Fig 1.
Comparison of genetic and environmental covariance estimation of different methods in simulations.
Compared methods include GECKO (purple), GNOVA (grey), and LDSC (green). Results are shown for the one study design (first row: A, B, G, H), two partially overlapped study design (second row: C, D, I, J), and two separate study design (third row: E, F). Boxplots display estimated genetic covariances (A, C, E) and environmental covariances (G, I) on y-axis versus the true covariances on x-axis across simulation replicates. Estimation accuracy is measured by the ratio of mean square errors (MSE), which contrast the MSE from GNOVA or LDSC with respect to GECKO, across various true covariances on x-axis, for genetic (B, D, F) and environmental covariances (H, J). An MSE ratio below one suggests that GECKO performs worse than the other method; above one otherwise.
Fig 2.
Power comparison of different methods in detecting non-zero genetic and environmental covariances in simulations.
Compared methods include GECKO (purple, solid line), GNOVA (grey, dotted line), and LDSC (green, dashed line). Results are shown for the one study design (first row: A, D), two partially overlapped study design (second row: B, E) and two separate study design (third row: C). Power (y-axis) of different methods are shown with respect to the true covariances (x-axis) for detecting non-zero genetic covariances (A, B, C) and environmental covariances (D, E). Power is shown based on a type I error of 0.05 but not a nominal p-value of 0.05. The power of GNOVA and LDSC for detecting non-zero environmental covariance are not shown because GNOVA and LDSC cannot test for environmental covariance.
Table 1.
Type I error control of different methods under null simulations.
Fig 3.
Comparison of genetic and environmental covariance estimation in presence of stratified genetic components for different methods in simulations.
Compared methods include GECKO (purple) and GNOVA (grey). Results are obtained in the presence of stratified genetic components. Results are shown for the one study design (first row: A, B, G, H), two partially overlapped study design (second row: C, D, I, J) and two separate study design (third row: E, F). Boxplots display the estimated total genetic covariances (A, C, E) and environmental covariances (G, I) on y-axis versus the true covariances on x-axis across simulation replicates. Estimation accuracy is measured by the ratio of mean square errors (MSE), which contrast the MSE from GNOVA or LDSC with respect to GECKO, across various true covariances on x-axis, for genetic (B, D, F) and environmental covariances (H, J). An MSE ratio below one suggests that GECKO performs worse than the other method; above one otherwise.
Fig 4.
Genetic and environmental correlation estimation by GECKO across pairs of 22 GWAS traits.
A: heatmap displays genetic (lower triangle) and environmental (upper triangle) correlation estimates for pairs of traits from GECKO. A cross in the square represents statistically significant correlation estimates for the trait pair after Bonferroni correction. Environmental correlation estimation is carried out only for pairs of traits that are collected in the same study (non-grey boxes). B: violin plot displays the proportion of significantly enriched gene sets detected for pairs of traits with non-significant genetic correlation estimates (purple) and for pairs of traits with significant genetic correlation estimates (green). C: scatterplot contrasts the genetic correlation estimates (y-axis) versus the environmental correlation estimates (x-axis) for pairs of traits that are collected in the same study. Traits are organized into five different phenotype categories (figure legend).