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

Author

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

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

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

Published
2023
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3. A Model of Minor Histocompatibility Antigens in Allogeneic Hematopoietic Cell Transplantation

Author

Paul J. Martin, David M. Levine, Barry E. Storer, Xiuwen Zheng, Deepti Jain, Ben Heavner, Brandon M. Norris, Daniel E. Geraghty, Stephen R. Spellman, Cassie L. Sather, Feinan Wu, and John A. Hansen

Subjects
genetic variation, graft-versus-host disease, single nucleotide polymorphisms (SNPs), hematopoietic cell transplantation, minor histocompatibility antigens, Immunologic diseases. Allergy, RC581-607
Abstract

Minor histocompatibility antigens (mHAg) composed of peptides presented by HLA molecules can cause immune responses involved in graft-versus-host disease (GVHD) and graft-versus-leukemia effects after allogeneic hematopoietic cell transplantation (HCT). The current study was designed to identify individual graft-versus-host genomic mismatches associated with altered risks of acute or chronic GVHD or relapse after HCT between HLA-genotypically identical siblings. Our results demonstrate that in allogeneic HCT between a pair of HLA-identical siblings, a mHAg manifests as a set of peptides originating from annotated proteins and non-annotated open reading frames, which i) are encoded by a group of highly associated recipient genomic mismatches, ii) bind to HLA allotypes in the recipient, and iii) evoke a donor immune response. Attribution of the immune response and consequent clinical outcomes to individual peptide components within this set will likely differ from patient to patient according to their HLA types.

Published
2021
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4. BinomiRare: A robust test for association of a rare genetic variant with a binary outcome for mixed models and any case-control proportion

Author

Tamar Sofer, Jiwon Lee, Nuzulul Kurniansyah, Deepti Jain, Cecelia A. Laurie, Stephanie M. Gogarten, Matthew P. Conomos, Ben Heavner, Yao Hu, Charles Kooperberg, Jeffrey Haessler, Ramachandran S. Vasan, L. Adrienne Cupples, Brandon J. Coombes, Amanda Seyerle, Sina A. Gharib, Han Chen, Jeffrey R. O’Connell, Man Zhang, Daniel J. Gottlieb, Bruce M. Psaty, W.T. Longstreth, Jr., Jerome I. Rotter, Kent D. Taylor, Stephen S. Rich, Xiuqing Guo, Eric Boerwinkle, Alanna C. Morrison, James S. Pankow, Andrew D. Johnson, Nathan Pankratz, Alex P. Reiner, Susan Redline, Nicholas L. Smith, Kenneth M. Rice, and Elizabeth D. Schifano

Subjects
genome sequencing, rare variant assocaition testing, diverse populations, targeted gene analysis, mixed models, Genetics, QH426-470
Abstract

Summary: Whole-genome sequencing (WGS) and whole-exome sequencing studies have become increasingly available and are being used to identify rare genetic variants associated with health and disease outcomes. Investigators routinely use mixed models to account for genetic relatedness or other clustering variables (e.g., family or household) when testing genetic associations. However, no existing tests of the association of a rare variant with a binary outcome in the presence of correlated data control the type 1 error where there are (1) few individuals harboring the rare allele, (2) a small proportion of cases relative to controls, and (3) covariates to adjust for. Here, we address all three issues in developing a framework for testing rare variant association with a binary trait in individuals harboring at least one risk allele. In this framework, we estimate outcome probabilities under the null hypothesis and then use them, within the individuals with at least one risk allele, to test variant associations. We extend the BinomiRare test, which was previously proposed for independent observations, and develop the Conway-Maxwell-Poisson (CMP) test and study their properties in simulations. We show that the BinomiRare test always controls the type 1 error, while the CMP test sometimes does not. We then use the BinomiRare test to test the association of rare genetic variants in target genes with small-vessel disease (SVD) stroke, short sleep, and venous thromboembolism (VTE), in whole-genome sequence data from the Trans-Omics for Precision Medicine (TOPMed) program.

Published
2021
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5. Reproducible big data science: A case study in continuous FAIRness.

Author

Ravi Madduri, Kyle Chard, Mike D'Arcy, Segun C Jung, Alexis Rodriguez, Dinanath Sulakhe, Eric Deutsch, Cory Funk, Ben Heavner, Matthew Richards, Paul Shannon, Gustavo Glusman, Nathan Price, Carl Kesselman, and Ian Foster

Subjects
Medicine, Science
Abstract

Big biomedical data create exciting opportunities for discovery, but make it difficult to capture analyses and outputs in forms that are findable, accessible, interoperable, and reusable (FAIR). In response, we describe tools that make it easy to capture, and assign identifiers to, data and code throughout the data lifecycle. We illustrate the use of these tools via a case study involving a multi-step analysis that creates an atlas of putative transcription factor binding sites from terabytes of ENCODE DNase I hypersensitive sites sequencing data. We show how the tools automate routine but complex tasks, capture analysis algorithms in understandable and reusable forms, and harness fast networks and powerful cloud computers to process data rapidly, all without sacrificing usability or reproducibility-thus ensuring that big data are not hard-to-(re)use data. We evaluate our approach via a user study, and show that 91% of participants were able to replicate a complex analysis involving considerable data volumes.

Published
2019
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6. Predictive Big Data Analytics: A Study of Parkinson's Disease Using Large, Complex, Heterogeneous, Incongruent, Multi-Source and Incomplete Observations.

Author

Ivo D Dinov, Ben Heavner, Ming Tang, Gustavo Glusman, Kyle Chard, Mike Darcy, Ravi Madduri, Judy Pa, Cathie Spino, Carl Kesselman, Ian Foster, Eric W Deutsch, Nathan D Price, John D Van Horn, Joseph Ames, Kristi Clark, Leroy Hood, Benjamin M Hampstead, William Dauer, and Arthur W Toga

Subjects
Medicine, Science
Abstract

BACKGROUND:A unique archive of Big Data on Parkinson's Disease is collected, managed and disseminated by the Parkinson's Progression Markers Initiative (PPMI). The integration of such complex and heterogeneous Big Data from multiple sources offers unparalleled opportunities to study the early stages of prevalent neurodegenerative processes, track their progression and quickly identify the efficacies of alternative treatments. Many previous human and animal studies have examined the relationship of Parkinson's disease (PD) risk to trauma, genetics, environment, co-morbidities, or life style. The defining characteristics of Big Data-large size, incongruency, incompleteness, complexity, multiplicity of scales, and heterogeneity of information-generating sources-all pose challenges to the classical techniques for data management, processing, visualization and interpretation. We propose, implement, test and validate complementary model-based and model-free approaches for PD classification and prediction. To explore PD risk using Big Data methodology, we jointly processed complex PPMI imaging, genetics, clinical and demographic data. METHODS AND FINDINGS:Collective representation of the multi-source data facilitates the aggregation and harmonization of complex data elements. This enables joint modeling of the complete data, leading to the development of Big Data analytics, predictive synthesis, and statistical validation. Using heterogeneous PPMI data, we developed a comprehensive protocol for end-to-end data characterization, manipulation, processing, cleaning, analysis and validation. Specifically, we (i) introduce methods for rebalancing imbalanced cohorts, (ii) utilize a wide spectrum of classification methods to generate consistent and powerful phenotypic predictions, and (iii) generate reproducible machine-learning based classification that enables the reporting of model parameters and diagnostic forecasting based on new data. We evaluated several complementary model-based predictive approaches, which failed to generate accurate and reliable diagnostic predictions. However, the results of several machine-learning based classification methods indicated significant power to predict Parkinson's disease in the PPMI subjects (consistent accuracy, sensitivity, and specificity exceeding 96%, confirmed using statistical n-fold cross-validation). Clinical (e.g., Unified Parkinson's Disease Rating Scale (UPDRS) scores), demographic (e.g., age), genetics (e.g., rs34637584, chr12), and derived neuroimaging biomarker (e.g., cerebellum shape index) data all contributed to the predictive analytics and diagnostic forecasting. CONCLUSIONS:Model-free Big Data machine learning-based classification methods (e.g., adaptive boosting, support vector machines) can outperform model-based techniques in terms of predictive precision and reliability (e.g., forecasting patient diagnosis). We observed that statistical rebalancing of cohort sizes yields better discrimination of group differences, specifically for predictive analytics based on heterogeneous and incomplete PPMI data. UPDRS scores play a critical role in predicting diagnosis, which is expected based on the clinical definition of Parkinson's disease. Even without longitudinal UPDRS data, however, the accuracy of model-free machine learning based classification is over 80%. The methods, software and protocols developed here are openly shared and can be employed to study other neurodegenerative disorders (e.g., Alzheimer's, Huntington's, amyotrophic lateral sclerosis), as well as for other predictive Big Data analytics applications.

Published
2016
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7. Building a collaborative cloud platform to accelerate heart, lung, blood, and sleep research

Author

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

Subjects
Health Informatics
Abstract

Research increasingly relies on interrogating large-scale data resources. The NIH National Heart, Lung, and Blood Institute developed the NHLBI BioData CatalystⓇ (BDC), a community-driven ecosystem where researchers, including bench and clinical scientists, statisticians, and algorithm developers, find, access, share, store, and compute on large-scale datasets. This ecosystem provides secure, cloud-based workspaces, user authentication and authorization, search, tools and workflows, applications, and new innovative features to address community needs, including exploratory data analysis, genomic and imaging tools, tools for reproducibility, and improved interoperability with other NIH data science platforms. BDC offers straightforward access to large-scale datasets and computational resources that support precision medicine for heart, lung, blood, and sleep conditions, leveraging separately developed and managed platforms to maximize flexibility based on researcher needs, expertise, and backgrounds. Through the NHLBI BioData Catalyst Fellows Program, BDC facilitates scientific discoveries and technological advances. BDC also facilitated accelerated research on the coronavirus disease-2019 (COVID-19) pandemic.

Published
2023
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8. Whole Genome Sequence Analysis of the Plasma Proteome in Black Adults Provides Novel Insights Into Cardiovascular Disease

Author

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

Subjects
Adult, Male, Proteomics, Aging, Whole genome sequence analysis, Proteome, Clinical Sciences, Black People, Disease, Computational biology, race and ethnicity, Cardiorespiratory Medicine and Haematology, Cardiovascular, Article, proteomics, cardiovascular disease, Physiology (medical), Genetics, 2.1 Biological and endogenous factors, Humans, Medicine, Aetiology, Lung, Heart Disease - Coronary Heart Disease, and Blood Institute TOPMed (Trans-Omics for Precision Medicine) Consortium†, business.industry, Prevention, Human Genome, National Heart, Genomics, Blood proteins, Genetic architecture, Heart Disease, Good Health and Well Being, Cardiovascular System & Hematology, Cardiovascular Diseases, Public Health and Health Services, Female, Cardiology and Cardiovascular Medicine, business, Biotechnology, Genome-Wide Association Study
Abstract

Background: Plasma proteins are critical mediators of cardiovascular processes and are the targets of many drugs. Previous efforts to characterize the genetic architecture of the plasma proteome have been limited by a focus on individuals of European descent and leveraged genotyping arrays and imputation. Here we describe whole genome sequence analysis of the plasma proteome in individuals with greater African ancestry, increasing our power to identify novel genetic determinants. Methods: Proteomic profiling of 1301 proteins was performed in 1852 Black adults from the Jackson Heart Study using aptamer-based proteomics (SomaScan). Whole genome sequencing association analysis was ascertained for all variants with minor allele count ≥5. Results were validated using an alternative, antibody-based, proteomic platform (Olink) as well as replicated in the Multi-Ethnic Study of Atherosclerosis and the HERITAGE Family Study (Health, Risk Factors, Exercise Training and Genetics). Results: We identify 569 genetic associations between 479 proteins and 438 unique genetic regions at a Bonferroni-adjusted significance level of 3.8×10 -11 . These associations include 114 novel locus-protein relationships and an additional 217 novel sentinel variant-protein relationships. Novel cardiovascular findings include new protein associations at the APOE gene locus including ZAP70 (sentinel single nucleotide polymorphism [SNP] rs7412-T, β=0.61±0.05, P =3.27×10 -30 ) and MMP-3 (β=-0.60±0.05, P =1.67×10 -32 ), as well as a completely novel pleiotropic locus at the HPX gene, associated with 9 proteins. Further, the associations suggest new mechanisms of genetically mediated cardiovascular disease linked to African ancestry; we identify a novel association between variants linked to APOL1-associated chronic kidney and heart disease and the protein CKAP2 (rs73885319-G, β=0.34±0.04, P =1.34×10 -17 ) as well as an association between ATTR amyloidosis and RBP4 levels in community-dwelling individuals without heart failure. Conclusions: Taken together, these results provide evidence for the functional importance of variants in non-European populations, and suggest new biological mechanisms for ancestry-specific determinants of lipids, coagulation, and myocardial function.

Published
2022
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9. Whole Genome Sequencing Identifies CRISPLD2 as a Lung Function Gene in Children With Asthma

Author

Priyadarshini Kachroo, Julian Hecker, Bo L. Chawes, Tarunveer S. Ahluwalia, Michael H. Cho, Dandi Qiao, Rachel S. Kelly, Su H. Chu, Yamini V. Virkud, Mengna Huang, Kathleen C. Barnes, Esteban G. Burchard, Celeste Eng, Donglei Hu, Juan C. Celedón, Michelle Daya, Albert M. Levin, Hongsheng Gui, L. Keoki Williams, Erick Forno, Angel C.Y. Mak, Lydiana Avila, Manuel E. Soto-Quiros, Michelle M. Cloutier, Edna Acosta-Pérez, Glorisa Canino, Klaus Bønnelykke, Hans Bisgaard, Benjamin A. Raby, Christoph Lange, Scott T. Weiss, Jessica A. Lasky-Su, Namiko Abe, Goncalo Abecasis, Christine Albert, Nicholette (Nichole) Palmer Allred, Laura Almasy, Alvaro Alonso, Seth Ament, Peter Anderson, Pramod Anugu, Deborah Applebaum-Bowden, Dan Arking, Donna K. Arnett, Allison Ashley-Koch, Stella Aslibekyan, Tim Assimes, Paul Auer, Dimitrios Avramopoulos, John Barnard, Kathleen Barnes, R. Graham Barr, Emily Barron-Casella, Terri Beaty, Diane Becker, Lewis Becker, Rebecca Beer, Ferdouse Begum, Amber Beitelshees, Emelia Benjamin, Marcos Bezerra, Larry Bielak, Joshua Bis, Thomas Blackwell, John Blangero, Eric Boerwinkle, Ingrid Borecki, Russell Bowler, Jennifer Brody, Ulrich Broeckel, Jai Broome, Karen Bunting, Esteban Burchard, Jonathan Cardwell, Cara Carty, Richard Casaburi, James Casella, Mark Chaffin, Christy Chang, Daniel Chasman, Sameer Chavan, Bo-Juen Chen, Wei-Min Chen, Yii-Der Ida Chen, Seung Hoan Choi, Lee-Ming Chuang, Mina Chung, Elaine Cornell, Adolfo Correa, Carolyn Crandall, James Crapo, L. Adrienne Cupples, Joanne Curran, Jeffrey Curtis, Brian Custer, Coleen Damcott, Dawood Darbar, Sayantan Das, Sean David, Colleen Davis, Mariza de Andrade, Michael DeBaun, Ranjan Deka, Dawn DeMeo, Scott Devine, Ron Do, Qing Duan, Ravi Duggirala, Peter Durda, Susan Dutcher, Charles Eaton, Lynette Ekunwe, Patrick Ellinor, Leslie Emery, Charles Farber, Leanna Farnam, Tasha Fingerlin, Matthew Flickinger, Myriam Fornage, Nora Franceschini, Mao Fu, Stephanie M. Fullerton, Lucinda Fulton, Stacey Gabriel, Weiniu Gan, Yan Gao, Margery Gass, Bruce Gelb, Xiaoqi (Priscilla) Geng, Soren Germer, Chris Gignoux, Mark Gladwin, David Glahn, Stephanie Gogarten, Da-Wei Gong, Harald Goring, C. Charles Gu, Yue Guan, Xiuqing Guo, Jeff Haessler, Michael Hall, Daniel Harris, Nicola Hawley, Jiang He, Ben Heavner, Susan Heckbert, Ryan Hernandez, David Herrington, Craig Hersh, Bertha Hidalgo, James Hixson, John Hokanson, Kramer Holly, Elliott Hong, Karin Hoth, Chao (Agnes) Hsiung, Haley Huston, Chii Min Hwu, Marguerite Ryan Irvin, Rebecca Jackson, Deepti Jain, Cashell Jaquish, Min A. Jhun, Jill Johnsen, Andrew Johnson, Craig Johnson, Rich Johnston, Kimberly Jones, Hyun Min Kang, Robert Kaplan, Sharon Kardia, Sekar Kathiresan, Laura Kaufman, Shannon Kelly, Eimear Kenny, Michael Kessler, Alyna Khan, Greg Kinney, Barbara Konkle, Charles Kooperberg, Stephanie Krauter, Ethan Lange, Leslie Lange, Cathy Laurie, Cecelia Laurie, Meryl LeBoff, Seunggeun Shawn Lee, Wen-Jane Lee, Jonathon LeFaive, David Levine, Dan Levy, Joshua Lewis, Yun Li, Honghuang Lin, Keng Han Lin, Simin Liu, Yongmei Liu, Ruth Loos, Steven Lubitz, Kathryn Lunetta, James Luo, Michael Mahaney, Barry Make, Ani Manichaikul, JoAnn Manson, Lauren Margolin, Lisa Martin, Susan Mathai, Rasika Mathias, Patrick McArdle, Merry-Lynn McDonald, Sean McFarland, Stephen McGarvey, Hao Mei, Deborah A. Meyers, Julie Mikulla, Nancy Min, Mollie Minear, Ryan L. Minster, Braxton Mitchell, May E. Montasser, Solomon Musani, Stanford Mwasongwe, Josyf C. Mychaleckyj, Girish Nadkarni, Rakhi Naik, Pradeep Natarajan, Sergei Nekhai, Deborah Nickerson, Kari North, Jeff O'Connell, Tim O'Connor, Heather Ochs-Balcom, James Pankow, George Papanicolaou, Margaret Parker, Afshin Parsa, Sara Penchev, Juan Manuel Peralta, Marco Perez, James Perry, Ulrike Peters, Patricia Peyser, Lawrence S. Phillips, Sam Phillips, Toni Pollin, Wendy Post, Julia Powers Becker, Meher Preethi Boorgula, Michael Preuss, Dmitry Prokopenko, Bruce Psaty, Pankaj Qasba, Zhaohui Qin, Nicholas Rafaels, Laura Raffield, Vasan Ramachandran, D.C. Rao, Laura Rasmussen-Torvik, Aakrosh Ratan, Susan Redline, Robert Reed, Elizabeth Regan, Alex Reiner, Ken Rice, Stephen Rich, Dan Roden, Carolina Roselli, Jerome Rotter, Ingo Ruczinski, Pamela Russell, Sarah Ruuska, Kathleen Ryan, Phuwanat Sakornsakolpat, Shabnam Salimi, Steven Salzberg, Kevin Sandow, Vijay Sankaran, Christopher Scheller, Ellen Schmidt, Karen Schwander, David Schwartz, Frank Sciurba, Christine Seidman, Jonathan Seidman, Vivien Sheehan, Amol Shetty, Aniket Shetty, Wayne Hui-Heng Sheu, M. Benjamin Shoemaker, Brian Silver, Edwin Silverman, Jennifer Smith, Josh Smith, Nicholas Smith, Tanja Smith, Sylvia Smoller, Beverly Snively, Tamar Sofer, Nona Sotoodehnia, Adrienne Stilp, Elizabeth Streeten, Yun Ju Sung, Jessica Su-Lasky, Jody Sylvia, Adam Szpiro, Carole Sztalryd, Daniel Taliun, Hua Tang, Margaret Taub, Kent Taylor, Simeon Taylor, Marilyn Telen, Timothy A. Thornton, Lesley Tinker, David Tirschwell, Hemant Tiwari, Russell Tracy, Michael Tsai, Dhananjay Vaidya, Peter VandeHaar, Scott Vrieze, Tarik Walker, Robert Wallace, Avram Walts, Emily Wan, Fei Fei Wang, Karol Watson, Daniel E. Weeks, Bruce Weir, Scott Weiss, Lu-Chen Weng, Cristen Willer, Kayleen Williams, Carla Wilson, James Wilson, Quenna Wong, Huichun Xu, Lisa Yanek, Ivana Yang, Rongze Yang, Norann Zaghloul, Maryam Zekavat, Yingze Zhang, Snow Xueyan Zhao, Wei Zhao, Xiuwen Zheng, Degui Zhi, Xiang Zhou, Michael Zody, and Sebastian Zoellner

Subjects
Adult, Costa Rica, Male, Pulmonary and Respiratory Medicine, Adolescent, Vital Capacity, Single-nucleotide polymorphism, Pedigree chart, Critical Care and Intensive Care Medicine, Young Adult, 03 medical and health sciences, FEV1/FVC ratio, 0302 clinical medicine, Polymorphism (computer science), Forced Expiratory Volume, Humans, Medicine, SNP, 030212 general & internal medicine, Child, Asthma, Whole Genome Sequencing, business.industry, Middle Aged, respiratory system, medicine.disease, respiratory tract diseases, Minor allele frequency, 030228 respiratory system, Genetic epidemiology, Child, Preschool, Interferon Regulatory Factors, Immunology, Respiratory Physiological Phenomena, Female, Cardiology and Cardiovascular Medicine, business, Cell Adhesion Molecules
Abstract

BACKGROUND: Asthma is a common respiratory disorder with a highly heterogeneous nature that remains poorly understood. The objective was to use whole genome sequencing (WGS) data to identify regions of common genetic variation contributing to lung function in individuals with a diagnosis of asthma. METHODS: WGS data were generated for 1,053 individuals from trios and extended pedigrees participating in the family-based Genetic Epidemiology of Asthma in Costa Rica study. Asthma affection status was defined through a physician’s diagnosis of asthma, and most participants with asthma also had airway hyperresponsiveness (AHR) to methacholine. Family-based association tests for single variants were performed to assess the associations with lung function phenotypes. RESULTS: A genome-wide significant association was identified between baseline FEV(1)/FVC ratio and a single-nucleotide polymorphism in the top hit cysteine-rich secretory protein LCCL domain-containing 2 (CRISPLD2) (rs12051168; P = 3.6 × 10(−8) in the unadjusted model) that retained suggestive significance in the covariate-adjusted model (P = 5.6 × 10(−6)). Rs12051168 was also nominally associated with other related phenotypes: baseline FEV(1) (P = 3.3 × 10(−3)), postbronchodilator (PB) FEV(1) (7.3 × 10(−3)), and PB FEV(1)/FVC ratio (P = 2.7 × 10(−3)). The identified baseline FEV(1)/FVC ratio and rs12051168 association was meta-analyzed and replicated in three independent cohorts in which most participants with asthma also had confirmed AHR (combined weighted z-score P = .015) but not in cohorts without information about AHR. CONCLUSIONS: These findings suggest that using specific asthma characteristics, such as AHR, can help identify more genetically homogeneous asthma subgroups with genotype-phenotype associations that may not be observed in all children with asthma. CRISPLD2 also may be important for baseline lung function in individuals with asthma who also may have AHR.

Published
2019
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10. BinomiRare: A robust test for association of a rare genetic variant with a binary outcome for mixed models and any case-control proportion

Author

Ramachandran S. Vasan, Nathan Pankratz, Eric Boerwinkle, Charles Kooperberg, Nicholas L. Smith, Jiwon Lee, Xiuqing Guo, Elizabeth D. Schifano, Matthew P. Conomos, Man Zhang, Andrew D. Johnson, Kent D. Taylor, Jeffrey Haessler, Yao Hu, Alanna C. Morrison, Jeffrey R. O'Connell, W. T. Longstreth, Brandon J. Coombes, Jerome I. Rotter, Deepti Jain, James S. Pankow, Nuzulul Kurniansyah, Amanda A. Seyerle, Stephen S. Rich, Ben Heavner, Daniel J. Gottlieb, Sina A. Gharib, Bruce M. Psaty, Stephanie M. Gogarten, Han Chen, L. Adrienne Cupples, Kenneth Rice, Tamar Sofer, Cecelia A. Laurie, Alexander P. Reiner, and Susan Redline

Subjects
Genetics, Mixed model, mixed models, Disease, Biology, QH426-470, DNA sequencing, Article, targeted gene analysis, Test (assessment), genome sequencing, Covariate, Molecular Medicine, diverse populations, Allele, Null hypothesis, rare variant assocaition testing, Genetics (clinical), Type I and type II errors
Abstract

Summary: Whole-genome sequencing (WGS) and whole-exome sequencing studies have become increasingly available and are being used to identify rare genetic variants associated with health and disease outcomes. Investigators routinely use mixed models to account for genetic relatedness or other clustering variables (e.g., family or household) when testing genetic associations. However, no existing tests of the association of a rare variant with a binary outcome in the presence of correlated data control the type 1 error where there are (1) few individuals harboring the rare allele, (2) a small proportion of cases relative to controls, and (3) covariates to adjust for. Here, we address all three issues in developing a framework for testing rare variant association with a binary trait in individuals harboring at least one risk allele. In this framework, we estimate outcome probabilities under the null hypothesis and then use them, within the individuals with at least one risk allele, to test variant associations. We extend the BinomiRare test, which was previously proposed for independent observations, and develop the Conway-Maxwell-Poisson (CMP) test and study their properties in simulations. We show that the BinomiRare test always controls the type 1 error, while the CMP test sometimes does not. We then use the BinomiRare test to test the association of rare genetic variants in target genes with small-vessel disease (SVD) stroke, short sleep, and venous thromboembolism (VTE), in whole-genome sequence data from the Trans-Omics for Precision Medicine (TOPMed) program.

Published
2021

11. Deep coverage whole genome sequences and plasma lipoprotein(a) in individuals of European and African ancestries

Author

Cashell E. Jaquish, Craig P. Hersh, Emily Barron-Casella, Larry Phillips, James S. Pankow, JoAnn E. Manson, Mariza de Andrade, Vivien A. Sheehan, Norann A. Zaghloul, Yongmei Liu, Sergei Nekhai, Russell P. Tracy, Bruce S. Weir, Pankaj Qasba, Adrienne M. Stilp, Tasha E. Fingerlin, Heather M. Ochs-Balcom, L. Adrienne Cupples, Rongze Yang, David A. Schwartz, Shannon Kelly, Scott E. Devine, Tanja Smith, G.L. Kinney, Beverly Snively, Kent D. Taylor, Manolis Kellis, Ruth J. F. Loos, Seth A. Ament, Kathleen C. Barnes, Robert C. Kaplan, Ulrich Broeckel, Hemant K. Tiwari, Karin F. Hoth, Peter Durda, Julie L. Mikulla, Charles B. Eaton, Cotton Seed, Merry Lynn McDonald, Eric Boerwinkle, Josh Smith, Honghuang Lin, Ingrid B. Borecki, Jeffrey L. Curtis, Emelia J. Benjamin, Kenneth Rice, Sharon L.R. Kardia, Nicholas L. Smith, Coleen M. Damcott, Jeffrey Haessler, Kimberly L. Jones, Elaine Cornell, Nona Sotoodehnia, Adolfo Correa, Rich Johnston, C. Charles Gu, Chii Min Hwu, Peter C. Anderson, Lesley F. Tinker, Colleen Davis, R. Graham Barr, Scott T. Weiss, Leanna Farnam, Chao Hsiung, Brian Silver, Patrick F. McArdle, Lisa W. Martin, Yii-Der Ida Chen, John Barnard, Holly Kramer, Sekar Kathiresan, Gonçalo R. Abecasis, Barry Make, Michael C. Mahaney, Marco V Perez, Donna K. Arnett, Mark Chaffin, Xiuqing Guo, Joshua C. Bis, Pamela Russell, Paul L. Auer, James G. Wilson, Marilyn J. Telen, David K. Levine, Jennifer A. Smith, Christopher Scheller, Meher Preethi Boorgula, Aakrosh Ratan, Kari E. North, Dan E. Arking, Elizabeth A. Regan, Margaret G. Parker, Andres Metspalu, Jai G. Broome, Daniel Taliun, Lynette Ekunwe, Alyna T. Khan, Hao Mei, Dhananjay Vaidya, Ellen M. Schmidt, Stanford Mwasongwe, Joshua P. Lewis, Stacey Gabriel, Christine E. Seidman, Margery Gass, Deborah A. Nickerson, Nicola L. Hawley, Rebecca L. Beer, Afshin Parsa, Steven L. Salzberg, Terri H. Beaty, Stella Aslibekyan, Girish N. Nadkarni, Wei Zhao, David L. Tirschwell, Karen Bunting, Dawn L. DeMeo, Laura J. Rasmussen-Torvik, Julia Powers Becker, Dmitry Prokopenko, Karen Schwander, Bruce D. Gelb, Stephanie Krauter, Laura M. Raffield, Michael E. Hall, Frank C. Sciurba, Lauren Margolin, Nora Franceschini, Daniel N. Harris, Seyedeh M. Zekavat, Dawood Darbar, Keng-Han Lin, Haley Huston, Steven A. Lubitz, Andrea Ganna, Carole Sztalryd, Cathy C. Laurie, Christy Chang, James E. Hixson, Steven A. McCarroll, Barbara A. Konkle, Michael D. Kessler, Scott I. Vrieze, Yingze Zhang, Sarah Ruuska, George J. Papanicolaou, Weiniu Gan, Maris Alver, Elizabeth A. Streeten, Fei Fei Wang, Lu-Chen Weng, Braxton D. Mitchell, Lee-Ming Chuang, Sean P. David, Wei-Min Chen, Susan R. Heckbert, Ryan D. Hernandez, Andrew D. Johnson, Cristen J. Willer, Ani Manichaikul, Jonathan M. Bloom, Timothy D. O’Connor, Sylvia Smoller, Marguerite R. Irvin, Seung Hoan Choi, Ida Surakka, Veikko Salomaa, Diane M. Becker, Ron Do, W. Craig Johnson, Cecelia A. Laurie, Meryl S. LeBoff, Aniket Shetty, Tõnu Esko, Michael C. Zody, Xiaoqi Geng, Da Wei Gong, Snow Xueyan Zhao, Esteban G. Burchard, Xiuwen Zheng, Cara L. Carty, Alexander P. Reiner, Tim Assimes, Deborah A. Meyers, Mina K. Chung, Harald H H Göring, Benjamin M. Neale, Pradeep Natarajan, Yan Gao, Stephen S. Rich, Yun Ju Sung, Susan K. Dutcher, Ferdouse Begum, Stephanie M. Gogarten, John Blangero, Jonathan Cardwell, Kayleen Williams, Rakhi P. Naik, Amol C. Shetty, Sayantan Das, Myriam Fornage, May E. Montasser, Michelle Daya, Edwin K. Silverman, Daniel E. Weeks, Laura J. Kaufman, Dimitrios Avramopoulos, Michael Y. Tsai, Christine M. Albert, Vijay G. Sankaran, Jeffrey R. O'Connell, Thomas W. Blackwell, Stephen T. McGarvey, Robert M. Reed, Leslie A. Lange, Qing Duan, Bruce M. Psaty, Leslie S. Emery, Bertha Hidalgo, Jennifer A. Brody, L. Keoki Williams, Soren Germer, Zhaohui S. Qin, Deepti Jain, Deborah Applebaum-Bowden, Carla Wilson, Degui Zhi, Christoph Lange, Elliott Hong, L.F. Bielak, Wen Jane Lee, Yue Guan, Tarik Walker, Rebecca D. Jackson, Charles R. Farber, Mark J. Daly, Stephanie M. Fullerton, John E. Hokanson, Karol E. Watson, James Luo, Richard Casaburi, Seunggeun Lee, Jill M. Johnsen, Margaret A. Taub, Ryan L. Minster, Ravi Duggirala, Susan K. Mathai, Yun Li, Quenna Wong, Matthew Flickinger, Huichun Xu, Susan Redline, Ulrike Peters, Ivana V. Yang, Jesse M. Engreitz, Sanni Ruotsalainen, Sean K. McFarland, Avram D Walts, Patricia A. Peyser, Allison E. Ashley-Koch, Lewis C. Becker, Nicholette Palmer Allred, James A. Perry, Jody S. Sylvia, Tamar Sofer, Vasan S. Ramachandran, Laura Almasy, Matthew J. Budoff, Ranjan Deka, David M. Herrington, Simeon I. Taylor, Daniel Levy, Chaojie Yang, Sameer Chavan, Kathleen A. Ryan, Josyf C. Mychaleckyj, Ingo Ruczinski, Sebastian Zoellner, Michael Preuss, Tim Poterba, Wendy S. Post, Amber L. Beitelshees, Ben Heavner, Carolina Roselli, Namiko Abe, Jonathon LeFaive, Mark T. Gladwin, Brian Custer, Robert B. Wallace, Simin Liu, Michael H. Cho, Robert E. Handsaker, Dabeeru C. Rao, Bo Juen Chen, Wayne Hui Heng Sheu, Xiang Zhou, Marcos Bezerra, Solomon K. Musani, Eimear E. Kenny, James F. Casella, Kathryn L. Lunetta, Nancy Min, Nicholas Rafaels, Lisa R. Yanek, Min A. Jhun, David C. Glahn, Rasika A. Mathias, Mollie A. Minear, Russell P. Bowler, Jason Ernst, Carolyn J. Crandall, Sara Penchev, Alvaro Alonso, Timothy A. Thornton, Toni I. Pollin, Charles Kooperberg, Ethan M. Lange, Juan M. Peralta, Pramod Anugu, Lucinda Fulton, Adam A. Szpiro, Michael R. DeBaun, M. Benjamin Shoemaker, Sam Phillips, Dan M. Roden, Mao Fu, Daniel I. Chasman, James D. Crapo, Hua Tang, Gina M. Peloso, Hyun Min Kang, Samuli Ripatti, Phuwanat Sakornsakolpat, Christopher R. Gignoux, Peter VandeHaar, Jerome I. Rotter, Dandi Qiao, Emily S. Wan, Shabnam Salimi, Kevin Sandow, Jiang He, Patrick T. Ellinor, Joanne E. Curran, Institute for Molecular Medicine Finland, Samuli Olli Ripatti / Principal Investigator, University of Helsinki, Centre of Excellence in Complex Disease Genetics, Clinicum, Doctoral Programme in Population Health, Department of Public Health, and Complex Disease Genetics

Subjects
0301 basic medicine, Apolipoprotein B, General Physics and Astronomy, Gene Expression, Genome-wide association study, 030204 cardiovascular system & hematology, Cardiovascular, Lipoprotein particle, chemistry.chemical_compound, 0302 clinical medicine, Risk Factors, GENETIC-VARIANTS, 2.1 Biological and endogenous factors, WIDE ASSOCIATION, Aetiology, lcsh:Science, African Continental Ancestry Group, Genetics, Multidisciplinary, Genome, biology, CHOLESTEROL, Adaptor Proteins, Lipoprotein(a), Single Nucleotide, NHLBI TOPMed Lipids Working Group, 3142 Public health care science, environmental and occupational health, 3. Good health, Cholesterol, Heart Disease, Cardiovascular Diseases, CARDIOVASCULAR-DISEASE, RARE VARIANTS, lipids (amino acids, peptides, and proteins), LOW-FREQUENCY, Human, DNA Copy Number Variations, Science, Quantitative Trait Loci, European Continental Ancestry Group, Black People, POPULATION-SCALE, Quantitative trait locus, Article, White People, General Biochemistry, Genetics and Molecular Biology, LDL, Structural variation, 03 medical and health sciences, COMPONENTS-ANALYSIS, RISK-FACTOR, Humans, CORONARY-HEART-DISEASE, Polymorphism, Whole Genome Sequencing, Prevention, Human Genome, General Chemistry, Atherosclerosis, Vesicular Transport, Good Health and Well Being, 030104 developmental biology, chemistry, biology.protein, lcsh:Q, 3111 Biomedicine, Lipoprotein, Genome-Wide Association Study
Abstract

Lipoprotein(a), Lp(a), is a modified low-density lipoprotein particle that contains apolipoprotein(a), encoded by LPA, and is a highly heritable, causal risk factor for cardiovascular diseases that varies in concentrations across ancestries. Here, we use deep-coverage whole genome sequencing in 8392 individuals of European and African ancestry to discover and interpret both single-nucleotide variants and copy number (CN) variation associated with Lp(a). We observe that genetic determinants between Europeans and Africans have several unique determinants. The common variant rs12740374 associated with Lp(a) cholesterol is an eQTL for SORT1 and independent of LDL cholesterol. Observed associations of aggregates of rare non-coding variants are largely explained by LPA structural variation, namely the LPA kringle IV 2 (KIV2)-CN. Finally, we find that LPA risk genotypes confer greater relative risk for incident atherosclerotic cardiovascular diseases compared to directly measured Lp(a), and are significantly associated with measures of subclinical atherosclerosis in African Americans., Circulating lipoprotein(a) is an important risk factor for cardiovascular disease and shows variability between different ethnic groups. Here, Zekavat et al. perform whole-genome sequencing in individuals of European and African ancestries and find ancestry-specific genetic determinants for lipoprotein(a) levels.

Published
2018
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12. Atlas of Transcription Factor Binding Sites from ENCODE DNase Hypersensitivity Data across 27 Tissue Types

Author

Cory C. Funk, Nilufer Ertekin-Taner, Leroy Hood, Matthew A. Richards, Nathan D. Price, Alex Rodriguez, Gustavo Glusman, Yukai Xiao, Alex M. Casella, Segun Jung, Ben Heavner, Ian Foster, Kyle Chard, Paul Shannon, Rory Donovan-Maiye, Carl Kesselman, John D. Van Horn, Todd E. Golde, Arthur W. Toga, Ravi Madduri, and Seth A. Ament

Subjects
0301 basic medicine, genetic processes, information science, Gene regulatory network, Computational biology, Biology, ENCODE, General Biochemistry, Genetics and Molecular Biology, DNase-Seq, Article, 03 medical and health sciences, 0302 clinical medicine, Genetic variation, Humans, natural sciences, Transcription factor, Binding Sites, Deoxyribonucleases, Genomics, Footprinting, DNA binding site, 030104 developmental biology, health occupations, Human genome, Sequence motif, Hypersensitive site, 030217 neurology & neurosurgery, Transcription Factors
Abstract

There is intense interest in mapping the tissue-specific binding sites of transcription factors in the human genome to reconstruct gene regulatory networks and predict functions for non-coding genetic variation. DNase-seq footprinting provides a means to predict genome-wide binding sites for hundreds of transcription factors (TFs) simultaneously. However, despite the public availability of DNase-seq data for hundreds of samples, there is neither a unified analytical workflow nor a publicly accessible database providing the locations of footprints across all available samples. Here, we implemented a workflow for uniform processing of footprints using two state-of-the-art footprinting algorithms: Wellington and HINT. Our workflow scans the footprints generated by these algorithms for 1,530 sequence motifs to predict binding sites for 1,515 human transcription factors. We applied our workflow to detect footprints in 192 DNase-seq experiments from ENCODE spanning 27 human tissues. This collection of footprints describes an expansive landscape of potential TF occupancy. At thresholds optimized through machine learning, we report high-quality footprints covering 9.8% of the human genome. These footprints were enriched for true positive TF binding sites as defined by ChIP-seq peaks, as well as for genetic variants associated with changes in gene expression. Integrating our footprint atlas with summary statistics from genome-wide association studies revealed that risk for neuropsychiatric traits was enriched specifically at highly-scoring footprints in human brain, while risk for immune traits was enriched specifically at highly-scoring footprints in human lymphoblasts. Our cloud-based workflow is available at github.com/globusgenomics/genomics-footprint and a database with all footprints and TF binding site predictions are publicly available at http://data.nemoarchive.org/other/grant/sament/sament/footprint_atlas.

Published
2018

13. Reproducible big data science: A case study in continuous FAIRness

Author

Nathan D. Price, Kyle Chard, Paul Shannon, Cory C. Funk, Alexis A. Rodriguez, Matthew A. Richards, Eric W. Deutsch, Dinanath Sulakhe, Ravi Madduri, Segun Jung, Mike D'Arcy, Gustavo Glusman, Carl Kesselman, Ben Heavner, and Ian Foster

Subjects
Big Data, Databases, Factual, Process (engineering), Computer science, Science, Data management, Big data, Interoperability, Cloud computing, Terabyte, 03 medical and health sciences, 0302 clinical medicine, Humans, Longitudinal Studies, 030304 developmental biology, 0303 health sciences, Multidisciplinary, business.industry, Information Dissemination, Data Science, Usability, Data science, Metadata, Medicine, business, 030217 neurology & neurosurgery, Algorithms, Software
Abstract

Big biomedical data create exciting opportunities for discovery but make it difficult to capture analyses and outputs in forms that are findable, accessible, interoperable, and reusable (FAIR). In response, we describe tools that make it easy to capture, and assign identifiers to, data and code throughout the data lifecycle. We illustrate the use of these tools via a case study involving a multi- step analysis that creates an atlas of putative transcription factor binding sites from terabytes of ENCODE DNase I hypersensitive sites sequencing data. We show how the tools automate routine but complex tasks, capture analysis algorithms in understandable and reusable forms, and harness fast networks and powerful cloud computers to process data rapidly, all without sacrificing usability or reproducibility--thus ensuring that big data are not hard-to-(re)use data. In this talk, we will describe the enhancements made to the Galaxy framework to support working with datasets referred to by minids, support analyzing BagIt-based research objects called BDBags, execution using software encapsulated using docker containers with unique identifiers. We will describe the tools, services developed to create an end-to-end reproducible analysis pipelines while adhering to FAIR principles., Accepted talk at RO2018

Published
2018

14. I'll take that to go: Big data bags and minimal identifiers for exchange of large, complex datasets

Author

Eric W. Deutsch, Kristi A. Clark, Carl Kesselman, Arthur W. Toga, Stian Soiland-Reyes, Mike D'Arcy, Ravi Madduri, Ben Heavner, Carole Goble, Kyle Chard, Nathan D. Price, Ian Foster, Alexis Rodriguez, Ivo D. Dinov, and Jung, Segun

Subjects
0301 basic medicine, Big Data, BDBags, identifiers, Computer science, Big data, data analysis, Big Data bags, data assembling, computer.software_genre, Uniform resource locators, 03 medical and health sciences, Software, Big Data analysis, research objects, MiniD, Robustness, computer.programming_language, bdbag, Metadata, business.industry, data descriptions, data collections, datasets, Payloads, Data science, Identifier, 030104 developmental biology, Workflow, Encoding, Minid, Data mining, business, computer, Big Data sharing
Abstract

Big data workflows often require the assembly and exchange of complex, multi-element datasets. For example, in biomedical applications, the input to an analytic pipeline can be a dataset consisting thousands of images and genome sequences assembled from diverse repositories, requiring a description of the contents of the dataset in a concise and unambiguous form. Typical approaches to creating datasets for big data workflows assume that all data reside in a single location, requiring costly data marshaling and permitting errors of omission and commission because dataset members are not explicitly specified. We address these issues by proposing simple methods and tools for assembling, sharing, and analyzing large and complex datasets that scientists can easily integrate into their daily workflows. These tools combine a simple and robust method for describing data collections (BDBags), data descriptions (Research Objects), and simple persistent identifiers (Minids) to create a powerful ecosystem of tools and services for big data analysis and sharing. We present these tools and use biomedical case studies to illustrate their use for the rapid assembly, sharing, and analysis of large datasets.

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