Instrumental Variable Genetic Risk Prediction
I developed a new method to estimate the genetic correlation between key phenotypes and polygenic risk scores. This approach is both robust and privacy-protecting - it can correct for sample overlap and only requires GWAS summary-level statistics as input.
By Li Ge in Statistical Genetics Causal Inference
August 20, 2019
Rotation advisor: Qiongshi Lu, PhD.
Motivation
- We can utilize GWAS summary statistics from major reference data sources such as UK Biobank to calculate polygenic risk scores (PRSs) for genetic risk prediction on another data set.
- Even better, PRSs are natural instrumental variables. We can use them as proxies to investigate the genetic associations between different phenotypes.
- However, two data set might suffer from overfitting due to sample-overlapping.
- How can we make valid and robust inferences under sample-overlapping?
Results
- I developed a new method to estimate the genetic correlation between key phenotypes and polygenic risk scores. This approach is both robust and privacy-protecting - it can correct for sample overlap and only requires GWAS summary-level statistics as input.
Methods
See details in the slides.
- Posted on:
- August 20, 2019
- Length:
- 1 minute read, 123 words
- Categories:
- Statistical Genetics Causal Inference
- See Also: