| Title: | Inferring disease architecture and predictive ability with LDpred2-auto |
| Journal: | American Journal of Human Genetics |
| Published: | 8 Nov 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/37944514/ |
| DOI: | https://doi.org/10.1016/j.ajhg.2023.10.010 |
| URL: | https://doi.org/10.1101/2022.10.10.511629 |
Publication 9274
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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 h2 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 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 we show that this new set of variants captures 12% more heritability and provides 6% more predictive performance, on average, in UK Biobank analyses.</p>
5 Authors
- Florian Privé
- Clara Albiñana
- Julyan Arbel
- Bogdan Pasaniuc
- Bjarni J Vilhjálmsson
1 Application
| Application ID | Title |
|---|---|
| 58024 | Combining Genetic and Environmental Information to Predict Disease Risk |
Enabling scientific discoveries that improve human health