Isabelle Gregga

Project Personnel, Loyola University Chicago

3 active projects

Duplicate of Metabolic Syndrome

We want to look at metabolic syndrome, and how it affects individuals. We will build a dataset for metabolic syndrome consisting of blood pressure, serum glucose, serum triglycerides, waist circumference and HDL.

Scientific Questions Being Studied

We want to look at metabolic syndrome, and how it affects individuals. We will build a dataset for metabolic syndrome consisting of blood pressure, serum glucose, serum triglycerides, waist circumference and HDL.

Project Purpose(s)

  • Ancestry

Scientific Approaches

We will be focusing on creating a diverse dataset in our investigation. Our research methods consist of creating a dataset, and further analyzing the data.

Anticipated Findings

We anticipate to identify potential genetic contribution to an individual having metabolic syndrome.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Subsetting SNPs

These results will be used for running future GWAS in the Wheeler Lab. We are subsetting the available genomic data to the SNPS included in Wheeler Lab models to make GWAS more time- and cost-effective. We will then use the…

Scientific Questions Being Studied

These results will be used for running future GWAS in the Wheeler Lab. We are subsetting the available genomic data to the SNPS included in Wheeler Lab models to make GWAS more time- and cost-effective. We will then use the GWAS sumstats with our prediction models to study complex trait genetics and potential biological pathways/mechanisms.

Project Purpose(s)

  • Other Purpose (This data will be used for future academic research projects in the Wheeler Lab)

Scientific Approaches

These results will be used for running future GWAS in the Wheeler Lab. We are subsetting the available All of Us genomic data to the SNPS included in Wheeler Lab omics models to make GWAS more time- and cost-effective. We will then use the GWAS sumstats with our omics prediction models (using tools such as PRS-CSX and PrediXcan).

Anticipated Findings

These results will be used for running future GWAS in the Wheeler Lab. We will use the future GWAS sumstats with our omics prediction models to study complex trait genetics and potential biological pathways/mechanisms.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Collaborators:

  • Vir Trivedi - Undergraduate Student, Loyola University Chicago
  • Maya Sharma - Undergraduate Student, Loyola University Chicago
  • Jacob Grandinetti - Undergraduate Student, Loyola University Chicago
  • Heather Wheeler - Mid-career Tenured Researcher, Loyola University Chicago
  • Elizaveta Kolbunova - Undergraduate Student, Loyola University Chicago

Duplicate of PRS in populations across the epidemiological transition

Evaluate and optimize the performance of polygenic risk scores (PRS) in populations across the epidemiological transition. Rationale: Polygenic risk scores trained in one population may not perform as well in another due to both genetic and environmental effects and thus…

Scientific Questions Being Studied

Evaluate and optimize the performance of polygenic risk scores (PRS) in populations across the epidemiological transition. Rationale: Polygenic risk scores trained in one population may not perform as well in another due to both genetic and environmental effects and thus be of limited utility when defining disease risk. Methods have been developed to model differences in genetic ancestry, but performance assessment and optimization across diverse environments is less common. We are interested in how PRS performance varies across both genetic ancestries and geographies.

Project Purpose(s)

  • Methods Development
  • Ancestry

Scientific Approaches

Approach: Use multi-ancestry polygenic risk score (PRS) approaches like PRS-CSx to train and validate PRS in All of Us and other cohorts, like the UK Biobank. Test and optimize the performance of chronotype, sleep duration, type 2 diabetes, BMI, height, and hypertension polygenic risk scores trained in UK Biobank and All of Us populations in populations across the epidemiological transition.

Anticipated Findings

Hypothesis: Polygenic risk scores trained with African-origin populations included in the modeling will perform best in African genetic ancestry individuals, with optimal performance in test populations with the closest geographic and/or genetic distance to the training population.

Demographic Categories of Interest

  • Race / Ethnicity

Data Set Used

Controlled Tier

Research Team

Owner:

Collaborators:

  • Che Alyssa Zulaik - Undergraduate Student, Loyola University Chicago
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