Kylee Bates
Undergraduate Student, Brigham Young University
6 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
Germline Mutations that Increase Cancer Risk
Scientific Questions Being Studied
Previous studies have reported numerous, heritable gene variants that can increase risk of developing cancer. We look to increase understanding of these gene variants and their connection to cancer risk in a more diverse population. We are also interested in exploring how these variants connect to other reported health problems in individuals who later develop cancer. Specifically, we intend to ask the following questions:
1. Are harmful, germline, gene variants a good predictor of whether or not an individual will develop cancer during their life?
2. Are there other commonly reported health problems that can be linked to greater risk for cancer in people with these gene variants?
3. Do these findings hold across a diverse population?
Project Purpose(s)
- Disease Focused Research (cancer)
- Ancestry
Scientific Approaches
We will create workflows that will align, intersect, extract, integrate, and analyze known predisposition cancer mutations in the All of Us cohort.
Align: We will use the UCSC genome browser tool to ensure that predisposition variants match the human reference build of All of Us.
Intersect: We will use “bedtools intersect” and “BigQuery” to identify predisposition variants in whole genome sequencing mutation files (VCFs).
Extract: We will store all suspected cancer predisposition variants as a first “data freeze”. This dataset will be our “training-set”. We will use subsequent All of Us data releases as “test-sets” for any novel associations or statical models we identify.
Integrate: Using genomics data and insurance billing codes, we will visualize the relationships between predisposition variants, cancer occurrences, and other reported health problems.
Analyze: We will build custom scripts in Python and R to identify associations found when combining genomics and phenotypic data.
Anticipated Findings
We expect to see that the presence of pathogenic gene variants can help predict a person’s risk for developing cancer. We anticipate that this finding will hold across a diverse population. We also expect to find other frequently reported health problems that associate with increased occurrence of cancer.
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
- Kylee Bates - Undergraduate Student, Brigham Young University
- Matthew Bailey - Early Career Tenure-track Researcher, Brigham Young University
- Adam Bates - Undergraduate Student, Brigham Young University
Collaborators:
- Justin Bryan - Undergraduate Student, Brigham Young University
- David Stone - Undergraduate Student, Brigham Young University
- Spencer Boris - Undergraduate Student, Brigham Young University
- Christian Betteridge - Undergraduate Student, Brigham Young University
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)
- 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 TierFamily History vs Predisposition Variants in Cancer v7
Scientific Questions Being Studied
We are interested in comparing the impact of having a family history of cancer versus having genetic predisposition variants associated with cancer. This information will give us better insight into what might have a bigger impact on the development of the cancer as a whole and will further the investigation into what puts a patient at risk for developing cancer.
Project Purpose(s)
- Disease Focused Research (cancer)
- Ancestry
Scientific Approaches
We will be using the survey data that includes information about family history of cancer as well as genetic data to determine those participants who have predisposition variants. We will use ICD codes from EHRs to identify who has cancer in the dataset and evaluate whether these people have a family history of cancer, predisposition variants or both.
Anticipated Findings
We expect to find an association between family history of cancer and development of cancer, because this has been previously demonstrated. We hope to see what the difference in effect size between having a family history of cancer and having predisposition variants.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Controlled TierFamily History vs Predisposition Variants in Cancer
Scientific Questions Being Studied
We are interested in comparing the impact of having a family history of cancer versus having genetic predisposition variants associated with cancer. This information will give us better insight into what might have a bigger impact on the development of the cancer as a whole and will further the investigation into what puts a patient at risk for developing cancer.
Project Purpose(s)
- Disease Focused Research (cancer)
- Ancestry
Scientific Approaches
We will be using the survey data that includes information about family history of cancer as well as genetic data to determine those participants who have predisposition variants. We will use ICD codes from EHRs to identify who has cancer in the dataset and evaluate whether these people have a family history of cancer, predisposition variants or both.
Anticipated Findings
We expect to find an association between family history of cancer and development of cancer, because this has been previously demonstrated. We hope to see what the difference in effect size between having a family history of cancer and having predisposition variants.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Controlled TierRequest a Review of this Research Project
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