| dc.identifier.citation | Joseph H. Breeyear, Brian S. Mautz, Jacob M. Keaton, Jacklyn N. Hellwege, Eric S. Torstenson, Jingjing Liang, Michael J. Bray, Ayush Giri, Helen R. Warren, Patricia B. Munroe, Digna R. Velez Edwards, Xiaofeng Zhu, Chun Li, Todd L. Edwards, A new test for trait mean and variance detects unreported loci for blood-pressure variation, The American Journal of Human Genetics, 2024, , ISSN 0002-9297, https://doi.org/10.1016/j.ajhg.2024.03.014. (https://www.sciencedirect.com/science/article/pii/S0002929724000879) Abstract: Summary Variability in quantitative traits has clinical, ecological, and evolutionary significance. Most genetic variants identified for complex quantitative traits have only a detectable effect on the mean of trait. We have developed the mean-variance test (MVtest) to simultaneously model the mean and log-variance of a quantitative trait as functions of genotypes and covariates by using estimating equations. The advantages of MVtest include the facts that it can detect effect modification, that multiple testing can follow conventional thresholds, that it is robust to non-normal outcomes, and that association statistics can be meta-analyzed. In simulations, we show control of type I error of MVtest over several alternatives. We identified 51 and 37 previously unreported associations for effects on blood-pressure variance and mean, respectively, in the UK Biobank. Transcriptome-wide association studies revealed 633 significant unique gene associations with blood-pressure mean variance. MVtest is broadly applicable to studies of complex quantitative traits and provides an important opportunity to detect novel loci. Keywords: variability; genetic association; interaction; canalization; estimating equations; blood pressure | en_US |
| dc.description.abstract | Variability in quantitative traits has clinical, ecological, and evolutionary significance. Most genetic variants identified for complex quantitative traits have only a detectable effect on the mean of trait. We have developed the mean-variance test (MVtest) to simultaneously model the mean and log-variance of a quantitative trait as functions of genotypes and covariates by using estimating equations. The advantages of MVtest include the facts that it can detect effect modification, that multiple testing can follow conventional thresholds, that it is robust to non-normal outcomes, and that association statistics can be meta-analyzed. In simulations, we show control of type I error of MVtest over several alternatives. We identified 51 and 37 previously unreported associations for effects on blood-pressure variance and mean, respectively, in the UK Biobank. Transcriptome-wide association studies revealed 633 significant unique gene associations with blood-pressure mean variance. MVtest is broadly applicable to studies of complex quantitative traits and provides an important opportunity to detect novel loci. | en_US |
