“Inferring Disease Architecture and Predictive Ability With LDpred2-Auto”, Florian Privé, Clara Albiñana, Bogdan Pasaniuc, Bjarni J. Vilhjálmsson2022-10-12 (rare mutations)⁠:

LDpred2 is a widely used Bayesian method for building polygenic scores (PGS). LDpred2-auto can infer the two parameters from the LDpred model, h² and p, so that it does not require an additional validation dataset to choose best-performing parameters.

Here, we present a new version of LDpred2-auto, which adds a third parameter α to its model for modeling negative selection. Additional changes are also made to provide better sampling of these parameters. We then validate the inference of these 3 parameters.

LDpred2-auto also provides per-variant probabilities of being causal that are well calibrated, and can therefore be used for fine-mapping purposes. We also derive a new formula to infer the out-of-sample predictive performance R² of the resulting PGS directly from the Gibbs sampler of LDpred2-auto.

Finally, we extend the set of HapMap3 variants recommended to use with LDpred2 with 37% more variants to improve the coverage of this set, and show that this new set of variants captures 12% more heritability and provides 6% more predictive performance, on average.