Haiquan Li

Early Career Tenure-track Researcher, University of Arizona

5 active projects

COPC

Chronic pains are often overlapping with each other, forming COPC. The project will use all of us data to identify COPC developing trajectories and genetic mechanisms once the genetic data is available

Scientific Questions Being Studied

Chronic pains are often overlapping with each other, forming COPC. The project will use all of us data to identify COPC developing trajectories and genetic mechanisms once the genetic data is available

Project Purpose(s)

  • Disease Focused Research (Chronic overlapping pain conditions (COPC))

Scientific Approaches

We will use logistic regression to study the pairwise overlapping, counting the time series of the occurrence of the diseases. We will also use similar models with lasso to identify most relevant pairs and their trajectories.

Anticipated Findings

We expect to identify true COPC developing pairs and clusters, providing insights for the development of COPC conditions, and the underlying conditions, such as mental status

Demographic Categories of Interest

  • Race / Ethnicity
  • Age
  • Sex at Birth

Research Team

Owner:

  • Jungwei Fan - Early Career Tenure-track Researcher, Mayo Clinic
  • Haiquan Li - Early Career Tenure-track Researcher, University of Arizona

Collaborators:

  • Wenting luo - Graduate Trainee, University of Arizona
  • Edwin Baldwin - Graduate Trainee, University of Arizona

Built_environment_covid

Study the COVID-19 spread and mental health associated with built environment using COPE COVID-19 survey data. COPE data provides unique opportunity to study the medical and social impacts of built environment, such as the household types. The study will conduct…

Scientific Questions Being Studied

Study the COVID-19 spread and mental health associated with built environment using COPE COVID-19 survey data. COPE data provides unique opportunity to study the medical and social impacts of built environment, such as the household types. The study will conduct secondary use of the survey to study the association, providing evidence for policy makers.

Project Purpose(s)

  • Disease Focused Research (COVID-19)
  • Social / Behavioral
  • Ethical, Legal, and Social Implications (ELSI)

Scientific Approaches

We will use the COPE survey data and conduct logistic regression analyses to study the associations.

Anticipated Findings

We expect built environment types will be associated with the spread of COVID-19 and potentially impose stress to the residents. We also expect indoor behaviors (e.g., shopping) will be related to COVID-19 spread.

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

Collaborators:

  • Wenting luo - Graduate Trainee, University of Arizona
  • Edwin Baldwin - Graduate Trainee, University of Arizona

Disease_convergence_and_lifestyle_v2

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

BAT498_Spring21_capstone_course_project

This project is for a capstone course for seniors in Biosystems Analytics and Technology, University of Arizona. With the unique data sources, students are exposed to the unprecedented national biobank and practice the whole process of a research project: such…

Scientific Questions Being Studied

This project is for a capstone course for seniors in Biosystems Analytics and Technology, University of Arizona. With the unique data sources, students are exposed to the unprecedented national biobank and practice the whole process of a research project: such as formalizing a question, studying the background, proposing a hypothesis, designing a solution, and most importantly, synthesizing all knowledge learned from this degree program to implement the solution. They will integrate their computational and statistical skills to solve the problem, which is the program's core components. Students are still exploring the data but have shown a strong interest in studying risk factors for irritable bowel syndrome (IBS).

Project Purpose(s)

  • Educational

Scientific Approaches

Students will form their own datasets, including the cases and controls. Very likely, they will use logistical regression in R to investigate the problem. However, it is up to the students to choose their preferred solution.

Anticipated Findings

This project is for training purposes. Novel findings may or may not be resulted, but the students can recapitulate existing findings using the new dataset if the results are not unknown.

Demographic Categories of Interest

  • Race / Ethnicity
  • Age
  • Gender Identity
  • Sexual Orientation
  • Geography
  • Disability Status
  • Access to Care
  • Education Level
  • Income Level

Research Team

Owner:

  • Haiquan Li - Early Career Tenure-track Researcher, University of Arizona

Collaborators:

  • Alvin Onyango - Undergraduate Student, University of Arizona
  • Amarta Singh - Undergraduate Student, University of Arizona

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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