Madeline Dailey
Graduate Trainee, Stanford University
2 active projects
PRISM2
Scientific Questions Being Studied
Genetics of T2D in diverse patient cohorts. Going to see what genes in tandem work well together. Going to multiply genetic Risk SNPs prescence to other genetic risk SNPs prescence and create a new interaction term variable. Then going to make Polygenic Risk Scores for T2D.
Project Purpose(s)
- Educational
Scientific Approaches
T2D cases and healthy cohort. Can we predict t2D equally well in different racial groups? We will use statistical analysis and plink to do a lot of the work. Its just a lot of genomic manipulation.
Anticipated Findings
We hope to see how these gene-gene interactions will show their relatedness to T2D. The important gene gene interactions can then be used for further research.
Demographic Categories of Interest
- Race / Ethnicity
- Age
Data Set Used
Controlled TierResearch Team
Owner:
- Noah Fields - Graduate Trainee, Stanford University
- Madeline Dailey - Graduate Trainee, Stanford University
PRISM
Scientific Questions Being Studied
Clinicians today rely on imprecise, generalized methods in the treatment of complex diseases, leading to less effective care. Polygenic Risk Scores (PRS’s) are one method to help providers make more informed decisions, yet they are sparsely adopted due to two key limitations: PRS’s consider only individual genetic variants in a summative manner and they are trained on data representative of primarily European populations. However, new precision medicine initiatives and genomic data will bring PRS’s back to the forefront of innovation. Therefore, we present a plan with the following specific aims: 1) To design a study cohort ethnically representative of the U.S. population and limited in confounding diagnoses, 2) To compile a list of risk variants and associated pathways for a well-studied disease with a PRS, 3) To develop a more generally applicable PRS that factors in gene-gene interactions, and 4) To evaluate the accuracy of the PRS in ethnicity-stratified data.
Project Purpose(s)
- Population Health
- Methods Development
- Ancestry
Scientific Approaches
We plan to use the genomics data from this database, examining the alleles present for a number of selected risk variants for a disease of choice (chosen simply for comparison purposes, not to seek greater understanding). We hope to design a cohort with a distribution of ancestry likened to the actual distribution of ancestry in the U.S. and limit the presence of confounding diagnoses as much as feasible. We will then train a non-linear model that takes into account biologically relevant cross-terms, or gene-gene relationships, to generate a polygenic risk score that will be more accurate in general and for a broader range of patients.
Anticipated Findings
We anticipate finding that training an algorithm that makes a risk estimate on more ethnically diverse data will increase the accuracy for patients of non-European ancestry. This will hopefully indicate the necessity to train other risk estimates on more diverse data to improve clinical decision-making. We also anticipate finding that including more complex interactions in our risk estimate will increase the granularity (ie. discerning disease stages) and accuracy of the estimate. This again will improve clinical decision-making and increase our ability to use risk modeling algorithms such as these to learn more about the interactions underlying complex diseases.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Controlled TierResearch Team
Owner:
- Madeline Dailey - Graduate Trainee, Stanford University
Collaborators:
- Noah Fields - Graduate Trainee, Stanford University
- Allie Littleton - Graduate Trainee, Stanford University
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