Session 2 : Creating a User-friendly Shiny App for Reproducible Two-Sample Mendelian Randomization Studies
Presentation Type
Invited
Track
Genomics
Abstract
We present a Shiny app that supports and facilitates two-sample Mendelian randomization studies with genome-wide association study (GWAS) summary statistics. The proliferation of GWAS and the sharing of their marginal SNP association statistics have enabled researchers to address causal inference questions between two complex traits. Two-sample Mendelian randomization posits a causal relationship between a putative exposure and a putative outcome. Our Shiny app will enable researchers to input GWAS summary statistics for the putative outcome and putative exposure. The app supports diverse sensitivity analyses to assess the assumptions that underlie Mendelian randomization. To ensure computational reproducibility, the user can download a Rmarkdown file with all analysis code from our app. We also briefly discuss anticipated issues with app deployment.
Start Date
2-7-2025 8:50 AM
End Date
2-7-2025 9:50 AM
Session 2 : Creating a User-friendly Shiny App for Reproducible Two-Sample Mendelian Randomization Studies
Dakota A & C (Room 250)
We present a Shiny app that supports and facilitates two-sample Mendelian randomization studies with genome-wide association study (GWAS) summary statistics. The proliferation of GWAS and the sharing of their marginal SNP association statistics have enabled researchers to address causal inference questions between two complex traits. Two-sample Mendelian randomization posits a causal relationship between a putative exposure and a putative outcome. Our Shiny app will enable researchers to input GWAS summary statistics for the putative outcome and putative exposure. The app supports diverse sensitivity analyses to assess the assumptions that underlie Mendelian randomization. To ensure computational reproducibility, the user can download a Rmarkdown file with all analysis code from our app. We also briefly discuss anticipated issues with app deployment.