Hi,

I was wondering what one should do if the estimates of the model have a negative component for the variances of a given component. I am familiar that in the direct variance approach paper it mentions there can be negative A estimates, and this can make biological sense. Similarly, at least for the ACE/ADE base model, there is a negative/positive parameter estimate relationship in which interpretability should win out (so whichever one is positive here, since the AIC will be the same in any case for ACE/ADE). However, if the A estimate is negative, or any of the submodels have a negative estimate, and this corresponds to the lowest AIC, is there a potential explanation to this, other than that the model is not well-suited (given the paper itself mentions there can be negative A estimates and biologically this may be feasible)?

I appreciate it!

One thing you might want to do is to examine the significance of the negative estimate. If, in reality, the variance component is zero, then half the time its estimate will be below zero. Even if the variance component in the true world is small, it may result in a negative estimate. So if you fix it to zero and examine the likelihood ratio test of fixing that parameter to zero, you can get an idea of whether it is "significantly" negative. If not, the data are compatible with fixing the component to zero. If it is significantly negative, you may want to consider alternative models for the data. In the case of C in an ACE model, it is reasonable to note that C is merely an aggregate of C, Assortative mating and age effects (if not corrected), non-additive genetic effects including dominance and epistasis. Significantly negative C implies that variance due to non-additivity has overwhelmed any C that was there.

HTH!