Estimation of a genetic Gaussian network using GWAS summary data.
| Autor: | Yang Y; Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106, United States., Lorincz-Comi N; Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106, United States., Zhu X; Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106, United States. |
|---|---|
| Jazyk: | angličtina |
| Zdroj: | Biometrics [Biometrics] 2024 Oct 03; Vol. 80 (4). |
| DOI: | 10.1093/biomtc/ujae148 |
| Abstrakt: | A genetic Gaussian network of multiple phenotypes, constructed through the inverse matrix of the genetic correlation matrix, is informative for understanding the biological dependencies of the phenotypes. However, its estimation may be challenging because the genetic correlation estimates are biased due to estimation errors and idiosyncratic pleiotropy inherent in GWAS summary statistics. Here, we introduce a novel approach called estimation of genetic graph (EGG), which eliminates the estimation error bias and idiosyncratic pleiotropy bias with the same techniques used in multivariable Mendelian randomization. The genetic network estimated by EGG can be interpreted as shared common biological contributions between phenotypes, conditional on others. We use both simulations and real data to demonstrate the superior efficacy of our novel method in comparison with the traditional network estimators. (© The Author(s) 2024. Published by Oxford University Press on behalf of The International Biometric Society.) |
| Databáze: | MEDLINE |
| Externí odkaz: |
|