Lindsay Guare
Graduate Trainee, University of Pennsylvania
5 active projects
Ritchie Lab Common Files
Scientific Questions Being Studied
This workspace contains files that we wish to make commonly available to members of the Ritchie Lab at the University of Pennsylvania.
Project Purpose(s)
- Ancestry
Scientific Approaches
The project will contain notebooks and code for general use within our research lab.
Anticipated Findings
The project will be used as a repository for notebooks and code that can be useful to all members of our research lab.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Controlled TierResearch Team
Owner:
- Anurag Verma - Early Career Tenure-track Researcher, University of Pennsylvania
- Tess Cherlin - Research Fellow, University of Pennsylvania
- Scott Dudek - Project Personnel, University of Pennsylvania
- Katie Cardone - Project Personnel, University of Pennsylvania
- Karl Keat - Graduate Trainee, University of Pennsylvania
- Lindsay Guare - Graduate Trainee, University of Pennsylvania
- David Zhang - Graduate Trainee, University of Pennsylvania
- Alexis Garofalo - Graduate Trainee, University of Pennsylvania
The Genetics of Endometriosis in Diverse Ancestries
Scientific Questions Being Studied
More than 200,000 women are diagnosed with endometriosis every year and over half of those women do not receive a definitive diagnosis until 8.5 years after the onset of symptoms and many times when they present with additional comorbidities. While several studies have suggested that genomic markers, environmental risk factors and inflammatory markers play crucial roles in endometriosis symptomatology, there are no effective tools available to predict an individual’s risk of developing endometriosis or to predict its downstream effects. The long-term goal is to develop effective and non-invasive early screening tools to identify patients at risk of developing endometriosis and predict long-term effects. The main objective of this project is the development of models to predict the risk of endometriosis across varied clinical manifestations and associated long-term health outcomes, integrating genetic and non-genetic risk factors extracted from Electronic Health Records.
Project Purpose(s)
- Disease Focused Research (endometriosis)
- Ancestry
Scientific Approaches
We plan to use phenotype and genotype data for this project. We will approach the analyses in multiple ways:
- Genetic architecture elucidation vai common varaint and rare variant association analyses
- Testing of polygenic risk scores
- Genotypic and biometric clustering approaches
- Mediation and mendelian randomization analyses
Anticipated Findings
The expected outcomes will be rigorously evaluated non-invasive computational methods for screening and diagnosing endometriosis across various clinical manifestations and its long-term effects based on genetic and non-genetic factors. In addition, our screening and diagnostic methods will be applicable to women of diverse ancestries.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Controlled TierResearch Team
Owner:
- Shefali Verma - Early Career Tenure-track Researcher, University of Pennsylvania
- Lindsay Guare - Graduate Trainee, University of Pennsylvania
DETECT_MI
Scientific Questions Being Studied
I am exploring the comorbidity architecture of complex diseases using a temporal. Specifically, how temporal data in the EHR can best be utilized to predict longitudinal clinical outcomes.
Project Purpose(s)
- Disease Focused Research (Cardiometabolic, neurological, immune-related conditions)
- Methods Development
- Control Set
- Ancestry
Scientific Approaches
We aim to develop novel methods to model temporality in EHR data. We want to implement both deep learning and statistical models to generate patient disease trajectories. We will utilize lab, medication, diagnosis, vital, and procedural data to this end for subphenotyping of patients. We then plan to explore genetic variation underlying patients in different disease trajectory paths to determine what, if any, a genetic basis exists for diversity in health paths in individuals with the same initial diagnoses.
Anticipated Findings
We anticipate identifying common sequences of conditions resulting in clinical outcomes. By first identifying common disease trajectory models, we hope to then classify patients into different trajectory phenotypes and leverage predictive modeling methods for health forecasting questions. Our findings will enhance the medical understanding of disease precursors and early indicators, ideally shedding light on ways of widening intervention windows. Additionally, this line of questioning is especially important to understand how disparities at the health system level may predispose certain groups of people to earlier or more severe clinical outcomes.
Demographic Categories of Interest
- Race / Ethnicity
- Age
- Access to Care
Data Set Used
Controlled TierResearch Team
Owner:
- Pankhuri Singhal - Graduate Trainee, University of Pennsylvania
- Karl Keat - Graduate Trainee, University of Pennsylvania
- Lindsay Guare - Graduate Trainee, University of Pennsylvania
The Genetics of Glaucoma in Diverse Ancestry Groups
Scientific Questions Being Studied
There is evidence for health disparities in glaucoma; racial minorities are disproportionately affected. We have performed previous genetic analyses regarding case/control status of POAG, specifically in African-ancestry cohorts. We intend to investigate whether our significant results replicate in the All of Us dataset.
Project Purpose(s)
- Disease Focused Research (primary open angle glaucoma)
- Ancestry
Scientific Approaches
As this is a replication analysis, the approach is relatively straightforward.
1 ) We will extract participants of African-ancestry (according to PCA projection on the thousand genomes dataset)
2) We will extract the SNPs from our dataset which passed the genome-wide significant threshold in any of the sex-stratified or overall analyses.
3 ) We will perform simple association testing and a very small-scale GWAS to determine if the variants are nominally significant in this dataset.
Anticipated Findings
The aim of the over-arching project has been to better understand the genetic and molecular mechanisms of primary open-angle glaucoma in African-ancestry individuals. Elucidating the biological basis of the disease may assist screening and therapeutic plans in the future in order to avoid severe outcomes like irreversible blindness. In particular, the All of Us dataset will serve as an important replication set to assess our pervious results.
Demographic Categories of Interest
- Race / Ethnicity
Data Set Used
Controlled TierThyroid Function Genetics
Scientific Questions Being Studied
The distribution of thyroid stimulating hormone, which regulates the secretion of thyroid hormone, has been observed to vary by race. This affects accurate diagnosis of thyroid dysfunction. We intend to study the genomic variants associated with thyroid function and thyroid dysfunction in multi-ancestry populations.
Project Purpose(s)
- Disease Focused Research (Thyroid Dysfunction)
- Ancestry
Scientific Approaches
Overall, we intend to perform stratified-ancestry GWAS for thyroid stimulating hormone, free thyroxine, free triiodothyronine and thyroid antibodies, as well as for hypothyroidism and hyperthyroidism.
As appropriate, we will perform separate exclusions for thyroid function test, thyroid antibody, and thyroid dysfunction analyses. We will prepare phenotype files using thyroid measurements and thyroid dysfunction from the EHR data.
We will use plots and statistical analyses to examine the distribution differences between cohorts.
We will subset the WGS data based on predicted genetic ancestry and then perform variant-level filtering for: MAF 0.01, Missingness 0.01, and Bi-Allelic variants only. We will subset the genotype array data based on predicted genetic ancestry and compute local PCs to use as covariates in the GWAS, reducing inflation.
We will use the tool REGENIE to run the GWAS.
Anticipated Findings
We will determine associations between phenotypic differences in thyroid physiology and pathology between Black and White individuals and differences in ancestral genomic variants. This will inform the need for ancestry specific reference ranges for thyroid stimulating hormone. We will also contribute to understanding of the genomic influences on thyroid hormone regulation.
Demographic Categories of Interest
- Race / Ethnicity
Data Set Used
Controlled TierRequest a Review of this Research Project
You can request that the All of Us Resource Access Board (RAB) review a research purpose description if you have concerns that this research project may stigmatize All of Us participants or violate the Data User Code of Conduct in some other way. To request a review, you must fill in a form, which you can access by selecting ‘request a review’ below.
