Young A Lee

Research Fellow, Mass General Brigham

6 active projects

Trans-ethnic genetic studies of neuropsychiatric conditions

Trans-ethnic genetic analysis can inform the discovery of trait- or disease-associated loci and characterize both shared and unique genetic architectures across populations. Unlike most existing biobank studies, the All of Us Research Program provides unique and valuable resources, notably its…

Scientific Questions Being Studied

Trans-ethnic genetic analysis can inform the discovery of trait- or disease-associated loci and characterize both shared and unique genetic architectures across populations. Unlike most existing biobank studies, the All of Us Research Program provides unique and valuable resources, notably its large genetic samples collected from participants having diverse racial and ethnic backgrounds. In the present study, we propose to use those diverse genetic samples to characterize genetic underpinnings that are potentially unique and/or shared across populations. We expect the findings from our trans-ethnic genetic investigation would improve the delivery of precision healthcare by facilitating personalized approaches to prevent and treat psychopathology in real-world clinical settings.

Project Purpose(s)

  • Population Health
  • Ancestry

Scientific Approaches

We plan to leverage both electronic health records and survey measures collected from the All of Us study participants to ascertain neuropsychiatric (both diagnosed and self-reported). Upon ascertainment of these traits, we plan to conduct genome-wide association studies and identity variants that might be associated with neuropsychiatric traits of interest. In addition, we plan to quantify the polygenic contribution to psychopathology (e.g. major depression, schizophrenia) within and across diverse populations.

Anticipated Findings

We anticipate being able to assess both shared and unique aspects in the genetic etiology of psychopathology in diverse samples and additionally evaluate the utility of trans-ethnic polygenic scores to improve risk stratification and prediction of neuropsychiatric conditions, especially among participants from communities that are historically underrepresented from biomedical studies.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Collaborators:

  • Yingzhe Zhang - Graduate Trainee, Mass General Brigham
  • Tian Ge - Early Career Tenure-track Researcher, Mass General Brigham

Mental health impact of COVID-related policies

Previous studies have reported substantial variations in the prevalence of psychiatric disorders and the distributions of social determinants of mental health outcomes. However, it remains unknown if we would observe similar patterns during the COVID-19 pandemic. We are interested in…

Scientific Questions Being Studied

Previous studies have reported substantial variations in the prevalence of psychiatric disorders and the distributions of social determinants of mental health outcomes. However, it remains unknown if we would observe similar patterns during the COVID-19 pandemic. We are interested in exploring social determinants of health that are known to vary substantially across the geographic regions in the United States and examining their relationship with mental health outcomes during the pandemic.

Project Purpose(s)

  • Disease Focused Research (psychiatric disorders)
  • Population Health
  • Social / Behavioral

Scientific Approaches

We plan to use demographic and socioeconomic characteristics measured using the baseline survey, COVID-related measures from both the COPE surveys, and electronic health records in terms of datasets. We will analyze these data using statistical methods ranging from mixed-effects regression models (when modeling repeated COPE survey measurements) to causal inference methods to estimate the potential causal effects of COVID-related policies on mental health outcomes during the pandemic.

Anticipated Findings

Taking into account the regional variations in both the COVID-related policies and baseline prevalence of mental health outcomes would help us minimize confounding by geographic regions and thus allow us to produce potential causal evidence that could inform policies and interventions.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Collaborators:

  • Yingzhe Zhang - Graduate Trainee, Mass General Brigham
  • Devika Godbole - Graduate Trainee, Mass General Brigham
  • Karmel Choi - Early Career Tenure-track Researcher, Mass General Brigham
  • Chris Kennedy - Early Career Tenure-track Researcher, Mass General Brigham

Genetic investigation of participation bias (v6)

We are interested in examining the genetic underpinnings of potential participation bias in this volunteer-based sample.

Scientific Questions Being Studied

We are interested in examining the genetic underpinnings of potential participation bias in this volunteer-based sample.

Project Purpose(s)

  • Population Health
  • Ancestry

Scientific Approaches

As the first step, we plan to compare the sociodemographic characteristics between the overall All of Us sample and the subset that contributed genetic data.

Anticipated Findings

We expect to observe substantial healthy volunteer bias among participants who contributed their genetic samples to the All of Us Research Program.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Covid Mental Health

There is a rapidly growing body of literature--both national and international--reporting increased incidence of mental health conditions due to the COVID-19 pandemic. However, most of the risk and protective factors reported by recent COVID studies are known predictors of major…

Scientific Questions Being Studied

There is a rapidly growing body of literature--both national and international--reporting increased incidence of mental health conditions due to the COVID-19 pandemic. However, most of the risk and protective factors reported by recent COVID studies are known predictors of major psychiatric conditions. Further, most prior studies focus on the association of one risk factor with mental health risk at a time using linear models and do not examine multiple risk factors concurrently. More flexible machine learning (ML) approaches that can automatically detect main and interaction effects may provide novel insights for psychopathology by capturing the hidden nonlinear dependence between risk/protective characteristics.

Project Purpose(s)

  • Disease Focused Research (disease of mental health)

Scientific Approaches

We plan to leverage interpretable ML models to discover actionable insights into the factors associated with psychological vulnerability and resilience using high-dimensional data including sociodemographic, clinical, survey, and lifestyle features.

Anticipated Findings

We expect that the findings from our study may help discover new insights into predictors of psychological vulnerability and resilience that may be unique to the period of the COVID-19 pandemic.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Collaborators:

  • zhaowen liu - Research Fellow, Mass General Brigham
  • Karmel Choi - Early Career Tenure-track Researcher, Mass General Brigham

participation_analysis

In this analysis, we aim to compare the characteristics of allofUS participants vs. the general population. This will inform if the allofUS participants are representative of the general population and if different participation characteristics vary across ancestry groups. This is…

Scientific Questions Being Studied

In this analysis, we aim to compare the characteristics of allofUS participants vs. the general population. This will inform if the allofUS participants are representative of the general population and if different participation characteristics vary across ancestry groups. This is important because to generalize the results observed in allofUS to the entire US population we need to understand how representative is this study.

Project Purpose(s)

  • Methods Development

Scientific Approaches

We will select specific socio-demographic variables that are available in allofUS and in the US census: education level, health and labour market. We will then compare the distribution of the individuals within each category in allofUS and in the US census. We will run these analyses separately for each ethnicity.

Anticipated Findings

We will provide a better understanding of how representative is allofUS compared to the general US population. This might information can be used to "re-weight" analysis obtained from allofUS to be representative of the general US population.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Young A Lee - Research Fellow, Mass General Brigham
  • andrea ganna - Early Career Tenure-track Researcher, The Broad Institute

Covid Mental Health v5

Our team has been collaborating with ELSA UK and ELSA Brazil cohorts through the International Hundred K+ Cohort Consortium to examine the mental health consequences of the global COVID-19 pandemic in different countries. As the site based in the U.S.,…

Scientific Questions Being Studied

Our team has been collaborating with ELSA UK and ELSA Brazil cohorts through the International Hundred K+ Cohort Consortium to examine the mental health consequences of the global COVID-19 pandemic in different countries. As the site based in the U.S., we decided to use data from the All of Us Research Program as they reflect the remarkable diversity of the U.S. population and provide a unique opportunity to explore risk/protective factors that shape the mental health impact of the pandemic across various sociodemographic contexts.

Project Purpose(s)

  • Disease Focused Research (disease of mental health)
  • Population Health
  • Social / Behavioral

Scientific Approaches

We plan to use a mixed-modeling approach to analyze COPE surveys to estimate the time-varying effects of risk/protective factors on mental health outcomes during the COVID-19 pandemic.

Anticipated Findings

We expect that the findings from our study may help discover new insights into predictors of psychological vulnerability and resilience that may be unique to the period of the COVID-19 pandemic.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Collaborators:

  • Karmel Choi - Early Career Tenure-track Researcher, Mass General Brigham
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