Amy Moore
Research Associate, Research Triangle Institute
6 active projects
Duplicate of Substance use disorder spectrum and related traits - v6
Scientific Questions Being Studied
Drug misuse and drug overdose deaths remain critical public health crises in the United States. Approximately 50% of the risk of developing a substance use disorder (SUD) is driven by genetic factors. SUDs have many health consequences, including diseases whose natural history is affected by SUDs (e.g. worsening HIV progression) and co-occurring traits (e.g. high risk taking). Unfortunately, studies looking for genetic variation associated with opioid use disorder (OUD), other SUDs, and related traits have found relatively few such variants. We know from other studies that greater numbers of study participants are needed to identify those variants. Our main goal is to identify genetic variants associated with these traits using genome-wide association methods, which leverage genetic data along with information from surveys and healthcare records available in All of Us. We plan to combine results from All of Us with other cohorts to increase variant-trait association identification.
Project Purpose(s)
- Disease Focused Research (Substance use and related disorders)
- Methods Development
- Control Set
- Ancestry
Scientific Approaches
The cornerstone approach of our research will be the genome-wide association study (GWAS) of the SUD spectrum and related traits. We will apply additional approaches such as polygenic risk score (PRS) analysis, LD score regression (LDSC), genetic correlation analysis, biological annotation, and integration with publicly available, appropriate omics-based datasets.
Anticipated Findings
We expect to discover novel genetic loci associated with SUD spectrum and related traits, increase the scientific confidence in previously-discovered genetic associations, and produce better estimates of the association size. Our goals are to thoroughly characterize the findings from the GWAS and relate each genetic variant to its causal role in the SOUD spectrum. These findings could be translated into future work, such as the identification of new drug targets for evidence-based SOUD treatment.
Demographic Categories of Interest
- Race / Ethnicity
Data Set Used
Controlled TierResearch Team
Owner:
- Amy Moore - Research Associate, Research Triangle Institute
Collaborators:
- Fang Fang - Senior Researcher, All of Us Researcher Academy/RTI International
- Javan Carter - Research Associate, All of Us Researcher Academy/RTI International
Substance use disorder spectrum and related traits
Scientific Questions Being Studied
Drug misuse and drug overdose deaths remain critical public health crises in the United States. Approximately 50% of the risk of developing a substance use disorder (SUD) is driven by genetic factors. SUDs have many health consequences, including diseases whose natural history is affected by SUDs (e.g. worsening HIV progression) and co-occurring traits (e.g. high risk taking). Unfortunately, studies looking for genetic variation associated with opioid use disorder (OUD), other SUDs, and related traits have found relatively few such variants. We know from other studies that greater numbers of study participants are needed to identify those variants. Our main goal is to identify genetic variants associated with these traits using genome-wide association methods, which leverage genetic data along with information from surveys and healthcare records available in All of Us. We plan to combine results from All of Us with other cohorts to increase variant-trait association identification.
Project Purpose(s)
- Disease Focused Research (Substance use and related disorders)
- Methods Development
- Control Set
- Ancestry
Scientific Approaches
The cornerstone approach of our research will be the genome-wide association study (GWAS) of the SUD spectrum and related traits. We will apply additional approaches such as polygenic risk score (PRS) analysis, LD score regression (LDSC), genetic correlation analysis, biological annotation, and integration with publicly available, appropriate omics-based datasets.
Anticipated Findings
We expect to discover novel genetic loci associated with SUD spectrum and related traits, increase the scientific confidence in previously-discovered genetic associations, and produce better estimates of the association size. Our goals are to thoroughly characterize the findings from the GWAS and relate each genetic variant to its causal role in the SOUD spectrum. These findings could be translated into future work, such as the identification of new drug targets for evidence-based SOUD treatment.
Demographic Categories of Interest
- Race / Ethnicity
Data Set Used
Controlled TierResearch Team
Owner:
- Amy Moore - Research Associate, Research Triangle Institute
Collaborators:
- Julie White - Research Associate, All of Us Researcher Academy/RTI International
- Javan Carter - Research Associate, All of Us Researcher Academy/RTI International
- Bradley Webb - Other, Virginia Commonwealth University
Duplicate of Demo - Medication Sequencing - ALM for exploration
Scientific Questions Being Studied
1- What are the main prescribed medication sequences that participants with type 2 diabetes and depression took over three years of treatment?
In this questions, we are extracting the anti-diabetes and anti-depressant medications used to to treated participants who have T2D and depression codes. We retrieved medications prescribed after the first diagnosis code for each disease. We represented the medications using their ATC 4th level.
2- What is the most common first anti-diabetic and anti-depressant that were prescribed for All of Us participants? We extracted the first medications prescribed to treat T2D and depression. We identified the most common first medication with the highest number of participants.
3- Is there a change in the percentages of participants who were prescribed first common medication, treated using one medication, treated only using one common medication between 2000-2018?
Project Purpose(s)
- Disease Focused Research (type 2 diabetes, depression)
- 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
In this project, we plan on using the medication sequencing developed at Columbia University and the OHDSI network as a means to characterize treatment pathways at scale. Further, we want to demonstrate implementation of these medication sequencing algorithms in the All of Us research dataset to show how the various sources of data contained within the program can be used to characterize treatment pathways at scale. We will perform separate medication sequence analyses for three different common, complex diseases: type 2 diabetes, depression
1- Data manipulation
Using python and BigQuery to:
A- Retrieve medication and their classes
B-Create the medications sequences
2- Visualization:
A- Creating sunburst to visualize the sequences
B- Plotting the percentages of participants the first common medication and one medication during three years
Anticipated Findings
For this study, we anticipate demonstrating the validity of the data by showing expected treatment patterns despite gathering data from over 30 individual EHR sites. Specifically, we expect to find:
1- Variation in the medication sequences prescribed to treat All of Us participants who had type 2 diabetes and depression.
2- The most common medication used to treat participants as first line treatment with type 2 diabetes and depression diagnosis.
3- A trend or change over time of prescribing the first common medication over the study period
4- Trend overtime for the percentage of participants
Importantly, the detailed code developed herein is made available within the Researcher Workbench to researchers, so that they may more easily extract medication data and class information using a common medication ontology, an approach useful in many discovery studies.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Registered TierDuplicate of How to Get Started with Controlled Tier Data - ALM for exploration
Scientific Questions Being Studied
1. Socio-Economic Metrics: How to retrieve participants' socio-economic data from the CDR.
2. Observation Date: How to query and plot an observation date using survey completion date as example.
3. Demographics: Examples of how to query and plot participant demographic data.
4. Death Cause: How to retrieve and plot deceased participants' death causes.
The above questions are taken directly from the All of Us-provided description of this workspace. I intend to use this example workspace to familiarize myself with using controlled tier data so that my original work will be of high quality.
Project Purpose(s)
- Educational
- Methods Development
- Other Purpose (This is an All of Us Featured Workspace: - teaches the users how to set up this notebook, install and import software packages, and select the correct version of the CDR. - gives an overview of the data types available in the current Controlled Tier Curated Data Repository (CDR) that are not available in the Registered Tier - shows how to retrieve and summarize this data.)
Scientific Approaches
We recommend that all researchers explore the notebooks in this workspace to learn the basics of All of Us Program Data. The tutorial Workspace contains two Jupyter Notebooks (one written in Python, the other in R). It contains helper functions for repeatedly, code readability and efficiency and repeatedly.
The above questions are taken directly from the All of Us-provided description of this workspace. I intend to use this example workspace to familiarize myself with using controlled tier data so that my original work will be of high quality.
Anticipated Findings
By reading and running the notebooks in this Tutorial Workspace, you will understand the following: All of Us data are made available in two Curated Data Repository: the Registered Tier and Controlled Tier. The latter was subject to more relaxed privacy rules relative to the Registered Tier. As a result, you can expect to find more concept ids in certain data types such as EHR and Survey.
The above questions are taken directly from the All of Us-provided description of this workspace. I intend to use this example workspace to familiarize myself with using controlled tier data so that my original work will be of high quality.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Controlled TierDuplicate of How to Work with All of Us Genomic Data - ALM for exploration
Scientific Questions Being Studied
NA - I intend to use this example workspace to familiarize myself with using genetic data so that my original work will be of high quality. I will use this duplicate dataset to learn how to use All of Us genomic data.
Project Purpose(s)
- Other Purpose (I will use this duplicate dataset provided by All of Us to learn how to use All of Us genomic data. )
Scientific Approaches
NA - I intend to use this example workspace to familiarize myself with using genetic data so that my original work will be of high quality. I will use this duplicate dataset to learn how to use All of Us genomic data.
Anticipated Findings
NA - I intend to use this example workspace to familiarize myself with using genetic data so that my original work will be of high quality. I will use this duplicate dataset to learn how to use All of Us genomic data.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Controlled TierDuplicate of Demo - Hypertension Prevalence
Scientific Questions Being Studied
We are using the All of Us Researcher Workbench interface to answer the question, "Is hypertension prevalence in the All of Us Research Program similar to hypertension prevalence in the 2015–2016 National Health and Nutrition Examination Survey (NHANES) ?". Clinical approaches to understanding and treating hypertension may benefit from the integration of a precision medicine approach that integrates data on environments, social determinants of health, behaviors, and genomic factors that contribute to hypertension risk. Hypertension is a major public health concern and remains a leading risk factor for stroke and cardiovascular disease.
Project Purpose(s)
- Other Purpose (This work is an AoU demo project. Demo projects are efforts by the AoU Research Program designed to meet the program goal of ensuring the quality and utility of the Research Hub as a resource for accelerating discovery in science and medicine. As an approved demo project, this work was reviewed and overseen by the AoU Research Program Science Committee and the AoU Data and Research Center to ensure compliance with program policy, including policies for acceptable data access and use. )
Scientific Approaches
In this cross-sectional, population-based study, we used All of Us baseline data from patient (age>18) provided information (PPI) surveys and electronic health record (EHR) blood pressure measurements and retrospectively examined the prevalence of hypertension in the EHR cohort using Systemized Nomenclature of Medicine (SNOMED codes and blood pressure medications recorded in the EHR. We used the EHR data (SNOMED codes on 2 distinct dates and at least one hypertension medication) as the primary definition, and then add subjects with elevated systolic or elevated diastolic blood pressure on measurements 2 and 3 from PPI. We extracted each participant’s detailed dates of SNOMED code for essential hypertension from the Researcher Workbench table ‘cb_search_all_events’. We calculated an age-standardized HTN prevalence according to the age distribution of the U.S. Census, using 3 groups (18-39, 40-59, ≥ 60).
Anticipated Findings
The prevalence of hypertension in the All of Us cohort is similar to that of published literature. All of Us age-adjusted HTN prevalence was 27.9% compared to 29.6% in National Health and Nutrition Examination Survey. The All of Us cohort is a growing source of diverse longitudinal data that can be utilized to study hypertension nationwide. The prevalence of hypertension varies in the United States (U.S.) by age, sex, and socioeconomic status. Hypertension can often be treated successfully with medication, and prevented or delayed with lifestyle modifications. Even with these established hypertension intervention and prevention strategies, the prevalence of hypertension continues to be at levels of public health concern. The diversity within All of Us may provide insight into factors relevant to hypertension prevention and treatments in a variety of social and geographic contexts and population strata in the U.S.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Registered TierRequest a Review of this Research Project
You can request that the All of Us Resource Access Board (RAB) review a research purpose description if you have concerns that this research project may stigmatize All of Us participants or violate the Data User Code of Conduct in some other way. To request a review, you must fill in a form, which you can access by selecting ‘request a review’ below.
