Моделирование тканей

0 for noncausal tissues at p < 0.05. (b) Power to detect the causal tissue per true value of \({h}_{ge(t{\prime} )}^{2}\) in the causal tissue. A true positive event is defined as \({h}_{ge(t{\prime} )}^{2}\) > 0 for causal tissues at p < 0.05. (c) Bias on causal estimates of \({h}_{ge(t{\prime} )}^{2}\) for different true values of the causal tissue \({h}_{ge(t{\prime} )}^{2}\). Dashed lines indicate true values of \({h}_{ge(t{\prime} )}^{2}\). (d) Bias on noncausal estimates of \({h}_{ge(t{\prime} )}^{2}\) for different true values of the causal tissue \({h}_{ge(t{\prime} )}^{2}\). In all panels, we performed n = 1,000 independent simulated genetic architectures across different eQTL sample sizes (n = 100, 200, 300, 500, 1000, 1500); we used a one-sided z-test and the genomic block jackknife standard error to obtain p-values and data are presented as mean values ± 1.96 × s.e.m. The value of \({G}_{t{\prime} }\) is set to the total number of unique cis-heritable genes across all tissues./p>

0 for noncausal tissues at p < 0.05. (b) Power to detect the causal tissue per true value of \({\omega }_{ge(t{\prime} )}\) in the causal tissue. A true positive event is defined as \({\omega }_{ge(t{\prime} )}\) > 0 for causal tissues at p < 0.05. (c) Bias on causal estimates of \({\omega }_{ge(t{\prime} )}\) for different true values of the causal tissue \({\omega }_{ge(t{\prime} )}\). Dashed lines indicate true values of \({\omega }_{ge(t{\prime} )}\). (d) Bias on noncausal estimates of \({\omega }_{ge(t{\prime} )}\) for different true values of the causal tissue \({\omega }_{ge(t{\prime} )}\). In all panels, we performed n = 1,000 independent simulated genetic architectures across different eQTL sample sizes (n = 100, 200, 300, 500, 1000, 1500); we used a one-sided z-test and the genomic block jackknife standard error to obtain p-values and data are presented as mean values ± 1.96 × s.e.m. The value of \({G}_{t{\prime} }\) is set to the total number of unique cis-heritable genes across all tissues./p>

0 for noncausal tissues at p < 0.05. Note, when there are 0 tagging tissues, there is no measurement of type I error. (b) Power to detect the causal tissue in which \({h}_{ge(t{\prime} )}^{2} > 0\) for causal tissues at p < 0.05. (c) Bias on estimates of \({h}_{ge(t{\prime} )}^{2}\) for the causal tissue, while changing the number of noncausal tissues in the model. The dashed line indicates that the true value of \({h}_{ge(t{\prime} )}^{2}\). (d) Bias on estimates of \({h}_{ge(t{\prime} )}^{2}\) for noncausal tissues, while changing the number of noncausal tissues in the model. In all panels, we performed n = 1,000 independent simulated genetic architectures across different eQTL sample sizes (n = 100, 200, 300, 500, 1000, 1500); we used a one-sided z-test and the genomic block jackknife standard error to obtain p-values and data are presented as mean values ± 1.96 × s.e.m. The value of \({G}_{t{\prime} }\) is set to the total number of unique cis-heritable genes across all tissues./p>

0 for noncausal tissues at p < 0.05. Note, when there are 0 tagging tissues, there is no measurement of type I error. (b) Power to detect the causal tissue in which \({\omega }_{ge(t{\prime} )} > 0\) for causal tissues at p < 0.05. (c) Bias on estimates of \({\omega }_{ge(t{\prime} )}\) for the causal tissue, while changing the number of noncausal tissues in the model. The dashed line indicates that the true value of \({\omega }_{ge(t{\prime} )}\). (d) Bias on estimates of \({\omega }_{ge(t{\prime} )}\) for noncausal tissues, while changing the number of noncausal tissues in the model. In all panels, we performed n = 1,000 independent simulated genetic architectures across different eQTL sample sizes (n = 100, 200, 300, 500, 1000, 1500); we used a one-sided z-test and the genomic block jackknife standard error to obtain p-values and data are presented as mean values ± 1.96 × s.e.m. The value of \({G}_{t{\prime} }\) is set to the total number of unique cis-heritable genes across all tissues./p>