In a previous contribution, we implemented a finite locus model (FLM) for estimating additive and
dominance genetic variances via a Bayesian method and a single-site Gibbs sampler. We observed
a dependency of dominance variance estimates on locus number in the analysis FLM. Here, we
extended the FLM to include two-locus epistasis, and implemented the analysis with two genotype
samplers (Gibbs and descent graph) and three different priors for genetic effects (uniform and
variable across loci, uniform and constant across loci, and normal). Phenotypic data were
simulated for two pedigrees with 6300 and 12300 individuals in closed populations, using several
different, non-additive genetic models. Replications of these data were analysed with FLMs
differing in the number of loci. Simulation results indicate that the dependency of non-additive
genetic variance estimates on locus number persisted in all implementation strategies we
investigated. However, this dependency was considerably diminished with normal priors for genetic
effects as compared with uniform priors (constant or variable across loci). Descent graph sampling
of genotypes modestly improved variance components estimation compared with Gibbs sampling.
Moreover, a larger pedigree produced considerably better variance components estimation,
suggesting this dependency might originate from data insufficiency. As the FLM represents an
appealing alternative to the infinitesimal model for genetic parameter estimation and for inclusion
of polygenic background variation in QTL mapping analyses, further improvements are warranted
and might be achieved via improvement of the sampler or treatment of the number of loci as an
unknown.
The Application of Firth Bias Correction in Variance Components Estimation of Clustered Random Intercept Binary Model
