Jasmin Divers, Daniel Shriner, Adebowale Adeyemo, Charles N Rotimi, Maggie C Y Ng, Carl D Langefeld, Barry I Freedman, Donald W Bowden, Benjamin F Voight, Laura J Scott, Valgerdur Steinthorsdottir, Andrew P Morris, Christian Dina, Ryan P Welch, Eleftheria Zeggini, Cornelia Huth, Yurii S Aulchenko, Gudmar Thorleifsson, Laura J McCulloch, Teresa Ferreira, Harald Grallert, Najaf Amin, Guanming Wu, Cristen J Willer, Soumya Raychaudhuri, Steve A McCarroll, Claudia Langenberg, Oliver M Hofmann, Josée Dupuis, Lu Qi, Ayellet V Segrè, Mandy van Hoek, Pau Navarro, Kristin Ardlie, Beverley Balkau, Rafn Benediktsson, Amanda J Bennett, Roza Blagieva, Eric Boerwinkle, Lori L Bonnycastle, Kristina Bengtsson Boström, Bert Bravenboer, Suzannah Bumpstead, Noël P Burtt, Guillaume Charpentier, Peter S Chines, Marilyn Cornelis, David J Couper, Gabe Crawford, Alex S F Doney, Katherine S Elliott, Amanda L Elliott, Michael R Erdos, Caroline S Fox, Christopher S Franklin, Martha Ganser, Christian Gieger, Niels Grarup, Todd Green, Simon Griffin, Christopher J Groves, Candace Guiducci, Samy Hadjadj, Neelam Hassanali, Christian Herder, Bo Isomaa, Anne U Jackson, Paul R V Johnson, Torben Jørgensen, Wen H L Kao, Norman Klopp, Augustine Kong, Peter Kraft, Johanna Kuusisto, Torsten Lauritzen, Man Li, Aloysius Lieverse, Cecilia M Lindgren, Valeriya Lyssenko, Michel Marre, Thomas Meitinger, Kristian Midthjell, Mario A Morken, Narisu Narisu, Peter Nilsson, Katharine R Owen, Felicity Payne, John R B Perry, Ann-Kristin Petersen, Carl Platou, Christine Proença, Inga Prokopenko, Wolfgang Rathmann, N William Rayner, Neil R Robertson, Ghislain Rocheleau, Michael Roden, Michael J Sampson, Richa Saxena, Beverley M Shields, Peter Shrader, Gunnar Sigurdsson, Thomas Sparsø, Klaus Strassburger, Heather M Stringham, Qi Sun, Amy J Swift, Barbara Thorand, Jean Tichet, Tiinamaija Tuomi, Rob M van Dam, Timon W van Haeften, Thijs van Herpt, Jana V van Vliet-Ostaptchouk, G Bragi Walters, Michael N Weedon, Cisca Wijmenga, Jacqueline Witteman, Richard N Bergman, Stephane Cauchi, Francis S Collins, Anna L Gloyn, Ulf Gyllensten, Torben Hansen, Winston A Hide, Graham A Hitman, Albert Hofman, David J Hunter, Kristian Hveem, Markku Laakso, Karen L Mohlke, Andrew D Morris, Colin N A Palmer, Peter P Pramstaller, Igor Rudan, Eric Sijbrands, Lincoln D Stein, Jaakko Tuomilehto, Andre Uitterlinden, Mark Walker, Nicholas J Wareham, Richard M Watanabe, Goncalo R Abecasis, Bernhard O Boehm, Harry Campbell, Mark J Daly, Andrew T Hattersley, Frank B Hu, James B Meigs, James S Pankow, Oluf Pedersen, H-Erich Wichmann, Inês Barroso, Jose C Florez, Timothy M Frayling, Leif Groop, Rob Sladek, Unnur Thorsteinsdottir, James F Wilson, Thomas Illig, Philippe Froguel, Cornelia M van Duijn, Kari Stefansson, David Altshuler, Michael Boehnke, Mark I McCarthy, Nicole Soranzo, Eleanor Wheeler, Nicole L Glazer, Nabila Bouatia-Naji, Reedik Mägi, Joshua Randall, Toby Johnson, Paul Elliott, Denis Rybin, Peter Henneman, Abbas Dehghan, Jouke Jan Hottenga, Kijoung Song, Anuj Goel, Josephine M Egan, Taina Lajunen, Alex Doney, Stavroula Kanoni, Christine Cavalcanti-Proença, Meena Kumari, Nicholas J Timpson, Carina Zabena, Erik Ingelsson, Ping An, Jeffrey O'Connell, Jian'an Luan, Amanda Elliott, Steven A McCarroll, Rosa Maria Roccasecca, François Pattou, Praveen Sethupathy, Yavuz Ariyurek, Philip Barter, John P Beilby, Yoav Ben-Shlomo, Sven Bergmann, Murielle Bochud, Amélie Bonnefond, Knut Borch-Johnsen, Yvonne Böttcher, Eric Brunner, Suzannah J Bumpstead, Yii-Der Ida Chen, Peter Chines, Robert Clarke, Lachlan J M Coin, Matthew N Cooper, Laura Crisponi, Ian N M Day, Eco J C de Geus, Jerome Delplanque, Annette C Fedson, Antje Fischer-Rosinsky, Nita G Forouhi, Rune Frants, Maria Grazia Franzosi, Pilar Galan, Mark O Goodarzi, Jürgen Graessler, Scott Grundy, Rhian Gwilliam, Göran Hallmans, Naomi Hammond, Xijing Han, Anna-Liisa Hartikainen, Caroline Hayward, Simon C Heath, Serge Hercberg, Andrew A Hicks, David R Hillman, Aroon D Hingorani, Jennie Hui, Joe Hung, Antti Jula, Marika Kaakinen, Jaakko Kaprio, Y Antero Kesaniemi, Mika Kivimaki, Beatrice Knight, Seppo Koskinen, Peter Kovacs, Kirsten Ohm Kyvik, G Mark Lathrop, Debbie A Lawlor, Olivier Le Bacquer, Cécile Lecoeur, Yun Li, Robert Mahley, Massimo Mangino, Alisa K Manning, María Teresa Martínez-Larrad, Jarred B McAteer, Ruth McPherson, Christa Meisinger, David Melzer, David Meyre, Braxton D Mitchell, Sutapa Mukherjee, Silvia Naitza, Matthew J Neville, Ben A Oostra, Marco Orrù, Ruth Pakyz, Giuseppe Paolisso, Cristian Pattaro, Daniel Pearson, John F Peden, Nancy L Pedersen, Markus Perola, Andreas F H Pfeiffer, Irene Pichler, Ozren Polasek, Danielle Posthuma, Simon C Potter, Anneli Pouta, Michael A Province, Bruce M Psaty, Nigel W Rayner, Kenneth Rice, Samuli Ripatti, Fernando Rivadeneira, Olov Rolandsson, Annelli Sandbaek, Manjinder Sandhu, Serena Sanna, Avan Aihie Sayer, Paul Scheet, Udo Seedorf, Stephen J Sharp, Beverley Shields, Eric J G Sijbrands, Angela Silveira, Laila Simpson, Andrew Singleton, Nicholas L Smith, Ulla Sovio, Amy Swift, Holly Syddall, Ann-Christine Syvänen, Toshiko Tanaka, Anke Tönjes, André G Uitterlinden, Ko Willems van Dijk, Dhiraj Varma, Sophie Visvikis-Siest, Veronique Vitart, Nicole Vogelzangs, Gérard Waeber, Peter J Wagner, Andrew Walley, Kim L Ward, Hugh Watkins, Sarah H Wild, Gonneke Willemsen, Jaqueline C M Witteman, John W G Yarnell, Diana Zelenika, Björn Zethelius, Guangju Zhai, Jing Hua Zhao, M Carola Zillikens, Ingrid B Borecki, Ruth J F Loos, Pierre Meneton, Patrik K E Magnusson, David M Nathan, Gordon H Williams, Kaisa Silander, Veikko Salomaa, George Davey Smith, Stefan R Bornstein, Peter Schwarz, Joachim Spranger, Fredrik Karpe, Alan R Shuldiner, Cyrus Cooper, George V Dedoussis, Manuel Serrano-Ríos, Lars Lind, Lyle J Palmer, Paul W Franks, Shah Ebrahim, Michael Marmot, W H Linda Kao, Peter Paul Pramstaller, Alan F Wright, Michael Stumvoll, Anders Hamsten, Thomas A Buchanan, Timo T Valle, Jerome I Rotter, David S Siscovick, Brenda W J H Penninx, Dorret I Boomsma, Panos Deloukas, Timothy D Spector, Luigi Ferrucci, Antonio Cao, Angelo Scuteri, David Schlessinger, Manuela Uda, Aimo Ruokonen, Marjo-Riitta Jarvelin, Dawn M Waterworth, Peter Vollenweider, Leena Peltonen, Vincent Mooser, and Robert Sladek

Subjects
Medicine, Science
Abstract

African Americans are disproportionately affected by type 2 diabetes (T2DM) yet few studies have examined T2DM using genome-wide association approaches in this ethnicity. The aim of this study was to identify genes associated with T2DM in the African American population. We performed a Genome Wide Association Study (GWAS) using the Affymetrix 6.0 array in 965 African-American cases with T2DM and end-stage renal disease (T2DM-ESRD) and 1029 population-based controls. The most significant SNPs (n = 550 independent loci) were genotyped in a replication cohort and 122 SNPs (n = 98 independent loci) were further tested through genotyping three additional validation cohorts followed by meta-analysis in all five cohorts totaling 3,132 cases and 3,317 controls. Twelve SNPs had evidence of association in the GWAS (P

Published
2012
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7. Common genetic variants and modification of penetrance of BRCA2-associated breast cancer.

Author

Mia M Gaudet, Tomas Kirchhoff, Todd Green, Joseph Vijai, Joshua M Korn, Candace Guiducci, Ayellet V Segrè, Kate McGee, Lesley McGuffog, Christiana Kartsonaki, Jonathan Morrison, Sue Healey, Olga M Sinilnikova, Dominique Stoppa-Lyonnet, Sylvie Mazoyer, Marion Gauthier-Villars, Hagay Sobol, Michel Longy, Marc Frenay, GEMO Study Collaborators, Frans B L Hogervorst, Matti A Rookus, J Margriet Collée, Nicoline Hoogerbrugge, Kees E P van Roozendaal, HEBON Study Collaborators, Marion Piedmonte, Wendy Rubinstein, Stacy Nerenstone, Linda Van Le, Stephanie V Blank, Trinidad Caldés, Miguel de la Hoya, Heli Nevanlinna, Kristiina Aittomäki, Conxi Lazaro, Ignacio Blanco, Adalgeir Arason, Oskar T Johannsson, Rosa B Barkardottir, Peter Devilee, Olofunmilayo I Olopade, Susan L Neuhausen, Xianshu Wang, Zachary S Fredericksen, Paolo Peterlongo, Siranoush Manoukian, Monica Barile, Alessandra Viel, Paolo Radice, Catherine M Phelan, Steven Narod, Gad Rennert, Flavio Lejbkowicz, Anath Flugelman, Irene L Andrulis, Gord Glendon, Hilmi Ozcelik, OCGN, Amanda E Toland, Marco Montagna, Emma D'Andrea, Eitan Friedman, Yael Laitman, Ake Borg, Mary Beattie, Susan J Ramus, Susan M Domchek, Katherine L Nathanson, Tim Rebbeck, Amanda B Spurdle, Xiaoqing Chen, Helene Holland, kConFab, Esther M John, John L Hopper, Saundra S Buys, Mary B Daly, Melissa C Southey, Mary Beth Terry, Nadine Tung, Thomas V Overeem Hansen, Finn C Nielsen, Mark H Greene, Phuong L Mai, Ana Osorio, Mercedes Durán, Raquel Andres, Javier Benítez, Jeffrey N Weitzel, Judy Garber, Ute Hamann, EMBRACE, Susan Peock, Margaret Cook, Clare Oliver, Debra Frost, Radka Platte, D Gareth Evans, Fiona Lalloo, Ros Eeles, Louise Izatt, Lisa Walker, Jacqueline Eason, Julian Barwell, Andrew K Godwin, Rita K Schmutzler, Barbara Wappenschmidt, Stefanie Engert, Norbert Arnold, Dorothea Gadzicki, Michael Dean, Bert Gold, Robert J Klein, Fergus J Couch, Georgia Chenevix-Trench, Douglas F Easton, Mark J Daly, Antonis C Antoniou, David M Altshuler, and Kenneth Offit

Subjects
Genetics, QH426-470
Abstract

The considerable uncertainty regarding cancer risks associated with inherited mutations of BRCA2 is due to unknown factors. To investigate whether common genetic variants modify penetrance for BRCA2 mutation carriers, we undertook a two-staged genome-wide association study in BRCA2 mutation carriers. In stage 1 using the Affymetrix 6.0 platform, 592,163 filtered SNPs genotyped were available on 899 young (

Published
2010
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8. Common inherited variation in mitochondrial genes is not enriched for associations with type 2 diabetes or related glycemic traits.

Author

Ayellet V Segrè, DIAGRAM Consortium, MAGIC investigators, Leif Groop, Vamsi K Mootha, Mark J Daly, and David Altshuler

Subjects
Genetics, QH426-470
Abstract

Mitochondrial dysfunction has been observed in skeletal muscle of people with diabetes and insulin-resistant individuals. Furthermore, inherited mutations in mitochondrial DNA can cause a rare form of diabetes. However, it is unclear whether mitochondrial dysfunction is a primary cause of the common form of diabetes. To date, common genetic variants robustly associated with type 2 diabetes (T2D) are not known to affect mitochondrial function. One possibility is that multiple mitochondrial genes contain modest genetic effects that collectively influence T2D risk. To test this hypothesis we developed a method named Meta-Analysis Gene-set Enrichment of variaNT Associations (MAGENTA; http://www.broadinstitute.org/mpg/magenta). MAGENTA, in analogy to Gene Set Enrichment Analysis, tests whether sets of functionally related genes are enriched for associations with a polygenic disease or trait. MAGENTA was specifically designed to exploit the statistical power of large genome-wide association (GWA) study meta-analyses whose individual genotypes are not available. This is achieved by combining variant association p-values into gene scores and then correcting for confounders, such as gene size, variant number, and linkage disequilibrium properties. Using simulations, we determined the range of parameters for which MAGENTA can detect associations likely missed by single-marker analysis. We verified MAGENTA's performance on empirical data by identifying known relevant pathways in lipid and lipoprotein GWA meta-analyses. We then tested our mitochondrial hypothesis by applying MAGENTA to three gene sets: nuclear regulators of mitochondrial genes, oxidative phosphorylation genes, and approximately 1,000 nuclear-encoded mitochondrial genes. The analysis was performed using the most recent T2D GWA meta-analysis of 47,117 people and meta-analyses of seven diabetes-related glycemic traits (up to 46,186 non-diabetic individuals). This well-powered analysis found no significant enrichment of associations to T2D or any of the glycemic traits in any of the gene sets tested. These results suggest that common variants affecting nuclear-encoded mitochondrial genes have at most a small genetic contribution to T2D susceptibility.

Published
2010
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9. High-resolution mutation mapping reveals parallel experimental evolution in yeast.

Author

Ayellet V Segrè, Andrew W Murray, and Jun-Yi Leu

Subjects
Biology (General), QH301-705.5
Abstract

Understanding the genetic basis of evolutionary adaptation is limited by our ability to efficiently identify the genomic locations of adaptive mutations. Here we describe a method that can quickly and precisely map the genetic basis of naturally and experimentally evolved complex traits using linkage analysis. A yeast strain that expresses the evolved trait is crossed to a distinct strain background and DNA from a large pool of progeny that express the trait of interest is hybridized to oligonucleotide microarrays that detect thousands of polymorphisms between the two strains. Adaptive mutations are detected by linkage to the polymorphisms from the evolved parent. We successfully tested our method by mapping five known genes to a precision of 0.2-24 kb (0.1-10 cM), and developed computer simulations to test the effect of different factors on mapping precision. We then applied this method to four yeast strains that had independently adapted to a fluctuating glucose-galactose environment. All four strains had acquired one or more missense mutations in GAL80, the repressor of the galactose utilization pathway. When transferred into the ancestral strain, the gal80 mutations conferred the fitness advantage that the evolved strains show in the transition from glucose to galactose. Our results show an example of parallel adaptation caused by mutations in the same gene.

Published
2006
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10. The impact of rare variation on gene expression across tissues.

Author

Xin Li, Yungil Kim, Emily K. Tsang, Joe R. Davis, Farhan N. Damani, Colby Chiang, Gaelen T. Hess, Zachary Zappala, Benjamin J. Strober, Alexandra J. Scott, Amy Li, Andrea Ganna, Michael C. Bassik, Jason D. Merker, François Aguet, Kristin G. Ardlie, Beryl B. Cummings, Ellen T. Gelfand, Gad Getz, Kane Hadley, Robert E. Handsaker, Katherine H. Huang, Seva Kashin, Konrad J. Karczewski, Monkol Lek, Xiao Li, Daniel G. MacArthur, Jared L. Nedzel, Duyen T. Nguyen, Michael S. Noble, Ayellet V. Segrè, Casandra A. Trowbridge, Taru Tukiainen, Nathan S. Abell, Brunilda Balliu, Ruth Barshir, Omer Basha, Alexis J. Battle, Gireesh K. Bogu, Andrew Brown, Christopher D. Brown, Stephane E. Castel, Lin S. Chen, Donald F. Conrad, Nancy J. Cox, Olivier Delaneau, Emmanouil T. Dermitzakis, Barbara E. Engelhardt, Eleazar Eskin, Pedro G. Ferreira, Laure Frésard, Eric R. Gamazon, Diego Garrido-Martín, Ariel D. H. Gewirtz, Genna Gliner, Michael J. Gloudemans, Roderic Guigó, Ira M. Hall, Buhm Han, Yuan He, Farhad Hormozdiari, Cedric Howald, Hae Kyung Im, Brian Jo, Eun Yong Kang, Sarah Kim-Hellmuth, Tuuli Lappalainen, Gen Li 0004, Boxiang Liu, Serghei Mangul, Mark I. McCarthy, Ian C. McDowell, Pejman Mohammadi 0001, Jean Monlong, and Stephen B. Montgomery

Published
2017
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11. Insights into human health from phenome- and genome-wide analyses of UK Biobank retinal optical coherence tomography phenotypes

Author

Seyedeh Maryam Zekavat, Saman Doroodgar Jorshery, Yusrah Shweikh, Katrin Horn, Franziska G. Rauscher, Sayuri Sekimitsu, Satoshi Kayoma, Yixuan Ye, Vineet Raghu, Hongyu Zhao, Marzyeh Ghassemi, Tobias Elze, Ayellet V. Segrè, Janey L. Wiggs, Markus Scholz, Lucian Del Priore, Jay C. Wang, Pradeep Natarajan, and Nazlee Zebardast

Subjects
Article
Abstract

The human retina is a complex multi-layered tissue which offers a unique window into systemic health and disease. Optical coherence tomography (OCT) is widely used in eye care and allows the non-invasive, rapid capture of retinal measurements in exquisite detail. We conducted genome- and phenome-wide analyses of retinal layer thicknesses using macular OCT images from 44,823 UK Biobank participants. We performed phenome-wide association analyses, associating retinal thicknesses with 1,866 incident ICD-based conditions (median 10-year follow-up) and 88 quantitative traits and blood biomarkers. We performed genome-wide association analyses, identifying inherited genetic markers which influence the retina, and replicated our associations among 6,313 individuals from the LIFE-Adult Study. And lastly, we performed comparative association of phenome- and genome-wide associations to identify putative causal links between systemic conditions, retinal layer thicknesses, and ocular disease.Independent associations with incident mortality were detected for photoreceptor thinning and ganglion cell complex thinning. Significant phenotypic associations were detected between retinal layer thinning and ocular, neuropsychiatric, cardiometabolic and pulmonary conditions. Genome-wide association of retinal layer thicknesses yielded 259 loci. Consistency between epidemiologic and genetic associations suggested putative causal links between thinning of the retinal nerve fiber layer with glaucoma, photoreceptor segment with AMD, as well as poor cardiometabolic and pulmonary function with PS thinning, among other findings.In conclusion, retinal layer thinning predicts risk of future ocular and systemic disease. Furthermore, systemic cardio-metabolic-pulmonary conditions promote retinal thinning. Retinal imaging biomarkers, integrated into electronic health records, may inform risk prediction and potential therapeutic strategies.One Sentence SummaryPhenome- and genome-wide associations of retinal OCT images across nearly 50,000 individuals identifies ocular and systemic phenotypes linked to retinal layer thinning, inherited genetic variants linked to retinal layer thickness, and putative causal links between systemic conditions, retinal layer thickness, and ocular disease.

Published
2023

12. Transcriptomic Analysis of the Ocular Posterior Segment Completes a Cell Atlas of the Human Eye

Author

Aboozar Monavarfeshani, Wenjun Yan, Christian Pappas, Kenechukwu A. Odenigbo, Zhigang He, Ayellet V. Segrè, Tavé van Zyl, Gregory S. Hageman, and Joshua R. Sanes

Abstract

Although the visual system extends through the brain, most vision loss originates from defects in the eye. Its central element is the neural retina, which senses light, processes visual signals, and transmits them to the rest of the brain through the optic nerve (ON). Surrounding the retina are numerous other structures, conventionally divided into anterior and posterior segments. Here we used high-throughput single nucleus RNA sequencing (snRNA-seq) to classify and characterize cells in the extraretinal components of the posterior segment: ON, optic nerve head (ONH), peripheral sclera, peripapillary sclera (PPS), choroid, and retinal pigment epithelium (RPE). Defects in each of these tissues are associated with blinding diseases – for example, glaucoma (ONH and PPS), optic neuritis (ON), retinitis pigmentosa (RPE), and age-related macular degeneration (RPE and choroid). From ∼151,000 single nuclei, we identified 37 transcriptomically distinct cell types, including multiple types of astrocytes, oligodendrocytes, fibroblasts, and vascular endothelial cells. Our analyses revealed a differential distribution of many cell types among distinct structures. Together with our previous analyses of the anterior segment and retina, the new data complete a “Version 1” cell atlas of the human eye. We used this atlas to map the expression of >180 genes associated with the risk of developing glaucoma, which is known to involve ocular tissues in both anterior and posterior segments as well as neural retina. Similar methods can be used to investigate numerous additional ocular diseases, many of which are currently untreatable.

Published
2023

14. Deep Learning of the Retina Enables Phenome- and Genome-Wide Analyses of the Microvasculature

Author

Sarah Urbut, Ron A. Adelman, Pradeep Natarajan, Tobias Elze, Seyedeh M. Zekavat, Janey L. Wiggs, Declan P. O'Regan, Vineet K. Raghu, Mark Trinder, Yixuan Ye, Hongyu Zhao, Zhi Yu, Michael C. Honigberg, Ayellet V. Segrè, Sara Haidermota, Nazlee Zebardast, James F. Martone, Jay C. Wang, Akhil Pampana, Patrick T. Ellinor, Satoshi Koyama, Lucian V. Del Priore, Imperial College Healthcare NHS Trust- BRC Funding, and British Heart Foundation

Subjects
Male, retina, microvessels, Phenome, Fundus (eye), Genome, 1117 Public Health and Health Services, chemistry.chemical_compound, Original Research Articles, Physiology (medical), medicine, Humans, 1102 Cardiorespiratory Medicine and Haematology, Retina, business.industry, deep learning, 1103 Clinical Sciences, Retinal, Genomics, Mendelian Randomization Analysis, Middle Aged, medicine.anatomical_structure, Cardiovascular System & Hematology, chemistry, ComputingMethodologies_DOCUMENTANDTEXTPROCESSING, Female, epidemiology, Cardiology and Cardiovascular Medicine, business, Neuroscience, Genome-Wide Association Study
Abstract

Supplemental Digital Content is available in the text., Background: The microvasculature, the smallest blood vessels in the body, has key roles in maintenance of organ health and tumorigenesis. The retinal fundus is a window for human in vivo noninvasive assessment of the microvasculature. Large-scale complementary machine learning-based assessment of the retinal vasculature with phenome-wide and genome-wide analyses may yield new insights into human health and disease. Methods: We used 97 895 retinal fundus images from 54 813 UK Biobank participants. Using convolutional neural networks to segment the retinal microvasculature, we calculated vascular density and fractal dimension as a measure of vascular branching complexity. We associated these indices with 1866 incident International Classification of Diseases–based conditions (median 10-year follow-up) and 88 quantitative traits, adjusting for age, sex, smoking status, and ethnicity. Results: Low retinal vascular fractal dimension and density were significantly associated with higher risks for incident mortality, hypertension, congestive heart failure, renal failure, type 2 diabetes, sleep apnea, anemia, and multiple ocular conditions, as well as corresponding quantitative traits. Genome-wide association of vascular fractal dimension and density identified 7 and 13 novel loci, respectively, that were enriched for pathways linked to angiogenesis (eg, vascular endothelial growth factor, platelet-derived growth factor receptor, angiopoietin, and WNT signaling pathways) and inflammation (eg, interleukin, cytokine signaling). Conclusions: Our results indicate that the retinal vasculature may serve as a biomarker for future cardiometabolic and ocular disease and provide insights into genes and biological pathways influencing microvascular indices. Moreover, such a framework highlights how deep learning of images can quantify an interpretable phenotype for integration with electronic health record, biomarker, and genetic data to inform risk prediction and risk modification.

Published
2022

15. Gene Set Enrichment Analsyes Identify Pathways Involved in Genetic Risk for Diabetic Retinopathy

Author

Jie Jin Wang, Robert P. Igo, Barry I. Freedman, Jerome I. Rotter, Kathyrn P. Burdon, Alan D. Penman, Albert V. Smith, Xiaohui Li, Jamie E. Craig, Paul Mitchell, Brian L. Yaspan, Mary Frances Cotch, John M. Rouhana, Ayellet V. Segrè, Barbara E.K. Klein, Ashley Li, Richard A. Jensen, Gayatri Susarla, Lynn K. Stanwyck, Sudha K. Iyengar, Maggie C.Y. Ng, Ching J. Chen, Kent D. Taylor, Donald W. Bowden, Lucia Sobrin, Tien Yin Wong, Emily Y. Chew, Sharon G. Adler, Samuela Pollack, and Jane Z. Kuo

Subjects
Lipid catabolic process, Clinical Sciences, Genome-wide association study, Type 2 diabetes, Biology, Ophthalmology & Optometry, Polymorphism, Single Nucleotide, Article, Opthalmology and Optometry, Risk Factors, Diabetes Mellitus, Genetics, medicine, 2.1 Biological and endogenous factors, Humans, Interferon gamma, Polymorphism, Aetiology, Eye Disease and Disorders of Vision, Gene, Metabolic and endocrine, Lipid Transport, Diabetic Retinopathy, Catabolism, Diabetes, Human Genome, Single Nucleotide, medicine.disease, Ophthalmology, Diabetes Mellitus, Type 2, Public Health and Health Services, Lipid digestion, Type 2, Biotechnology, Genome-Wide Association Study, medicine.drug
Abstract

To identify functionally related genes associated with diabetic retinopathy (DR) risk using gene set enrichment analyses applied to genome-wide association study meta-analyses.MethodsWe analyzed DR GWAS meta-analyses performed on 3246 Europeans and 2611 African Americans with type 2 diabetes. Gene sets relevant to 5 key DR pathophysiology processes were investigated: tissue injury, vascular events, metabolic events and glial dysregulation, neuronal dysfunction, and inflammation. Keywords relevant to these processes were queried in 4 pathway and ontology databases. Two GSEA methods, Meta-Analysis Gene set Enrichment of variaNT Associations (MAGENTA) and Multi-marker Analysis of GenoMic Annotation (MAGMA), were used. Gene sets were defined to be enriched for gene associations with DR if the P value corrected for multiple testing (Pcorr) was

Published
2022

16. Single-nucleus cross-tissue molecular reference maps toward understanding disease gene function

Author

Gökcen Eraslan, Eugene Drokhlyansky, Shankara Anand, Evgenij Fiskin, Ayshwarya Subramanian, Michal Slyper, Jiali Wang, Nicholas Van Wittenberghe, John M. Rouhana, Julia Waldman, Orr Ashenberg, Monkol Lek, Danielle Dionne, Thet Su Win, Michael S. Cuoco, Olena Kuksenko, Alexander M. Tsankov, Philip A. Branton, Jamie L. Marshall, Anna Greka, Gad Getz, Ayellet V. Segrè, François Aguet, Orit Rozenblatt-Rosen, Kristin G. Ardlie, and Aviv Regev

Subjects
Multidisciplinary
Abstract

Understanding gene function and regulation in homeostasis and disease requires knowledge of the cellular and tissue contexts in which genes are expressed. Here, we applied four single-nucleus RNA sequencing methods to eight diverse, archived, frozen tissue types from 16 donors and 25 samples, generating a cross-tissue atlas of 209,126 nuclei profiles, which we integrated across tissues, donors, and laboratory methods with a conditional variational autoencoder. Using the resulting cross-tissue atlas, we highlight shared and tissue-specific features of tissue-resident cell populations; identify cell types that might contribute to neuromuscular, metabolic, and immune components of monogenic diseases and the biological processes involved in their pathology; and determine cell types and gene modules that might underlie disease mechanisms for complex traits analyzed by genome-wide association studies.

Published
2022

17. Photoreceptor layer thinning is an early biomarker for age-related macular degeneration development: Epidemiological and genetic evidence from UK Biobank optical coherence tomography data

Author

Seyedeh M. Zekavat, Sayuri Sekimitsu, Tobias Elze, Pradeep Natarajan, Lucian V. Del Priore, Nazlee Zebardast, Ayellet V. Segrè, Janey L. Wiggs, Hongyu Zhao, Jay C. Wang, and Yixuan Ye

Subjects
medicine.medical_specialty, Retina, Retinal pigment epithelium, genetic structures, Proportional hazards model, business.industry, Retinal, Drusen, Macular degeneration, medicine.disease, eye diseases, chemistry.chemical_compound, medicine.anatomical_structure, chemistry, Ophthalmology, HTRA1, medicine, Biomarker (medicine), sense organs, business
Abstract

IntroductionAge-related macular degeneration (AMD) is a blinding condition for which there is currently no early-stage clinical biomarker. AMD is characterized by thinning of the outer retina and drusen formation leading to thickening of the Bruch’s membrane and RPE complex, but the timing between these two events, as well as the role of genetic variants in these processes, are unclear. Here, we jointly analyzed genomic, electronic health record, and optical coherence tomography (OCT) data across 44,823 individuals from the UK Biobank to characterize the epidemiological and genetic associations between retinal layer thicknesses and AMD.MethodsThe Topcon Advanced Boundary Segmentation algorithm was used for automated retinal layer segmentation. We associated 9 retinal layer thicknesses with prevalent AMD (present at enrollment) in a logistic regression model, and with incident AMD (diagnosed after enrollment) in a Cox proportional hazards model. Next, we tested the association of AMD-associated genetic alleles, individually and as a polygenic risk score (PRS), with retinal layer thicknesses. All analyses were adjusted for age, age2, sex, smoking status, and principal components of ancestry.ResultsPhotoreceptor segment (PS) thinning was observed throughout the lifespan of individuals analyzed and accelerated at age 45, while retinal pigment epithelium and Bruch’s membrane complex (RPE+BM) thickening started after age 57. Each standard deviation (SD) of PS thinning and RPE+BM thickening were associated with prevalent AMD (PS: OR 1.37, 95% CI 1.25-1.49, P=2.5×10−12; RPE+BM: OR=1.34, 95% CI 1.27-1.41, P=8.4×10−28) and incident AMD (PS: HR 1.35, 95% CI 1.23-1.47, P=3.7×10−11; RPE+BM: HR 1.14, 95% CI 1.06-1.22, P=0.00024). An AMD polygenic risk score (PRS) was associated with PS thinning (Beta -0.21 SD per 2-fold genetically increased risk of AMD, 95% CI -0.23 to -0.19, P=2.8×10−74), and its association with RPE+BM was U-shaped (thinning with AMD PRSndpercentile and thickening with AMD PRS>92ndpercentile suggestive of drusen formation). The loci with strongest support were AMD risk-raising variantsCFH:rs570618-T,CFH:10922109-C, andARMS2/HTRA1:rs3750846-C on PS thinning, andSYN3/TIMP3:rs5754227-T on RPE+BM thickening.ConclusionsEpidemiologically, PS thinning precedes RPE+BM thickening by decades, and is the retinal layer most strongly predictive of future AMD risk. Genetically, AMD risk variants are associated with decreased PS thickness. Overall, these findings support PS thinning as an early-stage clinical biomarker for future AMD development.

Published
2021

18. Single-nucleus cross-tissue molecular reference maps to decipher disease gene function

Author

Olena Kuksenko, John M. Rouhana, Anna Greka, Aviv Regev, Orit Rozenblatt-Rosen, François Aguet, Evgenij Fiskin, Nicholas Van Wittenberghe, Philip A. Branton, Julia Waldman, Danielle Dionne, Eugene Drokhlyansky, Ayellet V. Segrè, Michael S. Cuoco, Ayshwarya Subramanian, Jiali Wang, Kristin G. Ardlie, Gökcen Eraslan, Michal Slyper, Thet Su Win, Shankara Anand, Gad Getz, Orr Ashenberg, and Jamie L. Marshall

Subjects
Cell type, Context (language use), Genomics, Genome-wide association study, Human leukocyte antigen, Computational biology, Biology, Gene, Function (biology), Tissue homeostasis
Abstract

Understanding the function of genes and their regulation in tissue homeostasis and disease requires knowing the cellular context in which genes are expressed in tissues across the body. Single cell genomics allows the generation of detailed cellular atlases in human tissues, but most efforts are focused on single tissue types. Here, we establish a framework for profiling multiple tissues across the human body at single-cell resolution using single nucleus RNA-Seq (snRNA-seq), and apply it to 8 diverse, archived, frozen tissue types (three donors per tissue). We apply four snRNA-seq methods to each of 25 samples from 16 donors, generating a cross-tissue atlas of 209,126 nuclei profiles, and benchmark them vs. scRNA-seq of comparable fresh tissues. We use a conditional variational autoencoder (cVAE) to integrate an atlas across tissues, donors, and laboratory methods. We highlight shared and tissue-specific features of tissue-resident immune cells, identifying tissue-restricted and non-restricted resident myeloid populations. These include a cross-tissue conserved dichotomy between LYVE1- and HLA class II-expressing macrophages, and the broad presence of LAM-like macrophages across healthy tissues that is also observed in disease. For rare, monogenic muscle diseases, we identify cell types that likely underlie the neuromuscular, metabolic, and immune components of these diseases, and biological processes involved in their pathology. For common complex diseases and traits analyzed by GWAS, we identify the cell types and gene modules that potentially underlie disease mechanisms. The experimental and analytical frameworks we describe will enable the generation of large-scale studies of how cellular and molecular processes vary across individuals and populations.

Published
2021

19. Erratum to Gene Set Enrichment Analyses Identify Pathways Involved in Genetic Risk for Diabetic Retinopathy. Am J Ophthalmol 2022;233:111-123

Author

Lucia Sobrin, Gayatri Susarla, Lynn Stanwyck, John M. Rouhana, Ashley Li, Samuela Pollack, Robert P. Igo, Richard A. Jensen, Xiaohui Li, Maggie C.Y. Ng, Albert V. Smith, Jane Z. Kuo, Kent D. Taylor, Barry I. Freedman, Donald W. Bowden, Alan Penman, Ching J. Chen, Jamie E. Craig, Sharon G. Adler, Emily Y. Chew, Mary Frances Cotch, Brian Yaspan, Paul Mitchell, Jie Jin Wang, Barbara E.K. Klein, Tien Y. Wong, Jerome I. Rotter, Kathyrn P. Burdon, Sudha K. Iyengar, and Ayellet V. Segrè

Subjects
Ophthalmology
Published
2022

20. Multiethnic Genome-Wide Association Study of Diabetic Retinopathy Using Liability Threshold Modeling of Duration of Diabetes and Glycemic Control

Author

Mark I. McCarthy, Weihua Meng, Brian L. Yaspan, M Imamura, Mark W. Christiansen, Niina Sandholm, Yii-Der Ida Chen, Sharon G. Adler, Lucia Sobrin, Craig L. Hanis, Valeriya Lyssenko, Shiro Maeda, Yang Hai, Paul Mitchell, Roberta McKean-Cowdin, Xiuqing Guo, John R. Sedor, David S. Siscovick, Sudha K. Iyengar, Heather Hancock, Jane Z. Kuo, Barbara E.K. Klein, David-Alexandre Tregouet, Elisabet Agardh, Kent D. Taylor, Andrew D. Morris, S. Mohsen Hosseini, Andrew D. Paterson, I-Te Lee, Wayne Huey-Herng Sheu, Emma Ahlqvist, Kathryn P. Burdon, Leif Groop, Ayellet V. Segrè, Samy Hadjadj, Samaneh Davoudi, Lynn K. Stanwyck, Emily Y. Chew, Xiaohui Li, Michael A. Grassi, Jie Jin Wang, Samuela Pollack, Albert V. Smith, Kyu Hyung Park, Michiaki Kubo, Mary Frances Cotch, Yucheng Jia, Ching J. Chen, Colin N. A. Palmer, Helen M. Colhoun, Alan D. Penman, R. Varma, Per-Henrik Groop, Tien Yin Wong, Barry I. Freedman, Eli Ipp, Alex S. F. Doney, Gavin Tan, Ronald Klein, Kaanan P. Shah, Jamie E Craig, Donald W. Bowden, Jerome I. Rotter, Robert P. Igo, Darryl Nousome, Ching-Yu Cheng, Michel Marre, Maggie C.Y. Ng, Latchezar Dimitrov, Jeeyun Ahn, Atsushi Takahashi, Richard A. Jensen, Aaron Leong, Jihye Kim, Iiro Toppila, Elizabeth J. Rossin, Alkes L. Price, Diabetes and Obesity Research Program, University of Helsinki, Department of Medicine, Nefrologian yksikkö, Research Programs Unit, Clinicum, HUS Abdominal Center, and Per Henrik Groop / Principal Investigator

Subjects
Blood Glucose, 0301 basic medicine, Oncology, medicine.medical_specialty, Genotype, Endocrinology, Diabetes and Metabolism, LOCI, 030209 endocrinology & metabolism, Genome-wide association study, VARIANTS, Polymorphism, Single Nucleotide, 03 medical and health sciences, 0302 clinical medicine, Meta-Analysis as Topic, RESOURCE, Internal medicine, Diabetes mellitus, REVEALS, Genetic variation, Internal Medicine, medicine, Humans, Genetic Predisposition to Disease, CHINESE PATIENTS, Allele, METAANALYSIS, POLYMORPHISMS, Glycemic, Glycated Hemoglobin, Diabetic Retinopathy, business.industry, Diabetic retinopathy, medicine.disease, PREVALENCE, 3. Good health, SEVERITY, 030104 developmental biology, Diabetes Mellitus, Type 2, 3121 General medicine, internal medicine and other clinical medicine, Multiple comparisons problem, RISK-FACTORS, Medical genetics, Erratum, business, Genome-Wide Association Study, Protein Binding
Abstract

To identify genetic variants associated with diabetic retinopathy (DR), we performed a large multiethnic genome-wide association study. Discovery included eight European cohorts (n = 3,246) and seven African American cohorts (n = 2,611). We meta-analyzed across cohorts using inverse-variance weighting, with and without liability threshold modeling of glycemic control and duration of diabetes. Variants with a P value

Published
2018

21. A tissue-aware machine learning framework enhances the mechanistic understanding and genetic diagnosis of Mendelian and rare diseases

Author

Hae Kyung Im, Lior Kerber, Eyal Simonovsky, Maya Ziv, Ekaterina Vinogradov, Ayellet V. Segrè, Juman Jubran, Lior Rokach, Yuval Yogev, Ohad S. Birk, Idan Hekselman, Omry Mauer, Moran Sharon, Omer Basha, Esti Yeger-Lotem, and Chanan M Argov

Subjects
Candidate gene, business.industry, Disease, Biology, Machine learning, computer.software_genre, Mendelian disease, symbols.namesake, Mendelian inheritance, symbols, Human genome, Artificial intelligence, Genetic diagnosis, business, Disease manifestation, computer, Gene
Abstract

Genetic studies of Mendelian and rare diseases face the critical challenges of identifying pathogenic gene variants and their modes-of-action. Previous efforts rarely utilized the tissue-selective manifestation of these diseases for their elucidation. Here we introduce an interpretable machine learning (ML) platform that utilizes heterogeneous and large-scale tissue-aware datasets of human genes, and rigorously, concurrently and quantitatively assesses hundreds of candidate mechanisms per disease. The resulting tissue-aware ML platform is applicable in gene-specific, tissue-specific, or patient-specific modes. Application of the platform to selected Mendelian disease genes pinpointed mechanisms that lead to tissue-specific disease manifestation. When applied jointly to diseases that manifest in the same tissue, the models revealed common known and previously underappreciated factors that underlie tissue-selective disease manifestation. Lastly, we harnessed our ML platform toward genetic diagnosis of tissue-selective rare diseases. Patient-specific models of candidate disease-causing genes from 50 patients successfully prioritized the pathogenic gene in 86% of the cases, implying that the tissue-selectivity of rare diseases aids in filtering out unlikely candidate genes. Thus, interpretable tissue-aware ML models can boost mechanistic understanding and genetic diagnosis of tissue-selective heritable diseases. A webserver supporting gene prioritization is available at https://netbio.bgu.ac.il/trace/.

Published
2021

22. Additional file 10 of Exploiting the GTEx resources to decipher the mechanisms at GWAS loci

Author

Barbeira, Alvaro N., Bonazzola, Rodrigo, Gamazon, Eric R., Yanyu Liang, YoSon Park, Kim-Hellmuth, Sarah, Wang, Gao, Zhuoxun Jiang, Zhou, Dan, Hormozdiari, Farhad, Boxiang Liu, Abhiram Rao, Hamel, Andrew R., Pividori, Milton D., Aguet, François, Bastarache, Lisa, Jordan, Daniel M., Verbanck, Marie, Do, Ron, Stephens, Matthew, Ardlie, Kristin, McCarthy, Mark, Montgomery, Stephen B., Ayellet V. Segrè, Brown, Christopher D., Lappalainen, Tuuli, Xiaoquan Wen, and Im, Hae Kyung

Abstract

Additional file 10 Review history

Published
2021
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23. The GTEx Consortium atlas of genetic regulatory effects across human tissues

Author

Deborah C. Mash, Kevin S. Smith, Lappalainen T, Jeffrey A. Thomas, Rajinder Kaul, Paul Flicek, Maghboeba Mosavel, Yuxin Zou, Barbara E. Stranger, Brandon L. Pierce, Yanyu Liang, Andrew R. Hamel, Lihua Jiang, Marcus Hunter, Jimmie B. Vaught, Hae Kyung Im, John M. Rouhana, François Aguet, Ferran Reverter, Jason Bridge, Farzana Jasmine, Scott D. Jewell, William F. Leinweber, Gad Getz, Jonah Einson, Kevin Myer, SE Castel, Barbara E. Engelhardt, Stephen B. Montgomery, Brunilda Balliu, Gary Walters, Helen M. Moore, Daniel Nachun, Zerbino, Lori E. Brigham, Gao Wang, Farhad Hormozdiari, Pejman Mohammadi, Kasper D. Hansen, Nicole A. Teran, Fred A. Wright, Bryan Gillard, Sarah Kim-Hellmuth, CC Powell, Susan E. Koester, Wucher, Aaron Graubert, Duyen T. Nguyen, Shin Lin, Mike Moser, John A. Stamatoyannopoulos, Liqun Qi, Princy Parsana, Peter Hickey, Latarsha J. Carithers, Saboor Shad, Eric R. Gamazon, Jennifer A. Doherty, Stephen J. Trevanion, Kane Hadley, Kate R. Rosenbloom, Anita H. Undale, Robert E. Handsaker, Debra Bradbury, Shankara Anand, Meng Wang, David E. Tabor, Karna Robinson, S. Gabriel, Esti Yeger-Lotem, Kimberly Ramsey, Mary Barcus, Daniel G. MacArthur, Yuan He, Nancy Roche, Alvaro N. Barbeira, Ayellet V. Segrè, Dan Sheppard, Souvik Das, AR Little, Nathan S. Abell, Xiaoquan Wen, Elise D. Flynn, Nicole M. Ferraro, Hua Tang, Jared L. Nedzel, Jessica Wheeler, Abhi Rao, Meier, Thomas Juettemann, Sandra Linder, Bruce A. Roe, Daniel J. Cotter, David A. Davis, Christopher Johns, Lin Chen, Seva Kashin, Muhammad G. Kibriya, Ana Viñuela, Ellen Todres, Ashis Saha, Matthew Stephens, Chiara Sabatti, Manolis Kellis, Laura A. Siminoff, Phillip Branton, Xiao Li, Michael Snyder, Kathryn Demanelis, Gen Li, Barbara A. Foster, Leslie H. Sobin, Simona Volpi, Magali Ruffier, Christopher D. Brown, Ping Guan, Benjamin J. Strober, Alexis Battle, Michael J. Gloudemans, Silva Kasela, Manuel Muñoz-Aguirre, Ellen Karasik, OM deGoede, Roderic Guigó, Michael Washington, Alisa McDonald, Andrew A. Brown, Meritxell Oliva, Kieron Taylor, Nancy J. Cox, Daniel C. Rohrer, Paul J. Hoffman, Gene Kopen, Qin Li, Andrew D Skol, Rodrigo Bonazzola, Tiffany Eulalio, Mark H. Johnson, Laure Fresard, Lindsay F. Rizzardi, Abhiram Rao, T Krubit, W. J. Kent, Alan Kwong, Anna M. Smith, Pedro G. Ferreira, HM Gardiner, Andrew P. Feinberg, Rick Hasz, Lei Hou, Marta Melé, Andrew B. Nobel, Katherine H. Huang, Laura Barker, Maximilian Haeussler, Kristin G. Ardlie, Concepcion R. Nierras, Christopher Lee, Joshua M. Akey, Eskin E, Jeffrey McLean, Donald F. Conrad, Jin Billy Li, YoSon Park, Serghei Mangul, Emmanouil T. Dermitzakis, Brian Jo, D Garrido-Martin, and Barcelona Supercomputing Center

Subjects
Informàtica::Aplicacions de la informàtica::Bioinformàtica [Àrees temàtiques de la UPC], 0303 health sciences, Linkage disequilibrium, Multidisciplinary, Genotype-Tissue Expression (GTEx), Atlas (topology), Human tissues, Cancer susceptibility, Diseases, Genome-wide association study, RNA-Seq, Computational biology, Biology, Expressió gènica, Human genetics, 03 medical and health sciences, Tissues, 0302 clinical medicine, Allelic heterogeneity, Gene expression, Genètica, 030217 neurology & neurosurgery, 030304 developmental biology, Teixits (Histologia)
Abstract

The Genotype-Tissue Expression (GTEx) project dissects how genetic variation affects gene expression and splicing. Some human genetic variants affect the amount of RNA produced and the splicing of gene transcripts, crucial steps in development and maintaining a healthy individual. However, some of these changes only occur in a small number of tissues within the body. The Genotype-Tissue Expression (GTEx) project has been expanded over time, and, looking at the final data in version 8, Aguet et al. present a deep characterization of genetic associations and gene expression and splicing in 838 individuals over 49 tissues (see the Perspective by Wilson). This large study was able to characterize the details underlying many aspects of gene expression and provides a resource with which to better understand the fundamental molecular mechanisms of how genetic variants affect gene regulation and complex traits in humans. Science, this issue p. 1318; see also p. 1298 The Genotype-Tissue Expression (GTEx) project was established to characterize genetic effects on the transcriptome across human tissues and to link these regulatory mechanisms to trait and disease associations. Here, we present analyses of the version 8 data, examining 15,201 RNA-sequencing samples from 49 tissues of 838 postmortem donors. We comprehensively characterize genetic associations for gene expression and splicing in cis and trans, showing that regulatory associations are found for almost all genes, and describe the underlying molecular mechanisms and their contribution to allelic heterogeneity and pleiotropy of complex traits. Leveraging the large diversity of tissues, we provide insights into the tissue specificity of genetic effects and show that cell type composition is a key factor in understanding gene regulatory mechanisms in human tissues. We thank the donors and their families for their generous gifts of organ donation for transplantation and tissue donations for the GTEx research project; the Genomics Platform at the Broad Institute for data generation; J. Struewing for support and leadership of the GTEx project; M. Khan and C. Stolte for the illustrations in Fig. 1; and R. Do, D. Jordan, and M. Verbanck for providing GWAS pleiotropy scores. Funding: This work was supported by the Common Fund of the Office of the Director, U.S. National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, NIA, NIAID, and NINDS through NIH contracts HHSN261200800001E (Leidos Prime contract with NCI: A.M.S., D.E.T., N.V.R., J.A.M., L.S., M.E.B., L.Q., T.K., D.B., K.R., and A.U.), 10XS170 (NDRI: W.F.L., J.A.T., G.K., A.M., S.S., R.H., G.Wa., M.J., M.Wa., L.E.B., C.J., J.W., B.R., M.Hu., K.M., L.A.S., H.M.G., M.Mo., and L.K.B.), 10XS171 (Roswell Park Cancer Institute: B.A.F., M.T.M., E.K., B.M.G., K.D.R., and J.B.), 10X172 (Science Care Inc.), 12ST1039 (IDOX), 10ST1035 (Van Andel Institute: S.D.J., D.C.R., and D.R.V.), HHSN268201000029C (Broad Institute: F.A., G.G., K.G.A., A.V.S., X.Li., E.T., S.G., A.G., S.A., K.H.H., D.T.N., K.H., S.R.M., and J.L.N.), 5U41HG009494 (F.A., G.G., and K.G.A.), and through NIH grants R01 DA006227-17 (University of Miami Brain Bank: D.C.M. and D.A.D.), Supplement to University of Miami grant DA006227 (D.C.M. and D.A.D.), R01 MH090941 (University of Geneva), R01 MH090951 and R01 MH090937 (University of Chicago), R01 MH090936 (University of North Carolina–Chapel Hill), R01MH101814 (M.M.-A., V.W., S.B.M., R.G., E.T.D., D.G.-M., and A.V.), U01HG007593 (S.B.M.), R01MH101822 (C.D.B.), U01HG007598 (M.O. and B.E.S.), U01MH104393 (A.P.F.), extension H002371 to 5U41HG002371 (W.J.K.), as well as other funding sources: R01MH106842 (T.L., P.M., E.F., and P.J.H.), R01HL142028 (T.L., Si.Ka., and P.J.H.), R01GM122924 (T.L. and S.E.C.), R01MH107666 (H.K.I.), P30DK020595 (H.K.I.), UM1HG008901 (T.L.), R01GM124486 (T.L.), R01HG010067 (Y.Pa.), R01HG002585 (G.Wa. and M.St.), Gordon and Betty Moore Foundation GBMF 4559 (G.Wa. and M.St.), 1K99HG009916-01 (S.E.C.), R01HG006855 (Se.Ka. and R.E.H.), BIO2015-70777-P, Ministerio de Economia y Competitividad and FEDER funds (M.M.-A., V.W., R.G., and D.G.-M.), la Caixa Foundation ID 100010434 under agreement LCF/BQ/SO15/52260001 (D.G.-M.), NIH CTSA grant UL1TR002550-01 (P.M.), Marie-Skłodowska Curie fellowship H2020 Grant 706636 (S.K.-H.), R35HG010718 (E.R.G.), FPU15/03635, Ministerio de Educación, Cultura y Deporte (M.M.-A.),R01MH109905, 1R01HG010480 (A.Ba.), Searle Scholar Program (A.Ba.), R01HG008150 (S.B.M.), 5T32HG000044-22, NHGRI Institutional Training Grant in Genome Science (N.R.G.), EU IMI program (UE7-DIRECT-115317-1) (E.T.D. and A.V.), FNS funded project RNA1 (31003A_149984) (E.T.D. and A.V.), DK110919 (F.H.), F32HG009987 (F.H.), Massachusetts Lions Eye Research Fund Grant (A.R.H.), Wellcome grant WT108749/Z/15/Z (P.F.), and European Molecular Biology Laboratory (P.F. and D.Z.). Peer Reviewed "Article signat per 1 autors/es del BSC membres del THE GTEX CONSORTIUM: Marta Mele Messeguer"

Published
2020

24. The impact of sex on gene expression across human tissues

Author

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

Subjects
Male, Single sex, Quantitative Trait Loci, Gene Expression, Genome-wide association study, Quantitative trait locus, Biology, Genoma humà, Article, Epigenesis, Genetic, Transcriptome, Cellular origin, Sex Factors, Gene expression, Genetic variation, Humans, Disease, Precision Medicine, Promoter Regions, Genetic, Gene, Genetics, Regulation of gene expression, Chromosomes, Human, X, Sex Characteristics, Multidisciplinary, Human genome, Cromosomes sexuals, Genetic Variation, Sex chromosomes, Phenotype, Expressió gènica, Gene Expression Regulation, Organ Specificity, Expression quantitative trait loci, Female, Genome-Wide Association Study
Abstract

Many complex human phenotypes exhibit sex-differentiated characteristics. However, the molecular mechanisms underlying these differences remain largely unknown. We generated a catalog of sex differences in gene expression and in the genetic regulation of gene expression across 44 human tissue sources surveyed by the Genotype-Tissue Expression project (GTEx, v8 release). We demonstrate that sex influences gene expression levels and cellular composition of tissue samples across the human body. A total of 37% of all genes exhibit sex-biased expression in at least one tissue. We identify cis expression quantitative trait loci (eQTLs) with sex-differentiated effects and characterize their cellular origin. By integrating sex-biased eQTLs with genome-wide association study data, we identify 58 gene-trait associations that are driven by genetic regulation of gene expression in a single sex. These findings provide an extensive characterization of sex differences in the human transcriptome and its genetic regulation. INTRODUCTION: Many complex human pheno-types, including diseases, exhibit sex-differentiated characteristics. These sex differences have been variously attributed to hormones, sex chromosomes, genotype × sex effects, differences in behavior, and differences in environmental exposures; however, their mechanisms and underlying biology remain largely unknown. The Genotype-Tissue Expression (GTEx) project provides an opportunity to investigate the prevalence and genetic mechanisms of sex differences in the human transcriptome by surveying many tissues that have not previously been characterized in this manner. RATIONALE: To characterize sex differences in the human transcriptome and its regulation, and to discover how sex and genetics interact to influence complex traits and disease, we generated a catalog of sex differences in gene expression and its genetic regulation across 44 human tissue sources surveyed by the GTEx project (v8 data release), analyzing 16,245 RNA-sequencing samples and genotypes of 838 adult individuals. We report sex differences in gene expression levels, tissue cell type composition, and cis expression quantitative trait loci (cis-eQTLs). To assess their impact, we integrated these results with gene function, transcription factor binding annotation, and genome-wide association study (GWAS) summary statistics of 87 GWASs. RESULTS: Sex effects on gene expression are ubiquitous (13,294 sex-biased genes across all tissues). However, these effects are small and largely tissue-specific. Genes with sex-differentiated expression are not primarily driven by tissue-specific gene expression and are involved in a diverse set of biological functions, such as drug and hormone response, embryonic development and tissue morphogenesis, fertilization, sexual reproduction and spermatogenesis, fat metabolism, cancer, and immune response. Whereas X-linked genes with higher expression in females suggest candidates for escape from X-chromosome inactivation, sex-biased expression of autosomal genes suggests hormone-related transcription factor regulation and a role for additional transcription factors, as well as sex-differentiated distribution of epigenetic marks, particularly histone H3 Lys(27) trimethylation (H3K27me3). Sex differences in the genetic regulation of gene expression are much less common (369 sex-biased eQTLs across all tissues) and are highly tissue-specific. We identified 58 gene-trait associations driven by genetic regulation of gene expression in a single sex. These include loci where sex-differentiated cell type abundances mediate genotype-phenotype associations, as well as loci where sex may play a more direct role in the underlying molecular mechanism of the association. For example, we identified a female-specific eQTL in liver for the hexokinase HKDC1 that influences glucose metabolism in pregnant females, which is subsequently reflected in the birth weight of the offspring. CONCLUSION: By integrating sex-aware analyses of GTEx data with gene function and transcription factor binding annotations, we describe tissue-specific and tissue-shared drivers and mechanisms contributing to sex differences in the human transcriptome and eQTLs. We discovered multiple sex-differentiated genetic effects on gene expression that colocalize with complex trait genetic associations, thereby facilitating the mechanistic interpretation of GWAS signals. Because the causative tissue is unknown for many phenotypes, analysis of the diverse GTEx tissue collection can serve as a powerful resource for investigations into the basis of sex-biased traits. This work provides an extensive characterization of sex differences in the human transcriptome and its genetic regulation. ■

Published
2020

25. Evaluating Methods for Differential Gene Expression And Alternative Splicing Using Internal Synthetic Controls

Author

Eric A. Pierce, Daniel Navarro-Gomez, Sudeep Mehrotra, Revital Bronstein, and Ayellet V. Segrè

Subjects
Gene isoform, Identification (information), Computer science, Alternative splicing, Gene expression, Transcriptome profiling, Computational biology, Deep sequencing, Transcriptome Sequencing
Abstract

High-throughput transcriptome sequencing has become a powerful tool in the study of human diseases. Identification of causal mechanisms may entail analysis of differential gene expression (DGE), differential transcript/isoform expression (DTE) and identification, classification and quantification of alternative splicing (AS) and/or detection of novel AS events. For such a global transcriptome profiling execution of multi-level data analysis methodologies is required. Each level presents its own unique challenges and the questions about their performance remains. In this work we present results from systematic and consistent assessing and comparing a number of widely used methods for detecting DGE, DTE and AS using internal control “spike-in” sequences (Sequins) in RNA-seq data. We demonstrated that inclusion of internal controls in RNA-seq experiments allows accurate determination of lower bounds detection levels, and better assessment of DGE, DTE and AS accuracy and sensitivity. Tools for RNA-seq read alignment and detection of DGE performed reasonably. More efforts are needed to improve specificity and sensitivity of DTE and AS detection. Low expression of isoforms accompanied with sequencing depth does impact sensitivity and specificity of DTE and AS tools.

Published
2020

26. Meta-analysis of 542,934 subjects of European ancestry identifies new genes and mechanisms predisposing to refractive error and myopia

Author

Eric Jorgenson, Mark James Simcoe, Alex W. Hewitt, Omar A. Mahroo, Anthony P Khawaja, Pirro G. Hysi, Paul J. Foster, Virginie J. M. Verhoeven, UK Eye, Jie Yin, David A. Mackey, Ayellet V. Segrè, Christopher J Hammond, Milly S. Tedja, Peng T. Khaw, Richard A. Stone, Jugnoo S Rahi, Hélène Choquet, Jeremy A. Guggenheim, Karina Patasova, Phillippa M. Cumberland, Khanh K. Thai, Caroline C W Klaver, Ronald B. Melles, Robert Wojciechowski, Nicholas J. Wareham, Myopia, Veronique Vitart, Stuart MacGregor, Ophthalmology, Epidemiology, and Clinical Genetics

Subjects
Adult, Male, Refractive error, genetic structures, Genome-wide association study, Biology, Bioinformatics, Polymorphism, Single Nucleotide, Article, White People, Sensory disorders Donders Center for Medical Neuroscience [Radboudumc 12], 03 medical and health sciences, 0302 clinical medicine, All institutes and research themes of the Radboud University Medical Center, Myopia, Genetics, medicine, Humans, Genetic Predisposition to Disease, Gene, Aged, 030304 developmental biology, Genetic association, 0303 health sciences, Genetic heterogeneity, Middle Aged, Heritability, Refractive Errors, medicine.disease, eye diseases, 3. Good health, Abnormal eye, Meta-analysis, Female, 030217 neurology & neurosurgery, Genome-Wide Association Study
Abstract

Refractive errors, in particular myopia, are a leading cause of morbidity and disability world-wide. Genetic investigation can improve understanding of the molecular mechanisms underlying abnormal eye development and impaired vision. We conducted a meta-analysis of genome-wide association studies involving 542,934 European participants and identified 336 novel genetic loci associated with refractive error. Collectively, all associated genetic variants explain 18.4% of heritability and improve the accuracy of myopia prediction (AUC=0.75). Our results suggest that refractive error is genetically heterogeneous, driven by genes participating in the development of every anatomical component of the eye. In addition, our analyses suggest that genetic factors controlling circadian rhythm and pigmentation are also involved in the development of myopia and refractive error. These results may make possible predicting refractive error and the development of personalized myopia prevention strategies in the future., Reporting summary Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Published
2020

27. A large cross-ancestry meta-analysis of genome-wide association studies identifies 69 novel risk loci for primary open-angle glaucoma and includes a genetic link with Alzheimer’s disease

Author

Peter Kraft, Adriana Iglesias, Steffen Uebe, Alberta A H J Thiadens, Jessica N. Cooke Bailey, Paul Mitchell, Olusola Olawoye, Michele Ramsay, Caroline C.W. Klaver, Eranga N. Vithana, Ayub Qassim, Mark James Simcoe, Pirro G. Hysi, Xin Wang, Angela J. Cree, Juha Karjalainen, René Höhn, Cornelia van Duijn, Andrew J. Lotery, Veronique Vitart, Anthony P Khawaja, Terri L. Young, James E. Morgan, UK Biobank Eye, Yukihiro Shiga, Owen M. Siggs, Yukihide Momozawa, Stephen Akafo, Puya Gharahkhani, Robert P. Igo, Masato Akiama, Gen Tamiya, Sarah A. Pendergrass, Navya Shilpa Josyula, Chris Hammond, David A. Mackey, FinnGen study, Chiea Chuen Khor, Francesca Pasutto, Ewan Birney, Pieter W.M. Bonnemaijer, Aarno Palotie, Susan E. Williams, John Rouhana, Nishani Amersinghe, Peng T. Khaw, Stuart MacGregor, Ching-Yu Cheng, Yoichito Kamatani, Calvin Chi Pui Pang, Xikun Han, Alex W. Hewitt, Louis R. Pasquale, Jae Hee Kang, Eric Jorgenson, Ronald B. Melles, Michael Hauser, Li Jia Chen, Jonathan Haines, Masayuki Yamamoto, Alicia Poplawski, Ayellet V. Segrè, Jue-Sheng Ong, Kazuki Hashimoto, Hélène Choquet, Hannah Currant, Jamie E. Craig, Robert Luben, Adam Auton, Tin Aung, Paul J. Foster, Toru Nakazawa, Nicholas G. Strouthidis, Michiaki Kubo, K. Saidas Nair, Janey L. Wiggs, and Adeyinka O. Ashaye

Subjects
Genetics, 0303 health sciences, genetic structures, Open angle glaucoma, Glaucoma, Genome-wide association study, Disease, Biology, medicine.disease, eye diseases, 3. Good health, 03 medical and health sciences, 0302 clinical medicine, Meta-analysis, medicine, sense organs, Gene, 030217 neurology & neurosurgery, 030304 developmental biology, Genetic association
Abstract

We conducted a large multi-ethnic meta-analysis of genome-wide association studies for primary open-angle glaucoma (POAG) on a total of 34,179 cases vs 349,321 controls, and identified 127 independent risk loci, almost doubling the number of known loci for POAG. The majority of loci have broadly consistent effect across European, Asian and African ancestries. We identify a link, both genome-wide and at specific loci, between POAG and Alzheimer’s disease. Gene expression data and bioinformatic functional analyses provide further support for the functional relevance of the POAG risk genes. Several drug compounds target these risk genes and may be potential candidates for developing novel POAG treatments.

Published
2020

28. A vast resource of allelic expression data spanning human tissues

Author

Nancy J. Cox, Sayantan Das, Abhi Rao, Pejman Mohammadi, Alan Kwong, Brandon L. Pierce, Yanyu Liang, Yuxin Zou, Anna M. Smith, Matthew Stephens, Chiara Sabatti, Yuan He, Kasper D. Hansen, Lei Hou, Meritxell Oliva, W. James Kent, Stacey Gabriel, Andrew R. Hame, Tanya Krubit, Gary Walters, Lori E. Brigham, Gao Wang, Kevin S. Smith, Michael J. Gloudemans, Barbara E. Engelhardt, Yongjin Park, Nicole A. Teran, David A. Davis, Thomas Juettemann, Kimberley Ramsey, Fred A. Wright, Lin Chen, Valentin Wucher, Benjamin J. Strober, Duyen T. Nguyen, Eleazar Eskin, Kane Hadley, Deborah C. Mash, Michael Snyder, Sarah Kim-Hellmuth, Laura A. Siminoff, Maghboeba Mosavel, Shin Lin, Richard Hasz, Daniel C. Rohrer, Latarsha J. Carithers, Kevin Myer, Rajinder Kaul, Andrew D. Skol, Bryan Gillard, Dana R. Valley, Philip A. Branton, Stephane E. Castel, Robert E. Handsaker, Debra Bradbury, Meng Wang, Mary Barcus, Xiaoquan Wen, Hua Tang, Daniel J. Cotter, Lihua Jiang, Jason Bridge, Ashis Saha, Gen Li, Susan E. Koester, Qin Li, Mark H. Johnson, Barbara E. Stranger, Jimmie B. Vaught, Hae Kyung Im, Paul Flicek, Marcus Hunter, François Aguet, Elise D. Flynn, Sandra Linder, Nancy Roche, Daniel R. Zerbino, Xiao Li, Barbara A. Foster, Stephen B. Montgomery, Daniel Nachun, Serghei Mangul, Emmanouil T. Dermitzakis, Brian Jo, Simona Volpi, Farzana Jasmine, Scott D. Jewell, Jonah Einson, Tuuli Lappalainen, Farhad Hormozdiari, John M. Rouhana, Ana Viñuela, Daniel G. MacArthur, William F. Leinweber, Gad Getz, Peter Hickey, Eric R. Gamazon, Brunilda Balliu, Jennifer A. Doherty, Christopher D. Brown, Roderic Guigó, Gene Kopen, Rodrigo Bonazzola, Pedro G. Ferreira, Andrew P. Feinberg, Shankara Anand, Helen M. Moore, Paul J. Hoffman, Heather M. Gardiner, Ping Guan, Ferran Reverter, Jin Billy Li, Tiffany Eulalio, Joseph Wheeler, Alvaro N. Barbeira, Jared L. Nedzel, Seva Kashin, Laure Fresard, Lindsay F. Rizzardi, Abhiram Rao, Muhammad G. Kibriya, David Tabor, Leslie H. Sobin, A. Roger Little, Stephen J. Trevanion, Nicole M. Ferraro, Kate R. Rosenbloom, John A. Stamatoyannopoulos, Liqun Qi, Princy Parsana, Ayellet V. Segrè, Dan Sheppard, Nathan S. Abell, Kathryn Demanelis, Manolis Kellis, Silva Kasela, Xin Li, Conner C. Powell, YoSon Park, Michael Washington, Magali Ruffier, Saboor Shad, Christopher Johns, Jeffrey A. Thomas, Andrew Brown, Alisa McDonald, Karna Robinson, Esti Yeger-Lotem, Manuel Muñoz-Aguirre, Kieron Taylor, Marta Melé, Diego Garrido-Martín, Brian Roe, Michael T. Moser, Andrew B. Nobel, Alexis Battle, Maximilian Haeussler, Concepcion R. Nierras, Ellen Karasik, Sam Meier, Anita H. Undale, Ellen Todres, Aaron Graubert, Joshua M. Akey, Jeffrey McLean, Donald F. Conrad, Olivia M. De Goede, Katherine H. Huang, Laura Barker, Kristin G. Ardlie, and Christopher Lee

Subjects
animal structures, Future studies, lcsh:QH426-470, Short Report, Gene Expression, Genomics, Computational biology, Biology, eQTL, Polymorphism, Single Nucleotide, ASE, Regulatory variation, 03 medical and health sciences, 0302 clinical medicine, Resource (project management), Humans, SNP, Allele, lcsh:QH301-705.5, Alleles, Allele specific, 030304 developmental biology, 0303 health sciences, Allelic expression, Genome, Human, Sequence Analysis, RNA, Haplotype, Functional genomics, Human genetics, lcsh:Genetics, Haplotypes, lcsh:Biology (General), Expression data, Expression quantitative trait loci, GTEx, 030217 neurology & neurosurgery
Abstract

Allele expression (AE) analysis robustly measures cis-regulatory effects. Here, we present and demonstrate the utility of a vast AE resource generated from the GTEx v8 release, containing 15,253 samples spanning 54 human tissues for a total of 431 million measurements of AE at the SNP level and 153 million measurements at the haplotype level. In addition, we develop an extension of our tool phASER that allows effect sizes of cis-regulatory variants to be estimated using haplotype-level AE data. This AE resource is the largest to date, and we are able to make haplotype-level data publicly available. We anticipate that the availability of this resource will enable future studies of regulatory variation across human tissues.

Published
2020

29. Exploiting the GTEx resources to decipher the mechanisms at GWAS loci

Author

Christopher D. Brown, Gao Wang, Lisa Bastarache, Boxiang Liu, Tuuli Lappalainen, Marie Verbanck, Xiaoquan Wen, Zhuoxun Jiang, Farhad Hormozdiari, Eric R. Gamazon, Mark I. McCarthy, Sarah Kim-Hellmuth, Rodrigo Bonazzola, Milton Pividori, Alvaro N. Barbeira, Kristin G. Ardlie, Abhiram Rao, Daniel M. Jordan, Yanyu Liang, Andrew R. Hamel, Dan Zhou, YoSon Park, Stephen B. Montgomery, Matthew Stephens, Hae Kyung Im, François Aguet, Ron Do, Ayellet V. Segrè, Im, Hae Kyung [0000-0003-0333-5685], and Apollo - University of Cambridge Repository

Subjects
Multifactorial Inheritance, Genotype, lcsh:QH426-470, Research, Gene Expression, Genome-wide association study, Computational biology, Biology, Phenotype, Human genetics, Transcriptome, lcsh:Genetics, Rna expression, Genes, lcsh:Biology (General), RNA splicing, Humans, DECIPHER, Genetic Predisposition to Disease, Allelic heterogeneity, lcsh:QH301-705.5, Gene, Genome-Wide Association Study, Genetic association
Abstract

The resources generated by the GTEx consortium offer unprecedented opportunities to advance our understanding of the biology of human diseases. Here, we present an in-depth examination of the phenotypic consequences of transcriptome regulation and a blueprint for the functional interpretation of genome-wide association study-discovered loci. Across a broad set of complex traits and diseases, we demonstrate widespread dose-dependent effects of RNA expression and splicing. We develop a data-driven framework to benchmark methods that prioritize causal genes and find no single approach outperforms the combination of multiple approaches. Using colocalization and association approaches that take into account the observed allelic heterogeneity of gene expression, we propose potential target genes for 47% (2,519 out of 5,385) of the GWAS loci examined. Our results demonstrate the translational relevance of the GTEx resources and highlight the need to increase their resolution and breadth to further our understanding of the genotype-phenotype link.

Published
2019

30. Population-scale tissue transcriptomics maps long non-coding RNAs to complex disease

Author

Olivia M. de Goede, Daniel C. Nachun, Nicole M. Ferraro, Michael J. Gloudemans, Abhiram S. Rao, Craig Smail, Tiffany Y. Eulalio, François Aguet, Bernard Ng, Jishu Xu, Alvaro N. Barbeira, Stephane E. Castel, Sarah Kim-Hellmuth, YoSon Park, Alexandra J. Scott, Benjamin J. Strober, Christopher D. Brown, Xiaoquan Wen, Ira M. Hall, Alexis Battle, Tuuli Lappalainen, Hae Kyung Im, Kristin G. Ardlie, Sara Mostafavi, Thomas Quertermous, Karla Kirkegaard, Stephen B. Montgomery, Shankara Anand, Stacey Gabriel, Gad A. Getz, Aaron Graubert, Kane Hadley, Robert E. Handsaker, Katherine H. Huang, Xiao Li, Daniel G. MacArthur, Samuel R. Meier, Jared L. Nedzel, Duyen T. Nguyen, Ayellet V. Segrè, Ellen Todres, Brunilda Balliu, Rodrigo Bonazzola, Andrew Brown, Donald F. Conrad, Daniel J. Cotter, Nancy Cox, Sayantan Das, Emmanouil T. Dermitzakis, Jonah Einson, Barbara E. Engelhardt, Eleazar Eskin, Elise D. Flynn, Laure Fresard, Eric R. Gamazon, Diego Garrido-Martín, Nicole R. Gay, Roderic Guigó, Andrew R. Hamel, Yuan He, Paul J. Hoffman, Farhad Hormozdiari, Lei Hou, Brian Jo, Silva Kasela, Seva Kashin, Manolis Kellis, Alan Kwong, Xin Li, Yanyu Liang, Serghei Mangul, Pejman Mohammadi, Manuel Muñoz-Aguirre, Andrew B. Nobel, Meritxell Oliva, Yongjin Park, Princy Parsana, Ferran Reverter, John M. Rouhana, Chiara Sabatti, Ashis Saha, Matthew Stephens, Barbara E. Stranger, Nicole A. Teran, Ana Viñuela, Gao Wang, Fred Wright, Valentin Wucher, Yuxin Zou, Pedro G. Ferreira, Gen Li, Marta Melé, Esti Yeger-Lotem, Debra Bradbury, Tanya Krubit, Jeffrey A. McLean, Liqun Qi, Karna Robinson, Nancy V. Roche, Anna M. Smith, David E. Tabor, Anita Undale, Jason Bridge, Lori E. Brigham, Barbara A. Foster, Bryan M. Gillard, Richard Hasz, Marcus Hunter, Christopher Johns, Mark Johnson, Ellen Karasik, Gene Kopen, William F. Leinweber, Alisa McDonald, Michael T. Moser, Kevin Myer, Kimberley D. Ramsey, Brian Roe, Saboor Shad, Jeffrey A. Thomas, Gary Walters, Michael Washington, Joseph Wheeler, Scott D. Jewell, Daniel C. Rohrer, Dana R. Valley, David A. Davis, Deborah C. Mash, Mary E. Barcus, Philip A. Branton, Leslie Sobin, Laura K. Barker, Heather M. Gardiner, Maghboeba Mosavel, Laura A. Siminoff, Paul Flicek, Maximilian Haeussler, Thomas Juettemann, W. James Kent, Christopher M. Lee, Conner C. Powell, Kate R. Rosenbloom, Magali Ruffier, Dan Sheppard, Kieron Taylor, Stephen J. Trevanion, Daniel R. Zerbino, Nathan S. Abell, Joshua Akey, Lin Chen, Kathryn Demanelis, Jennifer A. Doherty, Andrew P. Feinberg, Kasper D. Hansen, Peter F. Hickey, Farzana Jasmine, Lihua Jiang, Rajinder Kaul, Muhammad G. Kibriya, Jin Billy Li, Qin Li, Shin Lin, Sandra E. Linder, Brandon L. Pierce, Lindsay F. Rizzardi, Andrew D. Skol, Kevin S. Smith, Michael Snyder, John Stamatoyannopoulos, Hua Tang, Meng Wang, Latarsha J. Carithers, Ping Guan, Susan E. Koester, A. Roger Little, Helen M. Moore, Concepcion R. Nierras, Abhi K. Rao, Jimmie B. Vaught, and Simona Volpi

Subjects
Multifactorial Inheritance, Population, Quantitative Trait Loci, Coronary Artery Disease, Disease, Computational biology, Quantitative trait locus, Biology, Article, General Biochemistry, Genetics and Molecular Biology, Transcriptome, 03 medical and health sciences, 0302 clinical medicine, Humans, education, Gene, 030304 developmental biology, 0303 health sciences, education.field_of_study, Gene Expression Profiling, Genetic Variation, Inflammatory Bowel Diseases, Long non-coding RNA, Diabetes Mellitus, Type 1, Diabetes Mellitus, Type 2, Organ Specificity, Expression quantitative trait loci, Trait, RNA, Long Noncoding, 030217 neurology & neurosurgery
Abstract

Long non-coding RNA (lncRNA) genes have well-established and important impacts on molecular and cellular functions. However, among the thousands of lncRNA genes, it is still a major challenge to identify the subset with disease or trait relevance. To systematically characterize these lncRNA genes, we used Genotype Tissue Expression (GTEx) project v8 genetic and multi-tissue transcriptomic data to profile the expression, genetic regulation, cellular contexts, and trait associations of 14,100 lncRNA genes across 49 tissues for 101 distinct complex genetic traits. Using these approaches, we identified 1,432 lncRNA gene-trait associations, 800 of which were not explained by stronger effects of neighboring protein-coding genes. This included associations between lncRNA quantitative trait loci and inflammatory bowel disease, type 1 and type 2 diabetes, and coronary artery disease, as well as rare variant associations to body mass index.

Published
2021

31. RNA sequence analysis reveals macroscopic somatic clonal expansion across normal tissues

Author

Jonna Grimsby, Nicholas J. Haradhvala, Julian M. Hess, Hailei Zhang, Eila Arich-Landkof, Paz Polak, Chip Stewart, Philip A. Branton, Gad Getz, Noam Shoresh, Ruslana Frazer, Kristin G. Ardlie, Jaegil Kim, Keren Yizhak, Dimitri Livitz, Daniel Rosebrock, K Kübler, François Aguet, Ayellet V. Segrè, and Xiao Li

Subjects
Male, Genetics, Mutation, Multidisciplinary, Sequence Analysis, RNA, Somatic cell, Sequence analysis, DNA Mutational Analysis, Cell, Normal tissue, RNA, Disease, Biology, medicine.disease_cause, Clone Cells, medicine.anatomical_structure, Organ Specificity, Neoplasms, RNA Sequence, medicine, Humans, Female
Abstract

Somatic mosaicism in normal tissues Somatic cells can accumulate mutations over the course of an individual's lifetime. This generates cells that differ genetically at specific loci within the genome. To explore how this genetic diversity in individuals contributes to disease, Yizhak et al. developed a method to detect mutations from RNA sequencing data (see the Perspective by Tomasetti). Applying this method to Cancer Genome Atlas samples and normal samples from the Genotype-Tissue Expression (GTEx) project generated a tissue-specific study of mutation accumulation. Somatic mutations were detected in nearly all individuals and across many normal human tissues in genomic regions called cancer hotspots and in genes that play a role in cancer. Interestingly, the skin, lung, and esophagus exhibited the most mutations, suggesting that the environment generates many human mutations. Science , this issue p. eaaw0726 ; see also p. 938

Published
2019

32. Cell type specific genetic regulation of gene expression across human tissues

Author

Diego Garrido-Martín, Stephane E. Castel, Valentin Wucher, Manuel Muñoz-Aguirre, Gad Getz, José Manuel Soria, Kristin G. Ardlie, Barbara E. Stranger, Hae Kyung Im, Gao Wang, François Aguet, Serghei Mangul, Sarah Kim-Hellmuth, Matthew Stephens, Andrew A. Brown, Emmanouil T. Dermitzakis, Alvaro N. Barbeira, Hualin S. Xi, Rodrigo Bonazzola, Roderic Guigó, Kerrin S. Small, Andrew R. Hamel, Angel Martinez-Perez, Ayellet V. Segrè, Ana Viñuela, Amy L. Roberts, Jie Quan, Xiaoquan Wen, Silva Kasela, Yuxin Zou, Tuuli Lappalainen, Meritxell Oliva, Brian B. Nadel, and Universitat Politècnica de Catalunya. Doctorat en Estadística i Investigació Operativa

Subjects
Cell type, Bioinformatics, Cell, Cell type specific, Quantitative trait locus, Biology, Article, Transcriptome, 03 medical and health sciences, 0302 clinical medicine, Bioinformàtica, Genotype, medicine, Gene, 030304 developmental biology, Genetics, Regulation of gene expression, 0303 health sciences, Multidisciplinary, food and beverages, Matemàtiques i estadística [Àrees temàtiques de la UPC], Expressió gènica, Tissue specificity, medicine.anatomical_structure, RNA splicing, Gene expression, 030217 neurology & neurosurgery
Abstract

The Genotype-Tissue Expression (GTEx) project has identified expression and splicing quantitative trait loci (cis-QTLs) for the majority of genes across a wide range of human tissues. However, the interpretation of these QTLs has been limited by the heterogeneous cellular composition of GTEx tissue samples. Here, we map interactions between computational estimates of cell type abundance and genotype to identify cell type interaction QTLs for seven cell types and show that cell type interaction eQTLs provide finer resolution to tissue specificity than bulk tissuecis-eQTLs. Analyses of genetic associations to 87 complex traits show a contribution from cell type interaction QTLs and enables the discovery of hundreds of previously unidentified colocalized loci that are masked in bulk tissue.One Sentence SummaryEstimated cell type abundances from bulk RNA-seq across tissues reveal the cellular specificity of quantitative trait loci.

Published
2019

33. A Quantitative Proteome Map of the Human Body

Author

Lihua Jiang, Meng Wang, Shin Lin, Ruiqi Jian, Xiao Li, Joanne Chan, Guanlan Dong, Huaying Fang, Aaron E. Robinson, Michael P. Snyder, François Aguet, Shankara Anand, Kristin G. Ardlie, Stacey Gabriel, Gad Getz, Aaron Graubert, Kane Hadley, Robert E. Handsaker, Katherine H. Huang, Seva Kashin, Daniel G. MacArthur, Samuel R. Meier, Jared L. Nedzel, Duyen Y. Nguyen, Ayellet V. Segrè, Ellen Todres, Brunilda Balliu, Alvaro N. Barbeira, Alexis Battle, Rodrigo Bonazzola, Andrew Brown, Christopher D. Brown, Stephane E. Castel, Don Conrad, Daniel J. Cotter, Nancy Cox, Sayantan Das, Olivia M. de Goede, Emmanouil T. Dermitzakis, Barbara E. Engelhardt, Eleazar Eskin, Tiffany Y. Eulalio, Nicole M. Ferraro, Elise Flynn, Laure Fresard, Eric R. Gamazon, Diego Garrido-Martín, Nicole R. Gay, Roderic Guigó, Andrew R. Hamel, Yuan He, Paul J. Hoffman, Farhad Hormozdiari, Lei Hou, Hae Kyung Im, Brian Jo, Silva Kasela, Manolis Kellis, Sarah Kim-Hellmuth, Alan Kwong, Tuuli Lappalainen, Xin Li, Yanyu Liang, Serghei Mangul, Pejman Mohammadi, Stephen B. Montgomery, Manuel Muñoz-Aguirre, Daniel C. Nachun, Andrew B. Nobel, Meritxell Oliva, YoSon Park, Yongjin Park, Princy Parsana, Ferran Reverter, John M. Rouhana, Chiara Sabatti, Ashis Saha, Andrew D. Skol, Matthew Stephens, Barbara E. Stranger, Benjamin J. Strober, Nicole A. Teran, Ana Viñuela, Gao Wang, Xiaoquan Wen, Fred Wright, Valentin Wucher, Yuxin Zou, Pedro G. Ferreira, Gen Li, Marta Melé, Esti Yeger-Lotem, Mary E. Barcus, Debra Bradbury, Tanya Krubit, Jeffrey A. McLean, Liqun Qi, Karna Robinson, Nancy V. Roche, Anna M. Smith, Leslie Sobin, David E. Tabor, Anita Undale, Jason Bridge, Lori E. Brigham, Barbara A. Foster, Bryan M. Gillard, Richard Hasz, Marcus Hunter, Christopher Johns, Mark Johnson, Ellen Karasik, Gene Kopen, William F. Leinweber, Alisa McDonald, Michael T. Moser, Kevin Myer, Kimberley D. Ramsey, Brian Roe, Saboor Shad, Jeffrey A. Thomas, Gary Walters, Michael Washington, Joseph Wheeler, Scott D. Jewell, Daniel C. Rohrer, Dana R. Valley, David A. Davis, Deborah C. Mash, Philip A. Branton, Laura K. Barker, Heather M. Gardiner, Maghboeba Mosavel, Laura A. Siminoff, Paul Flicek, Maximilian Haeussler, Thomas Juettemann, W. James Kent, Christopher M. Lee, Conner C. Powell, Kate R. Rosenbloom, Magali Ruffier, Dan Sheppard, Kieron Taylor, Stephen J. Trevanion, Daniel R. Zerbino, Nathan S. Abell, Joshua Akey, Lin Chen, Kathryn Demanelis, Jennifer A. Doherty, Andrew P. Feinberg, Kasper D. Hansen, Peter F. Hickey, Farzana Jasmine, Rajinder Kaul, Muhammad G. Kibriya, Jin Billy Li, Qin Li, Sandra E. Linder, Brandon L. Pierce, Lindsay F. Rizzardi, Kevin S. Smith, John Stamatoyannopoulos, Hua Tang, Latarsha J. Carithers, Ping Guan, Susan E. Koester, A. Roger Little, Helen M. Moore, Concepcion R. Nierras, Abhi K. Rao, Jimmie B. Vaught, and Simona Volpi

Subjects
Proteomics, Cell signaling, Proteome, Quantitative proteomics, Gene Expression, Disease, Computational biology, Biology, ENCODE, Article, General Biochemistry, Genetics and Molecular Biology, Transcriptome, 03 medical and health sciences, 0302 clinical medicine, Humans, Secretion, RNA, Messenger, Gene, 030304 developmental biology, 0303 health sciences, Gene Expression Profiling, RNA, Metabolism, Phenotype, Secretory protein, 030217 neurology & neurosurgery
Abstract

Determining protein levels in each tissue and how they compare with RNA levels is important for understanding human biology and disease as well as regulatory processes that control protein levels. We quantified the relative protein levels from 12,627 genes across 32 normal human tissue types prepared by the GTEx project. Known and new tissue specific or enriched proteins (5,499) were identified and compared to transcriptome data. Many ubiquitous transcripts are found to encode highly tissue specific proteins. Discordance in the sites of RNA expression and protein detection also revealed potential sites of synthesis and action of protein signaling molecules. Overall, these results provide an extraordinary resource, and demonstrate that understanding protein levels can provide insights into metabolism, regulation, secretome, and human diseases.SummaryQuantitative proteome study of 32 human tissues and integrated analysis with transcriptome data revealed that understanding protein levels could provide in-depth knowledge to post transcriptional or translational regulations, human metabolism, secretome, and diseases.

Published
2020

34. ForestPMPlot: A Flexible Tool for Visualizing Heterogeneity Between Studies in Meta-analysis

Author

Eleazar Eskin, Xiao Li, Eun Yong Kang, Yu Rang Park, Buhm Han, and Ayellet V. Segrè

Subjects
0301 basic medicine, genetic association studies, Quantitative Trait Loci, Genome-wide association study, Computational biology, Investigations, Quantitative trait locus, Biology, QH426-470, 03 medical and health sciences, Mice, Genetic Heterogeneity, 0302 clinical medicine, Meta-Analysis as Topic, Models, Computer Graphics, Genetics, Animals, Humans, GWAS, Molecular Biology, Genetics (clinical), Selection Bias, Models, Statistical, Genetic heterogeneity, Statistical, Visualization, meta-analysis, 030104 developmental biology, Phenotype, Meta-analysis, Sample Size, Expression quantitative trait loci, heterogeneity, 030217 neurology & neurosurgery, Software
Abstract

Meta-analysis has become a popular tool for genetic association studies to combine different genetic studies. A key challenge in meta-analysis is heterogeneity, or the differences in effect sizes between studies. Heterogeneity complicates the interpretation of meta-analyses. In this paper, we describe ForestPMPlot, a flexible visualization tool for analyzing studies included in a meta-analysis. The main feature of the tool is visualizing the differences in the effect sizes of the studies to understand why the studies exhibit heterogeneity for a particular phenotype and locus pair under different conditions. We show the application of this tool to interpret a meta-analysis of 17 mouse studies, and to interpret a multi-tissue eQTL study.

Published
2016

35. Erratum. Multiethnic Genome-Wide Association Study of Diabetic Retinopathy Using Liability Threshold Modeling of Duration of Diabetes and Glycemic Control. Diabetes 2019;68:441—456

Author

Aaron Leong, Yii-Der Ida Chen, Iiro Toppila, Elizabeth J. Rossin, Alan D. Penman, I-Te Lee, Kathryn P. Burdon, Yucheng Jia, Sudha K. Iyengar, Helen M. Colhoun, Michael A. Grassi, Jie Jin Wang, Tien Yin Wong, Barry I. Freedman, Kaanan P. Shah, Ching J. Chen, Weihua Meng, Brian L. Yaspan, Samaneh Davoudi, David S. Siscovick, Per-Henrik Groop, Samy Hadjadj, Michel Marre, Ching-Yu Cheng, John R. Sedor, Xiuqing Guo, Albert V. Smith, Robert P. Igo, Kyu Hyung Park, Jerome I. Rotter, Andrew D. Paterson, Samuela Pollack, Sharon G. Adler, Alkes L. Price, Emily Y. Chew, Emma Ahlqvist, Eli Ipp, Jamie E Craig, Richard A. Jensen, Barbara E.K. Klein, Maggie C.Y. Ng, Leif Groop, Craig L. Hanis, Xiaohui Li, Atsushi Takahashi, Roberta McKean-Cowdin, Mark P. Christiansen, Lynn K. Stanwyck, Paul Mitchell, Shiro Maeda, Alex S. F. Doney, Darryl Nousome, Ronald Klein, Yang Hai, Mark I. McCarthy, Niina Sandholm, Kent D. Taylor, Andrew D. Morris, Rohit Varma, David-Alexandre Trégouët, Jihye Kim, Valeriya Lyssenko, Colin N. A. Palmer, Mary Frances Cotch, Jane Z. Kuo, Elisabet Agardh, S. Mohsen Hosseini, Michiaki Kubo, Jeeyun Ahn, Gavin Tan, Minako Imamura, Lucia Sobrin, Heather Hancock, Wayne Huey-Herng Sheu, Donald W. Bowden, Ayellet V. Segrè, and Latchezar Dimitrov

Subjects
Pediatrics, medicine.medical_specialty, Complications, business.industry, Endocrinology, Diabetes and Metabolism, Genome-wide association study, Diabetic retinopathy, medicine.disease, Diabetes mellitus, Internal Medicine, medicine, Duration (project management), business, Glycemic
Abstract

To identify genetic variants associated with diabetic retinopathy (DR), we performed a large multiethnic genome-wide association study. Discovery included eight European cohorts (n = 3,246) and seven African American cohorts (n = 2,611). We meta-analyzed across cohorts using inverse-variance weighting, with and without liability threshold modeling of glycemic control and duration of diabetes. Variants with a P value

Published
2020

36. Using an atlas of gene regulation across 44 human tissues to inform complex disease- and trait-associated variation

Author

Nancy J. Cox, Mark I. McCarthy, Martijn van de Bunt, Anuar Konkashbaev, Halit Ongen, Farhad Hormozdiari, Eric R. Gamazon, Xiaoquan Wen, Hualin S. Xi, Jie Quan, Manolis Kellis, Eleazar Eskin, Emmanouil T. Dermitzakis, François Aguet, Ayellet V. Segrè, Gad Getz, Eske M. Derks, Kristin G. Ardlie, Dan L. Nicolae, and Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory

Subjects
0301 basic medicine, Linkage disequilibrium, Genotype, ddc:025.063/570, Quantitative Trait Loci, Gene Expression, Genome-wide association study, Biology, Quantitative trait locus, Polymorphism, Single Nucleotide, Article, 03 medical and health sciences, Quantitative Trait, Heritable, ddc:590, Genetics, Humans, Disease, ddc:576.5, Regulation of gene expression, Gene Expression Profiling, Heritability, 3. Good health, Phenotype, 030104 developmental biology, Gene Expression Regulation, Expression quantitative trait loci, Trait, Functional genomics, Genome-Wide Association Study
Abstract

We apply integrative approaches to expression quantitative loci (eQTLs) from 44 tissues from the Genotype-Tissue Expression project and genome-wide association study data. About 60% of known trait-associated loci are in linkage disequilibrium with a cis-eQTL, over half of which were not found in previous large-scale whole blood studies. Applying polygenic analyses to metabolic, cardiovascular, anthropometric, autoimmune, and neurodegenerative traits, we find that eQTLs are significantly enriched for trait associations in relevant pathogenic tissues and explain a substantial proportion of the heritability (40-80%). For most traits, tissue-shared eQTLs underlie a greater proportion of trait associations, although tissue-specific eQTLs have a greater contribution to some traits, such as blood pressure. By integrating information from biological pathways with eQTL target genes and applying a gene-based approach, we validate previously implicated causal genes and pathways, and propose new variant and gene associations for several complex traits, which we replicate in the UK BioBank and BioVU., National Institutes of Health (U.S.) (Contract HHSN268201000029C)

Published
2018

37. Abstract 021: ARHGEF26 is a Novel Genetic Risk Factor for Vascular Inflammation and Coronary Artery Disease

Author

Brian J. McMillan, Chris C. A. Spencer, Pradeep Natarajan, Ayellet V. Segrè, François Aguet, Amit Khera, Connor A. Emdin, Mark Chaffin, Sekar Kathiresan, Derek Klarin, Alison Leed, Michael E. Weale, Qiuyu M Zhu, Virendar K. Kaushik, Steven Horner, and Kristin G. Ardlie

Subjects
Coronary artery disease, medicine.medical_specialty, Vascular inflammation, business.industry, Internal medicine, medicine, Cardiology, Inflammation, medicine.symptom, Genetic risk factor, Cardiology and Cardiovascular Medicine, medicine.disease, business
Abstract

Vascular inflammation drives the initiation and progression of coronary artery disease (CAD). However, the underlying genetic factors are not well understood. We performed a genome-wide association study in UK Biobank testing 9 million DNA variants for association with CAD (4,831 cases, 115,455 controls), followed by a meta-analysis with previous results. We identified ARHGEF26 (Rho guanine nucleotide exchange factor 26) as a novel locus significantly associated with CAD (combined OR=1.08, 95% CI 1.06-1.11, P=1.02 х 10 –9 ). We hypothesized that ARHGEF26 regulates vascular inflammation by affecting the function of vascular cells. Focusing on the haplotype tagged by the lead variant rs12493885 (ARHGEF26 p.Val29Leu), we performed eQTL and allele-specific expression analyses. There is no significant ARHGEF26 transcription alteration or allelic imbalance associated with the risk allele in human coronary artery samples. Promoter luciferase assay showed no significant difference between the reference and alternative haplotypes. In contrast, expression of exogenous Leu29 mutant after depletion of endogenous ARHGEF26 led to rescued phenotypes consistently exceeding those observed with overexpression of wild-type ARHGEF26, including increased leukocyte transendothelial migration, leukocyte adhesion on endothelial cells, and smooth muscle cell proliferation. These data suggest that the CAD-risk allele (Leu29) may lead to a gain of protein function in vascular cells. To identify the molecular mechanism, we compared the nucleotide-exchange activity between the wild-type and mutant proteins and found no significant difference. Evaluation of ARHGEF26 protein stability by cycloheximide chase showed the Leu29 mutant displayed longer half-life than wild-type ARHGEF26, suggesting the gain-of-function phenotypes of Leu29 in cells may be secondary to its resistance to degradation. Quantitative proteomics revealed differential protein interaction between the wild-type and Leu29 ARHGEF26 in endothelial cells, highlighting critical inflammatory pathways impacted by the CAD-risk allele. In summary, our work identified a novel genetic risk factor for CAD, and enabled discovery of novel CAD-causing pathways in vascular inflammation.

Published
2018

38. Co-expression networks reveal the tissue-specific regulation of transcription and splicing

Author

Morgan Diegel, Laure Fresard, Lindsay F. Rizzardi, Yuan He, Monkol Lek, Daniel C. Rohrer, Boxiang Liu, Maximilian Haeussler, Heather M. Traino, Concepcion R. Nierras, Joseph Wheeler, Serghei Mangul, Fan Wu, Hualin S. Xi, Andrew D. Skol, Steven Hunter, Yaping Liu, Casandra A. Trowbridge, Brandon L. Pierce, Daniel Bates, Peter Hickey, Susan E. Koester, Bryan Gillard, Eric R. Gamazon, Jennifer A. Doherty, Jared L. Nedzel, Eric Haugen, Lori E. Brigham, Gao Wang, Dana R. Valley, Zachary Zappala, Emmanouil T. Dermitzakis, Seva Kashin, Ira M. Hall, John Vivian, Philip A. Branton, Barbara E. Stranger, Magali Ruffier, Melina Claussnitzer, Nancy Roche, Michael Washington, Halit Ongen, Brian Jo, Rachna Kumar, Jean Monlong, Yi-Hui Zhou, Kristen Lee, Stephane E. Castel, Mark Miklos, Alisa McDonald, Diego Garrido-Martín, Jimmie B. Vaught, Hae Kyung Im, Leslie H. Sobin, John T. Lonsdale, Audra K. Johnson, Rui Zhang, Nancy J. Cox, Christopher D. Brown, Paul Flicek, Ferran Reverter, Roderic Guigó, Tuuli Lappalainen, Sarah E. Gould, Deborah C. Mash, Michael T. Moser, Andrew B. Nobel, Takunda Matose, Jingchun Zhu, Joe R. Davis, Andrey A. Shabalin, Jie Quan, Pedro G. Ferreira, Taru Tukiainen, Ellen Gelfand, Cédric Howald, Buhm Han, Emily K. Tsang, Andrew P. Feinberg, Caroline Linke, Kane Hadley, Richard Sandstrom, Mark D. Johnson, Joshua M. Akey, Ian C. McDowell, Daniel R. Zerbino, Alexis Battle, Brian Roe, Daniel G. MacArthur, Ellen Karasik, Marcus Hunter, Anjené M. Addington, Thomas Juettemann, Konrad J. Karczewski, Duyen T. Nguyen, Lei Hou, Stephen B. Montgomery, YoSon Park, Nicole C. Lockart, Lin Chen, Rajinder Kaul, Ruiqi Jian, Robert G. Montroy, Xiao Li, Michael Snyder, Beryl B. Cummings, Kimberly M. Valentino, Ariel D. H. Gewirtz, François Aguet, Jeffrey McLean, Gary Walters, Farhad Hormozdiari, William F. Leinweber, Gad Getz, Jeffery P. Struewing, Anne Ndungu, Dan L. Nicolae, Benoit Molinie, Lihua Jiang, Michael Sammeth, W. James Kent, John Palowitch, Brian Craft, Donald F. Conrad, Kathryn Demanelis, Jason Bridge, Jin Billy Li, A. Roger Little, Nicholas Van Wittenberghe, Stephen J. Trevanion, Pejman Mohammadi, Michael S. Noble, Kate R. Rosenbloom, Marian S. Fernando, Benjamin J. Strober, Ping Guan, Brunilda Balliu, Yungil Kim, Kevin Myer, Christine B. Peterson, Pushpa Hariharan, Jae Hoon Sul, Abhi Rao, Michael F. Salvatore, Qin Li, Eun Yong Kang, Matthew T. Maurano, Ayellet V. Segrè, Dan Sheppard, Fred A. Wright, Matthew Stephens, Kasper D. Hansen, Chiara Sabatti, Kevin S. Smith, Xin Li, Ruth Barshir, Muhammad G. Kibriya, Farhan N. Damani, Manolis Kellis, Olivier Delaneau, Shin Lin, Richard Hasz, Michael J. Gloudemans, Anita H. Undale, Mary Goldman, Fidencio J. Neri, Katherine H. Huang, David E. Tabor, Manuel Muñoz-Aguirre, Maghboeba Mosavel, Simona Volpi, Latarsha J. Carithers, Anna M. Smith, Genna Gliner, Eleazar Eskin, Nikolaos I Panousis, Benedict Paten, Andrew A. Brown, Jessica Lin, Kieron Taylor, Robert E. Handsaker, Laura Barker, Casey Martin, Meng Wang, Farzana Jasmine, Scott D. Jewell, Nathan S. Abell, Kristin G. Ardlie, Shilpi Singh, Mary Barcus, Anthony Payne, Christopher Lee, Xiaoquan Wen, Nicola J. Rinaldi, Hua Tang, Yongjin Park, Christopher Johns, Saboor Shad, Judith B. Zaugg, Reza Sodaei, Maria M. Tomaszewski, David A. Davis, Joanne Chan, Laura A. Siminoff, Mark I. McCarthy, Ki Sung Um, Karna Robinson, Esti Yeger-Lotem, Martijn van de Bunt, Meritxell Oliva, Jemma Nelson, Negin Vatanian, Colby Chiang, Jeffrey A. Thomas, Alexandra J. Scott, Omer Basha, Jessica Halow, Panagiotis Papasaikas, Barbara A. Foster, Barbara E. Engelhardt, Sarah Kim-Hellmuth, Li Wang, Gireesh K. Bogu, Sandra Linder, Sarah Urbut, Ashis Saha, Gen Li, Bernadette Mestichelli, Chuan Gao, John A. Stamatoyannopoulos, Liqun Qi, Princy Parsana, Helen M. Moore, Gene Kopen, and GTEx, Consortium

Subjects
Gene isoform, 0301 basic medicine, Genotyping Techniques, Bioinformatics, RNA Splicing, 1.1 Normal biological development and functioning, Gene regulatory network, Method, Genomics, Computational biology, Biology, Medical and Health Sciences, GTEx Consortium, Transcriptome, 03 medical and health sciences, Databases, 0302 clinical medicine, Genetic, Transcription (biology), Underpinning research, Genetic variation, Gene expression, Genetics, Humans, ddc:576.5, Gene Regulatory Networks, Polymorphism, Gene, Genetics (clinical), 030304 developmental biology, Regulation of gene expression, 0303 health sciences, Gene Expression Profiling, Human Genome, Bayes Theorem, Single Nucleotide, Biological Sciences, Gene expression profiling, 030104 developmental biology, Gene Expression Regulation, Organ Specificity, RNA splicing, RNA, Generic health relevance, Sequence Analysis, 030217 neurology & neurosurgery, Biotechnology
Abstract

Gene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of regulatory genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single or small sets of tissues. Here, we have reconstructed networks that capture a much more complete set of regulatory relationships, specifically including regulation of relative isoform abundance and splicing, and tissue-specific connections unique to each of a diverse set of tissues. Using the Genotype-Tissue Expression (GTEx) project v6 RNA-sequencing data across 44 tissues in 449 individuals, we evaluated shared and tissue-specific network relationships. First, we developed a framework called Transcriptome Wide Networks (TWNs) for combining total expression and relative isoform levels into a single sparse network, capturing the complex interplay between the regulation of splicing and transcription. We built TWNs for sixteen tissues, and found that hubs with isoform node neighbors in these networks were strongly enriched for splicing and RNA binding genes, demonstrating their utility in unraveling regulation of splicing in the human transcriptome, and providing a set of candidate shared and tissue-specific regulatory hub genes. Next, we used a Bayesian biclustering model that identifies network edges between genes with co-expression in a single tissue to reconstruct tissue-specific networks (TSNs) for 27 distinct GTEx tissues and for four subsets of related tissues. Using both TWNs and TSNs, we characterized gene co-expression patterns shared across tissues. Finally, we found genetic variants associated with multiple neighboring nodes in our networks, supporting the estimated network structures and identifying 33 genetic variants with distant regulatory impact on transcription and splicing. Our networks provide an improved understanding of the complex relationships between genes in the human transcriptome, including tissue-specificity of gene co-expression, regulation of splicing, and the coordinated impact of genetic variation on transcription.

Published
2017

39. A Novel Approach to High-Quality Postmortem Tissue Procurement: The GTEx Project

Author

Latarsha J, Carithers, Kristin, Ardlie, Mary, Barcus, Philip A, Branton, Angela, Britton, Stephen A, Buia, Carolyn C, Compton, David S, DeLuca, Joanne, Peter-Demchok, Ellen T, Gelfand, Ping, Guan, Greg E, Korzeniewski, Nicole C, Lockhart, Chana A, Rabiner, Abhi K, Rao, Karna L, Robinson, Nancy V, Roche, Sherilyn J, Sawyer, Ayellet V, Segrè, Charles E, Shive, Anna M, Smith, Leslie H, Sobin, Anita H, Undale, Kimberly M, Valentino, Jim, Vaught, Taylor R, Young, Helen M, Moore, Jun, Zhu, and Other departments

Subjects
Knowledge management, Biomedical Research, Tissue and Organ Procurement, Genomic data, media_common.quotation_subject, Medicine (miscellaneous), Tissue Banks, Biospecimen Collection, General Biochemistry, Genetics and Molecular Biology, World Wide Web, Procurement, Common fund, Humans, Quality (business), Guest Editorial, media_common, business.industry, Cell Biology, General Medicine, Expressió gènica, 3. Good health, Organ procurement, Tissue bank, Postmortem tissue, Business
Abstract

The Genotype-Tissue Expression (GTEx) project, sponsored by the NIH Common Fund, was established to study the correlation between human genetic variation and tissue-specific gene expression in non-diseased individuals. A significant challenge was the collection of high-quality biospecimens for extensive genomic analyses. Here we describe how a successful infrastructure for biospecimen procurement was developed and implemented by multiple research partners to support the prospective collection, annotation, and distribution of blood, tissues, and cell lines for the GTEx project. Other research projects can follow this model and form beneficial partnerships with rapid autopsy and organ procurement organizations to collect high quality biospecimens and associated clinical data for genomic studies. Biospecimens, clinical and genomic data, and Standard Operating Procedures guiding biospecimen collection for the GTEx project are available to the research community. This work was supported by the National Institutes of Health (HHSN261200800001E (Leidos Prime contract with NCI); 10XS170 (NDRI), 10XS171 (Roswell Park Cancer Institute), 10X172 (Science Care Inc.), 12ST1039 (IDOX); 10ST1035 (Van Andel Institute); HHSN268201000029C (Broad Institute); and R01 DA006227-17 (U Miami Brain Bank).

Published
2015

40. Pathways Targeted by Antidiabetes Drugs Are Enriched for Multiple Genes Associated With Type 2 Diabetes Risk

Author

Nancy Wei, Ayellet V. Segrè, David Altshuler, Jose C. Florez, Magic Investigators, Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), and Center for Liver, Digestive and Metabolic Diseases (CLDM)

Subjects
MECHANISM, Peroxisome proliferator-activated receptor gamma, SUSCEPTIBILITY LOCI, Genotype, endocrine system diseases, METFORMIN, medicine.drug_class, Endocrinology, Diabetes and Metabolism, Genome-wide association study, Type 2 diabetes, Polymorphism, Single Nucleotide, ABCC8, GLUCOSE, Databases, Genetic, GLYCEMIC TRAITS, Internal Medicine, medicine, Humans, Hypoglycemic Agents, Gene Regulatory Networks, Genetic Predisposition to Disease, GENOME-WIDE ASSOCIATION, Thiazolidinedione, Gene, Genetic association, Genetics, INSULIN-RESISTANCE, biology, LARGE-SCALE ASSOCIATION, COMMON VARIANTS, nutritional and metabolic diseases, Genetics/Genomes/Proteomics/Metabolomics, medicine.disease, 3. Good health, Diabetes Mellitus, Type 2, Drug development, biology.protein, PPAR-GAMMA
Abstract

Genome-wide association studies (GWAS) have uncovered >65 common variants associated with type 2 diabetes (T2D); however, their relevance for drug development is not yet clear. Of note, the first two T2D-associated loci (PPARG and KCNJ11/ABCC8) encode known targets of antidiabetes medications. We therefore tested whether other genes/pathways targeted by antidiabetes drugs are associated with T2D. We compiled a list of 102 genes in pathways targeted by marketed antidiabetic medications and applied Gene Set Enrichment Analysis (MAGENTA [Meta-Analysis Gene-set Enrichment of variaNT Associations]) to this gene set, using available GWAS meta-analyses for T2D and seven quantitative glycemic traits. We detected a strong enrichment of drug target genes associated with T2D (P = 2 × 10−5; 14 potential new associations), primarily driven by insulin and thiazolidinedione (TZD) targets, which was replicated in an independent meta-analysis (Metabochip). The glycemic traits yielded no enrichment. The T2D enrichment signal was largely due to multiple genes of modest effects (P = 4 × 10−4, after removing known loci), highlighting new associations for follow-up (ACSL1, NFKB1, SLC2A2, incretin targets). Furthermore, we found that TZD targets were enriched for LDL cholesterol associations, illustrating the utility of this approach in identifying potential side effects. These results highlight the potential biomedical relevance of genes revealed by GWAS and may provide new avenues for tailored therapy and T2D treatment design.

Published
2014

41. Widespread Allelic Heterogeneity in Complex Traits

Author

Chelsea J.-T. Ju, Sriram Sankararaman, Anthony Zhu, Sagiv Shifman, Hyejung Won, Eleazar Eskin, Gleb Kichaev, Jong Wha J. Joo, Bogdan Pasaniuc, Farhad Hormozdiari, and Ayellet V. Segrè

Subjects
Models, Molecular, 0301 basic medicine, Quantitative Trait Loci, causal variants, Genome-wide association study, Locus (genetics), Biology, eQTL, Medical and Health Sciences, Article, Linkage Disequilibrium, complex traits, 03 medical and health sciences, Databases, 0302 clinical medicine, Gene Frequency, Genetic, Models, Databases, Genetic, Genetics, Humans, Association mapping, Allele frequency, Genetics (clinical), Alleles, Genetic Association Studies, 030304 developmental biology, Genetic association, Genetics & Heredity, 0303 health sciences, allelic heterogeneity, Prevention, Human Genome, Molecular, Biological Sciences, Genetic architecture, 030104 developmental biology, Phenotype, Sample size determination, Expression quantitative trait loci, gene expression, Allelic heterogeneity, 030217 neurology & neurosurgery, Biotechnology
Abstract

Recent successes in genome-wide association studies (GWASs) make it possible to address important questions about the genetic architecture of complex traits, such as allele frequency and effect size. One lesser-known aspect of complex traits is the extent of allelic heterogeneity (AH) arising from multiple causal variants at a locus. We developed a computational method to infer the probability of AH and applied it to three GWAS and four expression quantitative trait loci (eQTL) datasets. We identified a total of 4152 loci with strong evidence of AH. The proportion of all loci with identified AH is 4-23% in eQTLs, 35% in GWAS of High-Density Lipoprotein (HDL), and 23% in schizophrenia. For eQTLs, we observed a strong correlation between sample size and the proportion of loci with AH (R2=0.85, P = 2.2e-16), indicating that statistical power prevents identification of AH in other loci. Understanding the extent of AH may guide the development of new methods for fine mapping and association mapping of complex traits.

Published
2017

42. Genetic analysis in UK Biobank links insulin resistance and transendothelial migration pathways to coronary artery disease

Author

Steven Horner, Sekar Kathiresan, Chris C. A. Spencer, Virendar K. Kaushik, Pradeep Natarajan, Kristin G. Ardlie, Amit Khera, Connor A. Emdin, Michael E. Weale, François Aguet, Mark Chaffin, Ayellet V. Segrè, Derek Klarin, Alison Leed, Qiuyu Martin Zhu, and Brian J. McMillan

Subjects
0301 basic medicine, Adult, Male, Genotype, Genome-wide association study, Coronary Artery Disease, 030204 cardiovascular system & hematology, Biology, Genetic analysis, Polymorphism, Single Nucleotide, Article, Coronary artery disease, 03 medical and health sciences, Health Information Systems, 0302 clinical medicine, Insulin resistance, Polymorphism (computer science), Genetics, medicine, Human Umbilical Vein Endothelial Cells, Guanine Nucleotide Exchange Factors, Humans, Genetic Predisposition to Disease, Leukocyte Rolling, Cells, Cultured, Aged, Transendothelial and Transepithelial Migration, population genetics, Middle Aged, medicine.disease, Phenotype, Biobank, gene-expression, United Kingdom, 3. Good health, Cytoskeletal Proteins, 030104 developmental biology, HEK293 Cells, Logistic Models, Immunology, genome-wide association studies, Female, Insulin Resistance, Carrier Proteins, Rho Guanine Nucleotide Exchange Factors, Genome-Wide Association Study
Abstract

UK Biobank is among the world's largest repositories for phenotypic and genotypic information in individuals of European ancestry. We performed a genome-wide association study in UK Biobank testing ∼9 million DNA sequence variants for association with coronary artery disease (4,831 cases and 115,455 controls) and carried out meta-analysis with previously published results. We identified 15 new loci, bringing the total number of loci associated with coronary artery disease to 95 at the time of analysis. Phenome-wide association scanning showed that CCDC92 likely affects coronary artery disease through insulin resistance pathways, whereas experimental analysis suggests that ARHGEF26 influences the transendothelial migration of leukocytes.

Published
2017

43. Genomic analyses identify hundreds of variants associated with age at menarche and support a role for puberty timing in cancer risk

Author

Emmi Tikkanen, Pascal Guénel, Rico Rueedi, Hamdi Mbarek, Gerardo Heiss, Hilary K. Finucane, Jing Hua Zhao, Peter K. Joshi, Mark I. McCarthy, Frank B. Hu, Stephen J. Chanock, Manjeet K. Bolla, Sandosh Padmanabhan, Vilmundur Gudnason, Chunyan He, Aarno Palotie, Reedik Mägi, Joe Dennis, Nicholas G. Martin, Behrooz Z. Alizadeh, Elisabeth Widen, Caroline Hayward, Penelope A. Lind, Magdalena Zoledziewska, Ayellet V. Segrè, Fergus J. Couch, Massimo Mangino, Melissa C. Southey, Immaculata De Vivo, Yongmei Liu, Ute Hamann, Angela Cox, Maartje J. Hooning, Iffat Rahman, Mitul Shah, Eric Boerwinkle, Matthias W. Beckmann, Catharina A. Hartman, Tanguy Corre, Kathryn L. Lunetta, Thérèse Truong, Anna Marie Mulligan, Caterina Barbieri, Robert Winqvist, Lili Milani, Cristina Menni, Frits R. Rosendaal, Pau Navarro, Christian Gieger, Ken K. Ong, Stig E. Bojesen, Susan M. Ring, Marjanka K. Schmidt, George McMahon, Deborah J. Thompson, Robert Karlsson, Nicholas J. Timpson, M. Arfan Ikram, Alexander Teumer, Jingmei Li, Christa Meisinger, Diana L. Cousminer, Doris Stöckl, Heli Nevanlinna, Teresa Nutile, Tim D. Spector, Eva Albrecht, Murielle Bochud, Linda Broer, Felix R. Day, Sven Bergmann, Renée de Mutsert, Nicholas J. Wareham, Marjo-Riitta Järvelin, Marzyeh Amini, Elisabeth Altmaier, Isabel dos-Santos-Silva, Peter Vollenweider, Douglas F. Easton, Tamara B. Harris, Ellen A. Nohr, Paul D.P. Pharoah, David J. Porteous, Jenny A. Visser, Ilja M. Nolte, Genevieve Lachance, Thomas Meitinger, Archie Campbell, Thomas Brüning, Graham G. Giles, Johan G. Eriksson, Nancy L. Pedersen, George Davey Smith, Gonneke Willemsen, Joanne M. Murabito, Francesco Cucca, Daniel I. Chasman, Eleonora Porcu, Nora Franceschini, Thibaud Boutin, Ulla Sovio, Roger L. Milne, Annette Peters, Lisette Stolk, Paul M. Ridker, Claudia Langenberg, Patrick Sulem, Toshiko Tanaka, Marike Gabrielson, Judith S. Brand, Unnur Thorsteindottir, Alison M. Dunning, Marina Ciullo, Julie E. Buring, Harold Snieder, Lude Franke, Meir J. Stampfer, Po-Ru Loh, Julian Peto, Ellen W. Demerath, Sheila Ulivi, Ivana Kolcic, Maristella Steri, Jouke J. Hottenga, Amanda B. Spurdle, Tõnu Esko, Katherine S. Pollard, Eulalia Catamo, Anneli Pouta, Kyriaki Michailidou, Dieter Flesch-Janys, James F. Wilson, Kamila Czene, Sean Whalen, Diether Lambrechts, Abhishek Sarkar, Laura Crisponi, Jonathan Tyrer, Antonietta Robino, Sara Lindström, Harald Grallert, Stefania Lenarduzzi, Peter Kraft, Patrik K. E. Magnusson, Lavinia Paternoster, Digna R. Velez Edwards, Mike A. Nalls, Per Hall, Joyce Y. Tung, Cinzia Sala, Lindsay Fernández-Rhodes, Ayush Giri, Hiltrud Brauch, André G. Uitterlinden, Alkes L. Price, Tracy A. O'Mara, Lynda M. Rose, John L. Hopper, David A. Hinds, Katherine S. Ruth, Arto Mannermaa, Uwe Völker, Rossella Sorice, Zoltán Kutalik, Debbie A Lawlor, Georgia Chenevix-Trench, Anna Murray, Albertine J. Oldehinkel, Bruce H. R. Wolffenbuttel, Hae Kyung Im, Dale R. Nyholt, Lenore J. Launer, Andres Metspalu, Irene L. Andrulis, Konstantin Strauch, Albert V. Smith, Daniela Toniolo, Eco J. C. de Geus, Jenny Chang-Claude, Marek Zygmunt, Dorret I. Boomsma, Harry Campbell, Peter Devilee, Henry Völzke, Daniela Ruggiero, Dennis O. Mook-Kanamori, John R. B. Perry, Javier Benitez, Grant W. Montgomery, David J. Hunter, David Karasik, Luigi Ferrucci, Emily Hallberg, Stefania Bandinelli, Hermann Brenner, Raymond Noordam, Sarah E. Medland, Peter A. Fasching, Qin Wang, Ilaria Gandin, Manolis Kellis, Hannes Helgason, Igor Rudan, Paolo Radice, Michela Traglia, Jian'an Luan, Robert A. Scott, Brumat Marco, Veronique Vitart, Kari Stefansson, Katharina E. Schraut, Ko Willems van Dijk, Ozren Polasek, Daniel F. Gudbjartsson, Natalia Perjakova, Joop S.E. Laven, Life Course Epidemiology (LCE), Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Interdisciplinary Centre Psychopathology and Emotion regulation (ICPE), Lifestyle Medicine (LM), Center for Liver, Digestive and Metabolic Diseases (CLDM), Stem Cell Aging Leukemia and Lymphoma (SALL), Real World Studies in PharmacoEpidemiology, -Genetics, -Economics and -Therapy (PEGET), Biological Psychology, APH - Health Behaviors & Chronic Diseases, APH - Personalized Medicine, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, APH - Mental Health, APH - Methodology, Art and Culture, History, Antiquity, Day, Felix R., Thompson, Deborah J., Helgason, Hanne, Chasman, Daniel I., Finucane, Hilary, Sulem, Patrick, Ruth, Katherine S., Whalen, Sean, Sarkar, Abhishek K., Albrecht, Eva, Altmaier, Elisabeth, Amini, Marzyeh, Barbieri, CATERINA MARIA, Boutin, Thibaud, Campbell, Archie, Demerath, Ellen, Giri, Ayush, He, Chunyan, Hottenga, Jouke J., Karlsson, Robert, Kolcic, Ivana, Loh, Po Ru, Lunetta, Kathryn L., Mangino, Massimo, Brumat, Marco, Mcmahon, George, Medland, Sarah E., Nolte, Ilja M., Noordam, Raymond, Nutile, Teresa, Paternoster, Lavinia, Perjakova, Natalia, Porcu, Eleonora, Rose, Lynda M., Schraut, Katharina E., Segrã¨, Ayellet V., Smith, Albert V., Stolk, Lisette, Teumer, Alexander, Andrulis, Irene L., Bandinelli, Stefania, Beckmann, Matthias W., Benitez, Javier, Bergmann, Sven, Bochud, Murielle, Boerwinkle, Eric, Bojesen, Stig E., Bolla, Manjeet K., Brand, Judith S., Brauch, Hiltrud, Brenner, Hermann, Broer, Linda, Brã¼ning, Thoma, Buring, Julie E., Campbell, Harry, Catamo, Eulalia, Chanock, Stephen, Chenevix Trench, Georgia, Corre, Tanguy, Couch, Fergus J., Cousminer, Diana L., Cox, Angela, Crisponi, Laura, Czene, Kamila, Davey Smith, George, De Geus, Eco J. C. N., De Mutsert, Renã©e, De Vivo, Immaculata, Dennis, Joe, Devilee, Peter, Dos Santos Silva, Isabel, Dunning, Alison M., Eriksson, Johan G., Fasching, Peter A., Fernández Rhodes, Lindsay, Ferrucci, Luigi, Flesch Janys, Dieter, Franke, Lude, Gabrielson, Marike, Gandin, Ilaria, Giles, Graham G., Grallert, Harald, Gudbjartsson, Daniel F., Guã©nel, Pascal, Hall, Per, Hallberg, Emily, Hamann, Ute, Harris, Tamara B., Hartman, Catharina A., Heiss, Gerardo, Hooning, Maartje J., Hopper, John L., Hu, Frank, Hunter, David J., Ikram, M. Arfan, Im, Hae Kyung, Jã¤rvelin, Marjo Riitta, Joshi, Peter K., Karasik, David, Kellis, Manoli, Kutalik, Zoltan, Lachance, Genevieve, Lambrechts, Diether, Langenberg, Claudia, Launer, Lenore J., Laven, Joop S. E., Lenarduzzi, Stefania, Li, Jingmei, Lind, Penelope A., Lindstrom, Sara, Liu, Yongmei, Luan, Jian'An, Mã¤gi, Reedik, Mannermaa, Arto, Mbarek, Hamdi, Mccarthy, Mark I., Meisinger, Christa, Meitinger, Thoma, Menni, Cristina, Metspalu, Andre, Michailidou, Kyriaki, Milani, Lili, Milne, Roger L., Montgomery, Grant W., Mulligan, Anna M., Nalls, Mike A., Navarro, Pau, Nevanlinna, Heli, Nyholt, Dale R., Oldehinkel, Albertine J., O'Mara, Tracy A., Padmanabhan, Sandosh, Palotie, Aarno, Pedersen, Nancy, Peters, Annette, Peto, Julian, Pharoah, Paul D. P., Pouta, Anneli, Radice, Paolo, Rahman, Iffat, Ring, Susan M., Robino, Antonietta, Rosendaal, Frits R., Rudan, Igor, Rueedi, Rico, Ruggiero, Daniela, Sala, Cinzia F., Schmidt, Marjanka K., Scott, Robert A., Shah, Mitul, Sorice, Rossella, Southey, Melissa C., Sovio, Ulla, Stampfer, Meir, Steri, Maristella, Strauch, Konstantin, Tanaka, Toshiko, Tikkanen, Emmi, Timpson, Nicholas J., Traglia, Michela, Truong, Thã©rã¨se, Tyrer, Jonathan P., Uitterlinden, André G., Edwards, Digna R. Velez, Vitart, Veronique, Vã¶lker, Uwe, Vollenweider, Peter, Wang, Qin, Widen, Elisabeth, Van Dijk, Ko Willem, Willemsen, Gonneke, Winqvist, Robert, Wolffenbuttel, Bruce H. R., Zhao, Jing Hua, Zoledziewska, Magdalena, Zygmunt, Marek, Alizadeh, Behrooz Z., Boomsma, Dorret I., Ciullo, Marina, Cucca, Francesco, Esko, Tãµnu, Franceschini, Nora, Gieger, Christian, Gudnason, Vilmundur, Hayward, Caroline, Kraft, Peter, Lawlor, Debbie A., Magnusson, Patrik K. E., Martin, Nicholas G., Mook Kanamori, Dennis O., Nohr, Ellen A., Polasek, Ozren, Porteous, David, Price, Alkes L., Ridker, Paul M., Snieder, Harold, Spector, Tim D., Stã¶ckl, Dori, Toniolo, Daniela, Ulivi, Sheila, Visser, Jenny A., Vã¶lzke, Henry, Wareham, Nicholas J., Wilson, James F., LifeLines Cohort, Study, Interact, Consortium, Kconfab/aocs, Investigator, Endometrial Cancer Association, Consortium, Ovarian Cancer Association, Consortium, Practical, Consortium, Spurdle, Amanda B., Thorsteindottir, Unnur, Pollard, Katherine S., Easton, Douglas F., Tung, Joyce Y., Chang Claude, Jenny, Hinds, David, Murray, Anna, Murabito, Joanne M., Stefansson, Kari, Ong, Ken K., Perry, John R. B., Internal Medicine, Erasmus MC other, Medical Oncology, Epidemiology, Obstetrics & Gynecology, Day, Felix [0000-0003-3789-7651], Thompson, Deborah [0000-0003-1465-5799], Dennis, Joe [0000-0003-4591-1214], Dunning, Alison [0000-0001-6651-7166], Langenberg, Claudia [0000-0002-5017-7344], Luan, Jian'an [0000-0003-3137-6337], Pharoah, Paul [0000-0001-8494-732X], Sovio, Ulla [0000-0002-0799-1105], Tyrer, Jonathan [0000-0003-3724-4757], Zhao, Jing Hua [0000-0003-4930-3582], Wareham, Nicholas [0000-0003-1422-2993], Easton, Douglas [0000-0003-2444-3247], Ong, Kenneth [0000-0003-4689-7530], Perry, John [0000-0001-6483-3771], Apollo - University of Cambridge Repository, Massachusetts Institute of Technology. Department of Mathematics, and Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory

Subjects
0301 basic medicine, Male, Netherlands Twin Register (NTR), Ovarian Cancer Association Consortium, LOCI, Genome-wide association study, cancer risk, Menarche/genetics, Bioinformatics, GWAS, menarche, puberty, Body Mass Index, Risk Factors, Neoplasms, Databases, Genetic, WIDE ASSOCIATION, Endometrial Cancer Association Consortium, Ribonucleoproteins/genetics, 2. Zero hunger, Genetics, Age Factors, Genetics GWAS menarche, reproductive disorders, Neoplasms/genetics, 3. Good health, Ribonucleoproteins, Menarche, MENDELIAN RANDOMIZATION, Intercellular Signaling Peptides and Proteins, GROWTH, Female, TRAITS, Population variance, Intercellular Signaling Peptides and Proteins/genetics, Adolescent, Ubiquitin-Protein Ligases, kConFab/AOCS Investigators, Quantitative Trait Loci, Quantitative trait locus, Biology, METABOLISM, INHERITANCE, Polymorphism, Single Nucleotide, PRACTICAL consortium, 03 medical and health sciences, Genomic Imprinting, SDG 3 - Good Health and Well-being, Mendelian randomization, REGRESSION, medicine, Journal Article, Humans, cancer, Genetic Predisposition to Disease, 1000 Genomes Project, Membrane Proteins/genetics, METAANALYSIS, Puberty, Calcium-Binding Proteins, Membrane Proteins, Cancer, InterAct Consortium, Heritability, medicine.disease, BODY-MASS INDEX, 030104 developmental biology, genome-wide association studies, Puberty/genetics, LifeLines Cohort Study, Genome-Wide Association Study
Abstract

The timing of puberty is a highly polygenic childhood trait that is epidemiologically associated with various adult diseases. Using 1000 Genomes Project-imputed genotype data in up to similar to 370,000 women, we identify 389 independent signals (P

Published
2017

44. Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility

Author

Tazeen H. Jafar, Lars Lind, Peter Almgren, Wendy Winckler, Eitaro Nakashima, Young Min Cho, Annette Peters, Rona J. Strawbridge, Ananda R. Wickremasinghe, Katharine R. Owen, Lee-Ming Chuang, Tien-Jyun Chang, Graeme I. Bell, James B. Meigs, Bill Musk, Timo A. Lakka, Elin Grundberg, Wei Lu, Sarah Edkins, George Dedoussis, Weiping Jia, Danish Saleheen, Suthesh Sivapalaratnam, Maria Samuel, Tien Yin Wong, Lu Qi, Pierre Fontanillas, Momoko Horikoshi, Jirong Long, Abdul Basit, Anubha Mahajan, Andrew T. Hattersley, Markus M. Nöthen, Denis Rybin, Inger Njølstad, S. Krithika, Miguel Cruz, Delilah Zabaneh, Leena Peltonen, Jasmina Kravic, Sangsoo Kim, David Couper, Lori L. Bonnycastle, Heather M. Stringham, Yi-Cheng Chang, Paul Elliott, Eric J.G. Sijbrands, Nita G. Forouhi, Alena Stančáková, Ghazala Mirza, Robert W. Lawrence, Ruth J. F. Loos, Norman Klopp, Shiro Maeda, Martina Müller-Nurasyid, Jer-Yuarn Wu, Jianjun Liu, Kee Seng Chia, Elodie Eury, Loukianos S. Rallidis, Timothy M. Frayling, Ken Yamamoto, David Altshuler, Gunnar Sigurosson, Harald Grallert, Jackie F. Price, Barbara Thorand, Jouko Saramies, Malene M. Kristensen, Sonali Pechlivanis, Inês Barroso, Jong-Young Lee, Melissa Parkin, Josée Dupuis, Stéphane Lobbens, Jesús Kumate, Elena Tremoli, Sudhir Kowlessur, Xueling Sim, Norihiro Kato, Philippe Froguel, Kathleen Stirrups, Eero Lindholm, Alex S. F. Doney, Andres Metspalu, Yu-Tang Gao, Roman Wennauer, Xiao-Ou Shu, Marilyn C. Cornelis, Veikko Salomaa, Nanette R. Lee, Johanna Kuusisto, Caroline S. Fox, Man Li, James Scott, Wing-Yee So, Andrew R. Wood, Inga Prokopenko, Oddgeir L. Holmen, Tin Aung, Ryoichi Takayanagi, Chen Suo, Hara Kazuo, Carl G. P. Platou, Ann M. Kelly, Teresa Ferreira, Karl-Heinz Jöckel, Wei-Yen Lim, James F. Wilson, Tom Forsén, Qi Sun, Valur Emilsson, Gonçalo R. Abecasis, Fan Zhang, Timo Saaristo, Harry Campbell, Ying Wu, Mark Seielstad, Wei Zheng, Han Chen, Stavroula Kanoni, Yuqian Bao, Jose C. Florez, Wan Ting Tay, Ronald C. WMa, Gerald Steinbach, Min Jin Go, Rong Zhang, Junbin Liang, Vasiliki Lagou, Leif Groop, Emil Rehnberg, Nabi Shah, Weihua Zhang, Yun Li, Bengt Sennblad, Olle Melander, Nancy L. Pedersen, Muhammed Islam, Jaakko Tuomilehto, Young Jin Kim, Richard N. Bergman, Juliana C.N. Chan, Eleftheria Zeggini, Andrew D. Johnson, Kees Hovingh, Joban Sehmi, Rainer Rauramaa, Satu Männistö, Reedik Mägi, Samuel Liju, Mingyu Yang, Ayellet V. Segrè, Noël P. Burtt, Mozhgan Dorkhan, Beverley Balkau, Neelam Hassanali, Hyun Min Kang, Fabrizio Veglia, Eeva Korpi-Hyövälti, Loic Yengo, Elizabeth J. Rossin, Angela Silveira, Maggie C.Y. Ng, Yuan-Tsong Chen, Anders Hamsten, David R. Matthews, Mark J. Caulfield, Emmi Tikkanen, Tanya M. Teslovich, John R. B. Perry, Karen L. Mohlke, Sarah E. Hunt, Soo Heon Kwak, Jorge Escobedo, Christopher J. Groves, Ulf de Faire, Jeremy B M Jowett, Gudmar Thorleifsson, Michael Roden, Evelin Mihailov, Viswanathan Mohan, Craig L. Hanis, Thomas WMühleisen, Congrong Wang, Sonia Shah, Kyle J. Gaulton, Jaspal S. Kooner, Jiro Nakamura, Mustafa Atalay, Linda S. Adair, S Wiltshire, Tõnu Esko, Anna Jonsson, Antigone S. Dimas, Karin Leander, Li Ching Chang, George B. Grant, Bo Isomaa, Anne U. Jackson, Claudia Langenberg, Kristian Hveem, Yoon Shin Cho, Astradur B. Hreidarsson, Xu Wang, Keizo Ohnaka, Alexandra C. Nica, Simon D. Rees, Pau Navarro, Sekar Kathiresan, Rob M. van Dam, Zafar I. Hydrie, Bok Ghee Han, Francis S. Collins, Fuu Jen Tsai, Unnur Thorsteinsdottir, Ross M. Fraser, Caroline Hayward, Cornelia M. van Duijn, Samuli Ripatti, Mieke D. Trip, Hyung Lae Kim, Rafn Benediktsson, Candace Guiducci, Bruna Gigante, Kyong Soo Park, Wen Hong L. Kao, Tom Wilsgaard, Leena Kinnunen, John Danesh, Alan James, Alan R. Shuldiner, Mitsuhiro Yokota, Jen Mai Lee, Kari Stefansson, Erik Ingelsson, Colin N. A. Palmer, David J. Hunter, Paul Zimmet, Manickam Chidambaram, Sirkka Keinänen-Kiukaanniemi, Laura J. Scott, Susanne Moebus, Benjamin F. Voight, Wolfgang Rathmann, Hassan Khan, Thomas Illig, Prasad Katulanda, Christian Gieger, Andrew D. Morris, Yik Ying Teo, Andrew P. Morris, Venkatesan Radha, N. William Rayner, Johan G. Eriksson, Christian Dina, Igor Rudan, Sailaja Vedantam, Cheng Hu, James S. Pankow, Ann-Christine Syvänen, Karl Gertow, Valeriya Lyssenko, Guillaume Charpentier, Albert Hofman, Chiea Chuen Khor, Joseph Trakalo, Peter Kraft, Takashi Kadowaki, Qiuyin Cai, John C. Chambers, André G. Uitterlinden, Simon C. Potter, Nicholas J. Wareham, Soumya Raychaudhuri, Jian'an Luan, Tiinamaija Tuomi, Anthony H. Barnett, Juha Saltevo, Robert A. Scott, Valgerdur Steinthorsdottir, Peng Keat Ng, Mark I. McCarthy, Åsa K. Hedman, Kerrin S. Small, Julia Meyer, Frank B. Hu, Cecilia M. Lindgren, Jennifer E. Below, Nancy J. Cox, Jennie Hui, Andrew Crenshaw, Latonya F. Been, Nam H. Cho, Janani Pinidiyapathirage, A. Samad Shera, Bernhard OBoehm, Jason Carey, Augustine Kong, Twee Hee Ong, Philippe M. Frossard, Donald W. Bowden, Toshimasa Yamauchi, Steve E. Humphries, Cordelia Langford, Xinzhong Li, Hiroshi Ikegami, Stéphane Cauchi, Ching-Ti Liu, Michael Boehnke, Peter M. Nilsson, Debashish Das, John Beilby, Robin Young, Christian Herder, Asif Rasheed, Neil Robertson, Raimund Erbel, Fumihiko Takeuchi, Markku Laakso, Esteban J. Parra, Panos Deloukas, Eric Boerwinkle, Adan Valladares-Salgado, Chien-Hsiun Chen, Kay-Tee Khaw, Damiano Baldassarre, Ashok Kumar, E. Shyong Tai, Peter S. Chines, Dharambir KSanghera, Peter Donnelly, [ 1 ] Univ Oxford, Wellcome Trust Ctr Human Genet, Oxford, England [ 2 ] Natl Inst Hlth, Ctr Genome Sci, Gangoe Myeon, Yeonje Ri, South Korea [ 3 ] Univ London Imperial Coll Sci Technol & Med, London, England [ 4 ] Univ Washington, Dept Genome Sci, Seattle, WA 98195 USA [ 5 ] Univ Oxford, Oxford Ctr Diabet Endocrinol & Metab, Oxford OX1 2JD, England [ 6 ] NHLBI, Framingham Heart Study, Framingham, MA USA [ 7 ] Wake Forest Sch Med, Ctr Genom & Personalized Med Res, Winston Salem, NC USA [ 8 ] Wake Forest Sch Med, Ctr Diabet Res, Winston Salem, NC USA [ 9 ] Univ London Imperial Coll Sci Technol & Med, Hammersmith Hosp, London, England [ 10 ] Univ Cambridge, Dept Publ Hlth & Primary Care, Cambridge, England [ 11 ] Ctr Noncommunicable Dis Pakistan, Karachi, Pakistan [ 12 ] Natl Univ Singapore, Dept Epidemiol & Publ Hlth, Singapore 117548, Singapore [ 13 ] Wellcome Trust Sanger Inst, Cambridge, England [ 14 ] Univ Michigan, Dept Biostat, Ann Arbor, MI 48109 USA [ 15 ] Univ N Carolina, Dept Nutr, Chapel Hill, NC USA [ 16 ] Lund Univ, Scania Univ Hosp, Dept Clin Sci Malmo, Ctr Diabet, Malmo, Sweden [ 17 ] Univ Eastern Finland, Inst Biomed, Kuopio, Finland [ 18 ] Singapore Natl Eye Ctr, Singapore Eye Res Inst, Singapore, Singapore [ 19 ] Natl Univ Singapore, Dept Ophthalmol, Singapore 117548, Singapore [ 20 ] IRCCS, Ctr Cardiol Monzino, Milan, Italy [ 21 ] Univ Milan, Dept Pharmacol Sci, Milan, Italy [ 22 ] INSERM, Ctr Rech Epidemiol & Sante Populat CESP, U1018, Villejuif, France [ 23 ] Univ Paris 11, UMRS 1018, Villejuif, France [ 24 ] Shanghai Jiao Tong Univ, Affiliated Peoples Hosp 6, Dept Endocrinol & Metab, Shanghai Key Lab Diabet Mellitus,Shanghai Diabet, Shanghai 200030, Peoples R China [ 25 ] Univ Birmingham, Coll Med & Dent Sci, Birmingham, W Midlands, England [ 26 ] Heart England Natl Hlth Serv NHS Fdn Trust, Ctr Biomed Res, Birmingham, W Midlands, England [ 27 ] Univ Cambridge, Addenbrookes Hosp, Metab Res Labs, Inst Metab Sci, Cambridge CB2 2QQ, England [ 28 ] Addenbrookes Hosp, Inst Metab Sci, Cambridge Biomed Res Ctr, Natl Inst Hlth Res, Cambridge, England [ 29 ] Baqai Inst Diabetol & Endocrinol, Karachi, Pakistan [ 30 ] Univ Oklahoma, Hlth Sci Ctr, Coll Med, Dept Pediat,Sect Genet, Oklahoma City, OK 73190 USA [ 31 ] Sir Charles Gairdner Hosp, Busselton Populat Med Res Inst, Nedlands, WA 6009, Australia [ 32 ] Queen Elizabeth II Med Ctr, PathWest Lab Med Western Australia, Nedlands, WA, Australia [ 33 ] Univ Western Australia, Sch Pathol & Lab Med, Nedlands, WA 6009, Australia [ 34 ] Univ Chicago, Dept Med, Chicago, IL 60637 USA [ 35 ] Univ Chicago, Dept Human Genet, Chicago, IL 60637 USA [ 36 ] Univ Iceland, Fac Med, Reykjavik, Iceland [ 37 ] Landspitali Univ Hosp, Reykjavik, Iceland [ 38 ] Cedars Sinai Med Ctr, Diabet & Obes Res Inst, Los Angeles, CA 90048 USA [ 39 ] Univ Med Ctr Ulm, Div Endocrinol & Diabet, Dept Internal Med, Ulm, Germany [ 40 ] Nanyang Technol Univ, Lee Kong Chian LKC Sch Med, Singapore 639798, Singapore [ 41 ] Univ Texas Hlth Sci Ctr Houston, Ctr Human Genet, Houston, TX 77030 USA [ 42 ] Baylor Coll Med, Human Genome Sequencing Ctr, Houston, TX 77030 USA [ 43 ] NHGRI, NIH, Bethesda, MD 20892 USA [ 44 ] Broad Inst Harvard & Massachusetts Inst Technol M, Cambridge, MA USA [ 45 ] Vanderbilt Univ, Sch Med, Vanderbilt Ingram Canc Ctr, Vanderbilt Epidemiol Ctr,Dept Med, Nashville, TN 37212 USA [ 46 ] Univ Edinburgh, Ctr Populat Hlth Sci, Edinburgh, Midlothian, Scotland [ 47 ] Univ Edinburgh, Western Gen Hosp, MRC, Inst Genet & Mol Med, Edinburgh, Midlothian, Scotland [ 48 ] CNRS, Unite Mixte Rech UMR 8199, Inst Biol, Lille, France [ 49 ] Univ Lille 2, Inst Pasteur, Lille, France [ 50 ] Queen Mary Univ London, Barts & London Sch Med, William Harvey Res Inst, London, England [ 51 ] Queen Mary Univ London, Barts & London Sch Med, William Harvey Res Inst, Barts & London Genome Ctr, London, England [ 52 ] Chinese Univ Hong Kong, Prince Wales Hosp, Dept Med & Therapeut, Hong Kong, Hong Kong, Peoples R China [ 53 ] Acad Sinica, Inst Biomed Sci, Taipei, Taiwan [ 54 ] Natl Taiwan Univ Hosp, Dept Internal Med, Taipei 100, Taiwan [ 55 ] Corbeil Essonnes Hosp, Endocrinol Diabetol Unit, Corbeil Essonnes, France [ 56 ] China Med Univ, Sch Chinese Med, Taichung, Taiwan [ 57 ] Boston Univ, Sch Publ Hlth, Dept Biostat, Boston, MA USA [ 58 ] Natl Univ Singapore, Ctr Mol Epidemiol, Singapore 117548, Singapore [ 59 ] Indian Council Med Res, Adv Ctr Genom Diabet, Madras Diabet Res Fdn, Dept Mol Genet, Madras, Tamil Nadu, India [ 60 ] Ajou Univ, Sch Med, Dept Prevent Med, Suwon 441749, South Korea [ 61 ] Seoul Natl Univ, Coll Med, Dept Internal Med, Seoul 151, South Korea [ 62 ] Natl Taiwan Univ, Sch Med, Grad Inst Clin Med, Taipei 10764, Taiwan [ 63 ] Harvard Univ, Sch Publ Hlth, Dept Nutr & Epidemiol, Boston, MA 02115 USA [ 64 ] Univ N Carolina, Dept Biostat, Collaborat Studies Coordinating Ctr, Chapel Hill, NC USA [ 65 ] Natl Univ Singapore, Saw Swee Hock Sch Publ Hlth, Singapore 117548, Singapore [ 66 ] Ealing Hosp NHS Trust, Southall, Middx, England [ 67 ] Karolinska Inst, Inst Environm Med, Divis Cardiovasc Epidemiol, S-10401 Stockholm, Sweden [ 68 ] Harokopio Univ, Dept Dietet Nutr, Athens, Greece [ 69 ] Univ Geneva, Sch Med, Dept Genet Med & Dev, CH-1211 Geneva, Switzerland [ 70 ] Biomed Sci Res Ctr Alexander Fleming, Vari, Greece [ 71 ] INSERM, UMR 1087, Nantes, France [ 72 ] CNRS, UMR 6291, Nantes, France [ 73 ] Univ Nantes, Nantes, France [ 74 ] Univ Dundee, Ninewells Hosp, Biomed Res Inst, Diabet Res Ctr, Dundee, Scotland [ 75 ] Univ Dundee, Ninewells Hosp, Biomed Res Inst, Pharmacogen Ctr, Dundee, Scotland [ 76 ] Univ Oxford, Dept Stat, Oxford OX1 3TG, England [ 77 ] Erasmus Univ, Med Ctr, Dept Epidemiol, Rotterdam, Netherlands [ 78 ] Netherlands Consortium Healthy Ageing, Netherland Genom Initiat, Rotterdam, Netherlands [ 79 ] Ctr Med Syst Biol, Rotterdam, Netherlands [ 80 ] Univ London Imperial Coll Sci Technol & Med, MRC, Hlth Protect Agcy, Ctr Environm & Hlth, London, England [ 81 ] Iceland Heart Assoc, Kopavogur, Iceland [ 82 ] Univ Duisburg Essen, Univ Hosp Essen, West German Heart Ctr, Clin Cardiol, Essen, Germany [ 83 ] Natl Inst Hlth & Welf, Dept Chron Dis Prevent, Helsinki, Finland [ 84 ] Univ Helsinki, Dept Gen Practice & Primary Hlth Care, Helsinki, Finland [ 85 ] Univ Helsinki, Gen Hosp, Unit Gen Practice, Helsinki, Finland [ 86 ] Folkhalsan Res Ctr, Helsinki, Finland [ 87 ] Inst Mexicano Seguro Social, Hosp Gen Reg 1, Unidad Invest Epidemiol Clin, Mexico City, DF, Mexico [ 88 ] Univ Tartu, Estonian Genome Ctr, EE-50090 Tartu, Estonia [ 89 ] Univ Tartu, Inst Mol & Cell Biol, EE-50090 Tartu, Estonia [ 90 ] Broad Inst, Program Med & Populat Genet, Cambridge, MA USA [ 91 ] Childrens Hosp, Div Genet & Endocrinol, Boston, MA 02115 USA [ 92 ] Harvard Univ, Sch Med, Dept Med, Boston, MA USA [ 93 ] Massachusetts Gen Hosp, Ctr Human Genet Res, Boston, MA 02114 USA [ 94 ] Massachusetts Gen Hosp, Diabet Res Ctr, Diabet Unit, Boston, MA 02114 USA [ 95 ] Addenbrookes Hosp, Inst Metab Sci, MRC, Epidemiol Unit, Cambridge, England [ 96 ] Vaasa Hlth Care Ctr, Vaasa, Finland [ 97 ] Brigham & Womens Hosp, Div Endocrinol & Metab, Boston, MA 02115 USA [ 98 ] Harvard Univ, Sch Med, Boston, MA USA [ 99 ] Univ Exeter, Peninsula Med Sch, Inst Biomed & Clin Sci, Exeter, Devon, England [ 100 ] Shanghai Canc Inst, Dept Epidemiol, Shanghai, Peoples R China [ 101 ] Karolinska Inst, Dept Med Solna, Atherosclerosis Res Unit, Stockholm, Sweden [ 102 ] Karolinska Univ, Hosp Solna, Ctr Mol Med, Stockholm, Sweden [ 103 ] Helmholtz Zentrum Muenchen, Inst Genet Epidemiol, Neuherberg, Germany [ 104 ] Helmholtz Zentrum Muenchen, Res Unit Mol Epidemiol, Neuherberg, Germany [ 105 ] Univ Munich, Clin Cooperat Grp Diabet, Munich, Germany [ 106 ] Helmholtz Zentrum Muenchen, Munich, Germany [ 107 ] Tech Univ Munich, Clin Cooperat Grp Nutrigen & Type Diabet 2, Munich, Germany [ 108 ] German Ctr Diabet Res DZD, Neuherberg, Germany [ 109 ] Kings Coll London, Dept Twin Res & Genet Epidemiol, London, England [ 110 ] Univ Tokyo, Grad Sch Med, Dept Diabet & Metab Dis, Tokyo, Japan [ 111 ] Univ Exeter, Peninsula Med Sch, Inst Biomed & Clin Sci, Exeter, Devon, England [ 112 ] Univ Dusseldorf, Leibniz Ctr Diabet Res, German Diabet Ctr, Inst Clin Diabetol, Dusseldorf, Germany [ 113 ] Norwegian Univ Sci & Technol, Dept Publ Hlth & Gen Practice, HUNT Res Ctr, Levanger, Norway [ 114 ] Univ Amsterdam, Acad Med Ctr, Dept Vasc Med, NL-1105 AZ Amsterdam, Netherlands [ 115 ] Brigham & Womens Hosp, Dept Med, Channing Lab, Boston, MA USA [ 116 ] Univ Western Australia, Sch Populat Hlth, Nedlands, WA 6009, Australia [ 117 ] UCL, Inst Cardiovasc Sci, London, England [ 118 ] Harvard Univ, Sch Publ Hlth, Program Mol & Genet Epidemiol, Boston, MA 02115 USA [ 119 ] Kinki Univ, Sch Med, Dept Diabet Endocrinol & Metab, Osaka 589, Japan [ 120 ] Hannover Med Sch, Hannover Unified Biobank, Hannover, Germany [ 121 ] Univ Uppsala Hosp, Dept Mol Sci, Mol Epidemiol & Sci Life Lab, Uppsala, Sweden [ 122 ] Aga Khan Univ, Dept Community Hlth Sci, Karachi, Pakistan [ 123 ] Dept Social Serv & Hlth Care, Pietarsaari, Finland [ 124 ] Aga Khan Univ, Dept Med, Karachi, Pakistan [ 125 ] Queen Elizabeth II Med Ctr, West Australian Sleep Disorders Res Inst, Dept Pulm Physiol & Sleep Med, Nedlands, WA, Australia [ 126 ] Univ Western Australia, Sch Med & Pharmacol, Nedlands, WA 6009, Australia [ 127 ] Univ Hosp Essen, Inst Med Informat Biometry & Epidemiol, Essen, Germany [ 128 ] Heart & Diabet Inst, Baker Int Diabet Inst IDI, Melbourne, Australia [ 129 ] Johns Hopkins Bloomberg Sch Publ Hlth, Dept Epidemiol, Baltimore, MD USA [ 130 ] Massachusetts Gen Hosp, Cardiovasc Res Ctr, Boston, MA 02114 USA [ 131 ] Natl Ctr Global Hlth & Med, Res Inst, Shinjuku Ku, Tokyo, Japan [ 132 ] Univ Colombo, Dept Clin Med, Diabet Res Unit, Colombo, Sri Lanka [ 133 ] Univ Oulu, Inst Hlth Sci, Fac Med, Oulu, Finland [ 134 ] Oulu Univ Hosp, Unit Gen Practice, Oulu, Finland [ 135 ] Agcy Sci Technol & Res, Genome Inst Singapore, Singapore, Singapore [ 136 ] Ewha Womans Univ, Sch Med, Dept Biochem, Seoul, South Korea [ 137 ] Soongsil Univ, Sch Syst Biomed Sci, Seoul, South Korea [ 138 ] Natl Inst Hlth & Welf, Diabet Prevent Unit, Helsinki, Finland [ 139 ] deCODE Genet, Reykjavik, Iceland [ 140 ] South Ostrobothnia Cent Hosp, Seinajoki, Finland [ 141 ] Minist Hlth, Port Louis, Mauritius [ 142 ] Univ Toronto, Dept Anthropol, Mississauga, ON L5L 1C6, Canada [ 143 ] Fdn IMSS, Mexico City, DF, Mexico [ 144 ] Univ Eastern Finland, Dept Med, Kuopio, Finland [ 145 ] Kuopio Univ Hosp, SF-70210 Kuopio, Finland [ 146 ] Kuopio Res Inst Exercise Med, Kuopio, Finland [ 147 ] Univ Western Australia, Ctr Genet Epidemiol & Biostat, Nedlands, WA 6009, Australia [ 148 ] Univ San Carlos, Off Populat Studies Fdn Inc, Cebu, Philippines [ 149 ] Univ London Imperial Coll Sci Technol & Med, Hammersmith Hosp, Natl Heart & Lung Inst, London, England [ 150 ] Univ N Carolina, Dept Genet, Chapel Hill, NC USA [ 151 ] Univ N Carolina, Dept Biostat, Chapel Hill, NC USA [ 152 ] Beijing Genom Inst, Shenzhen, Peoples R China [ 153 ] Uppsala Univ, Akad Sjukhuset, Dept Med Sci, Uppsala, Sweden [ 154 ] Mt Sinai Sch Med, Charles R Bronfman Inst Personalized Med, New York, NY USA [ 155 ] Mt Sinai Sch Med, Child Hlth & Dev Inst, New York, NY USA [ 156 ] Mt Sinai Sch Med, Dept Prevent Med, New York, NY USA [ 157 ] Shanghai Inst Prevent Med, Shanghai, Peoples R China [ 158 ] Massachusetts Gen Hosp, Div Gen Med, Boston, MA 02114 USA [ 159 ] Dr Mohans Diabet Specialties Ctr, Madras, Tamil Nadu, India [ 160 ] Univ Bonn, Inst Human Genet, Bonn, Germany [ 161 ] Univ Bonn, Life & Brain Ctr, Dept Genom, Bonn, Germany [ 162 ] Univ Munich, Chair Genet Epidemiol, Inst Med Informat Biometry & Epidemiol, Munich, Germany [ 163 ] Univ Munich, Univ Hosp Grosshadern, Dept Med 1, Munich, Germany [ 164 ] Sir Charles Gairdner Hosp, Nedlands, WA 6009, Australia [ 165 ] Nagoya Univ, Grad Sch Med, Div Endocrinol & Diabet, Dept Internal Med, Nagoya, Aichi 4648601, Japan [ 166 ] Chubu Rosai Hosp, Dept Endocrinol & Diabet, Nagoya, Aichi, Japan [ 167 ] Univ Tromso, Fac Hlth Sci, Dept Community Med, Tromso, Norway [ 168 ] Kyushu Univ, Grad Sch Med Sci, Dept Geriatr Med, Higashi Ku, Fukuoka 812, Japan [ 169 ] Churchill Hosp, Hlth Res Biomed Res Ctr, Oxford Natl Inst, Oxford OX3 7LJ, England [ 170 ] Univ Minnesota, Div Epidemiol & Community Hlth, Minneapolis, MN USA [ 171 ] Seoul Natl Univ, Grad Sch Convergence Sci & Technol, Dept Mol Med & Biopharmaceut Sci, World Class Univ Program, Seoul, South Korea [ 172 ] Seoul Natl Univ, Coll Med, Seoul, South Korea [ 173 ] Karolinska Inst, Dept Med Epidemiol & Biostat, Stockholm, Sweden [ 174 ] Inst Mol Med Finland FIMM, Helsinki, Finland [ 175 ] Helmholtz Zentrum Muenchen, Inst Epidemiol 2, Neuherberg, Germany [ 176 ] Univ Kelaniya, Fac Med, Dept Publ Hlth, Ragama, Sri Lanka [ 177 ] Nord Trondelag Hlth Trust, Levanger Hosp, Dept Internal Med, Levanger, Norway [ 178 ] Univ Gen Hosp Attikon, Athens, Greece [ 179 ] Univ Dusseldorf, Leibniz Ctr Diabet Res, German Diabet Ctr, Inst Biometr & Epidemiol, Dusseldorf, Germany [ 180 ] Kuopio Univ Hosp, Dept Clin Physiol & Nucl Med, SF-70210 Kuopio, Finland [ 181 ] Harvard Univ, Brigham & Womens Hosp, Sch Med, Div Rheumatol Immunol & Allergy, Boston, MA 02115 USA [ 182 ] Partners Ctr Personalized Genom Med, Boston, MA USA [ 183 ] Univ Dusseldorf, Dept Endocrinol & Diabetol, Dusseldorf, Germany [ 184 ] Univ Dusseldorf, Dept Metab Dis, Dusseldorf, Germany [ 185 ] Harvard Univ, Hlth Sci & Technol MD Program, Boston, MA 02115 USA [ 186 ] MIT, Boston, MA USA [ 187 ] Harvard Univ, Harvard Biol & Biomed Sci Program, Boston, MA 02115 USA [ 188 ] Boston Univ, Data Coordinating Ctr, Boston, MA 02215 USA [ 189 ] Finnish Diabet Assoc, Tampere, Finland [ 190 ] Pirkanmaa Hosp Dist, Tampere, Finland [ 191 ] Cent Finland Cent Hosp, Dept Med, Jyvaskyla, Finland [ 192 ] South Karelia Cent Hosp, Lappeenranta, Finland [ 193 ] UCL, Dept Genet Evolut & Environm, Genet Inst, London, England [ 194 ] Diabet Assoc Pakistan, Karachi, Pakistan [ 195 ] Univ Maryland, Sch Med, Div Endocrinol Diabet & Nutr, Baltimore, MD 21201 USA [ 196 ] Baltimore Vet Adm Med Ctr, Geriatr Res Educ & Clin Ctr, Baltimore, MD USA [ 197 ] Univ Maryland, Sch Med, Program Personalised & Genom Med, Baltimore, MD 21201 USA [ 198 ] Erasmus Univ, Med Ctr, Dept Internal Med, Rotterdam, Netherlands [ 199 ] Univ Ulm, Dept Clin Chem, D-89069 Ulm, Germany [ 200 ] Univ Ulm, Cent Lab, D-89069 Ulm, Germany [ 201 ] Uppsala Univ, Dept Med Sci, Uppsala, Sweden [ 202 ] Kyushu Univ, Grad Sch Med Sci, Dept Internal Med & Bioregulatory Sci, Higashi Ku, Fukuoka 812, Japan [ 203 ] Univ Helsinki, Helsinki Univ Hosp, Dept Med, Helsinki, Finland [ 204 ] Hosp Univ LaPaz IdiPAZ, Inst Invest Sanitaria, Madrid, Spain [ 205 ] Danube Univ Krems, Ctr Vasc Prevent, Krems, Austria [ 206 ] King Abdulaziz Univ, Diabet Res Grp, Jeddah 21413, Saudi Arabia [ 207 ] IMSS, Ctr Med Nacl Siglo 21, Hosp Especialidades, Unidad Invest Med Bioquim, Mexico City, DF, Mexico [ 208 ] Univ Penn, Perelman Sch Med, Dept Pharmacol, Philadelphia, PA 19104 USA [ 209 ] Univ Melbourne, Ctr Eye Res Australia, East Melbourne, Vic, Australia [ 210 ] Kyushu Univ, Med Inst Bioregulat, Res Ctr Genet Informat, Div Genome Anal,Higashi Ku, Fukuoka 812, Japan [ 211 ] Univ Lille 1, Math Lab, CNRS UMR 8524, MODAL Team,INRIA Lille Nord Europe, F-59655 Villeneuve Dascq, France [ 212 ] Aichi Gakuin Univ, Sch Dent, Dept Genome Sci, Nagoya, Aichi 464, Japan [ 213 ] Harvard Univ, Sch Med, Dept Genet, Boston, MA USA [ 214 ] Harvard Univ, Sch Med, Dept Mol Biol, Boston, MA USA [ 215 ] Massachusetts Gen Hosp, Diabet Unit, Boston, MA 02114 USA [ 216 ] Wake Forest Sch Med, Dept Biochem, Winston Salem, NC USA [ 217 ] Wake Forest Sch Med, Dept Internal Med, Winston Salem, NC USA [ 218 ] Hallym Univ, Dept Biomed Sci, Chunchon, Gangwon Do, South Korea [ 219 ] Univ Texas Hlth Sci Ctr Houston, Ctr Human Genet, Houston, TX 77030 USA [ 220 ] Imperial Coll Healthcare NHS Trust, London, England [ 221 ] Univ Calif San Francisco, Inst Human Genet, San Francisco, CA 94143 USA [ 222 ] Blood Syst Res Inst, San Francisco, CA USA [ 223 ] Natl Univ Singapore, Grad Sch Integrat Sci & Engn, Singapore 117548, Singapore [ 224 ] Natl Univ Singapore, Dept Stat & Appl Probabil, Singapore 117548, Singapore [ 225 ] Natl Univ Singapore, Dept Med, Singapore 117548, Singapore [ 226 ] Duke Natl Univ Singapore, Grad Sch Med, Singapore, Singapore [ 227 ] Univ Liverpool, Dept Biostat, Liverpool L69 3BX, Merseyside, England, Obstetrics & Gynecology, Radiology & Nuclear Medicine, Surgery, Epidemiology, Dermatology, Internal Medicine, Medical Microbiology & Infectious Diseases, Medical Research Council (MRC), National Institute for Health Research, ACS - Amsterdam Cardiovascular Sciences, Vascular Medicine, and Cardiology

Subjects
CHROMATIN, endocrine system diseases, South Asian Type 2 Diabetes (SAT2D) Consortium, SCALE ASSOCIATION ANALYSIS, Medizin, LOCI, Genome-wide association study, VARIANTS, 0302 clinical medicine, Risk Factors, IMPUTATION, Glucose homeostasis, 11 Medical and Health Sciences, Genetics & Heredity, Genetics, 0303 health sciences, IDENTIFY, Hispanic or Latino, 3. Good health, MAP, POPULATIONS, Medical genetics, Type 2 Diabetes Genetic Exploration by Nex-generation sequencing in muylti-Ethnic Samples (T2D-GENES) Consortium, Hispanic Americans, Life Sciences & Biomedicine, Asian Continental Ancestry Group, medicine.medical_specialty, European Continental Ancestry Group, TRANSETHNIC METAANALYSIS, 030209 endocrinology & metabolism, Single-nucleotide polymorphism, Biology, Polymorphism, Single Nucleotide, Article, White People, Sykursýki, 03 medical and health sciences, Asian People, SDG 3 - Good Health and Well-being, medicine, Humans, Genetic Predisposition to Disease, Allele, Asian Genetic Epidemiology Network Type 2 Diabetes (AGEN-T2D) Consortium, Alleles, 030304 developmental biology, Genetic association, Science & Technology, DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium, Mexican American Type 2 Diabetes (MAT2D) Consortium, 06 Biological Sciences, Arfgengi, Genetic architecture, INDIVIDUALS, Diabetes Mellitus, Type 2, Case-Control Studies, GLUCOSE-HOMEOSTASIS, ASSOCIATION ANALYSES, Imputation (genetics), Developmental Biology, Genome-Wide Association Study
Abstract

To access publisher's full text version of this article click on the hyperlink at the bottom of the page To further understanding of the genetic basis of type 2 diabetes (T2D) susceptibility, we aggregated published meta-analyses of genome-wide association studies (GWAS), including 26,488 cases and 83,964 controls of European, east Asian, south Asian and Mexican and Mexican American ancestry. We observed a significant excess in the directional consistency of T2D risk alleles across ancestry groups, even at SNPs demonstrating only weak evidence of association. By following up the strongest signals of association from the trans-ethnic meta-analysis in an additional 21,491 cases and 55,647 controls of European ancestry, we identified seven new T2D susceptibility loci. Furthermore, we observed considerable improvements in the fine-mapping resolution of common variant association signals at several T2D susceptibility loci. These observations highlight the benefits of trans-ethnic GWAS for the discovery and characterization of complex trait loci and emphasize an exciting opportunity to extend insight into the genetic architecture and pathogenesis of human diseases across populations of diverse ancestry. Canadian Institutes of Health Research Medical Research Council UK G0601261 Mexico Convocatoria SSA/IMMS/ISSSTE-CONACYT 2012-2 clave 150352 IMSS R-2011-785-018 CONACYT Salud-2007-C01-71068 US National Institutes of Health DK062370 HG000376 DK085584 DK085545 DK073541 DK085501 Wellcome Trust WT098017 WT090532 WT090367 WT098381 WT081682 WT085475 info:eu-repo/grantAgreement/EC/FP7/201413

Published
2014

45. Distant regulatory effects of genetic variation in multiple human tissues

Author

Eskin E, Ariel D. H. Gewirtz, Jae Hoon Sul, Xiao Li, Donald F. Conrad, Ey Kang, Eric R. Gamazon, Tuuli Lappalainen, Genna Gliner, François Aguet, Christine B. Peterson, Alice He, Benjamin J. Strober, Ian C. McDowell, Ashis Saha, Emmanouil T. Dermitzakis, Brian Jo, Andrew A. Brown, SE Castel, Barbara E. Engelhardt, Nancy J. Cox, Alexis Battle, Ayellet V. Segrè, TJ Sullivan, Chiara Sabatti, Princy Parsana, Buhm Han, Pejman Mohammadi, Kristin G. Ardlie, Manolis Kellis, Gerald Quon, Christopher D. Brown, and Yiping He

Subjects
Genetics, Regulation of gene expression, 0303 health sciences, KLF14, Genomics, Biology, Phenotype, Transcriptome, 03 medical and health sciences, 0302 clinical medicine, 030220 oncology & carcinogenesis, Genetic variation, Expression quantitative trait loci, Gene, 030304 developmental biology
Abstract

Understanding the genetics of gene regulation provides information on the cellular mechanisms through which genetic variation influences complex traits. Expression quantitative trait loci, or eQTLs, are enriched for polymorphisms that have been found to be associated with disease risk. While most analyses of human data has focused on regulation of expression by nearby variants (cis-eQTLs), distal or trans-eQTLs may have broader effects on the transcriptome and important phenotypic consequences, necessitating a comprehensive study of the effects of genetic variants on distal gene transcription levels. In this work, we identify trans-eQTLs in the Genotype Tissue Expression (GTEx) project data1, consisting of 449 individuals with RNA-sequencing data across 44 tissue types. We find 81 genes with a trans-eQTL in at least one tissue, and we demonstrate that trans-eQTLs are more likely than cis-eQTLs to have effects specific to a single tissue. We evaluate the genomic and functional properties of trans-eQTL variants, identifying strong enrichment in enhancer elements and Piwi-interacting RNA clusters. Finally, we describe three tissue-specific regulatory loci underlying relevant disease associations: 9q22 in thyroid that has a role in thyroid cancer, 5q31 in skeletal muscle, and a previously reported master regulator near KLF14 in adipose. These analyses provide a comprehensive characterization of trans-eQTLs across human tissues, which contribute to an improved understanding of the tissue-specific cellular mechanisms of regulatory genetic variation.

Published
2016

46. Colocalization of GWAS and eQTL Signals Detects Target Genes

Author

Michael Bilow, Jae Hoon Sul, Eleazar Eskin, Jong Wha J. Joo, Bogdan Pasaniuc, Martijn van de Bunt, Ayellet V. Segrè, Xiao Li, Sriram Sankararaman, and Farhad Hormozdiari

Subjects
0301 basic medicine, Linkage disequilibrium, Genotype, Posterior probability, Quantitative Trait Loci, Datasets as Topic, Genome-wide association study, Locus (genetics), Disease, Computational biology, Biology, Medical and Health Sciences, Genome, Linkage Disequilibrium, Article, 03 medical and health sciences, 0302 clinical medicine, Genetic, Models, Genetics, 2.1 Biological and endogenous factors, Humans, Insulin, Genetic Predisposition to Disease, Aetiology, Gene, Genetics (clinical), 030304 developmental biology, Genetic association, Probability, Genetics & Heredity, 0303 health sciences, Models, Statistical, Models, Genetic, Prevention, Human Genome, Statistical, Biological Sciences, 030104 developmental biology, Glucose, Gene Expression Regulation, Organ Specificity, Sample Size, Expression quantitative trait loci, 030217 neurology & neurosurgery, Genome-Wide Association Study
Abstract

The vast majority of genome-wide association studies (GWAS) risk loci fall in non-coding regions of the genome. One possible hypothesis is that these GWAS risk loci alter the individual’s disease risk through their effect on gene expression in different tissues. In order to understand the mechanisms driving a GWAS risk locus, it is helpful to determine which gene is affected in specific tissue types. For example, the relevant gene and tissue may play a role in the disease mechanism if the same variant responsible for a GWAS locus also affects gene expression. Identifying whether or not the same variant is causal in both GWAS and eQTL studies is challenging due to the uncertainty induced by linkage disequilibrium (LD) and the fact that some loci harbor multiple causal variants. However, current methods that address this problem assume that each locus contains a single causal variant. In this paper, we present a new method, eCAVIAR, that is capable of accounting for LD while computing the quantity we refer to as the colocalization posterior probability (CLPP). The CLPP is the probability that the same variant is responsible for both the GWAS and eQTL signal. eCAVIAR has several key advantages. First, our method can account for more than one causal variant in any loci. Second, it can leverage summary statistics without accessing the individual genotype data. We use both simulated and real datasets to demonstrate the utility of our method. Utilizing publicly available eQTL data on 45 different tissues, we demonstrate that computing CLPP can prioritize likely relevant tissues and target genes for a set of Glucose and Insulin-related traits loci. eCAVIAR is available at http://genetics.cs.ucla.edu/caviar/

Published
2016

47. Genetic and environmental risk factors in congenital heart disease functionally converge in protein networks driving heart development

Author

Leslie B. Smoot, Blake C. Ballif, Patricia K. Donahoe, Lars Allan Larsen, Amy E. Roberts, Sonia M Mesquita, Lisa G. Shaffer, Jonathan G. Seidman, Alexandre C. Pereira, Søren Brunak, Ayellet V. Segrè, Kasper Lage, Niels Tommerup, Steven C. Greenway, Mark J. Daly, Hiroko Wakimoto, Jill A. Rosenfeld, Joshua M. Gorham, Christine E. Seidman, and William T. Pu

Subjects
Heart disease, Anatomical structures, ved/biology.organism_classification_rank.species, Environment, Biology, Bioinformatics, Statistics, Nonparametric, Environmental risk, Risk Factors, Databases, Genetic, medicine, Humans, Genetic Predisposition to Disease, Protein Interaction Maps, cardiovascular diseases, Model organism, Genetics, Multidisciplinary, Heart development, ved/biology, Infant, Newborn, Heart, Biological Sciences, medicine.disease, Atrial septum, Transcriptome, Hand Deformities, Congenital, Protein network, Chd risk
Abstract

Congenital heart disease (CHD) occurs in ∼1% of newborns. CHD arises from many distinct etiologies, ranging from genetic or genomic variation to exposure to teratogens, which elicit diverse cell and molecular responses during cardiac development. To systematically explore the relationships between CHD risk factors and responses, we compiled and integrated comprehensive datasets from studies of CHD in humans and model organisms. We examined two alternative models of potential functional relationships between genes in these datasets: direct convergence, in which CHD risk factors significantly and directly impact the same genes and molecules and functional convergence, in which risk factors significantly impact different molecules that participate in a discrete heart development network. We observed no evidence for direct convergence. In contrast, we show that CHD risk factors functionally converge in protein networks driving the development of specific anatomical structures (e.g., outflow tract, ventricular septum, and atrial septum) that are malformed by CHD. This integrative analysis of CHD risk factors and responses suggests a complex pattern of functional interactions between genomic variation and environmental exposures that modulate critical biological systems during heart development.

Published
2012

48. Hundreds of variants clustered in genomic loci and biological pathways affect human height

Author

Vilmundur Gudnason, Robert J. Weyant, Robert C. Kaplan, Jeffrey R. O'Connell, Panos Deloukas, Barbara McKnight, Alessandro De Grandi, Gert-Jan B. van Ommen, Eric Boerwinkle, Liming Liang, Douglas F. Levinson, Richard B. Hayes, Mark I. McCarthy, Andrew P. Morris, Joel N. Hirschhorn, Jaakko Kaprio, Nilesh J. Samani, Inke R. König, Frank B. Hu, Nicholas J. Wareham, Ozren Polasek, Michael C. Turchin, Daniel F. Gudbjartsson, Wilmar Igl, Vincent Mooser, Aki S. Havulinna, Themistocles L. Assimes, Cameron D. Palmer, Lachlan J. M. Coin, Eva Albrecht, Hana Lango Allen, Inês Barroso, Eco J. C. de Geus, H.-Erich Wichmann, Michael A. Province, Sarah H. Wild, Patricia B. Munroe, Jeanette Erdmann, Mattias Lorentzon, Michael Preuss, Nelson B. Freimer, Mika Kähönen, Tushar Bhangale, Nicole L. Glazer, Markku S. Nieminen, Andrew A. Hicks, Jonathan Tyrer, Grant W. Montgomery, Ruth J. F. Loos, Shaun Purcell, Manfred Kayser, Marjo-Riitta Järvelin, Guillaume Paré, Terho Lehtimäki, Irene Pichler, Ida Surakka, Tõnu Esko, Peter Kraft, Talin Haritunians, Juha Sinisalo, Mari Nelis, Veronique Vitart, Alan R. Sanders, Alistair S. Hall, Carlos Iribarren, Mark E. Cooper, André G. Uitterlinden, Peter Kovacs, David Schlessinger, Florian Ernst, Marja-Liisa Lokki, M. Juhani Junttila, Martin Ridderstråle, Jorma Viikari, Inga Prokopenko, Christopher J. O'Donnell, Jennie Hui, Aila Rissanen, Gonçalo R. Abecasis, Jouke-Jan Hottenga, Tomi Pastinen, Kevin B. Jacobs, Jan Smit, Kristin G. Ardlie, Niina Pellikka, Neil R. Robertson, Martina Müller, John F. Peden, Mary F. Feitosa, Thomas W. Winkler, Kari Stefansson, Jianjun Liu, Jaana Laitinen, Shen Haiqing, Martin Farrall, Nilanjan Chatterjee, Eleanor Wheeler, Tamara B. Harris, Åsa Johansson, Anders Hamsten, Elizabeth K. Speliotes, Larry D. Atwood, Valgerdur Steinthorsdottir, Elin Grundberg, Nicole Soranzo, Anna-Liisa Hartikainen, Devin Absher, Cecilia M. Lindgren, Rany M. Salem, Lorena Citterio, Karol Estrada, Sven Bergmann, Tony Kwan, Thomas Meitinger, Alan F. Wright, Richard N. Bergman, Sailaja Vedantam, Kirsi H. Pietiläinen, Lina Zgaga, Anne U. Jackson, Stefan Schreiber, Ju-Hyun Park, Nicola Glorioso, Jianxin Shi, Najaf Amin, Dale R. Nyholt, Lenore J. Launer, Heribert Schunkert, Thomas Quertermous, Mari Liis Tammesoo, Dorret I. Boomsma, Harry Campbell, Albert V. Smith, Andrew R. Wood, Caroline S. Fox, Paavo Zitting, Gabrielle Boucher, Alex N. Parker, Jennifer G. Sambrook, Joyce B. J. van Meurs, Heather M. Stringham, Marjolein J. Peters, Sekar Kathiresan, Elisabeth Widen, Caroline Hayward, Tim D. Spector, Dawn M. Waterworth, Carlo Rivolta, Antti Jula, Albert Hofman, Hugh Watkins, Wendy L. McArdle, Roberto Elosua, Markku Koiranen, Paul M. Ridker, Iris M. Heid, Lee M. Kaplan, Brenda W.J.H. Penninx, Peter M. Visscher, Claes Ohlsson, Per Hall, Benjamin F. Voight, Ben A. Oostra, Anna F. Dominiczak, Martin den Heijer, Andrea Maschio, Quince Gibson, Peter P. Pramstaller, Nigel W. Rayner, Carsten Oliver Schmidt, Toby Johnson, Henrik Grönberg, M. Carola Zillikens, Alan R. Shuldiner, Cornelia M. van Duijn, Samuli Ripatti, Erik Ingelsson, John R. Thompson, Stephen J. Chanock, Johannes Kettunen, David J. Hunter, Lyle J. Palmer, Ulf Gyllensten, Jianfeng Xu, Astrid Petersmann, Johan G. Eriksson, Guillaume Lettre, Teresa Ferreira, Fredrik Wiklund, Andreas Ziegler, Andres Metspalu, Jerome I. Rotter, Suzanne Rafelt, Julius S. Ngwa, Francis S. Collins, Michael N. Weedon, Thomas Illig, Wolfgang Hoffman, Nancy L. Heard-Costa, Anke Tönjes, Daniel I. Chasman, Alice M. Arnold, Olli T. Raitakari, Anna L. Dixon, Lu Qi, Michael E. Goddard, Anneli Pouta, Mark J. Caulfield, Liesbeth Vandenput, Christian Gieger, L. Adrienne Cupples, Veikko Salomaa, Elizabeth L. Altmaier, Nicholas G. Martin, Thomas D. Kocher, Jubao Duan, David S. Siscovick, Heikki V. Huikuri, Willem H. Ouwehand, Kari E. North, Christian Hengstenberg, Jacques S. Beckmann, Constance Chen, John Perry, Ana Marušić, Olle Melander, John D. Rioux, Erika Salvi, Andrew T. Hattersley, Paul Elliott, Leif Groop, Cristen J. Willer, Manuela Uda, Gudmar Thorleifsson, Eric E. Schadt, Charles C. White, Pablo V. Gejman, Serena Sanna, Ulla Sovio, Robert W. Lawrence, Unnur Thorsteinsdottir, Ayellet V. Segrè, Daniele Cusi, G. Bragi Walters, Ken Sin Lo, Ivana Kolcic, Igor Rudan, Sonja I. Berndt, Keri L. Monda, Soumya Raychaudhuri, Jian'an Luan, Joshua C. Randall, Anthony J. Balmforth, Yurii S. Aulchenko, Peter Almgren, Karen L. Mohlke, Timothy M. Frayling, Shamika Ketkar, Thomas H. Mosley, Markus Perola, Henry Völzke, Laura Zagato, Leena Peltonen, Seppo Koskinen, Massimo Mangino, Ke Hao, Tsegaselassie Workalemahu, Lambertus A. Kiemeney, Helene Alavere, Jaakko Tuomilehto, Reedik Mägi, James F. Wilson, Ingrid B. Borecki, Zoltán Kutalik, Michael Stumvoll, Dominique J. Verlaan, Arthur W. Musk, Eero Kajantie, Jian Yang, Joshua W. Knowles, Tuomas O. Kilpeläinen, Michael Boehnke, G. Mark Lathrop, John Beilby, Fernando Rivadeneira, Gudny Eiriksdottir, Gonneke Willemsen, Andrew C. Heath, Antonella Mulas, Katja K.H. Aben, David P. Strachan, Thor Aspelund, Jing Hua Zhao, Psychiatry, NCA - Anxiety & Depression, EMGO - Mental health, Internal Medicine, Epidemiology, Intensive Care, Clinical Genetics, Genetic Identification, Medical Research Council (MRC), Biological Psychology, Neuroscience Campus Amsterdam - Anxiety & Depression, EMGO+ - Mental Health, Genetics of Complex Traits, University of Exeter, Department of Epidemiology, Erasmus Medical Centre, Netherlands Genomics Initiative (NGI), Netherlands Genomics Initiative, Department of Medicine, Montreal, National Cancer Institute [Bethesda] (NCI-NIH), National Institutes of Health [Bethesda] (NIH), Department of Biostatistics, University of Michigan [Ann Arbor], University of Michigan System-University of Michigan System, deCODE Genetics, deCODE genetics [Reykjavik], Divisions of Genetics and Endocrinology and Program in Genomics, Boston Children's Hospital, Metabolism Initiative and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard (BROAD INSTITUTE), Harvard Medical School [Boston] (HMS)-Massachusetts Institute of Technology (MIT)-Massachusetts General Hospital [Boston]-Harvard Medical School [Boston] (HMS)-Massachusetts Institute of Technology (MIT)-Massachusetts General Hospital [Boston], Institut de Génomique d'Evry (IG), Institut de Biologie François JACOB (JACOB), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, the Genetic Investigation of ANthropocentric Traits (GIANT) Consortium, Université Paris-Saclay-Institut de Biologie François JACOB (JACOB), and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)

Subjects
Netherlands Twin Register (NTR), Multifactorial Inheritance, [SDV]Life Sciences [q-bio], Genome-wide association study, Aetiology, screening and detection [ONCOL 5], 0302 clinical medicine, POPULATION, SNPS, Genetics, 0303 health sciences, Multidisciplinary, HERITABILITY, COMMON VARIANTS, Phenotype, DISEASES, ComputingMethodologies_DOCUMENTANDTEXTPROCESSING, Science & Technology - Other Topics, GROWTH, Allelic heterogeneity, Chromosomes, Human, Pair 3, Metabolic Networks and Pathways, TRAITS, Common disease-common variant, General Science & Technology, Locus (genetics), Biology, Polymorphism, Single Nucleotide, Article, Molecular epidemiology [NCEBP 1], 03 medical and health sciences, SDG 3 - Good Health and Well-being, ADULT, STRATIFICATION, MD Multidisciplinary, Humans, Genetic Predisposition to Disease, Genetic variability, 030304 developmental biology, Science & Technology, MULTIDISCIPLINARY SCIENCES, Genome, Human, Hormonal regulation [IGMD 6], Body Height, Genetic architecture, wide association analysis common variants heritability population adult stratification diseases traits growth snps, WIDE ASSOCIATION ANALYSIS, Genetic Loci, Evaluation of complex medical interventions [NCEBP 2], Polygene, Expression quantitative trait loci, 030217 neurology & neurosurgery, Genome-Wide Association Study
Abstract

Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence the phenotype. Genome-wide association (GWA) studies have identified more than 600 variants associated with human traits1, but these typically explain small fractions of phenotypic variation, raising questions about the use of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait2,3. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways ( P=0.016) and that underlie skeletal growth defects ( P

Published
2010

49. Heterothallism inSaccharomyces cerevisiaeisolates from nature: effect ofHOlocus on the mode of reproduction

Author

Zeev Frenkel, Tal Katz Ezov, Ayellet V. Segrè, Moran Bahalul, Shang-Lin Chang, Jun-Yi Leu, Yechezkel Kashi, Andrew W. Murray, and Abraham B. Korol

Subjects
Homothallism, Mating type, Evolution of sexual reproduction, Saccharomyces cerevisiae, Population, Biology, Article, Genetics, Cluster Analysis, Point Mutation, Heterothallic, DNA, Fungal, education, Phylogeny, reproductive and urinary physiology, Ecology, Evolution, Behavior and Systematics, education.field_of_study, Fungal genetics, Genetic Variation, Sequence Analysis, DNA, Genes, Mating Type, Fungal, biology.organism_classification, Diploidy, Genetics, Population, Mating of yeast, behavior and behavior mechanisms, Genome, Fungal, Microsatellite Repeats
Abstract

Understanding the evolution of sex and recombination, key factors in the evolution of life, is a major challenge in biology. Studies of reproduction strategies of natural populations are important to complement the theoretical and experimental models. Fungi with both sexual and asexual life cycles are an interesting system for understanding the evolution of sex. In a study of natural populations of yeast Saccharomyces cerevisiae, we found that the isolates are heterothallic, meaning their mating type is stable, while the general belief is that natural S. cerevisiae strains are homothallic (can undergo mating-type switching). Mating-type switching is a gene-conversion process initiated by a site-specific endonuclease HO; this process can be followed by mother-daughter mating. Heterothallic yeast can mate with unrelated haploids (amphimixis), or undergo mating between spores from the same tetrad (intratetrad mating, or automixis), but cannot undergo mother-daughter mating as homothallic yeasts can. Sequence analysis of HO gene in a panel of natural S. cerevisiae isolates revealed multiple mutations. Good correspondence was found in the comparison of population structure characterized using 19 microsatellite markers spread over eight chromosomes and the HO sequence. Experiments that tested whether the mating-type switching pathway upstream and downstream of HO is functional, together with the detected HO mutations, strongly suggest that loss of function of HO is the cause of heterothallism. Furthermore, our results support the hypothesis that clonal reproduction and intratetrad mating may predominate in natural yeast populations, while mother-daughter mating might not be as significant as was considered.

Published
2010

50. The human transcriptome across tissues and individuals

Author

Dmitri D. Pervouchine, Michael Sammeth, Pedro G. Ferreira, Ferran Reverter, Gad Getz, Timothy J. Sullivan, Miquel Calvo, Jakob M. Goldmann, Ayellet V. Segrè, Rory Johnson, Kristin G. Ardlie, Tuuli Lappalainen, Fred A. Wright, Emmanouil T. Dermitzakis, Anastasia Niarchou, Sarah Djebali, David S. DeLuca, Jean Monlong, Marta Melé, Roderic Guigó, and Taylor Young

Subjects
Gene expression profiling, Transcriptome, Regulation of gene expression, Genetics, Multidisciplinary, Alternative splicing, RNA splicing, Transcriptional regulation, Human genome, Biology, Gene, Article
Abstract

Expression, genetic variation, and tissues Human genomes show extensive genetic variation across individuals, but we have only just started documenting the effects of this variation on the regulation of gene expression. Furthermore, only a few tissues have been examined per genetic variant. In order to examine how genetic expression varies among tissues within individuals, the Genotype-Tissue Expression (GTEx) Consortium collected 1641 postmortem samples covering 54 body sites from 175 individuals. They identified quantitative genetic traits that affect gene expression and determined which of these exhibit tissue-specific expression patterns. Melé et al. measured how transcription varies among tissues, and Rivas et al. looked at how truncated protein variants affect expression across tissues. Science , this issue p. 648 , p. 660 , p. 666 ; see also p. 640

Published
2015

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