To our knowledge, BEAGLE is the only method other than ours that has proposed a strategy for overcoming these difficulties. (This strategy is not discussed in the paper [13], but it is detailed in the documentation accompanying the BEAGLE v3.0 software.) When BEAGLE encounters multiple reference panels, as in Scenario B, it simply downweights the less complete panels during the burn-in stage of its model-fitting procedure. Specifically, every individual in the dataset is assigned a weight that reflects the completeness of that individual's genotypes – individuals with more missing data get lower weights, and therefore have less influence on the early steps of the model-fitting algorithm. This detail aside, BEAGLE still fits a joint model to the complete dataset in Scenario B, in contrast to the IMPUTE v2 approach of modeling the observed data jointly but the missing data independently.
