Polygenic Risk Scores

Polygenic Risk Scores sits within gwas and addresses aggregated genetic-risk estimators built from gwas summary statistics. The page below sketches the conceptual scope of the area, the methodological tools it relies on, and the recent literature anchoring its current frontier.

The area organises around a small number of recurring axes: scope (what biological scales the work spans), method (the dominant experimental or computational tools), data regime (what kinds of measurements are now routine vs. still frontier), and open questions (what the field cannot yet do reliably). The sources below cover different combinations of these axes.

Frontier results

A primary recent reference for this area is Modeling linkage disequilibrium increases accuracy of polygenic risk scores (Vilhjalmsson, 2015), which contributes to the methodological or empirical conversation that defines the current frontier of polygenic risk scores. It illustrates the kind of question the field is actively pursuing — the specific technical claim, the dataset or system on which it was validated, and the way subsequent work builds on it.

Supporting context

Supporting context comes from 10 years of GWAS discovery: biology, function, and translation (Visscher et al., 2017), cited here as a representative entry into adjacent results that reinforce the framing of polygenic risk scores without being the central methodological claim.

Open questions

Open questions in polygenic risk scores cluster around scaling current methods to larger systems, integrating measurements across modalities, and producing predictive rather than descriptive models. The references above mark the work that the next iteration of this page should engage with in more specific detail.