Hey,

I would honestly just write the code myself. You could consider a conditional logistic regression model with family as the matching stratum. In R, one can perform conditional logistic regression with clogit. I previously did this in 2016 for a Costa Rican trios study, where I also had to adjust for population stratification (via inclusion of PC1 and PC2) and other covariates. The code for that eventually became a Bioconductor Experiment Package called RegParallel; however, the manuscript is still being published, I believe.

You could consider encoding the variants as categorical (Het, Hom, Ref) or continuous (allele tallies). You could also consider first 'summarising' the rare variants across genes. Obviously, there is no single correct approach - for some genes, a single variant will be sufficient to 'knock out' or disrupt the function of the gene; whereas, in others, it may very well require 'compound' (i.e. multiple) variants, thus increasing the 'burden' of the rare variants on the gene's functionality.

Another idea is to use penalized regression, as these did: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3842118/ (Heather Cordell is an international leader in family-based analyses, and is a pride of the UK genetics community).

Kevin