Christian Betteridge
Undergraduate Student, Brigham Young University
5 active projects
Metabolism v7 mfd
Scientific Questions Being Studied
Metabolic diseases are a global epidemic. They occur when cells allocate too many of their primary resources towards one pathway, such as lipid production, at the expense of other crucial pathways, such as energy production. Understanding how cells control the pivotal point of allocating glucose towards lipid or energy production is key to developing effective treatments for these and other common metabolic diseases. PAS kinase (PASK) is a nutrient sensing protein kinase that regulates this critical metabolic node of lipid versus respiratory metabolism. PASK controls many of the hallmark pathways associated with heart disease, diabetes, cancer, and even neurodegenerative disease yet little is known about PASK alleles associated with human disease. Herein we propose to conduct the first large-scale analysis to identify PASK alleles associated with human disease.
Project Purpose(s)
- Ancestry
Scientific Approaches
Analyze common and rare variants in USF1, ATXN2, and PASK compared to triglycerides, weight, cardiovascular measures, exercise levels and COVID-19 results.
Perform a phenome-wide association study (PheWAS) with common and rare variants in USF1, ATXN2, and PASK. Common variants in each of these three genes will be regressed against phenotypes from electronic health record data.
Anticipated Findings
We target PASK as well as two of its substrates for our study. Due to their regulation of the critical node of glucose partitioning to lipid versus respiratory metabolism, we propose to uncover the influence of PASK, USF1, and ATXN2 variants on a variety of human phenotypes and classes of disease, from hyperlipidemia and diabetes to neurodegenerative disorders using the All of Us dataset.
In addition to the wide variety of phenotypic data that will aid in understanding the influence of variation in cellular respiration and triglyceride levels, the ancestral diversity represented by the individuals in the dataset will allow us to identify patterns and variants that differ or are similar across ancestry, defining effects common to humankind while opening further studies on multigene, secondary variants.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Controlled TierResearch Team
Owner:
- Miranda Sharp - Graduate Trainee, Brigham Young University
- Mary Davis - Early Career Tenure-track Researcher, Brigham Young University
- Lydia Howell - Graduate Trainee, Brigham Young University
- Kylee Bates - Undergraduate Student, Brigham Young University
- Christian Betteridge - Undergraduate Student, Brigham Young University
Metabolism v7
Scientific Questions Being Studied
Metabolic diseases are a global epidemic. They occur when cells allocate too many of their primary resources towards one pathway, such as lipid production, at the expense of other crucial pathways, such as energy production. Understanding how cells control the pivotal point of allocating glucose towards lipid or energy production is key to developing effective treatments for these and other common metabolic diseases. PAS kinase (PASK) is a nutrient sensing protein kinase that regulates this critical metabolic node of lipid versus respiratory metabolism. PASK controls many of the hallmark pathways associated with heart disease, diabetes, cancer, and even neurodegenerative disease yet little is known about PASK alleles associated with human disease. Herein we propose to conduct the first large-scale analysis to identify PASK alleles associated with human disease.
Project Purpose(s)
- Ancestry
Scientific Approaches
Analyze common and rare variants in USF1, ATXN2, and PASK compared to triglycerides, weight, cardiovascular measures, exercise levels and COVID-19 results.
Perform a phenome-wide association study (PheWAS) with common and rare variants in USF1, ATXN2, and PASK. Common variants in each of these three genes will be regressed against phenotypes from electronic health record data.
Anticipated Findings
We target PASK as well as two of its substrates for our study. Due to their regulation of the critical node of glucose partitioning to lipid versus respiratory metabolism, we propose to uncover the influence of PASK, USF1, and ATXN2 variants on a variety of human phenotypes and classes of disease, from hyperlipidemia and diabetes to neurodegenerative disorders using the All of Us dataset.
In addition to the wide variety of phenotypic data that will aid in understanding the influence of variation in cellular respiration and triglyceride levels, the ancestral diversity represented by the individuals in the dataset will allow us to identify patterns and variants that differ or are similar across ancestry, defining effects common to humankind while opening further studies on multigene, secondary variants.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Controlled TierResearch Team
Owner:
- Mary Davis - Early Career Tenure-track Researcher, Brigham Young University
- Lydia Howell - Graduate Trainee, Brigham Young University
- Kylee Bates - Undergraduate Student, Brigham Young University
- Christian Betteridge - Undergraduate Student, Brigham Young University
Collaborators:
- Spencer Boris - Undergraduate Student, Brigham Young University
Learning
Scientific Questions Being Studied
I am looking to understand the interface of All of Us through the application of statistical regression strategies. I will be looking at data related to Autism and survey data. My aim is to gain greater clarity on the functions and potential of All of Us.
Project Purpose(s)
- Disease Focused Research (autistic disorder)
- Social / Behavioral
- Educational
Scientific Approaches
Mainly linear regression and other statistical approaches. I look to find relationships between clinical and survey data as a means to improve my coding abilities and to more efficiently learn concepts of research.
Anticipated Findings
I do not intend to find anything of extreme significance or value outside of what is already known; I may attempt to replicate the methods of previously executed research to see what differences exist between approaches.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Registered TierDuplicate of Data Wrangling in All of Us Program (v6)
Scientific Questions Being Studied
For Educational purpose to show best practices when using jupyter notebooks for data access, storage, data manipulations - transformations, conversions, cleaning, optimization and other research support related issues that is useful for multiple AoU researchers.
Project Purpose(s)
- Educational
- Other Purpose (For use with Office hours. notebooks for adding code snippets useful for researchers. This is a placeholder for creating notebooks for best practices among other things)
Scientific Approaches
For Educational purpose to show best practices when using jupyter notebooks for data access, storage, data manipulations - transformations, conversions, cleaning, optimization and other research support related issues that is useful for multiple AoU researchers.
Anticipated Findings
For Educational purpose to show best practices when using jupyter notebooks for data access, storage, data manipulations - transformations, conversions, cleaning, optimization and other research support related issues that is useful for multiple AoU researchers.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Registered TierPASK, USF1, ATXN2 genomic/lab value analysis
Scientific Questions Being Studied
Due to the far-reaching effects of metabolic dysregulation, the All of Us dataset makes it possible for us to examine PASK, USF1, and ATXN2 variants on various phenotypes and disorders.
PAS Kinase (PASK) is a protein that responds to glucose availability and regulates glucose metabolism in yeast, mice, and humans. Metabolic issues contribute to diabetes, cancer, and heart disease. Publications have shown a relationship between conditions such as those listed above and PASK. Our research aims to find genetic associations between PASK and two of its substrates with various disease phenotypes. We will accomplish this using data provided by the All of Us dataset, which includes lab measurements and whole genome sequencing. We will perform a linear regression analysis to find associations between PASK gene variants among diverse ancestries to find new trends and associations between various metabolic issues and phenotypical expression.
Project Purpose(s)
- Ancestry
Scientific Approaches
We propose to discover associations between USF1, ATXN2, and PASK to analyze their variation in conjunction with blood pressure, heart rate, hemoglobin, blood count, lipid panels, and urinalysis using the All of Us dataset.
We will utilize the All of Us dataset to find associations between PASK, USF1, and ATXN2and their phenotypes in diverse ancestry groups. Regression analyses will present our findings.
Our genome-wide association study (GWAS) will look at common and rare variants found in the PASK, USF1, and ATXN2. Using diverse data, we can better indicate significant variations between these genes and make applicable findings for all people. Efforts to ensure ancestrally varying datasets improve contributions made in medicine to make progress towards personalized care. European, African, Asian, and Hispanic populations are all included in the All of Us dataset, which will guarantee more accurate associations made in our study.
Anticipated Findings
Our final goal will be to show associations between these three genes of interest with disease phenotypes. We expect to see significant phenotype-gene associations in various areas due to the central nature of lipid and respiratory metabolism and the role of their dysregulation in many diseases. We will publish our findings in a peer-reviewed article which will be part of a more extensive publication. The publication will include information from the genome-wide association study described in this proposal. It will also include data collected from a phenome-wide association study, identifying associated disease phenotypes for PASK, USF1, and ATXN2. Next fall, I will present the data as a poster at the American Society of Human Genetics (ASHG). Lastly, if selected for the award, I will give my research at the College of Life Sciences CURA research conference next spring.
Demographic Categories of Interest
- Race / Ethnicity
- Sex at Birth
Data Set Used
Controlled TierRequest a Review of this Research Project
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