| Title: | A gene-specific variance-control approach corrects polygenicity-driven inflation observed in transcriptome-wide association studies |
| Journal: | American Journal of Human Genetics |
| Published: | 20 Jan 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41564881/ |
| DOI: | https://doi.org/10.1016/j.ajhg.2025.12.014 |
Publication 18611
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Abstract
Transcriptome-wide association studies (TWASs) and related methods (xWASs) have been widely adopted in genetic studies to understand molecular traits as mediators between genetic variation and disease. However, the effect of polygenicity on the validity of these mediator-trait association tests has largely been overlooked. Given the widespread polygenicity of complex traits, it is necessary to assess the accuracy of these mediator-trait association tests. We found that, for highly polygenic target traits, the standard test based on linear regression is inflated, leading to dramatically increased false-positive rates that grow linearly with sample size and heritability. To address this inflation, we propose an effective variance-control method-similar to genomic control but allowing for a different correction factor for each gene. Using simulated and real data, as well as theoretical derivations, we show that our method yields calibrated false-positive rates, outperforming existing approaches. We further demonstrate that methods analogous to TWASs, namely those that associate genetic predictors of mediating traits with target traits, suffer from similar inflation issues. We advise developers of genetic predictors for molecular traits (including polygenic risk scores, PRSs) to compute and provide the necessary inflation parameters to ensure proper false-positive control. Finally, we have updated our PrediXcan software package and resources to facilitate this correction for end users.</p>
9 Keywords
- Computer Simulation
- Genetic Predisposition to Disease
- Genetic Variation
- Genome-Wide Association Study
- Humans
- Models, Genetic
- Multifactorial Inheritance
- Polymorphism, Single Nucleotide
- Transcriptome
3 Authors
- Yanyu Liang
- Festus Nyasimi
- Hae Kyung Im
1 Application
| Application ID | Title |
|---|---|
| 89052 | Developing and applying advanced statistical methods to dissect genetic and genomic heterogeneity in diverse complex traits |
Enabling scientific discoveries that improve human health