Mary Davis

Early Career Tenure-track Researcher, Brigham Young University

3 active projects

Infectious disease

We are interested in predictors of infectious disease (especially bacterial infections). We hypothesize that human genetic variation can cause individuals to be more or less susceptible to infection by specific types of bacteria. We will identify specific types of infections…

Scientific Questions Being Studied

We are interested in predictors of infectious disease (especially bacterial infections). We hypothesize that human genetic variation can cause individuals to be more or less susceptible to infection by specific types of bacteria. We will identify specific types of infections (such as pneumonia or urinary tract infections) and perform genome wide associations studies to identify common variants that are associated with the infections.

Project Purpose(s)

  • Disease Focused Research (infectious disease)
  • Ancestry

Scientific Approaches

We will identify an infectious event (such as pneumonia with Staph aureus) and identify commonalities between patients to try and predict these events in the future. In this case, we will identify patients who were diagnosed with influenza in the month preceding the Staph aureus pneumonia, and also look for genetic variants using logistic regression to identify patients who are most susceptible.

Anticipated Findings

We anticipate identifying genetic and other characteristics of patients who are at high risk for specific infectious diseases. This will then allow clinicians and patients to identify those at high risk and provide careful monitoring or prophylactic treatment.

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

Multiple sclerosis

We will analyze real-world data of individuals with and without multiple sclerosis (MS) to identify risk factors of disease and better predict who will respond positively to different types of MS treatments.

Scientific Questions Being Studied

We will analyze real-world data of individuals with and without multiple sclerosis (MS) to identify risk factors of disease and better predict who will respond positively to different types of MS treatments.

Project Purpose(s)

  • Disease Focused Research (multiple sclerosis)
  • Population Health
  • Social / Behavioral
  • Ancestry

Scientific Approaches

We will use insurance billing codes, medications, and free text to identify individuals with and without multiple sclerosis (MS). We will perform statistical genetic analyses to better understand the genetic variations that contribute to development of MS across diverse ancestry. We will analyze other symptoms and variables in the dataset to find earlier in life events that are associated with later MS development. We will use natural language processing and structured data to identify individuals with MS who are on MS treatments and use available data to identify indicators of which medications are effective and tolerable for patients.

Anticipated Findings

We anticipate the results of these studies will contribute to understanding how MS develops in individuals, and what earlier in life events may be predictors of who will develop MS, hopefully shortening the time to diagnosis. We hope to identify genetic or environmental predictors of which treatments are effective and tolerable so a patient in the future can be placed on the best medication at the onset of disease course.

Demographic Categories of Interest

  • Race / Ethnicity
  • Age
  • Geography
  • Disability Status
  • Access to Care
  • Education Level
  • Income Level

Data Set Used

Controlled Tier

Research Team

Owner:

  • Mary Davis - Early Career Tenure-track Researcher, Brigham Young University

Identification of PAS Kinase Alleles Associated with Diabetes

PAS kinase is a protein kinase associated with diabetes and hypertriglyceridemia and known to regulate neurodegenerative processes, all ailments common to the elderly. Herein we propose a multidisciplinary approach to understand human variants of PAS kinase through the collaboration of…

Scientific Questions Being Studied

PAS kinase is a protein kinase associated with diabetes and hypertriglyceridemia and known to regulate neurodegenerative processes, all ailments common to the elderly. Herein we propose a multidisciplinary approach to understand human variants of PAS kinase through the collaboration of bioinformatics (Mary Davis) and molecular biology (Julianne Grose).
Aim A: Identify variants of PAS kinase associated with diabetes, high A1C levels, hypertriglyceridemia, cardiovascular disease, insulin resistance, macrovascular disorders or neuropathy in medical databases.
Aim B: Characterize the effects of the variant on PAS kinase activity, including triglyceride accumulation, insulin production, metabolic rate and neurodegenerative pathways in a model organism.
Our results will likely uncover novel pathways and therefore, novel treatment and diagnosis targets in the development and progression of geriatric disease.

Project Purpose(s)

  • Disease Focused Research (type 1 diabetes mellitus)
  • Ancestry

Scientific Approaches

Aim A:
1. Search Medical databases for PAS kinase alleles associated with diabetes, high A1C levels, hypertriglyceridemia, cardiovascular disease, insulin resistance macrovascular disorders or neuropathy (1000 cases and 1000 controls)

2. Use structural prediction to predict the mutational effects of pas kinase variants

3. Perform statistical analysis of co-occurrence/interdependence of phenotypes

Aim B:
1. Characterize the effects of over-expression of PAS kinase variants in various cell lines
2. Phenotypic analysis including triglyceride levels, respiration rate and insulin production. (Analyzed twice in triplicate)
3. Direct effects on activity through co-purificaiton and in vitro kinase assays

Anticipated Findings

We hypothesize that human variants of PAS kinase will be associated with diabetes, insulin resistance, and neuropathies in medical databases. Isolation and characterization of such variants will identify novel pathways and putative targets in the diagnosis and treatment of geriatric diabetes.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Matt Porter - Graduate Trainee, Brigham Young University
  • Mary Davis - Early Career Tenure-track Researcher, Brigham Young University
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