| Title: | A mediation analysis framework based on variance component to remove genetic confounding effect |
| Journal: | Journal of Human Genetics |
| Published: | 25 Mar 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/38528049/ |
| DOI: | https://doi.org/10.1038/s10038-024-01232-x |
| Citations: | 1 (1 in last 2 years) as of 8 Aug 2024 |
Publication 10056
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Abstract
Identification of pleiotropy at the single nucleotide polymorphism (SNP) level provides valuable insights into shared genetic signals among phenotypes. One approach to study these signals is through mediation analysis, which dissects the total effect of a SNP on the outcome into a direct effect and an indirect effect through a mediator. However, estimated effects from mediation analysis can be confounded by the genetic correlation between phenotypes, leading to inaccurate results. To address this confounding effect in the context of genetic mediation analysis, we propose a restricted-maximum-likelihood (REML)-based mediation analysis framework called REML-mediation, which can be applied to either individual-level or summary statistics data. Simulations demonstrated that REML-mediation provides unbiased estimates of the true cross-trait causal effect, assuming certain assumptions, albeit with a slightly inflated standard error compared to traditional linear regression. To validate the effectiveness of REML-mediation, we applied it to UK Biobank data and analyzed several mediator-outcome trait pairs along with their corresponding sets of pleiotropic SNPs. REML-mediation successfully identified and corrected for genetic confounding effects in these trait pairs, with correction magnitudes ranging from 7% to 39%. These findings highlight the presence of genetic confounding effects in cross-trait epidemiological studies and underscore the importance of accounting for them in data analysis.</p>
3 Authors
- Zihan Dong
- Hongyu Zhao
- Andrew T. DeWan
1 Application
| Application ID | Title |
|---|---|
| 32285 | Detecting pleiotropic effects through integration of omics data |
Enabling scientific discoveries that improve human health