Double trouble: Predicting new variant counts across two heterogeneous populations

Music City Center 

Collecting genomics data across multiple heterogeneous populations (e.g., across different cancer types) has the potential to improve our understanding of disease. Despite sequencing advances, though, resources often remain a constraint when gathering data. So it would be useful for experimental design if experimenters with access to a pilot study could predict the number of new variants they might expect to find in a follow-up study: both the number of new variants shared between the populations and the total across the populations. While many authors have developed prediction methods for the single-population case, we show that these predictions can fare poorly across multiple populations that are heterogeneous. We show that the Bayesian nonparametric (BNP) framework of a state-of-the-art single-population predictor facilitates a natural extension to multiple populations. However, we prove that a particularly natural choice of prior within this framework fails for fundamental reasons. By supplying an alternative BNP prior choice, we provide the first predictor for the number of new shared variants and new total variants that can handle heterogeneity in multiple populations. We show that our proposed method works well empirically using real cancer and population genetics data.

Bayesian nonparametrics

genomics data

number of variants

beta process

Poisson point process