Lydia Howell
Graduate Trainee, Brigham Young University
3 active projects
updated association between Alzheimer's and infection
Scientific Questions Being Studied
There is ample evidence to suggest that some link may exist between chronic inflammation of the brain and development of Alzheimer's disease later in life. A potential trigger for such inflammation is an infectious pathogen, and understanding the potential links between infection and Alzheimer's will enable prevention of disease before symptoms even begin and a better understanding of how to treat the disease once it has already developed. My dataset explores the relative risk of developing Alzheimer's disease in individuals that have experienced a viral infection compared to those who have not.
Project Purpose(s)
- Disease Focused Research (Alzheimer's Disease)
- Population Health
- Educational
Scientific Approaches
This is preliminary exploration of the data, not an exhaustive study, so I will make some basic plots of the dataset I generate as I start planning a broader study.
Anticipated Findings
We expect to see that individuals that have experienced an infection of the brain or nervous system are more likely to develop AD than those who have not. This would shed greater light on how AD develops in the first place and how we could prevent it or lessen its impact using existing treatments or vaccinations for pathogens that may be linked to AD.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Controlled Tier
Duplicate of How to Work with All of Us Genomic Data (Hail - Plink)(v6)
Scientific Questions Being Studied
Not applicable - these notebooks demonstrate example analysis how to use Hail and PLINK to perform genome-wide association studies using the All of Us genomic data and phenotypic data.
Project Purpose(s)
- Disease Focused Research (metabolic diseases)
- Other Purpose (Demonstrate to the All of Us Researcher Workbench users how to get started with the All of Us genomic data and tools. It includes an overview of all the All of Us genomic data and shows some simple examples on how to use these data.)
Scientific Approaches
Not applicable - these notebooks demonstrate example analysis how to use Hail and PLINK to perform genome-wide association studies using the All of Us genomic data and phenotypic data.
Anticipated Findings
Not applicable - these notebooks demonstrate example analysis how to use Hail and PLINK to perform genome-wide association studies using the All of Us genomic data and phenotypic data.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Controlled TierDuplicate of Demo - PheWAS Smoking
Scientific Questions Being Studied
As a demonstration project, this study will present the results of Phenome-Wide Association Studies (PheWAS) to show how the various sources of data contained within All of Us research dataset can be used to inform scientific discovery. We will perform separate PheWAS studies with smoking status as the independent variable. Specific questions include:
1. How can one implement a PheWAS within the All of Us Researcher Workbench?
2. How can one use heterogeneous data sources within the All of Us dataset to explore disease associations using self-reported exposures (Participant Provided Information, or “PPI”) and exposures captured in the electronic medical record (EHR).
Project Purpose(s)
- Methods Development
- Other Purpose (This work is a result of an All of Us Research Program Demonstration Project. The projects are efforts by the Program designed to meet the program's goal of ensuring the quality and utility of the Research Hub as a resource for accelerating discovery in science and medicine. This work was reviewed and overseen by the All of Us Research Program Science Committee and the Data and Research Center to ensure compliance with program policy, including policies for acceptable data access and use.)
Scientific Approaches
As a method for assessing the health burden of smoking on potential observed phenotypes, we implement a Phenome-Wide Association study. A Phenome-wide association study consists of an array of association tests over an indexed representation of the human phenome. In this analysis, we will conduct PheWAS for EHR derived smoking and PPI derived smoking exposures included in the All of Us research dataset. We will be representing "Smoking Exposure” in three ways:
EHR Smoking ICD Billing Codes
Participant Provided Information (PPI) Smoking lifetime 100 cigarettes yes/no
Participant Provided Information (PPI) Smoking lifetime smoking everyday
To perform PheWAS, we will map ICD representations of disease to a common vocabulary of PheCodes. We then use Jupyter Notebooks to create reusable functions to perform PheWAS and generate Manhattan Plots to summarize associations.
Anticipated Findings
For this study, we anticipate that we will be able to replicate known disease associations with smoking exposure. This will serve to demonstrate the quality, utility, and diversity of the All of Us data and tools and the power of gathering multiple data sources for a single phenotype, providing researchers options for study design and validation. Importantly the entire pheWAS package is made available for reuse by researchers in the Workbench, for new hypothesis generation.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
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