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1. Electronic health record signatures identify undiagnosed patients with common variable immunodeficiency disease.

Author

Johnson, Ruth, Stephens, Alexis, Mester, Rachel, Knyazev, Sergey, Kohn, Lisa, Freund, Malika, Bondhus, Leroy, Hill, Brian, Schwarz, Tommer, Zaitlen, Noah, Arboleda, Valerie, A Bastarache, Lisa, Pasaniuc, Bogdan, and Butte, Manish

Subjects
Humans, Common Variable Immunodeficiency, Electronic Health Records, Machine Learning, Algorithms, Male, Female, Phenotype, Adult, Undiagnosed Diseases
Abstract

Human inborn errors of immunity include rare disorders entailing functional and quantitative antibody deficiencies due to impaired B cells called the common variable immunodeficiency (CVID) phenotype. Patients with CVID face delayed diagnoses and treatments for 5 to 15 years after symptom onset because the disorders are rare (prevalence of ~1/25,000), and there is extensive heterogeneity in CVID phenotypes, ranging from infections to autoimmunity to inflammatory conditions, overlapping with other more common disorders. The prolonged diagnostic odyssey drives excessive system-wide costs before diagnosis. Because there is no single causal mechanism, there are no genetic tests to definitively diagnose CVID. Here, we present PheNet, a machine learning algorithm that identifies patients with CVID from their electronic health records (EHRs). PheNet learns phenotypic patterns from verified CVID cases and uses this knowledge to rank patients by likelihood of having CVID. PheNet could have diagnosed more than half of our patients with CVID 1 or more years earlier than they had been diagnosed. When applied to a large EHR dataset, followed by blinded chart review of the top 100 patients ranked by PheNet, we found that 74% were highly probable to have CVID. We externally validated PheNet using >6 million records from disparate medical systems in California and Tennessee. As artificial intelligence and machine learning make their way into health care, we show that algorithms such as PheNet can offer clinical benefits by expediting the diagnosis of rare diseases.

Published
2024

2. Clinical associations with a polygenic predisposition to benign lower white blood cell counts

Author

Mosley, Jonathan D., Shelley, John P., Dickson, Alyson L., Zanussi, Jacy, Daniel, Laura L., Zheng, Neil S., Bastarache, Lisa, Wei, Wei-Qi, Shi, Mingjian, Jarvik, Gail P., Rosenthal, Elisabeth A., Khan, Atlas, Sherafati, Alborz, Kullo, Iftikhar J., Walunas, Theresa L., Glessner, Joseph, Hakonarson, Hakon, Cox, Nancy J., Roden, Dan M., Frangakis, Stephan G., Vanderwerff, Brett, Stein, C. Michael, Van Driest, Sara L., Borinstein, Scott C., Shu, Xiao-Ou, Zawistowski, Matthew, Chung, Cecilia P., and Kawai, Vivian K.

Published
2024
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7. Quantifying the phenome-wide disease burden of obesity using electronic health records and genomics.

Author

Robinson, Jamie, Carroll, Robert, Bastarache, Lisa, Chen, Qingxia, Pirruccello, James, Mou, Zongyang, Wei, Wei-Qi, Connolly, John, Mentch, Frank, Crane, Paul, Hebbring, Scott, Crosslin, David, Gordon, Adam, Rosenthal, Elisabeth, Stanaway, Ian, Hayes, M, Wei, Wei, Petukhova, Lynn, Namjou-Khales, Bahram, Zhang, Ge, Safarova, Mayya, Walton, Nephi, Still, Christopher, Bottinger, Erwin, Loos, Ruth, Murphy, Shawn, Jackson, Gretchen, Abumrad, Naji, Kullo, Iftikhar, Jarvik, Gail, Larson, Eric, Weng, Chunhua, Roden, Dan, Khera, Amit, and Denny, Joshua

Subjects
Humans, Phenomics, Electronic Health Records, Genome-Wide Association Study, Diabetes Mellitus, Type 2, Polymorphism, Single Nucleotide, Genomics, Genetic Predisposition to Disease, Obesity, Phenotype, Cost of Illness
Abstract

OBJECTIVE: High BMI is associated with many comorbidities and mortality. This study aimed to elucidate the overall clinical risk of obesity using a genome- and phenome-wide approach. METHODS: This study performed a phenome-wide association study of BMI using a clinical cohort of 736,726 adults. This was followed by genetic association studies using two separate cohorts: one consisting of 65,174 adults in the Electronic Medical Records and Genomics (eMERGE) Network and another with 405,432 participants in the UK Biobank. RESULTS: Class 3 obesity was associated with 433 phenotypes, representing 59.3% of all billing codes in individuals with severe obesity. A genome-wide polygenic risk score for BMI, accounting for 7.5% of variance in BMI, was associated with 296 clinical diseases, including strong associations with type 2 diabetes, sleep apnea, hypertension, and chronic liver disease. In all three cohorts, 199 phenotypes were associated with class 3 obesity and polygenic risk for obesity, including novel associations such as increased risk of renal failure, venous insufficiency, and gastroesophageal reflux. CONCLUSIONS: This combined genomic and phenomic systematic approach demonstrated that obesity has a strong genetic predisposition and is associated with a considerable burden of disease across all disease classes.

Published
2022

9. Heterozygous rare variants in NR2F2 cause a recognizable multiple congenital anomaly syndrome with developmental delays

Author

Ganapathi, Mythily, Matsuoka, Leticia S., March, Michael, Li, Dong, Brokamp, Elly, Benito-Sanz, Sara, White, Susan M., Lachlan, Katherine, Ahimaz, Priyanka, Sewda, Anshuman, Bastarache, Lisa, Thomas-Wilson, Amanda, Stoler, Joan M., Bramswig, Nuria C., Baptista, Julia, Stals, Karen, Demurger, Florence, Cogne, Benjamin, Isidor, Bertrand, Bedeschi, Maria Francesca, Peron, Angela, Amiel, Jeanne, Zackai, Elaine, Schacht, John P., Iglesias, Alejandro D., Morton, Jenny, Schmetz, Ariane, Seidel, Verónica, Lucia, Stephanie, Baskin, Stephanie M., Thiffault, Isabelle, Cogan, Joy D., Gordon, Christopher T., Chung, Wendy K., Bowdin, Sarah, and Bhoj, Elizabeth

Published
2023
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10. A multiancestry genome-wide association study of unexplained chronic ALT elevation as a proxy for nonalcoholic fatty liver disease with histological and radiological validation

Author

Vujkovic, Marijana, Ramdas, Shweta, Lorenz, Kim M, Guo, Xiuqing, Darlay, Rebecca, Cordell, Heather J, He, Jing, Gindin, Yevgeniy, Chung, Chuhan, Myers, Robert P, Schneider, Carolin V, Park, Joseph, Lee, Kyung Min, Serper, Marina, Carr, Rotonya M, Kaplan, David E, Haas, Mary E, MacLean, Matthew T, Witschey, Walter R, Zhu, Xiang, Tcheandjieu, Catherine, Kember, Rachel L, Kranzler, Henry R, Verma, Anurag, Giri, Ayush, Klarin, Derek M, Sun, Yan V, Huang, Jie, Huffman, Jennifer E, Creasy, Kate Townsend, Hand, Nicholas J, Liu, Ching-Ti, Long, Michelle T, Yao, Jie, Budoff, Matthew, Tan, Jingyi, Li, Xiaohui, Lin, Henry J, Chen, Yii-Der Ida, Taylor, Kent D, Chang, Ruey-Kang, Krauss, Ronald M, Vilarinho, Silvia, Brancale, Joseph, Nielsen, Jonas B, Locke, Adam E, Jones, Marcus B, Verweij, Niek, Baras, Aris, Reddy, K Rajender, Neuschwander-Tetri, Brent A, Schwimmer, Jeffrey B, Sanyal, Arun J, Chalasani, Naga, Ryan, Kathleen A, Mitchell, Braxton D, Gill, Dipender, Wells, Andrew D, Manduchi, Elisabetta, Saiman, Yedidya, Mahmud, Nadim, Miller, Donald R, Reaven, Peter D, Phillips, Lawrence S, Muralidhar, Sumitra, DuVall, Scott L, Lee, Jennifer S, Assimes, Themistocles L, Pyarajan, Saiju, Cho, Kelly, Edwards, Todd L, Damrauer, Scott M, Wilson, Peter W, Gaziano, J Michael, O’Donnell, Christopher J, Khera, Amit V, Grant, Struan FA, Brown, Christopher D, Tsao, Philip S, Saleheen, Danish, Lotta, Luca A, Bastarache, Lisa, Anstee, Quentin M, Daly, Ann K, Meigs, James B, Rotter, Jerome I, Lynch, Julie A, Rader, Daniel J, Voight, Benjamin F, and Chang, Kyong-Mi

Subjects
Genetics, Liver Disease, Human Genome, Digestive Diseases, Chronic Liver Disease and Cirrhosis, Aetiology, 2.1 Biological and endogenous factors, Alanine Transaminase, Alpha-Ketoglutarate-Dependent Dioxygenase FTO, Genome-Wide Association Study, Humans, Intracellular Signaling Peptides and Proteins, Lipase, Membrane Proteins, Non-alcoholic Fatty Liver Disease, Polymorphism, Single Nucleotide, Protein Serine-Threonine Kinases, Regeneron Genetics Center, Geisinger-Regeneron DiscovEHR Collaboration, EPoS Consortium, VA Million Veteran Program, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

Nonalcoholic fatty liver disease (NAFLD) is a growing cause of chronic liver disease. Using a proxy NAFLD definition of chronic elevation of alanine aminotransferase (cALT) levels without other liver diseases, we performed a multiancestry genome-wide association study (GWAS) in the Million Veteran Program (MVP) including 90,408 cALT cases and 128,187 controls. Seventy-seven loci exceeded genome-wide significance, including 25 without prior NAFLD or alanine aminotransferase associations, with one additional locus identified in European American-only and two in African American-only analyses (P

Published
2022

12. Commonalities across computational workflows for uncovering explanatory variants in undiagnosed cases.

Author

Kobren, Shilpa Nadimpalli, Baldridge, Dustin, Velinder, Matt, Krier, Joel B, LeBlanc, Kimberly, Esteves, Cecilia, Pusey, Barbara N, Züchner, Stephan, Blue, Elizabeth, Lee, Hane, Huang, Alden, Bastarache, Lisa, Bican, Anna, Cogan, Joy, Marwaha, Shruti, Alkelai, Anna, Murdock, David R, Liu, Pengfei, Wegner, Daniel J, Paul, Alexander J, Undiagnosed Diseases Network, Sunyaev, Shamil R, and Kohane, Isaac S

Subjects
Undiagnosed Diseases Network, Humans, Computational Biology, Genomics, Genome, Software, Genetic Testing, Workflow, Undiagnosed Diseases, Genetics, Human Genome, Genetics & Heredity, Clinical Sciences
Abstract

PurposeGenomic sequencing has become an increasingly powerful and relevant tool to be leveraged for the discovery of genetic aberrations underlying rare, Mendelian conditions. Although the computational tools incorporated into diagnostic workflows for this task are continually evolving and improving, we nevertheless sought to investigate commonalities across sequencing processing workflows to reveal consensus and standard practice tools and highlight exploratory analyses where technical and theoretical method improvements would be most impactful.MethodsWe collected details regarding the computational approaches used by a genetic testing laboratory and 11 clinical research sites in the United States participating in the Undiagnosed Diseases Network via meetings with bioinformaticians, online survey forms, and analyses of internal protocols.ResultsWe found that tools for processing genomic sequencing data can be grouped into four distinct categories. Whereas well-established practices exist for initial variant calling and quality control steps, there is substantial divergence across sites in later stages for variant prioritization and multimodal data integration, demonstrating a diversity of approaches for solving the most mysterious undiagnosed cases.ConclusionThe largest differences across diagnostic workflows suggest that advances in structural variant detection, noncoding variant interpretation, and integration of additional biomedical data may be especially promising for solving chronically undiagnosed cases.

Published
2021

14. Use of Electronic Health Records to Support a Public Health Response to the COVID-19 Pandemic in the United States: A Perspective from Fifteen Academic Medical Centers

Author

Madhavan, Subha, Bastarache, Lisa, Brown, Jeffrey S, Butte, Atul J, Dorr, David A, Embi, Peter J, Friedman, Charles P, Johnson, Kevin B, Moore, Jason H, Kohane, Isaac S, Payne, Philip RO, Tenenbaum, Jessica D, Weiner, Mark G, Wilcox, Adam B, and Ohno-Machado, Lucila

Subjects
Patient Safety, Networking and Information Technology R&D (NITRD), Clinical Research, Generic health relevance, Good Health and Well Being, Academic Medical Centers, COVID-19, Electronic Health Records, Humans, Information Dissemination, Information Systems, Public Health Practice, Registries, United States, covid-19, ehr, public health, data network, policy, Information and Computing Sciences, Engineering, Medical and Health Sciences, Medical Informatics
Abstract

Our goal is to summarize the collective experience of 15 organizations in dealing with uncoordinated efforts that result in unnecessary delays in understanding, predicting, preparing for, containing, and mitigating the COVID-19 pandemic in the US. Response efforts involve the collection and analysis of data corresponding to healthcare organizations, public health departments, socioeconomic indicators, as well as additional signals collected directly from individuals and communities. We focused on electronic health record (EHR) data, since EHRs can be leveraged and scaled to improve clinical care, research, and to inform public health decision-making. We outline the current challenges in the data ecosystem and the technology infrastructure that are relevant to COVID-19, as witnessed in our 15 institutions. The infrastructure includes registries and clinical data networks to support population-level analyses. We propose a specific set of strategic next steps to increase interoperability, overall organization, and efficiencies.

Published
2021

15. Use of electronic health records to support a public health response to the COVID-19 pandemic in the United States: a perspective from 15 academic medical centers.

Author

Madhavan, Subha, Bastarache, Lisa, Brown, Jeffrey S, Butte, Atul J, Dorr, David A, Embi, Peter J, Friedman, Charles P, Johnson, Kevin B, Moore, Jason H, Kohane, Isaac S, Payne, Philip RO, Tenenbaum, Jessica D, Weiner, Mark G, Wilcox, Adam B, and Ohno-Machado, Lucila

Subjects
Humans, Registries, Information Dissemination, Public Health Practice, Information Systems, Academic Medical Centers, United States, Electronic Health Records, COVID-19, covid-19, data network, ehr, policy, public health, Information and Computing Sciences, Engineering, Medical and Health Sciences, Medical Informatics
Abstract

Our goal is to summarize the collective experience of 15 organizations in dealing with uncoordinated efforts that result in unnecessary delays in understanding, predicting, preparing for, containing, and mitigating the COVID-19 pandemic in the US. Response efforts involve the collection and analysis of data corresponding to healthcare organizations, public health departments, socioeconomic indicators, as well as additional signals collected directly from individuals and communities. We focused on electronic health record (EHR) data, since EHRs can be leveraged and scaled to improve clinical care, research, and to inform public health decision-making. We outline the current challenges in the data ecosystem and the technology infrastructure that are relevant to COVID-19, as witnessed in our 15 institutions. The infrastructure includes registries and clinical data networks to support population-level analyses. We propose a specific set of strategic next steps to increase interoperability, overall organization, and efficiencies.

Published
2021

20. Stroke genetics informs drug discovery and risk prediction across ancestries

Author

Mishra, Aniket, Malik, Rainer, Hachiya, Tsuyoshi, Jürgenson, Tuuli, Namba, Shinichi, Posner, Daniel C., Kamanu, Frederick K., Koido, Masaru, Le Grand, Quentin, Shi, Mingyang, He, Yunye, Georgakis, Marios K., Caro, Ilana, Krebs, Kristi, Liaw, Yi-Ching, Vaura, Felix C., Lin, Kuang, Winsvold, Bendik Slagsvold, Srinivasasainagendra, Vinodh, Parodi, Livia, Bae, Hee-Joon, Chauhan, Ganesh, Chong, Michael R., Tomppo, Liisa, Akinyemi, Rufus, Roshchupkin, Gennady V., Habib, Naomi, Jee, Yon Ho, Thomassen, Jesper Qvist, Abedi, Vida, Cárcel-Márquez, Jara, Nygaard, Marianne, Leonard, Hampton L., Yang, Chaojie, Yonova-Doing, Ekaterina, Knol, Maria J., Lewis, Adam J., Judy, Renae L., Ago, Tetsuro, Amouyel, Philippe, Armstrong, Nicole D., Bakker, Mark K., Bartz, Traci M., Bennett, David A., Bis, Joshua C., Bordes, Constance, Børte, Sigrid, Cain, Anael, Ridker, Paul M., Cho, Kelly, Chen, Zhengming, Cruchaga, Carlos, Cole, John W., de Jager, Phil L., de Cid, Rafael, Endres, Matthias, Ferreira, Leslie E., Geerlings, Mirjam I., Gasca, Natalie C., Gudnason, Vilmundur, Hata, Jun, He, Jing, Heath, Alicia K., Ho, Yuk-Lam, Havulinna, Aki S., Hopewell, Jemma C., Hyacinth, Hyacinth I., Inouye, Michael, Jacob, Mina A., Jeon, Christina E., Jern, Christina, Kamouchi, Masahiro, Keene, Keith L., Kitazono, Takanari, Kittner, Steven J., Konuma, Takahiro, Kumar, Amit, Lacaze, Paul, Launer, Lenore J., Lee, Keon-Joo, Lepik, Kaido, Li, Jiang, Li, Liming, Manichaikul, Ani, Markus, Hugh S., Marston, Nicholas A., Meitinger, Thomas, Mitchell, Braxton D., Montellano, Felipe A., Morisaki, Takayuki, Mosley, Thomas H., Nalls, Mike A., Nordestgaard, Børge G., O’Donnell, Martin J., Okada, Yukinori, Onland-Moret, N. Charlotte, Ovbiagele, Bruce, Peters, Annette, Psaty, Bruce M., Rich, Stephen S., Rosand, Jonathan, Sabatine, Marc S., Sacco, Ralph L., Saleheen, Danish, Sandset, Else Charlotte, Salomaa, Veikko, Sargurupremraj, Muralidharan, Sasaki, Makoto, Satizabal, Claudia L., Schmidt, Carsten O., Shimizu, Atsushi, Smith, Nicholas L., Sloane, Kelly L., Sutoh, Yoichi, Sun, Yan V., Tanno, Kozo, Tiedt, Steffen, Tatlisumak, Turgut, Torres-Aguila, Nuria P., Tiwari, Hemant K., Trégouët, David-Alexandre, Trompet, Stella, Tuladhar, Anil Man, Tybjærg-Hansen, Anne, van Vugt, Marion, Vibo, Riina, Verma, Shefali S., Wiggins, Kerri L., Wennberg, Patrik, Woo, Daniel, Wilson, Peter W. F., Xu, Huichun, Yang, Qiong, Yoon, Kyungheon, Millwood, Iona Y., Gieger, Christian, Ninomiya, Toshiharu, Grabe, Hans J., Jukema, J. Wouter, Rissanen, Ina L., Strbian, Daniel, Kim, Young Jin, Chen, Pei-Hsin, Mayerhofer, Ernst, Howson, Joanna M. M., Irvin, Marguerite R., Adams, Hieab, Wassertheil-Smoller, Sylvia, Christensen, Kaare, Ikram, Mohammad A., Rundek, Tatjana, Worrall, Bradford B., Lathrop, G. Mark, Riaz, Moeen, Simonsick, Eleanor M., Kõrv, Janika, França, Paulo H. C., Zand, Ramin, Prasad, Kameshwar, Frikke-Schmidt, Ruth, de Leeuw, Frank-Erik, Liman, Thomas, Haeusler, Karl Georg, Ruigrok, Ynte M., Heuschmann, Peter Ulrich, Longstreth, W. T., Jung, Keum Ji, Bastarache, Lisa, Paré, Guillaume, Damrauer, Scott M., Chasman, Daniel I., Rotter, Jerome I., Anderson, Christopher D., Zwart, John-Anker, Niiranen, Teemu J., Fornage, Myriam, Liaw, Yung-Po, Seshadri, Sudha, Fernández-Cadenas, Israel, Walters, Robin G., Ruff, Christian T., Owolabi, Mayowa O., Huffman, Jennifer E., Milani, Lili, Kamatani, Yoichiro, Dichgans, Martin, and Debette, Stephanie

Published
2022
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21. IgG4‐related disease: Association with a rare gene variant expressed in cytotoxic T cells

Author

Newman, John H, Shaver, Aaron, Sheehan, Jonathan H, Mallal, Simon, Stone, John H, Pillai, Shiv, Bastarache, Lisa, Riebau, Derek, Allard‐Chamard, Hugues, Stone, William M, Perugino, Cory, Pilkinton, Mark, Smith, Scott A, McDonnell, Wyatt J, Capra, John A, Meiler, Jens, Cogan, Joy, Xing, Kelly, Mahajan, Vinay S, Mattoo, Hamid, Hamid, Rizwan, Phillips, John A, Adams, David R, Aday, Aaron, Alejandro, Mercedes E, Allard, Patrick, Ashley, Euan A, Azamian, Mahshid S, Bacino, Carlos A, Balasubramanyam, Ashok, Barseghyan, Hayk, Batzli, Gabriel F, Beggs, Alan H, Behnam, Babak, Bellen, Hugo J, Bernstein, Jonathan A, Bican, Anna, Bick, David P, Birch, Camille L, Bonner, Devon, Boone, Braden E, Bostwick, Bret L, Briere, Lauren C, Brown, Donna M, Brush, Matthew, Burke, Elizabeth A, Burrage, Lindsay C, Butte, Manish J, Chen, Shan, Clark, Gary D, Coakley, Terra R, Cooper, Cynthia M, Cope, Heidi, Craigen, William J, D'Souza, Precilla, Davids, Mariska, Davidson, Jean M, Dayal, Jyoti G, Dell'Angelica, Esteban C, Dhar, Shweta U, Dipple, Katrina M, Donnell‐Fink, Laurel A, Dorrani, Naghmeh, Dorset, Daniel C, Douine, Emilie D, Draper, David D, Dries, Annika M, Eckstein, David J, Emrick, Lisa T, Eng, Christine M, Enns, Gregory M, Eskin, Ascia, Esteves, Cecilia, Estwick, Tyra, Fernandez, Liliana, Ferreira, Carlos, Fisher, Paul G, Fogel, Brent L, Friedman, Noah D, Gahl, William A, Glanton, Emily, Godfrey, Rena A, Goldman, Alica M, Goldstein, David B, Gould, Sarah E, Gourdine, Jean‐Philippe F, Groden, Catherine A, Gropman, Andrea L, Haendel, Melissa, Hanchard, Neil A, Handley, Lori H, Herzog, Matthew R, High, Francis, Holm, Ingrid A, Hom, Jason, Howerton, Ellen M, Huang, Yong, Jamal, Fariha, Jiang, Yong‐hui, and Johnston, Jean M

Subjects
Biological Sciences, Genetics, Rare Diseases, Clinical Research, Aetiology, 2.1 Biological and endogenous factors, Adolescent, CD4-Positive T-Lymphocytes, Genetic Variation, Humans, Immunoglobulin G, Immunoglobulin G4-Related Disease, Male, Middle Aged, T-Lymphocytes, Cytotoxic, cytotoxic lymphocytes, heritable, IgG4-RD, Undiagnosed Disease Network, Medicinal and Biomolecular Chemistry, Clinical Sciences, Medicinal and biomolecular chemistry
Abstract

BackgroundFamily screening of a 48-year-old male with recently diagnosed IgG4-related disease (IgG4-RD) revealed unanticipated elevations in plasma IgG4 in his two healthy teenaged sons.MethodsWe performed gene sequencing, immune cell studies, HLA typing, and analyses of circulating cytotoxic CD4+ T lymphocytes and plasmablasts to seek clues to pathogenesis. DNA from a separate cohort of 99 patients with known IgG4-RD was also sequenced for the presence of genetic variants in a specific gene, FGFBP2.ResultsThe three share a previously unreported heterozygous single base deletion in fibroblast growth factor binding protein type 2 (FGFBP2), which causes a frameshift in the coding sequence. The FGFBP2 protein is secreted by cytotoxic T-lymphocytes and binds fibroblast growth factor. The variant sequence in the FGFBP2 protein is predicted to form a disordered random coil rather than a helical-turn-helix structure, unable to adopt a stable conformation. The proband and the two sons had 5-10-fold higher numbers of circulating cytotoxic CD4 + T cells and plasmablasts compared to matched controls. The three members also share a homozygous missense common variant in FGFBP2 found in heterozygous form in ~40% of the population. This common variant was found in 73% of an independent, well characterized IgG4-RD cohort, showing enrichment in idiopathic IgG4-RD.ConclusionsThe presence of a shared deleterious variant and homozygous common variant in FGFBP2 in the proband and sons strongly implicates this cytotoxic T cell product in the pathophysiology of IgG4-RD. The high prevalence of a common FGFBP2 variant in sporadic IgG4-RD supports the likelihood of participation in disease.

Published
2019

23. Relationships Between Hearing-Related and Health-Related Variables in Academic Progress of Children With Unilateral Hearing Loss.

Author

Picou, Erin M., Davis, Hilary, Tang, Leigh Anne, Bastarache, Lisa, and Tharpe, Anne Marie

Subjects
EAR abnormalities, RISK assessment, CHILDREN'S health, PARENTS, OTITIS media, HEALTH status indicators, RESEARCH funding, HEARING aids, PREMATURE infants, SEX distribution, EAR diseases, DESCRIPTIVE statistics, SEVERITY of illness index, LONGITUDINAL method, ODDS ratio, TEACHERS, ACADEMIC achievement, ANALYSIS of variance, HEARING disorders, DATA analysis software, CONFIDENCE intervals, SPECIAL education, DISEASE relapse, SPEECH therapy, DISEASE complications, CHILDREN
Abstract

Purpose: School-age children with unilateral hearing loss are at an increased risk of exhibiting academic difficulties. Yet, approximately half of children with unilateral hearing loss will not require additional support. There is a dearth of information to assist in determining which of these children will express academic deficits and which will not. The purpose of this study was to identify hearing- and health-related factors that contribute to adverse educational progress in children with permanent unilateral hearing loss. Specific indicators of academic concern identified during school age included the need for specialized academic services, receipt of speech-language therapy, or parent/teacher concerns for academics or speech-language development. Method: This study provides an in-depth analysis of a previously described patient cohort developed from de-identified electronic health records. Factors of interest included potentially relevant hearing-related risk factors (e.g., degree, type, and laterality of hearing loss), in addition to health-related factors that could be extracted from the electronic health records (e.g., sex, premature birth, history of significant otitis media). Results: Being born preterm, having a history of pressure equalization tubes or having conductive or mixed hearing loss more than doubled the risk of demonstrating adverse educational progress. Laterality and degree of loss were generally not significantly related to academic progress. Conclusions: Approximately half of school-age children with permanent unilateral hearing loss in this cohort experienced some academic challenges. Birth history and middle ear pathology were important predictors of adverse educational progress. [ABSTRACT FROM AUTHOR]

Published
2025
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24. Protein-coding variants implicate novel genes related to lipid homeostasis contributing to body-fat distribution

Author

Justice, Anne E, Karaderi, Tugce, Highland, Heather M, Young, Kristin L, Graff, Mariaelisa, Lu, Yingchang, Turcot, Valérie, Auer, Paul L, Fine, Rebecca S, Guo, Xiuqing, Schurmann, Claudia, Lempradl, Adelheid, Marouli, Eirini, Mahajan, Anubha, Winkler, Thomas W, Locke, Adam E, Medina-Gomez, Carolina, Esko, Tõnu, Vedantam, Sailaja, Giri, Ayush, Lo, Ken Sin, Alfred, Tamuno, Mudgal, Poorva, Ng, Maggie CY, Heard-Costa, Nancy L, Feitosa, Mary F, Manning, Alisa K, Willems, Sara M, Sivapalaratnam, Suthesh, Abecasis, Goncalo, Alam, Dewan S, Allison, Matthew, Amouyel, Philippe, Arzumanyan, Zorayr, Balkau, Beverley, Bastarache, Lisa, Bergmann, Sven, Bielak, Lawrence F, Blüher, Matthias, Boehnke, Michael, Boeing, Heiner, Boerwinkle, Eric, Böger, Carsten A, Bork-Jensen, Jette, Bottinger, Erwin P, Bowden, Donald W, Brandslund, Ivan, Broer, Linda, Burt, Amber A, Butterworth, Adam S, Caulfield, Mark J, Cesana, Giancarlo, Chambers, John C, Chasman, Daniel I, Chen, Yii-Der Ida, Chowdhury, Rajiv, Christensen, Cramer, Chu, Audrey Y, Collins, Francis S, Cook, James P, Cox, Amanda J, Crosslin, David S, Danesh, John, de Bakker, Paul IW, Denus, Simon de, Mutsert, Renée de, Dedoussis, George, Demerath, Ellen W, Dennis, Joe G, Denny, Josh C, Di Angelantonio, Emanuele, Dörr, Marcus, Drenos, Fotios, Dubé, Marie-Pierre, Dunning, Alison M, Easton, Douglas F, Elliott, Paul, Evangelou, Evangelos, Farmaki, Aliki-Eleni, Feng, Shuang, Ferrannini, Ele, Ferrieres, Jean, Florez, Jose C, Fornage, Myriam, Fox, Caroline S, Franks, Paul W, Friedrich, Nele, Gan, Wei, Gandin, Ilaria, Gasparini, Paolo, Giedraitis, Vilmantas, Girotto, Giorgia, Gorski, Mathias, Grallert, Harald, Grarup, Niels, Grove, Megan L, Gustafsson, Stefan, Haessler, Jeff, Hansen, Torben, and Hattersley, Andrew T

Subjects
Clinical Research, Obesity, Genetics, Nutrition, Prevention, Biotechnology, Human Genome, Aetiology, 2.1 Biological and endogenous factors, Metabolic and endocrine, Cardiovascular, Animals, Body Fat Distribution, Body Mass Index, Case-Control Studies, Drosophila, Exome, Female, Gene Frequency, Genetic Predisposition to Disease, Genetic Variation, Genome-Wide Association Study, Homeostasis, Humans, Lipids, Male, Proteins, Risk Factors, Waist-Hip Ratio, CHD Exome+ Consortium, Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium, EPIC-CVD Consortium, ExomeBP Consortium, Global Lipids Genetic Consortium, GoT2D Genes Consortium, InterAct, ReproGen Consortium, T2D-Genes Consortium, MAGIC Investigators, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

Body-fat distribution is a risk factor for adverse cardiovascular health consequences. We analyzed the association of body-fat distribution, assessed by waist-to-hip ratio adjusted for body mass index, with 228,985 predicted coding and splice site variants available on exome arrays in up to 344,369 individuals from five major ancestries (discovery) and 132,177 European-ancestry individuals (validation). We identified 15 common (minor allele frequency, MAF ≥5%) and nine low-frequency or rare (MAF

Published
2019

25. Cardiovascular toxicities associated with immune checkpoint inhibitors: an observational, retrospective, pharmacovigilance study.

Author

Salem, Joe-Elie, Manouchehri, Ali, Moey, Melissa, Lebrun-Vignes, Bénédicte, Bastarache, Lisa, Pariente, Antoine, Gobert, Aurélien, Spano, Jean-Philippe, Balko, Justin, Bonaca, Marc, Roden, Dan, Johnson, Douglas, and Moslehi, Javid

Subjects
Adult, Adverse Drug Reaction Reporting Systems, Aged, Aged, 80 and over, Antineoplastic Agents, Immunological, Bayes Theorem, Cardiotoxicity, Cardiovascular Diseases, Databases, Factual, Female, Humans, Immunotherapy, Male, Middle Aged, Pharmacovigilance, Retrospective Studies, Risk Assessment, Risk Factors, Time Factors
Abstract

BACKGROUND: Immune checkpoint inhibitors (ICIs) have substantially improved clinical outcomes in multiple cancer types and are increasingly being used in early disease settings and in combinations of different immunotherapies. However, ICIs can also cause severe or fatal immune-related adverse-events (irAEs). We aimed to identify and characterise cardiovascular irAEs that are significantly associated with ICIs. METHODS: In this observational, retrospective, pharmacovigilance study, we used VigiBase, WHOs global database of individual case safety reports, to compare cardiovascular adverse event reporting in patients who received ICIs (ICI subgroup) with this reporting in the full database. This study included all cardiovascular irAEs classified by group queries according to the Medical Dictionary for Regulatory Activities, between inception on Nov 14, 1967, and Jan 2, 2018. We evaluated the association between ICIs and cardiovascular adverse events using the reporting odds ratio (ROR) and the information component (IC). IC is an indicator value for disproportionate Bayesian reporting that compares observed and expected values to find associations between drugs and adverse events. IC025 is the lower end of the IC 95% credibility interval, and an IC025 value of more than zero is deemed significant. This study is registered with ClinicalTrials.gov, number NCT03387540. FINDINGS: We identified 31 321 adverse events reported in patients who received ICIs and 16 343 451 adverse events reported in patients treated with any drugs (full database) in VigiBase. Compared with the full database, ICI treatment was associated with higher reporting of myocarditis (5515 reports for the full database vs 122 for ICIs, ROR 11·21 [95% CI 9·36-13·43]; IC025 3·20), pericardial diseases (12 800 vs 95, 3·80 [3·08-4·62]; IC025 1·63), and vasculitis (33 289 vs 82, 1·56 [1·25-1·94]; IC025 0·03), including temporal arteritis (696 vs 18, 12·99 [8·12-20·77]; IC025 2·59) and polymyalgia rheumatica (1709 vs 16, 5·13 [3·13-8·40]; IC025 1·33). Pericardial diseases were reported more often in patients with lung cancer (49 [56%] of 87 patients), whereas myocarditis (42 [41%] of 103 patients) and vasculitis (42 [60%] of 70 patients) were more commonly reported in patients with melanoma (χ2 test for overall subgroup comparison, p80%), with death occurring in 61 (50%) of 122 myocarditis cases, 20 (21%) of 95 pericardial disease cases, and five (6%) of 82 vasculitis cases (χ2 test for overall comparison between pericardial diseases, myocarditis, and vasculitis, p

Published
2018

26. Publisher Correction: Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity.

Author

Turcot, Valérie, Lu, Yingchang, Highland, Heather M, Schurmann, Claudia, Justice, Anne E, Fine, Rebecca S, Bradfield, Jonathan P, Esko, Tõnu, Giri, Ayush, Graff, Mariaelisa, Guo, Xiuqing, Hendricks, Audrey E, Karaderi, Tugce, Lempradl, Adelheid, Locke, Adam E, Mahajan, Anubha, Marouli, Eirini, Sivapalaratnam, Suthesh, Young, Kristin L, Alfred, Tamuno, Feitosa, Mary F, Masca, Nicholas GD, Manning, Alisa K, Medina-Gomez, Carolina, Mudgal, Poorva, Ng, Maggie CY, Reiner, Alex P, Vedantam, Sailaja, Willems, Sara M, Winkler, Thomas W, Abecasis, Gonçalo, Aben, Katja K, Alam, Dewan S, Alharthi, Sameer E, Allison, Matthew, Amouyel, Philippe, Asselbergs, Folkert W, Auer, Paul L, Balkau, Beverley, Bang, Lia E, Barroso, Inês, Bastarache, Lisa, Benn, Marianne, Bergmann, Sven, Bielak, Lawrence F, Blüher, Matthias, Boehnke, Michael, Boeing, Heiner, Boerwinkle, Eric, Böger, Carsten A, Bork-Jensen, Jette, Bots, Michiel L, Bottinger, Erwin P, Bowden, Donald W, Brandslund, Ivan, Breen, Gerome, Brilliant, Murray H, Broer, Linda, Brumat, Marco, Burt, Amber A, Butterworth, Adam S, Campbell, Peter T, Cappellani, Stefania, Carey, David J, Catamo, Eulalia, Caulfield, Mark J, Chambers, John C, Chasman, Daniel I, Chen, Yii-Der I, Chowdhury, Rajiv, Christensen, Cramer, Chu, Audrey Y, Cocca, Massimiliano, Collins, Francis S, Cook, James P, Corley, Janie, Corominas Galbany, Jordi, Cox, Amanda J, Crosslin, David S, Cuellar-Partida, Gabriel, D'Eustacchio, Angela, Danesh, John, Davies, Gail, Bakker, Paul IW, Groot, Mark CH, Mutsert, Renée, Deary, Ian J, Dedoussis, George, Demerath, Ellen W, Heijer, Martin, Hollander, Anneke I, Ruijter, Hester M, Dennis, Joe G, Denny, Josh C, Di Angelantonio, Emanuele, Drenos, Fotios, Du, Mengmeng, Dubé, Marie-Pierre, Dunning, Alison M, and Easton, Douglas F

Subjects
CHD Exome+ Consortium, EPIC-CVD Consortium, ExomeBP Consortium, Global Lipids Genetic Consortium, GoT2D Genes Consortium, EPIC InterAct Consortium, INTERVAL Study, ReproGen Consortium, T2D-Genes Consortium, MAGIC Investigators, Understanding Society Scientific Group, Nutrition, Obesity, Developmental Biology, Medical and Health Sciences, Biological Sciences
Abstract

In the version of this article originally published, one of the two authors with the name Wei Zhao was omitted from the author list and the affiliations for both authors were assigned to the single Wei Zhao in the author list. In addition, the ORCID for Wei Zhao (Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA) was incorrectly assigned to author Wei Zhou. The errors have been corrected in the HTML and PDF versions of the article.

Published
2018

27. Neptune: an environment for the delivery of genomic medicine

Author

Eric, Venner, Yi, Victoria, Murdock, David, Kalla, Sara E., Wu, Tsung-Jung, Sabo, Aniko, Li, Shoudong, Meng, Qingchang, Tian, Xia, Murugan, Mullai, Cohen, Michelle, Kovar, Christie, Wei, Wei-Qi, Chung, Wendy K., Weng, Chunhua, Wiesner, Georgia L., Jarvik, Gail P., Muzny, Donna, Gibbs, Richard A., Abrams, Debra, Adunyah, Samuel E., Albertson-Junkans, Ladia, Almoguera, Berta, Ames, Darren C., Appelbaum, Paul, Aronson, Samuel, Aufox, Sharon, Babb, Lawrence J., Balasubramanian, Adithya, Bangash, Hana, Basford, Melissa, Bastarache, Lisa, Baxter, Samantha, Behr, Meckenzie, Benoit, Barbara, Bhoj, Elizabeth, Bielinski, Suzette J., Bland, Harris T., Blout, Carrie, Borthwick, Kenneth, Bottinger, Erwin P., Bowser, Mark, Brand, Harrison, Brilliant, Murray, Brodeur, Wendy, Caraballo, Pedro, Carrell, David, Carroll, Andrew, Castillo, Lisa, Castro, Victor, Chandanavelli, Gauthami, Chiang, Theodore, Chisholm, Rex L., Christensen, Kurt D., Chung, Wendy, Chute, Christopher G., City, Brittany, Cobb, Beth L., Connolly, John J., Crane, Paul, Crew, Katherine, Crosslin, David R., Dayal, Jyoti, De Andrade, Mariza, De la Cruz, Jessica, Denny, Josh C., Denson, Shawn, DeSmet, Tim, Dikilitas, Ozan, Dinsmore, Michael J., Dodge, Sheila, Dunlea, Phil, Edwards, Todd L., Eng, Christine M., Fasel, David, Fedotov, Alex, Feng, Qiping, Fleharty, Mark, Foster, Andrea, Freimuth, Robert, Friedrich, Christopher, Fullerton, Stephanie M., Funke, Birgit, Gabriel, Stacey, Gainer, Vivian, Gharavi, Ali, Glazer, Andrew M., Glessner, Joseph T., Goehringer, Jessica, Gordon, Adam S., Graham, Chet, Green, Robert C., Gundelach, Justin H., Hain, Heather S., Hakonarson, Hakon, Harden, Maegan V., Harley, John, Harr, Margaret, Hartzler, Andrea, Hayes, M. Geoffrey, Hebbring, Scott, Henrikson, Nora, Hershey, Andrew, Hoell, Christin, Holm, Ingrid, Howell, Kayla M., Hripcsak, George, Hu, Jianhong, Hynes, Elizabeth Duffy, Jayaseelan, Joy C., Jiang, Yunyun, Joo, Yoonjung Yoonie, Jose, Sheethal, Josyula, Navya Shilpa, Justice, Anne E., Kalra, Divya, Karlson, Elizabeth W., Keating, Brendan J., Kelly, Melissa A., Kenny, Eimear E., Key, Dustin, Kiryluk, Krzysztof, Kitchner, Terrie, Klanderman, Barbara, Klee, Eric, Kochan, David C., Korchina, Viktoriya, Kottyan, Leah, Kudalkar, Emily, Rahm, Alanna Kulchak, Kullo, Iftikhar J., Lammers, Philip, Larson, Eric B., Lebo, Matthew S., Leduc, Magalie, Lee, Ming Ta (Michael), Lennon, Niall J., Leppig, Kathleen A., Leslie, Nancy D., Li, Rongling, Liang, Wayne H., Lin, Chiao-Feng, Linder, Jodell E., Lindor, Noralane M., Lingren, Todd, Linneman, James G., Liu, Cong, Liu, Wen, Liu, Xiuping, Lynch, John, Lyon, Hayley, Macbeth, Alyssa, Mahadeshwar, Harshad, Mahanta, Lisa, Malin, Bradley, Manolio, Teri, Marasa, Maddalena, Marsolo, Keith, McGowan, Michelle L., McNally, Elizabeth, Meldrim, Jim, Mentch, Frank, Rasouly, Hila Milo, Mosley, Jonathan, Mukherjee, Shubhabrata, Mullen, Thomas E., Muniz, Jesse, Murdock, David R., Murphy, Shawn, Myers, Melanie F., Namjou, Bahram, Ni, Yizhao, Onofrio, Robert C., Obeng, Aniwaa Owusu, Person, Thomas N., Peterson, Josh F., Petukhova, Lynn, Pisieczko, Cassandra J., Pratap, Siddharth, Prows, Cynthia A., Puckelwartz, Megan J., Raj, Ritika, Ralston, James D., Ramaprasan, Arvind, Ramirez, Andrea, Rasmussen, Luke, Rasmussen-Torvik, Laura, Raychaudhuri, Soumya, Rehm, Heidi L., Ritchie, Marylyn D., Rives, Catherine, Riza, Beenish, Roden, Dan M., Rosenthal, Elisabeth A., Santani, Avni, Dan, Schaid, Scherer, Steven, Scott, Stuart, Scrol, Aaron, Sengupta, Soumitra, Shang, Ning, Sharma, Himanshu, Sharp, Richard R., Singh, Rajbir, Sleiman, Patrick M.A., Slowik, Kara, Smith, Joshua C., Smith, Maureen E., Smoot, Duane T., Smoller, Jordan W., Sohn, Sunghwan, Stanaway, Ian B., Starren, Justin, Stroud, Mary, Su, Jessica, Taylor, Casey Overby, Tolwinski, Kasia, Van Driest, Sara L., Vargas, Sean M., Varugheese, Matthew, Veenstra, David, Venner, Eric, Verbitsky, Miguel, Vicente, Gina, Wagner, Michael, Walker, Kimberly, Walunas, Theresa, Wang, Liwen, Wang, Qiaoyan, Weiss, Scott T., Wells, Quinn S., White, Peter S., Wiley, Ken L., Jr, Williams, Janet L., Williams, Marc S., Wilson, Michael W., Witkowski, Leora, Woods, Laura Allison, Woolf, Betty, Wynn, Julia, Yang, Yaping, Zhang, Ge, Zhang, Lan, and Zouk, Hana

Published
2021
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28. Analysis of predicted loss-of-function variants in UK Biobank identifies variants protective for disease

Author

Emdin, Connor A, Khera, Amit V, Chaffin, Mark, Klarin, Derek, Natarajan, Pradeep, Aragam, Krishna, Haas, Mary, Bick, Alexander, Zekavat, Seyedeh M, Nomura, Akihiro, Ardissino, Diego, Wilson, James G, Schunkert, Heribert, McPherson, Ruth, Watkins, Hugh, Elosua, Roberto, Bown, Matthew J, Samani, Nilesh J, Baber, Usman, Erdmann, Jeanette, Gupta, Namrata, Danesh, John, Chasman, Daniel, Ridker, Paul, Denny, Joshua, Bastarache, Lisa, Lichtman, Judith H, D’Onofrio, Gail, Mattera, Jennifer, Spertus, John A, Sheu, Wayne H-H, Taylor, Kent D, Psaty, Bruce M, Rich, Stephen S, Post, Wendy, Rotter, Jerome I, Chen, Yii-Der Ida, Krumholz, Harlan, Saleheen, Danish, Gabriel, Stacey, and Kathiresan, Sekar

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Epidemiology, Health Sciences, Heart Disease - Coronary Heart Disease, Diabetes, Biotechnology, Obesity, Cardiovascular, Heart Disease, Human Genome, 2.1 Biological and endogenous factors, Metabolic and endocrine, Good Health and Well Being, Databases, Genetic, Diabetes Mellitus, Type 2, Disease, Gene Frequency, Genetic Testing, Genetic Variation, Humans, Phenotype, Proteins, Respiratory Hypersensitivity, United Kingdom
Abstract

Less than 3% of protein-coding genetic variants are predicted to result in loss of protein function through the introduction of a stop codon, frameshift, or the disruption of an essential splice site; however, such predicted loss-of-function (pLOF) variants provide insight into effector transcript and direction of biological effect. In >400,000 UK Biobank participants, we conduct association analyses of 3759 pLOF variants with six metabolic traits, six cardiometabolic diseases, and twelve additional diseases. We identified 18 new low-frequency or rare (allele frequency

Published
2018

29. Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity

Author

Turcot, Valérie, Lu, Yingchang, Highland, Heather M, Schurmann, Claudia, Justice, Anne E, Fine, Rebecca S, Bradfield, Jonathan P, Esko, Tõnu, Giri, Ayush, Graff, Mariaelisa, Guo, Xiuqing, Hendricks, Audrey E, Karaderi, Tugce, Lempradl, Adelheid, Locke, Adam E, Mahajan, Anubha, Marouli, Eirini, Sivapalaratnam, Suthesh, Young, Kristin L, Alfred, Tamuno, Feitosa, Mary F, Masca, Nicholas GD, Manning, Alisa K, Medina-Gomez, Carolina, Mudgal, Poorva, Ng, Maggie CY, Reiner, Alex P, Vedantam, Sailaja, Willems, Sara M, Winkler, Thomas W, Abecasis, Gonçalo, Aben, Katja K, Alam, Dewan S, Alharthi, Sameer E, Allison, Matthew, Amouyel, Philippe, Asselbergs, Folkert W, Auer, Paul L, Balkau, Beverley, Bang, Lia E, Barroso, Inês, Bastarache, Lisa, Benn, Marianne, Bergmann, Sven, Bielak, Lawrence F, Blüher, Matthias, Boehnke, Michael, Boeing, Heiner, Boerwinkle, Eric, Böger, Carsten A, Bork-Jensen, Jette, Bots, Michiel L, Bottinger, Erwin P, Bowden, Donald W, Brandslund, Ivan, Breen, Gerome, Brilliant, Murray H, Broer, Linda, Brumat, Marco, Burt, Amber A, Butterworth, Adam S, Campbell, Peter T, Cappellani, Stefania, Carey, David J, Catamo, Eulalia, Caulfield, Mark J, Chambers, John C, Chasman, Daniel I, Chen, Yii-Der I, Chowdhury, Rajiv, Christensen, Cramer, Chu, Audrey Y, Cocca, Massimiliano, Collins, Francis S, Cook, James P, Corley, Janie, Corominas Galbany, Jordi, Cox, Amanda J, Crosslin, David S, Cuellar-Partida, Gabriel, D’Eustacchio, Angela, Danesh, John, Davies, Gail, Bakker, Paul IW, Groot, Mark CH, Mutsert, Renée, Deary, Ian J, Dedoussis, George, Demerath, Ellen W, Heijer, Martin, Hollander, Anneke I, Ruijter, Hester M, Dennis, Joe G, Denny, Josh C, Di Angelantonio, Emanuele, Drenos, Fotios, Du, Mengmeng, Dubé, Marie-Pierre, Dunning, Alison M, and Easton, Douglas F

Subjects
Biological Sciences, Genetics, Nutrition, Human Genome, Prevention, Clinical Research, Obesity, 2.1 Biological and endogenous factors, Oral and gastrointestinal, Metabolic and endocrine, Cancer, Stroke, Adult, Animals, Body Mass Index, Drosophila, Energy Intake, Energy Metabolism, Female, Gene Frequency, Genetic Variation, Humans, Male, Proteins, Syndrome, CHD Exome+ Consortium, EPIC-CVD Consortium, ExomeBP Consortium, Global Lipids Genetic Consortium, GoT2D Genes Consortium, EPIC InterAct Consortium, INTERVAL Study, ReproGen Consortium, T2D-Genes Consortium, MAGIC Investigators, Understanding Society Scientific Group, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract

Genome-wide association studies (GWAS) have identified >250 loci for body mass index (BMI), implicating pathways related to neuronal biology. Most GWAS loci represent clusters of common, noncoding variants from which pinpointing causal genes remains challenging. Here we combined data from 718,734 individuals to discover rare and low-frequency (minor allele frequency (MAF) < 5%) coding variants associated with BMI. We identified 14 coding variants in 13 genes, of which 8 variants were in genes (ZBTB7B, ACHE, RAPGEF3, RAB21, ZFHX3, ENTPD6, ZFR2 and ZNF169) newly implicated in human obesity, 2 variants were in genes (MC4R and KSR2) previously observed to be mutated in extreme obesity and 2 variants were in GIPR. The effect sizes of rare variants are ~10 times larger than those of common variants, with the largest effect observed in carriers of an MC4R mutation introducing a stop codon (p.Tyr35Ter, MAF = 0.01%), who weighed ~7 kg more than non-carriers. Pathway analyses based on the variants associated with BMI confirm enrichment of neuronal genes and provide new evidence for adipocyte and energy expenditure biology, widening the potential of genetically supported therapeutic targets in obesity.

Published
2018

30. Limited clinical utility for GWAS or polygenic risk score for postoperative acute kidney injury in non-cardiac surgery in European-ancestry patients

Author

Larach, Daniel B., Lewis, Adam, Bastarache, Lisa, Pandit, Anita, He, Jing, Sinha, Anik, Douville, Nicholas J., Heung, Michael, Mathis, Michael R., Mosley, Jonathan D., Wanderer, Jonathan P., Kheterpal, Sachin, Zawistowski, Matthew, Brummett, Chad M., Siew, Edward D., Robinson-Cohen, Cassianne, and Kertai, Miklos D.

Published
2022
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33. Investigating the Genetic Architecture of the PR Interval Using Clinical Phenotypes

Author

Mosley, Jonathan D, Shoemaker, M Benjamin, Wells, Quinn S, Darbar, Dawood, Shaffer, Christian M, Edwards, Todd L, Bastarache, Lisa, McCarty, Catherine A, Thompson, Will, Chute, Christopher G, Jarvik, Gail P, Crosslin, David R, Larson, Eric B, Kullo, Iftikhar J, Pacheco, Jennifer A, Peissig, Peggy L, Brilliant, Murray H, Linneman, James G, Witte, John S, Denny, Josh C, and Roden, Dan M

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Heart Disease, Atherosclerosis, Genetics, Cardiovascular, Aetiology, 2.1 Biological and endogenous factors, Good Health and Well Being, Adolescent, Adult, Aged, Atrial Fibrillation, Body Mass Index, Case-Control Studies, Electrocardiography, Female, Genotype, Humans, Male, Metabolic Syndrome, Middle Aged, Odds Ratio, Phenotype, Polymorphism, Single Nucleotide, Risk Factors, Waist Circumference, Young Adult, PR interval, atrial fibrillation, biomarker, cardiac electrophysiology, molecular epidemiology, risk factors, Medical Biotechnology, Cardiorespiratory Medicine and Haematology, Cardiovascular System & Hematology, Cardiovascular medicine and haematology
Abstract

One potential use for the PR interval is as a biomarker of disease risk. We hypothesized that quantifying the shared genetic architectures of the PR interval and a set of clinical phenotypes would identify genetic mechanisms contributing to PR variability and identify diseases associated with a genetic predictor of PR variability. We used ECG measurements from the ARIC study (Atherosclerosis Risk in Communities; n=6731 subjects) and 63 genetically modulated diseases from the eMERGE network (Electronic Medical Records and Genomics; n=12 978). We measured pairwise genetic correlations (rG) between PR phenotypes (PR interval, PR segment, P-wave duration) and each of the 63 phenotypes. The PR segment was genetically correlated with atrial fibrillation (rG=-0.88; P=0.0009). An analysis of metabolic phenotypes in ARIC also showed that the P wave was genetically correlated with waist circumference (rG=0.47; P=0.02). A genetically predicted PR interval phenotype based on 645 714 single-nucleotide polymorphisms was associated with atrial fibrillation (odds ratio=0.89 per SD change; 95% confidence interval, 0.83-0.95; P=0.0006). The differing pattern of associations among the PR phenotypes is consistent with analyses that show that the genetic correlation between the P wave and PR segment was not significantly different from 0 (rG=-0.03 [0.16]). The genetic architecture of the PR interval comprises modulators of atrial fibrillation risk and obesity.

Published
2017

35. Genetic variants associated with sepsis-associated acute kidney injury.

Author

Douville, Nicholas J., Bastarache, Lisa, Bertucci-Richter, Emily, Patil, Snehal, Jewell, Elizabeth S., Freundlich, Robert E., Kertai, Miklos D., and Engoren, Milo C.

Subjects
*ACUTE kidney failure, *GENOME-wide association studies, *DISEASE risk factors, *GENETIC variation, *KIDNEY physiology
Abstract

Background: Kidney dysfunction is a common complication in septic patients. Studies have identified numerous risk factors for sepsis-associated acute kidney injury (S-AKI), yet there is wide variability in the incidence even among patients with similar risk factors, suggesting the presence of additional uncharacterized risk factors, including genetic differences. The expansion of biobanks, advances in genotyping, and standardized diagnostic criteria have enabled large-scale, hypothesis-generating studies into the genetic mechanisms underlying S-AKI. We hypothesize that the genetic pathway behind S-AKI has overlapping mechanisms with key differences based upon the specific subtype of acute kidney injury (AKI). Methods: To test this hypothesis, we performed a genome-wide association study (GWAS) of S-AKI in three logistic regression models. Model 1, controlled for 1) age, 2) sex, 3) genotyping chip, and 4) the first five principal components. In Model 2, pre-sepsis baseline serum creatinine was added to the variables in Model 1. Finally, in Model 3, we controlled for the full range of patient, clinical, and ICU-related risk factors. Each of the 3-models were repeated in a pre-specified sensitivity analysis of higher severity S-AKI, defined as KDIGO Stage 2 or 3. We then compare associated variants and genes from our GWAS with previously published AKI sub-types and model other factors associated with S-AKI in our dataset. Findings: 3,348 qualifying Sepsis-3 patients have been genotyped in our dataset. Of these patients, 383 (11.4%) developed Stage 1, 2, or 3 AKI (primary outcome) and 181 (5.4%) developed Stage 2 or 3 AKI (sensitivity analysis). The median age was 61 years (interquartile range (IQR): 51,69), 42% were female, and the increase in SOFA score (between 48-hours before to 24-hours after the onset of suspected infection) was 2 (2–3). No variants exceeded our threshold for genome-wide significance (P<5x10-8), however, a total of 13 variants exceeded the suggestive (P<1x10-6) threshold. Notably, rs184516290 (chr1:199814965:G:A), near the NR5A2 gene, chr1:199805801:T:TA, also near the NR5A2 gene, and rs117313146 (chr15:31999784:G:C), near the CHRNA7 gene, were associated with S-AKI at the suggestive level in all three models presented. Variants in the suppressor of fused homolog (SUFU) gene, previously shown to be correlated with renal function in bacteremic patients, consistently exceeded the P<0.05 threshold in our models. Conclusions: While failing to identify any novel association for S-AKI at the level of genome-wide significance, our study did suggest multiple variants in previously characterized pathways for S-AKI including CHRNA7, NR5A2, and SUFU. We failed to replicate associations from multiple prior studies which may result from differences in how the phenotype was defined or, alternatively, limited genetic contribution and low heritability. [ABSTRACT FROM AUTHOR]

Published
2024
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37. Phenotypic signatures in clinical data enable systematic identification of patients for genetic testing

Author

Morley, Theodore J., Han, Lide, Castro, Victor M., Morra, Jonathan, Perlis, Roy H., Cox, Nancy J., and Bastarache, Lisa

Subjects
Diagnosis, Genetic aspects, Methods, Genetic testing -- Methods, Rare diseases -- Diagnosis -- Genetic aspects, Genetic screening -- Methods
Abstract

Author(s): Theodore J. Morley [sup.1] [sup.2] , Lide Han [sup.1] [sup.2] , Victor M. Castro [sup.3] , Jonathan Morra [sup.4] , Roy H. Perlis [sup.3] , Nancy J. Cox [sup.1] [...], Around 5% of the population is affected by a rare genetic disease, yet most endure years of uncertainty before receiving a genetic test. A common feature of genetic diseases is the presence of multiple rare phenotypes that often span organ systems. Here, we use diagnostic billing information from longitudinal clinical data in the electronic health records (EHRs) of 2,286 patients who received a chromosomal microarray test, and 9,144 matched controls, to build a model to predict who should receive a genetic test. The model achieved high prediction accuracies in a held-out test sample (area under the receiver operating characteristic curve (AUROC), 0.97; area under the precision-recall curve (AUPRC), 0.92), in an independent hospital system (AUROC, 0.95; AUPRC, 0.62), and in an independent set of 172,265 patients in which cases were broadly defined as having an interaction with a genetics provider (AUROC, 0.9; AUPRC, 0.63). Patients carrying a putative pathogenic copy number variant were also accurately identified by the model. Compared with current approaches for genetic test determination, our model could identify more patients for testing while also increasing the proportion of those tested who have a genetic disease. We demonstrate that phenotypic patterns representative of a wide range of genetic diseases can be captured from EHRs to systematize decision-making for genetic testing, with the potential to speed up diagnosis, improve care and reduce costs. Machine learning of electronic health records identifies individuals with a high suspicion of a wide range of genetic diseases and prioritizes those individuals for genetic testing.

Published
2021
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38. Hemochromatosis risk genotype is not associated with colorectal cancer or age at its diagnosis

Author

Jarvik, Gail P., Wang, Xiaoliang, Fontanillas, Pierre, Kim, Esther, Chanprasert, Sirisak, Gordon, Adam S., Bastarache, Lisa, Kowdley, Kris V., Harrison, Tabitha, Rosenthal, Elisabeth A., Stanaway, Ian B., Bézieau, Stéphane, Weinstein, Stephanie J., Newcomb, Polly A., Casey, Graham, Platz, Elizabeth A., Visvanathan, Kala, Le Marchand, Loic, Ulrich, Cornelia M., Hardikar, Sheetal, Li, Christopher I., van Duijnhoven, Franzel J.B., Gsur, Andrea, Campbell, Peter T., Moreno, Victor, Vodička, Pavel, Brenner, Hermann, Chang-Claude, Jenny, Hoffmeister, Michael, Slattery, Martha L., Gunter, Marc J., Aglago, Elom K., Castellví-Bel, Sergi, Kweon, Sun-Seog, Chan, Andrew T., Li, Li, Zheng, Wei, Bishop, D. Timothy, Giles, Graham G., Rennert, Gad, Offit, Kenneth, Keku, Temitope O., Woods, Michael O., Hampe, Jochen, Van Guelpen, Bethan, Gallinger, Steven J., de la Chapelle, Albert, Hampel, Heather, Berndt, Sonja I., Tangen, Catherine M., Lindblom, Annika, Wolk, Alicja, Burnett-Hartman, Andrea, Wu, Anna H., White, Emily, Gruber, Stephen B., Jenkins, Mark A., Mountain, Joanna, Peters, Ulrike, and Crosslin, David R.

Published
2020
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40. Phenome-wide association analysis suggests the APOL1 linked disease spectrum primarily drives kidney-specific pathways

Author

Bajaj, Archna, Ihegword, Andrea, Qiu, Chengxiang, Small, Aeron M., Wei, Wei-Qi, Bastarache, Lisa, Feng, QiPing, Kember, Rachel L., Risman, Marjorie, Bloom, Roy D., Birtwell, David L., Williams, Heather, Shaffer, Christian M., Chen, Jinbo, Center, Regeneron Genetics, Denny, Joshua C., Rader, Daniel J., Stein, C. Michael, Damrauer, Scott M., and Susztak, Katalin

Published
2020
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42. Large-Scale Exome-wide Association Analysis Identifies Loci for White Blood Cell Traits and Pleiotropy with Immune-Mediated Diseases

Author

Tajuddin, Salman M, Schick, Ursula M, Eicher, John D, Chami, Nathalie, Giri, Ayush, Brody, Jennifer A, Hill, W David, Kacprowski, Tim, Li, Jin, Lyytikäinen, Leo-Pekka, Manichaikul, Ani, Mihailov, Evelin, O’Donoghue, Michelle L, Pankratz, Nathan, Pazoki, Raha, Polfus, Linda M, Smith, Albert Vernon, Schurmann, Claudia, Vacchi-Suzzi, Caterina, Waterworth, Dawn M, Evangelou, Evangelos, Yanek, Lisa R, Burt, Amber, Chen, Ming-Huei, van Rooij, Frank JA, Floyd, James S, Greinacher, Andreas, Harris, Tamara B, Highland, Heather M, Lange, Leslie A, Liu, Yongmei, Mägi, Reedik, Nalls, Mike A, Mathias, Rasika A, Nickerson, Deborah A, Nikus, Kjell, Starr, John M, Tardif, Jean-Claude, Tzoulaki, Ioanna, Edwards, Digna R Velez, Wallentin, Lars, Bartz, Traci M, Becker, Lewis C, Denny, Joshua C, Raffield, Laura M, Rioux, John D, Friedrich, Nele, Fornage, Myriam, Gao, He, Hirschhorn, Joel N, Liewald, David CM, Rich, Stephen S, Uitterlinden, Andre, Bastarache, Lisa, Becker, Diane M, Boerwinkle, Eric, de Denus, Simon, Bottinger, Erwin P, Hayward, Caroline, Hofman, Albert, Homuth, Georg, Lange, Ethan, Launer, Lenore J, Lehtimäki, Terho, Lu, Yingchang, Metspalu, Andres, O’Donnell, Chris J, Quarells, Rakale C, Richard, Melissa, Torstenson, Eric S, Taylor, Kent D, Vergnaud, Anne-Claire, Zonderman, Alan B, Crosslin, David R, Deary, Ian J, Dörr, Marcus, Elliott, Paul, Evans, Michele K, Gudnason, Vilmundur, Kähönen, Mika, Psaty, Bruce M, Rotter, Jerome I, Slater, Andrew J, Dehghan, Abbas, White, Harvey D, Ganesh, Santhi K, Loos, Ruth JF, Esko, Tõnu, Faraday, Nauder, Wilson, James G, Cushman, Mary, Johnson, Andrew D, Edwards, Todd L, Zakai, Neil A, Lettre, Guillaume, Reiner, Alex P, and Auer, Paul L

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Immunology, Hematology, Human Genome, Stem Cell Research, 1.1 Normal biological development and functioning, 2.1 Biological and endogenous factors, Inflammatory and immune system, Blood Cell Count, Exome, Genetic Loci, Genetic Pleiotropy, Genome-Wide Association Study, Humans, Immune System Diseases, Leukocytes, Quality Control, Medical and Health Sciences, Genetics & Heredity, Biological sciences, Biomedical and clinical sciences, Health sciences
Abstract

White blood cells play diverse roles in innate and adaptive immunity. Genetic association analyses of phenotypic variation in circulating white blood cell (WBC) counts from large samples of otherwise healthy individuals can provide insights into genes and biologic pathways involved in production, differentiation, or clearance of particular WBC lineages (myeloid, lymphoid) and also potentially inform the genetic basis of autoimmune, allergic, and blood diseases. We performed an exome array-based meta-analysis of total WBC and subtype counts (neutrophils, monocytes, lymphocytes, basophils, and eosinophils) in a multi-ancestry discovery and replication sample of ∼157,622 individuals from 25 studies. We identified 16 common variants (8 of which were coding variants) associated with one or more WBC traits, the majority of which are pleiotropically associated with autoimmune diseases. Based on functional annotation, these loci included genes encoding surface markers of myeloid, lymphoid, or hematopoietic stem cell differentiation (CD69, CD33, CD87), transcription factors regulating lineage specification during hematopoiesis (ASXL1, IRF8, IKZF1, JMJD1C, ETS2-PSMG1), and molecules involved in neutrophil clearance/apoptosis (C10orf54, LTA), adhesion (TNXB), or centrosome and microtubule structure/function (KIF9, TUBD1). Together with recent reports of somatic ASXL1 mutations among individuals with idiopathic cytopenias or clonal hematopoiesis of undetermined significance, the identification of a common regulatory 3' UTR variant of ASXL1 suggests that both germline and somatic ASXL1 mutations contribute to lower blood counts in otherwise asymptomatic individuals. These association results shed light on genetic mechanisms that regulate circulating WBC counts and suggest a prominent shared genetic architecture with inflammatory and autoimmune diseases.

Published
2016

43. Identifying genetically driven clinical phenotypes using linear mixed models.

Author

Mosley, Jonathan D, Witte, John S, Larkin, Emma K, Bastarache, Lisa, Shaffer, Christian M, Karnes, Jason H, Stein, C Michael, Phillips, Elizabeth, Hebbring, Scott J, Brilliant, Murray H, Mayer, John, Ye, Zhan, Roden, Dan M, and Denny, Joshua C

Subjects
Humans, Disease, Genetic Predisposition to Disease, HLA Antigens, Genotype, Phenotype, Polymorphism, Single Nucleotide, Models, Genetic, Adult, Aged, Aged, 80 and over, Middle Aged, European Continental Ancestry Group, Female, Male, Genetic Variation, Genome-Wide Association Study, Exome, and over, Models, Genetic, Polymorphism, Single Nucleotide
Abstract

We hypothesized that generalized linear mixed models (GLMMs), which estimate the additive genetic variance underlying phenotype variability, would facilitate rapid characterization of clinical phenotypes from an electronic health record. We evaluated 1,288 phenotypes in 29,349 subjects of European ancestry with single-nucleotide polymorphism (SNP) genotyping on the Illumina Exome Beadchip. We show that genetic liability estimates are primarily driven by SNPs identified by prior genome-wide association studies and SNPs within the human leukocyte antigen (HLA) region. We identify 44 (false discovery rate q

Published
2016

45. Harmonizing Clinical Sequencing and Interpretation for the eMERGE III Network

Author

Zouk, Hana, Venner, Eric, Lennon, Niall J., Muzny, Donna M., Abrams, Debra, Adunyah, Samuel, Albertson-Junkans, Ladia, Ames, Darren C., Appelbaum, Paul, Aronson, Samuel, Aufox, Sharon, Babb, Lawrence J., Balasubramanian, Adithya, Bangash, Hana, Basford, Melissa, Bastarache, Lisa, Baxter, Samantha, Behr, Meckenzie, Benoit, Barbara, Bhoj, Elizabeth, Bielinski, Suzette J., Bland, Harris T., Blout, Carrie, Borthwick, Kenneth, Bottinger, Erwin P., Bowser, Mark, Brand, Harrison, Brilliant, Murray, Brodeur, Wendy, Caraballo, Pedro, Carrell, David, Carroll, Andrew, Almoguera, Berta, Castillo, Lisa, Castro, Victor, Chandanavelli, Gauthami, Chiang, Theodore, Chisholm, Rex L., Christensen, Kurt D., Chung, Wendy, Chute, Christopher G., City, Brittany, Cobb, Beth L., Connolly, John J., Crane, Paul, Crew, Katherine, Crosslin, David, De Andrade, Mariza, De la Cruz, Jessica, Denson, Shawn, Denny, Josh, DeSmet, Tim, Dikilitas, Ozan, Friedrich, Christopher, Fullerton, Stephanie M., Funke, Birgit, Gabriel, Stacey, Gainer, Vivian, Gharavi, Ali, Glazer, Andrew M., Glessner, Joseph T., Goehringer, Jessica, Gordon, Adam S., Graham, Chet, Green, Robert C., Gundelach, Justin H., Dayal, Jyoti, Hain, Heather S., Hakonarson, Hakon, Harden, Maegan V., Harley, John, Harr, Margaret, Hartzler, Andrea, Hayes, M. Geoffrey, Hebbring, Scott, Henrikson, Nora, Hershey, Andrew, Hoell, Christin, Holm, Ingrid, Howell, Kayla M., Hripcsak, George, Hu, Jianhong, Jarvik, Gail P., Jayaseelan, Joy C., Jiang, Yunyun, Joo, Yoonjung Yoonie, Jose, Sheethal, Josyula, Navya Shilpa, Justice, Anne E., Kalla, Sara E., Kalra, Divya, Karlson, Elizabeth, Kelly, Melissa A., Keating, Brendan J., Kenny, Eimear E., Key, Dustin, Kiryluk, Krzysztof, Kitchner, Terrie, Klanderman, Barbara, Klee, Eric, Kochan, David C., Korchina, Viktoriya, Kottyan, Leah, Kovar, Christie, Kudalkar, Emily, Kullo, Iftikhar J., Lammers, Philip, Larson, Eric B., Lebo, Matthew S., Leduc, Magalie, Lee, Ming Ta (Michael), Leppig, Kathleen A., Leslie, Nancy D., Li, Rongling, Liang, Wayne H., Lin, Chiao-Feng, Linder, Jodell, Lindor, Noralane M., Lingren, Todd, Linneman, James G., Liu, Cong, Liu, Wen, Liu, Xiuping, Lynch, John, Lyon, Hayley, Macbeth, Alyssa, Mahadeshwar, Harshad, Mahanta, Lisa, Malin, Brad, Manolio, Teri, Marasa, Maddalena, Marsolo, Keith, Dinsmore, Michael J., Dodge, Sheila, Hynes, Elizabeth Duffy, Dunlea, Phil, Edwards, Todd L., Eng, Christine M., Fasel, David, Fedotov, Alex, Feng, Qiping, Fleharty, Mark, Foster, Andrea, Freimuth, Robert, McGowan, Michelle L., McNally, Elizabeth, Meldrim, Jim, Mentch, Frank, Mosley, Jonathan, Mukherjee, Shubhabrata, Mullen, Thomas E., Muniz, Jesse, Murdock, David R., Murphy, Shawn, Murugan, Mullai, Myers, Melanie F., Namjou, Bahram, Ni, Yizhao, Obeng, Aniwaa Owusu, Onofrio, Robert C., Taylor, Casey Overby, Person, Thomas N., Peterson, Josh F., Petukhova, Lynn, Pisieczko, Cassandra J., Pratap, Siddharth, Prows, Cynthia A., Puckelwartz, Megan J., Rahm, Alanna Kulchak, Raj, Ritika, Ralston, James D., Ramaprasan, Arvind, Ramirez, Andrea, Rasmussen, Luke, Rasmussen-Torvik, Laura, Rasouly, Hila Milo, Raychaudhuri, Soumya, Ritchie, Marylyn D., Rives, Catherine, Riza, Beenish, Roden, Dan, Rosenthal, Elisabeth A., Santani, Avni, Schaid, Dan, Scherer, Steven, Scott, Stuart, Scrol, Aaron, Sengupta, Soumitra, Shang, Ning, Sharma, Himanshu, Sharp, Richard R., Singh, Rajbir, Sleiman, Patrick M.A., Slowik, Kara, Smith, Joshua C., Smith, Maureen E., Smoller, Jordan W., Sohn, Sunghwan, Stanaway, Ian B., Starren, Justin, Stroud, Mary, Su, Jessica, Tolwinski, Kasia, Van Driest, Sara L., Vargas, Sean M., Varugheese, Matthew, Veenstra, David, Verbitsky, Miguel, Vicente, Gina, Wagner, Michael, Walker, Kimberly, Walunas, Theresa, Wang, Liwen, Wang, Qiaoyan, Wei, Wei-Qi, Weiss, Scott T., Wiesner, Georgia L., Wells, Quinn, Weng, Chunhua, White, Peter S., Wiley, Ken L., Jr., Williams, Janet L., Williams, Marc S., Wilson, Michael W., Witkowski, Leora, Woods, Laura Allison, Woolf, Betty, Wu, Tsung-Jung, Wynn, Julia, Yang, Yaping, Yi, Victoria, Zhang, Ge, Zhang, Lan, Rehm, Heidi L., and Gibbs, Richard A.

Published
2019
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46. Publisher Correction: Stroke genetics informs drug discovery and risk prediction across ancestries

Author

Mishra, Aniket, Malik, Rainer, Hachiya, Tsuyoshi, Jürgenson, Tuuli, Namba, Shinichi, Posner, Daniel C., Kamanu, Frederick K., Koido, Masaru, Le Grand, Quentin, Shi, Mingyang, He, Yunye, Georgakis, Marios K., Caro, Ilana, Krebs, Kristi, Liaw, Yi-Ching, Vaura, Felix C., Lin, Kuang, Winsvold, Bendik Slagsvold, Srinivasasainagendra, Vinodh, Parodi, Livia, Bae, Hee-Joon, Chauhan, Ganesh, Chong, Michael R., Tomppo, Liisa, Akinyemi, Rufus, Roshchupkin, Gennady V., Habib, Naomi, Jee, Yon Ho, Thomassen, Jesper Qvist, Abedi, Vida, Cárcel-Márquez, Jara, Nygaard, Marianne, Leonard, Hampton L., Yang, Chaojie, Yonova-Doing, Ekaterina, Knol, Maria J., Lewis, Adam J., Judy, Renae L., Ago, Tetsuro, Amouyel, Philippe, Armstrong, Nicole D., Bakker, Mark K., Bartz, Traci M., Bennett, David A., Bis, Joshua C., Bordes, Constance, Børte, Sigrid, Cain, Anael, Ridker, Paul M., Cho, Kelly, Chen, Zhengming, Cruchaga, Carlos, Cole, John W., de Jager, Phil L., de Cid, Rafael, Endres, Matthias, Ferreira, Leslie E., Geerlings, Mirjam I., Gasca, Natalie C., Gudnason, Vilmundur, Hata, Jun, He, Jing, Heath, Alicia K., Ho, Yuk-Lam, Havulinna, Aki S., Hopewell, Jemma C., Hyacinth, Hyacinth I., Inouye, Michael, Jacob, Mina A., Jeon, Christina E., Jern, Christina, Kamouchi, Masahiro, Keene, Keith L., Kitazono, Takanari, Kittner, Steven J., Konuma, Takahiro, Kumar, Amit, Lacaze, Paul, Launer, Lenore J., Lee, Keon-Joo, Lepik, Kaido, Li, Jiang, Li, Liming, Manichaikul, Ani, Markus, Hugh S., Marston, Nicholas A., Meitinger, Thomas, Mitchell, Braxton D., Montellano, Felipe A., Morisaki, Takayuki, Mosley, Thomas H., Nalls, Mike A., Nordestgaard, Børge G., O’Donnell, Martin J., Okada, Yukinori, Onland-Moret, N. Charlotte, Ovbiagele, Bruce, Peters, Annette, Psaty, Bruce M., Rich, Stephen S., Rosand, Jonathan, Sabatine, Marc S., Sacco, Ralph L., Saleheen, Danish, Sandset, Else Charlotte, Salomaa, Veikko, Sargurupremraj, Muralidharan, Sasaki, Makoto, Satizabal, Claudia L., Schmidt, Carsten O., Shimizu, Atsushi, Smith, Nicholas L., Sloane, Kelly L., Sutoh, Yoichi, Sun, Yan V., Tanno, Kozo, Tiedt, Steffen, Tatlisumak, Turgut, Torres-Aguila, Nuria P., Tiwari, Hemant K., Trégouët, David-Alexandre, Trompet, Stella, Tuladhar, Anil Man, Tybjærg-Hansen, Anne, van Vugt, Marion, Vibo, Riina, Verma, Shefali S., Wiggins, Kerri L., Wennberg, Patrik, Woo, Daniel, Wilson, Peter W. F., Xu, Huichun, Yang, Qiong, Yoon, Kyungheon, Millwood, Iona Y., Gieger, Christian, Ninomiya, Toshiharu, Grabe, Hans J., Jukema, J. Wouter, Rissanen, Ina L., Strbian, Daniel, Kim, Young Jin, Chen, Pei-Hsin, Mayerhofer, Ernst, Howson, Joanna M. M., Irvin, Marguerite R., Adams, Hieab, Wassertheil-Smoller, Sylvia, Christensen, Kaare, Ikram, Mohammad A., Rundek, Tatjana, Worrall, Bradford B., Lathrop, G. Mark, Riaz, Moeen, Simonsick, Eleanor M., Kõrv, Janika, França, Paulo H. C., Zand, Ramin, Prasad, Kameshwar, Frikke-Schmidt, Ruth, de Leeuw, Frank-Erik, Liman, Thomas, Haeusler, Karl Georg, Ruigrok, Ynte M., Heuschmann, Peter Ulrich, Longstreth, W. T., Jung, Keum Ji, Bastarache, Lisa, Paré, Guillaume, Damrauer, Scott M., Chasman, Daniel I., Rotter, Jerome I., Anderson, Christopher D., Zwart, John-Anker, Niiranen, Teemu J., Fornage, Myriam, Liaw, Yung-Po, Seshadri, Sudha, Fernández-Cadenas, Israel, Walters, Robin G., Ruff, Christian T., Owolabi, Mayowa O., Huffman, Jennifer E., Milani, Lili, Kamatani, Yoichiro, Dichgans, Martin, and Debette, Stephanie

Published
2022
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48. Exploiting the GTEx resources to decipher the mechanisms at GWAS loci

Author

Barbeira, Alvaro N., Bonazzola, Rodrigo, Gamazon, Eric R., Liang, Yanyu, Park, YoSon, Kim-Hellmuth, Sarah, Wang, Gao, Jiang, Zhuoxun, Zhou, Dan, Hormozdiari, Farhad, Liu, Boxiang, Rao, Abhiram, 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., Segrè, Ayellet V., Brown, Christopher D., Lappalainen, Tuuli, Wen, Xiaoquan, and Im, Hae Kyung

Published
2021
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49. Inactivating Mutations in NPC1L1 and Protection from Coronary Heart Disease

Author

Stitziel, Nathan O, Won, Hong-Hee, Morrison, Alanna C, Peloso, Gina M, Do, Ron, Lange, Leslie A, Fontanillas, Pierre, Gupta, Namrata, Duga, Stefano, Goel, Anuj, Farrall, Martin, Saleheen, Danish, Ferrario, Paola, König, Inke, Asselta, Rosanna, Merlini, Piera A, Marziliano, Nicola, Notarangelo, Maria Francesca, Schick, Ursula, Auer, Paul, Assimes, Themistocles L, Reilly, Muredach, Wilensky, Robert, Rader, Daniel J, Hovingh, G Kees, Meitinger, Thomas, Kessler, Thorsten, Kastrati, Adnan, Laugwitz, Karl-Ludwig, Siscovick, David, Rotter, Jerome I, Hazen, Stanely L, Tracy, Russell, Cresci, Sharon, Spertus, John, Jackson, Rebecca, Schwartz, Stephen M, Natarajan, Pradeep, Crosby, Jacy, Muzny, Donna, Ballantyne, Christie, Rich, Stephen S, O'Donnell, Christopher J, Abecasis, Goncalo, Sunaev, Shamil, Nickerson, Deborah A, Buring, Julie E, Ridker, Paul M, Chasman, Daniel I, Austin, Erin, Kullo, Iftikhar J, Weeke, Peter E, Shaffer, Christian M, Bastarache, Lisa A, Denny, Joshua C, Roden, Dan M, Palmer, Colin, Deloukas, Panos, Lin, Dan-Yu, Tang, Zheng-zheng, Erdmann, Jeanette, Schunkert, Heribert, Danesh, John, Marrugat, Jaume, Elosua, Roberto, Ardissino, Diego, McPherson, Ruth, Watkins, Hugh, Reiner, Alex P, Wilson, James G, Altshuler, David, Gibbs, Richard A, Lander, Eric S, Boerwinkle, Eric, Gabriel, Stacey, and Kathiresan, Sekar

Subjects
Biomedical and Clinical Sciences, Cardiovascular, Atherosclerosis, Heart Disease, Genetics, Heart Disease - Coronary Heart Disease, 5.1 Pharmaceuticals, Adult, Asian People, Black People, Case-Control Studies, Cholesterol, LDL, Coronary Disease, Exons, Female, Gene Silencing, Genotype, Humans, Male, Membrane Proteins, Membrane Transport Proteins, Middle Aged, Mutation, Protein Conformation, Risk, Sequence Analysis, DNA, Triglycerides, White People, Myocardial Infarction Genetics Consortium Investigators, Medical and Health Sciences, General & Internal Medicine, Biomedical and clinical sciences, Health sciences
Abstract

BackgroundEzetimibe lowers plasma levels of low-density lipoprotein (LDL) cholesterol by inhibiting the activity of the Niemann-Pick C1-like 1 (NPC1L1) protein. However, whether such inhibition reduces the risk of coronary heart disease is not known. Human mutations that inactivate a gene encoding a drug target can mimic the action of an inhibitory drug and thus can be used to infer potential effects of that drug.MethodsWe sequenced the exons of NPC1L1 in 7364 patients with coronary heart disease and in 14,728 controls without such disease who were of European, African, or South Asian ancestry. We identified carriers of inactivating mutations (nonsense, splice-site, or frameshift mutations). In addition, we genotyped a specific inactivating mutation (p.Arg406X) in 22,590 patients with coronary heart disease and in 68,412 controls. We tested the association between the presence of an inactivating mutation and both plasma lipid levels and the risk of coronary heart disease.ResultsWith sequencing, we identified 15 distinct NPC1L1 inactivating mutations; approximately 1 in every 650 persons was a heterozygous carrier for 1 of these mutations. Heterozygous carriers of NPC1L1 inactivating mutations had a mean LDL cholesterol level that was 12 mg per deciliter (0.31 mmol per liter) lower than that in noncarriers (P=0.04). Carrier status was associated with a relative reduction of 53% in the risk of coronary heart disease (odds ratio for carriers, 0.47; 95% confidence interval, 0.25 to 0.87; P=0.008). In total, only 11 of 29,954 patients with coronary heart disease had an inactivating mutation (carrier frequency, 0.04%) in contrast to 71 of 83,140 controls (carrier frequency, 0.09%).ConclusionsNaturally occurring mutations that disrupt NPC1L1 function were found to be associated with reduced plasma LDL cholesterol levels and a reduced risk of coronary heart disease. (Funded by the National Institutes of Health and others.).

Published
2014

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