On polygenic risk scores for complex traits prediction

Bingxin Zhao and Fei Zou

Biometrics, 2022, vol. 78, issue 2, 499-511

Abstract: Polygenic risk scores (PRS) have gained substantial attention for complex traits prediction in genome‐wide association studies (GWAS). Motivated by the polygenic model of complex traits, we study the statistical properties of PRS under the high‐dimensional but sparsity free setting where the triplet (n,p,m)→(∞,∞,∞)$(n,p,m) \rightarrow (\infty , \infty , \infty )$ with n,p,m$n, p, m$ being the sample size, the number of assayed single‐nucleotide polymorphisms (SNPs), and the number of assayed causal SNPs, respectively. First, we derive asymptotic results on the out‐of‐sample (prediction) R‐squared for PRS. These results help understand the widespread observed gap between the in‐sample heritability (or partial R‐squared due to the genetic features) estimate and the out‐of‐sample R‐squared for most complex traits. Next, we investigate how features should be selected (e.g., by a p‐value threshold) for constructing optimal PRS. We reveal that the optimal threshold depends largely on the genetic architecture underlying the complex trait and the sample size of the training GWAS, or the m/n$m/n$ ratio. For highly polygenic traits with a large m/n$m/n$ ratio, it is difficult to separate causal and null SNPs and stringent feature selection in principle often leads to poor PRS prediction. We numerically illustrate the theoretical results with intensive simulation studies and real data analysis on 33 complex traits with a wide range of genetic architectures in the UK Biobank database.

Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1111/biom.13466

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:biomet:v:78:y:2022:i:2:p:499-511

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0006-341X

Access Statistics for this article

More articles in Biometrics from The International Biometric Society
Bibliographic data for series maintained by Wiley Content Delivery ().