Life Sciences Commons^™

Key Variants Via The Alzheimer's Disease Sequencing Project Whole Genome Sequence Data, Yanbing Wang, Chloé Sarnowski, Honghuang Lin, Achilleas N Pitsillides, Nancy L Heard-Costa, Seung Hoan Choi, Dongyu Wang, Joshua C Bis, Elizabeth E Blue, Eric Boerwinkle, Philip L De Jager, Myriam Fornage, Ellen M Wijsman, Sudha Seshadri, Josée Dupuis, Gina M Peloso, Anita L Destefano

Journal Articles

INTRODUCTION: Genome-wide association studies (GWAS) have identified loci associated with Alzheimer's disease (AD) but did not identify specific causal genes or variants within those loci. Analysis of whole genome sequence (WGS) data, which interrogates the entire genome and captures rare variations, may identify causal variants within GWAS loci.

METHODS: We performed single common variant association analysis and rare variant aggregate analyses in the pooled population (N cases = 2184, N controls = 2383) and targeted analyses in subpopulations using WGS data from the Alzheimer's Disease Sequencing Project (ADSP). The analyses were restricted to variants within 100 kb of 83 previously …

An Approach To Identify Gene-Environment Interactions And Reveal New Biological Insight In Complex Traits, Xiaofeng Zhu, Yihe Yang, Noah Lorincz-Comi, Gen Li, Amy R Bentley, Paul S De Vries, Michael Brown, Alanna C Morrison, Charles N Rotimi, W James Gauderman, Dabeeru C Rao, Hugues Aschard

Journal Articles

There is a long-standing debate about the magnitude of the contribution of gene-environment interactions to phenotypic variations of complex traits owing to the low statistical power and few reported interactions to date. to address this issue, the Gene-Lifestyle Interactions Working Group within the Cohorts for Heart and Aging Research in Genetic Epidemiology Consortium has been spearheading efforts to investigate G × E in large and diverse samples through meta-analysis. Here, we present a powerful new approach to screen for interactions across the genome, an approach that shares substantial similarity to the Mendelian randomization framework. We identify and confirm 5 loci …

Unsupervised Deep Representation Learning Enables Phenotype Discovery For Genetic Association Studies Of Brain Imaging, Khush Patel, Ziqian Xie, Hao Yuan, Sheikh Muhammad Saiful Islam, Yaochen Xie, Wei He, Wanheng Zhang, Assaf Gottlieb, Han Chen, Luca Giancardo, Alexander Knaack, Evan Fletcher, Myriam Fornage, Shuiwang Ji, Degui Zhi

Journal Articles

Understanding the genetic architecture of brain structure is challenging, partly due to difficulties in designing robust, non-biased descriptors of brain morphology. Until recently, brain measures for genome-wide association studies (GWAS) consisted of traditionally expert-defined or software-derived image-derived phenotypes (IDPs) that are often based on theoretical preconceptions or computed from limited amounts of data. Here, we present an approach to derive brain imaging phenotypes using unsupervised deep representation learning. We train a 3-D convolutional autoencoder model with reconstruction loss on 6130 UK Biobank (UKBB) participants' T1 or T2-FLAIR (T2) brain MRIs to create a 128-dimensional representation known as Unsupervised Deep learning …

A Multitrait Genetic Study Of Hemostatic Factors And Hemorrhagic Transformation After Stroke Treatment, Cristina Gallego-Fabrega, Gerard Temprano-Sagrera, Jara Cárcel-Márquez, Elena Muiño, Natalia Cullell, Miquel Lledós, Laia Llucià-Carol, Jesús M Martin-Campos, Tomás Sobrino, José Castillo, Mònica Millán, Lucía Muñoz-Narbona, Elena López-Cancio, Marc Ribó, Jose Alvarez-Sabin, Jordi Jiménez-Conde, Jaume Roquer, Silvia Tur, Victor Obach, Juan F Arenillas, Tomas Segura, Gemma Serrano-Heras, Joan Marti-Fabregas, Marimar Freijo-Guerrero, Francisco Moniche, Maria Del Mar Castellanos, Alanna C Morrison, Nicholas L Smith, Paul S De Vries, Israel Fernández-Cadenas, Maria Sabater-Lleal

Journal Articles

BACKGROUND: Thrombolytic recombinant tissue plasminogen activator (r-tPA) treatment is the only pharmacologic intervention available in the ischemic stroke acute phase. This treatment is associated with an increased risk of intracerebral hemorrhages, known as hemorrhagic transformations (HTs), which worsen the patient's prognosis.

OBJECTIVES: to investigate the association between genetically determined natural hemostatic factors' levels and increased risk of HT after r-tPA treatment.

METHODS: Using data from genome-wide association studies on the risk of HT after r-tPA treatment and data on 7 hemostatic factors (factor [F]VII, FVIII, von Willebrand factor [VWF], FXI, fibrinogen, plasminogen activator inhibitor-1, and tissue plasminogen activator), we performed …

Genetic Drivers Of Heterogeneity In Type 2 Diabetes Pathophysiology, Ken Suzuki, Konstantinos Hatzikotoulas, Lorraine Southam, Henry J Taylor, Xianyong Yin, Kim M Lorenz, Ravi Mandla, Alicia Huerta-Chagoya, Giorgio E M Melloni, Stavroula Kanoni, Nigel W Rayner, Ozvan Bocher, Ana Luiza Arruda, Kyuto Sonehara, Shinichi Namba, Simon S K Lee, Michael H Preuss, Lauren E Petty, Philip Schroeder, Brett Vanderwerff, Mart Kals, Fiona Bragg, Kuang Lin, Xiuqing Guo, Weihua Zhang, Jie Yao, Young Jin Kim, Mariaelisa Graff, Fumihiko Takeuchi, Jana Nano, Amel Lamri, Masahiro Nakatochi, Sanghoon Moon, Robert A Scott, James P Cook, Jung-Jin Lee, Ian Pan, Daniel Taliun, Esteban J Parra, Jin-Fang Chai, Lawrence F Bielak, Yasuharu Tabara, Yang Hai, Gudmar Thorleifsson, Niels Grarup, Tamar Sofer, Matthias Wuttke, Chloé Sarnowski, Christian Gieger, Darryl Nousome, Stella Trompet, Soo-Heon Kwak, Jirong Long, Meng Sun, Lin Tong, Wei-Min Chen, Suraj S Nongmaithem, Raymond Noordam, Victor J Y Lim, Claudia H T Tam, Yoonjung Yoonie Joo, Chien-Hsiun Chen, Laura M Raffield, Bram Peter Prins, Aude Nicolas, Lisa R Yanek, Guanjie Chen, Jennifer A Brody, Edmond Kabagambe, Ping An, Anny H Xiang, Hyeok Sun Choi, Brian E Cade, Jingyi Tan, K Alaine Broadaway, Alice Williamson, Zoha Kamali, Jinrui Cui, Manonanthini Thangam, Linda S Adair, Adebowale Adeyemo, Carlos A Aguilar-Salinas, Tarunveer S Ahluwalia, Sonia S Anand, Alain Bertoni, Jette Bork-Jensen, Ivan Brandslund, Thomas A Buchanan, Charles F Burant, Adam S Butterworth, Mickaël Canouil, Juliana C N Chan, Li-Ching Chang, Miao-Li Chee, Ji Chen, Shyh-Huei Chen, Yuan-Tsong Chen, Zhengming Chen, Lee-Ming Chuang, Mary Cushman, John Danesh, Swapan K Das, H Janaka De Silva, George Dedoussis, Latchezar Dimitrov, Ayo P Doumatey, Shufa Du, Qing Duan, Kai-Uwe Eckardt, Leslie S Emery, Daniel S Evans, Michele K Evans, Krista Fischer, James S Floyd, Ian Ford, Oscar H Franco, Timothy M Frayling, Barry I Freedman, Pauline Genter, Hertzel C Gerstein, Vilmantas Giedraitis, Clicerio González-Villalpando, Maria Elena González-Villalpando, Penny Gordon-Larsen, Myron Gross, Lindsay A Guare, Sophie Hackinger, Liisa Hakaste, Sohee Han, Andrew T Hattersley, Christian Herder, Momoko Horikoshi, Annie-Green Howard, Willa Hsueh, Mengna Huang, Wei Huang, Yi-Jen Hung, Mi Yeong Hwang, Chii-Min Hwu, Sahoko Ichihara, Mohammad Arfan Ikram, Martin Ingelsson, Md Tariqul Islam, Masato Isono, Hye-Mi Jang, Farzana Jasmine, Guozhi Jiang, Jost B Jonas, Torben Jørgensen, Frederick K Kamanu, Fouad R Kandeel, Anuradhani Kasturiratne, Tomohiro Katsuya, Varinderpal Kaur, Takahisa Kawaguchi, Jacob M Keaton, Abel N Kho, Chiea-Chuen Khor, Muhammad G Kibriya, Duk-Hwan Kim, Florian Kronenberg, Johanna Kuusisto, Kristi Läll, Leslie A Lange, Kyung Min Lee, Myung-Shik Lee, Nanette R Lee, Aaron Leong, Liming Li, Yun Li, Ruifang Li-Gao, Symen Ligthart, Cecilia M Lindgren, Allan Linneberg, Ching-Ti Liu, Jianjun Liu, Adam E Locke, Tin Louie, Jian'an Luan, Andrea O Luk, Xi Luo, Jun Lv, Julie A Lynch, Valeriya Lyssenko, Shiro Maeda, Vasiliki Mamakou, Sohail Rafik Mansuri, Koichi Matsuda, Thomas Meitinger, Olle Melander, Andres Metspalu, Huan Mo, Andrew D Morris, Filipe A Moura, Jerry L Nadler, Michael A Nalls, Uma Nayak, Ioanna Ntalla, Yukinori Okada, Lorena Orozco, Sanjay R Patel, Snehal Patil, Pei Pei, Mark A Pereira, Annette Peters, Fraser J Pirie, Hannah G Polikowsky, Bianca Porneala, Gauri Prasad, Laura J Rasmussen-Torvik, Alexander P Reiner, Michael Roden, Rebecca Rohde, Katheryn Roll, Charumathi Sabanayagam, Kevin Sandow, Alagu Sankareswaran, Naveed Sattar, Sebastian Schönherr, Mohammad Shahriar, Botong Shen, Jinxiu Shi, Dong Mun Shin, Nobuhiro Shojima, Jennifer A Smith, Wing Yee So, Alena Stančáková, Valgerdur Steinthorsdottir, Adrienne M Stilp, Konstantin Strauch, Kent D Taylor, Barbara Thorand, Unnur Thorsteinsdottir, Brian Tomlinson, Tam C Tran, Fuu-Jen Tsai, Jaakko Tuomilehto, Teresa Tusie-Luna, Miriam S Udler, Adan Valladares-Salgado, Rob M Van Dam, Jan B Van Klinken, Rohit Varma, Niels Wacher-Rodarte, Eleanor Wheeler, Ananda R Wickremasinghe, Ko Willems Van Dijk, Daniel R Witte, Chittaranjan S Yajnik, Ken Yamamoto, Kenichi Yamamoto, Kyungheon Yoon, Canqing Yu, Jian-Min Yuan, Salim Yusuf, Matthew Zawistowski, Liang Zhang, Wei Zheng, Leslie J Raffel, Michiya Igase, Eli Ipp, Susan Redline, Yoon Shin Cho, Lars Lind, Michael A Province, Myriam Fornage, Craig L Hanis, Erik Ingelsson, Alan B Zonderman, Bruce M Psaty, Ya-Xing Wang, Charles N Rotimi, Diane M Becker, Fumihiko Matsuda, Yongmei Liu, Mitsuhiro Yokota, Sharon L R Kardia, Patricia A Peyser, James S Pankow, James C Engert, Amélie Bonnefond, Philippe Froguel, James G Wilson, Wayne H H Sheu, Jer-Yuarn Wu, M Geoffrey Hayes, Ronald C W Ma, Tien-Yin Wong, Dennis O Mook-Kanamori, Tiinamaija Tuomi, Giriraj R Chandak, Francis S Collins, Dwaipayan Bharadwaj, Guillaume Paré, Michèle M Sale, Habibul Ahsan, Ayesha A Motala, Xiao-Ou Shu, Kyong-Soo Park, J Wouter Jukema, Miguel Cruz, Yii-Der Ida Chen, Stephen S Rich, Roberta Mckean-Cowdin, Harald Grallert, Ching-Yu Cheng, Mohsen Ghanbari, E-Shyong Tai, Josee Dupuis, Norihiro Kato, Markku Laakso, Anna Köttgen, Woon-Puay Koh, Donald W Bowden, Colin N A Palmer, Jaspal S Kooner, Charles Kooperberg, Simin Liu, Kari E North, Danish Saleheen, Torben Hansen, Oluf Pedersen, Nicholas J Wareham, Juyoung Lee, Bong-Jo Kim, Iona Y Millwood, Robin G Walters, Kari Stefansson, Emma Ahlqvist, Mark O Goodarzi, Karen L Mohlke, Claudia Langenberg, Christopher A Haiman, Ruth J F Loos, Jose C Florez, Daniel J Rader, Marylyn D Ritchie, Sebastian Zöllner, Reedik Mägi, Nicholas A Marston, Christian T Ruff, David A Van Heel, Sarah Finer, Joshua C Denny, Toshimasa Yamauchi, Takashi Kadowaki, John C Chambers, Maggie C Y Ng, Xueling Sim, Jennifer E Below, Philip S Tsao, Kyong-Mi Chang, Mark I Mccarthy, James B Meigs, Anubha Mahajan, Cassandra N Spracklen, Josep M Mercader, Michael Boehnke, Jerome I Rotter, Marijana Vujkovic, Benjamin F Voight, Andrew P Morris, Eleftheria Zeggini

Journal Articles

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.