Hi,
I have run LDSC on a GWAS for 2 psychometric traits, one aggressive behavior related (N ~17k), one a meta analysis of externalizing (ADHD, ODD CD etc) related traits ( N ~ 40k).
Both h2 are significant (Z = 2.22 and Z = 3.3), yet the genetic correlation is out of bounds (rg =1.3).
The sample overlap is considerable (as is evident from the large gcov_int: 0.176
Hypothesis 1:
Perhaps I am really to close to "no significant h2". The large gcov_int might even result in the need for more accurate estimates of h2 to enable estimation of rg?
Hypothesis 2:
Perhaps gcov_int is underestimated ("luck"of the draw so to say), and the low intercept estimate leads to an overestimated slope (as slope and intercept are often related).
Hypothesis 3:
Sample size is simply insufficient to estimate the rg given the h2.
My question is basically, in writing up these results, what further exploration within LDSC can I undertake to clarify and contextualize my findings? Is their any knowledge on the relationship between gcov_int and the ability to estimate rg?
Output below
Best,
Michel Nivard
Heritability of phenotype 1
---------------------------
Total Observed scale h2: 0.0547 (0.0246)
Lambda GC: 1.0165
Mean Chi^2: 1.013
Intercept: 0.9913 (0.0079)
Ratio < 0 (usually indicates GC correction).
Heritability of phenotype 2/2
-----------------------------
Total Observed scale h2: 0.0423 (0.0128)
Lambda GC: 1.0588
Mean Chi^2: 1.0617
Intercept: 1.0276 (0.0072)
Ratio: 0.4473 (0.1173)
Genetic Covariance
------------------
Total Observed scale gencov: 0.0626 (0.013)
Mean z1*z2: 0.2113
Intercept: 0.176 (0.0053)
Genetic Correlation
-------------------
Genetic Correlation: nan (nan) (rg out of bounds)
Z-score: nan (nan) (rg out of bounds)
P: nan (nan) (rg out of bounds)
WARNING: rg was out of bounds.
This often means that h2 is not significantly different from zero.
Summary of Genetic Correlation Results
p1 p2 rg se z p h2_obs h2_obs_se h2_int h2_int_se gcov_int gcov_int_se
AGRESS.sumstats.gz EXTER.sumstats.gz 1.3 0.353 3.68 2.330e-04 0.042 0.013 1.028 0.007 0.176 0.005