Age at natural menopause (ANM) polygenic risk scores (PRS) in the All of Us Research Program
Marisa Medina, Professor
UC San Francisco
Open. Apprentices needed for the fall semester. Enter your application online beginning August 21st. The deadline to apply is Monday, August 31st, 4pm.
Not all women reach menopause at the same time. Some of this inter-individual variation is due to underlying genetic differences between women. Genome-wide association studies (GWAS) have been conducted to identify a list of common genetic variants that are associated with age at natural menopause. We would like to calculate polygenic risk-scores (PRS) for age at natural menopause in female All of Us Research Program participants. We would then like to correlate these risk scores with electronic health record (EHR)-derived estimates of menopause timing from postmenopausal women.
Role: 1) The undergraduate would learn about human subjects research and be trained to protect human subjects before analyzing human data.
2) Under direction of the research supervisor, the undergraduate would curate and analyze data from the All of Us Research Program and other sources, generating descriptive statistics and performing quality control to arrive at a final dataset for analysis.
3) The undergraduate would learn and/or expand their knowledge of genome-wide association studies (GWAS) and polygenic risk scores (PRS).
4) Under direction of the research supervisor, the undergraduate would statistically compute age at natural menopause (ANM) polygenic risk scores using participant genomic data.
5) The ANM PRS (genetically-predicted menopause ages) will be correlated to electronic-health record (EHR)-derived estimates of menopause timing for validation.
Qualifications: An interest and some basic experience with statistics and coding would be a good foundation for this project.
A motivated student with a desire to analyze datasets that are relevant to human (women's) health would be ideal.
It would be desirable but not essential for the student to have prior experience with genetic analysis and/or have proficiency in at least one programming language.
Day-to-day supervisor for this project: Beth Theusch, Staff Researcher
Hours: to be negotiated
Off-Campus Research Site: This is a data analysis project, so project can be conducted completely remotely. The Medina lab is physically located at 5700 Martin Luther King Jr Way in Oakland, but the research supervisor (Beth Theusch) works remotely.
Related website: https://medinalab.ucsf.edu/
Related website: https://medinalab.ucsf.edu/