Biology subjects

Carroll, R. J.

Publications and source records attributed to Carroll, R. J..

6 recordsLinked to original sources

bioRxiv2025

FHIR as a Unifying Format for Genomic Research Data Tracking, Aggregation, and Integration

The increasing complexity of genomic research demands standardized data sharing and integration. The Fast Healthcare Interoperability Resources (FHIR) specification has become a well-established standard for exchanging data among health data systems. While designed primarily for clinical and patient data in health care environments, it also has applicability to represent genomic research data and offers a path for aggregating and integrating extremely rich datasets that have traditionally remained siloed and disparate. To study this potential, we developed FHIR Aggregator, an integration of seven major biomedical repositories, including the Genomic Data Commons, GTEx, HTAN, and DepMap, that covered 142334 patients, 819251 specimens, 1096491 observations and 711166 documents. We explore the various ways the FHIR standard can be applied to structure genomic research data and enable new possibilities. We demonstrate how FHIR can be used, where it succeeds or falls short, and which concepts must be extended to better support large-scale clinical and genomics research projects.

bioinformatics↗

bioRxiv2024

Targeting the menopause transition with metformin improves breast cancer outcomes, but discontinuation has deleterious effects on metabolic health: Findings from a preclinical model of postmenopausal breast cancer.

BackgroundWomen with obesity and/or type-II-diabetes have an increased breast cancer risk, increased metastasis, and poorer prognosis, especially after menopause. In a rat model of high-fat-diet and menopause-induced weight gain, we previously reported that treatment with the anti-diabetic drug metformin for 8-weeks after ovariectomy (OVX; modeling menopause) reduced growth of existing mammary tumors and inhibited new tumor formation. This identified the menopause transition as a potential window-of-opportunity for interventions to decrease obesity-associated breast cancer incidence and disease progression. Here, we extend these findings to determine if limiting metformin to the peak window of OVX-induced weight gain would have similar anti-cancer effects. FindingsMetformin during the first four weeks following OVX is critical to reducing tumor burden, as rats treated with metformin early (weeks0-4-postOVX) had reduced tumor burden. Conversely, initiating metformin later in the postOVX period (weeks 4-8postOVX) did not reduce cancer burden. Despite improved tumor outcomes, metformin withdrawal after the early postOVX time had detrimental metabolic effects, including weight gain and increased adiposity, insulin, IGF1, and HOMA-IR, which correlate with increased cancer risk. ConclusionsThese data reveal early-postmenopause as a critical window when metformin decreases progression of existing disease and highlights the importance of maintaining treatment to prevent metabolic dysregulation, which could promote secondary tumors/metastasis. These findings also help explain the disconnect between epidemiological studies reporting anticancer benefits of metformin and more recent clinical trials that failed to see similar efficacy, potentially due to issues of timing and/or inclusion of women outside the early postmenopausal window and/or without underlying metabolic dysfunction.

cancer biology↗

bioRxiv2023

Joint Modeling of Gene-Environment Correlations and Interactions using Polygenic Risk Scores in Case-Control Studies

Polygenic risk scores (PRS) are rapidly emerging as aggregated measures of disease-risk associated with many genetic variants. Understanding the interplay of PRS with environmental factors is critical for interpreting and applying PRS in a wide variety of settings. We develop an efficient method for simultaneously modeling gene-environment correlations and interactions using PRS in case-control studies. We use a logistic-normal regression modeling framework to specify the disease risk and PRS distribution in the underlying population and propose joint inference across the two models using the retrospective likelihood of the case-control data. Extensive simulation studies demonstrate the flexibility of the method in trading-off bias and efficiency for the estimation of various model parameters compared to the standard logistic regression or a case-only analysis for gene-environment interactions, or a control-only analysis for gene-environment correlations. Finally, using simulated case-control datasets within the UK Biobank study, we demonstrate the power of the proposed method for its ability to recover results from the full prospective cohort for the detection of an interaction between long-term oral contraceptive use and PRS on the risk of breast cancer. This method is computationally efficient and implemented in a user-friendly R package.

genetics↗

bioRxiv2022

The Phenotype-Genotype Reference Map: Improving biobank data science through replication.

Population-scale biobanks linked to electronic health record data provide vast opportunity to extend our knowledge of human genetics. While biobanks have already proven their value to research, data quality remains an important concern. Here we introduce the phenotype-genotype reference map (PGRM), a set of 5,879 genetic associations from 523 GWAS publications that can be used for high-throughput replication experiments in biobank data. We tested the PGRM on five ancestry-specific cohorts drawn from four established, independent biobanks and found evidence of robust replications across a wide array of phenotypes. We defined simple replication measures and show how these can be applied to any EHR-linked biobank to detect data corruption and to empirically assess parameters for phenome-wide studies. Finally, we used the PGRM to determine factors associated with reproducibility of GWAS results.

genetics↗

bioRxiv2021

Inverting the model of genomics data sharing with the NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL)

The traditional model of genomic data analysis - downloading data from centralized warehouses for analysis with local computing resources - is increasingly unsustainable. Not only are transfers slow and cost prohibitive, but this approach also leads to redundant and siloed compute infrastructure that makes it difficult to ensure security and compliance of protected data. The NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL; https://anvilproject.org) inverts this model, providing a unified cloud computing environment for data storage, management, and analysis. AnVIL eliminates the need for data movement, allows for active threat detection and monitoring, and provides scalable, shared computing resources that can be acquired by researchers as needed. This presents many new opportunities for collaboration and data sharing that will ultimately lead to scientific discoveries at scales not previously possible.

bioinformatics↗

bioRxiv2017

An atlas of genetic variation for linking pathogen-induced cellular traits to human disease

Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with disease. To facilitate moving from associations to disease mechanisms, we leveraged the role of pathogens in shaping human evolution with the Hi-HOST Phenome Project (H2P2): a catalog of cellular GWAS comprised of 79 phenotypes in response to 8 pathogens in 528 lymphoblastoid cell lines. Seventeen loci surpass genome-wide significance (p<5x10-8) for phenotypes ranging from pathogen replication to cytokine production. Combining H2P2 with clinical association data from the eMERGE Network and experimental validation revealed evidence for mechanisms of action and connections with diseases. We identified a SNP near CXCL10 as a cis-cytokine-QTL and a new risk factor for inflammatory bowel disease. A SNP in ZBTB20 demonstrated pleiotropy, partially mediated through NF{kappa}B signaling, and was associated with viral hepatitis. Data are available in an H2P2 web portal to facilitate further interpreting human genome variation through the lens of cell biology.

genetics↗