Browsing by Author "Weng, Chunhua"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    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.
  • Thumbnail Image
    Item
    EHR-based cohort assessment for multicenter RCTs: a fast and flexible model for identifying potential study sites
    (Oxford University Press, 2022) Nelson, Sarah J.; Drury, Bethany; Hood, Daniel; Harper, Jeremy; Bernard, Tiffany; Weng, Chunhua; Kennedy, Nan; LaSalle, Bernie; Gouripeddi, Ramkiran; Wilkins, Consuelo H.; Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and Engineering
    Objective: The Recruitment Innovation Center (RIC), partnering with the Trial Innovation Network and institutions in the National Institutes of Health-sponsored Clinical and Translational Science Awards (CTSA) Program, aimed to develop a service line to retrieve study population estimates from electronic health record (EHR) systems for use in selecting enrollment sites for multicenter clinical trials. Our goal was to create and field-test a low burden, low tech, and high-yield method. Materials and methods: In building this service line, the RIC strove to complement, rather than replace, CTSA hubs' existing cohort assessment tools. For each new EHR cohort request, we work with the investigator to develop a computable phenotype algorithm that targets the desired population. CTSA hubs run the phenotype query and return results using a standardized survey. We provide a comprehensive report to the investigator to assist in study site selection. Results: From 2017 to 2020, the RIC developed and socialized 36 phenotype-dependent cohort requests on behalf of investigators. The average response rate to these requests was 73%. Discussion: Achieving enrollment goals in a multicenter clinical trial requires that researchers identify study sites that will provide sufficient enrollment. The fast and flexible method the RIC has developed, with CTSA feedback, allows hubs to query their EHR using a generalizable, vetted phenotype algorithm to produce reliable counts of potentially eligible study participants. Conclusion: The RIC's EHR cohort assessment process for evaluating sites for multicenter trials has been shown to be efficient and helpful. The model may be replicated for use by other programs.
  • Thumbnail Image
    Item
    Missense variants in TAF1 and developmental phenotypes: Challenges of determining pathogenicity
    (Wiley, 2019-10-23) Cheng, Hanyin; Capponi, Simona; Wakeling, Emma; Marchi, Elaine; Li, Quan; Zhao, Mengge; Weng, Chunhua; Piatek, Stefan G.; Ahlfors, Helena; Kleyner, Robert; Rope, Alan; Lumaka, Aimé; Lukusa, Prosper; Devriendt, Koenraad; Vermeesch, Joris; Posey, Jennifer E.; Palmer, Elizabeth E.; Murray, Lucinda; Leon, Eyby; Diaz, Jullianne; Worgan, Lisa; Mallawaarachchi, Amali; Vogt, Julie; de Munnik, Sonja A.; Dreyer, Lauren; Baynam, Gareth; Ewans, Lisa; Stark, Zornitza; Lunke, Sebastian; Gonçalves, Ana R.; Soares, Gabriela; Oliveira, Jorge; Fassi, Emily; Willing, Marcia; Waugh, Jeff L.; Faivre, Laurence; Riviere, Jean-Baptiste; Moutton, Sebastien; Mohammed, Shehla; Payne, Katelyn; Walsh, Laurence; Begtrup, Amber; Guillen Sacoto, Maria J.; Douglas, Ganka; Alexander, Nora; Buckley, Michael F.; Mark, Paul R.; Adès, Lesley C.; Sandaradura, Sarah A.; Lupski, James R.; Roscioli, Tony; Agrawal, Pankaj B.; Kline, Antonie D.; Wang, Kai; Timmers, T. Marc; Lyon, Gholson J.; Neurology, School of Medicine
    We recently described a new neurodevelopmental syndrome (TAF1/MRXS33 intellectual disability syndrome) (MIM# 300966) caused by pathogenic variants involving the X-linked gene TAF1, which participates in RNA polymerase II transcription. The initial study reported eleven families, and the syndrome was defined as presenting early in life with hypotonia, facial dysmorphia, and developmental delay that evolved into intellectual disability (ID) and/or autism spectrum disorder (ASD). We have now identified an additional 27 families through a genotype-first approach. Familial segregation analysis, clinical phenotyping, and bioinformatics were capitalized on to assess potential variant pathogenicity, and molecular modelling was performed for those variants falling within structurally characterized domains of TAF1. A novel phenotypic clustering approach was also applied, in which the phenotypes of affected individuals were classified using 51 standardized Human Phenotype Ontology (HPO) terms. Phenotypes associated with TAF1 variants show considerable pleiotropy and clinical variability, but prominent among previously unreported effects were brain morphological abnormalities, seizures, hearing loss, and heart malformations. Our allelic series broadens the phenotypic spectrum of TAF1/MRXS33 intellectual disability syndrome and the range of TAF1 molecular defects in humans. It also illustrates the challenges for determining the pathogenicity of inherited missense variants, particularly for genes mapping to chromosome X.