I have loci based data, that I have aggregated over segments of varying length, and am now trying to fit a GLMM model, beta-binomial with random effects. $l_i= b_0+\beta x_i + \mu_{ma} + \mu_{pa}$ is the model, where the $\mu$ s are the random effects. The goal is to estimate the heritability. My dataset is small, I have about 45 individuals per segment, but I have over 1000 segments; each segment is modeled separately. Problem 1) I ran dharma to check the dispersion and to see if I could trust the random effect estimates, many models are flagged regarding dispersion; about 25% have significant pvalues and dispersion stats > 1.2 or smaller than 0.8. Interestingly (or not?) most of the problematic segments are underdispersed. I identified that those are the segments whose length/coverage ratios are larger than the rest, so low coverage and long segments lead to underdispersion. After splitting those, the problematic segments did go down to 20% of the total number of segments but still the problem remains. I also tried fitting a binomial model but then the underdispersed segments remain, and the number of overdispersed goes up. Problem 2) I simulated some data and fitted the models but I can't seem to replicate the dispersion issues so I am assuming my simulations are lacking something present in the data, I did use the same segments and coverage values seen in the data, as well as the fixed variables. Problem 3) I am not sure how to convert the random effect variance estimate…

Full article content could not be extracted automatically. Read the original below.