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Article ; Online: A New Age for African-Driven Genomics Research: Human Heredity and Health in Africa (H3Africa).

Peprah, Emmanuel / Wiley, Ken / Sampson, Uchechukwu / Narula, Jagat

2017 Volume 12, Issue 2, Page(s) 67–68

MeSH term(s)	Africa/epidemiology ; Biomedical Research/trends ; Communicable Diseases/epidemiology ; Communicable Diseases/genetics ; Genomics/trends ; Health Status ; Heredity/genetics ; Humans ; Morbidity/trends ; Noncommunicable Diseases/epidemiology
Language	English
Publishing date	2017-08-31
Publishing country	England
Document type	Editorial
ZDB-ID	2629633-0
ISSN	2211-8179 ; 2211-8160
ISSN (online)	2211-8179
ISSN	2211-8160
DOI	10.1016/j.gheart.2017.05.003
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Article ; Online: Genetic sex validation for sample tracking in next-generation sequencing clinical testing.

Hu, Jianhong / Korchina, Viktoriya / Zouk, Hana / Harden, Maegan V / Murdock, David / Macbeth, Alyssa / Harrison, Steven M / Lennon, Niall / Kovar, Christie / Balasubramanian, Adithya / Zhang, Lan / Chandanavelli, Gauthami / Pasham, Divya / Rowley, Robb / Wiley, Ken / Smith, Maureen E / Gordon, Adam / Jarvik, Gail P / Sleiman, Patrick /

Kelly, Melissa A / Bland, Harris T / Murugan, Mullai / Venner, Eric / Boerwinkle, Eric / Prows, Cynthia / Mahanta, Lisa / Rehm, Heidi L / Gibbs, Richard A / Muzny, Donna M

BMC research notes

2024 Volume 17, Issue 1, Page(s) 62

Abstract: Objective: Data from DNA genotyping via a 96-SNP panel in a study of 25,015 clinical samples were utilized for quality control and tracking of sample identity in a clinical sequencing network. The study aimed to demonstrate the value of both the precise ...

Abstract	Objective: Data from DNA genotyping via a 96-SNP panel in a study of 25,015 clinical samples were utilized for quality control and tracking of sample identity in a clinical sequencing network. The study aimed to demonstrate the value of both the precise SNP tracking and the utility of the panel for predicting the sex-by-genotype of the participants, to identify possible sample mix-ups. Results: Precise SNP tracking showed no sample swap errors within the clinical testing laboratories. In contrast, when comparing predicted sex-by-genotype to the provided sex on the test requisition, we identified 110 inconsistencies from 25,015 clinical samples (0.44%), that had occurred during sample collection or accessioning. The genetic sex predictions were confirmed using additional SNP sites in the sequencing data or high-density genotyping arrays. It was determined that discrepancies resulted from clerical errors (49.09%), samples from transgender participants (3.64%) and stem cell or bone marrow transplant patients (7.27%) along with undetermined sample mix-ups (40%) for which sample swaps occurred prior to arrival at genome centers, however the exact cause of the events at the sampling sites resulting in the mix-ups were not able to be determined.
MeSH term(s)	Humans ; High-Throughput Nucleotide Sequencing ; Bone Marrow Transplantation ; Clinical Laboratory Services ; Genotype ; Laboratories
Language	English
Publishing date	2024-03-03
Publishing country	England
Document type	Journal Article
ZDB-ID	2413336-X
ISSN	1756-0500 ; 1756-0500
ISSN (online)	1756-0500
ISSN	1756-0500
DOI	10.1186/s13104-024-06723-w
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Article ; Online: The Trial Innovation Network Liaison Team: building a national clinical and translational community of practice.

Journal of clinical and translational science

2023 Volume 7, Issue 1, Page(s) e249

Abstract: In 2016, the National Center for Advancing Translational Science launched the Trial Innovation Network (TIN) to address barriers to efficient and informative multicenter trials. The TIN provides a national platform, working in partnership with 60+ ... ...

Abstract	In 2016, the National Center for Advancing Translational Science launched the Trial Innovation Network (TIN) to address barriers to efficient and informative multicenter trials. The TIN provides a national platform, working in partnership with 60+ Clinical and Translational Science Award (CTSA) hubs across the country to support the design and conduct of successful multicenter trials. A dedicated Hub Liaison Team (HLT) was established within each CTSA to facilitate connection between the hubs and the newly launched Trial and Recruitment Innovation Centers. Each HLT serves as an expert intermediary, connecting CTSA Hub investigators with TIN support, and connecting TIN research teams with potential multicenter trial site investigators. The cross-consortium Liaison Team network was developed during the first TIN funding cycle, and it is now a mature national network at the cutting edge of team science in clinical and translational research. The CTSA-based HLT structures and the external network structure have been developed in collaborative and iterative ways, with methods for shared learning and continuous process improvement. In this paper, we review the structure, function, and development of the Liaison Team network, discuss lessons learned during the first TIN funding cycle, and outline a path toward further network maturity.
Language	English
Publishing date	2023-11-06
Publishing country	England
Document type	Journal Article
ISSN	2059-8661
ISSN (online)	2059-8661
DOI	10.1017/cts.2023.675
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Article ; Online: Workshop proceedings: GWAS summary statistics standards and sharing.

MacArthur, Jacqueline A L / Buniello, Annalisa / Harris, Laura W / Hayhurst, James / McMahon, Aoife / Sollis, Elliot / Cerezo, Maria / Hall, Peggy / Lewis, Elizabeth / Whetzel, Patricia L / Bahcall, Orli G / Barroso, Inês / Carroll, Robert J / Inouye, Michael / Manolio, Teri A / Rich, Stephen S / Hindorff, Lucia A / Wiley, Ken / Parkinson, Helen

Cell genomics

2022 Volume 1, Issue 1

Abstract: Genome-wide association studies (GWASs) have enabled robust mapping of complex traits in humans. The open sharing of GWAS summary statistics (SumStats) is essential in facilitating the larger meta-analyses needed for increased power in resolving the ... ...

Abstract	Genome-wide association studies (GWASs) have enabled robust mapping of complex traits in humans. The open sharing of GWAS summary statistics (SumStats) is essential in facilitating the larger meta-analyses needed for increased power in resolving the genetic basis of disease. However, most GWAS SumStats are not readily accessible because of limited sharing and a lack of defined standards. With the aim of increasing the availability, quality, and utility of GWAS SumStats, the National Human Genome Research Institute-European Bioinformatics Institute (NHGRI-EBI) GWAS Catalog organized a community workshop to address the standards, infrastructure, and incentives required to promote and enable sharing. We evaluated the barriers to SumStats sharing, both technological and sociological, and developed an action plan to address those challenges and ensure that SumStats and study metadata are findable, accessible, interoperable, and reusable (FAIR). We encourage early deposition of datasets in the GWAS Catalog as the recognized central repository. We recommend standard requirements for reporting elements and formats for SumStats and accompanying metadata as guidelines for community standards and a basis for submission to the GWAS Catalog. Finally, we provide recommendations to enable, promote, and incentivize broader data sharing, standards and FAIRness in order to advance genomic medicine.
Language	English
Publishing date	2022-09-03
Publishing country	United States
Document type	Journal Article
ISSN	2666-979X
ISSN (online)	2666-979X
DOI	10.1016/j.xgen.2021.100004
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Article: Genetic Sex Validation for Sample Tracking in Clinical Testing.

Kelly, Melissa A / Bland, Harris T / Murugan, Mullai / Venner, Eric / Boerwinkle, Eric / Prows, Cynthia / Mahanta, Lisa / Rehm, Heidi L / Gibbs, Richard A / Muzny, Donna M

Research square

2023

Abstract	Objective: Data from DNA genotyping via a 96-SNP panel in a study of 25,015 clinical samples were utilized for quality control and tracking of sample identity in a clinical sequencing network. The study aimed to demonstrate the value of both the precise SNP tracking and the utility of the panel for predicting the sex-by-genotype of the participants, to identify possible sample mix-ups. Results: Precise SNP tracking showed no sample swap errors within the clinical testing laboratories. In contrast, when comparing predicted sex-by-genotype to the provided sex on the test requisition, we identified 110 inconsistencies from 25,015 clinical samples (0.44%), that had occurred during sample collection or accessioning. The genetic sex predictions were confirmed using additional SNP sites in the sequencing data or high-density genotyping arrays. It was determined that discrepancies resulted from clerical errors, samples from transgender participants and stem cell or bone marrow transplant patients along with undetermined sample mix-ups.
Language	English
Publishing date	2023-09-11
Publishing country	United States
Document type	Preprint
DOI	10.21203/rs.3.rs-3304685/v1
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Article ; Online: Evaluation of the portability of computable phenotypes with natural language processing in the eMERGE network.

Pacheco, Jennifer A / Rasmussen, Luke V / Wiley, Ken / Person, Thomas Nate / Cronkite, David J / Sohn, Sunghwan / Murphy, Shawn / Gundelach, Justin H / Gainer, Vivian / Castro, Victor M / Liu, Cong / Mentch, Frank / Lingren, Todd / Sundaresan, Agnes S / Eickelberg, Garrett / Willis, Valerie / Furmanchuk, Al'ona / Patel, Roshan / Carrell, David S /

Scientific reports

2023 Volume 13, Issue 1, Page(s) 1971

Abstract: The electronic Medical Records and Genomics (eMERGE) Network assessed the feasibility of deploying portable phenotype rule-based algorithms with natural language processing (NLP) components added to improve performance of existing algorithms using ... ...

Abstract	The electronic Medical Records and Genomics (eMERGE) Network assessed the feasibility of deploying portable phenotype rule-based algorithms with natural language processing (NLP) components added to improve performance of existing algorithms using electronic health records (EHRs). Based on scientific merit and predicted difficulty, eMERGE selected six existing phenotypes to enhance with NLP. We assessed performance, portability, and ease of use. We summarized lessons learned by: (1) challenges; (2) best practices to address challenges based on existing evidence and/or eMERGE experience; and (3) opportunities for future research. Adding NLP resulted in improved, or the same, precision and/or recall for all but one algorithm. Portability, phenotyping workflow/process, and technology were major themes. With NLP, development and validation took longer. Besides portability of NLP technology and algorithm replicability, factors to ensure success include privacy protection, technical infrastructure setup, intellectual property agreement, and efficient communication. Workflow improvements can improve communication and reduce implementation time. NLP performance varied mainly due to clinical document heterogeneity; therefore, we suggest using semi-structured notes, comprehensive documentation, and customization options. NLP portability is possible with improved phenotype algorithm performance, but careful planning and architecture of the algorithms is essential to support local customizations.
MeSH term(s)	Electronic Health Records ; Natural Language Processing ; Genomics ; Algorithms ; Phenotype
Language	English
Publishing date	2023-02-03
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-023-27481-y
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Article ; Online: Genomic considerations for FHIR®; eMERGE implementation lessons.

Murugan, Mullai / Babb, Lawrence J / Overby Taylor, Casey / Rasmussen, Luke V / Freimuth, Robert R / Venner, Eric / Yan, Fei / Yi, Victoria / Granite, Stephen J / Zouk, Hana / Aronson, Samuel J / Power, Kevin / Fedotov, Alex / Crosslin, David R / Fasel, David / Jarvik, Gail P / Hakonarson, Hakon / Bangash, Hana / Kullo, Iftikhar J /

Connolly, John J / Nestor, Jordan G / Caraballo, Pedro J / Wei, WeiQi / Wiley, Ken / Rehm, Heidi L / Gibbs, Richard A

Journal of biomedical informatics

2021 Volume 118, Page(s) 103795

Abstract: Structured representation of clinical genetic results is necessary for advancing precision medicine. The Electronic Medical Records and Genomics (eMERGE) Network's Phase III program initially used a commercially developed XML message format for ... ...

Abstract	Structured representation of clinical genetic results is necessary for advancing precision medicine. The Electronic Medical Records and Genomics (eMERGE) Network's Phase III program initially used a commercially developed XML message format for standardized and structured representation of genetic results for electronic health record (EHR) integration. In a desire to move towards a standard representation, the network created a new standardized format based upon Health Level Seven Fast Healthcare Interoperability Resources (HL7® FHIR®), to represent clinical genomics results. These new standards improve the utility of HL7® FHIR® as an international healthcare interoperability standard for management of genetic data from patients. This work advances the establishment of standards that are being designed for broad adoption in the current health information technology landscape.
MeSH term(s)	Electronic Health Records ; Genomics ; Health Level Seven ; Humans ; Medical Informatics ; Precision Medicine
Language	English
Publishing date	2021-04-28
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2057141-0
ISSN	1532-0480 ; 1532-0464
ISSN (online)	1532-0480
ISSN	1532-0464
DOI	10.1016/j.jbi.2021.103795
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Article ; Online: Building a Platform to Enable NCD Research to Address Population Health in Africa: CVD Working Group Discussion at the Sixth H3Africa Consortium Meeting in Zambia.

Peprah, Emmanuel / Wiley, Ken / Troyer, Jennifer / Adebamowo, Sally N / Adu, Dwomoa / Mayosi, Bongani M / Ramsay, Michele / Motala, Ayesha A / Adebamowo, Clement / Ovbiagele, Bruce / Owolabi, Mayowa

Global heart

2016 Volume 11, Issue 1, Page(s) 165–170

MeSH term(s)	Africa ; Biomedical Research ; Cardiovascular Diseases ; Congresses as Topic ; Datasets as Topic ; Genomics ; Humans ; Translational Medical Research ; Zambia
Language	English
Publishing date	2016-04-22
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2629633-0
ISSN	2211-8179 ; 2211-8160
ISSN (online)	2211-8179
ISSN	2211-8160
DOI	10.1016/j.gheart.2015.11.002
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Article ; Online: Facilitating phenotype transfer using a common data model.

Journal of biomedical informatics

2019 Volume 96, Page(s) 103253

Abstract: Background: Implementing clinical phenotypes across a network is labor intensive and potentially error prone. Use of a common data model may facilitate the process.: Methods: Electronic Medical Records and Genomics (eMERGE) sites implemented the ... ...

Abstract	Background: Implementing clinical phenotypes across a network is labor intensive and potentially error prone. Use of a common data model may facilitate the process. Methods: Electronic Medical Records and Genomics (eMERGE) sites implemented the Observational Health Data Sciences and Informatics (OHDSI) Observational Medical Outcomes Partnership (OMOP) Common Data Model across their electronic health record (EHR)-linked DNA biobanks. Two previously implemented eMERGE phenotypes were converted to OMOP and implemented across the network. Results: It was feasible to implement the common data model across sites, with laboratory data producing the greatest challenge due to local encoding. Sites were then able to execute the OMOP phenotype in less than one day, as opposed to weeks of effort to manually implement an eMERGE phenotype in their bespoke research EHR databases. Of the sites that could compare the current OMOP phenotype implementation with the original eMERGE phenotype implementation, specific agreement ranged from 100% to 43%, with disagreements due to the original phenotype, the OMOP phenotype, changes in data, and issues in the databases. Using the OMOP query as a standard comparison revealed differences in the original implementations despite starting from the same definitions, code lists, flowcharts, and pseudocode. Conclusion: Using a common data model can dramatically speed phenotype implementation at the cost of having to populate that data model, though this will produce a net benefit as the number of phenotype implementations increases. Inconsistencies among the implementations of the original queries point to a potential benefit of using a common data model so that actual phenotype code and logic can be shared, mitigating human error in reinterpretation of a narrative phenotype definition.
MeSH term(s)	Attention Deficit Disorder with Hyperactivity/diagnosis ; Data Collection ; Databases, Factual ; Diabetes Mellitus, Type 2/diagnosis ; Electronic Health Records ; Humans ; Medical Informatics ; National Human Genome Research Institute (U.S.) ; Observational Studies as Topic ; Outcome Assessment, Health Care ; Phenotype ; Research Design ; Software ; United States
Language	English
Publishing date	2019-07-17
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2057141-0
ISSN	1532-0480 ; 1532-0464
ISSN (online)	1532-0480
ISSN	1532-0464
DOI	10.1016/j.jbi.2019.103253
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Article ; Online: Making work visible for electronic phenotype implementation: Lessons learned from the eMERGE network.

Journal of biomedical informatics

2019 Volume 99, Page(s) 103293

Abstract: Background: Implementation of phenotype algorithms requires phenotype engineers to interpret human-readable algorithms and translate the description (text and flowcharts) into computable phenotypes - a process that can be labor intensive and error prone. ...

Abstract	Background: Implementation of phenotype algorithms requires phenotype engineers to interpret human-readable algorithms and translate the description (text and flowcharts) into computable phenotypes - a process that can be labor intensive and error prone. To address the critical need for reducing the implementation efforts, it is important to develop portable algorithms. Methods: We conducted a retrospective analysis of phenotype algorithms developed in the Electronic Medical Records and Genomics (eMERGE) network and identified common customization tasks required for implementation. A novel scoring system was developed to quantify portability from three aspects: Knowledge conversion, clause Interpretation, and Programming (KIP). Tasks were grouped into twenty representative categories. Experienced phenotype engineers were asked to estimate the average time spent on each category and evaluate time saving enabled by a common data model (CDM), specifically the Observational Medical Outcomes Partnership (OMOP) model, for each category. Results: A total of 485 distinct clauses (phenotype criteria) were identified from 55 phenotype algorithms, corresponding to 1153 customization tasks. In addition to 25 non-phenotype-specific tasks, 46 tasks are related to interpretation, 613 tasks are related to knowledge conversion, and 469 tasks are related to programming. A score between 0 and 2 (0 for easy, 1 for moderate, and 2 for difficult portability) is assigned for each aspect, yielding a total KIP score range of 0 to 6. The average clause-wise KIP score to reflect portability is 1.37 ± 1.38. Specifically, the average knowledge (K) score is 0.64 ± 0.66, interpretation (I) score is 0.33 ± 0.55, and programming (P) score is 0.40 ± 0.64. 5% of the categories can be completed within one hour (median). 70% of the categories take from days to months to complete. The OMOP model can assist with vocabulary mapping tasks. Conclusion: This study presents firsthand knowledge of the substantial implementation efforts in phenotyping and introduces a novel metric (KIP) to measure portability of phenotype algorithms for quantifying such efforts across the eMERGE Network. Phenotype developers are encouraged to analyze and optimize the portability in regards to knowledge, interpretation and programming. CDMs can be used to improve the portability for some 'knowledge-oriented' tasks.
MeSH term(s)	Algorithms ; Electronic Health Records/classification ; Genomics ; Humans ; Medical Informatics/methods ; Phenotype ; Retrospective Studies
Language	English
Publishing date	2019-09-19
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2057141-0
ISSN	1532-0480 ; 1532-0464
ISSN (online)	1532-0480
ISSN	1532-0464
DOI	10.1016/j.jbi.2019.103293
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