Aubrey Jensen

Project Personnel, University of Arizona

4 active projects

DuplicateofNuclearGeneticControlofmtDNACopyNumberHeteroplasmy

This workspace was duplicated from an accessible repository generated as part of the study: "Nuclear genetic control of mtDNA copy number and heteroplasmy in humans". Please see https://github.com/rahulg603/mtSwirl. Current Project info: We hope to assess the relationship between mtDNA SNPs…

Scientific Questions Being Studied

This workspace was duplicated from an accessible repository generated as part of the study: "Nuclear genetic control of mtDNA copy number and heteroplasmy in humans". Please see https://github.com/rahulg603/mtSwirl.

Current Project info: We hope to assess the relationship between mtDNA SNPs and cardiometabolic and endocrine traits such as hypothyroidism and diabetes in the AoU cohort.

Project Purpose(s)

  • Disease Focused Research (mitochondrial phenotypes, common diseases (e.g., heart disease, type 2 diabetes))
  • Ancestry

Scientific Approaches

This workspace was duplicated from an accessible repository generated as part of the study: "Nuclear genetic control of mtDNA copy number and heteroplasmy in humans".

Project information: We plan to use the previously quantified mtDNA phenotypes to replicate findings from a limited mtDNA-wide PHEWAS of cardiometabolic- and endocrine-related phecodes that was performed in a different cohort.

Anticipated Findings

We anticipate that our approach help elucidate the relationship between mitochondrial SNPs and several important traits, and how these associations may differ by ancestry.

Demographic Categories of Interest

  • Race / Ethnicity

Data Set Used

Controlled Tier

Research Team

Owner:

  • Jin Zhou - Mid-career Tenured Researcher, University of California, Los Angeles
  • Aubrey Jensen - Project Personnel, University of Arizona

Collaborators:

  • Rahul Gupta - Graduate Trainee, Broad Institute

Duplicate of ZhouLab

This proposal develops several statistical methods and computational algorithms identifying genetic variants (when data available), biomarkers/medications and their trajectories associated with the onset of cardio-metabolic conditions, with and without diabetes. We focus on (1) developing statistical tools and optimization algorithms…

Scientific Questions Being Studied

This proposal develops several statistical methods and computational algorithms identifying genetic variants (when data available), biomarkers/medications and their trajectories associated with the onset of cardio-metabolic conditions, with and without diabetes. We focus on (1) developing statistical tools and optimization algorithms for analyzing temporal data; (2) robust phenotyping algorithms for studying diabetes and its complications; (3) identifying biomarkers/medications features and patterns associated with higher/lower incidence of events.

Project Purpose(s)

  • Population Health
  • Drug Development
  • Methods Development
  • Ancestry
  • Ethical, Legal, and Social Implications (ELSI)

Scientific Approaches

We will use observational study design to extract incidence of disease outcomes, e.g., heart failure, stroke, dementia, etc. We will use time-to-event models, e.g., Cox-PH models, to analyze the incidence of diseases.

Anticipated Findings

From our proposal, we expect to develop algorithms, user friendly open-source software, as well as analysis pipelines to encourage efficient and reproducible research. Additionally, from these studies, we also expect that we will identify novel genetic variants (when data available) or other clinical risk factors implicated in diabetes and its complications, a better understanding of how specific genetic variants (when data available) may impact diseases and traits, how they interact with each other and with lifestyle factors, and how this information could be used to pursue a more personalized approach to medicine. The uniqueness of our proposal is to incorporating time-dependent trajectories into disease predictions and early preventions.

Demographic Categories of Interest

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

Data Set Used

Registered Tier

Research Team

Owner:

  • Jin Zhou - Mid-career Tenured Researcher, University of California, Los Angeles
  • Aubrey Jensen - Project Personnel, University of Arizona

Collaborators:

  • Jonathan Hori - Graduate Trainee, University of California, Los Angeles

ZhouLab

This proposal develops several statistical methods and computational algorithms identifying genetic variants (when data available), biomarkers/medications and their trajectories associated with the onset of cardio-metabolic conditions, with and without diabetes. We focus on (1) developing statistical tools and optimization algorithms…

Scientific Questions Being Studied

This proposal develops several statistical methods and computational algorithms identifying genetic variants (when data available), biomarkers/medications and their trajectories associated with the onset of cardio-metabolic conditions, with and without diabetes. We focus on (1) developing statistical tools and optimization algorithms for analyzing temporal data; (2) robust phenotyping algorithms for studying diabetes and its complications; (3) identifying biomarkers/medications features and patterns associated with higher/lower incidence of events.

Project Purpose(s)

  • Population Health
  • Drug Development
  • Methods Development
  • Ancestry
  • Ethical, Legal, and Social Implications (ELSI)

Scientific Approaches

We will use observational study design to extract incidence of disease outcomes, e.g., heart failure, stroke, dementia, etc. We will use time-to-event models, e.g., Cox-PH models, to analyze the incidence of diseases.

Anticipated Findings

From our proposal, we expect to develop algorithms, user friendly open-source software, as well as analysis pipelines to encourage efficient and reproducible research. Additionally, from these studies, we also expect that we will identify novel genetic variants (when data available) or other clinical risk factors implicated in diabetes and its complications, a better understanding of how specific genetic variants (when data available) may impact diseases and traits, how they interact with each other and with lifestyle factors, and how this information could be used to pursue a more personalized approach to medicine. The uniqueness of our proposal is to incorporating time-dependent trajectories into disease predictions and early preventions.

Demographic Categories of Interest

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

Data Set Used

Registered Tier

Research Team

Owner:

  • Jin Zhou - Mid-career Tenured Researcher, University of California, Los Angeles
  • Aubrey Jensen - Project Personnel, University of Arizona

Collaborators:

  • Jonathan Hori - Graduate Trainee, University of California, Los Angeles

ZhouLab-ControlledTier

This proposal develops several statistical methods and computational algorithms identifying genetic variants, biomarkers/medications and their trajectories associated with the onset of cardio-metabolic conditions, with and without diabetes. We focus on (1) developing statistical tools and optimization algorithms for analyzing temporal…

Scientific Questions Being Studied

This proposal develops several statistical methods and computational algorithms identifying genetic variants, biomarkers/medications and their trajectories associated with the onset of cardio-metabolic conditions, with and without diabetes. We focus on (1) developing statistical tools and optimization algorithms for analyzing temporal data; (2) robust phenotyping algorithms for studying diabetes and its complications; (3) identifying biomarkers/medications features and patterns associated with higher/lower incidence of events.

Project Purpose(s)

  • Population Health
  • Drug Development
  • Methods Development
  • Ancestry
  • Ethical, Legal, and Social Implications (ELSI)

Scientific Approaches

We will use observational study design to extract incidence of disease outcomes, e.g., heart failure, stroke, dementia, etc. We will use time-to-event models, e.g., Cox-PH models, to analyze the incidence of diseases.

Anticipated Findings

From our proposal, we expect to develop algorithms, user friendly open-source software, as well as analysis pipelines to encourage efficient and reproducible research. Additionally, from these studies, we also expect that we will identify novel genetic variants or other clinical risk factors implicated in diabetes and its complications, a better understanding of how specific genetic variants may impact diseases and traits, how they interact with each other and with lifestyle factors, and how this information could be used to pursue a more personalized approach to medicine. The uniqueness of our proposal is to incorporating time-dependent trajectories into disease predictions and early preventions.

Demographic Categories of Interest

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

Data Set Used

Controlled Tier

Research Team

Owner:

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