Comparing subsampling strategies for efficient pairwise analysis of large pathogen genomic and spatial datasets: An application to Mycobacterium tuberculosis transmission.

Pairwise analysis of genomic and spatial data offers opportunities to identify and estimate the associations between covariates and the transmission of pathogens between individuals. However, such pairwise analyses are computationally intensive, and may not be feasible to conduct given the high dyad count in even moderately sized datasets. Here we compare two approaches to increase the efficiency of pairwise analysis for large datasets. We quantify and compare the performance of divide-and-conquer Bayesian model fitting and pairwise case-control approaches for estimating associations between individual- and pair-level covariates and shared membership in a transmission cluster. We utilize a large dataset (n = 4,154) of spatially-referenced, genomically-sequenced Mycobacterium tuberculosis isolates collected from a single city for this analysis, and conduct simulation studies under three representative tuberculosis genomic clustering settings. Across all simulation studies, the case-control approach produced negligible bias and expected 95% credible interval coverage, and performed comparably to the divide-and-conquer approach, with a somewhat narrower distribution of bias estimates and fewer outliers when effect sizes were large. Thus, we recommend using the case-control approach with five controls per case to downscale datasets for pairwise analysis when analysis of the entire dataset is not possible. This approach mitigates the computational challenges of pairwise Bayesian modeling on datasets that require significant computational resources while maintaining desired inferential properties.