Inferring disease architecture and predictive ability with LDpred2-auto

Florian Privé*, Clara Albiñana, Julyan Arbel, Bogdan Pasaniuc, Bjarni J Vilhjálmsson

*Corresponding author for this work

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

Abstract

LDpred2 is a widely used Bayesian method for building polygenic scores (PGSs). LDpred2-auto can infer the two parameters from the LDpred model, the SNP heritability h 2 and polygenicity p, so that it does not require an additional validation dataset to choose best-performing parameters. The main aim of this paper is to properly validate the use of LDpred2-auto for inferring multiple genetic parameters. Here, we present a new version of LDpred2-auto that adds an optional third parameter α to its model, for modeling negative selection. We then validate the inference of these three parameters (or two, when using the previous model). We also show that LDpred2-auto provides per-variant probabilities of being causal that are well calibrated and can therefore be used for fine-mapping purposes. We also introduce a formula to infer the out-of-sample predictive performance r 2 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 we show that this new set of variants captures 12% more heritability and provides 6% more predictive performance, on average, in UK Biobank analyses.

Original languageEnglish
JournalAmerican Journal of Human Genetics
Volume110
Issue12
Pages (from-to)2042-2055
Number of pages14
ISSN0002-9297
DOIs
Publication statusPublished - 7 Dec 2023

Keywords

  • LDpred2
  • inference
  • Polymorphism, Single Nucleotide/genetics
  • Genome-Wide Association Study/methods
  • Humans
  • Bayes Theorem
  • Multifactorial Inheritance/genetics

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