Christian Betteridge

Undergraduate Student, Brigham Young University

5 active projects

Monogenic Diabetes

Analysis of genetic contributors to monogenic diabetes or Maturity Onset Diabetes of the Young (MODY). We will look at all MODY and compare them to known phenotype criteria using ICD 9 and 10. Through this, we will see how closely…

Scientific Questions Being Studied

Analysis of genetic contributors to monogenic diabetes or Maturity Onset Diabetes of the Young (MODY). We will look at all MODY and compare them to known phenotype criteria using ICD 9 and 10. Through this, we will see how closely each gene and its variants are correlated to MODY phenotypes.

Project Purpose(s)

  • Disease Focused Research (MODY / Monogenic Diabetes)
  • Methods Development
  • Control Set
  • Ancestry

Scientific Approaches

We will use the genetic dataset of All of Us, R code on Jupyter Notebooks, and statistical regression to display the correlation of genes to their phenotypes, which will be pulled from ICD codes and other health record information.

Anticipated Findings

Findings we anticipate are gene variant associations to phenotypes within the genes known to be risk factors for MODY. Our findings could help us better understand MODY and create a clearer understanding of its various components and complications. MODY disproportionately affects non-European ancestry. With the AoU dataset, we can provide a more robust analysis of MODY because of the greater genetic diversity available to us in this dataset.

Demographic Categories of Interest

  • Race / Ethnicity
  • Age

Data Set Used

Controlled Tier

Research Team

Owner:

Collaborators:

  • Mary Davis - Early Career Tenure-track Researcher, Brigham Young University
  • Breckin Forstrom - Undergraduate Student, Brigham Young University

Metabolism v7

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…

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 Tier

Research 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

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…

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 Tier

Research Team

Owner:

Duplicate of Data Wrangling in All of Us Program (v6)

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.

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 Tier

Research Team

Owner:

PASK, USF1, ATXN2 genomic/lab value analysis

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…

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 Tier

Research Team

Owner:

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