Background
PRS methods require as input summary statistics from genome wide association studies. If unfamiliar with GWAS or in need of a refresher consider reading this paper.
- Genome-wide association studies (GWAS) involve analyzing the genomes of a large group of individuals.
- For a binary trait (like blue or brown eyes) this involves comparing the frequency of genetic variations in each group to produce an odds-ratio (measure of association) and a p-value to measure the signficance of the realtionship at each SNP.
- For continuous measures (like height) this results in an effect size (measure of continuous association) and p-value that measure the significance of our relationship.
- GWAS results can be summarized in a sumstats file which looks like this:
| CHR | ID | REF | A1 | A1_FREQ | OBS_CT | SE | BETA | P |
|---|---|---|---|---|---|---|---|---|
| 1 | rs100 | A | T | 0.1 | 100 | 0.01 | 1.10 | 0.01 |
| 2 | rs200 | C | G | 0.2 | 100 | 0.02 | -1.10 | 0.05 |
| 3 | rs300 | G | A | 0.3 | 100 | 0.03 | 1.02 | 0.10 |
PRS
- Polygenic scores combine genetic variant associations genome-wide by summing their effects to produce individual scores that predict the phenotype.
- Different PRS methods use different statistical methods to select variants and estimate their effect sizes for use in the PRS.
- The predictive power of PRS is usually assesed by residual variance explained \(R^2\) or other similar metrics.
POPout
- When genetic architecture is complex predictive performance can vary across the trait distribution which can make global metrics like \(R^2\) inaccurate at certain strata.
- Specifically when large rare variants are present in one tail of the trait distribtion we
often see performance loss or "POPout" whereby individuals with very high or low trait values do not have correspondingly high or low PRS values. - POPout quantifies and assesses the prescence of this phenonomena.