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Understanding factors that contribute to the increased likelihood of diseasetransmission between two individuals is important for infection control.However, analyzing measures of genetic relatedness is complicated due tocorrelation arising from the presence of the same individual across multipledyadic outcomes, potential spatial correlation caused by unmeasuredtransmission dynamics, and the distinctive distributional characteristics ofsome of the outcomes. We develop two novel hierarchical Bayesian spatialmethods for analyzing dyadic genetic relatedness data, in the form of patristicdistances and transmission probabilities, that simultaneously address each ofthese complications. Using individual-level spatially correlated random effectparameters, we account for multiple sources of correlation between the outcomesas well as other important features of their distribution. Through simulation,we show the limitations of existing approaches in terms of estimating keyassociations of interest, and the ability of the new methodology to correct forthese issues across datasets with different levels of correlation. All methodsare applied to Mycobacterium tuberculosis data from the Republic of Moldovawhere we identify previously unknown factors associated with diseasetransmission and, through analysis of the random effect parameters, keyindividuals and areas with increased transmission activity. Model comparisonsshow the importance of the new methodology in this setting. The methods areimplemented in the R package GenePair.

Spatial modeling of dyadic genetic relatedness data: Identifying factors associated with M. tuberculosis transmission in Moldova