Hi
I observe instances of very low GCP p-values, yet the GCP standard error is quite large. e.g. p= 5.4x10-8, where GCP is 0.47 (SE=0.52). By z-score alone, this is nowhere close to significant. It is difficult to even make an inference in this instance. I noticed that for one phenotype, h2SNP Zscore > 20, and for the other phenotype h2SNP Zscore =4, so one dataset has very limited power.
Accordingly, my suspicion is that the LCV result is a false positive due to the relatively weak signal in one dataset. Would you also suspect this? In that case, do you have recommendations for a minimum z-score to use for running LCV? e.g. the LDSC rule of thumb to not examine rg between phenotypes with h2 Z < 4. I imagine that the additional complication of the LCV model would require a more stringent z than for rg.
Thanks!
Adam
Hi
I observe instances of very low GCP p-values, yet the GCP standard error is quite large. e.g. p= 5.4x10-8, where GCP is 0.47 (SE=0.52). By z-score alone, this is nowhere close to significant. It is difficult to even make an inference in this instance. I noticed that for one phenotype, h2SNP Zscore > 20, and for the other phenotype h2SNP Zscore =4, so one dataset has very limited power.
Accordingly, my suspicion is that the LCV result is a false positive due to the relatively weak signal in one dataset. Would you also suspect this? In that case, do you have recommendations for a minimum z-score to use for running LCV? e.g. the LDSC rule of thumb to not examine rg between phenotypes with h2 Z < 4. I imagine that the additional complication of the LCV model would require a more stringent z than for rg.
Thanks!
Adam