ORIGINAL RESEARCH article

Front. Genet.

Sec. Statistical Genetics and Methodology

Volume 16 - 2025 | doi: 10.3389/fgene.2025.1416673

Winner's curse in rare variants analysis: effect size estimation bias depends on effect directions and the association methods used

Provisionally accepted
David  SoaveDavid Soave1,2*Melisa  HayaliogluMelisa Hayalioglu2Lei  SunLei Sun3,4
  • 1ontario institute for cancer research, Toronto, Ontario, Canada
  • 2Department of Mathematics, Wilfrid Laurier University, Waterloo, Ontario, Canada
  • 3Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada
  • 4Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada

The final, formatted version of the article will be published soon.

For complex human traits, a large portion of genetic heritability remains unaccounted for beyond common genetic variants; interest, therefore, exists in estimating the contribution of rare variants (RVs) to the etiology of complex traits. Research in this domain has primarily focused on genebased RV testing methods, wherein information from multiple variants is combined to maximize statistical power in detecting genes associated with the trait of interest. However, after discovering an association, estimating individual effects becomes challenging due to sample size limitations.Hence, the focus may be on estimating the average genetic effect (AGE) for the group of RVs analyzed. This study demonstrates that both AGEs and individual variant effects can be influenced by competing upward and downward biases, resulting from the winner's curse and the heterogeneity of individual variant effects, respectively. Various bias-correction techniques, including Bootstrap resampling and likelihood-based methods, have been proposed to address the winner's curse bias. We conduct a simulation study to illustrate the ramifications of these competing biases on variant effect size estimation and how they complicate the precision of pooled estimates obtained from the different bias-correction techniques. We then examine the individual effect estimates of the causal variants over the simulation replicates to show how they may contribute to the observed upward and downward biases when RVs are pooled.

Keywords: Genome-Wide Association Study, rare variants, Joint analysis, Estimation, selection bias, Winner's curse, Effect heterogeneity

Received: 12 Apr 2024; Accepted: 10 Jul 2025.

Copyright: © 2025 Soave, Hayalioglu and Sun. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: David Soave, ontario institute for cancer research, Toronto, Ontario, Canada

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.