Scott Kulm
Graduate Trainee, Cornell University
3 active projects
African American Prostate Cancer Polygenic Risk Score - WCM SPORE
Scientific Questions Being Studied
build a multiethnic polygenic risk score for prostate cancer onset, and identify modifiable risk factors associated with the score. this is important for disease screening and public health
Project Purpose(s)
- Disease Focused Research (prostate cancer)
Scientific Approaches
generate a polygenic risk score from all of us data and publicly available gwas summary statistics. regression models will be used to achieve these aims
Anticipated Findings
expect to a cross-ancestry risk model that can be deployed for preventative measures. existing models have poor cross-ancestry portability
Demographic Categories of Interest
- Race / Ethnicity
Research Team
Owner:
- Yajas Shah - Graduate Trainee, Cornell University
- Scott Kulm - Graduate Trainee, Cornell University
CRC and CBC
Scientific Questions Being Studied
The goal of the project is to use machine learning algorithms to predict colorectal cancer risk by monitoring complete blood counts.
Project Purpose(s)
- Disease Focused Research (colorectal cancer)
- Educational
- Commercial
Scientific Approaches
We plan to use various machine learning algorithms from SciKit Learn to predict those with onset of colorectal cancer by monitoring complete blood counts.
Anticipated Findings
We hope to find that by monitoring complete blood counts that we will be able to improve screening for individuals with a higher predictive risk of developing colorectal cancer.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Research Team
Owner:
- Scott Kulm - Graduate Trainee, Cornell University
- Matthew Wickersham - Graduate Trainee, Cornell University
CRC Testing
Scientific Questions Being Studied
At current times I wish to explore the dataset, examining available phenotypes and how the content is structured. Once the genetic data is released I plan on creating polygenic risk scores for several phenotypes for a wide cohort of individuals. As a starting point I will evaluate how well the polygenic risk scores predict disease within the cohort. Moving forward, I hope to enact fine-grain comparisons within and across phenotypes to improve portability of polygenic risk scores between population groups.
Project Purpose(s)
- Ancestry
Scientific Approaches
With the larger research subject of polygenic risk scores, I plan on using genetic material along with electronic health records to determine the accuracy of constructed polygenic risk scores. Along with the built-in research workbench tools that allow for the creation of disease specific cohorts, the analysis tools I will employ largely fall under the regression family. Specifically linear regression between score and continuous phenotypes, logistic regression to binary phenotypes, and cox proportional hazard models for survival analyses.
Anticipated Findings
The hypothesis is that polygenic risk scores are well able to stratify a cohort's risk for a given disease. In addition to providing evidence that polygenic risk scores can therefore be used in real clinical settings to inform screening schedules, medication regiments and other levels of care; this finding can improve upon the accuracy of current polygenic risk scores. This final gain in accuracy is hopefully specifically relevant to non-European populations.
Demographic Categories of Interest
This study will not center on underrepresented populations.
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