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1. High-dimensional multiple imputation (HDMI) for partially observed confounders including natural language processing-derived auxiliary covariates

Author

Weberpals, Janick, Shaw, Pamela A., Lin, Kueiyu Joshua, Wyss, Richard, Plasek, Joseph M, Zhou, Li, Ngan, Kerry, DeRamus, Thomas, Raman, Sudha R., Hammill, Bradley G., Lee, Hana, Toh, Sengwee, Connolly, John G., Dandreo, Kimberly J., Tian, Fang, Liu, Wei, Li, Jie, Hernández-Muñoz, José J., Schneeweiss, Sebastian, and Desai, Rishi J.

Subjects
Statistics - Methodology, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Multiple imputation (MI) models can be improved by including auxiliary covariates (AC), but their performance in high-dimensional data is not well understood. We aimed to develop and compare high-dimensional MI (HDMI) approaches using structured and natural language processing (NLP)-derived AC in studies with partially observed confounders. We conducted a plasmode simulation study using data from opioid vs. non-steroidal anti-inflammatory drug (NSAID) initiators (X) with observed serum creatinine labs (Z2) and time-to-acute kidney injury as outcome. We simulated 100 cohorts with a null treatment effect, including X, Z2, atrial fibrillation (U), and 13 other investigator-derived confounders (Z1) in the outcome generation. We then imposed missingness (MZ2) on 50% of Z2 measurements as a function of Z2 and U and created different HDMI candidate AC using structured and NLP-derived features. We mimicked scenarios where U was unobserved by omitting it from all AC candidate sets. Using LASSO, we data-adaptively selected HDMI covariates associated with Z2 and MZ2 for MI, and with U to include in propensity score models. The treatment effect was estimated following propensity score matching in MI datasets and we benchmarked HDMI approaches against a baseline imputation and complete case analysis with Z1 only. HDMI using claims data showed the lowest bias (0.072). Combining claims and sentence embeddings led to an improvement in the efficiency displaying the lowest root-mean-squared-error (0.173) and coverage (94%). NLP-derived AC alone did not perform better than baseline MI. HDMI approaches may decrease bias in studies with partially observed confounders where missingness depends on unobserved factors.

Published
2024

2. Considerations for first field trials of low-threshold gene drive for malaria vector control

Author

Connolly, John B, Burt, Austin, Christophides, George, Diabate, Abdoulaye, Habtewold, Tibebu, Hancock, Penelope A, James, Anthony A, Kayondo, Jonathan K, Lwetoijera, Dickson Wilson, Manjurano, Alphaxard, McKemey, Andrew R, Santos, Michael R, Windbichler, Nikolai, and Randazzo, Filippo

Subjects
Medical Microbiology, Biomedical and Clinical Sciences, Clinical Sciences, Malaria, Vector-Borne Diseases, Biotechnology, Genetics, Rare Diseases, Infectious Diseases, 3.2 Interventions to alter physical and biological environmental risks, Prevention of disease and conditions, and promotion of well-being, Infection, Good Health and Well Being, Mosquito Control, Mosquito Vectors, Animals, Anopheles, Gene Drive Technology, Adaptive trial design, Africa, Cluster randomized controlled trial, Genetic efficacy, Entomological efficacy, Epidemiological efficacy, Mosquito, Population modification, Population suppression, Spillover, Microbiology, Public Health and Health Services, Tropical Medicine, Medical microbiology, Public health
Abstract

Sustainable reductions in African malaria transmission require innovative tools for mosquito control. One proposal involves the use of low-threshold gene drive in Anopheles vector species, where a 'causal pathway' would be initiated by (i) the release of a gene drive system in target mosquito vector species, leading to (ii) its transmission to subsequent generations, (iii) its increase in frequency and spread in target mosquito populations, (iv) its simultaneous propagation of a linked genetic trait aimed at reducing vectorial capacity for Plasmodium, and (v) reduced vectorial capacity for parasites in target mosquito populations as the gene drive system reaches fixation in target mosquito populations, causing (vi) decreased malaria incidence and prevalence. Here the scope, objectives, trial design elements, and approaches to monitoring for initial field releases of such gene dive systems are considered, informed by the successful implementation of field trials of biological control agents, as well as other vector control tools, including insecticides, Wolbachia, larvicides, and attractive-toxic sugar bait systems. Specific research questions to be addressed in initial gene drive field trials are identified, and adaptive trial design is explored as a potentially constructive and flexible approach to facilitate testing of the causal pathway. A fundamental question for decision-makers for the first field trials will be whether there should be a selective focus on earlier points of the pathway, such as genetic efficacy via measurement of the increase in frequency and spread of the gene drive system in target populations, or on wider interrogation of the entire pathway including entomological and epidemiological efficacy. How and when epidemiological efficacy will eventually be assessed will be an essential consideration before decisions on any field trial protocols are finalized and implemented, regardless of whether initial field trials focus exclusively on the measurement of genetic efficacy, or on broader aspects of the causal pathway. Statistical and modelling tools are currently under active development and will inform such decisions on initial trial design, locations, and endpoints. Collectively, the considerations here advance the realization of developer ambitions for the first field trials of low-threshold gene drive for malaria vector control within the next 5 years.

Published
2024

8. Estimation of the non-linear parameter in Generalised Diversity-Interactions models is unaffected by change in structure of the interaction terms

Author

Vishwakarma, Rishabh, Byrne, Laura, Connolly, John, Moral, Rafael de Andrade, and Brophy, Caroline

Subjects
Statistics - Applications, Statistics - Computation
Abstract

Research over the past few decades has assumed the richness (number of species) to be the primary driver of the biodiversity and ecosystem function (BEF) relationship. However, biodiversity is multi-dimensional, and richness alone does not capture all its attributes. Diversity-Interactions modelling is a regression-based framework that models the biodiversity and ecosystem function relationship by incorporating species-specific effects along with the interactions between species. The species interactions in a Diversity-Interactions model can take several different forms ranging in complexity from a single interaction term (assuming all pairs of species interact in the same way) to many interaction terms (e.g. assuming a separate interaction for all pairs of species). The specification of the interactions may also include a non-linear parameter ({\theta}) as an exponent to the product of the species proportions to allow for non-linear relationship between the response and species interactions, giving rise to Generalized Diversity-Interactions modelling. The structure of the interaction terms describes the underlying biological processes and thus the selection of a correct structure is important. The inclusion of {\theta} introduces complexity to the model selection process: we could (a) select the interaction structure first by assuming {\theta}=1 and then estimate {\theta}, or (b) estimate {\theta} first and then select the appropriate interaction structure by fixing {\theta}, or (c) test for {\theta} and its inclusion for each interaction structure. It is also unknown whether {\theta} is robust to changes in the underlying interaction structure. Using a simulation study, we test the robustness of {\theta} and compare multiple model selection approaches to identify an optimal and computationally efficient model selection procedure for Generalized Diversity-Interactions models., Comment: For code to reproduce the analysis see https://github.com/rishvish/Theta-Simulation-Study

Published
2023

9. Going beyond richness: Modelling the BEF relationship using species identity, evenness, richness and species interactions via the DImodels R package

Author

Moral, Rafael A., Vishwakarma, Rishabh, Connolly, John, Byrne, Laura, Hurley, Catherine, Finn, John A., and Brophy, Caroline

Subjects
Quantitative Biology - Populations and Evolution, Statistics - Methodology
Abstract

BEF studies aim to understand how ecosystems respond to a gradient of species diversity. Diversity-Interactions (DI) models are suitable for analysing the BEF relationship. These models relate an ecosystem function response of a community to the identity of the species in the community, their evenness (proportions) and interactions. The number of species in the community (richness) is also implicitly modelled through this approach. It is common in BEF studies to model an ecosystem function as a function of richness; while this can uncover trends in the BEF relationship, by definition, species diversity is much broader than richness alone, and important patterns in the BEF relationship may remain hidden. In this paper, we introduce the DImodels R package for implementing DI models. We also compare DI models to traditional modelling approaches to highlight the advantages of using a multi-dimensional definition of species diversity. We show that using DI models can lead to considerably improved model fit over other methods; it does this by incorporating variation due to the multiple facets of species diversity. Predicting from a DI model is not limited to the study design points, the model can extrapolate to predict for any species composition and proportions (assuming there is sufficient coverage of this space in the study design). Expressing the BEF relationship as a function of richness alone can be useful to capture overall trends. However, collapsing the multiple dimensions of species diversity to a single dimension (such as richness) can result in valuable ecological information being lost. DI modelling provides a framework to test the multiple components of species diversity in the BEF relationship. It facilitates uncovering a deeper ecological understanding of the BEF relationship and can lead to enhanced inference.

Published
2022

10. Selection, optimization and validation of ten chronic disease polygenic risk scores for clinical implementation in diverse US populations

Author

Lennon, Niall J., Kottyan, Leah C., Kachulis, Christopher, Abul-Husn, Noura S., Arias, Josh, Belbin, Gillian, Below, Jennifer E., Berndt, Sonja I., Chung, Wendy K., Cimino, James J., Clayton, Ellen Wright, Connolly, John J., Crosslin, David R., Dikilitas, Ozan, Velez Edwards, Digna R., Feng, QiPing, Fisher, Marissa, Freimuth, Robert R., Ge, Tian, Glessner, Joseph T., Gordon, Adam S., Patterson, Candace, Hakonarson, Hakon, Harden, Maegan, Harr, Margaret, Hirschhorn, Joel N., Hoggart, Clive, Hsu, Li, Irvin, Marguerite R., Jarvik, Gail P., Karlson, Elizabeth W., Khan, Atlas, Khera, Amit, Kiryluk, Krzysztof, Kullo, Iftikhar, Larkin, Katie, Limdi, Nita, Linder, Jodell E., Loos, Ruth J. F., Luo, Yuan, Malolepsza, Edyta, Manolio, Teri A., Martin, Lisa J., McCarthy, Li, McNally, Elizabeth M., Meigs, James B., Mersha, Tesfaye B., Mosley, Jonathan D., Musick, Anjene, Namjou, Bahram, Pai, Nihal, Pesce, Lorenzo L., Peters, Ulrike, Peterson, Josh F., Prows, Cynthia A., Puckelwartz, Megan J., Rehm, Heidi L., Roden, Dan M., Rosenthal, Elisabeth A., Rowley, Robb, Sawicki, Konrad Teodor, Schaid, Daniel J., Smit, Roelof A. J., Smith, Johanna L., Smoller, Jordan W., Thomas, Minta, Tiwari, Hemant, Toledo, Diana M., Vaitinadin, Nataraja Sarma, Veenstra, David, Walunas, Theresa L., Wang, Zhe, Wei, Wei-Qi, Weng, Chunhua, Wiesner, Georgia L., Yin, Xianyong, and Kenny, Eimear E.

Published
2024
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13. 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

14. Recommendations for environmental risk assessment of gene drive applications for malaria vector control

Author

Connolly, John B, Mumford, John D, Glandorf, Debora CM, Hartley, Sarah, Lewis, Owen T, Evans, Sam Weiss, Turner, Geoff, Beech, Camilla, Sykes, Naima, Coulibaly, Mamadou B, Romeis, Jörg, Teem, John L, Tonui, Willy, Lovett, Brian, Mankad, Aditi, Mnzava, Abraham, Fuchs, Silke, Hackett, Talya D, Landis, Wayne G, Marshall, John M, and Aboagye-Antwi, Fred

Subjects
Rare Diseases, Gene Therapy, Vector-Borne Diseases, Infectious Diseases, Malaria, Prevention, Genetics, 2.2 Factors relating to the physical environment, Aetiology, Infection, Good Health and Well Being, Animals, Anopheles, Gene Drive Technology, Mosquito Control, Mosquito Vectors, Risk Assessment, Gene drive, Population suppression, Vector control, Environmental risk assessment, Engagement, Modelling, Ecological risk, Microbiology, Medical Microbiology, Public Health and Health Services, Tropical Medicine
Abstract

Building on an exercise that identified potential harms from simulated investigational releases of a population suppression gene drive for malaria vector control, a series of online workshops identified nine recommendations to advance future environmental risk assessment of gene drive applications.

Published
2022

17. Managing differential performance of polygenic risk scores across groups: Real-world experience of the eMERGE Network

Author

Lewis, Anna C.F., Chisholm, Rex L., Connolly, John J., Esplin, Edward D., Glessner, Joe, Gordon, Adam, Green, Robert C., Hakonarson, Hakon, Harr, Margaret, Holm, Ingrid A., Jarvik, Gail P., Karlson, Elizabeth, Kenny, Eimear E., Kottyan, Leah, Lennon, Niall, Linder, Jodell E., Luo, Yuan, Martin, Lisa J., Perez, Emma, Puckelwartz, Megan J., Rasmussen-Torvik, Laura J., Sabatello, Maya, Sharp, Richard R., Smoller, Jordan W., Sterling, Rene, Terek, Shannon, Wei, Wei-Qi, and Fullerton, Stephanie M.

Published
2024
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18. Author Correction: Genetic variation in the human leukocyte antigen region confers susceptibility to Clostridioides difficile infection

Author

Ferar, Kathleen, Hall, Taryn O., Crawford, Dana C., Rowley, Robb, Satterfield, Benjamin A., Li, Rongling, Gragert, Loren, Karlson, Elizabeth W., de Andrade, Mariza, Kullo, Iftikhar J., McCarty, Catherine A., Kho, Abel, Hayes, M. Geoffrey, Ritchie, Marylyn D., Crane, Paul K., Mirel, Daniel B., Carlson, Christopher, Connolly, John J., Hakonarson, Hakon, Crenshaw, Andrew T., Carrell, David, Luo, Yuan, Dikilitas, Ozan, Denny, Joshua C., Jarvik, Gail P., and Crosslin, David R.

Published
2023
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19. Genetic variation in the human leukocyte antigen region confers susceptibility to Clostridioides difficile infection

Author

Ferar, Kathleen, Hall, Taryn O., Crawford, Dana C., Rowley, Robb, Satterfield, Benjamin A., Li, Rongling, Gragert, Loren, Karlson, Elizabeth W., de Andrade, Mariza, Kullo, Iftikhar J., McCarty, Catherine A., Kho, Abel, Hayes, M. Geoffrey, Ritchie, Marylyn D., Crane, Paul K., Mirel, Daniel B., Carlson, Christopher, Connolly, John J., Hakonarson, Hakon, Crenshaw, Andrew T., Carrell, David, Luo, Yuan, Dikilitas, Ozan, Denny, Joshua C., Jarvik, Gail P., and Crosslin, David R.

Published
2023
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24. Conclusion

Author

Connolly, John M. and Keutner, Thomas

Published
2011

26. Infobuttons for Genomic Medicine: Requirements and Barriers

Author

Rasmussen, Luke V, Connolly, John J, Del Fiol, Guilherme, Freimuth, Robert R, Pet, Douglas B, Peterson, Josh F, Shirts, Brian H, Starren, Justin B, Williams, Marc S, Walton, Nephi, and Taylor, Casey Overby

Subjects
Health Services and Systems, Health Sciences, Genetics, Human Genome, Networking and Information Technology R&D (NITRD), Clinical Research, Good Health and Well Being, Electronic Health Records, Genomics, Humans, Medical Informatics, Medicine, Qualitative Research, facilitators and barriers, clinical decision support, genetics, infobuttons, open source, Information Systems, Clinical Sciences, Health services and systems
Abstract

ObjectivesThe study aimed to understand potential barriers to the adoption of health information technology projects that are released as free and open source software (FOSS).MethodsWe conducted a survey of research consortia participants engaged in genomic medicine implementation to assess perceived institutional barriers to the adoption of three systems: ClinGen electronic health record (EHR) Toolkit, DocUBuild, and MyResults.org. The survey included eight barriers from the Consolidated Framework for Implementation Research (CFIR), with additional barriers identified from a qualitative analysis of open-ended responses.ResultsWe analyzed responses from 24 research consortia participants from 18 institutions. In total, 14 categories of perceived barriers were evaluated, which were consistent with other observed barriers to FOSS adoption. The most frequent perceived barriers included lack of adaptability of the system, lack of institutional priority to implement, lack of trialability, lack of advantage of alternative systems, and complexity.ConclusionIn addition to understanding potential barriers, we recommend some strategies to address them (where possible), including considerations for genomic medicine. Overall, FOSS developers need to ensure systems are easy to trial and implement and need to clearly articulate benefits of their systems, especially when alternatives exist. Institutional champions will remain a critical component to prioritizing genomic medicine projects.

Published
2021

27. Prospective, multi-site study of healthcare utilization after actionable monogenic findings from clinical sequencing

Author

Linder, Jodell E., Tao, Ran, Chung, Wendy K., Kiryluk, Krzysztof, Liu, Cong, Weng, Chunhua, Connolly, John J., Hakonarson, Hakon, Harr, Margaret, Leppig, Kathleen A., Jarvik, Gail P., Veenstra, David L., Aufox, Sharon, Chisholm, Rex L., Gordon, Adam S., Hoell, Christin, Rasmussen-Torvik, Laura J., Smith, Maureen E., Holm, Ingrid A., Miller, Erin M., Prows, Cynthia A., Elskeally, Omar, Kullo, Iftikhar J., Lee, Christopher, Jose, Sheethal, Manolio, Teri A., Rowley, Robb, Padi-Adjirackor, Nana Addo, Wilmayani, Ni Ketut, City, Brittany, Wei, Wei-Qi, Wiesner, Georgia L., Rahm, Alanna Kulchak, Williams, Janet L., Williams, Marc S., and Peterson, Josh F.

Published
2023
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28. Atypical hemolytic uremic syndrome in the era of terminal complement inhibition: an observational cohort study

Author

Holt, Richard, Jones, Caroline, Ashman, Neil, Fan, Stanley, Forbes, Suzanne, Rajakariar, Ravindra, De Freitas, Declan, Magee, Colm, Hanko, Jennifer, Milford, David, Muorah, Mordi, Howie, Alexander J., Baharani, Jyoti, Dasgupta, Indranil, Roberts, Russell, MacDiarmaid-Gordon, Adam, Fry, Andrew, Torpey, Nicholas, Waldron, Mary, Awan, Atif, Raftery, Tara, Ratcliffe, Laura, Vilar, Enric, Newman, Joel, Seviar, Dale, Marks, Stephen D., Rees, Lesley, Veligratli, Faidra, Waters, Aoife, Chowdhury, Paramit, Pattison, James, Booth, Caroline, Williams, Nicole, Corbett, Richard, Amin, Beena, Pickering, Matthew, Bramham, Kate, Al-Hamed, Mohamed, Hill, Anita, Mooney, Andrew, Finlay, Eric, Newnham, Amanda, Yadav, Pallavi, Kaur, Amrit, Plant, Nicholas, Shenoy, Mohan, Choong, Lye Wai, Helps, Aileen, McLemon, Robert, Muniraju, Thalakunte, Khan, Izhar, Kidder, Dana, Padmanabhan, Neal, Cousland, Zoe, Price, Jonathan, Taylor, Alison, Jennings, Claudine, Ahmad, Sohail, Mead, Paul, Aslam, Mona, Bebb, Charlotte, Byrne, Catherine, Ferraro, Alastair, Christian, Martin, Evans, Jonathan, Kim, Jon Jin, Lunn, Andrew, Mallik, Meeta, Mason, Phil, Mole, David, Craze, Janet, Sangala, Nicholas, Uniacke, Mark, McGregor, Ellon, Lambie, Stewart, Bhandary, Nitin, Flossmann, Oliver, Mohteshamzadeh, Mobin, Abeyaratne, Asanga, Bingham, Coralie, Clissold, Rhian, Smyth, Lucy, Connolly, John, Reynolds, Ben, Neary, John, Bell, Joanne, Roxburgh, Sarah, Popoola, Joyce, Donne, Rosie, Fardon, Nicholas, Khwaja, Arif, Siddiqi, Shareen, Kumar, Gurinder, Kardasz, Steve, Moore, Iain, Tez, Didem, Willows, Jamie, Davison, Rachel, Harty, John, Wong, William, Williams, Andrew J., Gale, Daniel, Coward, Richard, Inward, Carol, Mraz, Martin, Ayub, Waqar, Short, Andrew, Lappin, David, Baines, Richard, Bommayya, Girish, Houlberg, Kris, Saif, Imran, Gilbert, Rodney, Griffin, Sian, Smith, Graham, Van Der Voort, Judith, Chandar, Jayanthi, Freundlich, Michael, McCarroll, Frank, Wong, Germaine, Laboi, Paul, Molyneux, Rebekah, Brocklebank, Vicky, Walsh, Patrick R., Smith-Jackson, Kate, Hallam, Thomas M., Marchbank, Kevin J., Wilson, Valerie, Bigirumurame, Theophile, Dutt, Tina, Montgomery, Emma K., Malina, Michal, Wong, Edwin K. S., Johnson, Sally, Sheerin, Neil S., and Kavanagh, David

Published
2023
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29. Education and electronic medical records and genomics network, challenges, and lessons learned from a large-scale clinical trial using polygenic risk scores

Author

Connolly, John J., Berner, Eta S., Smith, Maureen, Levy, Samuel, Terek, Shannon, Harr, Margaret, Karavite, Dean, Suckiel, Sabrina, Holm, Ingrid A., Dufendach, Kevin, Nelson, Catrina, Khan, Atlas, Chisholm, Rex L., Allworth, Aimee, Wei, Wei-Qi, Bland, Harris T., Clayton, Ellen Wright, Soper, Emily R., Linder, Jodell E., Limdi, Nita A., Miller, Alexandra, Nigbur, Scott, Bangash, Hana, Hamed, Marwan, Sherafati, Alborz, Lewis, Anna C.F., Perez, Emma, Orlando, Lori A., Rakhra-Burris, Tejinder K., Al-Dulaimi, Mustafa, Cifric, Selma, Scherr, Courtney Lynam, Wynn, Julia, Hakonarson, Hakon, and Sabatello, Maya

Published
2023
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31. First-in-Human Study to Evaluate Safety, Tolerability, Pharmacokinetics, and Pharmacodynamics of a Rapidly Developed SARS-CoV-2 Therapeutic Antibody, AOD01, in Healthy Adults

Author

Prativadibhayankaram, Venkateshan S., Lee, Lawrence Soon-U, Lye, David, Xiaoying, Xu, Nellore, Ranjani, Pendharkar, Vishal, Hentze, Hannes, Guan, Siyu, Ayers, Benjamin J., Seah, Shirley G. K., Chye, De Hoe, Talib, Najwa S. N., Kaliaperumal, Nivashini, Ong, Wei Yee, Wong, Zi Xin, Au, Veonice B., Alok, Anshula, Connolly, John E., Boyd-Kirkup, Jerome D., Ingram, Piers J., Hanson, Brendon J., Ethirajulu, Kantharaj, O’Connell, Damian, and Chan, Conrad E. Z.

Published
2022
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33. Returning integrated genomic risk and clinical recommendations: The eMERGE study

Author

Gordon, Adam, Sobowale, Agboade, Allworth, Aimee, Patel, Akshar, DiVietro, Alanna, Strong, Alanna, Sherafati, Alborz, Sherfati, Alborz, Bick, Alex, Miller, Alexandra, Chandel, Alka, Rosenthal, Alyssa, Khera, Amit, Kontorovich, Amy, Beck, Andrew, Beck, Andy, Espinoza, Angelica, Lewis, Anna, Prince, Anya, Khan, Atlas, Iverson, Ayuko, Khales, Bahram Namjou, Benoit, Barbara, Hernan, Becca, Kallman, Ben, Kerman, Ben, Shoemaker, Ben, Satterfield, Benjamin, Devine, Beth, Etheridge, Bethany, Goff, Blake, Freimuth, Bob, Grundmeier, Bob, Collier, Brenae, Mutai, Brenda, Harnett, Brett, Chang, Brian, Piening, Brian, Davis, Brittney, Korf, Bruce, Patterson, Candace, Demetriou, Carmen, Ta, Casey, Hammack, Catherine, Nelson, Catrina, Gascoigne, Caytie, Dorn, Chad, Moretz, Chad, Kachulis, Chris, Hoell, Christie, Cowles, Christine, Lange, Christoph, Weng, Chunhua, Prows, Cindy, Brokamp, Cole, Liu, Cong, Scherr, Courtney, Gonzalez, Crystal, Ramirez, Cynthia, Shimbo, Daichi, Roden, Dan, Schaid, Daniel, Kaufman, Dave, Crosslin, David, Kochan, David, Veenstra, David, Singh, Davinder, Karavite, Dean, Abrams, Debbie, Absher, Devin, Edwards, Digna Velez, Haverfield, Eden, Morales, Eduardo, Esplin, Edward, Malolepsza, Edyta, Alipour, Ehsan, Kenny, Eimear, Rosenthal, Elisabeth, Duvall, Eliza, McNally, Elizabeth, Bhoj, Elizabeth, Cohn, Elizabeth, Hibler, Elizabeth, Karlson, Elizabeth, Clayton, Ellen, Chesnut, Emily, DeFranco, Emily, Gallagher, Emily, Soper, Emily, Perez, Emma, Cash, Erin, Berner, Eta, Wang, Fei, Wehbe, Firas, Ricci, Francisco, Mentch, Frank, Shaibi, Gabriel, Jarvik, Gail, Hahn, George, Hripcsak, George, Wiesner, Georgia, Belbin, Gillian, Davogustto, Gio, Nadkarni, Girish, Qiu, Haijun, Hakonarson, Hakon, Bangash, Hana, Beasley, Hannah, Liu, Hao, Aungst, Heide, Tiwari, Hemant, Duckham, Hillary, Thomas, Hope, Kullo, Iftikhar, Holm, Ingrid, Allen, Isabelle, Ionita-Laza, Iuliana, Hellwege, Jacklyn, Petrzelka, Jacob, Odgis, Jacqueline, Narula, Jahnavi, Petrzelka, Jake, Patel, Jalpa, Cimino, James, Meigs, James, Snyder, James, Olson, Janet, Zahner, Janet, Pennington, Jeff, Pacheco, Jen, Pacheco, Jennifer Allen, Morse, Jennifer, Corsmo, Jeremy, Thayer, Jeritt, Cimino, Jim, Chen, Jingheng, Fournier, Jocelyn, Jackson, Jodell, Glessner, Joe, Pacyna, Joel, Smith, Johanna, Connolly, John, Lynch, John, Shelley, John, Mosley, Jonathan, Nestor, Jordan, Smoller, Jordan, Alsip, Jorge, Kannry, Joseph, Sutton, Joseph, Peterson, Josh, Smith, Joshua, Galasso, Julia, Smith, Julia, Wynn, Julia, Gundelach, Justin, Starren, Justin, Choi, Karmel, Mittendorf, Kate, Anderson, Katherine, Bonini, Katherine, Leppig, Kathleen, Muenzen, Kathleen, Larkin, Katie, Stuttgen, Kelsey, Wiley, Ken, Nguyen, Kenny, Dufendach, Kevin, Atkins, Kiley, Sawicki, Konrad, Norland, Kristjan, Kiryluk, Krzysztof, Beskow, Laura, Rasmussen-Torvik, Laura, Kottyan, Leah, Hsu, Li, Tian, Lifeng, Mahanta, Lisa, Martin, Lisa, Wang, Lisa, Gomez, Lizbeth, Thompson, Lorenzo, Orlando, Lori, Richter, Lucas, Rasmussen, Luke, Petukhova, Lynn, Seabolt, Lynn, O’Brien, Madison, Harden, Maegan, Fullerton, Malia, Harr, Margaret, Beasley, Mark, Guindo, Marta, Horike, Martha, Horike-Pyne, Martha, Abdalla, Marwah, Hamed, Marwan, Terry, Mary Beth, Maradik, Mary, Wyatt, Matt, Davis, Matthew, Lebo, Matthew, Smith, Maureen, Rosario, Maya del, Sabatello, Maya, Behr, Meckenzie, Roy-Puckelwartz, Meg, Habrat, Mel, Myers, Melanie, Yetisgen, Meliha, Iris, Merve, DaSilva, Michael, Preuss, Michael, McGowan, Michelle, Shi, Mingjian, Perera, Minoli, Thomas, Minta, Elkind, Mitch, Abbass, Mohammad, Saadatagah, Mohammad, Hess, Molly, Maradik, Molly, Vaitinadin, Nataraja “RJ”, Vaitinadin, Nataraja, Muthu, Naveen, Netherly, Neil, Lennon, Niall, Shang, Ning, Limdi, Nita, Forrest, Noah, Romero, Noheli, Robinson, Nora, Abul-Husn, Noura, Elsekaily, Omar, Dikilitas, Ozan, Kovatch, Patricia, Davis, Patrick, Appelbaum, Paul, Francaviglia, Paul, O’Reilly, Paul, Chandler, Paulette, Caraballo, Pedro, Tarczy-Hornoch, Peter, Shum, Pierre, Marathe, Priya, Murali, Priyanka, Feng, Qiping, Wells, Quinn, Atchley, Rachel, Narla, Radhika, Barton, Rene, Sterling, Rene, Chisholm, Rex, Green, Richard, Sharp, Richard, Peters, Riki, Kukafka, Rita, Rowley, Robb, Freimuth, Robert, Green, Robert, Winter, Robert, Mueller, Roger, Loos, Ruth, Irvin, Ryan, Suckiel, Sabrina, Hussain, Sajjad, Sharba, Samer, Aronson, Sandy, Jones, Sarah, Knerr, Sarah, Nigbur, Scott, Weiss, Scott, Mooney, Sean, Terek, Shannon, Aufox, Sharon, Nirenberg, Sharon, Murphy, Shawn, O’Byrne, Sheila, Wang (Sam) Choi, Shing, Aguilar, Sienna, Bland, S.T., Rodrigues, Stefanie, Ledbetter, Stephanie, Rutledge, Stephanie, Booth, Stuart James, Xian, Su, Trinidad, Susan Brown, Bakken, Suzanne, Schmidlen, Tara, Rakhra-Burris, Tejinder, Manolio, Teri, Mersha, Tesfaye, Walunas, Theresa, Chandereng, Thevaa, May, Thomas, Ge, Tian, Edwards, Todd, Kaszemacher, Tom, Hernandez, Valentina, Willis, Valerie, Desai, Vemi, Desai, Vimi, Lorenzi, Virginia, Gainer, Vivian, Wei, Wei-Qi, Chung, Wendy, Su, Wu-Chen, Chang, Xiao, Zhao, Yiqing, Luo, Yuan, Shen, Yufeng, Linder, Jodell E., Bland, Sarah T., Caraballo, Pedro J., Chisholm, Rex L., Clayton, Ellen Wright, Crosslin, David R., Esplin, Edward D., Forman, Sophie, Freimuth, Robert R., Gordon, Adam S., Harden, Maegan V., Holm, Ingrid A., Jarvik, Gail P., Karlson, Elizabeth W., Labrecque, Sofia, Lennon, Niall J., Limdi, Nita A., Mittendorf, Kathleen F., Murphy, Shawn N., Prows, Cynthia A., Rasmussen, Luke V., Sawicki, Konrad Teodor, Velez Edwards, Digna R., Abul-Husn, Noura S., Below, Jennifer E., Berner, Eta S., Booth, James, Chung, Wendy K., Cimino, James J., Fullerton, Stephanie M., Guiducci, Candace, Habrat, Melissa L., Hain, Heather, Hoell, Christin, Irvin, Marguerite R., Kachulis, Christopher, Kenny, Eimear E., Kullo, Iftikhar J., Manolio, Teri A., McNally, Elizabeth M., Mooney, Sean D., Namjou, Bahram, Perez, Emma F., Puckelwartz, Megan J., Roden, Dan M., Rosenthal, Elisabeth A., Saadatagah, Seyedmohammad, Schaid, Dan J., Schultz, Baergen, Shaibi, Gabriel Q., Sharp, Richard R., Shirts, Brian, Smith, Maureen E., Smoller, Jordan W., Suckiel, Sabrina A., Tiwari, Hemant K., Trinidad, Susan B., Wells, Quinn S., Wiesner, Georgia L., and Peterson, Josh F.

Published
2023
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34. Examining Construct Validity of the Quantitative Literacy VALUE Rubric in College-Level STEM Assignments

Author

Gray, Julie S., Brown, Melissa A., and Connolly, John P.

Abstract

Data-driven decision making is increasingly viewed as essential in a globally competitive society. Initiatives to augment standardized testing with performance-based assessment have increased as educators progressively respond to mandates for authentic measurement of student attainment. To meet this challenge, multidisciplinary rubrics were developed as a method of scoring student work samples. The current study utilized confirmatory factor analysis to examine ratings of student work (N = 245) using the Quantitative Literacy VALUE Rubric from the Association of American Colleges and Universities. The study examined a conceptual model of the six skill measures from the rubric to validate whether, taken together, they are reliable measures of a single general construct--Empirical and Quantitative Skill (EQS), a Texas Core Curriculum objective. The model confirmed that the six measures in the rubric ("Interpretation, Representation, Calculation, Application/Analysis, Assumptions, and Communication") appeared to describe a single construct. Results support using the Quantitative Literacy VALUE Rubric for assessing EQS.

Published
2017

35. Dendritic cell therapy with CD137L-DC-EBV-VAX in locally recurrent or metastatic nasopharyngeal carcinoma is safe and confers clinical benefit

Author

Nickles, Emily, Dharmadhikari, Bhushan, Yating, Li, Walsh, Robert J., Koh, Liang Piu, Poon, Michelle, Tan, Lip Kun, Wang, Ling-Zhi, Ang, Yvonne, Asokumaran, Yugarajah, Chong, Wan Qin, Huang, Yiqing, Loh, Kwok Seng, Tay, Joshua, Soo, Ross, Koh, Mickey, Ho, Liam Pock, Chan, Marieta, Niam, Madelaine, Soh, Melissa, Luah, Yen Hoon, Lim, Chwee Ming, Kaliaperumal, Nivashini, Au, Veonice B., Talib, Najwa Binte Said Nasir, Sng, Reina, Connolly, John E., Goh, Boon Cher, and Schwarz, Herbert

Published
2022
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36. Biotic homogenization destabilizes ecosystem functioning by decreasing spatial asynchrony

Author

Wang, Shaopeng, Loreau, Michel, de Mazancourt, Claire, Isbell, Forest, Beierkuhnlein, Carl, Connolly, John, Deutschman, Douglas H., Doležal, Jiří, Eisenhauer, Nico, Hector, Andy, Jentsch, Anke, Kreyling, Jürgen, Lanta, Vojtech, Lepš, Jan, Polley, H. Wayne, Reich, Peter B., van Ruijven, Jasper, Schmid, Bernhard, Tilman, David, Wilsey, Brian, and Craven, Dylan

Published
2021

38. A phase I study of an adenoviral vector delivering a MUC1/CD40-ligand fusion protein in patients with advanced adenocarcinoma

Author

Tan, Tira J., Ang, W. X. Gladys, Wang, Who-Whong, Chong, Hui-Shan, Tan, Sze Huey, Cheong, Rachael, Chia, John Whay-Kuang, Syn, Nicholas L., Shuen, Wai Ho, Ba, Rebecca, Kaliaperumal, Nivashini, Au, Bijin, Hopkins, Richard, Li, Xinhua, Tan, Aaron C., Seet, Amanda O. L., Connolly, John E., Arkachaisri, Thaschawee, Chew, Valerie, Lajam, Ahmad bin Mohamed, Guo, Dianyan, Chew, Marvin Z. W., Wasser, Martin, Kumar, Pavanish, Albani, Salvatore, and Toh, Han Chong

Published
2022
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41. Insight into the emerging insect to human pathogen Photorhabdus revealing geographic differences in immune cell tropism.

Author

Addison, Max, Hapeshi, Alexia, Zi Xin Wong, Connolly, John E., and Waterfield, Nicholas Robin

Subjects
MONONUCLEAR leukocytes, INSECT pathogens, TISSUE culture, CELL culture, CELL lines
Abstract

Background: Photorhabdus asymbiotica is a species of the insect pathogenic Photorhabdus genus that has been isolated as an etiological agent in human infections. Since then, multiple isolates have been identified worldwide; however, actual clinical infections have so far only been identified in North America, Australia, and Nepal. Previous research on the clinical isolates had shown that the strains differed in their behaviour when infecting cultured human cells. Methods: In this study, we investigate the differences between the pathogenic activities of P. asymbiotica isolates from different geographic locations. Pathogenicity was analysed using infection assays with both cultured cell lines (THP-1, CHO, and HEK cells) and primary immune cells, and peripheral blood mononuclear cells (PBMCs) isolated from human blood. Results: Here, we present the findings from the Australian (Kingscliff) and North American (ATCC43949) clinical isolates, and non-clinical soilborne nematode isolates from Thailand (PB68) and Northern Europe (HIT and JUN) of P. asymbiotica. We also show the first findings from a new clinical isolate of P. luminescens (Texas), the first non-asymbiotica species to cause a human infection, confirming its ability to infect and survive inside human immune cells. Conclusion: Here for the first time, we show how P. asymbiotica selectively infects certain immune cells while avoiding others and that infectivity varies depending on growth temperature. We also show that the tropism varies depending on the geographic location a strain is isolated from, with only the European HIT and JUN strains lack the ability to survive within mammalian cells in tissue culture. [ABSTRACT FROM AUTHOR]

Published
2024
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42. A Principled Approach to Characterize and Analyze Partially Observed Confounder Data from Electronic Health Records

Author

Weberpals, Janick, additional, Raman, Sudha, additional, Shaw, Pamela, additional, Lee, Hana, additional, Russo, Massimiliano, additional, Hammill, Bradley, additional, Toh, Sengwee, additional, Connolly, John, additional, Dandreo, Kimberly, additional, Tian, Fang, additional, Liu, Wei, additional, Li, Jie, additional, Hernández-Muñoz, José, additional, Glynn, Robert, additional, and Desai, Rishi, additional

Published
2024
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44. Abstract P46: Small Extracellular Vesicles from Therapy-induced Senescent TNBC Elicit Paracrine Anti-tumour Effects

Author

Robert, Matius, primary, Jakhar, Rekha, additional, Au, Bijin Veonice, additional, Eng, Gracie Wee Ling, additional, Gao, Alvin Kunyao, additional, Chuah, You Heng, additional, Sachaphibulkij, Karishma, additional, Lim, Lina Hsiu Kim, additional, Ong, Derrick Sek Tong, additional, Lim, Elaine Hsuen, additional, Itahana, Yoko, additional, Itahana, Koji, additional, Cheong, Jit Kong, additional, Connolly, John Edward, additional, and Crasta, Karen Carmelina, additional

Published
2024
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45. 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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46. Child and adolescent mental health : intervention and appropriate measurement

Author

Connolly, John Francis and Saville, Christopher

Abstract

This thesis explores issues relating to intervention in, and measurement of, child mental health and well-being. The first chapter was a systematic review of Positive psychology interventions used to improve mental health and well-being outcomes. This identified 12 randomised controlled trials (n= 1668) in children and adolescents identifying a growing but generally weak evidence base. The array of outcomes and confounding variables assessed were identified alongside implementation issues. The second chapter explored psychometric properties of a ubiquitous tool for measuring these same outcomes, namely the Strengths and Difficulties Questionnaire. Factor structure, reliability, and measurement invariance were investigated and normative data developed. A 5-factor model was found to best fit the data in a large nationally representative sample. Omega (but not alpha) reliability coefficients revealed acceptable reliability and measurement invariance (configural, metric, scalar) was found for gender, parent status, and socio-economic status. Findings suggested that the method of analysis employed greatly affected subsequent interpretation. Normative analysis revealed current norms underestimate at risk children (by 1-2 points) and new norms were created. The clinical, theoretical, and research implications of these findings are presented in the final part of the thesis, alongside a series of recommendations.

Published
2017

47. Has Endrew E improved the chances of proving a FAPE violation under the Individuals with Disabilities Education Act?

Author

Connolly, John P. and Wasserman, Lewis M.

Subjects
School districts, Disabled students, Special education, Psychology and mental health, Science and technology, Individuals with Disabilities Education Act
Abstract

We attempt to determine empirically what effect the Supreme Court's Endrew E u. Douglas County School District decision has had on case outcomes where parents challenged the substantive adequacy of [...]

Published
2021

48. Erratum: A genome-wide association study of anorexia nervosa suggests a risk locus implicated in dysregulated leptin signaling

Author

Li, Dong, Chang, Xiao, Connolly, John J, Tian, Lifeng, Liu, Yichuan, Bhoj, Elizabeth J, Robinson, Nora, Abrams, Debra, Li, Yun R, Bradfield, Jonathan P, Kim, Cecilia E, Li, Jin, Wang, Fengxiang, Snyder, James, Lemma, Maria, Hou, Cuiping, Wei, Zhi, Guo, Yiran, Qiu, Haijun, Mentch, Frank D, Thomas, Kelly A, Chiavacci, Rosetta M, Cone, Roger, Li, Bingshan, Sleiman, Patrick A, Eating Disorders Working Group of the Psychiatric Genomics Consortium, Price Foundation Collaborative Group, and Hakonarson, Hakon

Subjects
Biological Sciences, Mathematical Sciences, Genetics, Eating Disorders Working Group of the Psychiatric Genomics Consortium, Price Foundation Collaborative Group
Abstract

A correction to this article has been published and is linked from the HTML version of this paper. The error has been fixed in the paper.

Published
2017

49. A genome-wide association study of anorexia nervosa suggests a risk locus implicated in dysregulated leptin signaling.

Author

Li, Dong, Chang, Xiao, Connolly, John J, Tian, Lifeng, Liu, Yichuan, Bhoj, Elizabeth J, Robinson, Nora, Abrams, Debra, Li, Yun R, Bradfield, Jonathan P, Kim, Cecilia E, Li, Jin, Wang, Fengxiang, Snyder, James, Lemma, Maria, Hou, Cuiping, Wei, Zhi, Guo, Yiran, Qiu, Haijun, Mentch, Frank D, Thomas, Kelly A, Chiavacci, Rosetta M, Cone, Roger, Li, Bingshan, Sleiman, Patrick A, Eating Disorders Working Group of the Psychiatric Genomics Consortium, Price Foundation Collaborative Group, and Hakonarson, Hakon

Subjects
Eating Disorders Working Group of the Psychiatric Genomics Consortium, Price Foundation Collaborative Group
Abstract

We conducted a genome-wide association study (GWAS) of anorexia nervosa (AN) using a stringently defined phenotype. Analysis of phenotypic variability led to the identification of a specific genetic risk factor that approached genome-wide significance (rs929626 in EBF1 (Early B-Cell Factor 1); P = 2.04 × 10-7; OR = 0.7; 95% confidence interval (CI) = 0.61-0.8) with independent replication (P = 0.04), suggesting a variant-mediated dysregulation of leptin signaling may play a role in AN. Multiple SNPs in LD with the variant support the nominal association. This demonstrates that although the clinical and etiologic heterogeneity of AN is universally recognized, further careful sub-typing of cases may provide more precise genomic signals. In this study, through a refinement of the phenotype spectrum of AN, we present a replicable GWAS signal that is nominally associated with AN, highlighting a potentially important candidate locus for further investigation.

Published
2017

50. A Principled Approach to Characterize and Analyze Partially Observed Confounder Data from Electronic Health Records

Author

Weberpals,Janick, Raman,Sudha, Shaw,Pamela, Lee,Hana, Russo,Massimiliano, Hammill,Bradley, Toh,Sengwee, Connolly,John, Dandreo,Kimberly, Tian,Fang, Liu,Wei, Li,Jie, Hernández-Muñoz,José, Glynn,Robert, Desai,Rishi, Weberpals,Janick, Raman,Sudha, Shaw,Pamela, Lee,Hana, Russo,Massimiliano, Hammill,Bradley, Toh,Sengwee, Connolly,John, Dandreo,Kimberly, Tian,Fang, Liu,Wei, Li,Jie, Hernández-Muñoz,José, Glynn,Robert, and Desai,Rishi

Abstract

Janick Weberpals,1 Sudha R Raman,2 Pamela A Shaw,3 Hana Lee,4 Massimiliano Russo,1 Bradley G Hammill,2 Sengwee Toh,5 John G Connolly,5 Kimberly J Dandreo,6 Fang Tian,7 Wei Liu,7 Jie Li,7 José J Hernández-Muñoz,7 Robert J Glynn,1 Rishi J Desai1 1Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA; 2Department of Population Health Sciences, Duke University School of Medicine, Durham, NC, USA; 3Biostatistics Division, Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA; 4Office of Biostatistics, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA; 5Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA; 6Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston, MA, USA; 7Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USACorrespondence: Janick Weberpals, Instructor in Medicine, Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, 1620 Tremont Street, Suite 3030-R, Boston, MA, 02120, USA, Tel +1 617-278-0932, Fax +1 617-232-8602, Email jweberpals@bwh.harvard.eduObjective: Partially observed confounder data pose challenges to the statistical analysis of electronic health records (EHR) and systematic assessments of potentially underlying missingness mechanisms are lacking. We aimed to provide a principled approach to empirically characterize missing data processes and investigate performance of analytic methods.Methods: Three empirical sub-cohorts of diabetic SGLT2 or DPP4-inhibitor initiators with complete information on HbA1c, BMI and smoking as confounders of interest (COI) formed the basis of data simulation under a plasmode framework. A true null trea

Published
2024

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