Browsing by Author "Bastarache, Lisa"

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    A multiancestry genome-wide association study of unexplained chronic ALT elevation as a proxy for nonalcoholic fatty liver disease with histological and radiological validation
    (Springer Nature, 2022) 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.; Townsend Creasy, Kate; 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 F. A.; 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.; Regeneron Genetics Center; Geisinger-Regeneron DiscovEHR Collaboration; EPoS Consortium; VA Million Veteran Program; Rader, Daniel J.; Voight, Benjamin F.; Chang, Kyong-Mi; Medicine, School of Medicine
    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 < 5 × 10-8). External replication in histology-defined NAFLD cohorts (7,397 cases and 56,785 controls) or radiologic imaging cohorts (n = 44,289) replicated 17 single-nucleotide polymorphisms (SNPs) (P < 6.5 × 10-4), of which 9 were new (TRIB1, PPARG, MTTP, SERPINA1, FTO, IL1RN, COBLL1, APOH and IFI30). Pleiotropy analysis showed that 61 of 77 multiancestry and all 17 replicated SNPs were jointly associated with metabolic and/or inflammatory traits, revealing a complex model of genetic architecture. Our approach integrating cALT, histology and imaging reveals new insights into genetic liability to NAFLD.
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    A research agenda to support the development and implementation of genomics-based clinical informatics tools and resources
    (Oxford University Press, 2022) Wiley, Ken; Findley, Laura; Goldrich, Madison; Rakhra-Burris, Tejinder K.; Stevens, Ana; Williams, Pamela; Bult, Carol J.; Chisholm, Rex; Deverka, Patricia; Ginsburg, Geoffrey S.; Green, Eric D.; Jarvik, Gail; Mensah, George A.; Ramos, Erin; Relling, Mary V.; Roden, Dan M.; Rowley, Robb; Alterovitz, Gil; Aronson, Samuel; Bastarache, Lisa; Cimino, James J.; Crowgey, Erin L.; Del Fiol, Guilherme; Freimuth, Robert R.; Hoffman, Mark A.; Jeff, Janina; Johnson, Kevin; Kawamoto, Kensaku; Madhavan, Subha; Mendonca, Eneida A.; Ohno-Machado, Lucila; Pratap, Siddharth; Overby Taylor, Casey; Ritchie, Marylyn D.; Walton, Nephi; Weng, Chunhua; Zayas-Cabán, Teresa; Manolio, Teri A.; Williams, Marc S.; Pediatrics, School of Medicine
    Objective: The Genomic Medicine Working Group of the National Advisory Council for Human Genome Research virtually hosted its 13th genomic medicine meeting titled "Developing a Clinical Genomic Informatics Research Agenda". The meeting's goal was to articulate a research strategy to develop Genomics-based Clinical Informatics Tools and Resources (GCIT) to improve the detection, treatment, and reporting of genetic disorders in clinical settings. Materials and methods: Experts from government agencies, the private sector, and academia in genomic medicine and clinical informatics were invited to address the meeting's goals. Invitees were also asked to complete a survey to assess important considerations needed to develop a genomic-based clinical informatics research strategy. Results: Outcomes from the meeting included identifying short-term research needs, such as designing and implementing standards-based interfaces between laboratory information systems and electronic health records, as well as long-term projects, such as identifying and addressing barriers related to the establishment and implementation of genomic data exchange systems that, in turn, the research community could help address. Discussion: Discussions centered on identifying gaps and barriers that impede the use of GCIT in genomic medicine. Emergent themes from the meeting included developing an implementation science framework, defining a value proposition for all stakeholders, fostering engagement with patients and partners to develop applications under patient control, promoting the use of relevant clinical workflows in research, and lowering related barriers to regulatory processes. Another key theme was recognizing pervasive biases in data and information systems, algorithms, access, value, and knowledge repositories and identifying ways to resolve them.
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    Developing real‐world evidence from real‐world data: Transforming raw data into analytical datasets
    (Wiley, 2021-10-14) Bastarache, Lisa; Brown, Jeffrey S.; Cimino, James J.; Dorr, David A.; Embi, Peter J.; Payne, Philip R. O.; Wilcox, Adam B.; Weiner, Mark G.; Medicine, School of Medicine
    Development of evidence-based practice requires practice-based evidence, which can be acquired through analysis of real-world data from electronic health records (EHRs). The EHR contains volumes of information about patients-physical measurements, diagnoses, exposures, and markers of health behavior-that can be used to create algorithms for risk stratification or to gain insight into associations between exposures, interventions, and outcomes. But to transform real-world data into reliable real-world evidence, one must not only choose the correct analytical methods but also have an understanding of the quality, detail, provenance, and organization of the underlying source data and address the differences in these characteristics across sites when conducting analyses that span institutions. This manuscript explores the idiosyncrasies inherent in the capture, formatting, and standardization of EHR data and discusses the clinical domain and informatics competencies required to transform the raw clinical, real-world data into high-quality, fit-for-purpose analytical data sets used to generate real-world evidence.
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    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
    (Oxford University Press, 2020-11-03) Madhavan, Subha; Bastarache, Lisa; Brown, Jeffrey S.; Dorr, David A.; Embi, Peter J.; Friedman, Charles P.; Johnson, Kevin B.; Moore, Jason H.; Kohane, Isaac S.; Payne, Philip R.O.; Tenenbaum, Jessica D.; Weiner, Mark G.; Wilcox, Adam B.; Ohno-Machado, Lucila; Butte, Atul J.; Medicine, School of Medicine
    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