Edwin Baldwin

Graduate Trainee, University of Arizona

1 active project

Disease_convergence_and_lifestyle

Multiple genetic polymorphisms have been identified for complex diseases, but relationships, such as the biological underpinning of genetic interactions, are still elusive. Epigenomic studies have shown that genetic variants may have convergent effects, which increase the risk of developing complex…

Scientific Questions Being Studied

Multiple genetic polymorphisms have been identified for complex diseases, but relationships, such as the biological underpinning of genetic interactions, are still elusive. Epigenomic studies have shown that genetic variants may have convergent effects, which increase the risk of developing complex diseases and comorbidities. We aim to prioritize the genetic variants with convergent effects and diseases of excess epigenomic similarity from the abundant biological resources, such as ENCODE and GTEx. We will then study the agreement between the convergent effects and interactions of genetic variants in AllofUsRP and the agreement between disease epigenomic similarity and disease comorbidities in AllofUsRP. Lifestyle and environment exposures are critical risk factors, and their effects will be modeled as well. The research will help us understand disease mechanisms and missing heritability and foster applications like drug repositioning.

Project Purpose(s)

  • Population Health
  • Methods Development
  • Ancestry

Scientific Approaches

We have developed an information-theoretical based similarity for quantifying the similarity of genetic variants and disease pairs from GTEx data. We have also developed a multi-omics integration method to quantify the overall similarity of genetic variants in ENCODE. We will extend the latter method to quantify the epigenomic similarity for disease pairs. We aim to use AllofUsRP for validating the genetic interactions between genetic variants and comorbidities. Further, we will use, logistic regression, LASSO, and deep learning methods to model diseases from lifestyles and genetic interactions.

Anticipated Findings

We expect to find many unexpected biological links between the effects of distinct genetic variants, which may explain the increased risk of diseases and comorbidities. With machine learning models, we will build disease prediction models, particularly those impacted heavily by lifestyles, such as cancers. The research will generate candidates for novel drug targets and drug repositioning approaches.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Haiquan Li - Early Career Tenure-track Researcher, University of Arizona
  • Edwin Baldwin - Graduate Trainee, University of Arizona
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