Julia Sealock
Research Fellow, Broad Institute
6 active projects
antidepressant response genetics
Scientific Questions Being Studied
In this study, we will ask: Does genetic risk for psychiatric traits associate with worse response to antidepressants? Next, we will run a genome-wide association scan to determine if any variants associate with antidepressant response.
Project Purpose(s)
- Disease Focused Research (Psychiatric Disorders)
Scientific Approaches
We will calculate polygenic scores using either PLINK or PRS-CS. We will use SAIGE or Ordinal GWAS to conduct the GWAS.
Anticipated Findings
We anticipate that increased depression PGS associates with worse antidepressant response. We hope the GWAS will yield genome-wide significant variants.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Controlled Tier
Duplicate of Antidepressant Response Datav6
Scientific Questions Being Studied
Treatment of major depressive disorder typically begins with antidepressants. Response to antidepressants is highly variable. Around half of individuals will not respond to the first antidepressant they are prescribed, starting a long treatment odyssey to find the a drug or drug combination that works for them. EHRs provide detailed information on the medications individuals take, however, it is not always clear in an EHR how well a patient responds to a particular medication. Sometimes, physicians will administer a survey to a patient that aims to quantify their depression symptoms (patient health questionnaire, or PHQ). The responses to the PHQ are stored in EHRs. At Vanderbilt, we developed an algorithm that aims to infer antidepressant treatment response based on drug switching. We hope to implement our algorithm in the All of Us data and then use PHQ responses to validate how well our response variables track with survey questions on depression.
Project Purpose(s)
- Disease Focused Research (major depressive disorder)
Scientific Approaches
Our approach is to implement our drug switching algorithm for antidepressants and then use PHQ outcomes to determine how well our response outcome tracks with depression symptoms. This will require longitudinal data on antidepressants and PHQ responses.
Anticipated Findings
We hope that our algorithm will be a valid proxy for treatment response that can help increases sample sizes in antidepressant response studies. Future studies could integrate genetic information to determine if there are genetic variants contributing to treatment response. Overall, we hope the algorithm can be used by other EHR researchers and can serve as a paradigm for future treatment response algorithms for other medications.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Registered TierURVs in SCZ
Scientific Questions Being Studied
The main question for this workspace is, "do ultra rare damaging genetic variants associate with schizophrenia diagnosis?". This question is important because it can help shed light on the biology of schizophrenia to lead to new diagnostic methods and treatments.
Project Purpose(s)
- Disease Focused Research (Schizophrenia)
- Ancestry
Scientific Approaches
First, I will determine how many individuals with WGS data also have a diagnosis of schizophrenia. Next, I will use methods outlined in Singh et al Nature 2022 to find ultra rare damaging variants. Finally, I will run statistical analyses to determine if these variants associate with schizophrenia status.
Anticipated Findings
I anticipate to find an excess of rare variants in schizophrenia cases, and to replicate gene findings from Singh et al 2022.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Controlled TiermdCAD
Scientific Questions Being Studied
The scientific question we intend to study is the relationship between depression and cardiovascular disease outcomes. Cardiovascular disease and depression are commonly co-morbid. Understanding the prevalence and longitudinal relationship between the two diagnoses can help inform future disease monitoring tools and therapeutics.
Project Purpose(s)
- Disease Focused Research (Depression and Cardiovascular Disease)
Scientific Approaches
We plan to use phecodes for depression, coronary artery disease, and cardiomyopathy to calculate the prevalence of co-occurring depression and heart disease. We also plan to use sex and race information to stratify analyses.
Anticipated Findings
We anticipate to find that adverse cardiovascular events such as cardiomyopathy tend to occur more frequently in individuals with co-morbid depression and CAD compared to individuals with only depression or CAD. Our findings would contribute to a growing body of knowledge on the increased rate of heart disease among depression cases and motivate future studies into the shared biology between depression and heart disease.
Demographic Categories of Interest
- Race / Ethnicity
Data Set Used
Registered TierAntidepressant Response
Scientific Questions Being Studied
Treatment of major depressive disorder typically begins with antidepressants. Response to antidepressants is highly variable. Around half of individuals will not respond to the first antidepressant they are prescribed, starting a long treatment odyssey to find the a drug or drug combination that works for them. EHRs provide detailed information on the medications individuals take, however, it is not always clear in an EHR how well a patient responds to a particular medication. Sometimes, physicians will administer a survey to a patient that aims to quantify their depression symptoms (patient health questionnaire, or PHQ). The responses to the PHQ are stored in EHRs. At Vanderbilt, we developed an algorithm that aims to infer antidepressant treatment response based on drug switching. We hope to implement our algorithm in the All of Us data and then use PHQ responses to validate how well our response variables track with survey questions on depression.
Project Purpose(s)
- Disease Focused Research (major depressive disorder)
Scientific Approaches
Our approach is to implement our drug switching algorithm for antidepressants and then use PHQ outcomes to determine how well our response outcome tracks with depression symptoms. This will require longitudinal data on antidepressants and PHQ responses.
Anticipated Findings
We hope that our algorithm will be a valid proxy for treatment response that can help increases sample sizes in antidepressant response studies. Future studies could integrate genetic information to determine if there are genetic variants contributing to treatment response. Overall, we hope the algorithm can be used by other EHR researchers and can serve as a paradigm for future treatment response algorithms for other medications.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Registered TierLiver Transplant
Scientific Questions Being Studied
Our specific questions are about if MELDNa labs contribute to sex differences seen in liver transplant. We plan to use lab values (creatinine, INR, bilirubin, and sodium) to reconstruct MELDNa scores and compare lab and MELDNa values between sexes within control, liver disease cases, and transplant recipients. We next plan to build a sex-adjusted MELDNa score in conjunction with VUMC.
Project Purpose(s)
- Disease Focused Research (Liver Transplant)
Scientific Approaches
We plan to use lab values, ICD codes, and CPT codes to define lab traits, liver disease, and liver transplant.
Anticipated Findings
Our findings will contribute to the body of scientific knowledge by showing that all labs in the MELDNa score show sex differences that contribute to lower MELDNa score in females. A sex-adjusted MELDNa score will help close the gap between males and females in transplant.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Registered TierResearch Team
Owner:
- Julia Sealock - Research Fellow, Broad Institute
Collaborators:
- Francis Ratsimbazafy - Other, All of Us Program Operational Use
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