Grace Tietz

Graduate Trainee, Baylor College of Medicine

2 active projects

Duplicate of Demo - Cardiovascular Risk Scoring

1- Can we use All of Us data to calculate the cardiovascular pooled score? We want to use and utilize the unique data collected by the All of Us program including smoking information (status, age of starting smoking, age of…

Scientific Questions Being Studied

1- Can we use All of Us data to calculate the cardiovascular pooled score? We want to use and utilize the unique data collected by the All of Us program including smoking information (status, age of starting smoking, age of quieting), underrepresented race data (African american, Asian, others, ..), and measurements values such as blood pressure and cholesterol to calculate the score.
2- Can we identify the scores that we calculate within a year of All of Us enrollment? We wanted to know the participants who might have cardiovascular risk score within the time enrollment. This will help the program quantify the importance of collecting further longitudinal data for existing and future participants.
3- Will the risk score per race group be different? We compared the risk scores in each racial groups to quantify if some racial groups have higher or lower risk scores even if they have a cardiovascular disease in the future

Project Purpose(s)

  • Disease Focused Research (Cardiovascular disease )
  • 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 All of Us Data and Research Center to ensure compliance with program policy, including acceptable data access and use.)

Scientific Approaches

In this project, we plan on using the AHA algorithm/equation to calculate the cardiovascular risk scores ( https://ahajournals.org/doi/full/10.1161/01.cir.0000437741.48606.98). Further, we want to demonstrate the usage of smoking and race data collected by the program, which are data that usually researchers use natural language processing to extract, to facilitate the calculation of cardiovascular risk score.
We will calculate the scores using 1- Data manipulation: Using python and BigQuery to: A- Retrieve medications (diabetes), lab measurements including systolic blood pressure, diastolic blood pressure, cholesterol, race, and smoking information provided by participants 2- Visualization: A- Creating histogram for calculated scores using python visualization library Matplotlib

Anticipated Findings

For this study, we anticipate demonstrating the validity and importance of the data collected by the program and can be challenging to extract from medical records (smoking status) by showing by calculating the cardiovascular risk within 10 years. We expect to find: 1) the easiness in using data from different sources (EHR and survey data) to build a model or calculate a risk. 2) the heterogeneity in All of Us population where underrepresented population in clinical trials or clinical data set are more present in the All of Us 3) the cardiovascular risk score is different in racial groups.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Grace Tietz - Graduate Trainee, Baylor College of Medicine

Investigating Genetic Drivers of Cardiovascular Disease Variability

We aim to use differential penetrance among ancestries to investigate the role of epistasis and ancestral genomic background on a cardiovascular disease (CVD) pathogenic variant. More specifically, it was recently discovered in a paper by Murdock et al that the…

Scientific Questions Being Studied

We aim to use differential penetrance among ancestries to investigate the role of epistasis and ancestral genomic background on a cardiovascular disease (CVD) pathogenic variant.

More specifically, it was recently discovered in a paper by Murdock et al that the LPA variant rs3798220 known to increase CVD in European ancestry individuals was not strongly associated with CVD for those in the Hispanic community. To investigate this pattern more fully and understand genetic drivers for this difference in CVD, we are investigating whether ancestral haplotypes surrounding rs3798220, or an additional variant enriched in the Hispanic population explains the decreased CVD risk.

Project Purpose(s)

  • Disease Focused Research (cardiovascular system disease)
  • Ancestry

Scientific Approaches

Datasets
-All of Us genomic data
-All of Us data on cardiovascular disease
-All of Us data on blood lipid levels
-All of Us demographic data

Methods/Tools
-Local ancestry inference to identify influence of ancestry background of rs3798220 on the CVD phenotype
-Epistasis analyses to identify if other variants in combination with rs3798220 explain the difference in CVD between populations

Anticipated Findings

This study will generate findings regarding the impact of local ancestry and epistasis on the variant rs3798220. On a foundational level, this could contribute to our understanding of epistasis across diverse human populations and why individuals with the same genes may display differing traits/disease states. In a clinical context, this may refine CVD risk prediction for rs3798220 in historically underserved communities for whom genetic tests currently underperform. Further, this information can be applied broadly, giving it potential to improve precision-medicine approaches across populations and complex traits.

Demographic Categories of Interest

  • Race / Ethnicity

Data Set Used

Controlled Tier

Research Team

Owner:

  • Grace Tietz - Graduate Trainee, Baylor College of Medicine
  • Elizabeth Atkinson - Early Career Tenure-track Researcher, Baylor College of Medicine
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