  Understanding factors that contribute to the increased likelihood of disease
transmission between two individuals is important for infection control.
However, analyzing measures of genetic relatedness is complicated due to
correlation arising from the presence of the same individual across multiple
dyadic outcomes, potential spatial correlation caused by unmeasured
transmission dynamics, and the distinctive distributional characteristics of
some of the outcomes. We develop two novel hierarchical Bayesian spatial
methods for analyzing dyadic genetic relatedness data, in the form of patristic
distances and transmission probabilities, that simultaneously address each of
these complications. Using individual-level spatially correlated random effect
parameters, we account for multiple sources of correlation between the outcomes
as well as other important features of their distribution. Through simulation,
we show the limitations of existing approaches in terms of estimating key
associations of interest, and the ability of the new methodology to correct for
these issues across datasets with different levels of correlation. All methods
are applied to Mycobacterium tuberculosis data from the Republic of Moldova
where we identify previously unknown factors associated with disease
transmission and, through analysis of the random effect parameters, key
individuals and areas with increased transmission activity. Model comparisons
show the importance of the new methodology in this setting. The methods are
implemented in the R package GenePair.
